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Publisher: Elsevier   (Total: 3162 journals)

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Showing 1 - 200 of 3162 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 95, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 36, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 411, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 251, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 150, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 23, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 32, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 29, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 25)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 44, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 58, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 17, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 22)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 11)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 9)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 63)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 396, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 10, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 33, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 341, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 450, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 3)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 52, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 28, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 46)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 210, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 64, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 177, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 194, SJR: 1.58, CiteScore: 3)

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Journal Cover
NeuroImage
Journal Prestige (SJR): 3.679
Citation Impact (citeScore): 6
Number of Followers: 67  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1053-8119 - ISSN (Online) 1095-9572
Published by Elsevier Homepage  [3162 journals]
  • Depth-dependent intracortical myelin organization in the living human
           brain determined by in vivo ultra-high field magnetic resonance imaging
    • Abstract: Publication date: 15 January 2019Source: NeuroImage, Volume 185Author(s): Emma Sprooten, Rafael O'Halloran, Juliane Dinse, Won Hee Lee, Dominik Andreas Moser, Gaelle Eve Doucet, Morgan Goodman, Hannah Krinsky, Alejandro Paulino, Alexander Rasgon, Evan Leibu, Priti Balchandani, Matilde Inglese, Sophia Frangou BackgroundIntracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI.MethodsTwenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T1-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T1 values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T1 values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls.ResultsIntracortical T1 values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T1 values and non-linearity indices (P 
       
  • Probabilistic TFCE: A generalized combination of cluster size and voxel
           intensity to increase statistical power
    • Abstract: Publication date: 15 January 2019Source: NeuroImage, Volume 185Author(s): Tamás Spisák, Zsófia Spisák, Matthias Zunhammer, Ulrike Bingel, Stephen Smith, Thomas Nichols, Tamás Kincses The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test.Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting.The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data.The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster “leaking” than TFCE and, in many realistic cases, further improves its sensitivity.Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow.Implementation of pTFCE is available at https://spisakt.github.io/pTFCE.Graphical abstractImage 1
       
  • A neural mechanism of direct and observational conditioning for placebo
           and nocebo responses
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yiheng Tu, Joel Park, Seppo P. Ahlfors, Sheraz Khan, Natalia Egorova, Courtney Lang, Jin Cao, Jian Kong Classical theories suggest placebo analgesia and nocebo hyperalgesia are based on expectation and conditioned experience. Whereas the neural mechanism of how expectation modulates placebo and nocebo effects during pain anticipation have been extensively studied, little is known about how experience may change brain networks to produce placebo and nocebo responses. We investigated the neural pathways of direct and observational conditioning for conscious and nonconscious conditioned placebo/nocebo effects using magnetoencephalography and a face visual cue conditioning model. We found that both direct and observational conditioning produced conscious conditioned placebo and nocebo effects and a nonconscious conditioned nocebo effect. Alpha band brain connectivity changes before and after conditioning could predict the magnitude of conditioned placebo and nocebo effects. Particularly, the connectivity between the rostral anterior cingulate cortex and middle temporal gyrus was an important indicator for the manipulation of placebo and nocebo effects. Our study suggests that conditioning can mediate our pain experience by encoding experience and modulating brain networks.
       
  • Modeling regional dynamics in low-frequency fluctuation and its
           application to Autism spectrum disorder diagnosis
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Eunji Jun, Eunsong Kang, Jaehun Choi, Heung-Il Suk With the advent of neuroimaging techniques, many studies in the literature have validated the use of resting-state fMRI (rs-fMRI) for understanding functional mechanisms of the brain, as well as for identifying brain disorders or diseases. One of the main streams in recent studies of modeling and analyzing rs-fMRI data is to account for the dynamic characteristics of a brain. In this study, we propose a novel method that directly models the regional temporal BOLD fluctuations in a stochastic manner and estimates the dynamic characteristics in the form of likelihoods. Specifically, we modeled temporal BOLD fluctuation of individual Regions Of Interest (ROIs) by means of Hidden Markov Models (HMMs), and then estimated the ‘goodness-of-fit’ of each ROI's BOLD signals to the corresponding trained HMM in terms of a likelihood. Using estimated likelihoods of the ROIs over the whole brain as features, we built a classifier that can discriminate subjects with Autism Spectrum Disorder (ASD) from Typically Developing (TD) controls at an individual level.In order to interpret the trained HMMs and a classifier from a neuroscience perspective, we also conducted model analysis. First, we investigated the learned weight coefficients of a classifier by transforming them into activation patterns, from which we could identify the ROIs that are highly associated with ASD and TD groups. Second, we explored the characteristics of temporal BOLD signals in terms of functional networks by clustering them based on sequences of the hidden states decoded with the trained HMMs. We validated the effectiveness of the proposed method by achieving the state-of-the-art performance on the ABIDE dataset and observed insightful patterns related to ASD.
       
  • A simple geometric analysis method for measuring and mitigating RF induced
           currents on Deep Brain Stimulation leads by multichannel
           transmission/reception
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yigitcan Eryaman, Naoharu Kobayashi, Sean Moen, Joshua Aman, Andrea Grant, J. Thomas Vaughan, Gregory Molnar, Michael C. Park, Jerrold Vitek, Gregor Adriany, Kamil Ugurbil, Noam Harel The purpose of this work is to present a new method that can be used to estimate and mitigate RF induced currents on Deep Brain Stimulation (DBS) leads. Here, we demonstrate the effect of RF induced current mitigation on both RF heating and image quality for a variety of brain MRI sequences at 3 T.We acquired pre-scan images around a DBS lead (in-situ and ex-vivo) using conventional Gradient Echo Sequence (GRE) accelerated by parallel imaging (i.e GRAPPA) and quantified the magnitude and phase of RF induced current using the relative location of the B1+ null with respect to the lead position. We estimated the RF induced current on a DBS lead implanted in a gel phantom as well as in a cadaver head study for a variety of RF excitation patterns. We also measured the increase in tip temperature using fiber-optic probes for both phantom and cadaver studies. Using the magnitude and phase information of the current induced separately by two transmit channels of the body coil, we calculated an implant friendly (IF) excitation. Using the IF excitation, we acquired T1, T2 weighted Turbo Spin Echo (TSE), T2 weighted SPACE-Dark Fluid, and Ultra Short Echo Time (UTE) sequences around the lead.Our induced current estimation demonstrated linear relationship between the magnitude of the induced current and the square root SAR at the tip of the lead as measured in phantom studies. The “IF excitation pattern” calculated after the pre-scan mitigated RF artifacts and increased the image quality around the lead. In addition, it reduced the tip temperature significantly in both phantom and cadaver studies compared to a conventional quadrature excitation while keeping equivalent overall image quality.We present a relatively fast method that can be used to calculate implant friendly excitation, reducing image artifacts as well as the temperature around the DBS electrodes. When combined with a variety of MR sequences, the proposed method can improve the image quality and patient safety in clinical imaging scenarios.
       
  • Individual differences in analogical reasoning revealed by multivariate
           task-based functional brain imaging
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Rubi Hammer, Erick J. Paul, Charles H. Hillman, Arthur F. Kramer, Neal J. Cohen, Aron K. Barbey Although analogical reasoning (AR) plays a central role in higher-level cognition and constitutes a key source of individual differences in intellectual ability, the neural mechanisms that account for individual differences in AR remain to be well characterized. Here we investigated individual differences in AR within a large sample (n = 229), using multivariate fMRI analysis (a simple multiple kernel learning machine). The individual AR capability was positively correlated with activation level in a prefrontal executive network and a visuospatial network. Notably, the best predictors of individual differences in AR within these networks were activation in the dorsomedial prefrontal cortex (response selection) and the lingual gyrus (visual feature mapping). In contrast, AR capability was negatively correlated with activation in the default mode network. The implications of the reported findings are twofold: (i) Individual differences in AR depend on multiple executive and visuospatial brain regions, where their respective contributions are contingent upon the individuals' cognitive skills; (ii) Brain regions associated with individual differences in AR only partially overlap with brain regions sensitive to the associated task demands (i.e., brain regions sensitive to the analogy relational complexity, at the group-level). We discuss implications of such brain organization supporting AR as an example for brain architecture underlying higher-level cognitive processes.Graphical abstractImage 1
       
  • Measuring transient phase-amplitude coupling using local mutual
           information
    • Abstract: Publication date: Available online 18 October 2018Source: NeuroImageAuthor(s): Ramon Martinez-Cancino, Joseph Heng, Arnaud Delorme, Ken Kreutz-Delgado, Roberto C. Sotero, Scott Makeig Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges interact and transiently couple. A particular form of CFC, phase-amplitude coupling (PAC), has raised interest given the growing amount of evidence of its possible role in healthy and pathological brain information processing. Although several methods have been proposed for PAC estimation, only a few have addressed the estimation of the temporal evolution of PAC, and these typically require a large number of experimental trials to return a reliable estimate. Here we explore the use of mutual information to estimate a PAC measure (MIPAC) in both continuous and event-related multi-trial data. To validate these two applications of the proposed method, we first apply it to a set of simulated phase-amplitude modulated signals and show that MIPAC can successfully recover the temporal dynamics of the simulated coupling in either continuous or multi-trial data. Finally, to explore the use of MIPAC to analyze data from human event-related paradigms, we apply it to an actual event-related human electrocorticographic (ECoG) data set that exhibits strong PAC, demonstrating that the MIPAC estimator can be used to successfully characterize amplitude-modulation dynamics in electrophysiological data.
       
  • Apolipoprotein ε4 is associated with better cognitive control allocation
           in healthy young adults
    • Abstract: Publication date: Available online 18 October 2018Source: NeuroImageAuthor(s): Nicolas Zink, Wiebke Bensmann, Larissa Arning, Christian Beste, Ann-Kathrin Stock Many gene variants may impair our health and cognitive abilities at old age, but some of them paradoxically improve the same or similar functions at much younger age (antagonistic pleiotropy hypothesis). Such a diametric pattern may also hold true for the ancestral Apolipoprotein E (APOE) ε4 allele, which increases the risk for Alzheimer's disease and cognitive decline in old age, but may benefit (pre)frontal (executive) functions in young carriers. We therefore investigated potential cognitive benefits of the risk allele on cognitive control capacities and top-down control allocation (“metacontrol”) in n = 190 healthy young adults.On a behavioral level, we found young APOE ε4 carriers to better adapt to different degrees of cognitive control requirements, with superior performance in case of high control demands. On a neurophysiological level, these group differences were reflected by modulations of the N450 component, which were rooted in activation differences of the superior frontal gyrus (SFG, BA8). Taken together, our results suggest that young ε4 carriers are more efficient than non-carriers at allocating cognitive control resources based on the actual task requirements (i.e. metacontrol), as they seem to experience less conflict/exert less effort and recruit fewer additional prefrontal areas when task set complexity increases. We further found that ε2 carriers processed implicit spatial stimulus features to a stronger degree than ε3 and ε4 carriers, but failed to benefit from this, as the additional information likely increased response selection conflicts. This finding should however be treated with ample caution as the group of ε2 carriers was comparatively small.
       
  • The neural basis of hand choice: An fMRI investigation of the Posterior
           Parietal Interhemispheric Competition model
    • Abstract: Publication date: Available online 17 October 2018Source: NeuroImageAuthor(s): Aoife M. Fitzpatrick, Neil M. Dundon, Kenneth F. Valyear The current study investigates a new neurobiological model of human hand choice: The Posterior Parietal Interhemispheric Competition (PPIC) model. The model specifies that neural populations in bilateral posterior intraparietal and superior parietal cortex (pIP-SPC) encode actions in hand-specific terms, and compete for selection across and within hemispheres. Actions with both hands are encoded bilaterally, but the contralateral hand is overrepresented. We use a novel fMRI paradigm to test the PPIC model. Participants reach to visible targets while in the scanner, and conditions involving free choice of which hand to use (Choice) are compared with when hand-use is instructed. Consistent with the PPIC model, bilateral pIP-SPC is preferentially responsive for the Choice condition, and for actions made with the contralateral hand. In the right pIP-SPC, these effects include anterior intraparietal and superior parieto-occipital cortex. Left dorsal premotor cortex, and an area in the right lateral occipitotemporal cortex show the same response pattern, while the left inferior parietal lobule is preferentially responsive for the Choice condition and when using the ipsilateral hand. Behaviourally, hand choice is biased by target location – for targets near the left/right edges of the display, the hand in ipsilateral hemispace is favoured. Moreover, consistent with a competitive process, response times are prolonged for choices to more ambiguous targets, where hand choice is relatively unbiased, and fMRI responses in bilateral pIP-SPC parallel this pattern. Our data provide support for the PPIC model, and reveal a selective network of brain areas involved in free hand choice, including bilateral posterior parietal cortex, left-lateralized inferior parietal and dorsal premotor cortices, and the right lateral occipitotemporal cortex.
       
  • Lateral geniculate nucleus volumetry at 3T and 7T: Four different
           optimized magnetic-resonance-imaging sequences evaluated against a 7T
           reference acquisition
    • Abstract: Publication date: Available online 17 October 2018Source: NeuroImageAuthor(s): Njoud Aldusary, Lars Michels, Ghislaine L. Traber, Birgit Hartog-Keisker, Michael Wyss, Arwa Baeshen, Karen Huebel, Yassir Edrees Almalki, David Otto Brunner, Klaas Paul Pruessmann, Klara Landau, Spyridon Kollias, Marco Piccirelli PurposeThe lateral geniculate nucleus (LGN) is an essential nucleus of the visual pathway, occupying a small volume (60–160 mm3) among the other thalamic nuclei. The reported LGN volumes vary greatly across studies due to technical limitations and due to methodological differences of volume assessment. Yet, structural and anatomical alterations in ophthalmologic and neurodegenerative pathologies can only be revealed by a precise and reliable LGN representation. To improve LGN volume assessment, we first implemented a reference acquisition for LGN volume determination with optimized Contrast to Noise Ratio (CNR) and high spatial resolution. Next, we compared CNR efficiency and rating reliability of 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) images using white matter nulled (WMn) and grey matter nulled (GMn) sequences and its subtraction (WMn-GMn) relative to the clinical standard Proton Density Turbo Spin Echo (PD 2D TSE) and the reference acquisition. We hypothesized that 3D MPRAGE should provide a higher CNR and volume determination accuracy than the currently used 2D sequences.Materials and methodsIn 31 healthy subjects, we obtained at 3 and 7 T the following MR sequences: PD-TSE, MPRAGE with white/grey matter signal nulled (WMn/GMn), and a motion-corrected segmented MPRAGE sequence with a resolution of 0.4 × 0.4 × 0.4 mm3 (reference acquisition). To increase CNR, GMn were subtracted from WMn (WMn-GMn). Four investigators manually segmented the LGN independently.ResultsThe reference acquisition provided a very sharp depiction of the LGN and an estimated mean LGN volume of 124 ± 3.3 mm3. WMn-GMn had the highest CNR and gave the most reproducible LGN volume estimations between field strengths. Even with the highest CNR efficiency, PD-TSE gave inconsistent LGN volumes with the weakest reference acquisition correlation. The LGN WM rim induced a significant difference between LGN volumes estimated from WMn and GMn. WMn and GMn LGN volume estimations explained most of the reference acquisition volumes' variance. For all sequences, the volume rating reliability were good. On the other hand, the best CNR rating reliability, LGN volume and CNR correlations with the reference acquisition were obtained with GMn at 7 T.ConclusionWMn and GMn MPRAGE allow reliable LGN volume determination at both field strengths. The precise location and identification of the LGN (volume) can help to optimize neuroanatomical and neurophysiological studies, which involve the LGN structure. Our optimized imaging protocol may be used for clinical applications aiming at small nuclei volumetric and CNR quantification.
       
  • Apparent diffusion coefficient changes in human brain during sleep –
           Does it inform on the existence of a glymphatic system'
    • Abstract: Publication date: Available online 17 October 2018Source: NeuroImageAuthor(s): Şükrü Barış Demiral, Dardo Tomasi, Joelle Sarlls, Hedok Lee, Corinde E. Wiers, Amna Zehra, Tansha Srivastava, Kenneth Ke, Ehsan Shokri-Kojori, Clara R. Freeman, Elsa Lindgren, Veronica Ramirez, Gregg Miller, Peter Bandettini, Silvina Horovitz, Gene-Jack Wang, Helene Benveniste, Nora D. Volkow The role of sleep in brain physiology is poorly understood. Recently rodent studies have shown that the glymphatic system clears waste products from brain more efficiently during sleep compared to wakefulness due to the expansion of the interstitial fluid space facilitating entry of cerebrospinal fluid (CSF) into the brain. Here, we studied water diffusivity in the brain during sleep and awake conditions, hypothesizing that an increase in water diffusivity during sleep would occur concomitantly with an expansion of CSF volume - an effect that we predicted based on preclinical findings would be most prominent in cerebellum. We used MRI to measure slow and fast components of the apparent diffusion coefficient (ADC) of water in the brain in 50 healthy participants, in 30 of whom we compared awake versus sleep conditions and in 20 of whom we compared rested-wakefulness versus wakefulness following one night of sleep-deprivation. Sleep compared to wakefulness was associated with increases in slow-ADC in cerebellum and left temporal pole and with decreases in fast-ADC in thalamus, insula, parahippocampus and striatal regions, and the density of sleep arousals was inversely associated with ADC changes. The CSF volume was also increased during sleep and was associated with sleep-induced changes in ADCs in cerebellum. There were no differences in ADCs with wakefulness following sleep deprivation compared to rested-wakefulness. Although we hypothesized increases in ADC with sleep, our findings uncovered both increases in slow ADC (mostly in cerebellum) as well as decreases in fast ADC, which could reflect the distinct biological significance of fast- and slow-ADC values in relation to sleep. While preliminary, our findings suggest a more complex sleep-related glymphatic function in the human brain compared to rodents. On the other hand, our findings of sleep-induced changes in CSF volume provide preliminary evidence that is consistent with a glymphatic transport process in the human brain.
       
