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Publisher: Elsevier   (Total: 3183 journals)

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Showing 1 - 200 of 3183 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 37, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 25, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 101, 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: 434, 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: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 11, SJR: 0.18, CiteScore: 1)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 296, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 25, 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: 7, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 8)
Acute Pain     Full-text available via subscription   (Followers: 15, 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: 9, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 177, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 9, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 16, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 29, 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: 11, 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: 15, 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: 5)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 14)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 28, 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: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 20, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 13)
Advances in Digestive Medicine     Open Access   (Followers: 12)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 26)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 29, 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: 49, 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: 65, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 17)
Advances in Genetics     Full-text available via subscription   (Followers: 20, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 10, 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: 25, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 11, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 3, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 10, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 20, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 12, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 7, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
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: 4)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 2)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 18, 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: 25, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 17)
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: 9, 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: 19)
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: 65)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 418, 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: 13, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 36, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 20)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 53, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 373, 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: 467, 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: 44, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 58, 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: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 11)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 2, 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: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 53, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 6, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 6, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 58, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 63, 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: 12)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 35, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, 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: 50)
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: 241, 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: 30, 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: 39, 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: 64, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 23, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription   (Followers: 1)
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: 44, SJR: 1.512, CiteScore: 5)
Analytica Chimica Acta : X     Open Access  
Analytical Biochemistry     Hybrid Journal   (Followers: 207, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 13, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 14)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 25, 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: 210, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 6, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, CiteScore: 2)

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Similar Journals
Journal Cover
NeuroImage
Journal Prestige (SJR): 3.679
Citation Impact (citeScore): 6
Number of Followers: 74  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1053-8119 - ISSN (Online) 1095-9572
Published by Elsevier Homepage  [3183 journals]
  • Decoding the tradeoff between encoding and retrieval to predict memory for
           overlapping events
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Nicole M. Long, Brice A. Kuhl When new events overlap with past events, there is a natural tradeoff between encoding the new event and retrieving the past event. Given the ubiquity of overlap among memories, this tradeoff between memory encoding and retrieval is of central importance to computational models of episodic memory (O’Reilly & McClelland 1994; Hasselmo 2005). However, prior studies have not directly linked neural markers of encoding/retrieval tradeoffs to behavioral measures of how overlapping events are remembered. Here, by decoding patterns of scalp electroencephalography (EEG) from male and female human subjects, we show that tradeoffs between encoding and retrieval states are reflected in distributed patterns of neural activity and, critically, these neural tradeoffs predict how overlapping events will later be remembered. Namely, new events that overlapped with past events were more likely to be subsequently remembered if neural patterns were biased toward a memory encoding state–or, conversely, away from a retrieval state. Additionally, we show that neural markers of encoding vs. retrieval states are surprisingly independent from previously-described EEG predictors of subsequent memory. Instead, we demonstrate that previously-described EEG predictors of subsequent memory are better explained by task engagement than by memory encoding, per se. Collectively, our findings provide important insight into how the memory system balances memory encoding and retrieval states and, more generally, into the neural mechanisms that support successful memory formation.
       
  • Plasticity in auditory categorization is supported by differential
           engagement of the auditory-linguistic network
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Gavin M. Bidelman, Breya Walker To construct our perceptual world, the brain categorizes variable sensory cues into behaviorally-relevant groupings. Categorical representations are apparent within a distributed fronto-temporo-parietal brain network but how this neural circuitry is shaped by experience remains undefined. Here, we asked whether speech and music categories might be formed within different auditory-linguistic brain regions depending on listeners’ auditory expertise. We recorded EEG in highly skilled (musicians) vs. less experienced (nonmusicians) perceivers as they rapidly categorized speech and musical sounds. Musicians showed perceptual enhancements across domains, yet source EEG data revealed a double dissociation in the neurobiological mechanisms supporting categorization between groups. Whereas musicians coded categories in primary auditory cortex (PAC), nonmusicians recruited non-auditory regions (e.g., inferior frontal gyrus, IFG) to generate category-level information. Functional connectivity confirmed nonmusicians’ increased left IFG involvement reflects stronger routing of signal from PAC directed to IFG, presumably because sensory coding is insufficient to construct categories in less experienced listeners. Our findings establish auditory experience modulates specific engagement and inter-regional communication in the auditory-linguistic network supporting categorical perception. Whereas early canonical PAC representations are sufficient to generate categories in highly trained ears, less experienced perceivers broadcast information downstream to higher-order linguistic brain areas (IFG) to construct abstract sound labels.
       
  • Default mode and visual network activity in an attention task: Direct
           measurement with intracranial EEG
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Jiajia Li, Sharif I. Kronemer, Wendy X. Herman, Hunki Kwon, Jun Hwan Ryu, Christopher Micek, Ying Wu, Jason Gerrard, Dennis D. Spencer, Hal Blumenfeld Dynamic attention states are necessary to navigate the ever changing task demands of daily life. Previous investigations commonly utilize a block paradigm to study sustained and transient changes in attention networks. fMRI investigations have shown that sustained attention in visual block design attention tasks corresponds to decreased signal in the default mode and visual processing networks. While task negative networks are anticipated to decrease during active task engagement, it is unexpected that visual networks would also be suppressed during a visual task where event-related fMRI studies have found transient increases to visual stimuli. To resolve these competing results, the current investigations utilized intracranial EEG to directly interrogate visual and default mode network dynamics during a visual continuous performance task. We used the electrophysiological data to model expected fMRI signals and to maximize interpretation of current results with previous investigations. Results show broadband gamma power decreases in the default mode network, corresponding to previous EEG and fMRI findings. Meanwhile, visual processing regions including the primary visual cortex and fusiform gyrus demonstrate both sustained decreases during task engagement and stimuli-driven transient increases in gamma power. Modeled fMRI based on gamma power reproduces signal decreases reported in the fMRI literature, and emphasizes the insensitivity of fMRI to transient, regularly spaced signal changes embedded within sustained network dynamics. The signal processing functions of the dynamic visual and default mode network changes explored in this study are unknown but may be elucidated through further investigation.
       
  • The neurovascular response is attenuated by focused ultrasound-mediated
           disruption of the blood-brain barrier
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Nick Todd, Yongzhi Zhang, Margaret Livingstone, David Borsook, Nathan McDannold Focused ultrasound (FUS)-induced disruption of the blood-brain barrier (BBB) is a non-invasive method to target drug delivery to specific brain areas that is now entering into the clinic. Recent studies have shown that the method has several secondary effects on local physiology and brain function beyond making the vasculature permeable to normally non-BBB penetrant molecules. This study uses functional MRI methods to investigate how FUS BBB opening alters the neurovascular response in the rat brain. Nine rats underwent actual and sham FUS induced BBB opening targeted to the right somatosensory cortex (SI) followed by four runs of bilateral electrical hind paw stimulus-evoked fMRI. The neurovascular response was quantified using measurements of the blood oxygen level dependent (BOLD) signal and cerebral blood flow (CBF). An additional three rats underwent the same FUS-BBB opening followed by stimulus-evoked fMRI with high resolution BOLD imaging and BOLD imaging of a carbogen-breathing gas challenge. BOLD and CBF measurements at two different stimulus durations demonstrate that the neurovascular response to the stimulus is attenuated in both amplitude and duration in the region targeted for FUS-BBB opening. The carbogen results show that the attenuation in response amplitude, but not duration, is still present when the signaling mechanism originates from changes in blood oxygenation instead of stimulus-induced neuronal activity. There is some evidence of non-local effects, including a possible global decrease in baseline CBF. All effects are resolved by 24 h after FUS-BBB opening. Taken together, these results suggest that FUS-BBB opening alters that state of local brain neurovascular physiology in such a way that hinders its ability to respond to demands for increased blood flow to the region. The mechanisms for this effect need to be elucidated.
       
  • Centro-parietal EEG potentials index subjective evidence and confidence
           during perceptual decision making
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Jan Herding, Simon Ludwig, Alexander von Lautz, Bernhard Spitzer, Felix Blankenburg Recent studies suggest that a centro-parietal positivity (CPP) in the EEG signal tracks the absolute (unsigned) strength of accumulated evidence for choices that require the integration of noisy sensory input. Here, we investigated whether the CPP might also reflect the evidence for decisions based on a quantitative comparison between two sequentially presented stimuli (a signed quantity). We recorded EEG while participants decided whether the latter of two vibrotactile frequencies was higher or lower than the former in six variants of this task (n = 116). To account for biases in sequential comparisons, we applied a behavioral model based on Bayesian inference that estimated subjectively perceived frequency differences. Immediately after the second stimulus, parietal ERPs reflected the signed value of subjectively perceived differences and afterwards their absolute value. Strikingly, the modulation by signed difference was evident in trials without any objective evidence for either choice and correlated with choice-selective premotor beta band amplitudes. Modulations by the absolute strength of subjectively perceived evidence – a direct indicator of task difficulty - exhibited all features of statistical decision confidence. Together, our data suggest that parietal EEG signals first index subjective evidence, and later include a measure of confidence in the context of perceptual decision making.
       
  • Default-mode network activation underlies accurate contextual processing
           of exclusive disjunctions in older but not younger adults
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Chi-Chuan Chen, Yu-Shiang Su, Yu-Zhen Tu, Joshua Oon Soo Goh Young adults proactively engage frontoparietal processing of contextual cues to preempt subsequent events. Rather than being preemptive, older adults engage these brain areas reactively upon event occurrences. Reactive frontoparietal processes in older adults, however, might be insufficient for complex contextual neural computations where utilities of contexts are not straightforward but dependent on a set of stimulus-response rules. Applying non-linear logic (XOR) rules in an fMRI experiment, we found higher default-mode network (DMN) activity critical for correctly responding to such contingency in older but not younger adults. Moreover, older individuals with higher proactive cue processing showed better performances with less DMN activity. Thus, DMN processing provides critical support when older adults are faced with complex contextual contingencies. These findings suggest an age-related change in the neurocomputational role of introspective processes in decision-making from young to older adulthood.
       
  • Dynamic causal modeling for calcium imaging: Exploration of differential
           
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Kyesam Jung, Jiyoung Kang, Seungsoo Chung, Hae-Jeong Park Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI.
       
  • From neuronal to psychological noise – Long-range temporal correlations
           in EEG intrinsic activity reduce noise in internally-guided decision
           making
    • Abstract: Publication date: 1 November 2019Source: NeuroImage, Volume 201Author(s): Takashi Nakao, Madoka Miyagi, Ryosuke Hiramoto, Annemarie Wolff, Javier Gomez-Pilar, Makoto Miyatani, Georg Northoff Our personal internal preferences while making decisions are usually consistent. Recent psychological studies, however, show observable variability of internal criteria occurs by random noise. The neural correlates of said random noise - an instance of ‘psychological noise’ – yet remain unclear. Combining simulation, behavioral, and neural approaches, our study investigated the psychological and neural correlates of such random noise in our internal criteria during decision making. We applied well-established decision-making tasks which relied on either internal criteria - occupation choice task as internally-guided decision making (IDM) - or external criteria - salary judgment task as externally-guided decision making (EDM). Subjects underwent EEG for resting state and task-evoked activity during IDM and EDM. We measured resting state long-range temporal correlation (LRTC) in the alpha frequency range as the index of neuronal noise. Based on our simulation, we identified a measure of psychological noise (as distinguished from true preference change) in IDM. The main finding shows that the indices for psychological noise are directly related to frontocentral LRTC in the alpha range. Higher degrees of frontocentral LRTC, which index lower neuronal noise, were related to lower degrees of psychological noise during IDM. This was not found during EDM. Resting state LRTC was also related to task-evoked activity, such as conflict-related negativity, during IDM only. Taken together, our data demonstrate, for the first time, the direct relationship between neuronal noise in the brain’s intrinsic activity and psychological noise in the internal criteria of our decision making.Graphical abstractImage 1
       
  • Human visual cortical gamma reflects natural image structure
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Nicolas M. Brunet, Pascal Fries Many studies have reported visual cortical gamma-band activity related to stimulus processing and cognition. Most respective studies used artificial stimuli, and the few studies that used natural stimuli disagree. Electrocorticographic (ECoG) recordings from awake macaque areas V1 and V4 found gamma to be abundant during free viewing of natural images. In contrast, a study using ECoG recordings from V1 of human patients reported that many natural images induce no gamma and concluded that it is not necessary for seeing. To reconcile these apparently disparate findings, we reanalyzed those same human ECoG data recorded during presentation of natural images. We find that the strength of gamma is positively correlated with different image-computable metrics of image structure. This holds independently of the precise metric used to quantify gamma. In fact, an average of previously used gamma metrics reflects image structure most robustly. Gamma was sufficiently diagnostic of image structure to differentiate between any possible pair of images with>70% accuracy. Thus, while gamma might be weak for some natural images, the graded strength of gamma reflects the graded degree of image structure, and thereby conveys functionally relevant stimulus properties.
       
