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Publisher: Elsevier   (Total: 3160 journals)

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Showing 1 - 200 of 3160 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: 34, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 96, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 38, 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: 412, 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: 258, 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: 3, 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: 28, 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: 17, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 10, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 154, 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: 14, 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: 24, 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: 33, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, 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: 4)
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: 14)
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: 24)
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: 8)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 46, 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: 23, 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: 35, 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: 7, 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: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, 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: 23)
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: 12)
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: 10)
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: 64)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (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: 399, 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: 11, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, 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: 46, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 345, 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: 455, 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: 41, 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: 10)
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: 51, 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: 47)
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: 215, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, 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: 180, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 12, 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: 201, 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  [3160 journals]
  • Brain reading and behavioral methods provide complementary perspectives on
           the representation of concepts
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Andrew James Bauer, Marcel Adam Just The advent of brain reading techniques has enabled new approaches to the study of concept representation, based on the analysis of multivoxel activation patterns evoked by the contemplation of individual concepts such as animal concepts. The present fMRI study characterized the representation of 30 animal concepts. Dimensionality reduction of the multivoxel activation patterns underlying the individual animal concepts indicated that the semantic building blocks of the brain's representations of the animals corresponded to intrinsic animal properties (e.g. fierceness, intelligence, size). These findings were compared to behavioral studies of concept representation, which have typically collected pairwise similarity ratings between two concepts (e.g. Henley, 1969). Behavioral similarity judgments, by contrast, indicated that the animals were organized into taxonomically defined groups (e.g. canine, feline, equine). The difference in the results between the brain reading and behavioral approaches might derive from differences in cognitive processing during judging similarities versus contemplating one animal at a time. Brain reading approaches may have an advantage in describing thoughts about an individual concept, owing to the ability to decode brain activation patterns elicited by the brief consideration of a single concept (e.g. word reading) without a complex cognitive or behavioral task (e.g. similarity judgments). On the other hand, some behavioral tasks may tend to evoke a concept from numerous perspectives, yielding a representation of the breadth and sophistication of the concept knowledge. These results suggest that neural and behavioral measures offer complementary perspectives that together characterize the content and structure of concept representations.
       
  • Reduction of cerebral blood flow in community-based adults with
           subclinical cerebrovascular atherosclerosis: A 3.0T magnetic resonance
           imaging study
    • Abstract: Publication date: Available online 12 December 2018Source: NeuroImageAuthor(s): Hualu Han, Runhua Zhang, Gaifen Liu, Huiyu Qiao, Zhensen Chen, Yang Liu, Xiaoyi Chen, Dongye Li, Yongjun Wang, Xihai Zhao Reduction in cerebral blood flow (CBF), one of the major metrics for cerebral perfusion, is associated with many brain disorders. Therefore, early characterization of CBF prior to occurrence of symptoms is essential for prevention of cerebral ischemic disorders. We hypothesized that large artery atherosclerosis might be a potential indicator for decline in cerebral perfusion. The aim of this study was to investigate the relationship between large artery atherosclerosis and CBF in asymptomatic adults. A total of 134 asymptomatic subjects (mean age, 56.2 ± 12.8 years; 54 males) were recruited and underwent magnetic resonance (MR) imaging for brain and intracranial and extracranial carotid arteries. Presence or absence of cerebrovascular atherosclerosis was determined on MR vessel wall images. The CBF was measured with pseudo-continuous arterial spin labeling (pCASL) imaging. The CBF values in internal carotid artery (ICA) (37.2 ± 5.8 vs. 39.0 ± 4.9 ml/100 g/min, P = 0.049) and vertebrobasilar (VA-BA) artery territories (42.0 ± 6.8 vs. 44.8 ± 7.0 ml/100 g/min, P = 0.023) were significantly reduced in subjects with cerebrovascular plaque compared to those without. Presence of cerebrovascular plaque was significantly associated with CBF of VA-BA territory before (odds ratio, 2.89; 95% confidence interval, 1.37–6.08; P = 0.005) and after adjusted for confounding factors including age, gender, BMI, diabetes, systolic blood pressure, hyperlipidemia and history of cardiovascular disease (odds ratio, 2.76; 95% confidence interval, 1.18–6.46; P = 0.019). In conclusion, presence of cerebrovascular atherosclerosis is independently associated with reduction in CBF measured by pCASL in asymptomatic adults, suggesting that cerebrovascular large artery atherosclerosis might be an effective indicator for impairment of cerebral microcirculation hemodynamics.
       
  • A generative model of realistic brain cells with application to numerical
           simulation of the diffusion-weighted MR signal
    • Abstract: Publication date: Available online 12 December 2018Source: NeuroImageAuthor(s): Marco Palombo, Daniel C. Alexander, Hui Zhang To date, numerical simulations of the brain tissue have been limited by their lack of realism and flexibility. The purpose of this work is to propose a controlled and flexible generative model for brain cell morphology and an efficient computational pipeline for the reliable and robust simulation of realistic cellular structures with application to numerical simulation of intra-cellular diffusion-weighted MR (DW-MR) signal features. Inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables users to simulate molecular diffusion within realistic digital brain cells, such as neurons, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology (no statistically different 3D Sholl metrics, P > 0.05) and simulated intra-cellular DW-MR signal (mean relative difference 
       
  • The strength of alpha and gamma oscillations predicts behavioral switch
           costs
    • Abstract: Publication date: Available online 10 December 2018Source: NeuroImageAuthor(s): Amy L. Proskovec, Alex I. Wiesman, Tony W. Wilson Cognitive flexibility is often examined using task-switch paradigms, whereby individuals either switch between tasks or repeat the same task on successive trials. The behavioral costs of switching in terms of accuracy and reaction time are well-known, but the oscillatory dynamics underlying such costs are poorly understood. Herein, we examined 25 healthy adults who performed a task-switching paradigm during magnetoencephalography (MEG). All MEG data were transformed into the time-frequency domain and significant oscillatory responses were imaged separately per condition (i.e., switch, repeat) using a beamformer. To determine the impact of task switching on the neural dynamics, the resulting images were examined using paired-samples t-tests. Whole-brain correlations were also computed using the switch-related difference images (switch – repeat) and the switch-related behavioral data (i.e., switch costs). Our key results indicated stronger decreases in alpha and beta activity, and greater increases in gamma activity in nodes of the cingulo-opercular and fronto-parietal networks during switch relative to repeat trials. In addition, behavioral switch costs were positively correlated with switch-related differences in right frontal and inferior parietal alpha activity, and negatively correlated with switch effects in anterior cingulate and right temporoparietal gamma activity. In other words, participants who had a greater decrease in alpha or increase in gamma in these respective regions had smaller behavioral switch costs, which suggests that these oscillations are critical to supporting cognitive flexibility. In sum, we provide novel data linking switch effects and gamma oscillations, and employed a whole-brain approach to directly link switch-related oscillatory differences with switch-related performance differences.
       
  • Dynamics of aesthetic experience are reflected in the default-mode network
    • Abstract: Publication date: Available online 10 December 2018Source: NeuroImageAuthor(s): Amy M. Belfi, Edward A. Vessel, Aenne Brielmann, Ayse Ilkay Isik, Anjan Chatterjee, Helmut Leder, Denis G. Pelli, G. Gabrielle Starr Neuroaesthetics is a rapidly developing interdisciplinary field of research that aims to understand the neural substrates of aesthetic experience: While understanding aesthetic experience has been an objective of philosophers for centuries, it has only more recently been embraced by neuroscientists. Recent work in neuroaesthetics has revealed that aesthetic experience with static visual art engages visual, reward and default-mode networks. Very little is known about the temporal dynamics of these networks during aesthetic appreciation. Previous behavioral and brain imaging research suggests that critical aspects of aesthetic experience have slow dynamics, taking more than a few seconds, making them amenable to study with fMRI. Here, we identified key aspects of the dynamics of aesthetic experience while viewing art for various durations. In the first few seconds following image onset, activity in the DMN (and high-level visual and reward regions) was greater for very pleasing images; in the DMN this activity counteracted a suppressive effect that grew longer and deeper with increasing image duration. In addition, for very pleasing art, the DMN response returned to baseline in a manner time-locked to image offset. Conversely, for non-pleasing art, the timing of this return to baseline was inconsistent. This differential response in the DMN may therefore reflect the internal dynamics of the participant's state: The participant disengages from art-related processing and returns to stimulus-independent thought. These dynamics suggest that the DMN tracks the internal state of a participant during aesthetic experience.
       
  • Olfactory loss is associated with reduced hippocampal activation in
           response to emotional pictures
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): Pengfei Han, Thomas Hummel, Claudia Raue, Ilona Croy Emotional processing evolved within brain structures that were originally dedicated to olfactory function. Reduced olfactory function, absence of the olfactory bulb and the experimental removal of the olfactory bulb are associated with depressive behavior. Against this background, we hypothesized that olfactory dysfunction modifies the neural processing of non-olfactory emotion information. Using a functional magnetic resonance imaging design, we therefore tested whether people with and without impaired olfactory function differ in emotional perception and processing. Neural activity of 17 patients with acquired olfactory loss and 23 age- and sex-matched control participants were monitored in the MRI scanner, while they were presented with emotional and neutral pictures. Participants rated the valence and arousal for each picture after scanning. Patients showed reduced right hippocampal brain responses to emotional but not neutral pictures independent of their depressive symptoms. In addition, emotion-dependent activation in the hippocampus and insula was positively associated with the olfactory bulb (OB) volumes in healthy participants. Taken together, these findings suggest a disrupted neural processing of emotional pictures among patients with olfactory loss. This indicates a significant role of the neural olfactory trajectories for general emotion processing. Central emotion processing is reduced in olfactory disorders and relates to the OB volume in normosmic individuals.
       
  • Development of population receptive fields in the lateral visual stream
           improves spatial coding amid stable structural-functional coupling
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): Jesse Gomez, Alexis Drain, Brianna Jeska, Vaidehi S. Natu, Michael Barnett, Kalanit Grill-Spector Human visual cortex encompasses more than a dozen visual field maps across three major processing streams. One of these streams is the lateral visual stream, which extends from V1 to lateral-occipital (LO) and temporal-occipital (TO) visual field maps and plays a prominent role in shape as well as motion perception. However, it is unknown if and how population receptive fields (pRFs) in the lateral visual stream develop from childhood to adulthood, and what impact this development may have on spatial coding. Here, we used functional magnetic resonance imaging and pRF modeling in school-age children and adults to investigate the development of the lateral visual stream. Our data reveal four main findings: 1) The topographic organization of eccentricity and polar angle maps of the lateral stream is stable after age five. 2) In both age groups there is a reliable relationship between eccentricity map transitions and cortical folding: the middle occipital gyrus predicts the transition between the peripheral representation of LO and TO maps. 3) pRFs in LO and TO maps undergo differential development from childhood to adulthood, resulting in increasing coverage of the central visual field in LO and of the peripheral visual field in TO. 4) Model-based decoding shows that the consequence of pRF and visual field coverage development is improved spatial decoding from LO and TO distributed responses in adults vs. children. Together, these results explicate both the development and topography of the lateral visual stream. Our data show that the general structural-functional organization is laid out early in development, but fine-scale properties, such as pRF distribution across the visual field and consequently, spatial precision, become fine-tuned across childhood development. These findings advance understanding of the development of the human visual system from childhood to adulthood and provide an essential foundation for understanding developmental deficits.
       
  • Freely chosen and instructed actions are terminated by different neural
           mechanisms revealed by kinematics-informed EEG
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): Shivakumar Viswanathan, Bin A. Wang, Rouhollah O. Abdollahi, Silvia Daun, Christian Grefkes, Gereon R. Fink Neurophysiological accounts of human volition are dominated by debates on the origin of voluntary choices but the neural consequences that follow such choices remain poorly understood. For instance, could one predict whether or not an action was chosen voluntarily based only on how that action is motorically executed' We investigated this possibility by integrating scalp electroencephalograms and index-finger accelerometer recordings acquired while people chose between pressing a left or right button either freely or as instructed by a visual cue. Even though freely selected and instructed actions were executed with equal vigor, the timing of the movement to release the button was comparatively delayed for freely selected actions. This chronometric difference was six-times larger for the β-oscillations over the sensorimotor cortex that characteristically accompany an action's termination. This surprising modulation of an action's termination by volition was traceable to volition-modulated differences in how the competing yet non-selected action was represented and regulated.
       
  • Dynamic functional connectivity during task performance and rest predicts
           individual differences in attention across studies
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): Angus Ho Ching Fong, Kwangsun Yoo, Monica D. Rosenberg, Sheng Zhang, Chiang-Shan R. Li, Dustin Scheinost, R. Todd Constable, Marvin M. Chun Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10–60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.
       
  • Manipulating and decoding subjective gaming experience during active
           gameplay: a multivariate, whole-brain analysis
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): Uijong Ju, Christian Wallraven A large number of perceptual and cognitive processes are instantiated during active gameplay, culminating in what is termed the overall “gaming experience”, which encapsulates multiple, subjective dimensions of how one feels about the game. Although some research has been conducted into the neural mechanisms underlying the gaming experience, previous studies so far have relied on commercial games that provide little control over key aspects of gameplay and also have focused only on a few individual dimensions of the gaming experience. Here, we used a custom-made, immersive driving car game in four different gameplay versions (baseline, obstacle increase, goal decrease, speed increase) to assess and modulate the subjective gameplay experience while participants underwent a fMRI scan. A multivariate correlation analysis of whole-brain neural activity with behaviorally-identified subjective gaming experience uncovered brain networks associated with different experiences, including higher-level visual processing networks, the default network, and emotional areas. These regions were in addition able to decode the four different game conditions above chance. Our results for the first time describe the full range of cortical networks that become engaged to create the subjective experience during active gameplay.
       