  • Screen media activity and brain structure in youth: Evidence for diverse
           structural correlation networks from the ABCD study
    • Abstract: Publication date: Available online 16 October 2018Source: NeuroImageAuthor(s): Martin P. Paulus, Lindsay M. Squeglia, Kara Bagot, Joanna Jacobus, Rayus Kuplicki, Florence J. Breslin, Jerzy Bodurka, Amanda Sheffield Morris, Wesley K. Thompson, Hauke Bartsch, Susan F. Tapert The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06–0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08–0.1) and fluid intelligence (ES: 0.04–0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.
       
  • Limits to anatomical accuracy of diffusion tractography using modern
           approaches
    • Abstract: Publication date: 15 January 2019Source: NeuroImage, Volume 185Author(s): Kurt G. Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, Simona Schiavi, Alessandro Daducci, Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, David Romascano, Gaëtan Rensonnet, Marco Pizzolato, Alice Bates, Elda Fischi Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
       
  • BOLD mapping of human epileptic spikes recorded during simultaneous
           intracranial EEG-fMRI: The impact of automated spike classification
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Niraj K. Sharma, Carlos Pedreira, Umair J. Chaudhary, Maria Centeno, David W. Carmichael, Tinonkorn Yadee, Teresa Murta, Beate Diehl, Louis Lemieux ObjectivesSimultaneous intracranial EEG and functional MRI (icEEG-fMRI) can be used to map the haemodynamic (BOLD) changes associated with the generation of IEDs. Unlike scalp EEG-fMRI, in most patients who undergo icEEG-fMRI, IEDs recorded intracranially are numerous and show variability in terms of field amplitude and morphology. Therefore, visual marking can be highly subjective and time consuming. In this study, we applied an automated spike classification algorithm, Wave_clus (WC), to IEDs marked visually on icEEG data acquired during simultaneous fMRI acquisition. The motivation of this work is to determine whether using a potentially more consistent and unbiased automated approach can produce more biologically meaningful BOLD patterns compared to the BOLD patterns obtained based on the conventional, visual classification.MethodsWe analysed simultaneous icEEG-fMRI data from eight patients with severe drug resistant epilepsy, and who subsequently underwent resective surgery that resulted in a good outcome: confirmed epileptogenic zone (EZ). For each patient two fMRI analyses were performed: one based on the conventional visual IED classification and the other based on the automated classification. We used the concordance of the IED-related BOLD maps with the confirmed EZ as an indication of their biological meaning, which we compared for the automated and visual classifications for all IED originating in the EZ.ResultsAcross the group, the visual and automated classifications resulted in 32 and 24 EZ IED classes respectively, for which 75% vs 83% of the corresponding BOLD maps were concordant. At the single-subject level, the BOLD maps for the automated approach had greater concordance in four patients, and less concordance in one patient, compared to those obtained using the conventional visual classification, and equal concordance for three remaining patients. These differences did not reach statistical significance.ConclusionWe found automated IED classification on icEEG data recorded during fMRI to be feasible and to result in IED-related BOLD maps that may contain similar or greater biological meaning compared to the conventional approach in the majority of the cases studied. We anticipate that this approach will help to gain significant new insights into the brain networks associated with IEDs and in relation to postsurgical outcome.
       
  • Ventral striatum links motivational and motor networks during
           operant-conditioned movement in rats
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yuki Hori, Naoki Ihara, Chiaki Sugai, Jun Ogura, Manabu Honda, Koichi Kato, Yoshikazu Isomura, Takashi Hanakawa Voluntary actions require motives. It is already known that the medial prefrontal cortex (MPFC) assess the motivational values. However, it remains unclear how the motivational process gains access to the motor execution system in the brain. Here we present evidence that the ventral striatum (VS) plays a hub-like role in mediating motivational and motor processing in operant behavior. We used positron emission tomography (PET) to detect the neural activation areas associated with motivational action. Using obtained regions, partial correlation analysis was performed to examine how the motivational signals propagate to the motor system. The results revealed that VS activity propagated to both MPFC and primary motor cortex through the thalamus. Moreover, muscimol injection into the VS suppressed the motivational behavior, supporting the idea of representations of motivational signals in VS that trigger motivational behavior. These results suggest that the VS-thalamic pathway plays a pivotal role for both motivational processing through interactions with the MPFC and for motor processing through interactions with the motor BG circuits.
       
  • Gustatory responses in macaque monkeys revealed with fMRI: Comments on
           taste, taste preference, and internal state
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Peter M. Kaskan, Aaron M. Dean, Mark A. Nicholas, Andrew R. Mitz, Elisabeth A. Murray Studies of the neural mechanisms underlying value-based decision making typically employ food or fluid rewards to motivate subjects to perform cognitive tasks. Rewards are often treated as interchangeable, but it is well known that the specific tastes of foods and fluids and the values associated with their taste sensations influence choices and contribute to overall levels of food consumption. Accordingly, we characterized the gustatory system in three macaque monkeys (Macaca mulatta) and examined whether gustatory responses were modulated by preferences and hydration status. To identify taste-responsive cortex, we delivered small quantities (0.1 ml) of sucrose (sweet), citric acid (sour), or distilled water in random order without any predictive cues while scanning monkeys using event-related fMRI. Neural effects were evaluated by using each session in each monkey as a data point in a second-level analysis. By contrasting BOLD responses to sweet and sour tastes with those from distilled water in a group level analysis, we identified taste responses in primary gustatory cortex area G, an adjacent portion of the anterior insular cortex, and prefrontal cortex area 12o. Choice tests administered outside the scanner revealed that all three monkeys strongly preferred sucrose to citric acid or water. BOLD responses in the ventral striatum, ventral pallidum, and amygdala reflected monkeys’ preferences, with greater BOLD responses to sucrose than citric acid. Finally, we examined the influence of hydration level by contrasting BOLD responses to receipt of fluids when monkeys were thirsty and after ad libitum water consumption. BOLD responses in area G and area 12o in the left hemisphere were greater following full hydration. By contrast, BOLD responses in portions of medial frontal cortex were reduced after ad libitum water consumption. These findings highlight brain regions involved in representing taste, taste preference and internal state.
       
  • Towards microstructure fingerprinting: Estimation of tissue properties
           from a dictionary of Monte Carlo diffusion MRI simulations
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Gaëtan Rensonnet, Benoît Scherrer, Gabriel Girard, Aleksandar Jankovski, Simon K. Warfield, Benoît Macq, Jean-Philippe Thiran, Maxime Taquet Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties.This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints.Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies.This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.
       
  • Neural dynamics of verbal working memory processing in children and
           adolescents
    • Abstract: Publication date: Available online 16 October 2018Source: NeuroImageAuthor(s): Christine M. Embury, Alex I. Wiesman, Amy L. Proskovec, Mackenzie Mills, Elizabeth Heinrichs-Graham, Yu-Ping Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson Development of cognitive functions and the underlying neurophysiology is evident throughout childhood and adolescence, with higher order processes such as working memory (WM) being some of the last cognitive faculties to fully mature. Previous functional neuroimaging studies of the neurodevelopment of WM have largely focused on overall regional activity levels rather than the temporal dynamics of neural component recruitment. In this study, we used magnetoencephalography (MEG) to examine the neural dynamics of WM in a large cohort of children and adolescents who were performing a high-load, modified verbal Sternberg WM task. Consistent with previous studies in adults, our findings indicated left-lateralized activity throughout the task period, beginning in the occipital cortices and spreading anterior to include temporal and prefrontal cortices during later encoding and into maintenance. During maintenance, the occipital alpha increase that has been widely reported in adults was found to be relatively weak in this developmental sample, suggesting continuing development of this component of neural processing, which was supported by correlational analyses. Intriguingly, we also found sex-specific developmental effects in alpha responses in the right inferior frontal region during encoding and in parietal and occipital cortices during maintenance. These findings suggested a developmental divergence between males and females in the maturation of neural circuitry serving WM during the transition from childhood to adolescence.
       
  • The lifespan Human Connectome Project in aging: An overview
    • Abstract: Publication date: Available online 15 October 2018Source: NeuroImageAuthor(s): Susan Y. Bookheimer, David H. Salat, Melissa Terpstra, Beau M. Ances, Deanna M. Barch, Randy L. Buckner, Gregory C. Burgess, Sandra W. Curtiss, Mirella Diaz-Santos, Jennifer Stine Elam, Bruce Fischl, Douglas N. Greve, Hannah A. Hagy, Michael P. Harms, Olivia M. Hatch, Trey Hedden, Cynthia Hodge, Kevin C. Japardi, Taylor P. Kuhn, Timothy K. Ly The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., in press).
       
  • Cortical tracking of rhythm in music and speech
    • Abstract: Publication date: Available online 15 October 2018Source: NeuroImageAuthor(s): Eleanor E. Harding, Daniela Sammler, Molly J. Henry, Edward W. Large, Sonja A. Kotz Neural activity phase-locks to rhythm in both music and speech. However, the literature currently lacks a direct test of whether cortical tracking of comparable rhythmic structure is comparable across domains. Moreover, although musical training improves multiple aspects of music and speech perception, the relationship between musical training and cortical tracking of rhythm has not been compared directly across domains. We recorded the electroencephalograms (EEG) from 28 participants (14 female) with a range of musical training who listened to melodies and sentences with identical rhythmic structure. We compared cerebral-acoustic coherence between the EEG signal and single-trial stimulus envelopes (as measure of cortical entrainment) across domains and correlated years of musical training with cerebral-acoustic coherence. We hypothesized that neural activity would be comparably phase-locked across domains, and that the amount of musical training would be associated with increasingly strong phase locking in both domains. We found that participants with only a few years of musical training had a comparable cortical response to music and speech rhythm, partially supporting the hypothesis. However, the cortical response to music rhythm increased with years of musical training while the response to speech rhythm did not, leading to an overall greater cortical response to music rhythm across all participants. We suggest that task demands shaped the asymmetric cortical tracking across domains.
       
  • Fronto-central P3a to distracting sounds: An index of their arousing
           properties
    • Abstract: Publication date: Available online 15 October 2018Source: NeuroImageAuthor(s): Rémy Masson, Aurélie Bidet-Caulet The P3a observed after novel events is an event-related potential comprising an early fronto-central phase and a late fronto-parietal phase. It has classically been considered to reflect the attention processing of distracting stimuli. However, novel sounds can lead to behavioral facilitation as much as behavioral distraction. This illustrates the duality of the orienting response which includes both an attentional and an arousal component. Using a paradigm with visual or auditory targets to detect and irrelevant unexpected distracting sounds to ignore, we showed that the facilitation effect by distracting sounds is independent of the target modality and endures more than 1500 ms. These results confirm that the behavioral facilitation observed after distracting sounds is related to an increase in unspecific phasic arousal on top of the attentional capture. Moreover, the amplitude of the early phase of the P3a to distracting sounds positively correlated with subjective arousal ratings, contrary to other event-related potentials. We propose that the fronto-central early phase of the P3a would index the arousing properties of distracting sounds and would be linked to the arousal component of the orienting response. Finally, we discuss the relevance of the P3a as a marker of distraction.
       
  • Rapid solution of the Bloch-Torrey equation in anisotropic tissue:
           Application to dynamic susceptibility contrast MRI of cerebral white
           matter
    • Abstract: Publication date: Available online 15 October 2018Source: NeuroImageAuthor(s): Jonathan Doucette, Luxi Wei, Enedino Hernández-Torres, Christian Kames, Nils D. Forkert, Rasmus Aamand, Torben E. Lund, Brian Hansen, Alexander Rauscher Blood vessel related magnetic resonance imaging (MRI) contrast provides a window into the brain's metabolism and function. Here, we show that the spin echo dynamic susceptibility contrast (DSC) MRI signal of the brain's white matter (WM) strongly depends on the angle between WM tracts and the main magnetic field. The apparent cerebral blood flow and volume are 20% larger in fibres perpendicular to the main magnetic field compared to parallel fibres. We present a rapid numerical framework for the solution of the Bloch-Torrey equation that allows us to explore the isotropic and anisotropic components of the vascular tree. By fitting the simulated spin echo DSC signal to the measured data, we show that half of the WM vascular volume is comprised of vessels running in parallel with WM fibre tracts. The WM blood volume corresponding to the best fit to the experimental data was 2.82%, which is close to the PET gold standard of 2.6%.
       
  • A 3D population-based brain atlas of the mouse lemur primate with examples
           of applications in aging studies and comparative anatomy
    • Abstract: Publication date: Available online 13 October 2018Source: NeuroImageAuthor(s): Nachiket A. Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq The gray mouse lemur (Microcebus murinus) is a small prosimian of growing interest for studies of primate biology and evolution, and notably as a model organism of brain aging. As brain atlases are essential tools for brain investigation, the objective of the current work was to create the first 3D digital atlas of the mouse lemur brain. For this, a template image was constructed from in vivo magnetic resonance imaging (MRI) data of 34 animals. This template was then manually segmented into 40 cortical, 74 subcortical and 6 cerebro-spinal fluid (CSF) regions. Additionally, we generated probability maps of gray matter, white matter and CSF. The template, manual segmentation and probability maps, as well as imaging tools used to create and manipulate the template, can all be freely downloaded. The atlas was first used to automatically assess regional age-associated cerebral atrophy in a cohort of mouse lemurs previously studied by voxel based morphometry (VBM). Results based on the atlas were in good agreement with the VBM ones, showing age-associated atrophy in the same brain regions such as the insular, parietal or occipital cortices as well as the thalamus or hypothalamus. The atlas was also used as a tool for comparative neuroanatomy. To begin with, we compared measurements of brain regions in our MRI data with histology-based measures from a reference article largely used in previous comparative neuroanatomy studies. We found large discrepancies between our MRI-based data and those of the reference histology-based article. Next, regional brain volumes were compared amongst the mouse lemur and several other mammalian species where high quality volumetric MRI brain atlases were available, including rodents (mouse, rat) and primates (marmoset, macaque, and human). Unlike those based on histological atlases, measures from MRI atlases indicated similar cortical to cerebral volume indices in all primates, including in mouse lemurs, and lower values in mice. On the other hand, white matter to cerebral volume index increased from rodents to small primates (mouse lemurs and marmosets) to macaque, reaching their highest values in humans.Graphical abstractImage 1
       
  • Glutamatergic facilitation of neural responses in MT enhances motion
           perception in humans
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Michael-Paul Schallmo, Rachel Millin, Alex M. Kale, Tamar Kolodny, Richard A.E. Edden, Raphael A. Bernier, Scott O. Murray There is large individual variability in human neural responses and perceptual abilities. The factors that give rise to these individual differences, however, remain largely unknown. To examine these factors, we measured fMRI responses to moving gratings in the motion-selective region MT, and perceptual duration thresholds for motion direction discrimination. Further, we acquired MR spectroscopy data, which allowed us to quantify an index of neurotransmitter levels in the region of area MT. These three measurements were conducted in separate experimental sessions within the same group of male and female subjects. We show that stronger Glx (glutamate + glutamine) signals in the MT region are associated with both higher fMRI responses and superior psychophysical task performance. Our results suggest that greater baseline levels of glutamate within MT facilitate motion perception by increasing neural responses in this region.
       
  • Pattern-sensitive neurons reveal encoding of complex auditory regularities
           in the rat inferior colliculus
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Manuel S. Malmierca, Blanca E. Niño-Aguillón, Javier Nieto-Diego, Ángel Porteros, David Pérez-González, Carles Escera A ‘pattern alternation paradigm’ has been previously used in human ERP recordings to investigate the brain encoding of complex auditory regularities, but prior studies on regularity encoding in animal models to examine mechanisms of adaptation of auditory neuronal responses have used primarily oddball stimulus sequences to study stimulus-specific adaptation alone. In order to examine the sensitivity of neuronal adaptation to expected and unexpected events embedded in a complex sound sequence, we used a similar patterned sequence of sounds. We recorded single unit activity and compared neuronal responses in the rat inferior colliculus (IC) to sound stimuli conforming to pattern alternation regularity with those to stimuli in which occasional sound repetitions violated that alternation.Results show that some neurons in the rat inferior colliculus are sensitive to the history of patterned stimulation and to violations of patterned regularity, demonstrating that there is a population of subcortical neurons, located as early as the level of the midbrain, that can detect more complex stimulus regularities than previously supposed and that are as sensitive to complex statistics as some neurons in primary auditory cortex.Our findings indicate that these pattern-sensitive neurons can extract temporal and spectral regularities between successive acoustic stimuli. This is important because the extraction of regularities from the sound sequences will result in the development of expectancies for future sounds and hence, the present results are compatible with predictive coding models. Our results demonstrate that some collicular neurons, located as early as in the midbrain level, are involved in the generation and shaping of prediction errors in ways not previously considered and thus, the present findings challenge the prevailing view that perceptual organization of sound only emerges at the auditory cortex level.
       