  • Detailed mapping of human habenula resting-state functional connectivity
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Benjamin A. Ely, Emily R. Stern, Joo-won Kim, Vilma Gabbay, Junqian Xu The habenula (Hb) inhibits dopaminergic reward signaling in response to negative outcomes and has been linked to numerous functional domains relevant to mental health, including reward prediction, motivation, and aversion processing. Despite its important neuroscientific and clinical implications, however, the human Hb remains poorly understood due to its small size and the associated technical hurdles to in vivo functional magnetic resonance imaging (fMRI) investigation. Using high-resolution 3 T fMRI data from 68 healthy young adults acquired through the Human Connectome Project, we developed a rigorous approach for mapping the whole-brain resting-state functional connectivity of the human Hb. Our study combined an optimized strategy for defining subject-level connectivity seeds to maximize Hb blood-oxygen-level-dependent (BOLD) signal sensitivity with high-quality surface-based alignment for robust functional localization and cortical sensitivity. We identified significant positive Hb connectivity with: (i) conserved brainstem targets, including the dopaminergic ventral tegmental area, serotonergic raphe nuclei, and periaqueductal gray; (ii) subcortical structures related to reward and motor function, including the nucleus accumbens, dorsal striatum, pallidum, thalamus, and cerebellum; and (iii) cortical areas associated with the Salience Network and early sensory processing, including the dorsal anterior cingulate, anterior insula, and primary visual and auditory cortices. Hb connectivity was strongly biased towards task-positive brain regions, with weak or negative connectivity observed throughout the task-negative Default Mode Network. Our study provides a detailed characterization of Hb resting-state functional connectivity in healthy young adults, demonstrating both the feasibility and clinical potential of studying the human Hb using high-resolution 3 T fMRI.Graphical abstractImage 1
       
  • Motion sickness-susceptible participants exposed to coherent rotating dot
           patterns show excessive N2 amplitudes and impaired theta-band phase
           synchronization
    • Abstract: Publication date: Available online 18 July 2019Source: NeuroImageAuthor(s): Yue Wei, Yuka O. Okazaki, Richard H.Y. So, Winnie C.W. Chu, Keiichi Kitajo Visually induced motion sickness (VIMS) can occur via prolonged exposure to visual stimulation that generates the illusion of self-motion (vection). Not everyone is susceptible to VIMS and the neural mechanism underlying susceptibility is unclear. This study explored the differences of electroencephalographic (EEG) signatures between VIMS-susceptible and VIMS-resistant groups. Thirty-two-channel EEG data were recorded from 12 VIMS-susceptible and 15 VIMS-resistant university students while they were watching two patterns of moving dots: (1) a coherent rotation pattern (vection-inducing and potentially VIMS-provoking pattern), and (2) a random movement pattern (non-VIMS-provoking control). The VIMS-susceptible group exhibited a significantly larger increase in the parietal N2 response when exposed to the coherent rotating pattern than when exposed to control patterns. In members of the VIMS-resistant group, after vection onset, global connectivity from all other EEG electrodes to the right-temporal-parietal and to the right-central areas increased, whereas the global connectivity to the right-frontal area reduced. Such changes were not observed in the susceptible group. Further, the increases in N2 amplitude and the identified phase synchronization index were significantly correlated with individual motion sickness susceptibility. Results suggest that VIMS susceptibility is associated with systematic impairment of dynamic cortical coordination as captured by the phase synchronization of cortical activities. Analyses of dynamic EEG signatures could be a means to unlock the neural mechanism of VIMS.
       
  • Functional connectivity of brain associated with passive range of motion
           exercise: Proprioceptive input promoting motor activation'
    • Abstract: Publication date: Available online 17 July 2019Source: NeuroImageAuthor(s): Fatima A. Nasrallah, Abdalla Z. Mohamed, Megan EJ. Campbell, Hong Kai Yap, Chen-Hua Yeow, Jeong Hoon Lim Soft robotics have come to the forefront of devices available for rehabilitation following stroke; however, objective evaluation of the specific brain changes following rehabilitation with these devices is lacking. In this study, we utilized functional Magnetic Resonance Imaging (fMRI) and dynamic causal modeling (DCM) to characterize the activation of brain areas with a MRI compatible glove actuator compared to the conventional manual therapy. Thirteen healthy volunteers engaged in a motor-visual fMRI task under four different conditions namely active movement, manual passive movement, passive movement using a glove actuator, and crude tactile stimulation. Brain activity following each task clearly identified the somatosensory motor area (SMA) as a major hub orchestrating activity between the primary motor (M1) and sensory (S1) cortex.During the glove-induced passive movement, activity in the motor-somatosensory areas was reduced, but there were significant increases in motor cortical activity compared to manual passive movement. We estimated the modulatory signaling from within a defined sensorimotor network (SMA, M1, and S1), through DCM and highlighted a dual-gating of sensorimotor inputs to the SMA. Proprioceptive signaling from S1 to the SMA reflected positive coupling for the manually assisted condition, while M1 activity was positively coupled to the SMA during the glove condition. Importantly, both the S1 and M1 were shown to influence each other's connections with the SMA, with inhibitory nonlinear modulation by the M1 on the S1-SMA connection, and similarly S1 gated the M1-SMA connection. The work is one of the first to have applied effective connectivity to examine sensorimotor activity ensued by manual or robotic passive range of motion exercise, crude tactile stimulation, and voluntary movements to provide a basis for the mechanism by which soft actuators can alter brain activity.
       
  • Hebbian associative plasticity in the visuo-tactile domain: A cross-modal
           paired associative stimulation protocol
    • Abstract: Publication date: Available online 17 July 2019Source: NeuroImageAuthor(s): Agnese Zazio, Giacomo Guidali, Ottavia Maddaluno, Carlo Miniussi, Nadia Bolognini We developed and assessed the effects of a novel cross-modal protocol aimed at inducing associative (Hebbian-like) plasticity in the somatosensory cortical system through vision. Associative long-term potentiation can be induced in the primary somatosensory cortex (S1) by means of paired associative stimulation (PAS), in which a peripheral electrical stimulation of the median nerve is repeatedly paired with a transcranial magnetic stimulation (TMS) pulse over S1. Considering the mirror proprieties of S1, the cross-modal PAS (cm-PAS) consists of repetitive observation of bodily tactile stimulations, paired with TMS pulses over the contralateral S1. Through three experiments in healthy participants, we demonstrate that the cm-PAS is able to induce excitatory plastic effects with functional significance in S1, improving somatosensory processing at both behavioral (tactile acuity) and neurophysiological (somatosensory-evoked potentials) levels. The plastic effects induced by cm-PAS depend on the interval (20 m s) between the visual stimulus and the magnetic pulse, the targeted cortical site (S1), and the tactile content of the visual stimulus, which must represent a touch event. Such specificity implies the recruitment of cross-modal, mirror-like, mechanisms in S1, which are able to visually promote associative synaptic plasticity in S1 likely through the recruitment of predictive coding processes.
       
  • Towards a state-space geometry of neural responses to natural scenes: A
           steady-state approach
    • Abstract: Publication date: Available online 17 July 2019Source: NeuroImageAuthor(s): Bruce C. Hansen, David J. Field, Michelle R. Greene, Cassady Olson, Vladimir Miskovic Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance (∼73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments.
       
  • Mapping critical hubs of receptive and expressive language using MEG: A
           comparison against fMRI
    • Abstract: Publication date: Available online 17 July 2019Source: NeuroImageAuthor(s): Vahab Youssofzadeh, Abbas Babajani-Feremi The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20–37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3–30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q 
       
  • Structure-function associations of successful associative encoding
    • Abstract: Publication date: Available online 16 July 2019Source: NeuroImageAuthor(s): Nina Becker, Grégoria Kalpouzos, Alireza Salami, Erika J. Laukka, Yvonne Brehmer Functional magnetic resonance imaging (MRI) studies have demonstrated a critical role of hippocampus and inferior frontal gyrus (IFG) in associative memory. Similarly, evidence from structural MRI studies suggests a relationship between gray-matter volume in these regions and associative memory. However, how brain volume and activity relate to each other during associative-memory formation remains unclear. Here, we used joint independent component analysis (jICA) to examine how gray-matter volume and brain activity would be associated during associative encoding, especially in medial-temporal lobe (MTL) and IFG. T1-weighted images were collected from 27 young adults, and functional MRI was employed during intentional encoding of object pairs. A subsequent recognition task tested participants’ memory performance. Unimodal analyses using voxel-based morphometry revealed that participants with better associative memory showed larger gray-matter volume in left anterior hippocampus. Results from the jICA revealed one component that comprised a covariance pattern between gray-matter volume in anterior and posterior MTL and encoding-related activity in IFG. Our findings suggest that gray matter within the MTL modulates distally distinct parts of the associative encoding circuit, and extend previous studies that demonstrated MTL-IFG functional connectivity during associative memory tasks.
       
  • Spatio-temporal profile of brain activity during gentle touch investigated
           with magnetoencephalography
    • Abstract: Publication date: Available online 16 July 2019Source: NeuroImageAuthor(s): Elin Eriksson Hagberg, Rochelle Ackerley, Daniel Lundqvist, Justin Schneiderman, Veikko Jousmäki, Johan Wessberg Positive affective touch plays a central role in social and inter-personal interactions. Low-threshold mechanoreceptive afferents, including slowly-conducting C-tactile (CT) afferents found in hairy skin, transmit such signals from gentle touch to the brain. Tactile signals are processed, in part, by the posterior insula, where it is the thought to be the primary target for CTs. We used magnetoencephalography (MEG) to assess brain activity evoked by gentle, naturalistic stroking touch on the arm delivered by a new MEG-compatible brush robot. We aimed to use high temporal resolution MEG to allow us to distinguish between brain responses from fast-conducting Aβ and slowly-conducting CT afferents. Brush strokes were delivered to the left upper arm and left forearm of 15 healthy participants. We hypothesized that late brain responses, due to slow CT afference, would appear with a time shift between the two different locations on the arm. Our results show that gentle touch rapidly activated somatosensory, motor, and cingulate regions within the first 100 ms of skin contact, which was driven by fast-conducting mechanoreceptive afference, and that these responses were sustained during touch. Peak latencies in the posterior insula were shifted as a function of stimulus location and temporally-separate posterior insula activations were induced by Aβ and CT afference that may modulate the emotional processing of gentle touch on hairy skin. We conclude that the detailed information regarding temporal and spatial brain activity from MEG provides new insights into the central processing of gentle, naturalistic touch, which is thought to underpin affective tactile interactions.
       
  • White matter information flow mapping from diffusion MRI and EEG
    • Abstract: Publication date: Available online 15 July 2019Source: NeuroImageAuthor(s): Samuel Deslauriers-Gauthier, Jean-Marc Lina, Russell Butler, Kevin Whittingstall, Guillaume Gilbert, Pierre-Michel Bernier, Rachid Deriche, Maxime Descoteaux The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.
       
  • Exploring individual and group differences in latent brain networks using
           cross-validated simultaneous component analysis
    • Abstract: Publication date: Available online 15 July 2019Source: NeuroImageAuthor(s): Nathaniel E. Helwig, Matthew A. Snodgress Component models such as PCA and ICA are often used to reduce neuroimaging data into a smaller number of components, which are thought to reflect latent brain networks. When data from multiple subjects are available, the components are typically estimated simultaneously (i.e., for all subjects combined) using either tensor ICA or group ICA. As we demonstrate in this paper, neither of these approaches is ideal if one hopes to find latent brain networks that cross-validate to new samples of data. Specifically, we note that the tensor ICA model is too rigid to capture real-world heterogeneity in the component time courses, whereas the group ICA approach is too flexible to uniquely identify latent brain networks. For multi-subject component analysis, we recommend comparing a hierarchy of simultaneous component analysis (SCA) models. Our proposed model hierarchy includes a flexible variant of the SCA framework (the Parafac2 model), which is able to both (i) model heterogeneity in the component time courses, and (ii) uniquely identify latent brain networks. Furthermore, we propose cross-validation methods to tune the relevant model parameters, which reduces the potential of over-fitting the observed data. Using simulated and real data examples, we demonstrate the benefits of the proposed approach for finding credible components that reveal interpretable individual and group differences in latent brain networks.
       
  • Quantifying deep grey matter atrophy using automated segmentation
           approaches: A systematic review of structural MRI studies
    • Abstract: Publication date: Available online 15 July 2019Source: NeuroImageAuthor(s): Alex M. Pagnozzi, Jurgen Fripp, Stephen E. Rose The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a “silver standard” annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the “improved” segmentation from T1-sequences alone.
       
  • Joint modelling of diffusion MRI and microscopy
    • Abstract: Publication date: Available online 14 July 2019Source: NeuroImageAuthor(s): Amy FD. Howard, Jeroen Mollink, Michiel Kleinnijenhuis, Menuka Pallebage-Gamarallage, Matteo Bastiani, Michiel Cottaar, Karla L. Miller, Saad Jbabdi The combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine diffusion MRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a ‘brain-wide’ fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel.
       
  • Development and body mass inversely affect children's brain activation in
           dorsolateral prefrontal cortex during food choice
    • Abstract: Publication date: Available online 13 July 2019Source: NeuroImageAuthor(s): Floor van Meer, Laura N. van der Laan, Gabriele Eiben, Lauren Lissner, Maike Wolters, Stefan Rach, Manfred Herrmann, Peter Erhard, Denes Molnar, Gergely Orsi, Max A. Viergever, Roger A.H. Adan, Paul A.M. Smeets, I.Family Consortium Childhood obesity is a rising problem caused in part by unhealthy food choices. Food choices are based on a neural value signal encoded in the ventromedial prefrontal cortex, and self-control involves modulation of this signal by the dorsolateral prefrontal cortex (dlPFC). We determined the effects of development, body mass (BMI Cole score) and body mass history on the neural correlates of healthy food choice in children. 141 children (aged 10-17y) from Germany, Hungary and Sweden were scanned with fMRI while performing a food choice task. Afterwards health and taste ratings of the foods were collected. In the food choice task children were asked to consider the healthiness or tastiness of the food or to choose naturally. Overall, children made healthier choices when asked to consider healthiness. However, children who had a higher weight gain per year chose less healthy foods when considering healthiness but not when choosing naturally. Pubertal development stage correlated positively while current body mass correlated negatively with dlPFC activation when accepting foods. Pubertal development negatively and current body mass positively influenced the effect of considering healthiness on activation of brain areas involved in salience and motivation. In conclusion, children in earlier stages of pubertal development and children with a higher body weight exhibited less activation in the dlPFC, which has been implicated in self-control during food choice. Furthermore, pubertal development and body mass influenced neural responses to a health cue in areas involved in salience and motivation. Thus, these findings suggest that children in earlier stages of pubertal development, children with a higher body mass gain and children with overweight may possibly be less susceptible to healthy eating interventions that rely on self-control or that highlight health aspects of food.
       