  • Functional connectivity changes associated with fMRI neurofeedback of
           right inferior frontal cortex in adolescents with ADHD
    • Abstract: Publication date: March 2019Source: NeuroImage, Volume 188Author(s): K. Rubia, M. Criaud, M. Wulff, A. Alegria, H. Brinson, G. Barker, D. Stahl, V. Giampietro Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. We showed previously that 18 ADHD adolescents over 11 runs of 8.5 min of real-time functional magnetic resonance neurofeedback of the right inferior frontal cortex (rIFC) progressively increased activation in 2 regions of the rIFC which was associated with clinical symptom improvement. In this study, we used functional connectivity analyses to investigate whether fMRI-Neurofeedback of rIFC resulted in dynamic functional connectivity changes in underlying neural networks.Whole-brain seed-based functional connectivity analyses were conducted using the two clusters showing progressively increased activation in rIFC as seed regions to test for changes in functional connectivity before and after 11 fMRI-Neurofeedback runs. Furthermore, we tested whether the resulting functional connectivity changes were associated with clinical symptom improvements and whether they were specific to fMRI-Neurofeedback of rIFC when compared to a control group who had to self-regulate another region.rIFC showed increased positive functional connectivity after relative to before fMRI-Neurofeedback with dorsal caudate and anterior cingulate and increased negative functional connectivity with regions of the default mode network (DMN) such as posterior cingulate and precuneus. Furthermore, the functional connectivity changes were correlated with clinical improvements and the functional connectivity and correlation findings were specific to the rIFC-Neurofeedback group.The findings show for the first time that fMRI-Neurofeedback of a typically dysfunctional frontal region in ADHD adolescents leads to strengthening within fronto-cingulo-striatal networks and to weakening of functional connectivity with posterior DMN regions and that this may be underlying clinical improvement.Graphical abstractImage 1
       
  • MR fingerprinting enables quantitative measures of brain tissue relaxation
           times and myelin water fraction in the first five years of life
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Yong Chen, Meng-Hsiang Chen, Kristine R. Baluyot, Taylor M. Potts, Jordan Jimenez, Weili Lin, for the UNC/UMN Baby Connectome Project Consortium Quantitative assessments of normative brain development using MRI are of critical importance to gain insights into healthy neurodevelopment. However, quantitative MR imaging poses significant technical challenges and requires prohibitively long acquisition times, making it impractical for pediatric imaging. This is particularly relevant for healthy subjects, where imaging under sedation is not clinically indicated. MR Fingerprinting (MRF), a novel MR imaging framework, provides rapid, efficient, and simultaneous quantification of multiple tissue properties. In this study, a 2D MR Fingerprinting method was developed that achieves a spatial resolution of 1 × 1 × 3 mm3 with rapid and simultaneous quantification of T1, T2 and myelin water fraction (MWF). Phantom experiments demonstrated that accurate measurements of T1 and T2 relaxation times were achieved over a wide range of T1 and T2 values. MRF images were acquired cross-sectionally from 28 typically developing children, 0 to five years old, who were enrolled in the UNC/UMN Baby Connectome Project. Differences associated with age of R1 (=1/T1), R2 (=1/T2) and MWF were obtained from several predefined white matter regions. Both R1 and R2 exhibit a marked increase until ∼20 months of age, followed by a slower increase for all WM regions. In contrast, the MWF remains at a negligible level until ∼6 months of age for all predefined ROIs and gradually increases afterwards. Depending on the brain region, rapid increases are observed between 6 and 12 months to 6–18 months, followed by a slower pace of increase in MWF. Neither relaxivities nor MWF were significantly different between the left and right hemispheres. However, regional differences in age-related R1 and MWF measures were observed across different white matter regions. In conclusion, our results demonstrate that the MRF technique holds great potential for multi-parametric assessments of normative brain development in early childhood.
       
  • Using GPUs to accelerate computational diffusion MRI: From microstructure
           estimation to tractography and connectomes
    • Abstract: Publication date: Available online 8 December 2018Source: NeuroImageAuthor(s): Moises Hernandez-Fernandez, Istvan Reguly, Saad Jbabdi, Mike Giles, Stephen Smith, Stamatios N. Sotiropoulos The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performances than 200 CPU cores thanks to our parallel designs.
       
  • Age-related differences in task-induced brain activation is not task
           specific: Multivariate pattern generalization between metacognition,
           cognition and perception
    • Abstract: Publication date: Available online 8 December 2018Source: NeuroImageAuthor(s): Esther H.H. Keulers, María Björk Birkisdóttir, Luciana Falbo, Anique de Bruin, Peter L.J. Stiers Adolescence is associated with widespread maturation of brain structures and functional connectivity profiles that shift from local to more distributed and better integrated networks, which are active during a variety of cognitive tasks. Nevertheless, the approach to examine task-induced developmental brain changes is function-specific, leaving the question open whether functional maturation is specific to the particular cognitive demands of the task used, or generalizes across different tasks. In the present study we examine the hypothesis that functional brain maturation is driven by global changes in how the brain handles cognitive demands. Multivariate pattern classification analysis (MVPA) was used to examine whether age discriminative task-induced activation patterns generalize across a wide range of information processing levels. 25 young (13-years old) and 22 old (17-years old) adolescents performed three conceptually different tasks of metacognition, cognition and visual processing. MVPA applied within each task indicated that task-induced brain activation is consistent and reliably different between ages 13 and 17. These age-discriminative activation patterns proved to be common across the different tasks used, despite the differences in cognitive demands and brain structures engaged by each of the three tasks. MVP classifiers trained to detect age-discriminative patterns in brain activation during one task were significantly able to decode age from brain activation maps during execution of other tasks with accuracies between 63 and 75%. The results emphasize that age-specific characteristics of task-induced brain activation have to be understood at the level of brain-wide networks that show maturational changes in their organization and processing efficacy during adolescence.
       
  • Neural architecture supporting active emotion processing in children: A
           multivariate approach
    • Abstract: Publication date: Available online 8 December 2018Source: NeuroImageAuthor(s): M. Catalina Camacho, Helmet T. Karim, Susan B. Perlman BackgroundAdaptive emotion processing is critical for nearly all aspects of social and emotional functioning. There are distinct developmental trajectories associated with improved emotion processing, with a protracted developmental course for negative or complex emotions. The specific changes in neural circuitry that underlie this development, however are still scarcely understood. We employed a multivariate approach in order to elucidate distinctions in complex, naturalistic emotion processing between childhood and adulthood.MethodTwenty-one adults (M±SD age = 26.57 ± 5.08 years) and thirty children (age = 7.75 ± 1.80 years) completed a free-viewing movie task during BOLD fMRI scanning. This task was designed to assess naturalistic processing of movie clips portraying positive, negative, and neutral emotions. Multivariate support vector machines (SVM) were trained to classify age groups based on neural activation during the task.ResultsSVMs were able to successfully classify condition (positive, negative, and neutral) across all participants with high accuracy (61.44%). SVMs could successfully distinguish adults and children within each condition (ps 
       
  • Nutrient biomarker patterns, cognitive function, and fMRI measures of
           network efficiency in the aging brain
    • Abstract: Publication date: Available online 7 December 2018Source: NeuroImageAuthor(s): Christopher E. Zwilling, Tanveer Talukdar, Marta K. Zamroziewicz, Aron K. Barbey A central aim of research in the psychological and brain sciences is to establish therapeutic interventions to promote healthy brain aging. Accumulating evidence indicates that diet and the many bioactive substances present in food are reasonable interventions to examine for dementia prevention. However, interdisciplinary research that applies methods from nutritional epidemiology and network neuroscience to investigate the role of nutrition in shaping functional brain network efficiency remains to be conducted. The present study therefore sought to combine methods across disciplines, applying nutrient biomarker pattern (NBP) analysis to capture the effects of plasma nutrients in combination and to examine their collective influence on measures of functional brain network efficiency (small-world propensity). We examined the contribution of NBPs to multiple indices of cognition and brain health in non-demented elders (n = 116), investigating performance on measures of general intelligence, executive function, and memory, and resting-state fMRI measures of brain network efficiency within seven intrinsic connectivity networks. Statistical moderation investigated whether nutrient biomarker patterns influenced network efficiency and cognitive outcomes. The results revealed five NBPs that were associated with enhanced cognitive performance, including biomarker patterns high in plasma: (1) ω-3 and ω-6 polyunsaturated fatty acids (PUFAs), (2) lycopene, (3) ω-3 PUFAs, (4) carotenoids, and (5) vitamins B (riboflavin, folate, B12) and D. Furthermore, three NBPs were associated with enhanced functional brain network efficiency, including biomarker patterns high in plasma: (1) ω-6 PUFAs, (2) ω-3 PUFAs, and (3) carotene. Finally, ω-3 PUFAs moderated the fronto-parietal network and general intelligence, while ω-6 PUFAs and lycopene moderated the dorsal attention network and executive function. In sum, NBPs account for a significant proportion of variance in measures of cognitive performance and functional brain network efficiency. The results motivate a multidisciplinary approach that applies methods from nutritional epidemiology (nutrient biomarker pattern analysis) and cognitive neuroscience (functional brain network efficiency) to characterize the impact of nutrition on human health, aging, and disease.
       
  • Changes in functional connectivity dynamics with aging: A dynamical phase
           synchronization approach
    • Abstract: Publication date: Available online 7 December 2018Source: NeuroImageAuthor(s): Sou Nobukawa, Mitsuru Kikuchi, Tetsuya Takahashi The dynamics of the human brain network has attracted broad attention, in recognition of the concept that functional connectivity is not static, but changes its pattern over time, even in the resting state. We hypothesized that analysis of continuously captured time-varying instantaneous phase synchronization between signals from different brain regions might add another dimension to already identified network dynamics. To validate this hypothesis as an aid to elucidating the physiological mechanisms of aging, we examined time-series of instantaneous phase synchronization events in resting-state EEG activity across the brain, in healthy younger and healthy older subjects. We then characterized the temporal dynamics of phase synchronization using multiscale entropy, which quantifies the complexity of brain signal dynamics over multiple time scales. The results of surrogate analyses confirmed that the temporal dynamics of phase synchronization arise from deterministic processes in the neural network system. Group comparison showed region-specific enhanced complexity of temporal dynamics of phase synchronization in older subjects in alpha band predominantly in frontal brain regions, which was not identified by a comparative phase synchronization approach such as phase lag index. Enhanced complexity of temporal dynamics of functional connectivity in older subjects might reflect a general network alteration theory in aging. This is a first report describing the importance of capturing the dynamics of instantaneous phase synchronization and characterizing its temporal organization. Applying this method to neurophysiologic data may provide a novel understanding of dynamical neural network processes in both healthy and pathological conditions.
       
  • Relationship of critical dynamics, functional connectivity, and states of
           consciousness in large-scale human brain networks
    • Abstract: Publication date: Available online 6 December 2018Source: NeuroImageAuthor(s): Heonsoo Lee, Daniel Golkowski, Denis Jordan, Sebastian Berger, Rüdiger Ilg, Joseph Lee, George A. Mashour, UnCheol Lee, Michael S. Avidan, Stefanie Blain-Moraes, Goodarz Golmirzaie, Randall Hardie, Rosemary Hogg, Ellen Janke, Max B. Kelz, Kaitlyn Maier, George A. Mashour, Hannah Maybrier, Andrew McKinstry-Wu, Maxwell Muench Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric.To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.
       
  • Improved EEG source localization with Bayesian uncertainty modelling of
           unknown skull conductivity
    • Abstract: Publication date: Available online 6 December 2018Source: NeuroImageAuthor(s): Ville Rimpiläinen, Alexandra Koulouri, Felix Lucka, Jari P. Kaipio, Carsten H. Wolters Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
       
  • Passive exposure to speech sounds modifies change detection brain
           responses in adults
    • Abstract: Publication date: Available online 6 December 2018Source: NeuroImageAuthor(s): L.O. Kurkela Jari, A. Hämäläinen Jarmo, H.T. Leppänen Paavo, Shu Hua, Astikainen Piia In early life auditory discrimination ability can be enhanced by passive sound exposure. In contrast, in adulthood passive exposure seems to be insufficient to promote discrimination ability, but this has been tested only with a single short exposure session in humans. We tested whether passive exposure to unfamiliar auditory stimuli can result in enhanced cortical discrimination ability and change detection in adult humans, and whether the possible learning effect generalizes to different stimuli. To address these issues, we exposed adult Finnish participants to Chinese lexical tones passively for 2 h per day on 4 consecutive days. Behavioral responses and the brain's event-related potentials (ERPs) were measured before and after the exposure for the same stimuli applied in the exposure phase and to sinusoidal sounds roughly mimicking the frequency contour in speech sounds. Passive exposure modulated the ERPs to speech sound changes in both ignore (mismatch negativity latency, P3a amplitude and P3a latency) and attend (P3b amplitude) test conditions, but not the behavioral responses. Furthermore, effect of passive exposure transferred to the processing of the sinusoidal sounds as indexed by the latency of the mismatch negativity. No corresponding effects in the ERPs were found in a control group that participated to the test measurements, but received no exposure to the sounds. The results show that passive exposure to foreign speech sounds in adulthood can enhance cortical discrimination ability and attention orientation toward changes in speech sounds and that the learning effect can transfer to non-speech sounds.
       
  • Spatiotemporal analysis for detection of pre-symptomatic shape changes in
           neurodegenerative diseases: Initial application to the GENFI cohort
    • Abstract: Publication date: Available online 6 December 2018Source: NeuroImageAuthor(s): Claire Cury, Stanley Durrleman, David M. Cash, Marco Lorenzi, Jennifer M. Nicholas, Martina Bocchetta, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sebastien Ourselin Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.Graphical abstractImage 1
       
  • Memory entrainment by visually evoked theta-gamma coupling
    • Abstract: Publication date: Available online 5 December 2018Source: NeuroImageAuthor(s): Moritz Köster, Ulla Martens, Thomas Gruber The wake human brain constantly encodes novel information and integrates them into existing neuronal representations. It is posited that the formation of new memory traces is orchestrated by the synchronization of neuronal activity in the theta rhythm (3–8 Hz), theta coupled gamma activity (40–120 Hz), and decreases in the alpha rhythm (8–12 Hz). Critically, given the correlative nature of neurophysiological recordings, the functional relevance of oscillatory processes is not well understood. Here, we experimentally enhanced memory formation processes by a visual stimulation at an individual theta frequency, in contrast to the stimulation at an individual alpha frequency. This memory entrainment effect was not explained by theta power per se, but was driven by visually evoked theta-gamma coupling pattern. This underlines the functional role of the theta rhythm and the theta-gamma neuronal code in human episodic memory. The entrainment of mnemonic network mechanisms by a visual stimulation technique provides a proof of concept that visual pacemakers can entrain complex cognitive processes in the wake human brain.
       
  • A longitudinal study of infant view-invariant face processing during the
           first 3–8 months of life
    • Abstract: Publication date: Available online 5 December 2018Source: NeuroImageAuthor(s): Hiroko Ichikawa, Emi Nakato, Yasuhiko Igarashi, Masato Okada, So Kanazawa, Masami K. Yamaguchi, Ryusuke Kakigi View-invariant face processing emerges early in life. A previous study (Nakato et al., 2009) measured infant hemodynamic responses to faces from the frontal and profile views in the bilateral temporal areas, which have been reported to be involved in face processing using near-infrared spectroscopy. It was reported that 5-month-old infants showed increased oxyhemoglobin (oxy-Hb) responses to frontal faces, but not to profile faces. In contrast, 8-month-old infants displayed increased oxy-Hb responses to profile faces as well as to frontal faces. In this study, we used the experimental method developed in the previous study to investigate the development of view-invariant face processing, every month for 5 months (from the first 3–8 months of life). We longitudinally measured hemodynamic responses to faces from the frontal and profile views in 14 infants. The longitudinal measurements allowed us to investigate individual differences in each participant. We modeled each infant's hemodynamic oxy-Hb responses to frontal and profile faces using linear regression analysis. Processing of profile faces emerged later and underwent larger improvements than that of frontal faces. We also found an anticorrelation between the speed of improvement in face processing and the hemodynamic response to faces at the age of 3- months. Group analysis of the averaged hemodynamic data from the 14 infants using linear regression revealed that the processing of profile faces emerged between 5 and 6 months of age. Infant view-invariant face processing developed first for frontal faces. This was followed by the emergence of processing of profile faces.
       
  • Tonic thermonociceptive stimulation selectively modulates ongoing neural
           oscillations in the human posterior insula: Evidence from intracerebral
           EEG
    • Abstract: Publication date: Available online 5 December 2018Source: NeuroImageAuthor(s): Giulia Liberati, Maxime Algoet, Susana Ferrao Santos, Jose Geraldo Ribeiro-Vaz, Christian Raftopoulos, André Mouraux The human insula is an important target for spinothalamic input, but there is still no consensus on its role in pain perception and nociception. In this study, we show that the human insula exhibits activity preferential for sustained thermonociception. Using intracerebral EEG recorded from the insula of 8 patients (2 females) undergoing a presurgical evaluation of focal epilepsy (53 contacts: 27 anterior, 26 posterior), we “frequency-tagged” the insular activity elicited by sustained thermonociceptive and vibrotactile stimuli, by periodically modulating stimulation intensity at a fixed frequency of 0.2 Hz during 75 s. Both types of stimuli elicited an insular response at the frequency of stimulation (0.2 Hz) and its harmonics, whose magnitude was significantly greater in the posterior insula compared to the anterior insula. Compared to vibrotactile stimulation, thermonociceptive stimulation exerted a markedly greater 0.2 Hz modulation of ongoing theta-band (4–8 Hz) and alpha-band (8–12 Hz) oscillations. These modulations were also more prominent in the posterior insula compared to the anterior insula. The identification of oscillatory activities preferential for thermonociception could lead to new insights into the physiological mechanisms of nociception and pain perception in humans.
       