  • High detection sensitivity with antibody-based PET radioligand for amyloid
           beta in brain
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Xiaotian T. Fang, Greta Hultqvist, Silvio R. Meier, Gunnar Antoni, Dag Sehlin, Stina Syvänen PET imaging of amyloid-beta (Aβ) deposits in brain has become an important aid in Alzheimer's disease diagnosis, and an inclusion criterion for patient enrolment into clinical trials of new anti-Aβ treatments. Available PET radioligands visualizing Aβ bind to insoluble fibrils, i.e. Aβ plaques. Levels of prefibrillar Aβ forms, e.g. soluble oligomers and protofibrils, correlate better than plaques with disease severity and these soluble species are the neurotoxic form of Aβ leading to neurodegeneration. The goal was to create an antibody-based radioligand, recognizing not only fibrillary Aβ, but also smaller and still soluble aggregates. We designed and expressed a small recombinant bispecific antibody construct, di-scFv 3D6-8D3, targeting the Aβ N-terminus and the transferrin receptor (TfR). Natively expressed at the blood-brain barrier (BBB), TfR could thus be used as a brain-blood shuttle. Di-scFv 3D6-8D3 bound to Aβ1-40 with high affinity and to TfR with moderate affinity. Di-scFv [124I]3D6-8D3 was injected in two transgenic mouse models overexpressing human Aβ and wild-type control mice and PET scanned at 14, 24 or 72 h after injection. Di-scFv [124I]3D6-8D3 was retained in brain of transgenic animals while it was cleared from wild-type lacking Aβ. This difference was observed from 24 h onwards, and at 72 h, 18 months old transgenic animals, with high load of Aβ pathology, displayed SUVR of 2.2–3.5 in brain while wild-type showed ratios close to unity. A subset of the mice were also scanned with [11C]PIB. Again wt mice displayed ratios of unity while transgenes showed slightly, non-significantly, elevated SUVR of 1.2, indicating improved sensitivity with novel di-scFv [124I]3D6-8D3 compared with [11C]PIB. Brain concentrations of di-scFv [124I]3D6-8D3 correlated with soluble Aβ (p 
       
  • Predominantly global genetic influences on individual white matter tract
           microstructure
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Daniel E. Gustavson, Sean N. Hatton, Jeremy A. Elman, Matthew S. Panizzon, Carol E. Franz, Donald J. Hagler, Christine Fennema-Notestine, Lisa T. Eyler, Linda K. McEvoy, Michael C. Neale, Nathan Gillespie, Anders M. Dale, Michael J. Lyons, William S. Kremen Individual differences in white matter tract microstructure, measured with diffusion tensor imaging (DTI), demonstrate substantial heritability. However, it is unclear to what extent this heritability reflects global genetic influences or tract-specific genetic influences. The goal of the current study was to quantify the proportion of genetic and environmental variance in white matter tracts attributable to global versus tract-specific influences. We assessed fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) across 11 tracts and 22 subdivisions of these tracts in 392 middle-aged male twins from the Vietnam Era Twin Study of Aging (VETSA). In principal component analyses of the 11 white matter tracts, the first component, which represents the global signal, explained 50.1% and 62.5% of the variance in FA and MD, respectively. Similarly, the first principal component of the 22 tract subdivisions explained 38.4% and 47.0% of the variance in FA and MD, respectively. Twin modeling revealed that DTI measures of all tracts and subdivisions were heritable, and that genetic influences on global FA and MD accounted for approximately half of the heritability in the tracts or tract subdivisions. Similar results were observed for the AD and RD diffusion metrics. These findings underscore the importance of controlling for DTI global signals when measuring associations between specific tracts and outcomes such as cognitive ability, neurological and psychiatric disorders, and brain aging.
       
  • Measurement of Sylvian Fissure asymmetry and occipital bending in humans
           and Pan troglodytes
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Lewis Hou, Li Xiang, Timothy J. Crow, François Leroy, Denis Rivière, Jean-François Mangin, Neil Roberts The evolution of human-specific lateralised functions such as language has been linked to the development of structural asymmetries in the brain. Here we applied state of the art image analysis techniques to measure Sylvian Fissure (SF) asymmetry and Occipital Bending (OB) in 3D Magnetic Resonance (MR) images of the brain obtained in-vivo for 27 humans and 29 chimpanzees (Pan troglodytes).SF morphology differed between species, with the human SF terminating more superiorly in right inferior parietal lobe, an asymmetry that was on average absent in chimpanzees (F (1,52) = 5.963, p = 0.018). Irrespective of morphology, Total SF Length was, as previously reported, leftward in humans but not in chimpanzees, although the difference did not reach significance between species. However, when only brains possessing comparable bilateral SF bifurcation morphology were compared, humans showed previously reported “Typical” left-lateralised Anterior-Horizontal (AH-SF) and right-lateralised Vertical (V-SF) SF asymmetries. In contrast, chimpanzees lacked both asymmetries, and this approached being a significant difference between-species in the AH-SF segment (F (1, 34) = 3.680, p = 0.064).On average in humans the left occipital lobe crossed the midline toward the right (Rightward OB) which was significantly different from the chimpanzee cohort that showed no average OB (Independent-Samples Mann-Whitney U Test, p = 0.012). Furthermore, OB was related to SF asymmetry in humans, such that the more rightward V-SF and leftward AH-SF, the more rightward the OB. This “Default” pattern of SF and OB asymmetries was found in 41.7% of human individuals with bilateral SF bifurcation but none of the chimpanzees.To our knowledge, this is the first study highlighting that a pattern of SF and OB asymmetry distinguishes the human from the chimpanzee brain, and suggests this may be associated with a unique trajectory of brain development and functional abilities in humans.
       
  • A framework for linking resting-state chronnectome/genome features in
           schizophrenia: A pilot study
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Barnaly Rashid, Jiayu Chen, Ishtiaque Rashid, Eswar Damaraju, Jingyu Liu, Robyn Miller, Oktay Agcaoglu, Theo G.M. van Erp, Kelvin O. Lim, Jessica A. Turner, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah McEwen, Steven G. Potkin, Adrian Preda, Juan R. Bustillo, Godfrey D. Pearlson Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.
       
  • Sex difference in brain CB1 receptor availability in man
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Heikki Laurikainen, Lauri Tuominen, Maria Tikka, Harri Merisaari, Reetta-Liina Armio, Elina Sormunen, Faith Borgan, Mattia Veronese, Oliver Howes, Merja Haaparanta-Solin, Olof Solin, Jarmo Hietala, METSY group The endocannabinoid system (ECS) has a widespread neuromodulatory function in the central nervous system and is involved in important aspects of brain function including brain development, cortical rhythms, plasticity, reward, and stress sensitivity. Many of these effects are mediated via the cannabinoid CB1 receptor (CB1R) subtype. Animal studies convincingly show an interaction between the ECS and sex hormones, as well as a sex difference of higher brain CB1R in males. Human in vivo studies of sex difference have yielded discrepant findings.Gender differences in CB1R availability were investigated in vivo in 11 male and 11 female healthy volunteers using a specific CB1R tracer [18F]FMPEP-d2 and positron emission tomography (PET). Regional [18F]FMPEP-d2 distribution volume was used as a proxy for CB1R availability. In addition, we explored whether CB1R availability is linked to neuropsychological functioning.Relative to females, CB1R availability was on average 41% higher in males (p = 0.002) with a regionally specific effect larger in the posterior cingulate and retrosplenial cortices (p = 0.001). Inter-subject variability in CB1R availability was similar in both groups. Voxel-based analyses revealed an inverse association between CB1R availability and visuospatial working memory task performance in both groups (p 
       
  • Imaging glutamate redistribution after acute N-acetylcysteine
           administration: A simultaneous PET/MR study
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Ruth O'Gorman Tuura, Geoff Warnock, Simon Ametamey, Valerie Treyer, Ralph Noeske, Alfred Buck, Michael Sommerauer Glutamate is the most abundant excitatory neurotransmitter in the human brain, but in vivo imaging of acute fluctuations in glutamatergic levels has not been well established. The purpose of this study was to examine acute changes in glutamate after stimulation with N-acetylcysteine (NAC) using a simultaneous positron emission tomography/magnetic resonance spectroscopy (PET/MRS) approach.Ten healthy adult males were examined in two scanning sessions, and 5g NAC was administered 1 h prior to one of the scan sessions. Simultaneous PET/MR data were acquired using an integrated 3T PET/MR scanner. Glutamate (Glu), glutamine (Gln), and glutamate + glutamine (Glx) levels were assessed from MRS data collected from the basal ganglia with PRESS and from the left prefrontal cortex with PRESS and MEGAPRESS, and mGluR5 binding (BPND) was assessed from PET data collected with [18F]PSS232.NAC administration was associated with a significant reduction in Glx and Gln in the basal ganglia spectra, and in Glx in the frontal MEGAPRESS spectra (p 
       
  • Multimodal evidence on shape and surface information in individual face
           processing
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Dan Nemrodov, Marlene Behrmann, Matthias Niemeier, Natalia Drobotenko, Adrian Nestor The significance of shape and surface information for face perception is well established, yet their relative contribution to recognition and their neural underpinnings await clarification. Here, we employ image reconstruction to retrieve, assess and visualize such information using behavioral, electroencephalography and functional magnetic resonance imaging data.Our results indicate that both shape and surface information can be successfully recovered from each modality but that the latter is better recovered than the former, consistent with its key role for face representations. Further, shape and surface information exhibit similar spatiotemporal profiles, rely on the extraction of specific visual features, such as eye shape or skin tone, and reveal a systematic representational structure, albeit with more cross-modal consistency for shape than surface. More generally, the present work illustrates a novel approach to relating and comparing different modalities in terms of perceptual information content.Thus, our results help elucidate the representational basis of individual face recognition while, methodologically, they showcase the utility of image reconstruction and clarify its reliance on diagnostic visual information.
       
  • Automatic segmentation of the spinal cord and intramedullary multiple
           sclerosis lesions with convolutional neural networks
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Charley Gros, Benjamin De Leener, Atef Badji, Josefina Maranzano, Dominique Eden, Sara M. Dupont, Jason Talbott, Ren Zhuoquiong, Yaou Liu, Tobias Granberg, Russell Ouellette, Yasuhiko Tachibana, Masaaki Hori, Kouhei Kamiya, Lydia Chougar, Leszek Stawiarz, Jan Hillert, Elise Bannier, Anne Kerbrat, Gilles Edan The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
       
  • Altered temporal variance and functional connectivity of BOLD signal is
           associated with state anxiety during acute systemic inflammation
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Franziska Labrenz, Francesca Ferri, Karsten Wrede, Michael Forsting, Manfred Schedlowski, Harald Engler, Sigrid Elsenbruch, Sven Benson, Marcello Costantini Systemic inflammation is accompanied by complex behavioral changes and disturbed emotion regulation that have been related to the pathophysiology of mood disorders including depression and anxiety. However, the causal role of systemic inflammation on mood disorders is still unclear. We herein investigated neural resting state patterns of temporal variance of the amygdala and functional connectivity within the salience network underlying changes in state anxiety during experimentally-induced systemic inflammation. In this randomized, double-blind study, N = 43 healthy men received an intravenous injection of either low-dose lipopolysaccharide (LPS, 0.4 ng/kg body weight) or saline. Resting state functional magnetic resonance imaging was assessed before and 3.5 h after injection. State anxiety, assessed with a standardized questionnaire, and plasma cytokine concentrations were repeatedly measured. LPS administration induced a transient systemic inflammatory response reflected in increases in plasma Interleukin (IL)-6 and Tumor Necrosis Factor (TNF)-α concentration. Compared to placebo, state anxiety and temporal variance in the amygdala significantly increased while functional connectivity in the salience network decreased during LPS-induced systemic inflammation. Together, these data indicate that acute systemic inflammation alters temporal variance of the BOLD signal as well as functional connectivity in brain regions and networks implicated in emotion processing and regulation. These results are of translational importance to encourage further research on the role of inflammatory pathways in the pathophysiology of neuropsychiatric conditions including anxiety disorders.
       
  • Automated quality control for within and between studies diffusion MRI
           data using a non-parametric framework for movement and distortion
           correction
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Matteo Bastiani, Michiel Cottaar, Sean P. Fitzgibbon, Sana Suri, Fidel Alfaro-Almagro, Stamatios N. Sotiropoulos, Saad Jbabdi, Jesper L.R. Andersson Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.
       
  • Dynamic 23Na MRI - A non-invasive window on neuroglial-vascular mechanisms
           underlying brain function
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Mark Bydder, Wafaa Zaaraoui, Ben Ridley, Manon Soubrier, Marie Bertinetti, Sylviane Confort-Gouny, Lothar Schad, Maxime Guye, Jean-Philippe Ranjeva A novel magnetic resonance imaging (MRI) acquisition and reconstruction method for obtaining a series of dynamic sodium 23Na-MRI acquisitions was designed to non-invasively assess the signal variations of brain sodium during a hand motor task in 14 healthy human volunteers on an ultra high field (7T) MR scanner. Regions undergoing activation and deactivation were identified with reference to conventional task-related BOLD functional MRI (fMRI). Activation observed in the left central regions, the supplementary motor areas and the left cerebellum induced an increase in the sodium signal observed at ultra short echo time and a decrease in the 23Na signal observed at long echo time. Based on a simple model of two distinct sodium pools (namely, restricted and mobile sodium), the ultra short echo time measures the totality of sodium whereas the long echo time is mainly sensitive to mobile sodium. This activation pattern is consistent with previously described processes related to an influx of Na+ into the intracellular compartments and a moderate increase in the cerebral blood volume (CBV). In contrast, deactivation observed in the right central regions ipsilateral to the movement, the precuneus and the left cerebellum induced a slight decrease in sodium signal at ultra short echo time and an increase of sodium signal at longer echo times. This inhibitory pattern is compatible with a slight decrease in CBV and an efflux of intracellular Na+ to the extracellular compartments that may reflect neural dendritic spine and astrocytic shrinkage, and an increase of sodium in the extracellular fraction. In conclusion, cerebral dynamic 23Na MRI experiments can provide access to the ionic transients following a functional task occurring within the neuro-glial-vascular ensemble. This has the potential to open up a novel non-invasive window on the mechanisms underlying brain function.Graphical abstractImage 1
       
  • Brain functional and effective connectivity underlying the information
           processing speed assessed by the Symbol Digit Modalities Test
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): P.H.R. Silva, C.T. Spedo, C.R. Baldassarini, C.D. Benini, D.A. Ferreira, A.A. Barreira, R.F. Leoni Delayed Information Processing Speed (IPS) often underlies attention deficits and is particularly evident in patients with traumatic brain injury, Parkinson's disease, depression, dementia, and multiple sclerosis. Therefore, it is of interest to determine the brain network that is responsible for such essential cognitive function to understand IPS deficits and to develop effective rehabilitation programs. We assessed brain functional connectivity and effective connectivity during the performance of an adapted version of the Symbol Digit Modalities Test. Using dynamic causal modeling, we focused on obtaining a network model for IPS function in healthy subjects. Sixteen right-handed volunteers (seven women, age: 29.7 ± 5.0 years) were included in the study after giving written consent for participating. Functional magnetic resonance images were acquired in a 3T scanner. According to our results, two systems interact during the IPS task performance. One is formed by frontoparietal and fronto-occipital networks, related to the control of goal-directed (top-down) selection for stimuli and response, while the second is composed of the temporoparietal and inferior frontal cortices, which are associated with stimulus-driven attention in the brain. Additionally, the default-mode network showed a significant correlation with networks positively associated with the task, mainly those related to visual detection and processing, indicating its relevant role in functional integration involving IPS. Therefore, an IPS-related network was proposed through a methodology that may be useful for future studies considering other cognitive functions and tasks, clinical groups, and longitudinal assessments.Graphical abstractImage 1
       
  • How to control for confounds in decoding analyses of neuroimaging data
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Lukas Snoek, Steven Miletić, H. Steven Scholte Over the past decade, multivariate “decoding analyses” have become a popular alternative to traditional mass-univariate analyses in neuroimaging research. However, a fundamental limitation of using decoding analyses is that it remains ambiguous which source of information drives decoding performance, which becomes problematic when the to-be-decoded variable is confounded by variables that are not of primary interest. In this study, we use a comprehensive set of simulations as well as analyses of empirical data to evaluate two methods that were previously proposed and used to control for confounding variables in decoding analyses: post hoc counterbalancing and confound regression. In our empirical analyses, we attempt to decode gender from structural MRI data while controlling for the confound “brain size”. We show that both methods introduce strong biases in decoding performance: post hoc counterbalancing leads to better performance than expected (i.e., positive bias), which we show in our simulations is due to the subsampling process that tends to remove samples that are hard to classify or would be wrongly classified; confound regression, on the other hand, leads to worse performance than expected (i.e., negative bias), even resulting in significant below chance performance in some realistic scenarios. In our simulations, we show that below chance accuracy can be predicted by the variance of the distribution of correlations between the features and the target. Importantly, we show that this negative bias disappears in both the empirical analyses and simulations when the confound regression procedure is performed in every fold of the cross-validation routine, yielding plausible (above chance) model performance. We conclude that, from the various methods tested, cross-validated confound regression is the only method that appears to appropriately control for confounds which thus can be used to gain more insight into the exact source(s) of information driving one's decoding analysis.
       