  • Spoken language proficiency predicts print-speech convergence in beginning
           readers
    • Abstract: Publication date: Available online 13 July 2019Source: NeuroImageAuthor(s): Rebecca A. Marks, Ioulia Kovelman, Olga Kepinska, Myriam Oliver, Zhichao Xia, Stephanie L. Haft, Leo Zekelman, Yuuko Uchikoshi, Roeland Hancock, Fumiko Hoeft Learning to read transforms the brain, building on children's existing capacities for language and visuospatial processing. In particular, the development of print-speech convergence, or the spatial overlap of neural regions necessary for both auditory and visual language processing, is critical for literacy acquisition. Print-speech convergence is a universal signature of proficient reading, yet the antecedents of this convergence remain unknown. Here we examine the relationship between spoken language proficiency and the emergence of the print-speech network in beginning readers (ages 5–6). Results demonstrate that children's language proficiency, and not their early literacy skill, explains variance in their print-speech neural convergence in kindergarten. Furthermore, print-speech convergence in kindergarten predicts reading abilities one year later. These findings suggest that children's language ability is a core mechanism guiding the neural plasticity for learning to read, and extend theoretical perspectives on language and literacy acquisition across the lifespan.
       
  • Network analysis of whole-brain fMRI dynamics: A new framework based on
           dynamic communicability
    • Abstract: Publication date: Available online 12 July 2019Source: NeuroImageAuthor(s): Matthieu Gilson, Nikos E. Kouvaris, Gustavo Deco, Jean-François Mangin, Cyril Poupon, Sandrine Lefranc, Denis Rivière, Gorka Zamora-López Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.
       
  • Canonical maximization of coherence: A novel tool for investigation of
           neuronal interactions between two datasets
    • Abstract: Publication date: Available online 11 July 2019Source: NeuroImageAuthor(s): C. Vidaurre, G. Nolte, I.E.J. de Vries, M. Gómez, T.W. Boonstra, K.-R. Müller, A. Villringer, V.V. Nikulin Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.
       
  • Thalamic low frequency activity facilitates resting-state cortical
           interhemispheric MRI functional connectivity
    • Abstract: Publication date: Available online 9 July 2019Source: NeuroImageAuthor(s): Xunda Wang, Alex T.L. Leong, Russell W. Chan, Ed X. Wu Blood-oxygen-level-dependent (BOLD) resting-state functional MRI (rsfMRI) has emerged as a valuable tool to map complex brain-wide functional networks, predict cognitive performance and identify biomarkers for neurological diseases. However, interpreting these findings poses challenges, as the neural basis of rsfMRI connectivity remains poorly understood. The thalamus serves as a relay station and modulates diverse long-range cortical functional integrations, yet few studies directly interrogate its role in brain-wide rsfMRI connectivity. Utilizing a multi-modal approach of rsfMRI, optogenetic stimulation and multi-depth cortical electrophysiology recording, we examined whether and how the somatosensory thalamus contributes to cortical interhemispheric rsfMRI connectivity. We found that low frequency (1 Hz) optogenetic stimulation of somatosensory-specific ventral posteromedial (VPM) thalamocortical excitatory neurons increased the interhemispheric rsfMRI connectivity in all examined sensory cortices, somatosensory, visual and auditory, and the local intrahemispheric BOLD activity at infraslow frequency (0.01–0.1 Hz). In parallel, multi-depth local field potential recordings at bilateral primary somatosensory cortices revealed increased interhemispheric correlations of low frequency neural oscillations (i.e., mainly 
       
  • Using machine learning-based lesion behavior mapping to identify
           anatomical networks of cognitive dysfunction: Spatial neglect and
           attention
    • Abstract: Publication date: Available online 9 July 2019Source: NeuroImageAuthor(s): Daniel Wiesen, Christoph Sperber, Grigori Yourganov, Christopher Rorden, Hans-Otto Karnath Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inconsistencies, the idea of a wide-spread network that might underlie spatial orientation and neglect has been pushed forward. In such case, univariate lesion behavior mapping methods might have been inherently limited in detecting the presumed network due to limited statistical power. By comparing various univariate with a multivariate lesion-mapping based on support vector regression, we aimed to validate the network hypothesis directly in a large sample of 203 newly recruited right brain damaged patients. If the exact same correction factors and parameter combinations (FDR correction and dTLVC for lesion size control) were used, both univariate as well as multivariate approaches uncovered the same complex network pattern underlying spatial neglect. At the cortical level, lesion location dominantly affected temporal cortex and its borders into inferior parietal and occipital cortex. Beyond, frontal and subcortical gray matter regions as well as white matter tracts connecting these regions were affected. Our findings underline the importance of a right network in spatial exploration and attention and specifically in the emergence of the core symptoms of spatial neglect.
       
  • Brain structural differences in monozygotic twins discordant for body mass
           index
    • Abstract: Publication date: Available online 9 July 2019Source: NeuroImageAuthor(s): Christopher M. Weise, Tobias Bachmann, Burkhard Pleger BackgroundSubstantial efforts have been made to investigate the neurobiological underpinnings of human obesity with a number of studies indicating a profound influence of increased body weight on brain structure. Although body weight is known to be highly heritable, uncertainty remains regarding the respective contribution of genetic and environmental influences.MethodsIn this study we used structural magnetic resonance imaging (MRI) data from the Human Connectome Project (HCP). Voxel-based morphometry (VBM) was applied to study BMI-associated differences in gray matter volume (GMV) within monozygotic (MZ) twin pairs discordant for BMI (ΔBMI > 2.5 kg*m−2, n = 68 pairs). In addition, we investigated the relationship of ΔBMI (entire range) with GMV differences within the entire sample of MZ twin pairs (n = 153 pairs).ResultsAnalyses of BMI discordant twin pairs yielded less GMV in heavier twin siblings (p 
       
  • Faces and voices in the brain: A modality-general person-identity
           representation in superior temporal sulcus
    • Abstract: Publication date: Available online 9 July 2019Source: NeuroImageAuthor(s): Maria Tsantani, Nikolaus Kriegeskorte, Carolyn McGettigan, Lúcia Garrido Face-selective and voice-selective brain regions have been shown to represent face-identity and voice-identity, respectively. Here we investigated whether there are modality-general person-identity representations in the brain that can be driven by either a face or a voice, and that invariantly represent naturalistically varying face videos and voice recordings of the same identity. Models of face and voice integration suggest that such representations could exist in multimodal brain regions, and in unimodal regions via direct coupling between face- and voice-selective regions. Therefore, in this study we used fMRI to measure brain activity patterns elicited by the faces and voices of familiar people in face-selective, voice-selective, and person-selective multimodal brain regions. We used representational similarity analysis to (1) compare representational geometries (i.e. representational dissimilarity matrices) of face- and voice-elicited identities, and to (2) investigate the degree to which pattern discriminants for pairs of identities generalise from one modality to the other. We did not find any evidence of similar representational geometries across modalities in any of our regions of interest. However, our results showed that pattern discriminants that were trained to discriminate pairs of identities from their faces could also discriminate the respective voices (and vice-versa) in the right posterior superior temporal sulcus (rpSTS). Our findings suggest that the rpSTS is a person-selective multimodal region that shows a modality-general person-identity representation and integrates face and voice identity information.
       
  • The community structure of functional brain networks exhibits
           scale-specific patterns of inter- and intra-subject variability
    • Abstract: Publication date: Available online 7 July 2019Source: NeuroImageAuthor(s): Richard F. Betzel, Maxwell A. Bertolero, Evan M. Gordon, Caterina Gratton, Nico U.F. Dosenbach, Danielle S. Bassett The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain's modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability – which would be undetectable using past approaches – is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.
       
  • Genetic contribution to the phenotypic correlation between trait
           impulsivity and resting-state functional connectivity of the amygdala and
           its subregions
    • Abstract: Publication date: Available online 5 July 2019Source: NeuroImageAuthor(s): Dang Zheng, Jie Chen, Xiaoming Wang, Yuan Zhou Trait impulsivity, a predisposition to respond to stimuli without regard for the potentially negative consequences, contributes to many maladaptive behaviors. Studies have shown that both genetic factors and interregional functional interactions underlie trait impulsivity. However, whether common genes contribute to both trait impulsivity and its neural basis is still unknown. This study investigated the phenotypic correlations between trait impulsivity and the resting-state functional connectivity (rsFC) of the amygdala as well as its subregions and the genetic contribution to the phenotypic correlations. By recruiting a sample of 292 twins in late adolescence and young adulthood, we found that trait impulsivity was positively correlated with the rsFC between the left full amygdala and the right dorsolateral prefrontal cortex (DLPFC). Further analyses on the subregions of the amygdala showed that trait impulsivity was positively correlated with the rsFCs between the left basolateral (BL) amygdala and both the right DLPFC and the right inferior frontal gyrus and with the rsFCs between the right superficial (SF) amygdala and both the dorsal anterior cingulate cortex and right anterior insula. Bivariate genetic modelling analyses found genetic overlaps between trait impulsivity and the rsFC of the left full amygdala or the left BL amygdala with the right DLPFC. The proportions of phenotypic associations accounted for by overlapping genes were 82% and 60%, respectively. These results provide evidence for the genetic overlap between trait impulsivity and the intrinsic brain functional connectivity centred at the amygdala and especially at its BL subregion.
       
  • Optimization of data acquisition and analysis for fiber ball imaging
    • Abstract: Publication date: Available online 5 July 2019Source: NeuroImageAuthor(s): Hunter Moss, Emilie T. McKinnon, G. Russell Glenn, Joseph A. Helpern, Jens H. Jensen The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm2, for diffusion-encoding directions varying from 30 to 256 and for TE ranging from 90 to 120 m s. Our results suggest b-values above 4000 s/mm2 with least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).
       
  • Cognitive reward control recruits medial and lateral frontal cortices,
           which are also involved in cognitive emotion regulation a coordinate-based
           meta-analysis of fMRI studies
    • Abstract: Publication date: Available online 4 July 2019Source: NeuroImageAuthor(s): Felix Brandl, Zarah Le Houcq Corbi, Satja Mulej Bratec, Christian Sorg Cognitive reward control (CRC) refers to the cognitive control of one's craving for hedonic stimuli, like food, sex, or drugs. Numerous functional magnetic resonance imaging (fMRI) studies have investigated neural sources of CRC. However, a consistent pattern of brain activation across stimulus types has not been identified so far. We addressed this question using coordinate-based meta-analysis of task-fMRI studies during CRC. To further characterize such a potential common CRC activation pattern, we extended our approach to three additional questions: (i) Do CRC meta-analytic results overlap with those during the control of emotional states, such as in cognitive regulation of aversive emotions (CER)' (ii) How does the control of motivational/emotional states link to the control of action states with less motivational/emotional valence such as in response inhibition paradigms, i.e., do meta-anyltic result maps overlap' (iii) Does the control of motivational/emotional states constitute a consistent pattern of organized (i.e., coherent) ongoing or intrinsic brain activity' This question was tested by a seed-based intrinsic functional connectivity (iFC) analysis in an independent data set of resting-state fMRI.We found consistent CRC activation mainly in supplementary motor, dorsolateral prefrontal, and ventrolateral prefrontal cortices across studies. This activation pattern overlapped largely with CER-related activation, except for left-sided lateral temporal and parietal cortex activation, which was more pronounced during CER. It overlapped partly with activation during response inhibition in (pre-)supplementary motor, insular, and parietal cortices, but differed from it in dorsolateral and ventrolateral prefrontal cortices. Furthermore, it remarkably defined an iFC network covering activation patterns of both CRC and CER.Results demonstrate a consistent activation pattern of CRC across stimulus types, which overlaps largely with those of CER but only partly with those of response inhibition and constitutes an intrinsic co-activity network. These data suggest a common mechanism for the cognitive control of both motivational and emotional stimuli.results
       
  • Retrieval aids the creation of a generalised memory trace and strengthens
           episode-unique information
    • Abstract: Publication date: Available online 4 July 2019Source: NeuroImageAuthor(s): C.S. Ferreira, I. Charest, M. Wimber Generalised knowledge can adaptively guide our behaviour and help us navigate the world. In this study, we aim to test the role of memory retrieval in promoting such generalisation of memories. Retrieval is known to be a powerful memory enhancer. Both cognitive and neurobiological theories of retrieval-mediated learning propose that this benefit is due to the co-activation of related (semantic) information during retrieval, which strengthens this co-activated associative network. By doing so, retrieval might play an important role in the generalisation of the memory trace.Here, we used univariate and pattern fMRI analyses to investigate whether memory representations that undergo retrieval (vs. restudy) become generalised over time. Participants encoded scene-object pairs and either retrieved or restudied the objects over two sessions, two days apart. We analysed univariate and multivariate changes in brain activity specific to retrieval but not restudy, and tested whether predicted changes occur rapidly within a session, or evolve slowly, across the two days.Consistent with a role of retrieval in the semanticisation of memories, univariate analyses showed an increase in medial prefrontal cortex (mPFC) activation across consecutive retrieval attempts, and a multivariate increase in similarity between categorically related information. In addition to this semanticisation, we also observed that retrieval strengthened the patterns unique to the original study episodes. Semantic-categorical and episode-unique strengthening both evolved slowly, across two days, and were most pronounced in parietal areas. Our findings corroborate the hypothesis that retrieval supports the creation of a generalised memory trace, and show that this strengthening does not come at the expense of episode-unique information. Active remembering thus seems to promote a stable and adaptive memory that can be flexibly used to access both contextually specific and more abstract generalised information.
       
