A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow

Miller, Robyn L.; Vergara, Victor M.; Keator, David B.; Calhoun, Vince D.

doi:10.1109/tbme.2016.2600637

Cited by 17 publications

(22 citation statements)

References 21 publications

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“…We then evaluated the assumptions underlying naive network-based models, in particular the assumptions that brain networks are independent and stable. Inconsistent with the canonical model of localized and temporally stable networks (and consistent with the dynamic connectivity literature) 32,41 , we found that canonical brain networks are temporally decomposable into an array of possible connectivity states. Moreover, each local connectivity state also provides information about the global state of the entire brain.…”

Section: Discussionsupporting

confidence: 87%

“…Inconsistent with models that suggest uniformity of brain networks over time, and consistent with recent results using dynamic functional connectivity 41 , we found that networks could be resolved temporally into NC-states. This result parallels and extends the observation that the whole connectome can be resolved temporally into whole-brain states 32 .…”

Section: Each Canonical Network Is Resolvable Into a Set Of Network Csupporting

confidence: 91%

“…At a particular time, it is possible that network A's nodes have low moment-to-moment connectivity with nodes in network B, while the states of the two networks at the same time are highly concordant. Consistent with a similar approach in concurrent work 41 , such cases indicate a complex interplay between brain networks that may be reflective of nonlinear or multidimensional interactions among brain regions.…”

Section: Discussionsupporting

confidence: 65%

“…5B; p < 0.01 except SOM, permutation test). These findings suggest that, while fluctuations occurring on a dynamic level across networks are of importance 41 , canonical networks nevertheless retain a degree of independence from the rest of the cortex. As an exploratory aim, we used these measurements to characterize a maximally interdependent brain subsystem.…”

Section: Each Canonical Network Is Resolvable Into a Set Of Network Cmentioning

confidence: 84%

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Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Ćirić

Nomi

Uddin

et al. 2016

Preprint

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Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.A major endeavor in neuroscience is to characterize the spatiotemporal organization of the brain into functional systems 1 . By identifying patterns of synchronous brain activity, functional magnetic resonance neuroimaging (fMRI) techniques have partitioned the human brain into large-scale networks 2,3 . These functional networks are stable across individuals and populations [4][5][6] , are roughly consistent across task-evoked and resting data 7,8 , and are present across mammalian species 9, 10 . A hierarchically modularized set of canonical networks is now widely accepted as an organizational principle of the brain 11,12 . Indeed, an expanding literature relates networks to specific psychological functions and individual differences [13][14][15][16] , with the potential for improved clinical diagnosis or treatment outcome metrics [17][18][19] . However, a growing body of work has called into question how accurately this canonical network model represents underlying neural architecture. In particular, many methods used to delineate networks rely on two implicit assumptions. First is the 'spatial assumption' that each brain region participates in exactly one network. Casting doubt on this are models suggesting that brain regions can engage with several different networks [20][21][22][23][24][25][26] , dynamic causal models showing that connectivity between brain regions changes as a function of the experimental context 27 , and graph theoretic models intimating the existence of neural hubs that recruit multiple networks [28][29][30][31] . Second is the 'temporal assumption' that the connectivity within each network remains relatively stable over time. This, too, has been called into question, with recent work 32 suggesting that the brain is dynamically multistable. That is, the brain may occupy any of a number of connectivity states over time, each with a distinct ne...

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Section: Discussionsupporting

confidence: 87%

Section: Each Canonical Network Is Resolvable Into a Set Of Network Csupporting

confidence: 91%

Section: Discussionsupporting

confidence: 65%

Section: Each Canonical Network Is Resolvable Into a Set Of Network Cmentioning

confidence: 84%

See 2 more Smart Citations

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Ćirić

Nomi

Uddin

et al. 2016

Preprint

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show abstract

“…64 New methods being developed to optimize across multiple domains have yielded some interesting results. 65,66 Fig. 11 shows an example of estimated joint functional domains compared with the static (averaged) FNC matrix.…”

Section: Number 8: Labeling the Independent Component Analysis Componmentioning

confidence: 99%

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

Calhoun

Lacy

2017

Neuroimaging Clinics of North America

Self Cite

108

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Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment

Gao

Lewis

Calhoun

et al. 2023

Computers in Biology and Medicine

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A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow

Cited by 17 publications

References 21 publications

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment

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