  • Community and household-level socioeconomic disadvantage and functional
           organization of the salience and emotion network in children and
           adolescents
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Klara Gellci, Hilary A. Marusak, Craig Peters, Farrah Elrahal, Allesandra S. Iadipaolo, Christine A. Rabinak Socioeconomic disadvantage (SED) during childhood has been linked to disparities in physical and mental health. A growing body of research has focused on identifying neurodevelopmental consequences of SED, commonly measured using within-household factors (e.g., household income), to better understand the processes underlying SED-related disparities. These studies suggest that childhood SED has a widespread impact on brain development, altering development of multiple brain regions simultaneously. These findings also raise the possibility that childhood SED impacts development of key brain systems, such as the salience and emotion network (SEN), which is positioned at the intersection of brain systems involved in cognitive and emotion-related functioning and is thought to mediate information flow within and between these networks. The present study tests for associations between household- and community-level SED, as well as their interaction, and measures of SEN-based functional neural organization in 57 children and adolescents (ages 6–17). We applied graph theoretical analyses to resting-state functional magnetic resonance imaging (fMRI) data to examine SEN-based functional network topology. Results showed that youth residing in more distressed communities demonstrate lower hub-like properties (i.e., less efficient global information transfer and fewer connections) of two core SEN nodes – the anterior cingulate cortex and the left supramarginal gyrus. Similarly, lower household income was associated with lower efficiency of the anterior cingulate, but had no effect on the supramarginal gyrus. There was, however, an interaction between income and community SED in the rostral prefrontal cortex, such that higher income was associated with higher clustering coefficient and lower betweenness centrality, suggesting greater local processing and lower influence of this region on information flow across the network. These effects were significant only among youth living in low (but not high) SED communities, suggesting that within-household SED factors may not protect against the detrimental effects of a disadvantaged community context. Similarly, the age-related increase in average path length of the left rostral prefrontal cortex was only significant among youth living in low (but not high) SED communities. Given that maturation of the SEN is considered to be a critical functional backbone supporting the development of more flexible cognitive and emotional processes into adulthood, we tested for links between SEN graph metrics and measures of cognitive and emotion-related functioning. We found that higher community SED and lower income were both associated with lower IQ. Lower IQ, in turn, was associated with global efficiency of the left supramarginal gyrus. Observed effects of SED on SEN-based functional neural organization may help to explain the strong and pervasive link between childhood SED and disparities in cognitive and emotional outcomes.
       
  • Dual-calibrated fMRI measurement of absolute cerebral metabolic rate of
           oxygen consumption and effective oxygen diffusivity
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): M. Germuska, H.L. Chandler, R.C. Stickland, C. Foster, F. Fasano, T.W. Okell, J. Steventon, V. Tomassini, K. Murphy, R.G. Wise Dual-calibrated fMRI is a multi-parametric technique that allows for the quantification of the resting oxygen extraction fraction (OEF), the absolute rate of cerebral metabolic oxygen consumption (CMRO2), cerebral vascular reactivity (CVR) and baseline perfusion (CBF). It combines measurements of arterial spin labelling (ASL) and blood oxygenation level dependent (BOLD) signal changes during hypercapnic and hyperoxic gas challenges. Here we propose an extension to this methodology that permits the simultaneous quantification of the effective oxygen diffusivity of the capillary network (DC). The effective oxygen diffusivity has the scope to be an informative biomarker and useful adjunct to CMRO2, potentially providing a non-invasive metric of microvascular health, which is known to be disturbed in a range of neurological diseases. We demonstrate the new method in a cohort of healthy volunteers (n = 19) both at rest and during visual stimulation. The effective oxygen diffusivity was found to be highly correlated with CMRO2 during rest and activation, consistent with previous PET observations of a strong correlation between metabolic oxygen demand and effective diffusivity. The increase in effective diffusivity during functional activation was found to be consistent with previously reported increases in capillary blood volume, supporting the notion that measured oxygen diffusivity is sensitive to microvascular physiology.
       
  • Neural correlates of developing theory of mind competence in early
           childhood
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yaqiong Xiao, Fengji Geng, Tracy Riggins, Gang Chen, Elizabeth Redcay Theory of mind (ToM) encompasses a range of abilities that show different developmental time courses. However, relatively little work has examined the neural correlates of ToM during early childhood. In this study, we investigated the neural correlates of ToM in typically developing children aged 4–8 years using resting-state functional magnetic resonance imaging. We calculated whole-brain functional connectivity with the right temporo-parietal junction (RTPJ), a core region involved in ToM, and examined its relation to children's early, basic, and advanced components of ToM competence assessed by a parent-report measure. Total ToM and both basic and advanced ToM components, but not early, consistently showed a positive correlation with connectivity between RTPJ and posterior cingulate cortex/precuneus; advanced ToM was also correlated with RTPJ to left TPJ connectivity. However, early and advanced ToM components showed negative correlation with the right inferior/superior parietal lobe, suggesting that RTPJ network differentiation is also related to ToM abilities. We confirmed and extended these results using a Bayesian modeling approach demonstrating significant relations between multiple nodes of the mentalizing network and ToM abilities, with no evidence for differences in relations between ToM components. Our data provide new insights into the neural correlates of multiple aspects of ToM in early childhood and may have implications for both typical and atypical development of ToM.
       
  • Oxytocin differentially modulates specific dorsal and ventral striatal
           functional connections with frontal and cerebellar regions
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Zhiying Zhao, Xiaole Ma, Yayuan Geng, Weihua Zhao, Feng Zhou, Jiaojian Wang, Sebastian Markett, Bharat B. Biswal, Yina Ma, Keith M. Kendrick, Benjamin Becker Interactions between oxytocin and the basal ganglia are central in current overarching conceptualizations of its broad modulatory effects on behavior. Whereas evidence from animal models emphasizes the critical role of the ventral striatum in the behavioral effects of oxytocin, region-specific contributions of the basal ganglia have not been systematically explored in humans. The present study combined the randomized placebo-controlled administration of oxytocin versus placebo in healthy men (n = 144) with fMRI-based resting-state functional connectivity to determine the modulatory role of oxytocin on the major basal ganglia pathways. Oxytocin specifically increased connectivity between ventral striatal and pallidal nodes with upstream frontal regions, whereas it decreased the strengths of downstream pathways between the dorsal striatum and posterior cerebellum. These pathways have previously been implicated in salience, reward and behavioral flexibility, thus shaping goal-directed behavior. Given the importance of aberrant striatal intrinsic organization in autism, addiction and schizophrenia the present findings may suggest new mechanistic perspectives for the therapeutic potential of oxytocin in these disorders.
       
  • Background connectivity between frontal and sensory cortex depends on task
           state, independent of stimulus modality
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Abdurahman S. Elkhetali, Leland L. Fleming, Ryan J. Vaden, Rodolphe Nenert, Jane E. Mendle, Kristina M. Visscher The human brain has the ability to process identical information differently depending on the task. In order to perform a given task, the brain must select and react to the appropriate stimuli while ignoring other irrelevant stimuli. The dynamic nature of environmental stimuli and behavioral intentions requires an equally dynamic set of responses within the brain. Collectively, these responses act to set up and maintain states needed to perform a given task. However, the mechanisms that allow for setting up and maintaining a task state are not fully understood. Prior evidence suggests that one possible mechanism for maintaining a task state may be through altering 'background connectivity,' connectivity that exists independently of the trials of a task. Although previous studies have suggested that background connectivity contributes to a task state, these studies have typically not controlled for stimulus characteristics, or have focused primarily on relationships among areas involved with visual sensory processing. In the present study we examined background connectivity during tasks involving both visual and auditory stimuli. We examined the connectivity profiles of both visual and auditory sensory cortex that allow for selection of task-relevant stimuli, demonstrating the existence of a potentially universal pattern of background connectivity underlying attention to a stimulus. Participants were presented with simultaneous auditory and visual stimuli and were instructed to respond to only one, while ignoring the other. Using functional MRI, we observed task-based modulation of the background connectivity profile for both the auditory and visual cortex to certain brain regions. There was an increase in background connectivity between the task-relevant sensory cortex and control areas in the frontal cortex. This increase in synchrony when receiving the task-relevant stimulus as compared to the task irrelevant stimulus may be maintaining paths for passing information within the cortex. These task-based modulations of connectivity occur independently of stimuli and could be one way the brain sets up and maintains a task state.
       
  • Low-frequency direct cortical stimulation of left superior frontal gyrus
           enhances working memory performance
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Sankaraleengam Alagapan, Caroline Lustenberger, Eldad Hadar, Hae Won Shin, Flavio Frӧhlich The neural substrates of working memory are spread across prefrontal, parietal and cingulate cortices and are thought to be coordinated through low frequency cortical oscillations in the theta (3–8 Hz) and alpha (8–12 Hz) frequency bands. While the functional role of many subregions have been elucidated using neuroimaging studies, the role of superior frontal gyrus (SFG) is not yet clear. Here, we combined electrocorticography and direct cortical stimulation in three patients implanted with subdural electrodes to assess if superior frontal gyrus is indeed involved in working memory. We found left SFG exhibited task-related modulation of oscillations in the theta and alpha frequency bands specifically during the encoding epoch. Stimulation at the frequency matched to the endogenous oscillations resulted in reduced reaction times in all three participants. Our results provide evidence for SFG playing a functional role in working memory and suggest that SFG may coordinate working memory through low-frequency oscillations thus bolstering the feasibility of using intracranial electric stimulation for restoring cognitive function.
       
  • Training emotion regulation through real-time fMRI neurofeedback of
           amygdala activity
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): U. Herwig, J. Lutz, S. Scherpiet, H. Scheerer, J. Kohlberg, S. Opialla, A. Preuss, V.R. Steiger, J. Sulzer, S. Weidt, P. Stämpfli, M. Rufer, E. Seifritz, L. Jäncke, A.B. Brühl Being in control of one's emotions is not only desirable in many everyday situations but is also a great challenge in a variety of mental disorders. Successful intentional emotion regulation is related to down-regulation of amygdala activity. Training mental interventions supported by neurofeedback of one's own amygdala activity using real-time (rt-)fMRI might be beneficial for mental health and well-being. Rt-fMRI guided amygdala-downregulation using cognitive interventions such as a “reality check”, however, have not been well-investigated.Fifteen healthy subjects underwent four rt-fMRI sessions with neurofeedback of their own amygdala activity while applying a reality check as an emotion regulation strategy in order to down-regulate their amygdala signal during a stimulation with emotional pictures. The Control group comprised of eleven subjects also trained emotion regulation but without obtaining feedback. We hypothesized more prominent down-regulation of amygdala activity at the end of the training in the Feedback group. We investigated effects over time and between groups and further task specific connectivity of the amygdala by using psychophysiological interaction analyses.Four weekly amygdala-based feedback sessions resulted in significantly decreased amygdala activity (p = 0.003, d = 0.93), also compared to the Control group (p = 0.014, d = 1.12). Task specific connectivity of the amygdala with the anterior cingulate cortex, hippocampus and distinct prefrontal areas was increased in the Feedback group.Training of emotion regulation supported by rt-fMRI neurofeedback resulted in a prominent amygdala down-regulation compared to training without feedback. The finding implicates successful emotion regulation, compliant with emotion control models, through an easily applicable reality check strategy. Rt-fMRI neurofeedback may support emotion regulation learning and bears clinical potential for psychotherapy.Graphical abstractResults of the amygdala ROI analysis in the Feedback group: (a) Mean beta weights of the subjects' individual localizer amygdala ROI's. Conditions (regulate (green), view (red) and regulate > view (blue)) are presented over the four sessions (S1-S4). The blue regulate > view bar represents the effect of the training sessions with the decrease of amygdala activity through the mental intervention of reality check for emotion regulation in the regulate condition. Error bars represent standard deviations. (b) The mask of area in the amygdala region is covered by all individual ROIs of the Feedback group.Image 1
       
  • Layer-specific connectivity revealed by diffusion-weighted functional MRI
           in the rat thalamocortical pathway
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Daniel Nunes, Andrada Ianus, Noam Shemesh Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD-driven fMRI, and its activation maps relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4 T, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, these advanatages may have significant implications towards a much more specific and accurate mapping of neural activity in the global brain in-vivo.
       
  • Resting state dynamics meets anatomical structure: Temporal multiple
           kernel learning (tMKL) model
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Sriniwas Govinda Surampudi, Joyneel Misra, Gustavo Deco, Raju Surampudi Bapi, Avinash Sharma, Dipanjan Roy Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting-state functional magnetic resonance imaging (rsfMRI). The resting state patterns have been observed to be exhibiting complex spatio-temporal dynamics and substantial effort has been made to characterize the dynamic functional connectivity (dFC) configurations. However, the dynamics governing the state transitions that the brain undergoes and their relationship to stationary functional connectivity still remains an open problem.One class of approaches attempts to characterize the dynamics in terms of finite number of latent brain states, however, such attempts are yet to amalgamate the underlying anatomical structural connectivity (SC) with the dynamics. Another class of methods links individual dynamic FCs with the underlying SC but does not characterize the temporal evolution of FC. Further, the latent states discovered by previous approaches could not be directly linked to the SC, thereby motivating us to discover the underlying lower-dimensional manifold that represents the temporal structure. In the proposed approach, the discovered manifold is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to the latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) that inherently links dynamics to the structure and finally predicts the grand average FC of the test subjects by leveraging a state transition Markov model.The proposed solution does not make strong assumptions about the underlying data and is generally applicable to resting or task data for learning subject-specific state transitions and for successfully characterizing SC-dFC-FC relationship through a unifying framework. Training and testing were done using the rs-fMRI data of 46 healthy participants. tMKL model performs significantly better than the existing models for predicting resting state functional connectivity based on whole-brain dynamic mean-field model (DMF), single diffusion kernel (SDK) model and multiple kernel learning (MKL) model. Further, the learned model was tested on an independent cohort of 100 young, healthy participants from the Human Connectome Project (HCP) and the results establish the generalizability of the proposed solution. More importantly, the model retains sensitivity toward subject-specific anatomy, a unique contribution towards a holistic approach for SC-FC characterization.
       
  • Optimization and comparative evaluation of nonlinear deformation
           algorithms for atlas-based segmentation of DBS target nuclei
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Siobhan Ewert, Andreas Horn, Francisca Finkel, Ningfei Li, Andrea A. Kühn, Todd M. Herrington Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi).Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1-or T2-weighted acquisitions alone or a combination of T1-, T2-and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-the-art MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson's Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens.
       
  • Aggression modulates neural correlates of hostile intention attribution to
           laughter in children
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): A. Martinelli, B. Kreifelts, D. Wildgruber, K. Ackermann, A. Bernhard, C.M. Freitag, C. Schwenck The tendency to interpret nonverbal social signals as hostile in intention is associated with aggressive responding, poor social functioning and mental illness, and can already be observed in childhood. To investigate the neural correlates of such hostile attributions of social intention, we performed a functional magnetic imaging study in 10–18 year old children and adolescents. Fifty healthy participants rated videos of laughter, which they were told to imagine as being directed towards them, as friendly versus hostile in social intention. Hostile intention ratings were associated with neural response in the right temporal voice area (TVA). Moreover, self-reported trait physical aggression modulated this relationship in both the right TVA and bilateral lingual gyrus, with stronger associations between hostile intention ratings and neural activation in children with higher trait physical aggression scores. Functional connectivity results showed decreased connectivity between the right TVA and left dorsolateral prefrontal cortex with increasing trait physical aggression for making hostile social intention attributions. We conclude that children's social intention attributions are more strongly related to activation of early face and voice-processing regions with increasing trait physical aggression.
       
  • Integrating high-density ERP and fMRI measures of face-elicited brain
           activity in 9–12-year-old children: An ERP source localization study
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Pan Liu, Xiaoxiao Bai, Koraly E. Pérez-Edgar Social information processing is a critical mechanism underlying children's socio-emotional development. Central to this process are patterns of activation associated with one of our most salient socioemotional cues, the face. In this study, we obtained fMRI activation and high-density ERP source data evoked by parallel face dot-probe tasks from 9-to-12-year-old children. We then integrated the two modalities of data to explore the neural spatial-temporal dynamics of children's face processing. Our results showed that the tomography of the ERP sources broadly corresponded with the fMRI activation evoked by the same facial stimuli. Further, we combined complementary information from fMRI and ERP by defining fMRI activation as functional ROIs and applying them to the ERP source data. Indices of ERP source activity were extracted from these ROIs at three a priori ERP peak latencies critical for face processing. We found distinct temporal patterns among the three time points across ROIs. The observed spatial-temporal profiles converge with a dual-system neural network model for face processing: a core system (including the occipito-temporal and parietal ROIs) supports the early visual analysis of facial features, and an extended system (including the paracentral, limbic, and prefrontal ROIs) processes the socio-emotional meaning gleaned and relayed by the core system. Our results for the first time illustrate the spatial validity of high-density source localization of ERP dot-probe data in children. By directly combining the two modalities of data, our findings provide a novel approach to understanding the spatial-temporal dynamics of face processing. This approach can be applied in future research to investigate different research questions in various study populations.
       