  • Volitional modulation of higher-order visual cortex alters human
           perception
    • Abstract: Publication date: Available online 4 December 2018Source: NeuroImageAuthor(s): Jinendra Ekanayake, Gerard R. Ridgway, Joel S. Winston, Eva Feredoes, Adeel Razi, Yury Koush, Frank Scharnowski, Nikolaus Weiskopf, Geraint Rees Can we change our perception by controlling our brain activation' Awareness during binocular rivalry is shaped by the alternating perception of different stimuli presented separately to each monocular view. We tested the possibility of causally influencing the likelihood of a stimulus entering awareness. To do this, participants were trained with neurofeedback, using realtime functional magnetic resonance imaging (rt-fMRI), to differentially modulate activation in stimulus-selective visual cortex representing each of the monocular images. Neurofeedback training led to altered bistable perception associated with activity changes in the trained regions. The degree to which training influenced perception predicted changes in grey and white matter volumes of these regions. Short-term intensive neurofeedback training therefore sculpted the dynamics of visual awareness, with associated plasticity in the human brain.
       
  • Adaptive task difficulty influences neural plasticity and transfer of
           training
    • Abstract: Publication date: Available online 3 December 2018Source: NeuroImageAuthor(s): Kristin E. Flegal, J. Daniel Ragland, Charan Ranganath The efficacy of cognitive training is controversial, and research progress in the field requires an understanding of factors that promote transfer of training gains and their relationship to changes in brain activity. One such factor may be adaptive task difficulty, as adaptivity is predicted to facilitate more efficient processing by creating a prolonged mismatch between the supply of, and the demand upon, neural resources. To test this hypothesis, we measured behavioral and neural plasticity in fMRI sessions before and after 10 sessions of working memory updating (WMU) training, in which the difficulty of practiced tasks either adaptively increased in response to performance or was fixed. Adaptive training resulted in transfer to an untrained episodic memory task and activation decreases in striatum and hippocampus on a trained WMU task, and the amount of training task improvement was associated with near transfer to other WMU tasks and with hippocampal activation changes on both near and far transfer tasks. These findings suggest that cognitive training programs should incorporate adaptive task difficulty to broaden transfer of training gains and maximize efficiency of task-related brain activity.
       
  • Power and temporal dynamics of alpha oscillations at rest differentiate
           cognitive performance involving sustained and phasic cognitive control
    • Abstract: Publication date: Available online 2 December 2018Source: NeuroImageAuthor(s): Keyvan Mahjoory, Elena Cesnaite, Friederike U. Hohlefeld, Arno Villringer, Vadim V. Nikulin Resting state neuronal activity in EEG/MEG recordings is primarily characterized by the presence of alpha oscillations (approx. 8–12 Hz). However, their functional significance and link to cognitive task performance remains elusive. We investigated resting state neuronal activity and its relation to task performance by assessing traditional measures of alpha activity (power and individual alpha peak frequency) and dynamic properties of the signal measured by long-range temporal correlations (LRTC). Multichannel EEG was recorded at rest in 82 healthy male adults and compared to their cognitive performance, measured by tests involving executive functions, working memory, short- and long-term memory demands. Our results showed that attention span scores positively correlated with alpha power at rest, with corresponding neuronal sources located primarily in the left-hemispheric anterior cingulate cortex, supplementary motor area, and parietal regions. Furthermore, better working memory performance was related to increased LRTC of alpha oscillations at rest in the right hemispheric fronto-parietal, temporal, and occipital regions. Our findings suggest that resting state neuronal activity may reflect properties of brain networks that are functionally relevant for cognitive task performance. While alpha power measured at rest might relate to tasks that employ sustained inhibitory control, LRTC are suggested to reflect the capacity of neuronal networks to perform tasks that require phasic attention and quick adaptation to changing task demands.
       
  • Sex differences in network controllability as a predictor of executive
           function in youth
    • Abstract: Publication date: Available online 1 December 2018Source: NeuroImageAuthor(s): Eli J. Cornblath, Evelyn Tang, Graham L. Baum, Tyler M. Moore, Azeez Adebimpe, David R. Roalf, Ruben C. Gur, Raquel E. Gur, Fabio Pasqualetti, Theodore D. Satterthwaite, Danielle S. Bassett Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in impulsivity in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 879 youth aged 8–22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy-versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, controllability profiles in males are negatively related to the false positive rate on a continuous performance task, a common measure of impulsivity. Finally, we find associations between average controllability and individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.
       
  • Evaluation of a dual signal subspace projection algorithm in
           magnetoencephalographic recordings from patients with intractable epilepsy
           and vagus nerve stimulators
    • Abstract: Publication date: Available online 29 November 2018Source: NeuroImageAuthor(s): Chang Cai, Jiajing Xu, Jayabal Velmurugan, Robert Knowlton, Kensuke Sekihara, Srikantan S. Nagarajan, Heidi Kirsch Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been both common in clinical settings and difficult to reject. Artifacts from vagal nerve stimulators (VNS) are a common and difficult example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP) and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmarks for performance. We then evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.
       
  • Open-field PET: Simultaneous brain functional imaging and behavioural
           response measurements in freely moving small animals
    • Abstract: Publication date: Available online 28 November 2018Source: NeuroImageAuthor(s): Andre Z. Kyme, Georgios I. Angelis, John Eisenhuth, Roger R. Fulton, Victor Zhou, Genevra Hart, Kata Popovic, Mahmood Akhtar, Will J. Ryder, Kelly Clemens, Bernard Balleine, Arvind Parmar, Giancarlo Pascali, Gary Perkins, Steven R. Meikle A comprehensive understanding of how the brain responds to a changing environment requires techniques capable of recording functional outputs at the whole-brain level in response to external stimuli. Positron emission tomography (PET) is an exquisitely sensitive technique for imaging brain function but the need for anaesthesia to avoid motion artefacts precludes concurrent behavioural response studies. Here, we report a technique that combines motion-compensated PET with a robotically-controlled animal enclosure to enable simultaneous brain imaging and behavioural recordings in unrestrained small animals. The technique was used to measure in vivo displacement of [11C]raclopride from dopamine D2 receptors (D2R) concurrently with changes in the behaviour of awake, freely moving rats following administration of unlabelled raclopride or amphetamine. The timing and magnitude of [11C]raclopride displacement from D2R were reliably estimated and, in the case of amphetamine, these changes coincided with a marked increase in stereotyped behaviours and hyper-locomotion. The technique, therefore, allows simultaneous measurement of changes in brain function and behavioural responses to external stimuli in conscious unrestrained animals, giving rise to important applications in behavioural neuroscience.Graphical abstractImage 1
       
  • Accuracy and reliability of [11C]PBR28 specific binding estimated without
           the use of a reference region
    • Abstract: Publication date: Available online 27 November 2018Source: NeuroImageAuthor(s): Pontus Plaven-Sigray, Martin Schain, Francesca Zanderigo, Lars Farde, Christer Halldin, Anton Forsberg, Andrea Varrone, Aurelija Jucaite, Simon Cervenka, Per Stenkrona, Karin Collste, Mats Lekander, Eva Kosek, Jon Lampa, Caroline Olgart Höglund, Ilan Rabiner, Roger Gunn, Todd Ogden, Simon Cervenka [11C]PBR28 is a positron emission tomography radioligand used to estimate the expression of 18 kDa translocator protein (TSPO). TSPO is expressed on glial cells and can function as a marker for immune activation. Since TSPO is expressed throughout the brain, no true reference region exists. For this reason, an arterial input function is required for accurate quantification of [11C]PBR28 binding and the most common outcome measure is the total distribution volume (VT). Notably, VT reflects both specific binding and non-displaceable binding (VND). Therefore, estimates of specific binding, such as binding potentials (e.g., BPND) and specific distribution volume (VS) should theoretically be more sensitive to underlying differences in TSPO expression. It is unknown, however, if unbiased and accurate estimates of these measures are obtainable for [11C]PBR28.The Simultaneous Estimation (SIME) method uses time-activity-curves from multiple brain regions with the aim to obtain a brain-wide estimate of VND, which can subsequently be used to improve the estimation of BPND and VS. In this study we evaluated the accuracy of SIME-derived VND, and the reliability of resulting estimates of specific binding for [11C]PBR28, using a combination of simulation experiments and in vivo studies in healthy humans.The simulation experiments, based on data from 54 unique [11C]PBR28 examination, showed that VND values estimated using SIME were both precise and accurate. Data from a pharmacological competition challenge (n = 5) showed that SIME provided VND values that were on average 19% lower than those obtained using the Lassen plot, but similar to values obtained using the Likelihood-Estimation of Occupancy technique. Test-retest data (n = 11) showed that SIME-derived VS values exhibited good reliability and precision, while larger variability was observed in SIME-derived BPND values.The results support the use of SIME for quantifying specific binding of [11C]PBR28, and suggest that VS can be used in preference to, or as a complement to the conventional outcome measure VT. Additional studies in patient cohorts are warranted.
       
  • Fitness, cortical thickness and surface area in overweight/obese children:
           
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Irene Esteban-Cornejo, Jose Mora-Gonzalez, Cristina Cadenas-Sanchez, Oren Contreras-Rodriguez, Juan Verdejo-Román, Pontus Henriksson, Jairo H. Migueles, Maria Rodriguez-Ayllon, Pablo Molina-García, Chao Suo, Charles H. Hillman, Arthur F. Kramer, Kirk I. Erickson, Andrés Catena, Antonio Verdejo-García, Francisco B. Ortega Cortical thickness and surface area are thought to be genetically unrelated and shaped by independent neurobiological events suggesting that they should be considered separately in morphometric analyses. Although the developmental trajectories of cortical thickness and surface area may differ across brain regions and ages, there is no consensus regarding the relationships of physical fitness with cortical thickness and surface area as well as for its subsequent influence on intelligence. Thus, this study examines: (i) the associations of physical fitness components (i.e., cardiorespiratory fitness, speed-agility and muscular fitness) with overall and regional cortical thickness and surface area; (ii) whether body composition indicators (i.e., body mass index, fat-free mass index and fat mass index) mediate these associations; and (iii) the association of physical fitness and cortical thickness with intelligence in overweight/obese children. A total of 101 overweight/obese children aged 8–11 years were recruited in Granada, Spain. The physical fitness components were assessed following the ALPHA health-related fitness test battery. T1-weighted images were acquired with a 3.0 Tesla Siemens Magnetom Tim Trio system. We used FreeSurfer software version 5.3.0 to assess cortical thickness (mm) and surface area (mm2). The main results showed that cardiorespiratory fitness and speed-agility were related to overall cortical thickness (β = 0.321 and β = 0.302, respectively; both P 
       
  • Multiway canonical correlation analysis of brain data
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Alain de Cheveigné, Giovanni M. Di Liberto, Dorothée Arzounian, Daniel D.E. Wong, Jens Hjortkjær, Søren Fuglsang, Lucas C. Parra Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.
       
  • QuickNAT: A fully convolutional network for quick and accurate
           segmentation of neuroanatomy
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger, Alzheimer's Disease Neuroimaging Initiative Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.Graphical abstractImage 1
       
  • Stimulus-induced gamma power predicts the amplitude of the subsequent
           visual evoked response
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Mats W.J. van Es, Jan-Mathijs Schoffelen The efficiency of neuronal information transfer in activated brain networks may affect behavioral performance. Gamma-band synchronization has been proposed to be a mechanism that facilitates neuronal processing of behaviorally relevant stimuli. In line with this, it has been shown that strong gamma-band activity in visual cortical areas leads to faster responses to a visual go cue. We investigated whether there are directly observable consequences of trial-by-trial fluctuations in non-invasively observed gamma-band activity on the neuronal response. Specifically, we hypothesized that the amplitude of the visual evoked response to a go cue can be predicted by gamma power in the visual system, in the window preceding the evoked response. Thirty-three human subjects (22 female) performed a visual speeded response task while their magnetoencephalogram (MEG) was recorded. The participants had to respond to a pattern reversal of a concentric moving grating. We estimated single trial stimulus-induced visual cortical gamma power, and correlated this with the estimated single trial amplitude of the most prominent event-related field (ERF) peak within the first 100 ms after the pattern reversal. In parieto-occipital cortical areas, the amplitude of the ERF correlated positively with gamma power, and correlated negatively with reaction times. No effects were observed for the alpha and beta frequency bands, despite clear stimulus onset induced modulation at those frequencies. These results support a mechanistic model, in which gamma-band synchronization enhances the neuronal gain to relevant visual input, thus leading to more efficient downstream processing and to faster responses.
       
  • Neural substrates of cognitive reserve in Alzheimer's disease spectrum and
           normal aging
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Dong Hyuk Lee, Peter Lee, Sang Won Seo, Jee Hoon Roh, Minyoung Oh, Jungsu S. Oh, Seung Jun Oh, Jae Seung Kim, Yong Jeong The concept of cognitive reserve (CR) originated from discrepancies between the degree of brain pathology and the severity of clinical manifestations. CR has been characterized through CR proxies, such as education and occupation complexity; however, such approaches have inherent limitations. Although several methods have been developed to overcome these limitations, they fail to reflect the entire Alzheimer's disease (AD) pathology. Meanwhile, graph theory analysis, one of most powerful and flexible approaches, have established remarkable network properties of the brain. The functional and structural brain networks are damaged in neurodegenerative diseases. Therefore, network analysis has been applied to clarify the characteristics of the disease or give insight. Here, using multimodal neuroimaging, we propose an intuitive model to estimate CR based on its original definition, and explore the neural substrates of CR from the perspective of networks and functional connectivity. A total of 87 subjects (21 AD, 32 mild cognitive impairment, and 34 normal aging) underwent tau and amyloid PET, 3D T1-weighted MR, and resting-state fMRI. We hypothesized CR as a residual of actual cognitive performance and expected performance to be related to quantitative factors, such as AD pathology, demographics, and a genetic factor. Then, we correlated this marker using education and occupation complexity as conventional CR proxies. We validated this marker by testing whether it would modulate the effect of brain pathology on memory function. To examine the neural substrates associated with CR, we performed graph analysis to investigate the association between the CR marker and network measures at different granularities in total subjects, AD spectrum and normal aging, respectively. The CR marker from our model was well associated with education and occupation complexity. More directly, the CR marker was revealed to modify the relationship between brain pathology and memory function among AD spectrum. The CR marker was correlated with the global efficiency of the entire network, nodal clustering coefficient, and local efficiency of the right middle-temporal pole. In connectivity analysis, one cluster of edges centered on right middle-temporal pole was significantly correlated with the CR marker. In subgroup analysis, the network measures of right middle-temporal pole still correlated with the CR marker among AD spectrum. However, right precentral gyrus was revealed to be associated with the CR marker in normal aging. This study demonstrates that our intuitive model using multimodal neuroimaging and network perspective adequately and comprehensively captures CR. From a network perspective, CR is associated with the capacity to process information efficiently in the brain. The right middle-temporal pole was revealed to be a pivotal neural substrate of CR in AD spectrum. These findings foster understanding of AD and will be useful to help identify individuals with vulnerability or resistance to AD pathology, and characterize patients for intervention or drug trials.
       
