Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Network Neuroscience, Год журнала: 2019, Номер 4(1), С. 30 - 69

Опубликована: Дек. 16, 2019

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge the spatiotemporal organization these interactions critical for establishing solid understanding brain's functional architecture and relationship between neural dynamics cognition in health disease. possibility studying through careful analysis neuroimaging data has catalyzed substantial interest methods that estimate time-resolved fluctuations connectivity (often referred to as "dynamic" or time-varying connectivity; TVFC). At same time, debates have emerged regarding application TVFC analyses resting fMRI data, about statistical validity, physiological origins, cognitive behavioral relevance TVFC. These other unresolved issues complicate interpretation findings limit insights can be gained from this promising new research area. This article brings together scientists with variety perspectives on review current literature light issues. We introduce core concepts, define key terms, summarize controversies open questions, present forward-looking perspective how rigorously productively applied investigate wide range questions systems neuroscience.

Язык: Английский

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Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know

American Journal of Neuroradiology, Год журнала: 2018, Номер unknown

Опубликована: Янв. 18, 2018

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used both healthy subjects patients with various neurologic, neurosurgical, psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state does not require perform any specific task. The low-frequency oscillations of the signal have shown relate spontaneous neural activity. There are many ways analyze data. In this review article, we will briefly describe a few these highlight advantages limitations each. This description is facilitate adoption use clinical setting, helping neuroradiologists become familiar techniques applying them for care neurologic diseases.

Язык: Английский

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NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

NeuroImage Clinical, Год журнала: 2020, Номер 28, С. 102375 - 102375

Опубликована: Янв. 1, 2020

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize degree which unique and changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities study However, it still an open question replicating translating findings across studies. Standardized approaches for capturing reproducible comparable imaging markers greatly needed. Here, we propose a pipeline based priori-driven independent component analysis, NeuroMark, capable estimating functional network measures from magnetic resonance (fMRI) that can be used link abnormalities among different datasets, studies, NeuroMark automatically estimates features adaptable each individual subject datasets/studies/disorders by taking advantage reliable templates extracted 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar major depressive disorder) evaluate validity proposed perspectives (replication abnormalities, cross-study comparison, identification subtle changes, multi-disorder classification using identified biomarkers). Our results highlight effectively replicated schizophrenia datasets; revealed interesting neural clues overlap specificity between schizophrenia; demonstrated impairments present varying degrees in disease; captured biomarkers achieved good performance classifying disorder disorder.

Язык: Английский

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Resting brain dynamics at different timescales capture distinct aspects of human behavior

Nature Communications, Год журнала: 2019, Номер 10(1)

Опубликована: Май 24, 2019

Abstract Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The counterparts static connectivity (FC), resolution several minutes, have been studied but correlates dynamic measures FC few seconds unclear. Here, using fMRI and 58 phenotypic from Human Connectome Project, we find that captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported loneliness life satisfaction) equally well explained by FC. Furthermore, behaviorally relevant emerges interconnections across all networks, rather than within between pairs networks. Our findings shed new light on cognitive processes involved distinct facets behavior.

Язык: Английский

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Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Network Neuroscience, Год журнала: 2018, Номер 3(1), С. 1 - 26

Опубликована: Апрель 12, 2018

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into network organization of human brain. Studies brain disorders such as Alzheimer's disease or depression adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review clinical neuroscience, summarizing methodological details 106 RSFC studies. Although this approach is prevalent promising, our identified four challenges. First, composition networks varied remarkably in terms region parcellation edge definition, which are fundamental analyses. Second, many studies equated number connections across graphs, but conceptually problematic populations may induce spurious group differences. Third, few metrics were reported common, precluding meta-analyses. Fourth, some tested hypotheses at one level without clear neurobiological rationale considering how findings (e.g., global topology) contextualized by another modular structure). Based on these themes, simulations demonstrate impact specific decisions case-control comparisons. Finally, offer suggestions for promoting convergence order facilitate progress important field.

Язык: Английский

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Machine learning in resting-state fMRI analysis

Magnetic Resonance Imaging, Год журнала: 2019, Номер 64, С. 101 - 121

Опубликована: Июнь 4, 2019

Язык: Английский

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Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience

Human Brain Mapping, Год журнала: 2017, Номер 39(2), С. 811 - 821

Опубликована: Ноя. 14, 2017

Imagination and creative cognition are often associated with the brain's default network (DN). Recent evidence has also linked cognitive control systems to performance on tasks involving imagination creativity, a growing number of studies reporting functional interactions between DN regions. We sought extend emerging literature brain dynamics supporting by examining individual differences in large-scale connectivity relation Openness Experience, personality trait typified creativity. To this end, we obtained resting-state fMRI data from two large samples participants recruited United States China, examined contributions temporal shifts using multivariate structural equation modeling dynamic analysis. In Study 1, found that was related proportion scan time (i.e., "dwell time") spent state characterized positive correlations among default, executive, salience, dorsal attention networks. 2 replicated extended effect dwell correlated comparable further demonstrated robustness latent variable models including fluid intelligence other major factors. The findings suggest Experience is increased systems, profile may account for enhanced imaginative abilities people high Experience.

Язык: Английский

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Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease

Journal of Neuroscience Methods, Год журнала: 2020, Номер 339, С. 108701 - 108701

Опубликована: Апрель 8, 2020

Язык: Английский

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Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis

NeuroImage, Год журнала: 2017, Номер 180, С. 632 - 645

Опубликована: Окт. 14, 2017

Язык: Английский

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Structure–function relationships during segregated and integrated network states of human brain functional connectivity

Brain Structure and Function, Год журнала: 2017, Номер 223(3), С. 1091 - 1106

Опубликована: Окт. 31, 2017

Язык: Английский

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