  • On the pros and cons of using temporal derivatives to assess brain
           functional connectivity
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Jeremi K. Ochab, Wojciech Tarnowski, Maciej A. Nowak, Dante R. Chialvo The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed “Multiplication of Temporal Derivatives” (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.Graphical abstractImage 1
       
  • RF-induced heating in tissue near bilateral DBS implants during MRI at
           1.5 T and 3T: The role of surgical lead management
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Laleh Golestanirad, John Kirsch, Giorgio Bonmassar, Sean Downs, Behzad Elahi, Alastair Martin, Maria-Ida Iacono, Leonardo M. Angelone, Boris Keil, Lawrence L. Wald, Julie Pilitsis Access to MRI is limited for patients with deep brain stimulation (DBS) implants due to safety hazards, including radiofrequency (RF) heating of tissue surrounding the leads. Computational models provide an exquisite tool to explore the multi-variate problem of RF heating and help better understand the interaction of electromagnetic fields and biological tissues. This paper presents a computational approach to assess RF-induced heating, in terms of specific absorption rate (SAR) in the tissue, around the tip of bilateral DBS leads during MRI at 64MHz/1.5 T and 127 MHz/3T. Patient-specific realistic lead models were constructed from post-operative CT images of nine patients operated for sub-thalamic nucleus DBS. Finite element method was applied to calculate the SAR at the tip of left and right DBS contact electrodes. Both transmit head coils and transmit body coils were analyzed. We found a substantial difference between the SAR and temperature rise at the tip of right and left DBS leads, with the lead contralateral to the implanted pulse generator (IPG) exhibiting up to 7 times higher SAR in simulations, and up to 10 times higher temperature rise during measurements. The orientation of incident electric field with respect to lead trajectories was explored and a metric to predict local SAR amplification was introduced. Modification of the lead trajectory was shown to substantially reduce the heating in phantom experiments using both conductive wires and commercially available DBS leads. Finally, the surgical feasibility of implementing the modified trajectories was demonstrated in a patient operated for bilateral DBS.
       
  • Estimating and accounting for the effect of MRI scanner changes on
           longitudinal whole-brain volume change measurements
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Hyunwoo Lee, Kunio Nakamura, Sridar Narayanan, Robert A. Brown, Douglas L. Arnold, for the Alzheimer's Disease Neuroimaging Initiative ObjectiveLongitudinal MRI studies are often subjected to mid-study scanner changes, which may alter image characteristics such as contrast, signal-to-noise ratio, contrast-to-noise ratio, intensity non-uniformity and geometric distortion. Measuring brain volume loss under these conditions can render the results potentially unreliable across the timepoint of the change. Estimating and accounting for this effect may improve the reliability of estimates of brain atrophy rates.MethodsWe analyzed 237 subjects who were scanned at 1.5 T for the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and were subject to intra-vendor or inter-vendor scanner changes during follow-up (up to 8 years). Sixty-three subjects scanned on GE Signa HDx and HDxt platforms were also subject to a T1-weighted sequence change from Magnetization Prepared Rapid Gradient Echo (MP-RAGE) to Fast Spoiled Gradient Echo with IR Preparation (IR-FSPGR), as part of the transition from ADNI-1 to ADNI-2/GO. Two-timepoint percentage brain volume changes (PBVCs) between the baseline “screening” and the follow-up scans were calculated using SIENA. A linear mixed-effects model with subject-specific random slopes and intercepts was applied to estimate the fixed effects of scanner hardware changes on the PBVC measures. The same model also included a term to estimate the fixed effects of the T1-weighted sequence change.ResultsDifferent hardware upgrade or change combinations led to different offsets in the PBVC (SE; p): Philips Intera to Siemens Avanto, −1.81% (0.30; p 
       
  • Increased responses of the reward circuitry to positive task feedback
           following acute stress in healthy controls but not in siblings of
           schizophrenia patients
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): J.M.C. van Leeuwen, M. Vink, M. Joëls, R.S. Kahn, E.J. Hermans, C.H. Vinkers Acute stress is known to affect the way we process rewards. For example, during, or directly after stress, activity within key brain areas of the reward circuitry is reduced when a reward is presented. Generally, the effects of stress on the brain are time-dependent, changing neural and cognitive processing in the aftermath of stress to aid recovery. Such a dynamic response to stress is important for resilience on the longer term. However, relatively little is known about reward processing during the recovery phase of stress and whether this is changed in individuals at increased risk for stress-related psychopathology.Healthy male individuals (N = 40) and unaffected siblings of schizophrenia patients (N = 40) were randomized to either an acute stress task (Trier Social Stress Test) or a no-stress task. Neural responses during reward anticipation and reward feedback (monetary gain or no gain) were examined 50 min later using an fMRI monetary incentive delay task. The ventral striatum and orbitofrontal cortex (OFC) were used as predefined hypothesis-driven regions of interest.Neural responses following stress differed between controls and siblings during reward feedback (group × stress interaction OFC p = 0.003, ventral striatum p = 0.031), showing increased ventral striatum and OFC responses following stress in healthy controls only. Exploratory analyses revealed that this effect was most pronounced during hit trials (compared to when a reward was omitted), and independent of monetary value. Stress did not affect subsequent reward processing in siblings of schizophrenia patients. We found no significant differences between controls and siblings in ventral striatum and OFC responses during reward anticipation following stress.This study shows that ventral striatum and OFC responses to positive task feedback are increased in the aftermath of stress in healthy male controls, regardless of monetary value. This indicates a dynamic shift from previously reported reduced responses in the striatum and OFC to reward feedback directly after stress to increased responses to both reward and non-reward feedback during the recovery phase of stress. These increased neural responses following stress were absent in siblings of schizophrenia patients. Together, these findings indicate that stress recovery is affected in this at-risk group, particularly in responses to positive feedback following stress.
       
  • Interactions between neural decision-making circuits predict long-term
           dietary treatment success in obesity
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Martin Weygandt, Joachim Spranger, Verena Leupelt, Lukas Maurer, Thomas Bobbert, Knut Mai, John-Dylan Haynes Although dietary decision-making is regulated by multiple interacting neural controllers, their impact on dietary treatment success in obesity has only been investigated individually. Here, we used fMRI to test how well interactions between the Pavlovian system (automatically triggering urges of consumption after food cue exposure) and the goal-directed system (considering long-term consequences of food decisions) predict future dietary success achieved in 39 months. Activity of the Pavlovian system was measured with a cue-reactivity task by comparing perception of food versus control pictures, activity of the goal-directed system with a food-specific delay discounting paradigm. Both tasks were applied in 30 individuals with obesity up to five times: Before a 12-week diet, immediately thereafter, and at three annual follow-up visits. Brain activity was analyzed in two steps. In the first, we searched for areas involved in Pavlovian processes and goal-directed control across the 39-month study period with voxel-wise linear mixed-effects (LME) analyses. In the second, we computed network parameters reflecting the covariation of longitudinal voxel activity (i.e. principal components) in the regions identified in the first step and used them to predict body mass changes across the 39 months with LME models. Network analyses testing the link of dietary success with activity of the individual systems as reference found a moderate negative link to Pavlovian activity primarily in left hippocampus and a moderate positive association to goal-directed activity primarily in right inferior parietal gyrus. A cross-paradigm network analysis that integrated activity measured in both tasks revealed a strong positive link for interactions between visual Pavlovian areas and goal-directed decision-making regions mainly located in right insular cortex. We conclude that adaptation of food cue processing resources to goal-directed control activity is an important prerequisite of sustained dietary weight loss, presumably since the latter activity can modulate Pavlovian urges triggered by frequent cue exposure in everyday life.
       
  • Neural representations of aversive value encoding in pain catastrophizers
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Christopher A. Brown, Abeer F. Almarzouki, Richard J. Brown, Anthony K.P. Jones Chronic pain is exacerbated by maladaptive cognition such as pain catastrophizing (PC). Biomarkers of PC mechanisms may aid precision medicine for chronic pain. Here, we investigate EEG biomarkers using mass univariate and multivariate (machine learning) approaches. We test theoretical notions that PC results from a combination of augmented aversive-value encoding (“magnification”) and persistent expectations of pain (“rumination”). Healthy individuals with high or low levels of PC underwent an experimental pain model involving nociceptive laser stimuli preceded by cues predicting forthcoming pain intensity. Analysis of EEG acquired during the cue and laser stimulation provided event-related potentials (ERPs) identifying spatially and temporally-extended neural representations associated with pain catastrophizing. Specifically, differential neural responses to cues predicting high vs. low intensity pain (i.e. aversive value encoding) were larger in the high PC group, largely originating from mid-cingulate and superior parietal cortex. Multivariate spatiotemporal EEG patterns evoked from cues with high aversive value selectively and significantly differentiated the high PC from low PC group (64.6% classification accuracy). Regression analyses revealed that neural patterns classifying groups could be partially predicted (R2 = 28%) from those neural patterns classifying the aversive value of cues. In contrast, behavioural and EEG analyses did not provide evidence that PC modifies more persistent effects of prior expectation on pain perception and nociceptive responses. These findings support the hypothesis of magnification of aversive value encoding but not persistent expression of expectation in pain catastrophizers. Multivariate patterns of aversive value encoding provide promising biomarkers of maladaptive cognitive responses to chronic pain that have future potential for psychological treatment development and clinical stratification.
       
  • Mu rhythm desynchronization is specific to action execution and
           observation: Evidence from time-frequency and connectivity analysis
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Ranjan Debnath, Virginia C. Salo, George A. Buzzell, Kathryn H. Yoo, Nathan A. Fox Mu desynchronization is the attenuation of EEG power in the alpha frequency range recorded over central scalp locations thought to reflect motor cortex activation. Mu desynchronization during observation of an action is believed to reflect mirroring system activation in humans. However, this notion has recently been questioned because, among other reasons, the potential contamination of mu rhythm and occipital alpha activity induced by attention processes following presentation of visual stimuli in observation conditions. This study examined the validity of mu desynchronization as a measure of mirroring system activation in infants and further investigated the pattern of functional connectivity between the central and occipital regions during execution and observation of movement. EEG was recorded while 46 9-month-old infants executed grasping actions and observed an experimenter grasping. Current source density (CSD) was applied to EEG data and, time-frequency and connectivity analyses were performed in CSD transformed data. Mu desynchronization was evident over central regions during both execution and observation of movements. Independent alpha desynchronization over occipital region was also present in both conditions. The connectivity analyses revealed that central-occipital areas were functionally more connected compared to other areas of the brain during observation of movements. Collectively, the results demonstrate the validity of mu desynchronization as an index of infant mirroring system activity and support the proposal of a functional connection between distinct mirroring and attention processes during observation of action.
       
  • Exploring Alzheimer's disease mouse brain through X-ray phase contrast
           tomography: From the cell to the organ
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Lorenzo Massimi, Inna Bukreeva, Giulia Santamaria, Michela Fratini, Alessandro Corbelli, Francesco Brun, Stefano Fumagalli, Laura Maugeri, Alexandra Pacureanu, Peter Cloetens, Nicola Pieroni, Fabio Fiordaliso, Gianluigi Forloni, Antonio Uccelli, Nicole Kerlero de Rosbo, Claudia Balducci, Alessia Cedola Alzheimer's disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder associated with aberrant production of beta-amyloid (Aβ) peptide depositing in brain as amyloid plaques. While animal models allow investigation of disease progression and therapeutic efficacy, technology to fully dissect the pathological mechanisms of this complex disease at cellular and vascular levels is lacking.X-ray phase contrast tomography (XPCT) is an advanced non-destructive 3D multi-scale direct imaging from the cell through to the whole brain, with exceptional spatial and contrast resolution. We exploit XPCT to simultaneously analyse disease-relevant vascular and neuronal networks in AD mouse brain, without sectioning and staining. The findings clearly show the different typologies and internal structures of Aβ plaques, together with their interaction with patho/physiological cellular and neuro-vascular microenvironment. XPCT enables for the first time a detailed visualization of amyloid-angiopathy at capillary level, which is impossible to achieve with other approaches.XPCT emerges as added-value technology to explore AD mouse brain as a whole, preserving tissue chemistry and structure, enabling the comparison of physiological vs. pathological states at the level of crucial disease targets. In-vivo translation will permit to monitor emerging therapeutic approaches and possibly shed new light on pathological mechanisms of neurodegenerative diseases.Graphical abstractImage 1
       
  • Eccentricity-dependent temporal contrast tuning in human visual cortex
           measured with fMRI
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Marc M. Himmelberg, Alex R. Wade Cells in the peripheral retina tend to have higher contrast sensitivity and respond at higher flicker frequencies than those closer to the fovea. Although this predicts increased behavioural temporal contrast sensitivity in the peripheral visual field, this effect is rarely observed in psychophysical experiments. It is unknown how temporal contrast sensitivity is represented across eccentricity within cortical visual field maps and whether such sensitivities reflect the response properties of retinal cells or psychophysical sensitivities. Here, we used functional magnetic resonance imaging (fMRI) to measure contrast sensitivity profiles at four temporal frequencies in five retinotopically-defined visual areas. We also measured population receptive field (pRF) parameters (polar angle, eccentricity, and size) in the same areas. Overall contrast sensitivity, independent of pRF parameters, peaked at 10 Hz in all visual areas. In V1, V2, V3, and V3a, peripherally-tuned voxels had higher contrast sensitivity at a high temporal frequency (20 Hz), while hV4 more closely reflected behavioural sensitivity profiles. We conclude that our data reflect a cortical representation of the increased peripheral temporal contrast sensitivity that is already present in the retina and that this bias must be compensated later in the cortical visual pathway.
       
  • P3b amplitude as a signature of cognitive decline in the older population:
           An EEG study enhanced by Functional Source Separation
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Camillo Porcaro, Joshua Henk Balsters, Dante Mantini, Ian H. Robertson, Nicole Wenderoth With the greying population, it is increasingly necessary to establish robust and individualized markers of cognitive decline. This requires the combination of well-established neural mechanisms, and the development of increasingly sensitive methodologies. The P300 event-related potential (ERP) has been one of the most heavily investigated neural markers of attention and cognition, and studies have reliably shown that changes in the amplitude and latency of the P300 ERP index the process of aging. However, it is still not clear whether either the P3a or P3b sub-components additionally index levels of cognitive impairment. Here, we used a traditional visual three-stimulus oddball paradigm to investigate both the P3a and P3b ERP components in sixteen young and thirty-four healthy elderly individuals with varying degrees of cognitive ability. EEG data extraction was enhanced through the use of a novel signal processing method called Functional Source Separation (FSS) that increases signal-to-noise ratio by using a weighted sum of all electrodes rather than relying on a single, or a small sub-set, of EEG channels. Whilst clear differences in both the P3a and P3b ERPs were seen between young and elderly groups, only P3b amplitude differentiated older people with low memory performance relative to IQ from those with consistent memory and IQ. A machine learning analysis showed that P3b amplitude (derived from FSS analysis) could accurately categorise high and low performing elderly individuals (78% accuracy). A comparison of Bayes Factors found that differences in cognitive decline within the elderly group were 87 times more likely to be detected using FSS compared to the best performing single electrode (Cz). In conclusion, we propose that P3b amplitude could be a sensitive marker of early, age-independent, episodic memory dysfunction within a healthy older population. In addition, we advocate for the use of more advanced signal processing methods, such as FSS, for detecting subtle neural changes in clinical populations.Graphical abstractTopographic and functional behaviours differences between P3a and P3b: a comparison between channels and source space.ERPs and topographic maps for the three groups (Young vs. HP vs. LP) on Cz and Fz channels and FSP300. Top Panel (Functional Source Space) – Blue, magenta and red lines indicate FSP3a and green, cyan and brown lines indicate FSP3b for Young, HP and LP groups respectively. Last right column represents the superimposition of the P3a and P3b in the three groups. Bottom Panel (Channel Space) – Grey lines indicate the butterfly representation of all the EEG channels. Blue, magenta and red lines indicate CzP3a selected channel and green, cyan and brown lines indicate FzP3b selected channel for Young, HP and LP groups respectively. The black circle on the topographic map represent Cz and Fz channel positions.Image 1
       
