Large-scale dynamic modeling of task-fMRI signals via subspace system identification

Becker, Cassiano O.; Bassett, Danielle S.; Preciado, Víctor M.

doi:10.1088/1741-2552/aad8c7

Cited by 21 publications

(20 citation statements)

References 44 publications

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“…While this finding represents constraints of distance alone and not white matter topology, it quantitatively explains how that stimulusderived input alters the state-space trajectories of the brain. Future investigations may resolve the effects of structure on task dynamics using data-driven approaches that attempt to recover the full set of task-related inputs 60,61 .…”

Section: Discussionmentioning

confidence: 99%

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

Cornblath¹,

Ashourvan²,

Kim³

et al. 2020

Commun Biol

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A diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain's trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.

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Section: Discussionmentioning

confidence: 99%

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

Cornblath¹,

Ashourvan²,

Kim³

et al. 2020

Commun Biol

Self Cite

124

246

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“…For some questions, advances in the control of nonlinear systems could prove effective [99][100][101], including applications of Ising models [102,103] and considerations related to the dynamics of neural mass models [104]. Finally, moving beyond network controllability, recent work expanding system identification methods to identify specific form of nonlinear dynamics present in brain is particularly promising [105][106][107].…”

Section: Discussionmentioning

confidence: 99%

A practical guide to methodological considerations in the controllability of structural brain networks

et al. 2020

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Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units. Given the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 hour of scanning from ten healthy young adults. Following a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state of the art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints. Our hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.Recent advances in network control theory offer a formal means to study how the temporal dynamics of a complex system emerges from its underlying network structure [9][10][11]. Applying this theory to the brain requires that one first builds a network model in which brain regions (nodes) are anatomically connected to one another (edges) [12,13]. The state of the brain network system is then reflected in the pattern of neurophysiological activity across network nodes, and state trajectories represent the temporal sequence of brain states that the system traverses [14,15]. With definitions of the network and its state in hand, we can consider the problem of network controllability, which in essence amounts to asking how the system can be driven to specific target states by means of internal or external control input [16]. In the context of the brain, such input can intuitively take the form of electrical stimulation [17-21], task modulation [22][23][24], or other perturbations from the world or from different portions of the body [25,26]. Practically, network control theory and its associated toolkit e...

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“…Moreover, our modeling framework is currently only applicable to resting state dynamics with no inputs, and has been tested on the two modalities of fMRI and iEEG. Inclusion of input signals for system identification of task fMRI/iEEG data requires accurate data-driven 'input models' of how experimental stimuli, as well as subjects' voluntary responses, influence the BOLD or LFP signals in each brain region, and is a highly warranted avenue for future research [51,52]. Under intensive task conditions, moreover, it is more likely, or perhaps certain, to observe nonlinearities at least in the form of saturation effects in the BOLD/LFP signal.…”

Section: Discussionmentioning

confidence: 99%

Is the brain macroscopically linear? A system identification of resting state dynamics

Nozari

Stiso

Caciagli

et al. 2020

Preprint

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A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to only increase in their complexity, thereby supporting an expanded repertoire of nonlinear behaviors. An implicit assumption has thus formed that an “accurate” computational model of whole-brain dynamics must inevitably be nonlinear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide a rigorous and data-driven answer at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

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Large-scale dynamic modeling of task-fMRI signals via subspace system identification

Cited by 21 publications

References 44 publications

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

A practical guide to methodological considerations in the controllability of structural brain networks

Is the brain macroscopically linear? A system identification of resting state dynamics

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