  • The cerebral bases of the bouba-kiki effect
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Nathan Peiffer-Smadja, Laurent Cohen The crossmodal correspondence between some speech sounds and some geometrical shapes, known as the bouba-kiki (BK) effect, constitutes a remarkable exception to the general arbitrariness of the links between word meaning and word sounds. We have analyzed the association of shapes and sounds in order to determine whether it occurs at a perceptual or at a decisional level, and whether it takes place in sensory cortices or in supramodal regions. First, using an Implicit Association Test (IAT), we have shown that the BK effect may occur without participants making any explicit decision relative to sound-shape associations. Second, looking for the brain correlates of implicit BK matching, we have found that intermodal matching influences activations in both auditory and visual sensory cortices. Moreover, we found stronger prefrontal activation to mismatching than to matching stimuli, presumably reflecting a modulation of executive processes by crossmodal correspondence. Thus, through its roots in the physiology of object categorization and crossmodal matching, the BK effect provides a unique insight into some non-linguistic components of word formation.
       
  • Can we study 3D grid codes non-invasively in the human brain'
           Methodological considerations and fMRI findings
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Misun Kim, Eleanor A. Maguire Recent human functional magnetic resonance imaging (fMRI) and animal electrophysiology studies suggest that grid cells in entorhinal cortex are an efficient neural mechanism for encoding knowledge about the world, not only for spatial location but also for more abstract cognitive information. The world, be it physical or abstract, is often high-dimensional, but grid cells have been mainly studied on a simple two-dimensional (2D) plane. Recent theoretical studies have proposed how grid cells encode three-dimensional (3D) physical space, but it is unknown whether grid codes can be examined non-invasively in humans. Here, we investigated whether it was feasible to test different 3D grid models using fMRI based on the direction-modulated property of grid signals. In doing so, we developed interactive software to help researchers visualize 3D grid fields and predict grid activity in 3D as a function of movement directions. We found that a direction-modulated grid analysis was sensitive to one type of 3D grid model – a face-centred cubic (FCC) lattice model. As a proof of concept, we searched for 3D grid-like signals in human entorhinal cortex using a novel 3D virtual reality paradigm and a new fMRI analysis method. We found that signals in the left entorhinal cortex were explained by the FCC model. This is preliminary evidence for 3D grid codes in the human brain, notwithstanding the inherent methodological limitations of fMRI. We believe that our findings and software serve as a useful initial stepping-stone for studying grid cells in realistic 3D worlds and also, potentially, for interrogating abstract high-dimensional cognitive processes.
       
  • Spatially informed voxelwise modeling for naturalistic fMRI experiments
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Emin Çelik, Salman Ul Hassan Dar, Özgür Yılmaz, Ümit Keleş, Tolga Çukur Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring voxels, its sensitivity in detecting functional selectivity can be diminished in the presence of high levels of measurement noise. Here, we introduce spatially-informed voxelwise modeling (SPIN-VM) to take advantage of response correlations in spatial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model features, while still generating single-voxel response predictions. We demonstrated the performance of SPIN-VM on a rich dataset from a natural vision experiment. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. These results suggest that SPIN-VM offers improved performance in predicting single-voxel responses and recovering coherent information representations.
       
  • Volitional limbic neuromodulation exerts a beneficial clinical effect on
           Fibromyalgia
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Noam Goldway, Jacob Ablin, Omer Lubin, Yoav Zamir, Jackob Nimrod Keynan, Ayelet Or-Borichev, Marc Cavazza, Fred Charles, Nathan Intrator, Silviu Brill, Eti Ben-Simon, Haggai Sharon, Talma Hendler Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplificating effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation.We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal.Thirty-Four Fibromyalgia patients (31F; age 35.6 ± 11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion.REM latency and objective sleep quality index were robustly improved following the treatment course only in the real-neurofeedback group (time × group p 
       
  • Hierarchy of speech-driven spectrotemporal receptive fields in human
           auditory cortex
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Jonathan H. Venezia, Steven M. Thurman, Virginia M. Richards, Gregory Hickok Existing data indicate that cortical speech processing is hierarchically organized. Numerous studies have shown that early auditory areas encode fine acoustic details while later areas encode abstracted speech patterns. However, it remains unclear precisely what speech information is encoded across these hierarchical levels. Estimation of speech-driven spectrotemporal receptive fields (STRFs) provides a means to explore cortical speech processing in terms of acoustic or linguistic information associated with characteristic spectrotemporal patterns. Here, we estimate STRFs from cortical responses to continuous speech in fMRI. Using a novel approach based on filtering randomly-selected spectrotemporal modulations (STMs) from aurally-presented sentences, STRFs were estimated for a group of listeners and categorized using a data-driven clustering algorithm. ‘Behavioral STRFs’ highlighting STMs crucial for speech recognition were derived from intelligibility judgments. Clustering revealed that STRFs in the supratemporal plane represented a broad range of STMs, while STRFs in the lateral temporal lobe represented circumscribed STM patterns important to intelligibility. Detailed analysis recovered a bilateral organization with posterior-lateral regions preferentially processing STMs associated with phonological information and anterior-lateral regions preferentially processing STMs associated with word- and phrase-level information. Regions in lateral Heschl's gyrus preferentially processed STMs associated with vocalic information (pitch).
       
  • Deep neural network predicts emotional responses of the human brain from
           functional magnetic resonance imaging
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Hyun-Chul Kim, Peter A. Bandettini, Jong-Hwan Lee An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNNp) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI) data from individual subjects. During fMRI data acquisition, 10 healthy participants listened to 80 International Affective Digital Sound stimuli and rated their own emotions generated by each sound stimulus in terms of the arousal, dominance, and valence dimensions. The whole-brain spatial patterns from a general linear model (i.e., beta-valued maps) for each sound stimulus and the emotional response ratings were used as the input and output for the DNNP, respectively. Based on a nested five-fold cross-validation scheme, the paired input and output data were divided into training (three-fold), validation (one-fold), and test (one-fold) data. The DNNP was trained and optimized using the training and validation data and was tested using the test data. The Pearson's correlation coefficients between the rated and predicted emotional responses from our DNNP model with weight sparsity optimization (mean ± standard error 0.52 ± 0.02 for arousal, 0.51 ± 0.03 for dominance, and 0.51 ± 0.03 for valence, with an input denoising level of 0.3 and a mini-batch size of 1) were significantly greater than those of DNN models with conventional regularization schemes including elastic net regularization (0.15 ± 0.05, 0.15 ± 0.06, and 0.21 ± 0.04 for arousal, dominance, and valence, respectively), those of shallow models including logistic regression (0.11 ± 0.04, 0.10 ± 0.05, and 0.17 ± 0.04 for arousal, dominance, and valence, respectively; average of logistic regression and sparse logistic regression), and those of support vector machine-based predictive models (SVMps; 0.12 ± 0.06, 0.06 ± 0.06, and 0.10 ± 0.06 for arousal, dominance, and valence, respectively; average of linear and non-linear SVMps). This difference was confirmed to be significant with a Bonferroni-corrected p-value of less than 0.001 from a one-way analysis of variance (ANOVA) and subsequent paired t-test. The weights of the trained DNNPs were interpreted and input patterns that maximized or minimized the output of the DNNPs (i.e., the emotional responses) were estimated. Based on a binary classification of each emotion category (e.g., high arousal vs. low arousal), the error rates for the DNNP (31.2% ± 1.3% for arousal, 29.0% ± 1.7% for dominance, and 28.6% ± 3.0% for valence) were significantly lower than those for the linear SVMP (44.7% ± 2.0%, 50.7% ± 1.7%, and 47.4% ± 1.9% for arousal, dominance, and valence, respectively) and the non-linear SVMP (48.8% ± 2.3%, 52.2% ± 1.9%, and 46.4% ± 1.3% for arousal, dominance, and valence, respectively), as confirmed by the Bonferroni-corrected p 
       
  • Focus of attention modulates the heartbeat evoked potential
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Frederike H. Petzschner, Lilian A. Weber, Katharina V. Wellstein, Gina Paolini, Cao Tri Do, Klaas E. Stephan Theoretical frameworks such as predictive coding suggest that the perception of the body and world – interoception and exteroception – involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP) using EEG recordings. We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 524–620 ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. Our study thus provides direct evidence that the HEP is modulated by pure attention and suggests that this effect may provide a clinically relevant readout for assessing interoception.Graphical abstractImage 1
       
  • Combining resting state functional MRI with intraoperative cortical
           stimulation to map the mentalizing network
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Yordanka Nikolova Yordanova, Jérôme Cochereau, Hugues Duffau, Guillaume Herbet ObjectiveTo infer the face-based mentalizing network from resting-state functional MRI (rsfMRI) using a seed-based correlation analysis with regions of interest identified during intraoperative cortical electrostimulation.MethodsWe retrospectively included 23 patients in whom cortical electrostimulation induced transient face-based mentalizing impairment during ‘awake’ craniotomy for resection of a right-sided diffuse low-grade glioma. Positive stimulation sites were recorded and transferred to the patients' preoperative normalized MRI, and then used as seeds for subsequent seed-to-voxel functional connectivity analyses. The analyses, conducted with an uncorrected voxel-level p-value of 0.001 and a false-discovery-rate cluster-level p-value of 0.05, allowed identification of the cortical structures, functionally coupled with the mentalizing-related sites.ResultsTwo clusters of responsive stimulations were identified intraoperatively – one in the right dorsolateral prefrontal cortex (dlPFC, n = 13) and the other in the right inferior frontal gyrus (IFG, n = 10). A whole group level analysis revealed that stimulation sites correlated mainly with voxels located in the pars triangularis of the IFG, the dorsolateral and dorsomedial prefrontal cortices, the temporo-parietal junction, the posterior superior temporal sulcus, and the posterior inferior temporal/fusiform gyrus. Other analyses, taking into consideration the location of the responsive sites (IFG versus dlPFC cluster), highlighted only minor differences between both groups.ConclusionsThe present study successfully demonstrated the involvement of a large-scale neural network in the face-based mentalizing that strongly matches networks, classically identified using task-based fMRI paradigms. We thus validated the combination of rsfMRI and stimulation mapping as a powerful approach to identify functional networks in brain-damaged patients.
       
  • Detrended connectometry analysis to assess white matter correlates of
           performance in childhood
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Brady J. Williamson, Mekibib Altaye, Darren S. Kadis The white matter of the brain develops in a robust, regionally-variant, nonlinear manner during childhood. To relate white matter connectivity to performance, these regional nonlinear effects of age must be accounted for. Here, we identify white matter correlates of gross intellectual functioning using cutting-edge diffusion analyses inside a data-driven two-step regression framework. A total of 98 participants, ages 3–18 years, were included in the analyses. First, white matter connectivity was modeled as a function of age for each fiber direction at each voxel, extracted from the spin distribution function, using a 6th-order B-spline. The smoothing parameter for each direction was chosen by minimizing generalized cross-validation (GCV), which prevents overfitting while remaining sensitive to potentially nonlinear effects of age. In the second step, the resulting Gaussian residuals were modeled as a function of either full-scale IQ (FSIQ), or of verbal IQ (VIQ) and performance IQ (PIQ), using a linear regression framework (connectometry). Graph theoretical analyses were also performed to assess how each predictor relates to global topological changes, including average clustering coefficient, characteristic path length, global efficiency, average local efficiency, and small worldness. Analyses revealed widespread positive associations between white matter connectivity and FSIQ, including regions of the corpus callosum, fornix, and corticothalamic tracts (FDRq 
       
  • Decentralized temporal independent component analysis: Leveraging fMRI
           data in collaborative settings
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Bradley T. Baker, Anees Abrol, Rogers F. Silva, Eswar Damaraju, Anand D. Sarwate, Vince D. Calhoun, Sergey M. Plis The field of neuroimaging has recently witnessed a strong shift towards data sharing; however, current collaborative research projects may be unable to leverage institutional architectures that collect and store data in local, centralized data centers. Additionally, though research groups are willing to grant access for collaborations, they often wish to maintain control of their data locally. These concerns may stem from research culture as well as privacy and accountability concerns. In order to leverage the potential of these aggregated larger data sets, we require tools that perform joint analyses without transmitting the data. Ideally, these tools would have similar performance and ease of use as their current centralized counterparts. In this paper, we propose and evaluate a new Algorithm, decentralized joint independent component analysis (djICA), which meets these technical requirements. djICA shares only intermediate statistics about the data, plausibly retaining privacy of the raw information to local sites, thus making it amenable to further privacy protections, for example via differential privacy. We validate our method on real functional magnetic resonance imaging (fMRI) data and show that it enables collaborative large-scale temporal ICA of fMRI, a rich vein of analysis as of yet largely unexplored, and which can benefit from the larger-N studies enabled by a decentralized approach. We show that djICA is robust to different distributions of data over sites, and that the temporal components estimated with djICA show activations similar to the temporal functional modes analyzed in previous work, thus solidifying djICA as a new, decentralized method oriented toward the frontiers of temporal independent component analysis.
       
  • Modeling of dynamic cerebrovascular reactivity to spontaneous and
           externally induced CO2 fluctuations in the human brain using BOLD-fMRI
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Prokopis C. Prokopiou, Kyle T.S. Pattinson, Richard G. Wise, Georgios D. Mitsis In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO2 and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO2 challenges. To obtain dCVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs), as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnic challenges), suggesting a differential response of cerebral vasculature to spontaneous CO2 fluctuations and larger, externally induced CO2 changes that are possibly associated with the underlying differences in mean arterial CO2 levels. The clustering results suggest that anatomically distinct brain regions are characterized by different dCVR curves that in some cases do not exhibit the standard, positive valued curves that have been previously reported. They also reveal a consistent set of dCVR cluster shapes for resting and forcing conditions, which exhibit different distribution patterns across brain voxels.
       
  • BOLD-fMRI reveals the association between renal oxygenation and functional
           connectivity in the aging brain
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Hechun Li, Weifang Cao, Xingxing Zhang, Bo Sun, Sisi Jiang, Jianfu Li, Chang Liu, Wenjie Yin, Yu Wu, Tiejun Liu, Dezhong Yao, Cheng Luo Aging is accompanied by a decline in physical and cognitive function. Vascular aging may provide a major influence on these measures. The purpose of this study was to explore the relationship between renal oxygenation and functional connectivity of the aging brain because of the anatomic and hemodynamic similarities between cerebral and renal vessels. Fifty-two healthy older adults were recruited to undergo a BOLD-fMRI scan of the brain and kidneys, and forty-four healthy younger subjects were recruited as the control group. First, cerebral functional connectivity density (FCD) was used to evaluate functional connectivity. Renal medullary and cortical R2* values were extracted respectively, and the ratio of medullary and cortical R2* values (MCR) was calculated. Then, the association between brain FCD and renal MCR was analyzed. Compared with younger adults, the elderly group showed higher renal medullary R2* and MCR, which might reflect a slight abnormality of renal oxygenation with aging. The older subjects also showed enhanced FCD in bilateral motor-related regions and decreased FCD in regions of the default mode network (DMN). The findings indicated that the functional connectivity in the DMN and motor cortices was vulnerable to aging. Moreover, the altered brain FCD values in the watershed regions, DMN and motor cortices were significantly correlated with the renal MCR value in the elderly group. The association between renal oxygenation abnormalities and spontaneous activity in the brain might reflect vascular aging and its influence on the kidney and brain during aging to some extent. This study provided a new perspective for understanding the relationship between tissue oxygenation and brain functional connectivity.
       