  • Variational representational similarity analysis
    • Abstract: Publication date: Available online 28 June 2019Source: NeuroImageAuthor(s): Karl J. Friston, Jörn Diedrichsen, Emma Holmes, Peter Zeidman This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis.
       
  • Neurovascular decoupling in type 2 diabetes mellitus without mild
           cognitive impairment: Potential biomarker for early cognitive impairment
    • Abstract: Publication date: Available online 25 June 2019Source: NeuroImageAuthor(s): Ying Yu, Lin-Feng Yan, Qian Sun, Bo Hu, Jin Zhang, Yang Yang, Yu-Jie Dai, Wu-Xun Cui, Si-Jie Xiu, Yu-Chuan Hu, Chun-Ni Heng, Qing-Quan Liu, Jun-Feng Hou, Yu-Yun Pan, Liang-Hao Zhai, Teng-Hui Han, Guang-Bin Cui, Wen Wang Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.Graphical abstractImage 1
       
  • Structural covariance networks in children and their associations with
           maternal behaviors
    • Abstract: Publication date: Available online 20 June 2019Source: NeuroImageAuthor(s): Sally Richmond, Richard Beare, Katherine A. Johnson, Nicholas B. Allen, Marc L. Seal, Sarah Whittle There is a substantial body of research documenting the influence of early adverse experience on brain development. In contrast, relatively little attention has been directed toward the influence of ‘normative’ variation in parenting behaviors. This study investigated associations between parenting behaviors and structural brain networks, as measured by structural covariance, in a community sample of children. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed parent-child interaction tasks. Structural MRI scans were also obtained from the children. Structural covariance networks based on partial correlation between cortical thickness estimates were constructed, and estimates of efficiency were obtained using graph theoretical analysis. Associations between affective and communicative maternal behaviors and these network metrics were investigated. High levels of observed negative affective and communicative maternal behaviors were associated with decreased local efficiency, whereas high levels of positive affective maternal behaviors were associated with increased local efficiency. The regions implicated (including the cingulate cortex, temporal pole, and temporo-parietal junction) are thought to be involved in the processing of social information. Minimal support was found for an association between global efficiency and maternal behaviors. Our findings suggest that variations in parenting behaviors are associated with structural organization of socio-emotional brain networks in children.
       
  • Combining white matter diffusion and geometry for tract-specific alignment
           and variability analysis
    • Abstract: Publication date: Available online 13 May 2019Source: NeuroImageAuthor(s): Itay Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv We present a framework for along-tract analysis of white matter (WM) fiber bundles based on diffusion tensor imaging (DTI) and tractography. We introduce the novel concept of fiber-flux density for modeling fiber tracts’ geometry, and combine it with diffusion-based measures to define vector descriptors called Fiber-Flux Diffusion Density (FFDD). The proposed model captures informative features of WM tracts at both the microscopic (diffusion-related) and macroscopic (geometry-related) scales, thus enabling improved sensitivity to subtle structural abnormalities that are not reflected by either diffusion or geometrical properties alone. A key step in this framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tracts enable meaningful inter-subject comparisons and group-wise statistical analysis. Moreover, we show that the FMM alignment can be generalized in a straight forward manner to a single-shot co-alignment of multiple fiber bundles. The proposed alignment technique is shown to outperform a well-established, commonly used DTI registration algorithm. We demonstrate the FFDD framework on the Human Connectome Project (HCP) diffusion MRI dataset, as well as on two different datasets of contact sports players. We test our method using longitudinal scans of a basketball player diagnosed with a traumatic brain injury, showing compatibility with structural MRI findings. We further perform a group study comparing mid- and post-season scans of 13 active football players exposed to repetitive head trauma, to 17 non-player control (NPC) subjects. Results reveal statistically significant FFDD differences (p-values
       
  • Cardiac afferent activity modulates early neural signature of error
           detection during skilled performance
    • Abstract: Publication date: Available online 30 April 2019Source: NeuroImageAuthor(s): Gabriela Bury, Marta García-Huéscar, Joydeep Bhattacharya, María Herrojo Ruiz Behavioral adaptations during performance rely on predicting and evaluating the consequences of our actions through action monitoring. Previous studies revealed that proprioceptive and exteroceptive signals contribute to error-monitoring processes, which are implemented in the posterior medial frontal cortex. Interestingly, errors also trigger changes in autonomic nervous system activity such as pupil dilation or heartbeat deceleration. Yet, the contribution of implicit interoceptive signals of bodily states to error-monitoring during ongoing performance has been overlooked. This study investigated whether cardiovascular interoceptive signals influence the neural correlates of error processing during performance, with an emphasis on the early stages of error processing. We recorded musicians’ electroencephalography and electrocardiogram signals during the performance of highly-trained music pieces. Previous event-related potential (ERP) studies revealed that pitch errors during skilled musical performance are preceded by an error detection signal, the pre-error-negativity (preERN), and followed by a later error positivity (PE). In this study, by combining ERP, source localization and multivariate pattern classification analysis, we found that the error-minus-correct ERP waveform had an enhanced amplitude within 40–100 ms following errors in the systolic period of the cardiac cycle. This component could be decoded from single-trials, was dissociated from the preERN and PE, and stemmed from the inferior parietal cortex, which is a region implicated in cardiac autonomic regulation. In addition, the phase of the cardiac cycle influenced behavioral alterations resulting from errors, with a smaller post-error slowing and less perturbed velocity in keystrokes following pitch errors in the systole relative to the diastole phase of the cardiac cycle. Lastly, changes in the heart rate anticipated the upcoming occurrence of errors. This study provides the first evidence of preconscious visceral information modulating neural and behavioral responses related to early error monitoring during skilled performance.
       
  • Editorial overview: The 25th Anniversary of the Human Brain Mapping
           Meeting
    • Abstract: Publication date: Available online 5 April 2019Source: NeuroImageAuthor(s): Aina Puce, Bernard Mazoyer
       
  • Increased segregation of functional networks in developing brains
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Wei He, Paul F. Sowman, Jon Brock, Andrew C. Etchell, Cornelis J. Stam, Arjan Hillebrand A growing literature conceptualises typical brain development from a network perspective. However, largely due to technical and methodological challenges inherent in paediatric functional neuroimaging, there remains an important gap in our knowledge regarding the typical development of functional brain networks in “preschool” childhood (i.e., children younger than 6 years of age). In this study, we recorded brain oscillatory activity using age-appropriate magnetoencephalography in 24 children, including 14 preschool children aged from 4 to 6 years and 10 school children aged from 7 to 12 years. We compared the topology of the resting-state brain networks in these children, estimated using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series, with that of 24 adults. Our results show that during childhood the MST topology shifts from a star-like (centralised) toward a more line-like (de-centralised) configuration, indicating the functional brain networks become increasingly segregated. In addition, the increasing global network segregation is frequency-independent and accompanied by decreases in centrality (or connectedness) of cortical regions with age, especially in areas of the default mode network. We propose a heuristic MST model of “network space”, which posits a clear developmental trajectory for the emergence of complex brain networks. Our results not only revealed topological reorganisation of functional networks across multiple temporal and spatial scales in childhood, but also fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years of childhood.
       
  • Identifying predictors of within-person variance in MRI-based brain volume
           estimates
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Julian D. Karch, Elisa Filevich, Elisabeth Wenger, Nina Lisofsky, Maxi Becker, Oisin Butler, Johan Mårtensson, Ulman Lindenberger, Andreas M. Brandmaier, Simone Kühn Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-person variance. To this end, we applied both classical statistical methods and machine-learning-inspired approaches to structural magnetic resonance imaging (sMRI) data of six participants aged 24–31 years gathered at 40–50 occasions distributed over 6–8 months from the Day2day study. We explored the within-person associations between 21 variables covering physiological, affective, social, and environmental factors and global measures of brain volume estimated by VBM8 and FreeSurfer. Time since the first scan was reliably associated with Freesurfer estimates of grey matter volume and total cortex volume, in line with a rate of annual brain volume shrinkage of about 1 percent. For the same two structural measures, time of day also emerged as a reliable predictor with an estimated diurnal volume decrease of, again, about 1 percent. Furthermore, we found weak predictive evidence for the number of steps taken on the previous day and testosterone levels. The results suggest a need to control for time-of-day effects in sMRI research. In particular, we recommend that researchers interested in assessing longitudinal change in the context of intervention studies or longitudinal panels make sure that, at each measurement occasion, (a) a given participant is measured at the same time of day; (b) all participants are measured at about the same time of day. Furthermore, the potential effects of physical activity, including moderate amounts of aerobic exercise, and testosterone levels on MRI-based measures of brain structure deserve further investigation.
       
  • Corresponding anatomical and coactivation architecture of the human
           precuneus showing similar connectivity patterns with macaques
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Jiaojian Wang, Benjamin Becker, Lijie Wang, Hai Li, Xudong Zhao, Tianzi Jiang The precuneus (PCun) is one of the most expanded areas of the association cortex and plays an important role in integrating information from different modalities. However, whether the functional architecture of PCun is shared by humans and macaques is an open question. We used both anatomical connectivity and task-dependent coactivation patterns to parcellate the human PCun and consistently identified three subregions in the human PCun using two independent datasets. Two subregions were located in the dorsal PCun and one subregion was located in the ventral PCun. This parcellation scheme for the PCun was supported by identifying the subregion-specific networks and by functional characterization. Then, the absolute and relative gray matter volume of precuneus in human and macaque was calculated and significantly smaller absolute and relative gray matter volume in macaque was identified. Next, three macaque PCun subregions were defined based on our tractographic atlas. Finally, the whole brain anatomical connectivity patterns and connectivity fingerprints with 17 predefined homologous target brain areas were mapped for each PCun subregion and revealed that the PCun shares similar anatomical connectivity patterns in humans and macaques. The similar anatomical connectivity patterns of PCun were validated by an independent in-house dataset. Our findings demonstrated that anatomical connectivity patterns can reflect the functional architecture of the PCun in humans and that the functional architecture of the PCun is similar in humans and macaques.
       
  • The regional homogeneity patterns of the dorsal medial prefrontal cortex
           predict individual differences in decision impulsivity
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Chenyu Lv, Qiang Wang, Chuansheng Chen, Jiang Qiu, Gui Xue, Qinghua He Intertemporal choice refers to the process of making decisions by weighing short- and long-term benefits and costs. On average people prefer immediate rewards over delayed rewards with larger amounts, which is a form of decision impulsivity. Based on previous research showing the importance of the dorsal medial prefrontal cortex (DMPFC) in decision impulsivity, the present study examined whether regional homogeneity (ReHo) patterns in DMPFC were associated with individual differences in intertemporal choices. Two cohorts of college students (N = 239 and N = 227, respectively) were recruited and resting-state data were collected. Results from both univariate and multivariate pattern analyses of the two cohorts consistently showed that ReHo patterns in the DMPFC were associated with the delay discounting rate (i.e., log k). These results further support the important role of DMPFC in intertemporal choice and have potential practical implications for decision making in our daily life and at the level of national policies as well as for the treatment of clinical populations with decision impulsivity (e.g., gamblers, individuals with substance use disorders).
       
  • Brain controllability: Not a slam dunk yet
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Samir Suweis, Chengyi Tu, Rodrigo P. Rocha, Sandro Zampieri, Marzo Zorzi, Maurizio Corbetta In our recent article [1] published in this journal we provide quantitative evidence to show that there are warnings and caveats in the way Gu and collaborators [2] define controllability of brain networks and measure the contribution of each of its nodes.The comment by Pasqualetti et al. [3] confirms the need to go beyond the methodology and approach presented in Gu et al.’s original work. In fact, they recognize that “the source of confusion is due to the fact that assessing controllability via numerical analysis typically leads to ill-conditioned problems, and thus often generates results that are difficult to interpret”. This is indeed the first warning we discussed in [1]: our work was not meant to prove that brain networks are not controllable from one node, rather we wished to highlight that the one node controllability framework and all consequent results were not properly justified based on the methodology presented in Gu et al. [2]. We used in our work the same method of Gu et al. not because we believe it is the best methodology, but because we extensively investigated it with the aim of replicating, testing, and extending their results. The warning and caveats we have proposed are the results of this investigation.Indeed, on the basis of our controllability analyses of multiple human brain networks datasets, we concluded: “The λmin(WK) are statistically compatible with zero and thus the associated controllability Gramian cannot be inverted1. These results show that it is not possible to infer one node controllability of the brain numerically”. Hence both groups agree that one node controllability cannot be inferred numerically.
       
  • Superior emotional regulating effects of creative cognitive reappraisal
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Xiaofei Wu, Tingting Guo, Tengteng Tan, Wencai Zhang, Shaozheng Qin, Jin Fan, Jing Luo Although the effects of cognitive reappraisal in regulating negative emotion are generally well documented, its regulatory effects are usually not very strong because the ordinary reappraisals employed in previous studies were insufficient to overcome the mental set or response bias toward negative situations. In this study, we developed a new strategy employing creative reappraisals that provides an insightful reinterpretation of the negative stimulus. We believe this approach, through adopting a guided (creative) reappraisal rather than self-generation strategy, will greatly improve the emotion regulation effect of reappraisal through activating the neural networks representing the process of deep and structural mental representational change accompanied by the feeling of positive emotion and mental reward. The behavioral results suggested that 1) regarding the transient regulatory effect, creative reappraisal resulted in a positive rating for standardized negative pictures; 2) creative reappraisal had a long-lasting effect in reducing negative affect. In parallel with these behavioral results, the imaging data indicated that 1) creative reappraisal was specifically associated with greater engagement of the amygdala and hippocampus as well as regions in the ventral striatum, and 2) the engagement of the amygdala predicted the transient regulatory effect of creative reappraisal, while the involvement of the hippocampus and the ventral striatum predicted long-term regulatory effects. These findings suggest that the superior regulatory effect of creative reappraisal could be mediated by amygdala-based salient emotional arousal, hippocampus-based new association formation, and striatum-based mental rewarding to lead to a novel and positive experience that could be kept in long-term memory. This research indicates the key role of creative insight in reappraisal and presents a novel and highly efficient reappraisal strategy.
       