  • Whole-slice mapping of GABA and GABA+ at 7T via adiabatic MEGA-editing,
           real-time instability correction, and concentric circle readout
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Philipp Moser, Lukas Hingerl, Bernhard Strasser, Michal Považan, Gilbert Hangel, Ovidiu C. Andronesi, Andre van der Kouwe, Stephan Gruber, Siegfried Trattnig, Wolfgang Bogner An adiabatic MEscher-GArwood (MEGA)-editing scheme, using asymmetric hyperbolic secant editing pulses, was developed and implemented in a B1+-insensitive, 1D-semiLASER (Localization by Adiabatic SElective Refocusing) MR spectroscopic imaging (MRSI) sequence for the non-invasive mapping of γ-aminobutyric acid (GABA) over a whole brain slice. Our approach exploits the advantages of edited-MRSI at 7T while tackling challenges that arise with ultra-high-field-scans. Spatial-spectral encoding, using density-weighted, concentric circle echo planar trajectory readout, enabled substantial MRSI acceleration and an improved point-spread-function, thereby reducing extracranial lipid signals. Subject motion and scanner instabilities were corrected in real-time using volumetric navigators optimized for 7T, in combination with selective reacquisition of corrupted data to ensure robust subtraction-based MEGA-editing.Simulations and phantom measurements of the adiabatic MEGA-editing scheme demonstrated stable editing efficiency even in the presence of ±0.15 ppm editing frequency offsets and B1+ variations of up to ±30% (as typically encountered in vivo at 7T), in contrast to conventional Gaussian editing pulses. Volunteer measurements were performed with and without global inversion recovery (IR) to study regional GABA levels and their underlying, co-edited, macromolecular (MM) signals at 2.99 ppm. High-quality in vivo spectra allowed mapping of pure GABA and MM-contaminated GABA+ (GABA + MM) along with Glx (Glu + Gln), with high-resolution (eff. voxel size: 1.4 cm3) and whole-slice coverage in 24 min scan time. Metabolic ratio maps of GABA/tNAA, GABA+/tNAA, and Glx/tNAA were correlated linearly with the gray matter fraction of each voxel. A 2.15-fold increase in gray matter to white matter contrast was observed for GABA when enabling IR, which we attribute to the higher abundance of macromolecules at 2.99 ppm in the white matter than in the gray matter.In conclusion, adiabatic MEGA-editing with 1D-semiLASER selection is as a promising approach for edited-MRSI at 7T. Our sequence capitalizes on the benefits of ultra-high-field MRSI while successfully mitigating the challenges related to B0/B1+ inhomogeneities, prolonged scan times, and motion/scanner instability artifacts. Robust and accurate 2D mapping has been shown for the neurotransmitters GABA and Glx.Graphical abstractImage 1
       
  • The developmental trajectory of sensorimotor cortical oscillations
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Michael P. Trevarrow, Max J. Kurz, Timothy J. McDermott, Alex I. Wiesman, Mackenzie S. Mills, Yu-Ping Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson Numerous studies of motor control have confirmed beta and gamma oscillations in the primary motor cortices during basic movements. These responses include a robust beta decrease that precedes and extends through movement onset, a transient gamma response that coincides with the movement, and a post-movement beta rebound (PMBR) response that occurs after movement offset. While the existence of these responses has been confirmed by many studies, very few studies have examined their developmental trajectory. In the current study, we utilized magnetoencephalography (MEG) to investigate age-related changes in sensorimotor cortical oscillations in a large cross-section of children and adolescents (n = 94; age range = 9 -15 years-old). All participants performed a stimulus detection task with their right finger and the resulting MEG data were examined using oscillatory analysis methods and imaged using a beamformer. Consistent with adult studies, these youth participants exhibited characteristic beta (16–24 Hz) decreases prior to and during movement, as well as PMBR responses following movement offset, and a transient gamma (74–84 Hz) response during movement execution. Our primary findings were that the strength of the PMBR increased with age, while the strength of the gamma synchronization decreased with chronological age. In addition, the strength of each motor-related oscillatory response was significantly correlated with the power of spontaneous activity in the same frequency range and same voxel. This was the case for all three oscillatory responses. In conclusion, we investigated motor-related oscillatory activity in the largest cohort of children and adolescents reported to date, and our results indicated that beta and gamma cortical oscillations continue to develop as children transition into adolescents, and that these responses may not be fully matured until young to middle adulthood.
       
  • Low-frequency alternating current stimulation rhythmically suppresses
           gamma-band oscillations and impairs perceptual performance
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Jim D. Herring, Sophie Esterer, Tom R. Marshall, Ole Jensen, Til O. Bergmann Low frequency oscillations such as alpha (8–12 Hz) are hypothesized to rhythmically gate sensory processing, reflected by 40–100 Hz gamma band activity, via the mechanism of pulsed inhibition. We applied transcranial alternating current stimulation (TACS) at individual alpha frequency (IAF) and flanking frequencies (IAF-4 Hz, IAF+4 Hz) to the occipital cortex of healthy human volunteers during concurrent magnetoencephalography (MEG), while participants performed a visual detection task inducing strong gamma-band responses. Occipital (but not retinal) TACS phasically suppressed stimulus-induced gamma oscillations in the visual cortex and impaired target detection, with stronger phase-to-amplitude coupling predicting behavioral impairments. Retinal control TACS ruled out retino-thalamo-cortical entrainment resulting from (subthreshold) retinal stimulation. All TACS frequencies tested were effective, suggesting that visual gamma-band responses can be modulated by a range of low frequency oscillations. We propose that TACS-induced membrane potential modulations mimic the rhythmic change in cortical excitability by which spontaneous low frequency oscillations may eventually exert their impact when gating sensory processing via pulsed inhibition.
       
  • A representative template of the neonatal cerebellum
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Carlos R. Hernandez-Castillo, Catherine Limperopoulos, Jörn Diedrichsen The cerebellum plays an important role in human brain development. To improve the spatial specificity of the analysis of human cerebellar magnetic resonance imaging (MRI) data, we present a new template of the neonatal human cerebellum and brainstem based on the anatomy of 20 full-term healthy neonates. The template is spatially unbiased, which means that the location of each structure is not biased by the anatomy of the individuals used to create the template. In comparison to current whole-brain templates, it allows for an improved voxel-by-voxel normalization for MRI analysis. To align the cerebellum to the template, it needs to be isolated from the surrounding tissue, a process for which an automated algorithm has been developed. Our methodology outperforms normalization to a whole-brain neonatal template, using either linear or nonlinear transformations. Our algorithm reduces the spatial variability of the infratentorial area, while simultaneously increasing the overlap of the cerebellum. The template and the related software are freely available as part of SUIT v3.3 SPM toolbox.
       
  • The error-related negativity for error processing in interoception
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yafei Tan, Jolien Vandeput, Jiang Qiu, Omer Van den Bergh, Andreas von Leupoldt The error-related negativity (ERN) is an event-related potential in the electroencephalogram (EEG) observed within the first 100 ms after commission of an error. Increased ERN amplitudes have been observed in several psychological disorders characterized by high negative affect. While the ERN has extensively been studied in tasks using exteroceptive stimuli, its relation to interoceptive stimuli is unknown. Since errors related to interoception might be particularly relevant for survival and negative affect, this study aimed to explore the ERN for errors related to interoceptive, respiratory sensations (intERN). Moreover, we compared the intERN with a commonly observed ERN related to exteroceptive, visual stimuli (extERN) and examined their associations with interoception-related negative affect. We studied the ERN using a respiratory occlusion task (intERN) and a visual flanker task (extERN) in 40 healthy volunteers during continuous 129 channel EEG recordings. In the occlusion task, participants received inspiratory occlusions of two different durations and indicated whether each occlusion was short or long. In the Flanker task, participants indicated the direction of arrowheads. Interoception-related negative affect was assessed with the Anxiety Sensitivity Index. Comparable with the extERN, the intERN was observed at fronto-central scalp positions after error commission in the occlusion task, but it peaked significantly earlier than the extERN. Mean amplitudes of the intERN and extERN showed no significant difference and were not correlated. Moreover, higher levels of anxiety sensitivity were correlated with significantly greater amplitudes of the intERN, but with lower amplitudes of the extERN. The present results firstly demonstrate an error-related negativity EEG-potential that is related to interoceptive sensations (intERN). This intERN is not associated with a commonly observed ERN elicited by exteroceptive stimuli and is distinctly linked to higher levels of interoception-related negative affect. The intERN might be a promising neural marker for future studies on interoception, negative affect and error processing.
       
  • The effects of breastfeeding versus formula-feeding on cerebral cortex
           maturation in infant rhesus macaques
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Zheng Liu, Martha Neuringer, John W. Erdman, Matthew J. Kuchan, Lauren Renner, Emily E. Johnson, Xiaojie Wang, Christopher D. Kroenke Breastfeeding is positively associated with several outcomes reflecting early brain development and cognitive functioning. Brain neuroimaging studies have shown that exclusively breastfed children have increased white matter and subcortical gray matter volume compared to formula-fed children. However, it is difficult to disentangle the effects of nutrition in breast milk from other confounding factors that affect brain development, particularly in studies of human subjects. Among the nutrients provided by human breast milk are the carotenoid lutein and the natural form of tocopherol, both of which are selectively deposited in brain. Lutein is the predominant carotenoid in breast milk but not in most infant formulas, whereas infant formulas are supplemented with the synthetic form of tocopherol. In this study, a non-human primate model was used to investigate the effects of breastfeeding versus formula-feeding, as well as lutein and natural RRR-α-tocopherol supplementation of infant formula, on brain maturation under controlled experimental conditions. Infant rhesus macaques (Macaca mulatta) were exclusively breastfed, or were fed infant formulas with different levels and sources of lutein and α-tocopherol. Of note, the breastfed group were mother-reared whereas the formula-fed infants were nursery-reared. Brain structural and diffusion MR images were collected, and brain T2 was measured, at two, four and six months of age. The mother-reared breastfed group was observed to differ from the formula-fed groups by possessing higher diffusion fractional anisotropy (FA) in the corpus callosum, and lower FA in the cerebral cortex at four and six months of age. Cortical regions exhibiting the largest differences include primary motor, premotor, lateral prefrontal, and inferior temporal cortices. No differences were found between the formula groups. Although this study did not identify a nutritional component of breast milk that could be provided to infant formula to facilitate brain maturation consistent with that observed in breastfed animals, our findings indicate that breastfeeding promoted maturation of the corpus callosum and cerebral cortical gray matter in the absence of several confounding factors that affect studies in human infants. However, differences in rearing experience remain as a potential contributor to brain structural differences between breastfed and formula fed infants.
       
  • The alteration landscape of the cerebral cortex
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Franco Cauda, Andrea Nani, Jordi Manuello, Donato Liloia, Karina Tatu, Ugo Vercelli, Sergio Duca, Peter T. Fox, Tommaso Costa Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the “structural alteration variety” of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.
       
  • Water-exchange MRI detects subtle blood-brain barrier breakdown in
           Alzheimer's disease rats
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Ben R. Dickie, Matthias Vandesquille, José Ulloa, Hervé Boutin, Laura M. Parkes, Geoff J.M. Parker Blood-brain barrier (BBB) breakdown has been hypothesized to play a key role in the onset and progression of Alzheimer's disease (AD). However, the question of whether AD itself contributes to loss of BBB integrity is still uncertain, as many in-vivo studies have failed to detect signs of AD-related BBB breakdown. We hypothesize AD-related BBB damage is subtle, and that these negative results arise from a lack of measurement sensitivity. With the aim of developing a more sensitive measure of BBB breakdown, we have designed a novel MRI scanning protocol to quantify the trans-BBB exchange of endogenous water. Using this method, we detect increased BBB water permeability in a rat model of AD that is associated with reduced expression of the tight junction protein occludin. BBB permeability to MRI contrast agent, assessed using dynamic contrast-enhanced (DCE)-MRI, did not differ between transgenic and wild-type animals and was uncorrelated with occludin expression. Our data supports the occurrence of AD-related BBB breakdown, and indicates that such BBB pathology is subtle and may be undetectable using existing ‘tracer leakage’ methods. Our validated water-exchange MRI method provides a new powerful tool with which to study BBB damage in-vivo.
       
  • Normative pathways in the functional connectome
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Matthew Leming, Li Su, Shayanti Chattopadhyay, John Suckling Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.
       
  • Oculomotor inhibition reflects temporal expectations
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Roy Amit, Dekel Abeles, Marisa Carrasco, Shlomit Yuval-Greenberg The accurate extraction of signals out of noisy environments is a major challenge of the perceptual system. Forming temporal expectations and continuously matching them with perceptual input can facilitate this process. In humans, temporal expectations are typically assessed using behavioral measures, which provide only retrospective but no real-time estimates during target anticipation, or by using electrophysiological measures, which require extensive preprocessing and are difficult to interpret. Here we show a new correlate of temporal expectations based on oculomotor behavior. Observers performed an orientation-discrimination task on a central grating target, while their gaze position and EEG were monitored. In each trial, a cue preceded the target by a varying interval (“foreperiod”). In separate blocks, the cue was either predictive or non-predictive regarding the timing of the target. Results showed that saccades and blinks were inhibited more prior to an anticipated regular target than a less-anticipated irregular one. This consistent oculomotor inhibition effect enabled a trial-by-trial classification according to interval-regularity. Additionally, in the regular condition the slope of saccade-rate and drift were shallower for longer than shorter foreperiods, indicating their adjustment according to temporal expectations. Comparing the sensitivity of this oculomotor marker with those of other common predictability markers (e.g. alpha-suppression) showed that it is a sensitive marker for cue-related anticipation. In contrast, temporal changes in conditional probabilities (hazard-rate) modulated alpha-suppression more than cue-related anticipation. We conclude that pre-target oculomotor inhibition is a correlate of temporal predictions induced by cue-target associations, whereas alpha-suppression is more sensitive to conditional probabilities across time.
       
  • Hard to wake up' The cerebral correlates of sleep inertia assessed
           using combined behavioral, EEG and fMRI measures
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Raphael Vallat, David Meunier, Alain Nicolas, Perrine Ruby The first minutes following awakening from sleep are typically marked by reduced vigilance, increased sleepiness and impaired performance, a state referred to as sleep inertia. Although the behavioral aspects of sleep inertia are well documented, its cerebral correlates remain poorly understood. The present study aimed at filling this gap by measuring in 34 participants the changes in behavioral performance (descending subtraction task, DST), EEG spectral power, and resting-state fMRI functional connectivity across three time points: before an early-afternoon 45-min nap, 5 min after awakening from the nap and 25 min after awakening. Our results showed impaired performance at the DST at awakening and an intrusion of sleep-specific features (spectral power and functional connectivity) into wakefulness brain activity, the intensity of which was dependent on the prior sleep duration and depth for the functional connectivity (14 participants awakened from N2 sleep, 20 from N3 sleep). Awakening in N3 (deep) sleep induced the most robust changes and was characterized by a global loss of brain functional segregation between task-positive (dorsal attention, salience, sensorimotor) and task-negative (default mode) networks. Significant correlations were observed notably between the EEG delta power and the functional connectivity between the default and dorsal attention networks, as well as between the percentage of mistake at the DST and the default network functional connectivity. These results highlight (1) significant correlations between EEG and fMRI functional connectivity measures, (2) significant correlations between the behavioral aspect of sleep inertia and measures of the cerebral functioning at awakening (both EEG and fMRI), and (3) the important difference in the cerebral underpinnings of sleep inertia at awakening from N2 and N3 sleep.
       
  • Load modulates the alpha and beta oscillatory dynamics serving verbal
           working memory
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Amy L. Proskovec, Elizabeth Heinrichs-Graham, Tony W. Wilson A network of predominantly left-lateralized brain regions has been linked to verbal working memory (VWM) performance. However, the impact of memory load on the oscillatory dynamics serving VWM is far less understood. To further investigate this, we had 26 healthy adults perform a high-load (6 letter) and low-load (4 letter) variant of a VWM task while undergoing magnetoencephalography (MEG). MEG data were evaluated in the time-frequency domain and significant oscillatory responses spanning the encoding and maintenance phases were reconstructed using a beamformer. To determine the impact of load on the neural dynamics, the resulting images were examined using paired-samples t-tests and virtual sensor analyses. Our results indicated stronger increases in frontal theta activity in the high- relative to low-load condition during early encoding. Stronger decreases in alpha/beta activity were also observed during encoding in bilateral posterior cortices during the high-load condition, and the strength of these load effects increased as encoding progressed. During maintenance, stronger decreases in alpha activity in the left inferior frontal gyrus, middle temporal gyrus, supramarginal gyrus, and inferior parietal cortices were detected during high- relative to low-load performance, with the strength of these load effects remaining largely static throughout maintenance. Finally, stronger increases in occipital alpha activity were observed during maintenance in the high-load condition, and the strength of these effects grew stronger with time during the first half of maintenance, before dissipating during the latter half of maintenance. Notably, this was the first study to utilize a whole-brain approach to statistically evaluate the temporal dynamics of load-related oscillatory differences during encoding and maintenance processes, and our results highlight the importance of spatial, temporal, and spectral specificity in this regard.
       