  • Arterial stiffness and white matter integrity in the elderly: A diffusion
           tensor and magnetization transfer imaging study
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Atef Badji, Adrián Noriega de la Colina, Agah Karakuzu, Tanguy Duval, Laurence Desjardins-Crépeau, Sven Joubert, Louis Bherer, Maxime Lamarre-Cliche, Nikola Stikov, Hélène Girouard, Julien Cohen-Adad Background and purposeThe stiffness of large arteries and increased pulsatility can have an impact on the brain white matter (WM) microstructure, however those mechanisms are still poorly understood. The aim of this study was to investigate the association between central artery stiffness, axonal and myelin integrity in 54 cognitively unimpaired elderly subjects (65–75 years old).MethodsThe neuronal fiber integrity of brain WM was assessed using diffusion tensor metrics and magnetization transfer imaging as measures of axonal organization (Fractional anisotropy, Radial diffusivity) and state of myelination (Myelin volume fraction). Central artery stiffness was measured by carotid-femoral pulse wave velocity (cfPWV). Statistical analyses included 4 regions (the corpus callosum, the internal capsule, the corona radiata and the superior longitudinal fasciculus) which have been previously denoted as vulnerable to increased central artery stiffness.ResultscfPWV was significantly associated with fractional anisotropy and radial diffusivity (p 
       
  • Differentiating guilt and shame in an interpersonal context with
           univariate activation and multivariate pattern analyses
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Ruida Zhu, Chunliang Feng, Shen Zhang, Xiaoqin Mai, Chao Liu Guilt and shame are usually evoked during interpersonal interactions. However, no study has compared guilt and shame processing under such circumstances. In the present study, we investigated guilt and shame in an interpersonal context using functional magnetic resonance imaging (fMRI). Behaviorally, participants reported more “guilt” when their wrong advice caused a confederate's economic loss, whereas they reported more “shame” when their wrong advice were correctly refused by the confederate. The fMRI results showed that both guilt and shame activated regions related to the integration of theory of mind and self-referential information (dorsal medial prefrontal cortex, dmPFC) and to the emotional processing (anterior insula). Guilt relative to shame activated regions linked with theory of mind (supramarginal gyrus and temporo-parietal junction) and cognitive control (orbitofrontal cortex/ventrolateral prefrontal cortex and dorsolateral prefrontal cortex). Shame relative to guilt revealed no significant results. Using multivariate pattern analysis, we demonstrated that in addition to the regions found in the univariate activation analysis, the ventral anterior cingulate cortex and dmPFC could also distinguish guilt and shame. These results do not only echo previous studies of guilt and shame using recall and imagination paradigms but also provide new insights into the psychological and neural mechanisms of guilt and shame.
       
  • Lateral geniculate nucleus volumetry at 3T and 7T: Four different
           optimized magnetic-resonance-imaging sequences evaluated against a 7T
           reference acquisition
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Njoud Aldusary, Lars Michels, Ghislaine Lieselotte 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.
       
  • Optimizing fMRI experimental design for MVPA-based BCI control: Combining
           the strengths of block and event-related designs
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Giancarlo Valente, Amanda L. Kaas, Elia Formisano, Rainer Goebel Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback.In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design.The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks.Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.
       
  • Age-related changes in brain deactivation but not in activation after
           motor learning
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): K.M.M. Berghuis, S. Fagioli, N.M. Maurits, I. Zijdewind, J.B.C. Marsman, T. Hortobágyi, G. Koch, M. Bozzali It is poorly understood how healthy aging affects neural mechanisms underlying motor learning. We used blood-oxygen-level dependent (BOLD) contrasts to examine age-related changes in brain activation after acquisition and consolidation (24 h) of a visuomotor tracking skill. Additionally, structural magnetic resonance imaging and diffusion tensor imaging were used to examine age-related structural changes in the brain. Older adults had reduced gray matter volume (628 ± 57 ml) and mean white matter anisotropy (0.18 ± 0.03) compared with young adults (741 ± 59 ml and 0.22 ± 0.02, respectively). Although motor performance was 53% lower in older (n = 15, mean age 63.1 years) compared with young adults (n = 15, mean age 25.5 years), motor practice improved motor performance similarly in both age groups. While executing the task, older adults showed in general greater brain activation compared with young adults. BOLD activation decreased in parietal and occipital areas after skill acquisition but activation increased in these areas after consolidation in both age groups, indicating more efficient visuospatial processing immediately after skill acquisition. Changes in deactivation in specific areas were age-dependent after consolidating the motor skill into motor memory. Young adults showed greater deactivations from post-test to retention in parietal, occipital and temporal cortices, whereas older adults showed smaller deactivation in the frontal cortex. Since learning rate was similar between age groups, age-related changes in activation patterns may be interpreted as a compensatory mechanism for age-related structural decline.
       
  • How the brain makes sense beyond the processing of single words – An
           MEG study
    • Abstract: Publication date: Available online 24 November 2018Source: NeuroImageAuthor(s): Annika Hultén, Jan-Mathijs Schoffelen, Julia Uddén, Nietzsche H.L. Lam, Peter Hagoort Human language processing involves combinatorial operations that make human communication stand out in the animal kingdom. These operations rely on a dynamic interplay between the inferior frontal and the posterior temporal cortices. Using source reconstructed magnetoencephalography, we tracked language processing in the brain, in order to investigate how individual words are interpreted when part of sentence context. The large sample size in this study (n = 68) allowed us to assess how event-related activity is associated across distinct cortical areas, by means of inter-areal co-modulation within an individual. We showed that, within 500 ms of seeing a word, the word's lexical information has been retrieved and unified with the sentence context. This does not happen in a strictly feed-forward manner, but by means of co-modulation between the left posterior temporal cortex (LPTC) and left inferior frontal cortex (LIFC), for each individual word. The co-modulation of LIFC and LPTC occurs around 400 ms after the onset of each word, across the progression of a sentence. Moreover, these core language areas are supported early on by the attentional network. The results provide a detailed description of the temporal orchestration related to single word processing in the context of ongoing language.
       
  • Neural activity in human visual cortex is transformed by learning real
           world size
    • Abstract: Publication date: Available online 23 November 2018Source: NeuroImageAuthor(s): Marc N. Coutanche, Sharon L. Thompson-Schill The way that our brain processes visual information is directly affected by our experience. Repeated exposure to a visual stimulus triggers experience-dependent plasticity in the visual cortex of many species. Humans also have the unique ability to acquire visual knowledge through instruction. We introduced human participants to the real-world size of previously unfamiliar species, and to the functional motion of novel tools, during a functional magnetic resonance imaging scan. Using machine learning, we compared activity patterns evoked by images of the new items, before and after participants learned the animals' real-world size or tools' motion. We found that, after acquiring size information, participants' visual activity patterns for the new animals became more confusable with activity patterns evoked by similar-sized known animals in early visual cortex, but not in ventral temporal cortex, reflecting an influence of new size knowledge on posterior, but not anterior, components of the ventral stream. Learning the functional motion of new tools did not lead to an equivalent change in activity. Finally, time-points marked by evidence of new size information in early visual cortex were more likely to show size information and greater activation in the right angular gyrus, a key hub of semantic knowledge and spatial cognition. Overall, these findings suggest that learning an item's real-world size by instruction influences subsequent activity in visual cortex and a region that is central to semantic and spatial brain systems.
       
  • Cerebral blood flow changes after a day of wake, sleep, and sleep
           deprivation
    • Abstract: Publication date: Available online 22 November 2018Source: NeuroImageAuthor(s): Torbjørn Elvsåshagen, Henri JMM. Mutsaerts, Nathalia Zak, Linn B. Norbom, Sophia H. Quraishi, Per Ø. Pedersen, Ulrik F. Malt, Lars T. Westlye, Eus JW. van Someren, Atle Bjørnerud, Inge R. Groote Elucidating the neurobiological effects of sleep and wake is an important goal of the neurosciences. Whether and how human cerebral blood flow (CBF) changes during the sleep-wake cycle remain to be clarified. Based on the synaptic homeostasis hypothesis of sleep and wake, we hypothesized that a day of wake and a night of sleep deprivation would be associated with gray matter resting CBF (rCBF) increases and that sleep would be associated with rCBF decreases. Thirty-eight healthy adult males (age 22.1 ± 2.5 years) underwent arterial spin labeling perfusion magnetic resonance imaging at three time points: in the morning after a regular night's sleep, the evening of the same day, and the next morning, either after total sleep deprivation (n = 19) or a night of sleep (n = 19). All analyses were adjusted for hematocrit and head motion. rCBF increased from morning to evening and decreased after a night of sleep. These effects were most prominent in bilateral hippocampus, amygdala, thalamus, and in the occipital and sensorimotor cortices. Group × time interaction analyses for evening versus next morning revealed significant interaction in bilateral lateral and medial occipital cortices and in bilateral insula, driven by rCBF increases in the sleep deprived individuals and decreases in the sleepers, respectively. Furthermore, group × time interaction analyses for first morning versus next morning showed significant effects in medial and lateral occipital cortices, in anterior cingulate gyrus, and in the insula, in both hemispheres. These effects were mainly driven by CBF increases from TP1 to TP3 in the sleep deprived individuals. There were no associations between the rCBF changes and sleep characteristics, vigilant attention, or subjective sleepiness that remained significant after adjustments for multiple analyses. Altogether, these results encourage future studies to clarify mechanisms underlying sleep-related rCBF changes.
       
  • Disease progression timeline estimation for Alzheimer's disease using
           discriminative event based modeling
    • Abstract: Publication date: Available online 22 November 2018Source: NeuroImageAuthor(s): Vikram Venkatraghavan, Esther E. Bron, Wiro J. Niessen, Stefan Klein, for the Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis by staging patients. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for each subject an approximate ordering of events. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings based on a novel probabilistic Kendall's Tau distance. We also introduce the concept of relative distance between events which helps in creating a disease progression timeline. Subsequently, we propose a method to stage subjects by placing them on the estimated disease progression timeline. We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods. We also performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease. The event orderings obtained on ADNI data seem plausible and are in agreement with the current understanding of progression of AD. The proposed patient staging algorithm performed consistently better than that of state-of-the-art EBM methods. Event orderings obtained in simulation experiments were more accurate than those of other EBM methods and the estimated disease progression timeline was observed to correlate with the timeline of actual disease progression. The results of these experiments are encouraging and suggest that discriminative EBM is a promising approach to disease progression modeling.
       
  • Recruitment of the occipital cortex by arithmetic processing follows
           computational bias in the congenitally blind
    • Abstract: Publication date: Available online 22 November 2018Source: NeuroImageAuthor(s): Virginie Crollen, Latifa Lazzouni, Mohamed Rezk, Antoine Bellemare, Franco Lepore, Marie-Pascale Noël, Xavier Seron, Olivier Collignon Arithmetic reasoning activates the occipital cortex of congenitally blind people (CB). This activation of visual areas may highlight the functional flexibility of occipital regions deprived of their dominant inputs or relate to the intrinsic computational role of specific occipital regions. We contrasted these competing hypotheses by characterizing the brain activity of CB and sighted participants while performing subtraction, multiplication and a control letter task. In both groups, subtraction selectively activated a bilateral dorsal network commonly activated during spatial processing. Multiplication triggered activity in temporal regions thought to participate in memory retrieval. No between-group difference was observed for the multiplication task whereas subtraction induced enhanced activity in the right dorsal occipital cortex of the blind individuals only. As this area overlaps with regions showing selective tuning to auditory spatial processing and exhibits increased functional connectivity with a dorsal “spatial” network, our results suggest that the recruitment of occipital regions during high-level cognition in the blind actually relates to the intrinsic computational role of the activated regions.
       
  • Flexible proton density (PD) mapping using multi-contrast variable flip
           angle (VFA) data
    • Abstract: Publication date: Available online 19 November 2018Source: NeuroImageAuthor(s): Sara Lorio, Tim M. Tierney, Amy McDowell, Owen J. Arthurs, Antoine Lutti, Nikolaus Weiskopf, David W. Carmichael Quantitative proton density (PD) maps measure the amount of free water, which is important for non-invasive tissue characterization in pathology and across lifespan. PD mapping requires the estimation and subsequent removal of factors influencing the signal intensity other than PD. These factors include the T1, T2* relaxation effects, transmit field inhomogeneities, receiver coil sensitivity profile (RP) and the spatially invariant factor that is required to scale the data. While the transmit field can be reliably measured, the RP estimation is usually based on image post-processing techniques due to limitations of its measurement at magnetic fields higher than 1.5 T. The post-processing methods are based on unified bias-field/tissue segmentation, fitting the sensitivity profile from images obtained with different coils, or on the linear relationship between T1 and PD. The scaling factor is derived from the signal within a specific tissue compartment or reference object. However, these approaches for calculating the RP and scaling factor have limitations particularly in severe pathology or over a wide age range, restricting their application.We propose a new approach for PD mapping based on a multi-contrast variable flip angle acquisition protocol and a data-driven estimation method for the RP correction and map scaling. By combining all the multi-contrast data acquired at different echo times, we are able to fully correct the MRI signal for T2* relaxation effects and to decrease the variance and the entropy of PD values within tissue class of the final map. The RP is determined from the corrected data applying a non-parametric bias estimation, and the scaling factor is based on the median intensity of an external calibration object. Finally, we compare the signal intensity and homogeneity of the multi-contrast PD map with the well-established effective PD (PD*) mapping, for which the RP is based on concurrent bias field estimation and tissue classification, and the scaling factor is estimated from the mean white matter signal. The multi-contrast PD values homogeneity and accuracy within the cerebrospinal fluid (CSF) and deep brain structures are increased beyond that obtained using PD* maps. We demonstrate that the multi-contrast RP approach is insensitive to anatomical or a priori tissue information by applying it in a patient with extensive brain abnormalities and for whole body PD mapping in post-mortem foetal imaging.
       
  • Estimation of brain functional connectivity from hypercapnia BOLD MRI
           data: Validation in a lifespan cohort of 170 subjects
    • Abstract: Publication date: Available online 18 November 2018Source: NeuroImageAuthor(s): Xirui Hou, Peiying Liu, Hong Gu, Micaela Chan, Yang Li, Shin-Lei Peng, Gagan Wig, Yihong Yang, Denise Park, Hanzhang Lu Functional connectivity MRI, based on Blood-Oxygenation-Level-Dependent (BOLD) signals, is typically performed while the subject is at rest. On the other hand, BOLD is also widely used in physiological imaging such as cerebrovascular reactivity (CVR) mapping using hypercapnia (HC) as a modulator. We therefore hypothesize that hypercapnia BOLD data can be used to extract FC metrics after factoring out the effects of the physiological modulation, which will allow simultaneous assessment of neural and vascular function and may be particularly important in populations such as aging and cerebrovascular diseases. The present work aims to systematically examine the feasibility of hypercapnia BOLD-based FC mapping using three commonly applied analysis methods, specifically dual-regression Independent Component Analysis (ICA), region-based FC matrix analysis, and graph-theory based network analysis, in a large cohort of 170 healthy subjects ranging from 20 to 88 years old. To validate the hypercapnia BOLD results, we also compared these FC metrics with those obtained from conventional resting-state data. ICA analysis of the hypercapnia BOLD data revealed FC maps that strongly resembled those reported in the literature. FC matrix using region-based analysis showed a correlation of 0.97 on the group-level and 0.54 ± 0.10 on the individual-level, when comparing between hypercapnia and resting-state results. Although the correspondence on the individual-level was moderate, this was primarily attributed to variations intrinsic to FC mapping, because a corresponding resting-vs-resting comparison in a sub-cohort (N = 39) revealed a similar correlation of 0.57 ± 0.09. Graph-theory computations were also feasible in hypercapnia BOLD data and indices of global efficiency, clustering coefficient, modularity, and segregation were successfully derived. Hypercapnia FC results revealed age-dependent differences in which within-network connections generally exhibited an age-dependent decrease while between-network connections showed an age-dependent increase.
       
