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

Network Neuroscience, Journal Year: 2019, Volume and Issue: 4(1), P. 30 - 69

Published: Dec. 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.

Language: Английский

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Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises

Biological Psychiatry, Journal Year: 2020, Volume and Issue: 88(11), P. 818 - 828

Published: Feb. 27, 2020

Language: Английский

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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

NeuroImage, Journal Year: 2019, Volume and Issue: 206, P. 116276 - 116276

Published: Oct. 12, 2019

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts a variety applications, yet there are few direct comparisons relative utility. Here, we compared performance three DNN architectures machine learning algorithm (kernel regression) predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One was generic fully-connected feedforward network, while other two were recently published approaches specifically designed exploit structure connectome By using combined sample almost 10,000 participants Human Connectome Project (HCP) UK Biobank, showed kernel regression achieved similar across wide range behavioral demographic measures. Furthermore, network exhibited state-of-the-art connectome-specific DNNs. When fluid intelligence all algorithms dramatically improved when size increased 100 1000 subjects. Improvement smaller, but still significant, 5000 Importantly, competitive sizes. Overall, our study as effective for RSFC-based prediction, incurring significantly lower computational costs. Therefore, might serve useful baseline future studies.

Language: Английский

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The Human Connectome Project: A retrospective

NeuroImage, Journal Year: 2021, Volume and Issue: 244, P. 118543 - 118543

Published: Sept. 8, 2021

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances human neuroimaging, particularly for measures of brain connectivity; apply these study a large number healthy young adults; and freely share the data tools with scientific community. NIH awarded grants two consortia; this retrospective focuses on "WU-Minn-Ox" HCP consortium centered at Washington University, University Minnesota, Oxford. In just over 6 years, WU-Minn-Ox succeeded its core objectives by: 1) improving MR scanner hardware, pulse sequence design, image reconstruction methods, 2) acquiring analyzing multimodal MRI MEG unprecedented quality together behavioral from more than 1100 participants, 3) sharing (via ConnectomeDB database) associated analysis visualization tools. To date, 27 Petabytes have been shared, 1538 papers acknowledging use published. "HCP-style" neuroimaging paradigm has emerged set best-practice strategies optimizing acquisition analysis. This article reviews history HCP, including comments key events decisions major project components. We discuss several using data, improved cortical parcellations, analyses connectivity based functional diffusion MRI, brain-behavior relationships. also touch upon our efforts develop variety processing along detailed documentation, tutorials, educational course train next generation neuroimagers. conclude look forward opportunities challenges facing field perspective consortium.

Language: Английский

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Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Cerebral Cortex, Journal Year: 2021, Volume and Issue: 31(10), P. 4477 - 4500

Published: March 31, 2021

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality network-level Here, we extend the to estimate areal-level While parcellations comprise spatially distributed networks spanning cortex, consensus is that parcels should be localized, is, not span multiple lobes. There disagreement about whether strictly contiguous or noncontiguous components; therefore, considered three MS-HBM variants these range possibilities. Individual-specific estimated using 10 min data generalized better than other approaches 150 out-of-sample rs-fMRI and task-fMRI from same individuals. connectivity derived also achieved best behavioral prediction performance. Among variants, exhibited resting-state homogeneity most uniform within-parcel task activation. In terms prediction, gradient-infused was numerically best, but differences among were statistically significant. Overall, results suggest MS-HBMs can capture behaviorally meaningful parcellation features beyond group-level Multi-resolution trained models are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

Language: Английский

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Movie viewing elicits rich and reliable brain state dynamics

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Oct. 5, 2020

Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic activity. While dynamic switches between states have been well characterised in resting state acquisitions, remodelling these transitions by engagement naturalistic stimuli remains poorly understood. Here, we show temporal dynamics states, as measured fMRI, reshaped from predominantly bistable two relatively indistinct at rest, toward a sequence well-defined functional during movie viewing whose temporally aligned to specific features movie. The expression covaries with different physiological reflects subjectively rated In sum, data-driven decoding reveals distinct reshaping network reliable accompany switch perceptual immersion an ecologically valid experience.

Language: Английский

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Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: April 25, 2022

Abstract How individual differences in brain network organization track behavioral variability is a fundamental question systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the level. However, most studies focus on single traits, thus not capturing broader relationships across behaviors. In large sample of 1858 typically developing children from Adolescent Brain Cognitive Development (ABCD) study, we show predictive features are distinct domains cognitive performance, personality scores mental health assessments. On other hand, within each domain predicted by similar features. Predictive models generalize to measures same domain. Although tasks known modulate connectome, between resting task states. Overall, our findings reveal shared account for variation broad behavior childhood.

Language: Английский

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Intrinsic neural timescales: temporal integration and segregation

Trends in Cognitive Sciences, Journal Year: 2022, Volume and Issue: 26(2), P. 159 - 173

Published: Jan. 3, 2022

Language: Английский

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Heritability and interindividual variability of regional structure-function coupling

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Aug. 12, 2021

Abstract White matter structural connections are likely to support flow of functional activation or connectivity. While the relationship between and connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this at regional scale. Here we quantify coupling in healthy young adults using diffusion-weighted MRI resting-state data from Human Connectome Project study how may be heritable varies individuals. We show that strength widely across brain regions, but was strongest highly structurally connected visual subcortical areas. also interindividual differences based age, sex composite cognitive scores, within certain networks. These results suggest structure-function is an idiosyncratic feature organisation influenced by genetic factors.

Language: Английский

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Neurobiology of loneliness: a systematic review

Neuropsychopharmacology, Journal Year: 2021, Volume and Issue: 46(11), P. 1873 - 1887

Published: July 6, 2021

Language: Английский

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