  • Estimation of brain age delta from brain imaging
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Stephen M. Smith, Diego Vidaurre, Fidel Alfaro-Almagro, Thomas E. Nichols, Karla L. Miller It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.
       
  • Functional imaging of rostrocaudal spinal activity during upper limb motor
           tasks
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Nawal Kinany, Elvira Pirondini, Roberto Martuzzi, Loan Mattera, Silvestro Micera, Dimitri Van de Ville The spinal cord is the main interface between the brain and the periphery. It notably plays a central role in motor control, as spinal motoneurons activate skeletal muscles involved in voluntary movements. Yet, the spinal mechanisms underlying human movement generation have not been completely elucidated. In this regard, functional magnetic resonance imaging (fMRI) represents a potential tool to probe spinal cord function non-invasively and with high spatial resolution. Nonetheless, a thorough characterization of this approach is still lacking, currently limiting its impact. Here, we aimed at systematically quantifying to which extent fMRI can reveal spinal cord activity along the rostrocaudal direction. We investigated changes in the blood oxygenation level dependent signal of the human cervical spinal cord during bimanual upper limb movements (wrist extension, wrist adduction and finger abduction) in nineteen healthy volunteers. Prior to scanning, we recorded the muscle activity associated with these movements in order to reconstruct the theoretical motor-pool output pattern using an anatomy-based mapping of the electromyographic (EMG) waveforms. EMG-derived spinal maps were characterized by distinct rostrocaudal patterns of activation, thus confirming the task-specific features of the different movements. Analogous activation patterns were captured using spinal cord fMRI. Finally, an additional fMRI dataset was acquired from a subset of the participants (n = 6) to deploy a multivoxel pattern analysis, which allowed successful decoding of movements. These combined results suggest that spinal cord fMRI can be used to image rostrocaudal activation patterns reflecting the underlying activity of the motoneuron pools innervating the task-related muscles. Spinal cord fMRI offers the prospect of a novel tool to study motor processes and potentially their modification following neurological motor disorders.Graphical abstractImage 1
       
  • QUEST MRI assessment of fetal brain oxidative stress in utero
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Bruce A. Berkowitz, Roberto Romero, Robert H. Podolsky, Karen M. Lins-Childers, Yimin Shen, Tilman Rosales, Youssef Zaim Wadghiri, D. Minh Hoang, Marcia Arenas-Hernandez, Valeria Garcia-Flores, George Schwenkel, Bogdan Panaitescu, Nardhy Gomez-Lopez PurposeTo achieve sufficient precision of R1 (=1/T1) maps of the fetal brain in utero to perform QUEnch-assiSTed (QUEST) MRI in which a significant anti-oxidant-induced reduction in R1 indicates oxidative stress.MethodsC57BL/6 mouse fetuses in utero were gently and non-surgically isolated and secured using a homemade 3D printed clip. Using a commercial receive-only surface coil, brain maps of R1, an index sensitive to excessive and continuous free radical production, were collected using either a conventional Cartesian or a non-Cartesian (periodically rotated overlapping parallel lines with enhanced reconstruction) progressive saturation sequence. Data were normalized to the shortest TR time to remove bias. To assess oxidative stress, brain R1 maps were acquired on the lipopolysaccharide (LPS) model of preterm birth ± rosiglitazone (ROSI, which has anti-oxidant properties); phosphate buffered saline (PBS) controls ± ROSI were similarly studied.ResultsSufficient quality R1 maps were generated by a combination of the 3D printed clip, surface coil detection, non-Cartesian sequence, and normalization scheme ensuring minimal fetal movement, good detection sensitivity, reduced motion artifacts, and minimal baseline variations, respectively. In the LPS group, the combined caudate-putamen and thalamus region R1 was reduced (p 
       
  • Recommendations for motion correction of infant fNIRS data applicable to
           multiple data sets and acquisition systems
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Renata Di Lorenzo, Laura Pirazzoli, Anna Blasi, Chiara Bulgarelli, Yoko Hakuno, Yasuyo Minagawa, Sabrina Brigadoi Despite motion artifacts are a major source of noise in fNIRS infant data, how to approach motion correction in this population has only recently started to be investigated. Homer2 offers a wide range of motion correction methods and previous work on simulated and adult data suggested the use of Spline interpolation and Wavelet filtering as optimal methods for the recovery of trials affected by motion. However, motion artifacts in infant data differ from those in adults’ both in amplitude and frequency of occurrence. Therefore, artifact correction recommendations derived from adult data might not be optimal for infant data. We hypothesized that the combined use of Spline and Wavelet would outperform their individual use on data with complex profiles of motion artifacts. To demonstrate this, we first compared, on infant semi-simulated data, the performance of several motion correction techniques on their own and of the novel combined approach; then, we investigated the performance of Spline and Wavelet alone and in combination on real cognitive data from three datasets collected with infants of different ages (5, 7 and 10 months), with different tasks (auditory, visual and tactile) and with different NIRS systems. To quantitatively estimate and compare the efficacy of these techniques, we adopted four metrics: hemodynamic response recovery error, within-subject standard deviation, between-subjects standard deviation and number of trials that survived each correction method. Our results demonstrated that (i) it is always better correcting for motion artifacts than rejecting the corrupted trials; (ii) Wavelet filtering on its own and in combination with Spline interpolation seems to be the most effective approach in reducing the between- and the within-subject standard deviations. Importantly, the combination of Spline and Wavelet was the approach providing the best performance in semi-simulation both at low and high levels of noise, also recovering most of the trials affected by motion artifacts across all datasets, a crucial result when working with infant data.
       
  • Predicting domain-specific actions in expert table tennis players
           activates the semantic brain network
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Yingying Wang, Yingzhi Lu, Yuqin Deng, Nan Gu, Parviainen Tiina, Chenglin Zhou Motor expertise acquired during long-term training in sports enables top athletes to predict the outcomes of domain-specific actions better than nonexperts do. However, whether expert players encode actions, in addition to the concrete sensorimotor level, also at a more abstract, conceptual level, remains unclear. The present study manipulated the congruence between body kinematics and the subsequent ball trajectory in videos of an expert player performing table tennis serves. By using functional magnetic resonance imaging, the brain activity was evaluated in expert and nonexpert table tennis players during their predictions on the fate of the ball trajectory in congruent versus incongruent videos. Compared with novices, expert players showed greater activation in the sensorimotor areas (right precentral and postcentral gyri) in the comparison between incongruent vs. congruent videos. They also showed greater activation in areas related to semantic processing: the posterior inferior parietal lobe (angular gyrus), middle temporal gyrus, and ventromedial prefrontal cortex. These findings indicate that action anticipation in expert table tennis players engages both semantic and sensorimotor regions and suggests that skilled action observation in sports utilizes predictions both at motor-kinematic and conceptual levels.
       
  • Shared and connection-specific intrinsic interactions in the default mode
           network
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Jessica Samogin, Quanying Liu, Marco Marino, Nicole Wenderoth, Dante MantiniabstractElectrophysiological studies revealed that different neuronal oscillations, among which the alpha (8-13 Hz) rhythm in particular, but also the beta (13-30 Hz) and gamma (30-80 Hz) rhythms, are modulated during rest in the default mode network (DMN). Little is known, however, about the role of these rhythms in supporting DMN connectivity. Biophysical studies suggest that lower and higher frequencies mediate long- and short-range connectivity, respectively. Accordingly, we hypothesized that interactions between all DMN areas are supported by the alpha rhythm, and that the connectivity between specific DMN areas is established through other frequencies, mainly in the beta and/or gamma bands. To test this hypothesis, we used high-density electroencefalographic data collected in 19 healthy volunteers at rest. We analyzed frequency-dependent functional interactions between four main DMN nodes in a broad (1-80 Hz) frequency range. In line with our hypothesis, we found that the frequency-dependent connectivity profile between pairs of DMN nodes had a peak at 9-11 Hz. Also, the connectivity profile showed other peaks at higher frequencies, which depended on the specific connection. Overall, our findings suggest that frequency-dependent connectivity analysis may be a powerful tool to better understand how different neuronal oscillations support connectivity within and between brain networks.
       
  • Automagic: Standardized preprocessing of big EEG data
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Andreas Pedroni, Amirreza Bahreini, Nicolas Langer Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.
       
  • Modulation of the spontaneous hemodynamic response function across levels
           of consciousness
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Guo-Rong Wu, Carol Di Perri, Vanessa Charland-Verville, Charlotte Martial, Manon Carrière, Audrey Vanhaudenhuyse, Steven Laureys, Daniele Marinazzo Functional imaging research has already contributed with several results to the study of neural correlates of consciousness. Apart from task-related activation derived in fMRI, PET based glucose metabolism rate or cerebral blood flow account for a considerable proportion of the study of brain activity under different levels of consciousness. Resting state functional connectivity MRI is playing a crucial role to explore the consciousness related functional integration, successfully complementing PET, another widely used neuroimaging technique. Here, spontaneous hemodynamic response is introduced to characterize resting state brain activity giving information on the local metabolism (neurovascular coupling), and useful to improve the time-resolved activity and connectivity measures based on BOLD fMRI. This voxel-wise measure is then used to investigate the loss of consciousness under Propofol anesthesia and unresponsive wakefulness syndrome. Changes in the hemodynamic response in precuneus and posterior cingulate are found to be a common principle underlying loss of consciousness in both conditions. The thalamus appears to be less obviously modulated by Propofol, compared with frontoparietal regions. However, a significant increase in spontaneous thalamic hemodynamic response was found in patients in unresponsive wakefulness syndrome compared with healthy controls. Our results ultimately show that anesthesia- or pathology-induced neurovascular coupling could be tracked by modulated spontaneous hemodynamic response derived from resting state fMRI.
       
  • Topology highlights mesoscopic functional equivalence between imagery and
           perception: The case of hypnotizability
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Esther Ibáñez-Marcelo, Lisa Campioni, Angkoon Phinyomark, Giovanni Petri, Enrica L. Santarcangelo The functional equivalence (FE) between imagery and perception or motion has been proposed on the basis of neuroimaging evidence of large spatially overlapping activations between real and imagined sensori-motor conditions. However, similar local activation patterns do not imply the same mesoscopic integration of brain regions, which can be described by tools from Topological Data Analysis (TDA). On the basis of behavioral findings, stronger FE has been hypothesized in the individuals with high scores of hypnotizability scores (highs) with respect to low hypnotizable participants (lows) who differ between each other in the proneness to modify memory, perception and behavior according to specific imaginative suggestions. Here we present the first EEG evidence of stronger FE in highs. In fact, persistent homology shows that the highs EEG topological asset during real and imagined sensory conditions is significantly more similar than the lows. As a corollary finding, persistent homology shows lower restructuring of the EEG asset in highs than in lows during both sensory and imagery tasks with respect to basal conditions. Present findings support the view that greater embodiment of mental images may be responsible for the highs greater proneness to respond to sensori-motor suggestions and to report involuntariness in action. In addition, findings indicate hypnotizability-related sensory and cognitive information processing and suggest that the psycho-physiological trait of hypnotizability may modulate more than one aspect of the everyday life.
       
  • Finding the P3 in the P600: Decoding shared neural mechanisms of responses
           to syntactic violations and oddball targets
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Jona Sassenhagen, Christian J. Fiebach The P600 Event-Related Brain Potential, elicited by syntactic violations in sentences, is generally interpreted as indicating language-specific structural/combinatorial processing, with far-reaching implications for models of language. P600 effects are also often taken as evidence for language-like grammars in non-linguistic domains like music or arithmetic. An alternative account, however, interprets the P600 as a P3, a domain-general brain response to salience. Using time-generalized multivariate pattern analysis, we demonstrate that P3 EEG patterns, elicited in a visual Oddball experiment, account for the P600 effect elicited in a syntactic violation experiment: P3 pattern-trained MVPA can classify P600 trials just as well as P600-trained ones. A second study replicates and generalizes this finding, and demonstrates its specificity by comparing it to face- and semantic mismatch-associated EEG responses. These results indicate that P3 and P600 share neural patterns to a substantial degree, calling into question the interpretation of P600 as a language-specific brain response and instead strengthening its association with the P3. More generally, our data indicate that observing P600-like brain responses provides no direct evidence for the presence of language-like grammars, in language or elsewhere.
       
  • The anterior insula channels prefrontal expectancy signals during
           affective processing
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Vanessa Teckentrup, Johan N. van der Meer, Viola Borchardt, Yan Fan, Monja P. Neuser, Claus Tempelmann, Luisa Herrmann, Martin Walter, Nils B. Kroemer Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N1 = 37 & N2 = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.
       
  • Differential coupling between subcortical calcium and BOLD signals during
           evoked and resting state through simultaneous calcium fiber photometry and
           fMRI
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Chuanjun Tong, Jian-kun Dai, Yuyan Chen, Kaiwei Zhang, Yanqiu Feng, Zhifeng Liang Task based and resting state fMRI has been widely utilized to study brain functions. As the foundation of fMRI, the underlying neural basis of the BOLD signal has been extensively studied, but the detailed mechanism remains elusive, particularly during the resting state. To examine the neurovascular coupling, it is important to simultaneously record neural and vascular signals. Here we developed a novel setup of camera based, scalable simultaneous calcium fiber photometry and fMRI in rats. Using this setup, we recorded calcium signals of superior colliculus (SC) and lateral geniculate nucleus (LGN) and fMRI simultaneously during visual stimulation and the resting state. Our results revealed robust, region-specific coupling between calcium and BOLD signals in the task state and weaker, whole brain correlation in the resting state. Interestingly, the spatial specificity of such correlation in the resting state was improved upon regression of white matter, ventricle signals and global signals in fMRI data. Overall, our results suggest differential coupling of calcium and BOLD signals for subcortical regions between evoked and resting states, and the coupling relationship in the resting state was related with resting state BOLD preprocessing strategies.
       