  • Implicit visual cues tune oscillatory motor activity during
           decision-making
    • Abstract: Publication date: Available online 17 November 2018Source: NeuroImageAuthor(s): Andrea Alamia, Alexandre Zénon, Rufin VanRullen, Julie Duque, Gerard Derosiere Motor decisions entails a buildup of choice-selective activity in the motor cortex. The rate of this buildup crucially depends on the amount of evidence favoring the selection of each action choice in the visual environment. Though numerous studies have characterized how sensory evidence drives motor activity when processed consciously, very little is known about the neural mechanisms that underlie the integration of implicit sources of information.Here, we used electroencephalography to investigate the impact of implicit visual cues on response-locked potentials and oscillatory activity in the motor cortex during decision-making. Subjects were required to select between left and right index finger responses according to the motion direction of a cloud of dots presented in one of three possible colors. Unbeknown to the participants, the color cue could bring evidence either in favor of or against the selection of the correct response.Implicit color cues tuned choice-selective oscillatory activity in the low beta range (16–25 Hz), boosting the buildup of contralateral activity when evidence favored the selection of the correct action, while weakening it when evidence biased against the correct response. This modulation of oscillatory activity influenced the speed at which the correct action was eventually chosen. Implicit cues also altered oscillatory activity in a non-selective way in the low frequency oscillation (1–7 Hz) and high beta ranges (25–35 Hz), impacting both contralateral and ipsilateral activity. The current findings yield a critical extension of prior observations by indicating that the integration of both explicit and implicit sources of evidence tunes oscillatory motor activity during decision-making.
       
  • Pharmacological stress impairs working memory performance and attenuates
           dorsolateral prefrontal cortex glutamate modulation
    • Abstract: Publication date: Available online 17 November 2018Source: NeuroImageAuthor(s): Eric A. Woodcock, Mark K. Greenwald, Dalal Khatib, Vaibhav A. Diwadkar, Jeffrey A. Stanley Working memory processes are associated with the dorsolateral prefrontal cortex (dlPFC). Prior research using proton functional magnetic resonance spectroscopy (1H fMRS) observed significant dlPFC glutamate modulation during letter 2-back performance, indicative of working memory-driven increase in excitatory neural activity. Acute stress has been shown to impair working memory performance. Herein, we quantified dlPFC glutamate modulation during working memory under placebo (oral lactose) and acute stress conditions (oral yohimbine 54 mg + hydrocortisone 10 mg). Using a double-blind, randomized crossover design, participants (N = 19) completed a letter 2-back task during left dlPFC 1H fMRS acquisition (Brodmann areas 45/46; 4.5 cm3). An automated fitting procedure integrated with LCModel was used to quantify glutamate levels. Working memory-induced glutamate modulation was calculated as percentage change in glutamate levels from passive visual fixation to 2-back levels. Results indicated acute stress significantly attenuated working memory-induced glutamate modulation and impaired 2-back response accuracy, relative to placebo levels. Follow-up analyses indicated 2-back performance significantly modulated glutamate levels relative to passive visual fixation during placebo but not acute stress. Biomarkers, including blood pressure and saliva cortisol, confirmed that yohimbine + hydrocortisone dosing elicited a significant physiological stress response. These findings support a priori hypotheses and demonstrate that acute stress impairs dlPFC function and excitatory activity. This study highlights a neurobiological mechanism through which acute stress may contribute to psychiatric dysfunction and derail treatment progress. Future research is needed to isolate noradrenaline vs. cortisol effects and evaluate anti-stress medications and/or behavioral interventions.
       
  • Nonlinear Distributional Mapping (NoDiM) for harmonization across
           amyloid-PET radiotracers
    • Abstract: Publication date: Available online 17 November 2018Source: NeuroImageAuthor(s): Michael J. Properzi, Rachel F. Buckley, Jasmeer P. Chhatwal, Michael C. Donohue, Cristina Lois, Elizabeth C. Mormino, Keith A. Johnson, Reisa A. Sperling, Aaron P. Schultz IntroductionThere is a growing need in clinical research domains for direct comparability between amyloid-beta (Aβ) Positron Emission Tomography (PET) measures obtained via different radiotracers and processing methodologies. Previous efforts to provide a common measurement scale fail to account for non-linearities between measurement scales that can arise from these differences. We introduce a new application of distribution mapping, based on well established statistical orthodoxy, that we call Nonlinear Distribution Mapping (NoDiM). NoDiM uses cumulative distribution functions to derive mappings between Aβ-PET measurements from different tracers and processing streams that align data based on their location in their respective distributions.MethodsUtilizing large datasets of Florbetapir (FBP) from the Alzheimer's Disease Neuroimaging Initiative (n = 349 female (%) = 53) and Pittsburgh Compound B (PiB) from the Harvard Aging Brain Study (n = 305 female (%) = 59.3) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (n = 184 female (%) = 53.3), we fit explicit mathematical models of a mixture of two normal distributions, with parameter estimates from Gaussian Mixture Models, to each tracer's empirical data. We demonstrate the accuracy of these fits, and then show the ability of NoDiM to transform FBP measurements into PiB-like units.ResultsA mixture of two normal distributions fit both the FBP and PiB empirical data and provides a strong basis for derivation of a transfer function. Transforming Aβ-PET data with NoDiM results in FBP and PiB distributions that are closely aligned throughout their entire range, while a linear transformation does not. Additionally the NoDiM transform better matches true positive and false positive profiles across tracers.DiscussionThe NoDiM transformation provides a useful alternative to the linear mapping advocated in the Centiloid project, and provides improved correspondence between measurements from different tracers across the range of observed values. This improved alignment enables disparate measures to be merged on to continuous scale, and better enables the use of uniform thresholds across tracers.
       
  • Bundle-specific tractography with incorporated anatomical and
           orientational priors
    • Abstract: Publication date: Available online 17 November 2018Source: NeuroImageAuthor(s): Francois Rheault, Etienne St-Onge, Jasmeen Sidhu, Klaus Maier-Hein, Nathalie Tzourio-Mazoyer, Laurent Petit, Maxime Descoteaux Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track.To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility, sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.
       
  • Brain dynamics and temporal trajectories during task and naturalistic
           processing
    • Abstract: Publication date: Available online 16 November 2018Source: NeuroImageAuthor(s): Manasij Venkatesh, Joseph Jaja, Luiz Pessoa Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Here, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show the feasibility and potential of using it for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional “trajectories” of temporal data. Classification accuracy was approximately 90% for short clips of “social interactions” and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide “signatures” that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. Taken together, we believe the present approach provides a promising framework to characterize dynamic fMRI information during both tasks and naturalistic conditions.
       
  • Modes of operation: A topographic neural gradient supporting stimulus
           dependent and independent cognition
    • Abstract: Publication date: Available online 14 November 2018Source: NeuroImageAuthor(s): Charlotte Murphy, Hao-Ting Wang, Delali Konu, Rebecca Lowndes, Daniel S. Margulies, Elizabeth Jefferies, Jonathan Smallwood Human cognition is flexible - drawing on both sensory input, and representations from memory, to successfully navigate complex environments. Contemporary accounts suggest this flexibility is possible because neural function is organized into a hierarchy. Neural regions are organized along a macroscale gradient, anchored at one end by unimodal systems involved with perception and action, and at the other by transmodal systems, including the default mode network, supporting cognition less directly tied to immediate stimulus input. The current study tested whether this cortical hierarchy captures modes of behaviour that depend on immediate input, as well as those that depend on representations from memory. Participants made decisions regarding the location or identity of shapes using information in the environment (0-back) or from a prior trial (1-back). Using task based imaging we established that, regardless of the nature of the decision, medial and lateral visual cortex were recruited when decisions rely on immediate input, while transmodal regions were recruited when judgments depend on information from the prior trial. Using principal components analysis, we demonstrated that shifting decision-making from perception to memory altered the focus of neural activity from unimodal to transmodal regions (and vice versa). Notably, the more pronounced these shifts in neural activity from unimodal to transmodal regions when decisions relied on memory, the more efficiently individuals performed this task. These data illustrate how the macroscale organization of neural function into a hierarchy allows cognition to rely on input, or information from memory, in a flexible and efficient manner.
       
  • Assessment of mesoscopic properties of deep gray matter iron through a
           model-based simultaneous analysis of magnetic susceptibility and R2* - A
           pilot study in patients with multiple sclerosis and normal controls
    • Abstract: Publication date: Available online 14 November 2018Source: NeuroImageAuthor(s): Yanis Taege, Jesper Hagemeier, Niels Bergsland, Michael G. Dwyer, Bianca Weinstock-Guttman, Robert Zivadinov, Ferdinand Schweser Most studies of brain iron relied on the effect of the iron on magnetic resonance (MR) relaxation properties, such as R2∗, and bulk tissue magnetic susceptibility, as measured by quantitative susceptibility mapping (QSM). The present study exploited the dependence of R2∗ and magnetic susceptibility on physical interactions at different length-scales to retrieve information about the tissue microenvironment, rather than the iron concentration. We introduce a method for the simultaneous analysis of brain tissue magnetic susceptibility and R2∗ that aims to isolate those biophysical mechanisms of R2∗ -contrast that are associated with the micro- and mesoscopic distribution of iron, referred to as the Iron Microstructure Coefficient (IMC). The present study hypothesized that changes in the deep gray matter (DGM) magnetic microenvironment associated with aging and pathological mechanisms of multiple sclerosis (MS), such as changes of the distribution and chemical form of the iron, manifest in quantifiable contributions to the IMC. To validate this hypothesis, we analyzed the voxel-based association between R2∗ and magnetic susceptibility in different DGM regions of 26 patients with multiple sclerosis and 33 age- and sex-matched normal controls. Values of the IMC varied significantly between anatomical regions, were reduced in the dentate and increased in the caudate of patients compared to controls, and decreased with normal aging, most strongly in caudate, globus pallidus and putamen.
       
  • Spoken language coding neurons in the Visual Word Form Area: Evidence from
           a TMS adaptation paradigm
    • Abstract: Publication date: Available online 12 November 2018Source: NeuroImageAuthor(s): Chotiga Pattamadilok, Samuel Planton, Mireille Bonnard While part of the left ventral occipito-temporal cortex (left-vOT), known as the Visual Word Form Area, plays a central role in reading, the area also responds to speech. This cross-modal activation has been explained by three competing hypotheses. Firstly, speech is converted to orthographic representations that activate, in a top-down manner, written language coding neurons in the left-vOT. Secondly, the area contains multimodal neurons that respond to both language modalities. Thirdly, the area comprises functionally segregated neuronal populations that selectively encode different language modalities. A transcranial magnetic stimulation (TMS)-adaptation protocol was used to disentangle these hypotheses. During adaptation, participants were exposed to spoken or written words in order to tune the initial state of left-vOT neurons to one of the language modalities. After adaptation, they performed lexical decisions on spoken and written targets with TMS applied over the left-vOT. TMS showed selective facilitatory effects. It accelerated lexical decisions only when the adaptors and the targets shared the same modality, i.e., when left-vOT neurons had initially been adapted to the modality of the target stimuli. Since this within-modal adaptation was observed for both input modalities and no evidence for cross-modal adaptation was found, our findings suggest that the left-vOT contains neurons that selectively encode written and spoken language rather than purely written language coding neurons or multimodal neurons encoding language regardless of modality.
       
  • Intrinsic functional clustering of anterior cingulate cortex in the common
           marmoset
    • Abstract: Publication date: Available online 10 November 2018Source: NeuroImageAuthor(s): David J. Schaeffer, Kyle M. Gilbert, Maryam Ghahremani, Joseph S. Gati, Ravi S. Menon, Stefan Everling The common marmoset (Callithrix jacchus) has garnered recent attention as a potentially powerful preclinical model and complement to other canonical mammalian models of human brain diseases (e.g., rodents and Old World non-human primates). With a granular frontal cortex and the advent of transgenic modifications, marmosets are well positioned to serve as neuropsychiatric models of prefrontal cortex dysfunction. A critical step in the development of marmosets for such models is to characterize functional network topologies of frontal cortex in healthy, normally functioning marmosets. Here, we sought to characterize the intrinsic functional connectivity of anterior cingulate cortex (ACC) in marmosets using resting state functional magnetic resonance imaging (RS-fMRI). Seven lightly anesthetized marmosets were imaged at ultra-high field (9.4 T) and hierarchical clustering was employed to extract functional clusters of ACC from the RS-fMRI data. The data demonstrated three functionally discrete clusters within ACC. The functional connectivity between these clusters with the rest of the brain was also found to be distinct, supporting the hypothesis that ACC subregions serve different circuits and their concomitant functions. In a separate seed-based analysis, we also sought to delineate finer-grained patterns of ACC connectivity between marmoset primary motor area 4 ab and putative eye movement areas (8aD and 8 aV). This analysis demonstrated distinct patterns of ACC functional connectivity between motor and eye movement regions that overlapped well with what has been shown in humans and macaques. Overall, these results demonstrate that marmosets have a network topology of ACC that resembles that of Old World primates, giving further credence to the use of marmosets for preclinical studies of intractable human brain diseases.
       
  • Human or not human' Performance monitoring ERPs during human agent and
           machine supervision
    • Abstract: Publication date: Available online 10 November 2018Source: NeuroImageAuthor(s): Bertille Somon, Aurélie Campagne, Arnaud Delorme, Bruno Berberian Performance monitoring is a critical process which allows us to both learn from our own errors, and also interact with other human beings. However, our increasingly automated world requires us to interact more and more with automated systems, especially in risky environments. The present EEG study aimed at investigating and comparing the neuro-functional correlates associated with performance monitoring of an automated system and a human agent using a vertically-oriented arrowhead version of the flanker task. Given the influence of task difficulty on performance monitoring, two levels of difficulty were considered in order to assess their impact on supervision activity. A large N2P3 complex in fronto-central regions was observed for both human agent error detection and system error detection during supervision. Using a cluster-based permutation analysis, a significantly decreased P3-like component was found for system compared to human agent error detection. This variation is in line with various psychosocial behavioral studies showing a difference between human-human and human-machine interactions, even though it was not clearly anticipated. Finally, the activity observed during error detection was significantly reduced in the difficult condition compared to the easy one, for both system and human agent supervision. Overall, this study is a first step towards the characterization of the neurophysiological correlates underlying system supervision, and a better understanding of their evolution in more complex environments. To go further, these results need to be replicated in other experiments with various paradigms to assess the robustness of the pattern and decrease during system supervision.
       
  • Control freaks: Towards optimal selection of control conditions for fMRI
           neurofeedback studies
    • Abstract: Publication date: Available online 10 November 2018Source: NeuroImageAuthor(s): Bettina Sorger, Frank Scharnowski, David E.J. Linden, Michelle Hampson, Kymberly D. Young fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.
       