  • Decoding motion direction using the topography of sustained ERPs and alpha
           oscillations
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Gi-Yeul Bae, Steven J. Luck The present study sought to determine whether scalp electroencephalogram (EEG) signals contain decodable information about the direction of motion in random dot kinematograms (RDKs), in which the motion information is spatially distributed and mixed with random noise. Any direction of motion from 0 to 360° was possible, and observers reported the precise direction of motion at the end of a 1500-ms stimulus display. We decoded the direction of motion separately during the motion period (during which motion information was being accumulated) and the report period (during which a shift of attention was necessary to make a fine-tuned direction report). Machine learning was used to decode the precise direction of motion (within ±11.25°) from the scalp distribution of either alpha-band EEG activity or sustained event-related potentials (ERPs). We found that ERP-based decoding was above chance (1/16) during both the stimulus and the report periods, whereas alpha-based decoding was above chance only during the report period. Thus, sustained ERPs contain information about spatially distributed direction-of-motion, providing a new method for observing the accumulation of sensory information with high temporal resolution. By contrast, the scalp topography of alpha-band EEG activity appeared to mainly reflect spatially focused attentional processes rather than sensory information.
       
  • A comparative fMRI meta-analysis of altruistic and strategic decisions to
           give
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Jo Cutler, Daniel Campbell-MeiklejohnThe decision to share resources is fundamental for cohesive societies. Humans can be motivated to give for many reasons. Some generosity incurs a definite cost, with no extrinsic reward to the act, but instead provides intrinsic satisfaction (labelled here as ‘altruistic’ giving). Other giving behaviours are done with the prospect of improving one's own situation via reciprocity, reputation, or public good (labelled here as ‘strategic’ giving). These contexts differ in the source, certainty, and timing of rewards as well as the inferences made about others' mental states. We executed a combined statistical map and coordinate-based fMRI meta-analysis of decisions to give (36 studies, 1150 participants). Methods included a novel approach for accommodating variable signal dropout between studies in meta-analysis. Results reveal consistent, cross-paradigm neural correlates of each decision type, commonalities, and informative differences. Relative to being selfish, altruistic and strategic giving activate overlapping reward networks. However, strategic decisions showed greater activity in striatal regions than altruistic choices. Altruistic giving, more than strategic, activated subgenual anterior cingulate cortex (sgACC). Ventromedial prefrontal cortex (vmPFC) is consistently involved during generous decisions and processing across a posterior to anterior axis differentiates the altruistic/strategic context. Posterior vmPFC was preferentially recruited during altruistic decisions. Regions of the ‘social brain’ showed distinct patterns of activity between choice types, reflecting the different use of theory of mind in the two contexts. We provide the consistent neural correlates of decisions to give, and show that many will depend on the source of incentives.Graphical abstractImage 1
       
  • Data-driven tensor independent component analysis for model-based
           connectivity neurofeedback
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yury Koush, Nemanja Masala, Frank Scharnowski, Dimitri Van De Ville Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynamic causal modeling (DCM). DCM is an influential hypothesis-driven approach that requires prior knowledge about the target brain network dynamics and the modulatory influences. Data-driven approaches, such as tensor independent component analysis (ICA), can reveal spatiotemporal patterns of brain activity without prior assumptions. Tensor ICA allows flexible data decomposition and extraction of components consisting of spatial maps, time-series, and session/subject-specific weights, which can be used to characterize individual neurofeedback regulation per regulation trial, run, or session. In this study, we aimed to better understand the spatiotemporal brain patterns involved and affected by model-based feedback regulation using data-driven tensor ICA. We found that task-specific spatiotemporal brain patterns obtained using tensor ICA were highly consistent with model-based feedback estimates. However, we found that the DCM approach captured specific network interdependencies that went beyond what could be detected with either general linear model (GLM) or ICA approaches. We also found that neurofeedback-guided regulation resulted in activity changes that were characteristic of the mental strategies used to control the feedback signal, and that these activity changes were not limited to periods of active self-regulation, but were also evident in distinct gradual recovery processes during subsequent rest periods. Complementary data-driven and model-based approaches could aid in interpretation of the neurofeedback data when applied post-hoc, and in the definition of the target brain area/pattern/network/model prior to the neurofeedback training study when applied to the pilot data. Systematically investigating the triad of mental effort, spatiotemporal brain network changes, and activity and recovery processes might lead to a better understanding of how learning with neurofeedback is accomplished, and how such learning can cause plastic brain changes along with specific behavioral effects.
       
  • Comparing the potential of MEG and EEG to uncover brain tracking of speech
           temporal envelope
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Florian Destoky, Morgane Philippe, Julie Bertels, Marie Verhasselt, Nicolas Coquelet, Marc Vander Ghinst, Vincent Wens, Xavier De Tiège, Mathieu Bourguignon During connected speech listening, brain activity tracks speech rhythmicity at delta (∼0.5 Hz) and theta (4–8 Hz) frequencies. Here, we compared the potential of magnetoencephalography (MEG) and high-density electroencephalography (EEG) to uncover such speech brain tracking.Ten healthy right-handed adults listened to two different 5-min audio recordings, either without noise or mixed with a cocktail-party noise of equal loudness. Their brain activity was simultaneously recorded with MEG and EEG. We quantified speech brain tracking channel-by-channel using coherence, and with all channels at once by speech temporal envelope reconstruction accuracy.In both conditions, speech brain tracking was significant at delta and theta frequencies and peaked in the temporal regions with both modalities (MEG and EEG). However, in the absence of noise, speech brain tracking estimated from MEG data was significantly higher than that obtained from EEG. Furthemore, to uncover significant speech brain tracking, recordings needed to be ∼3 times longer in EEG than MEG, depending on the frequency considered (delta or theta) and the estimation method. In the presence of noise, both EEG and MEG recordings replicated the previous finding that speech brain tracking at delta frequencies is stronger with attended speech (i.e., the sound subjects are attending to) than with the global sound (i.e., the attended speech and the noise combined). Other previously reported MEG findings were replicated based on MEG but not EEG recordings: 1) speech brain tracking at theta frequencies is stronger with attended speech than with the global sound, 2) speech brain tracking at delta frequencies is stronger in noiseless than noisy conditions, and 3) when noise is added, speech brain tracking at delta frequencies dampens less in the left hemisphere than in the right hemisphere. Finally, sources of speech brain tracking reconstructed from EEG data were systematically deeper and more posterior than those derived from MEG.The present study demonstrates that speech brain tracking is better seen with MEG than EEG. Quantitatively, EEG recordings need to be ∼3 times longer than MEG recordings to uncover significant speech brain tracking. As a consequence, MEG appears more suited than EEG to pinpoint subtle effects related to speech brain tracking in a given recording time.
       
  • Retrospective harmonization of multi-site diffusion MRI data acquired with
           different acquisition parameters
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Suheyla Cetin Karayumak, Sylvain Bouix, Lipeng Ning, Anthony James, Tim Crow, Martha Shenton, Marek Kubicki, Yogesh Rathi A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we propose a dMRI harmonization method that is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions. We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8–22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14–54 years). In particular, we show that gender and age-related maturation differences in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Since we use matched control subjects from different scanners to estimate scanner-specific effects, our goal in this work is also to determine the minimum number of well-matched subjects needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.
       
  • Temporal Derivative Distribution Repair (TDDR): A motion correction method
           for fNIRS
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Frank A. Fishburn, Ruth S. Ludlum, Chandan J. Vaidya, Andrei V. Medvedev Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique of growing interest as a tool for investigation of cortical activity. Due to the on-head placement of optodes, artifacts arising from head motion are relatively less severe than for functional magnetic resonance imaging (fMRI). However, it is still necessary to remove motion artifacts. We present a novel motion correction procedure based on robust regression, which effectively removes baseline shift and spike artifacts without the need for any user-supplied parameters. Our simulations show that this method yields better activation detection performance than 5 other current motion correction methods. In our empirical validation on a working memory task in a sample of children 7–15 years, our method produced stronger and more extensive activation than any of the other methods tested. The new motion correction method enhances the viability of fNIRS as a functional neuroimaging modality for use in populations not amenable to fMRI.Graphical abstractImage 1
       
  • Shared understanding of narratives is correlated with shared neural
           responses
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Mai Nguyen, Tamara Vanderwal, Uri Hasson Humans have a striking ability to infer meaning from even the sparsest and most abstract forms of narratives. At the same time, flexibility in the form of a narrative is matched by inherent ambiguity in its interpretation. How does the brain represent subtle, idiosyncratic differences in the interpretation of abstract and ambiguous narratives' In this fMRI study, subjects were scanned either watching a novel 7-min animation depicting a complex narrative through the movement of geometric shapes, or listening to a narration of the animation's social story. Using an intersubject representational similarity analysis that compared interpretation similarity and neural similarity across subjects, we found that the more similar two people's interpretations of the abstract shapes animation were, the more similar were their neural responses in regions of the default mode network (DMN) and fronto-parietal network. Moreover, these shared responses were modality invariant: the shapes movie and the verbal interpretation of the movie elicited shared responses in linguistic areas and a subset of the DMN when subjects shared interpretations. Together, these results suggest a network of high-level regions that are not only sensitive to subtle individual differences in narrative interpretation during naturalistic conditions, but also resilient to large differences in the modality of the narrative.
       
  • Sparse wars: A survey and comparative study of spherical deconvolution
           algorithms for diffusion MRI
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Erick Jorge Canales-Rodríguez, Jon Haitz Legarreta, Marco Pizzolato, Gaëtan Rensonnet, Gabriel Girard, Jonathan Rafael- Patino, Muhamed Barakovic, David Romascano, Yasser Alemán-Gómez, Joaquim Radua, Edith Pomarol-Clotet, Raymond Salvador, Jean-Philippe Thiran, Alessandro Daducci Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection techniques based on the extended Bayesian information criterion (i.e., best subset selection and the least absolute shrinkage and selection operator), iteratively reweighted l2- and l1-norm approaches to approximate the l0-norm, sparse Bayesian learning, Cauchy deconvolution, and two accelerated Richardson-Lucy algorithms. Results from our exhaustive evaluation show that there is no single optimal method for all different fiber configurations, suggesting that further studies should be conducted to find the optimal way of combining solutions from different methods. We found l0-norm regularization algorithms to resolve more accurately fiber crossings with small inter-fiber angles. However, in voxels with very dominant fibers, algorithms promoting more sparsity are less accurate in detecting smaller fibers. In most cases, the best algorithm to reconstruct fiber crossings with two fibers did not perform optimally in voxels with one or three fibers. Therefore, simplified validation systems as employed in a number of previous studies, where only two fibers with similar volume fractions were tested, should be avoided as they provide incomplete information. Future studies proposing new reconstruction methods based on high angular resolution diffusion imaging data should validate their results by considering, at least, voxels with one, two, and three fibers, as well as voxels with dominant fibers and different diffusion anisotropies.
       
  • Neural correlates of the energetic value of food during visual processing
           and response inhibition
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): P. Mengotti, F. Foroni, R.I. Rumiati Previous research showed that human brain regions involved in reward and cognitive control are responsive to visually presented food stimuli, in particular high-energy foods. However, it is still to be determined whether the preference towards high-energy foods depends on their higher energy density (kcal/gram), or is based on the difference in energy content of the food items (total amount of kcal). Here we report the results of an fMRI study in which normal-weight healthy participants processed food images during a one-back task or were required to inhibit their response towards food stimuli during a Go/No-Go task. High-energy density (HD) and low-energy density (LD) foods were matched for energy content displayed. Food-related kitchen objects (OBJ) were used as control stimuli. The lateral occipital complex and the orbitofrontal cortex showed consistent higher activity in response to HD than LD foods, both during visual processing and response inhibition. This result suggests that images of HD foods, even when the amount of food shown is not associated with a higher energy content, elicit preferential visual processing - possibly involving attentional processes - and trigger a response from the reward system. We conclude that the human brain is able to distinguish food energy densities of food items during both active visual processing and response inhibition.
       
  • Functional MRS with J-edited lactate in human motor cortex at 4 T
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Yury Koush, Robin A. de Graaf, Lihong Jiang, Douglas L. Rothman, Fahmeed Hyder While functional MRI (fMRI) localizes regions of brain activation, functional MRS (fMRS) provides insights into metabolic underpinnings. Previous fMRS studies detected task-induced lactate increase using short echo-time non-edited 1H-MRS protocols, where lactate changes depended on accurate exclusion of overlapping lactate and lipid/macromolecule signals. Because long echo-time J-difference 1H-MRS detection of lactate is less susceptible to this shortcoming, we posited if J-edited fMRS protocol could reliably detect metabolic changes in the human motor cortex during a finger-tapping paradigm in relation to a reliable measure of basal lactate. Our J-edited fMRS protocol at 4T was guided by an fMRI pre-scan to determine the 1H-MRS voxel placement in the motor cortex. Because lactate and β-hydroxybutyrate (BHB) follow similar J-evolution profiles we observed both metabolites in all spectra, but only lactate showed reproducible task-induced modulation by 0.07 mM from a basal value of 0.82 mM. These J-edited fMRS results demonstrate good sensitivity and specificity for task-induced lactate modulation, suggesting that J-edited fMRS studies can be used to investigate the metabolic underpinning of human cognition by measuring lactate dynamics associated with activation and deactivation fMRI paradigms across brain regions at magnetic field lower than 7T.
       
  • A face is more than just the eyes, nose, and mouth: fMRI evidence that
           face-selective cortex represents external features
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Frederik S. Kamps, Ethan J. Morris, Daniel D. Dilks What is a face' Intuition, along with abundant behavioral and neural evidence, indicates that internal features (e.g., eyes, nose, mouth) are critical for face recognition, yet some behavioral and neural findings suggest that external features (e.g., hair, head outline, neck and shoulders) may likewise be processed as a face. Here we directly test this hypothesis by investigating how external (and internal) features are represented in the brain. Using fMRI, we found highly selective responses to external features (relative to objects and scenes) within the face processing system in particular, rivaling that observed for internal features. We then further asked how external and internal features are represented in regions of the cortical face processing system, and found a similar division of labor for both kinds of features, with the occipital face area and posterior superior temporal sulcus representing the parts of both internal and external features, and the fusiform face area representing the coherent arrangement of both internal and external features. Taken together, these results provide strong neural evidence that a “face” is composed of both internal and external features.
       
  • Somatosensory responses to nothing: An MEG study of expectations during
           omission of tactile stimulations
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Lau M. Andersen, Daniel Lundqvist The brain builds up expectations to future events based on the patterns of past events. This function has been studied extensively in the auditory and visual domains using various oddball paradigms, but only little exploration of this phenomenon has been done in the somatosensory domain. In this study, we explore how expectations of somatosensory stimulations are established and expressed in neural activity as measured with magnetoencephalography. Using tactile stimulations to the index finger, we compared conditions with actual stimulation to conditions with omitted stimulations, both of which were either expected or unexpected.Our results show that when a stimulation is expected but omitted, a time-locked response occurs ∼135 ms subsequent to the expected stimulation. This somatosensory response to “nothing” was source localized to the secondary somatosensory cortex and to the insula. This provides novel evidence of the capability of the brain of millisecond time-keeping of somatosensory patterns across intervals of 3000 ms.Our results also show that when stimuli are repeated and expectations are established, there is associated activity in the theta and beta bands. These theta and beta band expressions of expectation were localized to the primary somatosensory area, inferior parietal cortex and cerebellum. Furthermore, there was gamma band activity in the right insula for the first stimulation after an omission, which indicates the detection of a new stimulation event after an expected pattern has been broken.Finally, our results show that cerebellum play a crucial role in predicting upcoming stimulation and in predicting when stimulation may begin again.
       
  • Grouped sparse Bayesian learning for voxel selection in multivoxel pattern
           analysis of fMRI data
    • Abstract: Publication date: Available online 18 September 2018Source: NeuroImageAuthor(s): Zhenfu Wen, Tianyou Yu, Zhuliang Yu, Yuanqing Li Multivoxel pattern analysis (MVPA) methods have been widely applied in recent years to classify human brain states in functional magnetic resonance imaging (fMRI) data analysis. Voxel selection plays an important role in MVPA studies not only because it can improve decoding accuracy but also because it is useful for understanding brain functions. There are many voxel selection methods that have been proposed in fMRI literature. However, most of these methods either overlook the structure information of fMRI data or require additional cross-validation procedures to determine the hyperparameters of the models. In the present work, we proposed a voxel selection method for binary brain decoding called group sparse Bayesian logistic regression (GSBLR). This method utilizes the group sparse property of fMRI data by using a grouped automatic relevance determination (GARD) as a prior for model parameters. All the parameters in the GSBLR can be estimated automatically, thereby avoiding additional cross-validation. Experimental results based on two publicly available fMRI datasets and simulated datasets demonstrate that GSBLR achieved better classification accuracies and yielded more stable solutions than several state-of-the-art methods.
       