  • The role of ventromedial prefrontal cortex and temporo-parietal junction
           in third-party punishment behavior
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Emanuele Lo Gerfo, Alessia Gallucci, Rosalba Morese, Alessandra Vergallito, Stefania Ottone, Ferruccio Ponzano, Gaia Locatelli, Francesca Bosco, Leonor Josefina Romero Lauro Third parties punish, sacrificing personal interests, offenders who violate either fairness or cooperation norms. This behavior is defined altruistic punishment and the degree of punishment typically increases with the severity of the norm violation. An opposite and apparently paradoxical behavior, namely anti-social punishment, is the tendency to spend own money to punish cooperative or fair behaviors. Previous fMRI studies correlated punishment behavior with increased activation of brain areas belonging to the reward system (e.g. the ventromedial prefrontal cortex, VMPFC), the mentalizing (e.g. the temporoparietal junction, TPJ) and central-executive networks. In the present study, we aimed at investigating the causal role of VMPFC and TPJ in punishment behaviors through the application of anodal transcranial direct current stimulation (tDCS).Sixty healthy participants were randomly assigned to three tDCS conditions: (1) anodal tDCS over VMPFC, (2) anodal tDCS over right TPJ (rTPJ), (3) sham stimulation. At the end of the stimulation, participants played a third-party punishment game, consisting in viewing a series of fair or unfair monetary allocations between unknown proposers and recipients. Participants were asked whether and how much they would punish the proposers using their own monetary endowment. To test membership effects, proposers and recipients could be either Italian or Chinese.Anodal tDCS over VMPFC increased altruistic punishment behavior whereas anodal tDCS over rTPJ increased anti-social punishment choices compared with sham condition, while membership did not influence participant's choices. Our results support the idea that the two types of punishment behaviors rely upon different brain regions, suggesting that reward and mentalizing systems underlie, respectively, altruistic and anti-social punishment behaviors.
       
  • Complex diffusion-weighted image estimation via matrix recovery under
           general noise models
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Lucilio Cordero-Grande, Daan Christiaens, Jana Hutter, Anthony N. Price, Jo V. Hajnal We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
       
  • Seeing versus knowing: The temporal dynamics of real and implied colour
           processing in the human brain
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Lina Teichmann, Tijl Grootswagers, Thomas A. Carlson, Anina N. Rich Colour is a defining feature of many objects, playing a crucial role in our ability to rapidly recognise things in the world around us and make categorical distinctions. For example, colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries. That means our representation of many objects includes key colour-related information. The question addressed here is whether the neural representation activated by knowing that something is red is the same as that activated when we actually see something red, particularly in regard to timing. We addressed this question using neural timeseries (magnetoencephalography, MEG) data to contrast real colour perception and implied object colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain activation patterns evoked by colour accessed via real colour perception and implied colour activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of these processes. Male and female human participants (N = 18) viewed isoluminant red and green shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked by real colour perception is similar to implied colour activation, but that this pattern is instantiated at a later time. These results suggest that a common colour representation can be triggered by activating object representations from memory and perceiving colours.
       
  • Vigilance declines following sleep deprivation are associated with two
           previously identified dynamic connectivity states
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): James Teng, Ju Lynn Ong, Amiya Patanaik, Jesisca Tandi, Juan Helen Zhou, Michael W.L. Chee, Julian Lim Robustly linking dynamic functional connectivity (DFC) states to behaviour is important for establishing the utility of the method as a functional measurement. We previously used a sliding window approach to identify two dynamic connectivity states (DCS) related to vigilance. A new sample of 32 healthy participants underwent two sets of task-free functional magnetic resonance imaging (fMRI) scans, once in a well-rested state and once after a single night of total sleep deprivation. Using a temporal difference method, DFC and clustering analysis on the task-free fMRI data revealed five centroids that were highly correlated with those found in previous work. In particular, two of these states were associated with high and low arousal respectively. Individual differences in vulnerability to sleep deprivation were measured by assessing state-related changes in Psychomotor Vigilance Test (PVT) performance. Changes in the duration spent in each of the arousal states from the well-rested to the sleep-deprived condition correlated with declines in PVT performance. The reproducibility of DFC measures and their association with vigilance highlight their utility in serving as a neuroimaging method with behavioural relevance. (178 words).
       
  • Brain default-mode network dysfunction in addiction
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Rui Zhang, Nora D. Volkow Aberrant patterns of brain functional connectivity in the default mode network (DMN) have been observed across different classes of substance use disorder (SUD) and are associated with craving and relapse. In addicted individuals resting functional connectivity (RSFC) of the anterior DMN, which participates in attribution of personal value and emotional regulation, tends to be decreased, whereas RSFC of the posterior DMN, which directs attention to the internal world, tends to be increased. Aberrant RSFC within the DMN is believed to contribute to impaired self-awareness, negative emotions and to ruminations in addiction. Additionally, the disrupted connectivity between DMN and cortical regions involved with executive function, memory and emotion could be critical to drug-taking regardless of negative consequences and to stress-triggered relapse. At the system level, the dynamics of DMN interactions with the executive control and the salience networks are also disturbed in addiction. The DMN is prominently engaged during the withdrawal and preoccupation phases of the addiction cycle at the expense of the executive control network and with an enhanced participation of the salience network. In contrast, DMN prominence appears to be transitorily decreased during the intoxication phases. There is also growing evidence that disruption of the DMN in addiction reflects in part changes in dopaminergic, glutamatergic, and GABAergic signaling associated with acute and chronic drug use. Findings are starting to reveal DMN RSFC as a potential biomarker for predicting clinical outcomes in SUD and identify the DMN as a promising target for the treatment of addiction.
       
  • Latency analysis of resting-state BOLD-fMRI reveals traveling waves in
           visual cortex linking task-positive and task-negative networks
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): R. Hindriks, Mantini R, Gravel N, Deco G Due to the low temporal resolution of BOLD-fMRI, imaging studies on human brain function have almost exclusively focused on instantaneous correlations within the data. Developments in hardware and acquisition protocols, however, are offering data with higher sampling rates that allow investigating the latency structure of BOLD-fMRI data. In this study we describe a method for analyzing the latency structure within BOLD-fMRI data and apply it to resting-state data of 94 participants from the Human Connectome Project. The method shows that task-positive and task-negative networks are integrated through traveling BOLD waves within early visual cortex. The waves are initiated at the periphery of the visual field and propagate towards the fovea. This observation suggests a mechanism for the functional integration of task-positive and task-negative networks, argues for an eccentricity-based view on visual information processing, and contributes to the emerging view that resting-state BOLD-fMRI fluctuations are superpositions of inherently spatiotemporal patterns.
       
  • Visualization of thalamic calcium influx with quantitative susceptibility
           mapping as a potential imaging biomarker for repeated mild traumatic brain
           injury
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Ferdinand Schweser, Jenni Kyyriäinen, Marilena Preda, Asla Pitkänen, Kathryn Toffolo, Austin Poulsen, Kaitlynn Donahue, Benett Levy, David Poulsen A key event in the pathophysiology of traumatic brain injury (TBI) is the influx of substantial amounts of Ca2+ into neurons, particularly in the thalamus. Detection of this calcium influx in vivo would provide a window into the biochemical mechanisms of TBI with potentially significant clinical implications. In the present work, our central hypothesis was that the Ca2+ influx could be imaged in vivo with the relatively recent MRI technique of quantitative susceptibility mapping (QSM). Wistar rats were divided into five groups: naive controls, sham-operated experimental controls, single mild TBI, repeated mild TBI, and single severe TBI. We employed the lateral fluid percussion injury (FPI) model, which replicates clinical TBI without skull fracture, performed 9.4 Tesla MRI with a 3D multi-echo gradient-echo sequence at weeks 1 and 4 post-injury, computed susceptibility maps using V-SHARP and the QUASAR-HEIDI technique, and performed histology. Sham, experimental controls animals, and injured animals did not demonstrate calcifications at 1 week after the injury. At week 4, calcifications were found in the ipsilateral thalamus of 25–50% of animals after a single TBI and 83% of animals after repeated mild TBI. The location and appearance of calcifications on stained sections was consistent with the appearance on the in vivo susceptibility maps (correlation of volumes: r = 0.7). Our findings suggest that persistent calcium deposits represent a primary pathology of repeated injury and that FPI-QSM has the potential to become a sensitive tool for studying pathophysiology related to mild TBI in vivo.Graphical abstractImage 1
       
  • Neurofeedback helps to reveal a relationship between context reinstatement
           and memory retrieval
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Megan T. deBettencourt, Nicholas B. Turk-Browne, Kenneth A. Norman Theories of mental context and memory posit that successful mental context reinstatement enables better retrieval of memories from the same context, at the expense of memories from other contexts. To test this hypothesis, we had participants study lists of words, interleaved with task-irrelevant images from one category (e.g., scenes). Following encoding, participants were cued to mentally reinstate the context associated with a particular list, by thinking about the images that had appeared between the words. We measured context reinstatement by applying multivariate pattern classifiers to fMRI, and related this to performance on a free recall test that followed immediately afterwards. To increase sensitivity, we used a closed-loop neurofeedback procedure, whereby higher classifier evidence for the cued category elicited increased visibility of the images from the studied context onscreen. Our goal was to create a positive feedback loop that amplified small fluctuations in mental context reinstatement, making it easier to experimentally detect a relationship between context reinstatement and recall. As predicted, we found that greater amounts of classifier evidence were associated with better recall of words from the reinstated context, and worse recall of words from a different context. In a second experiment, we assessed the role of neurofeedback in identifying this brain-behavior relationship by presenting context images again and manipulating whether their visibility depended on classifier evidence. When neurofeedback was removed, the relationship between classifier evidence and memory retrieval disappeared. Together, these findings demonstrate a clear effect of context reinstatement on memory recall and suggest that neurofeedback can be a useful tool for characterizing brain-behavior relationships.
       
  • ICA-based denoising for ASL perfusion imaging
    • Abstract: Publication date: Available online 2 July 2019Source: NeuroImageAuthor(s): D. Carone, G.W.J. Harston, J. Garrard, F. De Angeli, L. Griffanti, T.W. Okell, M.A. Chappell, J. Kennedy Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation.72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data.The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p 
       
  • Development of a transcallosal tractography template and its application
           to dementia
    • Abstract: Publication date: Available online 28 June 2019Source: NeuroImageAuthor(s): Derek B. Archer, Stephen A. Coombes, Nikolaus R. McFarland, Steven T. DeKosky, David E. Vaillancourt Understanding the architecture of transcallosal connections would allow for more specific assessments of neurodegeneration across many fields of neuroscience, neurology, and psychiatry. To map these connections, we conducted probabilistic tractography in 100 Human Connectome Project subjects in 32 cortical areas using novel post-processing algorithms to create a spatially precise Trancallosal Tract Template (TCATT). We found robust transcallosal tracts in all 32 regions, and a topographical analysis in the corpus callosum largely agreed with well-established subdivisions of the corpus callosum. We then obtained diffusion MRI data from a cohort of patients with Alzheimer's disease (AD) and another with progressive supranuclear palsy (PSP) and used a two-compartment model to calculate free-water corrected fractional anisotropy (FAT) and free-water (FW) within the TCATT. These metrics were used to determine between-group differences and to determine which subset of tracts was best associated with cognitive function (Montreal Cognitive Assessment (MoCA)). In AD, we found robust between-group differences in FW (31/32 TCATT tracts) in the absence of between-group differences in FAT. FW in the inferior temporal gyrus TCATT tract was most associated with MoCA scores in AD. In PSP, there were widespread differences in both FAT and FW, and MoCA was predicted by FAT in the inferior frontal pars triangularis, preSMA, and medial frontal gyrus TCATT tracts as well as FW in the inferior frontal pars opercularis TCATT tract. The TCATT improves spatial localization of corpus callosum measurements to enhance the evaluation of treatment effects, as well as the monitoring of brain microstructure in relation to cognitive dysfunction and disease progression. Here, we have shown its direct relevance in capturing between-group differences and associating it with the MoCA in AD and PSP.
       
  • The peripheral preview effect with faces: Combined EEG and eye-tracking
           suggests multiple stages of trans-saccadic predictive and non-predictive
           processing
    • Abstract: Publication date: Available online 28 June 2019Source: NeuroImageAuthor(s): Christoph Huber-Huber, Antimo Buonocore, Olaf Dimigen, Clayton Hickey, David Melcher The world appears stable despite saccadic eye-movements. One possible explanation for this phenomenon is that the visual system predicts upcoming input across saccadic eye-movements based on peripheral preview of the saccadic target. We tested this idea using concurrent electroencephalography (EEG) and eye-tracking. Participants made cued saccades to peripheral upright or inverted face stimuli that changed orientation (invalid preview) or maintained orientation (valid preview) while the saccade was completed. Experiment 1 demonstrated better discrimination performance and a reduced fixation-locked N170 component (fN170) with valid than with invalid preview, demonstrating integration of pre- and post-saccadic information. Moreover, the early fixation-related potentials (FRP) showed a preview face inversion effect suggesting that some pre-saccadic input was represented in the brain until around 170 ms post fixation-onset. Experiment 2 replicated Experiment 1 and manipulated the proportion of valid and invalid trials to test whether the preview effect reflects context-based prediction across trials. A whole-scalp Bayes factor analysis showed that this manipulation did not alter the fN170 preview effect but did influence the face inversion effect before the saccade. The pre-saccadic inversion effect declined earlier in the mostly invalid block than in the mostly valid block, which is consistent with the notion of pre-saccadic expectations. In addition, in both studies, we found strong evidence for an interaction between the pre-saccadic preview stimulus and the post-saccadic target as early as 50 ms (Experiment 2) or 90 ms (Experiment 1) into the new fixation. These findings suggest that visual stability may involve three temporal stages: prediction about the saccadic target, integration of pre-saccadic and post-saccadic information at around 50-90 ms post fixation onset, and post-saccadic facilitation of rapid categorization.
       