  • Auditory predictions shape the neural responses to stimulus repetition and
           sensory change
    • Abstract: Publication date: Available online 9 November 2018Source: NeuroImageAuthor(s): Raffaele Cacciaglia, Jordi Costa-Faidella, Katarzyna Zarnowiec, Sabine Grimm, Carles Escera Perception is a highly active process relying on the continuous formulation of predictive inferences using short-term sensory memory templates, which are recursively adjusted based on new input. According to this idea, earlier studies have shown that novel stimuli preceded by a higher number of repetitions yield greater novelty responses, indexed by larger mismatch negativity (MMN). However, it is not clear whether this MMN memory trace effect is driven by more adapted responses to prior stimulation or rather by a heightened processing of the unexpected deviant, and only few studies have so far attempted to characterize the functional neuroanatomy of these effects. Here we implemented a modified version of the auditory frequency oddball paradigm that enables modeling the responses to both repeated standard and deviant stimuli. Fifteen subjects underwent functional magnetic resonance imaging (fMRI) while their attention was diverted from auditory stimulation. We found that deviants with longer stimulus history of standard repetitions yielded a more robust and widespread activation in the bilateral auditory cortex. Standard tones repetition yielded a pattern of response entangling both suppression and enhancement effects depending on the predictability of upcoming stimuli. We also observed that regularity encoding and deviance detection mapped onto spatially segregated cortical subfields. Our data provide a better understanding of the neural representations underlying auditory repetition and deviance detection effects, and further support that perception operates though the principles of Bayesian predictive coding.
       
  • Sensorimotor network segregation declines with age and is linked to GABA
           and to sensorimotor performance
    • Abstract: Publication date: Available online 9 November 2018Source: NeuroImageAuthor(s): Kaitlin Cassady, Holly Gagnon, Poortata Lalwani, Molly Simmonite, Bradley Foerster, Denise Park, Scott J. Peltier, Myria Petrou, Stephan F. Taylor, Daniel H. Weissman, Rachael D. Seidler, Thad A. Polk Aging is typically associated with declines in sensorimotor performance. Previous studies have linked some age-related behavioral declines to reductions in network segregation. For example, compared to young adults, older adults typically exhibit weaker functional connectivity within the same functional network but stronger functional connectivity between different networks. Based on previous animal studies, we hypothesized that such reductions of network segregation are linked to age-related reductions in the brain's major inhibitory transmitter, gamma aminobutyric acid (GABA). To investigate this hypothesis, we conducted graph theoretical analyses of resting state functional MRI data to measure sensorimotor network segregation in both young and old adults. We also used magnetic resonance spectroscopy to measure GABA levels in the sensorimotor cortex and collected a battery of sensorimotor behavioral measures. We report four main findings. First, relative to young adults, old adults exhibit both less segregated sensorimotor brain networks and reduced sensorimotor GABA levels. Second, less segregated networks are associated with lower GABA levels. Third, less segregated networks and lower GABA levels are associated with worse sensorimotor performance. Fourth, network segregation mediates the relationship between GABA and performance. These findings link age-related differences in network segregation to age-related differences in GABA levels and sensorimotor performance. More broadly, they suggest a neurochemical substrate of age-related dedifferentiation at the level of large-scale brain networks.
       
  • Optimising neonatal fMRI data analysis: Design and validation of an
           extended dHCP preprocessing pipeline to characterise noxious-evoked brain
           activity in infants
    • Abstract: Publication date: Available online 8 November 2018Source: NeuroImageAuthor(s): Luke Baxter, Sean Fitzgibbon, Fiona Moultrie, Sezgi Goksan, Mark Jenkinson, Stephen Smith, Jesper Andersson, Eugene Duff, Rebeccah Slater The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.
       
  • Intersubject similarity of personality is associated with intersubject
           similarity of brain connectivity patterns
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Wei Liu, Nils Kohn, Guillén Fernández Personality is a central high-level psychological concept that defines individual human beings and has been associated with a variety of real-world outcomes (e.g., mental health and academic performance). Using 2 h, high resolution, functional magnetic resonance imaging (fMRI) resting state data of 984 (primary dataset N = 801, hold-out dataset N = 183) participants from the Human Connectome Project (HCP), we investigated the relationship between personality (five-factor model, FFM) and intrinsic whole-brain functional connectome. We found a pattern of functional brain connectivity (“global personality network”) related to personality traits. Consistent with the heritability of personality traits, the connectivity strength of this global personality network is also heritable (more similar between monozygotic twin pairs compared to the dizygotic twin pairs). Validated by both the repeated family-based 10-fold cross-validation and hold-out dataset, our intersubject network similarity analysis allowed us to identify participants' pairs with similar personality profiles. Across all the identified pairs of participants, we found a positive correlation between the network similarity and personality similarity, supporting our “similar brain, similar personality” hypothesis. Furthermore, the global personality network can be used to predict the individual subject's responses in the personality questionnaire on an item level. In sum, based on individual brain connectivity pattern, we could predict different facets of personality, and this prediction is not based on localized regions, but rather relies on the individual connectivity pattern in large-scale brain networks.
       
  • Diffusion tensor imaging shows mechanism-specific differences in injury
           pattern and progression in rat models of acute spinal cord injury
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Andrew Yung, Stephen Mattucci, Barry Bohnet, Jie Liu, Caron Fournier, Wolfram Tetzlaff, Piotr Kozlowski, Thomas Oxland We investigate the ability of diffusion tensor imaging (DTI) to distinguish between three experimental rat models of spinal cord injury mechanism – contusion, dislocation, and distraction. Ex vivo DTI scans were performed on cord specimens that were preserved at different time points of the acute injury (3 hr, 24 hr, and 7 days post-injury) across all three injury mechanisms. White matter was classified as abnormal if their DTI metric was substantially different from regional values measured from a set of uninjured controls, thus allowing generation of binary “white matter damage maps” which categorizes each pixel in the DTI image as “normal” or “damaged”. Damage classification was most robust using thresholds in the longitudinal diffusivity, which supports previous studies that show that longitudinal diffusivity is the most robust DTI metric in depicting damage in SCI. Furthermore, the spatial damage patterns from all subjects in the same group were consolidated into a "damage occurrence ratio map", which illustrates an average damage shape that characterizes the injury mechanism. Our analysis has yielded a dataset which highlights the differences in injury pattern due to the initial mode of mechanical injury. For example, contusion produced an initial injury that emanated radially outward from the central canal, with subsequent damage along the caudal corticospinal tract and rostral gracile fasciculus; dislocation injuries showed a high level of involvement in the lateral and ventral white matter which became less apparent by 7 days post-injury, and distraction injuries were found to be less focal and more distributed rostrocaudally. This work represents a first step in adopting the use of the primary injury mechanism as a clinical prognostic factor in SCI, which may help to inform the trialing of existing neuroprotective treatment candidates, the development of new therapies as well as personalize the management of SCI for the individual patient.
       
  • Consistent pre-stimulus influences on auditory perception across the
           lifespan
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Steven W. McNair, Stephanie J. Kayser, Christoph Kayser As we get older, perception in cluttered environments becomes increasingly difficult as a result of changes in peripheral and central neural processes. Given the aging society, it is important to understand the neural mechanisms constraining perception in the elderly. In young participants, the state of rhythmic brain activity prior to a stimulus has been shown to modulate the neural encoding and perceptual impact of this stimulus – yet it remains unclear whether, and if so, how, the perceptual relevance of pre-stimulus activity changes with age. Using the auditory system as a model, we recorded EEG activity during a frequency discrimination task from younger and older human listeners. By combining single-trial EEG decoding with linear modelling we demonstrate consistent statistical relations between pre-stimulus power and the encoding of sensory evidence in short-latency EEG components, and more variable relations between pre-stimulus phase and subjects’ decisions in longer-latency components. At the same time, we observed a significant slowing of auditory evoked responses and a flattening of the overall EEG frequency spectrum in the older listeners. Our results point to mechanistically consistent relations between rhythmic brain activity and sensory encoding that emerge despite changes in neural response latencies and the relative amplitude of rhythmic brain activity with age.
       
  • Biobehavioral threat sensitivity and amygdala volume: A twin neuroimaging
           study
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Jens Foell, Isabella M. Palumbo, James R. Yancey, Nathalie Vizueta, Traute Demirakca, Christopher J. Patrick Current literature on the relationship between dispositional fear (or threat sensitivity) and amygdala gray matter volume (GMV) is heterogeneous, with findings including positive, negative, and null correlations. A clearer understanding of this relationship would help to determine the potential utility of amygdala volume as a biomarker of anxious/depressive (internalizing) disorders and contribute to understanding of neural mechanisms for variations in fearfulness. The study reported here used voxel-based morphometry to quantify amygdala GMV scores from structural neuroimaging data in a sample of 44 monozygotic twins (i.e., 22 pairs). Dispositional threat sensitivity (THT) was quantified using a biobehavioral cross-domain score that combined neurophysiological indicators with a psychological scale measure. Analyses revealed expected high concordance for amygdala GMV between co-twins. With respect to the major question of the study, a negative correlation was found between biobehavioral THT scores and amygdala volume – with individuals higher in THT showing smaller amygdala GMV scores. More modest associations of amygdala GMV with symptoms of social phobia, and fear disorder symptomology more broadly, were mediated by THT. These results provide insight into prior mixed findings and support the combined use of biological and behavioral measures to quantify characteristics relevant to mental health problems.
       
  • Connectome-based models predict attentional control in aging adults
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Stephanie Fountain-Zaragoza, Shaadee Samimy, Monica D. Rosenberg, Ruchika Shaurya Prakash There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18–30) and older adults (n = 21, ages 60–80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.
       
  • Late cortical tracking of ignored speech facilitates neural selectivity in
           acoustically challenging conditions
    • Abstract: Publication date: 1 February 2019Source: NeuroImage, Volume 186Author(s): Lorenz Fiedler, Malte Wöstmann, Sophie K. Herbst, Jonas Obleser Listening requires selective neural processing of the incoming sound mixture, which in humans is borne out by a surprisingly clean representation of attended-only speech in auditory cortex. How this neural selectivity is achieved even at negative signal-to-noise ratios (SNR) remains unclear. We show that, under such conditions, a late cortical representation (i.e., neural tracking) of the ignored acoustic signal is key to successful separation of attended and distracting talkers (i.e., neural selectivity). We recorded and modeled the electroencephalographic response of 18 participants who attended to one of two simultaneously presented stories, while the SNR between the two talkers varied dynamically between +6 and −6 dB. The neural tracking showed an increasing early-to-late attention-biased selectivity. Importantly, acoustically dominant (i.e., louder) ignored talkers were tracked neurally by late involvement of fronto-parietal regions, which contributed to enhanced neural selectivity. This neural selectivity, by way of representing the ignored talker, poses a mechanistic neural account of attention under real-life acoustic conditions.
       
  • Modular architecture of metabolic brain network and its effects on the
           spread of perturbation impact
    • Abstract: Publication date: Available online 5 November 2018Source: NeuroImageAuthor(s): Tianhao Zhang, Qi Huang, Chunxiang Jiao, Hua Liu, Binbin Nie, Shengxiang Liang, Panlong Li, Xi Sun, Ting Feng, Lin Xu, Baoci Shan Metabolic brain network, which is based on functional correlation patterns of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images, has been widely applied in both basic and clinical neuroscience. Exploring the properties of the metabolic brain network can provide valuable insight to the physiologic and pathologic processes of the brain. Based on the network theory, modular architecture has the ability to limit the spread of local perturbation impact and therefore modular networks are more robust against external damage. However, whether the metabolic brain network has modular architecture remains unknown.Methods77 rats performed 18F-FDG PET brain imaging. The metabolic brain network was then constructed by measuring interregional metabolic correlation in inter-subject manner. Afterwards, modular architecture of the network was detected by a greedy algorithm. Further, we perturbed the metabolic brain network by inducing focal photothrombotic ischemia in the bilateral motor cortex and then measured the glucose metabolic change of each brain region using FDG-PET.ResultsA significant modular architecture was found in the metabolic brain network. The network could be divided into four modules which corresponding approximately to executive, learning/memory, visual/auditory and sensorimotor processing functional domains. After inducing the focal ischemia on the bilateral motor cortex, most of the significantly changed brain regions (13 of 17) belong to the sensorimotor module.ConclusionOur results revealed an inherent modular architecture in the metabolic brain network and gave an experimental evidence that the modularity of the metabolism brain network could limit the spread of local perturbation impact.
       
  • Coarse-to-fine information integration in human vision
    • Abstract: Publication date: Available online 4 November 2018Source: NeuroImageAuthor(s): Kirsten Petras, Sanne ten Oever, Christianne Jacobs, Valerie Goffaux Coarse-to-fine theories of vision propose that the coarse information carried by the low spatial frequencies (LSF) of visual input guides the integration of finer, high spatial frequency (HSF) detail. Whether and how LSF modulates HSF processing in naturalistic broad-band stimuli is still unclear. Here we used multivariate decoding of EEG signals to separate the respective contribution of LSF and HSF to the neural response evoked by broad-band images. Participants viewed images of human faces, monkey faces and phase-scrambled versions that were either broad-band or filtered to contain LSF or HSF. We trained classifiers on EEG scalp-patterns evoked by filtered scrambled stimuli and evaluated the derived models on broad-band scrambled and intact trials. We found reduced HSF contribution when LSF was informative towards image content, indicating that coarse information does guide the processing of fine detail, in line with coarse-to-fine theories. We discuss the potential cortical mechanisms underlying such coarse-to-fine feedback.Graphical abstractImage 1
       
  • How do spatially distinct frequency specific MEG networks emerge from one
           underlying structural connectome' The role of the structural
           eigenmodes
    • Abstract: Publication date: Available online 3 November 2018Source: NeuroImageAuthor(s): Prejaas Tewarie, Romesh Abeysuriya, Áine Byrne, George C. O'Neill, Stamatios N. Sotiropoulos, Matthew J. Brookes, Stephen Coombes Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.
       
  • Fiber length profiling: A novel approach to structural brain organization
    • Abstract: Publication date: Available online 3 November 2018Source: NeuroImageAuthor(s): Claude J. Bajada, Jan Schreiber, Svenja Caspers There has been a recent increased interest in the structural connectivity of the cortex. However, an important feature of connectivity remains relatively unexplored; tract length. In this article, we develop an approach to characterize fiber length distributions across the human cerebral cortex. We used data from 76 participants of the Adult WU-Minn Human Connectome Project using probabilistic tractography. We found that connections of different lengths are not evenly distributed across the cortex. They form patterns where certain areas have a high density of fibers of a specific length while other areas have very low density. To assess the relevance of these new maps in relation to established characteristics, we compared them to structural indices such as cortical myelin content and cortical thickness. Additionally, we assessed their relation to resting state network organization. We noted that areas with very short fibers have relatively more myelin and lower cortical thickness while the pattern is inverted for longer fibers. Furthermore, the cortical fiber length distributions produce specific correlation patterns with functional resting state networks. Specifically, we find evidence that as resting state networks increase in complexity, their length profiles change. The functionally more complex networks correlate with maps of varying lengths while primary networks have more restricted correlations. We posit that these maps are a novel way of differentiating between ‘local modules’ that have restricted connections to ‘neighboring’ areas and ‘functional integrators’ that have more far reaching connectivity.
       