  • Early breast milk exposure modifies brain connectivity in preterm infants
    • Abstract: Publication date: Available online 18 September 2018Source: NeuroImageAuthor(s): Manuel Blesa, Gemma Sullivan, Devasuda Anblagan, Emma J. Telford, Alan J. Quigley, Sarah A. Sparrow, Ahmed Serag, Scott I. Semple, Mark E. Bastin, James P. Boardman Preterm infants are at increased risk of alterations in brain structure and connectivity, and subsequent neurocognitive impairment. Breast milk may be more advantageous than formula feed for promoting brain development in infants born at term, but uncertainties remain about its effect on preterm brain development and the optimal nutritional regimen for preterm infants. We test the hypothesis that breast milk exposure is associated with improved markers of brain development and connectivity in preterm infants at term equivalent age.We collected information about neonatal breast milk exposure and brain MRI at term equivalent age from 47 preterm infants (mean postmenstrual age [PMA] 29.43 weeks, range 23.28–33.0). Network-Based Statistics (NBS), Tract-based Spatial Statistics (TBSS) and volumetric analysis were used to investigate the effect of breast milk exposure on white matter water diffusion parameters, tissue volumes, and the structural connectome.Twenty-seven infants received exclusive breast milk feeds for ≥75% of days of in-patient care and this was associated with higher connectivity in the fractional anisotropy (FA)-weighted connectome compared with the group who had 
       
  • Childhood poverty and the organization of structural brain connectome
    • Abstract: Publication date: Available online 17 September 2018Source: NeuroImageAuthor(s): Dae-Jin Kim, Elysia Poggi Davis, Curt A. Sandman, Laura Glynn, Olaf Sporns, Brian F. O'Donnell, William P. Hetrick Socioeconomic disadvantage is associated with atypical development in specific brain regions, yet the relation between poverty and whole brain network organization (i.e., the connectome, a set of brain regions connected with neuronal pathways) has not been characterized. Developmental studies indicate that the connectome undergoes rapid change during childhood and is consequently likely to be highly sensitive to both salutary and detrimental influences. We investigated associations between the socioeconomic disparities measured by the income-to-needs ratio (INR) in childhood and structural brain network organization with 144 healthy children between 6 and 11 years of age (mean age = 8 years). INR of girls was positively and logarithmically associated with the extent to which brain networks were efficiently organized, suggesting that girls in more impoverished environments had less efficient brain network organization. Lower INR was associated with network inefficiency in multiple cortical regions including prefrontal cortex, cingulate, and insula, and in subcortical regions including the hippocampus and amygdala. These findings suggest that childhood poverty may result in wide-spread disruptions of the brain connectome among girls, particularly at the lowest INR levels, and are differentially expressed in females and males.
       
  • Human Connectome Project-style resting-state functional MRI at 7 Tesla
           using radiofrequency parallel transmission
    • Abstract: Publication date: Available online 17 September 2018Source: NeuroImageAuthor(s): Xiaoping Wu, Edward J. Auerbach, An T. Vu, Steen Moeller, Pierre-François Van de Moortele, Essa Yacoub, Kâmil Uğurbil We investigate the utility of RF parallel transmission (pTx) for whole-brain resting-state functional MRI (rfMRI) acquisition at 7 Tesla (7T). To this end, Human Connectome Project (HCP)-style data acquisitions were chosen as a showcase example. Five healthy subjects were scanned in pTx and single-channel transmit (1Tx) modes. The pTx data were acquired using a prototype 16-channel transmit system and a commercially available Nova 8-channel transmit 32-channel receive RF head coil. Additionally, pTx single-spoke multiband (MB) pulses were designed to image sagittal slices. HCP-style 7T rfMRI data (1.6-mm isotropic resolution, 5-fold slice and 2-fold in-plane acceleration, 3600 vol and ∼ 1-h scan) were acquired with pTx and the results were compared to those acquired with the original 7T HCP rfMRI protocol. The use of pTx significantly improved flip-angle uniformity across the brain, with coefficient of variation (i.e., std/mean) of whole-brain flip-angle distribution reduced on average by ∼39%. This in turn yielded ∼17% increase in group temporal SNR (tSNR) as averaged across the entire brain and ∼10% increase in group functional contrast-to-noise ratio (fCNR) as averaged across the grayordinate space (including cortical surfaces and subcortical voxels). Furthermore, when placing a seed in either the posterior parietal lobe or putamen estimate seed-based dense connectome, the increase in fCNR was observed to translate into stronger correlation of the seed with the rest of the grayordinate space. We have demonstrated the utility of pTx for slice-accelerated high-resolution whole-brain rfMRI at 7T; as compared to current state-of-the-art, the use of pTx improves flip-angle uniformity, increases tSNR, enhances fCNR and strengthens functional connectivity estimation.
       
  • Distinct modes of functional connectivity induced by movie-watching
    • Abstract: Publication date: Available online 17 September 2018Source: NeuroImageAuthor(s): Murat Demirtaş, Adrian Ponce-Alvarez, Matthieu Gilson, Patric Hagmann, Dante Mantini, Viviana Betti, Gian Luca Romani, Karl Friston, Maurizio Corbetta, Gustavo Deco A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices.
       
  • A realistic, accurate and fast source modeling approach for the EEG
           forward problem
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Tuuli Miinalainen, Atena Rezaei, Defne Us, Andreas Nüßing, Christian Engwer, Carsten H. Wolters, Sampsa Pursiainen The aim of this paper is to advance electroencephalography (EEG) source analysis using finite element method (FEM) head volume conductor models that go beyond the standard three compartment (skin, skull, brain) approach and take brain tissue inhomogeneity (gray and white matter and cerebrospinal fluid) into account. The new approach should enable accurate EEG forward modeling in the thin human cortical structures and, more specifically, in the especially thin cortices in children brain research or in pathological applications. The source model should thus be focal enough to be usable in the thin cortices, but should on the other side be more realistic than the current standard mathematical point dipole. Furthermore, it should be numerically accurate and computationally fast. We propose to achieve the best balance between these demands with a current preserving (divergence conforming) dipolar source model. We develop and investigate a varying number of current preserving source basis elements n (n=1,…,n=5). For validation, we conducted numerical experiments within a multi-layered spherical domain, where an analytical solution exists. We show that the accuracy increases along with the number of basis elements, while focality decreases. The results suggest that the best balance between accuracy and focality in thin cortices is achieved with n=4 (or in extreme cases even n=3) basis functions, while in thicker cortices n=5 is recommended to obtain the highest accuracy. We also compare the current preserving approach to two further FEM source modeling techniques, namely partial integration and St. Venant, and show that the best current preserving source model outperforms the competing methods with regard to overall balance. For all tested approaches, FEM transfer matrices enable high computational speed. We implemented the new EEG forward modeling approaches into the open source duneuro library for forward modeling in bioelectromagnetism to enable its broader use by the brain research community. This library is build upon the DUNE framework for parallel finite elements simulations and integrates with high-level toolboxes like FieldTrip. Additionally, an inversion test has been implemented using the realistic head model to demonstrate and compare the differences between the aforementioned source models.
       
  • Common and distinct neural substrates of the money illusion in win and
           loss domains
    • Abstract: Publication date: Available online 13 September 2018Source: NeuroImageAuthor(s): Yi Huang, Rongjun Yu People often evaluate money based on its face value and overlook its real purchasing power, a phenomenon known as the money illusion. In the present study, using functional magnetic resonance imaging (fMRI) combined with a gambling task, we examined the neural signatures of the money illusion in both win and loss domains. Behavioral results showed that self-reported satisfaction with outcomes was modulated by the face value but not the true value of money in both win and loss domains. At the neural level, activity in the posterior insula was associated with the true value of money in the win domain, but not in the loss domain. Importantly, we found that the ventral striatum, ventromedial prefrontal cortex (vmPFC) and amygdala encoded the money illusion in both domains, indicating a domain-general rather than domain-specific neural signature. Moreover, participants with a larger degree of money illusion at the behavioral level showed stronger functional connectivity between the ventral striatum and ventral anterior cingulate cortex (vACC) in the win domain, but stronger functional connectivity between the ventral striatum and amygdala in the loss domain. Our findings highlight the overlapping and distinct neural substrates underlying the money illusion in the context of wins and losses.
       
  • Inhibitory and excitatory mechanisms in the human cingulate-cortex support
           reinforcement learning: A functional Proton Magnetic Resonance
           Spectroscopy study
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Vered Bezalel, Rony Paz, Assaf Tal The dorsal anterior cingulate cortex (dACC) is crucial for motivation, reward- and error-guided decision-making, yet its excitatory and inhibitory mechanisms remain poorly explored in humans. In particular, the balance between excitation and inhibition (E/I), demonstrated to play a role in animal studies, is difficult to measure in behaving humans. Here, we used functional magnetic-resonance-spectroscopy (1H-fMRS) to measure the brain's major inhibitory (GABA) and excitatory (Glutamate) neurotransmitters during reinforcement learning with three different conditions: high cognitive load (uncertainty); probabilistic discrimination learning; and a control null-condition. Participants learned to prefer the gain option in the discrimination phase and had no preference in the other conditions. We found increased GABA levels during the uncertainty condition, potentially reflecting recruitment of inhibitory systems during high cognitive load when trying to learn. Further, higher GABA levels during the null (baseline) condition correlated with improved discrimination learning. Finally, glutamate and GABA levels were correlated during high cognitive load. These results suggest that availability of dACC inhibitory resources enables successful learning. Our approach helps elucidate the potential contribution of the balance between excitation and inhibition to learning and motivation in behaving humans.
       
  • Error-related modulations of the sensorimotor post-movement and foreperiod
           beta-band activities arise from distinct neural substrates and do not
           reflect efferent signal processing
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Julie Alayrangues, Flavie Torrecillos, Amirhossein Jahani, Nicole Malfait While beta activity has been extensively studied in relation to voluntary movement, its role in sensorimotor adaptation remains largely uncertain. Recently, it has been shown that the post-movement beta rebound as well as beta activity during movement-preparation are modulated by movement errors. However, there are critical functional differences between pre- and post-movement beta activities. Here, we addressed two related open questions. Do the pre- and post-movement error-related modulations arise from distinct neural substrates' Do these modulations relate to efferent signals shaping muscle-activation patterns or do they reflect integration of sensory information, intervening upstream of the motor output' For this purpose, first we exploited independent component analysis (ICA) which revealed a double dissociation suggesting that distinct neural substrates are recruited in error-related beta-power modulations observed before and after movement. Second, we compared error-related beta oscillation responses observed in two bimanual reaching tasks involving similar movements but different interlimb coordination, and in which the same mechanical perturbations induced different behavioral adaptive responses. While the task difference was not reflected in the post-movement beta rebound, the pre-movement beta activity was differently modulated according to the interlimb coordination. Critically, we show an uncoupling between the behavioral and the electrophysiological responses during the movement preparation phase, which demonstrates that the error-related modulation of the foreperiod beta activity does not reflect changes in the motor output from primary motor cortex. It seems instead to relate to higher level processing of sensory afferents, essential for sensorimotor adaptation.
       
  • Processing of parafoveally presented words. An fMRI study
    • Abstract: Publication date: 1 January 2019Source: NeuroImage, Volume 184Author(s): Lorenzo Vignali, Stefan Hawelka, Florian Hutzler, Fabio Richlan The present fMRI study investigated neural correlates of parafoveal preprocessing during reading and the type of information that is accessible from the upcoming - not yet fixated - word. Participants performed a lexical decision flanker task while the constraints imposed by the first three letters (the initial trigram) of parafoveally presented words were controlled. Behavioral results evidenced that the amount of information extracted from parafoveal stimuli, was affected by the difficulty of the foveal stimulus. Easy to process foveal stimuli (i.e., high frequency nouns) allowed parafoveal information to be extracted up to the lexical level. Conversely, when foveal stimuli were difficult to process (orthographically legal nonwords) only constraining trigrams modulated the task performance. Neuroimaging findings showed no effects of lexicality (i.e., difference between words and pseudowords) in the parafovea independently from the difficulty of the foveal stimulus. The constraints imposed by the initial trigrams, however, modulated the hemodynamic response in the left supramarginal gyrus. We interpreted the supramarginal activation as reflecting sublexical (phonological) processes. The missing parafoveal lexicality effect was discussed in relation to findings of experiments which observed effects of parafoveal semantic congruency on electrophysiological correlates.
       
  • Systematic comparison between a wireless EEG system with dry electrodes
           and a wired EEG system with wet electrodes
    • Abstract: Publication date: Available online 12 September 2018Source: NeuroImageAuthor(s): Julia W.Y. Kam, Sandon Griffin, Alan Shen, Shawn Patel, Hermann Hinrichs, Hans-Jochen Heinze, Leon Y. Deouell, Robert T. Knight Recent advances in dry electrodes technology have facilitated the recording of EEG in situations not previously possible, thanks to the relatively swift electrode preparation and avoidance of applying gel to subject's hair. However, to become a true alternative, these systems should be compared to state-of-the-art wet EEG systems commonly used in clinical or research applications. In our study, we conducted a systematic comparison of electrodes application speed, subject comfort, and most critically electrophysiological signal quality between the conventional and wired Biosemi EEG system using wet active electrodes and the compact and wireless F1 EEG system consisting of dry passive electrodes. All subjects (n = 27) participated in two recording sessions on separate days, one with the wet EEG system and one with the dry EEG system, in which the session order was counterbalanced across subjects. In each session, we recorded their EEG during separate rest periods with eyes open and closed followed by two versions of a serial visual presentation target detection task. Each task component allows for a neural measure reflecting different characteristics of the data, including spectral power in canonical low frequency bands, event-related potential components (specifically, the P3b), and single trial classification based on machine learning. The performance across the two systems was similar in most measures, including the P3b amplitude and topography, as well as low frequency (theta, alpha, and beta) spectral power at rest. Both EEG systems performed well above chance in the classification analysis, with a marginal advantage of the wet system over the dry. Critically, all aforementioned electrophysiological metrics showed significant positive correlations (r = 0.54–0.89) between the two EEG systems. This multitude of measures provides a comprehensive comparison that captures different aspects of EEG data, including temporal precision, frequency domain as well as multivariate patterns of activity. Taken together, our results indicate that the dry EEG system used in this experiment can effectively record electrophysiological measures commonly used across the research and clinical contexts with comparable quality to the conventional wet EEG system.
       
  • Irritability uniquely predicts prefrontal cortex activation during
           preschool inhibitory control among all temperament domains: A LASSO
           approach
    • Abstract: Publication date: Available online 11 September 2018Source: NeuroImageAuthor(s): Frank A. Fishburn, Christina O. Hlutkowsky, Lisa M. Bemis, Theodore J. Huppert, Lauren S. Wakschlag, Susan B. Perlman Temperament, defined as individual variation in the reactivity and regulation of emotional, motor, and attentional processes, has been shown to influence emotional and cognitive development during the preschool period (ages 4–5). While relationships between temperament and neural activity have been investigated previously, these have typically investigated individual temperament dimensions selected ad hoc. Since significant correlations exist between various temperament dimensions, it remains unclear whether these findings would replicate while analyzing all temperament dimensions simultaneously. Using functional near infrared spectroscopy (fNIRS), 4-5-year-old children (N = 118) were administered a Go/No-Go task to assess prefrontal cortex activation during inhibitory control. The relationship between PFC activation and all 15 temperament domains defined by the Children's Behavior Questionnaire (CBQ) was assessed using automatic feature selection via LASSO regression. Results indicate that only the Anger/Frustration dimension was predictive of activation during the inhibitory control task. These findings support previous work showing relationships between irritability and prefrontal activation during executive function and extend those findings by demonstrating the specificity of the activation-irritability relationship among temperament dimensions.Graphical abstractImage 1
       
  • The BOLD response in primary motor cortex and supplementary motor area
           during kinesthetic motor imagery based graded fMRI neurofeedback
    • Abstract: Publication date: Available online 8 September 2018Source: NeuroImageAuthor(s): David M.A. Mehler, Angharad N. Williams, Florian Krause, Michael Lührs, Richard G. Wise, Duncan L. Turner, David E.J. Linden, Joseph R. Whittaker There is increasing interest in exploring the use of functional MRI neurofeedback (fMRI-NF) as a therapeutic technique for a range of neurological conditions such as stroke and Parkinson's disease (PD). One main therapeutic potential of fMRI-NF is to enhance volitional control of damaged or dysfunctional neural nodes and networks via a closed-loop feedback model using mental imagery as the catalyst of self-regulation. The choice of target node/network and direction of regulation (increase or decrease activity) are central design considerations in fMRI-NF studies. Whilst it remains unclear whether the primary motor cortex (M1) can be activated during motor imagery, the supplementary motor area (SMA) has been robustly activated during motor imagery. Such differences in the regulation potential between primary and supplementary motor cortex are important because these areas can be differentially affected by a stroke or PD, and the choice of fMRI-NF target and grade of self-regulation of activity likely have substantial influence on the clinical effects and cost effectiveness of NF-based interventions. In this study we therefore investigated firstly whether healthy subjects would be able to achieve self-regulation of the hand-representation areas of M1 and the SMA using fMRI-NF training. There was a significant decrease in M1 neural activity during fMRI-NF, whereas SMA neural activity was increased, albeit not with the predicated graded effect. This study has important implications for fMRI-NF protocols that employ motor imagery to modulate activity in specific target regions of the brain and to determine how they may be tailored for neurorehabilitation.
       
  • Resting-state white matter-cortical connectivity in non-human primate
           brain
    • Abstract: Publication date: Available online 8 September 2018Source: NeuroImageAuthor(s): Tung-Lin Wu, Feng Wang, Muwei Li, Kurt G. Schilling, Yurui Gao, Adam W. Anderson, Li Min Chen, Zhaohua Ding, John C. Gore Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.
       
 
 
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