  • Resting state functional connectivity changes after MR-guided focused
           ultrasound mediated blood-brain barrier opening in patients with
           Alzheimer's disease
    • Abstract: Publication date: Available online 26 June 2019Source: NeuroImageAuthor(s): Ying Meng, Bradley J. MacIntosh, Zahra Shirzadi, Alex Kiss, Allison Bethune, Chris Heyn, Karim Mithani, Clement Hamani, Sandra E. Black, Kullervo Hynynen, Nir Lipsman MR-guided focused ultrasound (MRgFUS) can temporarily permeabilize the blood-brain barrier (BBB), noninvasively, to allow therapeutics access to the central nervous system. However, its secondary and potential neuromodulation effects are not well understood. We aimed to characterize the functional impact of MRgFUS BBB opening in human subjects, based the phase I trial in patients with Alzheimer's disease. We analyzed for changes in bilateral frontoparietal networks in resting state functional MRI from five subjects after BBB opening in the right prefrontal lobe. We found a transient functional connectivity decrease within only the ipsilateral frontoparietal network that was recovered by the next day. Additionally, baseline to month three comparisons did not reveal any significant differences from matched-controls from the Alzheimer's Disease Neuroimaging Initiative. MRgFUS may transiently affect neurologic function, but functional organization is restored at one day and remains unchanged at three months. This first in human data has implications for the development of MRgFUS as a drug delivery platform to pathologic brain tissue and potential use for non-invasive neuromodulation.
       
  • Visual imagery during real-time fMRI neurofeedback from occipital and
           superior parietal cortex
    • Abstract: Publication date: Available online 25 June 2019Source: NeuroImageAuthor(s): Patrik Andersson, Flavio Ragni, Angelika Lingnau Visual imagery has been suggested to recruit occipital cortex via feedback projections from fronto-parietal regions, suggesting that these feedback projections might be exploited to boost recruitment of occipital cortex by means of real-time neurofeedback. To test this prediction, we instructed a group of healthy participants to perform peripheral visual imagery while they received real-time auditory feedback based on the BOLD signal from either early visual cortex or the medial superior parietal lobe. We examined the amplitude and temporal aspects of the BOLD response in the two regions. Moreover, we compared the impact of self-rated mental focus and vividness of visual imagery on the BOLD responses in these two areas. We found that both early visual cortex and the medial superior parietal cortex are susceptible to auditory neurofeedback within a single feedback session per region. However, the signal in parietal cortex was sustained for a longer time compared to the signal in occipital cortex. Moreover, the BOLD signal in the medial superior parietal lobe was more affected by focus and vividness of the visual imagery than early visual cortex. Our results thus demonstrate that (a) participants can learn to self-regulate the BOLD signal in early visual and parietal cortex within a single session, (b) that different nodes in the visual imagery network respond differently to neurofeedback, and that (c) responses in parietal, but not in occipital cortex are susceptible to self-rated vividness of mental imagery. Together, these results suggest that medial superior parietal cortex might be a suitable candidate to provide real-time feedback to patients suffering from visual field defects.
       
  • Success, but not failure feedback guides learning during neurofeedback: An
           ERP study
    • Abstract: Publication date: 15 October 2019Source: NeuroImage, Volume 200Author(s): Ioanna Zioga, Rawan Hassan, Caroline Di Bernardi Luft Neurofeedback is a promising self-regulation technique used to modify specific targeted brain patterns. During neurofeedback, target brain activity is monitored in real time and fed back to the subject in a chosen format (e.g. visual stimulus). To date, we do not know how success and failure feedback are processed during neurofeedback learning. Here we analysed the event-related potentials (ERPs) in response to success and failure feedback during a single neurofeedback session in two experiments. Participants in experiment 1 (n = 127) took part in one of the three neurofeedback conditions: RLA: trained to increase alpha power on the right frontal in relation to the left; LRA: the reverse of the RLA; FPA: trained to increase alpha power on the mid-frontal in relation to the mid-parietal region. In experiment 2 (n = 45), participants took part in a similar session but one group received random feedback whereas the other received valid feedback to increase right frontal alpha power. We analysed the feedback related negativity (FRN), correct positivity (CP), and P3a and P3b in response to success and failure feedback. We observed stronger FRN and CP in response to success compared to failure feedback. Additionally, the P3a in response to success feedback was higher in epochs preceding subsequent good adjustments. Our findings indicate that people respond more strongly to success than failure feedback and that the P3a might mediate the encoding of the reinforced patterns in the brain.
       
  • Spectral signatures of serotonergic psychedelics and glutamatergic
           dissociatives
    • Abstract: Publication date: Available online 24 June 2019Source: NeuroImageAuthor(s): Carla Pallavicini, Martina G. Vilas, Mirta Villarreal, Federico Zamberlan, Suresh Muthukumaraswamy, David Nutt, Robin Carhart-Harris, Enzo Tagliazucchi Classic serotonergic psychedelics are remarkable for their capacity to induce reversible alterations in consciousness of the self and the surroundings, mediated by agonism at serotonin 5-HT2A receptors. The subjective effects elicited by dissociative drugs acting as N-methyl-D-aspartate (NMDA) antagonists (e.g. ketamine and phencyclidine) overlap in certain domains with those of serotonergic psychedelics, suggesting some potential similarities in the brain activity patterns induced by both classes of drugs, despite different pharmacological mechanisms of action. We investigated source-localized magnetoencephalography recordings to determine the frequency-specific changes in oscillatory activity and long-range functional coupling that are common to two serotonergic compounds (lysergic acid diethylamide [LSD] and psilocybin) and the NMDA-antagonist ketamine. Administration of the three drugs resulted in widespread and broadband spectral power reductions. We established their similarity by using different pairs of compounds to train and subsequently evaluate multivariate machine learning classifiers. After applying the same methodology to functional connectivity values, we observed a pattern of occipital, parietal and frontal decreases in the low alpha and theta bands that were specific to LSD and psilocybin, as well as decreases in the low beta band common to the three drugs. Our results represent a first effort in the direction of quantifying the similarity of large-scale brain activity patterns induced by drugs of different mechanism of action, confirming the link between changes in theta and alpha oscillations and 5-HT2A agonism, while also revealing the decoupling of activity in the beta band as an effect shared between NMDA antagonists and 5-HT2A agonists. We discuss how these frequency-specific convergences and divergences in the power and functional connectivity of brain oscillations might relate to the overlapping subjective effects of serotonergic psychedelics and glutamatergic dissociative compounds.
       
  • Do the posterior midline cortices belong to the electrophysiological
           default-mode network'
    • Abstract: Publication date: Available online 22 June 2019Source: NeuroImageAuthor(s): Martin Sjøgård, Xavier De Tiège, Alison Mary, Philippe Peigneux, Serge Goldman, Guy Nagels, Jeroen van Schependom, Andrew J. Quinn, Mark Woolrich, Vincent Wens The default-mode network (DMN) and its principal core hubs in the posterior midline cortices (PMC), i.e., the precuneus and the posterior cingulate cortex, play a critical role in the human brain structural and functional architecture. Because of their centrality, they are affected by a wide spectrum of brain disorders, e.g., Alzheimer's disease. Non-invasive electrophysiological techniques such as magnetoencephalography (MEG) are crucial to the investigation of the neurophysiology of the DMN and its alteration by brain disorders. However, MEG studies relying on band-limited power envelope correlation diverge in their ability to identify the PMC as a part of the DMN in healthy subjects at rest. Since these works were based on different MEG recording systems and different source reconstruction pipelines, we compared DMN functional connectivity estimated with two distinct MEG systems (Elekta, now MEGIN, and CTF) and two widely used reconstruction algorithms (Minimum Norm Estimation and linearly constrained minimum variance (LCMV) Beamformer). Our results identified the reconstruction method as the critical factor influencing PMC functional connectivity, which was significantly dampened by the LCMV Beamformer. On this basis, we recommend that future electrophysiological studies on the DMN should rely on Minimum Norm Estimation (or close variants) rather than on the classical LCMV Beamformer. Crucially, based on analytic knowledge about these two reconstruction algorithms, we demonstrated with simulations that this empirical observation could be explained by the existence of a spontaneous linear, approximately zero-lag synchronization structure between areas of the DMN or among multiple sources within the PMC. This finding highlights a novel property of the neural dynamics and functional architecture of a core human brain network at rest.
       
  • Prism adaptation enhances decoupling between the default mode network and
           the attentional networks
    • Abstract: Publication date: Available online 22 June 2019Source: NeuroImageAuthor(s): Meytal Wilf, Andrea Serino, Stephanie Clarke, Sonia Crottaz-Herbette Prism adaptation (PA) is a procedure used for studying visuomotor plasticity in healthy individuals, as well as for alleviating spatial neglect in patients. The adaptation is achieved by performing goal-directed movements while wearing prismatic lenses that induce a lateral displacement of visual information. This results in an initial movement error that is compensated by a recalibration of sensory-motor coordinates; consequently, a lateral bias in both motor and perceptual measurements occurs after prism removal, i.e., after effects. Neuroimaging studies have shown that a brief exposure to rightward prism changes the activations in the inferior parietal lobule (IPL) and modulates interhemispheric balance during attention tasks. However, it is yet unknown how PA changes global interplay between cortical networks as evident from task-free resting state connectivity. Thus we compared resting state functional connectivity patterns before (‘Pre’) and after (‘Post’) participants performed session of pointing movements with rightward-shifting prism (N = 14) or with neutral lenses (as a control condition; N = 12). Global connectivity analysis revealed significant decreases in functional connectivity following PA in two nodes of the Default Mode Network (DMN), and the left anterior insula. Further analyses of these regions showed specific connectivity decrease between either of the DMN nodes and areas within the attentional networks, including the inferior frontal gyrus, the anterior insula and the right superior temporal sulcus. On the other hand, the anterior insula decreased its connectivity to a large set of areas, all within the boundaries of the DMN. These results demonstrate that a brief exposure to PA enhances the decoupling between the DMN and the attention networks. The change in interplay between those pre-existing networks might be the basis of the rapid and wide-ranged behavioural changes induce by PA in healthy individuals.Graphical abstractImage 1
       
  • Neural patterns reveal single-trial information on absolute pitch and
           relative pitch perception
    • Abstract: Publication date: Available online 22 June 2019Source: NeuroImageAuthor(s): Simon Leipold, Marielle Greber, Silvano Sele, Lutz Jäncke Pitch is a fundamental attribute of sounds and yet is not perceived equally by all humans. Absolute pitch (AP) musicians perceive, recognize, and name pitches in absolute terms, whereas relative pitch (RP) musicians, representing the large majority of musicians, perceive pitches in relation to other pitches. In this study, we used electroencephalography (EEG) to investigate the neural representations underlying tone listening and tone labeling in a large sample of musicians (n = 105). Participants performed a pitch processing task with a listening and a labeling condition during EEG acquisition. Using a brain-decoding framework, we tested a prediction derived from both theoretical and empirical accounts of AP, namely that the representational similarity of listening and labeling is higher in AP musicians than in RP musicians. Consistent with the prediction, time-resolved single-trial EEG decoding revealed a higher representational similarity in AP musicians during late stages of pitch perception. Time-frequency-resolved EEG decoding further showed that the higher representational similarity was present in oscillations in the theta and beta frequency bands. Supplemental univariate analyses were less sensitive in detecting subtle group differences in the frequency domain. Taken together, the results suggest differences between AP and RP musicians in late pitch processing stages associated with cognition, rather than in early processing stages associated with perception.
       