  • Altered dynamic electroencephalography connectome phase-space features of
           emotion regulation in social anxiety
    • Abstract: Publication date: Available online 2 November 2018Source: NeuroImageAuthor(s): Mengqi Xing, Hyekyoung Lee, Zachery Morrissey, Moo K. Chung, K. Luan Phan, Heide Klumpp, Alex Leow, Olusola Ajilore Emotion regulation deficits are commonly observed in social anxiety disorder (SAD). We used manifold-learning to learn the phase-space connectome manifold of EEG brain dynamics in twenty SAD participants and twenty healthy controls. The purpose of the present study was to utilize manifold-learning to understand EEG brain dynamics associated with emotion regulation processes. Our emotion regulation task (ERT) contains three conditions: Neutral, Maintain and Reappraise. For all conditions and subjects, EEG connectivity data was converted into series of temporally-consecutive connectomes and aggregated to yield this phase-space manifold. As manifold geodesic distances encode intrinsic geometry, we visualized this space using its geodesic-informed minimum spanning tree and compared neurophysiological dynamics across conditions and groups using the corresponding trajectory length. Results showed that SAD participants had significantly longer trajectory lengths during Neutral and Maintain. Further, trajectory lengths during Reappraise were significantly associated with the habitual use of reappraisal strategies, while Maintain trajectory lengths were significantly associated with the negative affective state during Maintain. In sum, an unsupervised connectome manifold-learning approach can reveal emotion regulation associated phase-space features of brain dynamics.
       
  • Oscillatory signatures of reward prediction errors in declarative learning
    • Abstract: Publication date: Available online 2 November 2018Source: NeuroImageAuthor(s): Kate Ergo, Esther De Loof, Clio Janssens, Tom Verguts Reward prediction errors (RPEs) are crucial to learning. Whereas these mismatches between reward expectation and reward outcome are known to drive procedural learning, their role in declarative learning remains underexplored. Earlier work from our lab addressed this, and consistently found that signed reward prediction errors (SRPEs; “better-than-expected” signals) boost declarative learning. In the current EEG study, we sought to explore the neural signatures of SRPEs. Participants studied 60 Dutch-Swahili word pairs while RPE magnitudes were parametrically manipulated. Behaviorally, we replicated our previous findings that SRPEs drive declarative learning, with increased recognition for word pairs accompanied by large, positive RPEs. In the EEG data, at the start of reward feedback processing, we found an oscillatory (theta) signature consistent with unsigned reward prediction errors (URPEs; “different-than-expected” signals). Slightly later during reward feedback processing, we observed oscillatory (high-beta and high-alpha) signatures for SRPEs during reward feedback, similar to SRPE signatures during procedural learning. These findings illuminate the time course of neural oscillations in processing reward during declarative learning, providing important constraints for future theoretical work.
       
  • Stability of representational geometry across a wide range of fMRI
           activity levels
    • Abstract: Publication date: Available online 2 November 2018Source: NeuroImageAuthor(s): Spencer A. Arbuckle, Atsushi Yokoi, Andrew Pruszynski, Jörn Diedrichsen Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3–2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.
       
  • A framework for multi-component analysis of diffusion MRI data over the
           neonatal period
    • Abstract: Publication date: Available online 2 November 2018Source: NeuroImageAuthor(s): Maximilian Pietsch, Daan Christiaens, Jana Hutter, Lucilio Cordero-Grande, Anthony N. Price, Emer Hughes, A. David Edwards, Joseph V. Hajnal, Serena J. Counsell, J-Donald Tournier We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
       
  • Brain network disintegration during sedation is mediated by the complexity
           of sparsely connected regions
    • Abstract: Publication date: Available online 1 November 2018Source: NeuroImageAuthor(s): I. Pappas, R.M. Adapa, D.K. Menon, E.A. Stamatakis The precise mechanism of anaesthetic action on a neural level remains unclear. Recent approaches suggest that anaesthetics attenuate the complexity of interactions (connectivity) however evidence remains insufficient. We used tools from network and information theory to show that, during propofol-induced sedation, a collection of brain regions displayed decreased complexity in their connectivity patterns, especially so if they were sparsely connected. Strikingly, we found that, despite their low connectivity strengths, these regions exhibited an inordinate role in network integration. Their location and connectivity complexity delineated a specific pattern of sparse interactions mainly involving default mode regions while their connectivity complexity during the awake state also correlated with reaction times during sedation signifying its importance as a reliable indicator of the effects of sedation on individuals. Contrary to established views suggesting sedation affects only richly connected brain regions, we propose that suppressed complexity of sparsely connected regions should be considered a critical feature of any candidate mechanistic description for loss of consciousness.
       
  • High frequency repetitive transcranial magnetic stimulation to the left
           dorsolateral prefrontal cortex modulates sensorimotor cortex function in
           the transition to sustained muscle pain
    • Abstract: Publication date: Available online 1 November 2018Source: NeuroImageAuthor(s): Enrico DE. Martino, David A. Seminowicz, Siobhan M. Schabrun, Laura Petrini, Thomas Graven-Nielsen Based on reciprocal connections between the dorsolateral prefrontal cortex (DLPFC) and basal-ganglia regions associated with sensorimotor cortical excitability, it was hypothesized that repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC would modulate sensorimotor cortical excitability induced by muscle pain. Muscle pain was provoked by injections of nerve growth factor (end of Day-0 and Day-2) into the right extensor carpi radialis brevis (ECRB) muscle in two groups of 15 healthy participants receiving 5 daily sessions (Day-0 to Day-4) of active or sham rTMS. Muscle pain scores and pressure pain thresholds (PPTs) were collected (Day-0, Day-3, Day-5). Assessment of motor cortical excitability using TMS (mapping cortical ECRB muscle representation) and somatosensory evoked potentials (SEPs) from electrical stimulation of the right radial nerve were recorded at Day-0 and Day-5. At Day-0 versus Day-5, the sham compared to active group showed: Higher muscle pain scores and reduced PPTs (P 
       
  • Voxel-based meta-analysis via permutation of subject images (PSI): Theory
           and implementation for SDM
    • Abstract: Publication date: Available online 30 October 2018Source: NeuroImageAuthor(s): Anton Albajes-Eizagirre, Aleix Solanes, Eduard Vieta, Joaquim Radua Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple findings. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method.
       
  • Feasibility of functional MRI at ultralow magnetic field via changes in
           cerebral blood volume
    • Abstract: Publication date: Available online 28 October 2018Source: NeuroImageAuthor(s): Kai Buckenmaier, Anders Pedersen, Paul SanGiorgio, Klaus Scheffler, John Clarke, Ben Inglis We investigate the feasibility of performing functional MRI (fMRI) at ultralow field (ULF) with a Superconducting QUantum Interference Device (SQUID), as used for detecting magnetoencephalography (MEG) signals from the human head. While there is negligible magnetic susceptibility variation to produce blood oxygenation level-dependent (BOLD) contrast at ULF, changes in cerebral blood volume (CBV) may be a sensitive mechanism for fMRI given the five-fold spread in spin-lattice relaxation time (T1) values across the constituents of the human brain. We undertook simulations of functional signal strength for a simplified brain model involving activation of a primary cortical region in a manner consistent with a blocked task experiment. Our simulations involve measured values of T1 at ULF and experimental parameters for the performance of an upgraded ULFMRI scanner. Under ideal experimental conditions we predict a functional signal-to-noise ratio of between 3.1 and 7.1 for an imaging time of 30 min, or between 1.5 and 3.5 for a blocked task experiment lasting 7.5 min. Our simulations suggest it may be feasible to perform fMRI using a ULFMRI system designed to perform MRI and MEG in situ.
       
  • Dynamic low frequency EEG phase synchronization patterns during proactive
           control of task switching
    • Abstract: Publication date: Available online 28 October 2018Source: NeuroImageAuthor(s): María Eugenia López, Sandra Pusil, Ernesto Pereda, Fernando Maestú, Francisco Barceló Cognitive flexibility is critical for humans living in complex societies with ever growing multitasking demands. Yet the low frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.
       
  • L-DOPA reduces model-free control of behavior by attenuating the transfer
           of value to action
    • Abstract: Publication date: Available online 28 October 2018Source: NeuroImageAuthor(s): Nils B. Kroemer, Ying Lee, Shakoor Pooseh, Ben Eppinger, Thomas Goschke, Michael N. Smolka Dopamine is a key neurotransmitter in action control. However, influential theories of dopamine function make conflicting predictions about the effect of boosting dopamine neurotransmission. Here, we tested if increases in dopamine tone by administration of L-DOPA upregulate reward learning as predicted by reinforcement learning theories, and if increases are specific for deliberative “model-based” control or reflexive “model-free” control. Alternatively, L-DOPA may impair learning as suggested by “value” or “thrift” theories of dopamine. To this end, we employed a two-stage Markov decision-task to investigate the effect of L-DOPA (randomized cross-over) on behavioral control while brain activation was measured using fMRI. L-DOPA led to attenuated model-free control of behavior as indicated by the reduced impact of reward on choice. Increased model-based control was only observed in participants with high working memory capacity. Furthermore, L-DOPA facilitated exploratory behavior, particularly after a stream of wins in the task. Correspondingly, in the brain, L-DOPA decreased the effect of reward at the outcome stage and when the next decision had to be made. Critically, reward-learning rates and prediction error signals were unaffected by L-DOPA, indicating that differences in behavior and brain response to reward were not driven by differences in learning. Taken together, our results suggest that L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action. These findings provide support for the value and thrift accounts of dopamine and call for a refined integration of valuation and action signals in reinforcement learning models.
       
  • Reconfiguration of brain networks supporting inhibition of emotional
           challenge
    • Abstract: Publication date: Available online 27 October 2018Source: NeuroImageAuthor(s): Morgan E. Bartholomew, Cindy M. Yee, Wendy Heller, Gregory A. Miller, Jeffrey M. Spielberg Reacting to the salient emotional features of a stimulus is adaptive unless the information is irrelevant or interferes with goal-directed behavior. The ability to ignore salient but otherwise extraneous information involves restructuring of brain networks and is a key impairment in several psychological disorders. Despite the importance of understanding inhibitory control of emotional response, the associated brain network mechanisms remain unknown. Utilizing functional magnetic resonance imaging (fMRI) data obtained from 103 participants performing an emotion-word Stroop (EWS) task, the present study applied graph-theory analysis to identify how brain regions subserving emotion processing and cognitive control are integrated within the global brain network to promote more specialized and efficient processing during successful inhibition of response to emotional distractors. The present study identified two sub-networks associated with emotion inhibition, one involving hyper-connectivity to prefrontal cortex and one involving hyper-connectivity to thalamus. Brain regions typically associated with identifying emotion salience were more densely connected with the thalamic hub, consistent with thalamic amplification of prefrontal cortex control of these regions. Additionally, stimuli high in emotion arousal prompted restructuring of the global network to increase clustered processing and overall communication efficiency. These results provide evidence that inhibition of emotion relies on interactions between cognitive control and emotion salience sub-networks.
       
  • People got lost in solving a set of similar problems
    • Abstract: Publication date: Available online 26 October 2018Source: NeuroImageAuthor(s): Furong Huang, Qingbai Zhao, Zhijin Zhou, Jing Luo A mental set generally refers to the human brain's tendency to persist with a familiar solution and stubbornly ignore alternatives. However, if a familiar solution is unable to solve a problem similar to a previous problem, does it continue to hinder alternative solutions, and if so, how and why' To answer these questions, a Chinese character decomposition task was adopted in this study. Participants were asked to perform a practice problem that could be solved by a familiar loose chunk decomposition (LCD) solution followed by a test problem that was similar to the practice problem but could only be solved by an unfamiliar tight chunk decomposition (TCD) solution or were asked to repeatedly perform 3–5 practice problems followed by a test problem; the former is the base-set condition, and the latter is the enhanced-set condition. The results showed that the test problem recruited more activation of the inferior frontal gyrus (IFG), middle occipital cortex (MOG), superior parietal lobule (SPL) and dorsal anterior cingulate cortex (dACC) than the practice problem in the latter operation and verification stage, but almost equal activation of the dACC occurred in the early exploration stage. This likely implied that people did not think that the familiar but currently invalid LCD solution could not be used to solve the test problem; thus, it continuously competed for attention with the unfamiliar TCD solution, which required more executive control to suppress. Moreover, compared with the base-set condition, the test problem in the enhanced-set condition recruited greater activations of the IFG, SPL and dACC in the latter verification stage but less activations of regions in the left IFG and MOG in the early exploration stage. These results revealed that people less actively explored and had to work harder to operate the unfamiliar TCD solution, particularly to resolve competition from the familiar but currently invalid LCD solution. In conclusion, people lost the ability to identify errors in the familiar but currently invalid solution, which in turn decreased the exploration efforts and increased the processing demands associated with alternative solutions in the form of attentional bias and competition. This finding broadly explains the dilemma of creative problem solving.
       
  • Representation of spatial sequences using nested rules in human prefrontal
           cortex
    • Abstract: Publication date: Available online 25 October 2018Source: NeuroImageAuthor(s): Liping Wang, Marie Amalric, Wen Fang, Xinjian Jiang, Christophe Pallier, Santiago Figueira, Mariano Sigman, Stanislas Dehaene Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing.
       
  • Targeting reduced neural oscillations in patients with schizophrenia by
           transcranial alternating current stimulation
    • Abstract: Publication date: Available online 24 October 2018Source: NeuroImageAuthor(s): Sangtae Ahn, Juliann M. Mellin, Sankaraleengam Alagapan, Morgan L. Alexander, John H. Gilmore, L. Fredrik Jarskog, Flavio Fröhlich Transcranial alternating current stimulation (tACS) modulates endogenous neural oscillations in healthy human participants by the application of a low-amplitude electrical current with a periodic stimulation waveform. Yet, it is unclear if tACS can modulate and restore neural oscillations that are reduced in patients with psychiatric illnesses such as schizophrenia. Here, we asked if tACS modulates network oscillations in schizophrenia. We performed a randomized, double-blind, sham-controlled clinical trial to contrast tACS with transcranial direct current stimulation (tDCS) and sham stimulation in 22 schizophrenia patients with auditory hallucinations. We used high-density electroencephalography to investigate if a five-day, twice-daily 10Hz-tACS protocol enhances alpha oscillations and modulates network dynamics that are reduced in schizophrenia. We found that 10Hz-tACS enhanced alpha oscillations and modulated functional connectivity in the alpha frequency band. In addition, 10Hz-tACS enhanced the 40Hz auditory steady-state response (ASSR), which is reduced in patients with schizophrenia. Importantly, clinical improvement of auditory hallucinations correlated with enhancement of alpha oscillations and the 40Hz-ASSR. Together, our findings suggest that tACS has potential as a network-level approach to modulate reduced neural oscillations related to clinical symptoms in patients with schizophrenia.
       
  • Rapid adaptive adjustments of selective attention following errors
           revealed by the time course of steady-state visual evoked potentials
    • Abstract: Publication date: Available online 23 October 2018Source: NeuroImageAuthor(s): Marco Steinhauser, Søren K. Andersen Directing attention to task-relevant stimuli is crucial for successful task performance, but too much attentional selectivity implies that new and unexpected information in the environment remains undetected. A possible mechanism for optimizing this fundamental trade-off could be an error monitoring system that immediately triggers attentional adjustments following the detection of behavioral errors. However, the existence of rapid adaptive post-error adjustments has been controversially debated. While preconscious error processing reflected by an error-related negativity (Ne/ERN) in the event-related potential has been shown to occur within milliseconds after errors, more recent studies concluded that error detection even impairs attentional selectivity and that adaptive adjustments are implemented, if at all, only after errors are consciously detected. Here, we employ steady-state visual evoked potentials elicited by continuously presented stimuli to precisely track the emergence of error-induced attentional adjustments. Our results indicate that errors lead to an immediate reallocation of attention towards task-relevant stimuli, which occurs simultaneously with the Ne/ERN. Single-trial variation of this adjustment was correlated with the Ne/ERN amplitude and predicted adaptive behavioral adjustments on the post-error trial. This suggests that early error monitoring in the medial frontal cortex is directly involved in eliciting adaptive attentional adjustments.
       
 
 
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