Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders DOI Open Access
Jie Zhang, Wei Cheng,

Zhaowen Liu

и другие.

Brain, Год журнала: 2016, Номер 139(8), С. 2307 - 2321

Опубликована: Июль 14, 2016

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate of at scale single (micro) or whole-brain (macro) connectivity. However, mechanism underlying time-varying properties remains unclear, as coupling between network neural activity is not readily apparent when analysed either micro macroscales. We propose an intermediate (meso) analysis characterize functional architecture associated with a particular region. This yields topography that reflects and, importantly, creates analytical framework to establish fundamental relationship regional its structural find dynamical region into distinct modules different times may be indicative flexibility adaptability. Primary unimodal sensory-motor cortices low variability, while transmodal areas, including heteromodal association areas limbic system, high variability. In particular, regions highest such hippocampus/parahippocampus, inferior middle gyrus, olfactory gyrus caudate are all related learning, suggesting indicate level With simultaneously recorded electroencephalography/functional magnetic resonance imaging imaging/diffusion tensor data, we also modulated by local blood oxygen level-dependent α-band oscillation, governed ratio intra- inter-community Application mesoscale measure multicentre datasets three mental disorders matched controls involving 1180 subjects reveals those demonstrating extreme, i.e. highest/lowest liable change disorders. Specifically, draw attention identification diametrically opposing patterns changes schizophrenia deficit hyperactivity disorder/autism. Regions default-mode lower patients schizophrenia, but autism/attention disorder, compared respective controls. contrast, subcortical regions, especially thalamus, show higher patients, disorder. The these closely symptom scores. Our work provides insights organization how it nodal potentially useful predictor for learning rehabilitation.

Язык: Английский

Neural correlates of consciousness: progress and problems DOI
Christof Koch, Marcello Massimini, Mélanie Boly

и другие.

Nature reviews. Neuroscience, Год журнала: 2016, Номер 17(5), С. 307 - 321

Опубликована: Апрель 20, 2016

Язык: Английский

Процитировано

1437

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery DOI Creative Commons
Vince D. Calhoun, Robyn L. Miller, Godfrey D. Pearlson

и другие.

Neuron, Год журнала: 2014, Номер 84(2), С. 262 - 274

Опубликована: Окт. 1, 2014

Язык: Английский

Процитировано

1320

Precision Functional Mapping of Individual Human Brains DOI Creative Commons
Evan M. Gordon, Timothy O. Laumann, Adrian W. Gilmore

и другие.

Neuron, Год журнала: 2017, Номер 95(4), С. 791 - 807.e7

Опубликована: Июль 27, 2017

Язык: Английский

Процитировано

1278

Building better biomarkers: brain models in translational neuroimaging DOI
Choong‐Wan Woo, Luke J. Chang, Martin A. Lindquist

и другие.

Nature Neuroscience, Год журнала: 2017, Номер 20(3), С. 365 - 377

Опубликована: Фев. 23, 2017

Язык: Английский

Процитировано

969

Functional System and Areal Organization of a Highly Sampled Individual Human Brain DOI Creative Commons
Timothy O. Laumann, Evan M. Gordon,

Babatunde Adeyemo

и другие.

Neuron, Год журнала: 2015, Номер 87(3), С. 657 - 670

Опубликована: Июль 23, 2015

Язык: Английский

Процитировано

949

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation DOI Creative Commons
Caterina Gratton, Timothy O. Laumann, Ashley N. Nielsen

и другие.

Neuron, Год журнала: 2018, Номер 98(2), С. 439 - 452.e5

Опубликована: Апрель 1, 2018

Язык: Английский

Процитировано

863

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls DOI Creative Commons
Mohammad R. Arbabshirani, Sergey M. Plis, Jing Sui

и другие.

NeuroImage, Год журнала: 2016, Номер 145, С. 137 - 165

Опубликована: Март 22, 2016

Язык: Английский

Процитировано

859

Brain network dynamics are hierarchically organized in time DOI Open Access
Diego Vidaurre, Stephen M. Smith, Mark W. Woolrich

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2017, Номер 114(48), С. 12827 - 12832

Опубликована: Окт. 30, 2017

The brain recruits neuronal populations in a temporally coordinated manner task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed find repeating network patterns whole-brain resting fMRI data, where are defined as graphs of interacting areas. that transitions between nonrandom, with certain more likely occur after others. Further, this nonrandom sequencing itself hierarchically organized, revealing two distinct sets networks, or metastates, has tendency cycle within. One metastate associated sensory motor regions, other involves areas related higher order cognition. Moreover, we proportion time subject spends each consistent subject-specific measure, heritable, shows significant relationship cognitive traits.

Язык: Английский

Процитировано

797

Building a Science of Individual Differences from fMRI DOI
Julien Dubois, Ralph Adolphs

Trends in Cognitive Sciences, Год журнала: 2016, Номер 20(6), С. 425 - 443

Опубликована: Май 1, 2016

Язык: Английский

Процитировано

665

Rethinking segregation and integration: contributions of whole-brain modelling DOI
Gustavo Deco, Giulio Tononi, Mélanie Boly

и другие.

Nature reviews. Neuroscience, Год журнала: 2015, Номер 16(7), С. 430 - 439

Опубликована: Июнь 17, 2015

Язык: Английский

Процитировано

610