  • Quantitative magnetization transfer imaging of the human locus coeruleus
    • Abstract: Publication date: Available online 21 June 2019Source: NeuroImageAuthor(s): Paula Trujillo, Kalen J. Petersen, Matthew J. Cronin, Ya-Chen Lin, Hakmook Kang, Manus J. Donahue, Seth A. Smith, Daniel O. Claassena The locus coeruleus (LC) is the major origin of norepinephrine in the central nervous system, and is subject to age-related and neurodegenerative changes, especially in disorders such as Parkinson's disease and Alzheimer's disease. Previous studies have shown that neuromelanin (NM)-sensitive MRI can be used to visualize the LC, and it is hypothesized that magnetization transfer (MT) effects are the primary source of LC contrast. The aim of this study was to characterize the MT effects in LC imaging by applying high spatial resolution quantitative MT (qMT) imaging to create parametric maps of the macromolecular content of the LC and surrounding tissues. Healthy volunteers (n = 26; sex = 17 F/9M; age = 41.0 ± 19.1 years) underwent brain MRI on a 3.0 T scanner. qMT data were acquired using a 3D MT-prepared spoiled gradient echo sequence. A traditional NM scan consisting of a T1-weighted turbo spin echo sequence with MT preparation was also acquired. The pool-size ratio (PSR) was estimated for each voxel using a single-point qMT approach. The LC was semi-automatically segmented on the MT-weighted images. The MT-weighted images provided higher contrast-ratio between the LC and surrounding pontine tegmentum (PT) (0.215 ± 0.031) than the reference images without MT-preparation (−0.005 ± 0.026) and the traditional NM images (0.138 ± 0.044). The PSR maps showed significant differences between the LC (0.090 ± 0.009) and PT (0.188 ± 0.025). The largest difference between the PSR values in the LC and PT was observed in the central slices, which also correspond to those with the highest contrast-ratio. These results highlight the role of MT in generating NM-related contrast in the LC, and should serve as a foundation for future studies aiming to quantify pathological changes in the LC and surrounding structures in vivo.Graphical abstractImage 1
       
  • Association between the oral microbiome and brain resting state
           connectivity in smokers
    • Abstract: Publication date: Available online 13 June 2019Source: NeuroImageAuthor(s): Dongdong Lin, Kent E. Hutchison, Salvador Portillo, Victor Vegara, Jarrod M. Ellingson, Jingyu Liu, Kenneth S. Krauter, Amanda Carroll-Portillo, Vince D. Calhoun Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut–microbiome–brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the complex structural and functional networks in brain, our knowledge about the relationship between microbiome and brain function through specific brain circuits is still very limited. In this pilot study, we leveraged next generation microbial sequencing with functional neuroimaging techniques to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex-matched nonsmokers were recruited for 16S sequencing of their oral microbial community. Among them, 56 subjects were scanned by resting-state functional magnetic resonance imaging to derive brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the oral microbial community, functional network connectivity among brain regions, and the associations between microbial shifts and the brain functional network connectivity alternations. Compared to nonsmokers, we found a significant decrease of beta diversity (P = 6 × 10−3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) with significant alterations in microbial abundance. Pathway analysis on the predicted KEGG pathways shows that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (P = 0.032), mainly including connectivity between brain default network and other task-positive networks. This brain functional component was also significantly associated with smoking related oral microbiota, suggesting a related cross-individual pattern between smoking-induced oral microbiome dysbiosis and brain functional connectivity alternation, possibly involving immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.
       
  • MRI-based measures of intracortical myelin are sensitive to a history of
           TBI and are associated with functional connectivity
    • Abstract: Publication date: Available online 13 June 2019Source: NeuroImageAuthor(s): Evan M. Gordon, Geoffrey J. May, Steven M. Nelson Traumatic brain injuries (TBIs) induce persistent behavioral and cognitive deficits via diffuse axonal injury. Axonal injuries are often examined in vivo using diffusion MRI, which identifies damaged and demyelinated regions in deep white matter. However, TBI patients can exhibit impairment in the absence of diffusion-measured abnormalities, suggesting that axonal injury and demyelination may occur outside the deep white matter. Importantly, myelinated axons are also present within the cortex. Cortical myelination cannot be measured using diffusion imaging, but can be mapped in-vivo using the T1-w/T2-w ratio method. Here, we conducted the first work examining effects of TBI on intracortical myelin in living humans by applying myelin mapping to 46 US Military Veterans with a history of TBI. We observed that myelin maps could be created in TBI patients that matched known distributions of cortical myelin. After controlling for age and presence of blast injury, the number of lifetime TBIs was associated with reductions in the T1-w/T2-w ratio across the cortex, most significantly in a highly-myelinated lateral occipital region corresponding with the human MT + complex. Further, the T1-w/T2-w ratio in this MT + region predicted resting-state functional connectivity of that region. By contrast, a history of blast TBI did not affect the T1-w/T2-w ratio in either a diffuse or focal pattern. These findings suggest that intracortical myelin, as measured using the T1-w/T2-w ratio, may be a TBI biomarker that is anatomically complementary to diffusion MRI. Thus, myelin mapping could potentially be combined with diffusion imaging to improve MRI-based diagnostic tools for TBI.
       
  • Predictable tones elicit stimulus-specific suppression of evoked activity
           in auditory cortex
    • Abstract: Publication date: Available online 20 June 2019Source: NeuroImageAuthor(s): Biao Han, Pim Mostert, Floris P. de Lange The auditory cortex is sensitive to many forms of acoustic regularity, resulting in suppressed neural activity for expected auditory events. It is unclear whether this activity reduction for expected events is the result of suppression of neurons that are tuned to the expected stimulus (i.e., dampening), or alternatively suppression of neurons that are tuned away from the expected stimulus (i.e., sharpening). In the present study, we adjudicated between these models by characterizing the effect of expectation on the ability to classify the identity of auditory stimuli from auditory neural activity patterns, using magnetoencephalography (MEG) in healthy human observers. Participants listened to pure tone pairs, in which the identity of the second tone was either expected or unexpected. The task of the participants was to detect a target tone, which deviated strongly from both the expected and unexpected tones. We found a strong suppression of the overall neural response in the expected condition compared to the unexpected condition. Linear classifiers showed a reduced ability to decode stimulus identity from event-related auditory fields in the expected condition compared to the unexpected condition. This suggests that stimulus-specific event-related activity is dampened for expected tones in auditory cortex.
       
  • Dimensionality reduction of diffusion MRI measures for improved
           tractometry of the human brain
    • Abstract: Publication date: Available online 20 June 2019Source: NeuroImageAuthor(s): Maxime Chamberland, Erika P. Raven, Sila Genc, Kate Duffy, Maxime Descoteaux, Greg D. Parker, Chantal M.W. Tax, Derek K. Jones Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.Graphical abstractImage 1
       
  • Beyond consensus: Embracing heterogeneity in curated neuroimaging
           meta-analysis
    • Abstract: Publication date: Available online 20 June 2019Source: NeuroImageAuthor(s): Gia H. Ngo, Simon B. Eickhoff, Minh Nguyen, Gunes Sevinc, Peter T. Fox, R. Nathan Spreng, B.T. Thomas Yeo Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).
       
  • A guide to group effective connectivity analysis, part 1: First level
           analysis with DCM for fMRI
    • Abstract: Publication date: Available online 19 June 2019Source: NeuroImageAuthor(s): Peter Zeidman, Amirhossein Jafarian, Nadège Corbin, Mohamed L. Seghier, Adeel Razi, Cathy J. Price, Karl J. Friston Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.
       
  • Detecting resting-state brain activity using OEF-weighted imaging
    • Abstract: Publication date: Available online 19 June 2019Source: NeuroImageAuthor(s): Yang Yang, Yayan Yin, Jie Lu, Qihong Zou, Jia-Hong Gao Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.
       
  • Prospective motion correction in functional MRI using simultaneous
           multislice imaging and multislice-to-volume image registration
    • Abstract: Publication date: Available online 19 June 2019Source: NeuroImageAuthor(s): Daniel Christopher Hoinkiss, Peter Erhard, Nora-Josefin Breutigam, Federico von Samson-Himmelstjerna, Matthias Günther, David Andrew Porter The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50% when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion.
       
  • A guide to group effective connectivity analysis, part 2: Second level
           analysis with PEB
    • Abstract: Publication date: Available online 18 June 2019Source: NeuroImageAuthor(s): Peter Zeidman, Amirhossein Jafarian, Mohamed L. Seghier, Vladimir Litvak, Hayriye Cagnan, Cathy J. Price, Karl J. Friston This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.
       
  • Adaptive cognitive control attenuates the late positive potential to
           emotional distractors
    • Abstract: Publication date: Available online 18 June 2019Source: NeuroImageAuthor(s): Tobias Flaisch, Marco Steinhauser, Harald T. Schupp Emotional pictures are inherently prioritized during stimulus perception. While this preferential emotion processing promotes self-preservation and survival, it can be detrimental when it conflicts with current goals and intentions. Recent brain imaging research suggests that the brain resolves such conflicts by suppressing the processing of emotional distractors at the perceptual level. Beyond brain imaging, event-related scalp potential studies in humans have traced preferential emotion processing at distinct temporal stages. Comparing emotional to neutral pictures, an early stage is indexed by the early posterior negativity (EPN) component featuring a relative negativity over posterior sites, while a later stage is associated with the late positive potential (LPP), manifesting as relative positivity over centro-parietal sensors. However, little is known whether emotional response conflict is resolved at each of those processing stages, or whether conflict resolution operates selectively at early or late stages, respectively. The present study assessed EPN and LPP to emotional distractors in an emotional Stroop task as a function of response conflict in the previous trial. Conflict-related processing during the Stroop task was confirmed by a behavioral conflict adaptation effect and modulation of the congruency-sensitive N450 component. Preferential processing of emotional distractors was observed for the EPN as well as the LPP. While the EPN was completely unaffected by conflict in the previous trial, the LPP was selectively reduced subsequent to trials featuring high response conflict. This observation provides support for a conflict-based control of emotion processing and demonstrates that cognitive control acts selectively at specific stages of emotion perception.
       
  • Neural correlates of semantic and syntactic processing in German Sign
           Language
    • Abstract: Publication date: Available online 17 June 2019Source: NeuroImageAuthor(s): Anna-Lena Stroh, Frank Rösler, Giulia Dormal, Uta Salden, Nils Skotara, Barbara Hänel-Faulhaber, Brigitte Röder The study of deaf and hearing native users of signed languages can offer unique insights into how biological constraints and environmental input interact to shape the neural bases of language processing. Here, we use functional magnetic resonance imaging (fMRI) to address two questions: (1) Do semantic and syntactic processing in a signed language rely on anatomically and functionally distinct neural substrates as it has been shown for spoken languages' and (2) Does hearing status affect the neural correlates of these two types of linguistic processing' Deaf and hearing native signers performed a sentence judgement task on German Sign Language (Deutsche Gebärdensprache: DGS) sentences which were correct or contained either syntactic or semantic violations. We hypothesized that processing of semantic and syntactic violations in DGS relies on distinct neural substrates as it has been shown for spoken languages. Moreover, we hypothesized that effects of hearing status are observed within auditory regions, as deaf native signers have been shown to activate auditory areas to a greater extent than hearing native signers when processing a signed language. Semantic processing activated low-level visual areas and the left inferior frontal gyrus (IFG), suggesting both modality-dependent and independent processing mechanisms. Syntactic processing elicited increased activation in the right supramarginal gyrus (SMG). Moreover, psychophysiological interaction (PPI) analyses revealed a cluster in left middle occipital regions showing increased functional coupling with the right SMG during syntactic relative to semantic processing, possibly indicating spatial processing mechanisms that are specific to signed syntax. Effects of hearing status were observed in the right superior temporal cortex (STC): deaf but not hearing native signers showed greater activation for semantic violations than for syntactic violations in this region. Taken together, the present findings suggest that the neural correlates of language processing are partly determined by biological constraints but that they may additionally be influenced by the unique processing demands of the language modality and different sensory experiences.
       
  • Decoding of selective attention to continuous speech from the human
           auditory brainstem response
    • Abstract: Publication date: Available online 15 June 2019Source: NeuroImageAuthor(s): Octave Etard, Mikolaj Kegler, Chananel Braiman, Antonio Elia Forte, Tobias Reichenbach Humans are highly skilled at analysing complex acoustic scenes. The segregation of different acoustic streams and the formation of corresponding neural representations is mostly attributed to the auditory cortex. Decoding of selective attention from neuroimaging has therefore focussed on cortical responses to sound. However, the auditory brainstem response to speech is modulated by selective attention as well, as recently shown through measuring the brainstem's response to running speech. Although the response of the auditory brainstem has a smaller magnitude than that of the auditory cortex, it occurs at much higher frequencies and therefore has a higher information rate. Here we develop statistical models for extracting the brainstem response from multi-channel scalp recordings and for analysing the attentional modulation according to the focus of attention. We demonstrate that the attentional modulation of the brainstem response to speech can be employed to decode the attentional focus of a listener from short measurements of 10 s or less in duration. The decoding remains accurate when obtained from three EEG channels only. We further show how out-of-the-box decoding that employs subject-independent models, as well as decoding that is independent of the specific attended speaker is capable of achieving similar accuracy. These results open up new avenues for investigating the neural mechanisms for selective attention in the brainstem and for developing efficient auditory brain-computer interfaces.
       
  • Tracking dynamic brain networks using high temporal resolution MEG
           measures of functional connectivity
    • Abstract: Publication date: Available online 14 June 2019Source: NeuroImageAuthor(s): Prejaas Tewarie, Lucrezia Liuzzi, George C. O'Neill, Andrew Quinn, Alessandra Griffa, Mark W. Woolrich, Cornelis J. Stam, Arjan Hillebrand, Matthew J. Brookes Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.
       
  • A new blind source separation framework for signal analysis and artifact
           rejection in functional Near-Infrared Spectroscopy
    • Abstract: Publication date: Available online 14 June 2019Source: NeuroImageAuthor(s): Alexander von Lühmann, Zois Boukouvalas, Klaus-Robert Müller, Tülay Adalı In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statistical dependencies between signal components. We present a novel suitable BSS framework that tackles these issues by incorporating A) Independent Component Analysis methods that exploit both higher order statistics and sample dependency, B) multimodality, i.e., fNIRS with accelerometer signals, and C) Canonical-Correlation Analysis with temporal embedding. This enables analysis of signal components and rejection of motion-induced physiological hemodynamic artifacts that would otherwise be hard to identify. We implement a method for Blind Source Separation and Accelerometer based Artifact Rejection and Detection (BLISSA2RD). It allows the analysis of a novel n-back based cognitive workload paradigm in freely moving subjects, that is also presented in this manuscript. We evaluate on the corresponding data set and simulated ground truth data, making use of metrics based on 1st and 2nd order statistics and SNR and compare with three established methods: PCA, Spline and Wavelet-based artifact removal. Across 17 subjects, the method is shown to reduce movement induced artifacts by up to two orders of magnitude, improves the SNR of continuous hemodynamic signals in single channels by up to 10dB, and significantly outperforms conventional methods in the extraction of simulated Hemodynamic Response Functions from strongly contaminated data. The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm.Graphical abstractImage 1
       
 
 
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