Brain network communication: concepts, models and applications

Seguin, Caio; Sporns, Olaf; Zalesky, Andrew

doi:10.1038/s41583-023-00718-5

Cited by 87 publications

(40 citation statements)

References 168 publications

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“…The dominance of the hierarchical perspective is perhaps not surprising given that the models mainly inherited this organization from the classic neurocognitive framework for cognitive control (Figure 1, left panel), which is in contradistinction to the possibility that control is an emergent property of the system itself (Bizyaeva et al, 2023; Christophel et al, 2017; Eisenreich et al, 2017). The latter proposal is more similar to communication models that describe how signals propagate throughout the brain without any high-level control (Avena-Koenigsberger et al, 2018; Seguin et al, 2023). In fact, the control-theoretic and communication accounts of brain function share numerous similarities, such as that both use graph theory to represent connectome data, but the formal relationship between the two approaches is only starting to be examined (Srivastava et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

The controllosphere: The neural origin of cognitive effort.

Holroyd

2024

Psychological Review

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Section: Discussionmentioning

confidence: 99%

The controllosphere: The neural origin of cognitive effort.

Holroyd

2024

Psychological Review

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“…Considering the functional connectome as weights of a neural network distinguishes our methodology from conventional biophysical and phenomenological computational modeling strategies, which usually rely on the structural connectome to model polysynaptic connectivity (Cabral et al, 2017;Golos et al, 2015;Hansen et al, 2015). Given the challenges of accurately modelling the structure-function coupling in the brain (Seguin et al, 2023), such models are currently limited in terms of reconstruction accuracy, hindering translational applications. By working with direct, functional MRI-based activity flow estimates, fcHNNs bypass the challenge of modelling the structural-functional coupling and are able to provide a more accurate representation of the brain's dynamic activity propagation (although at the cost of losing the ability to provide biophysical details on the underlying mechanisms).…”

Section: Discussionmentioning

confidence: 99%

“…This hampers their ability to effectively bridge the gap between explanations at the level of single neurons and the complexity of behavior (Breakspear, 2017). Recent efforts using coarse-grained brain network models (Schirner et al, 2022;Schiff et al, 1994;Papadopoulos et al, 2017;Seguin et al, 2023) and linear network control theory (Chiêm et al, 2021;Scheid et al, 2021;Gu et al, 2015) opted to trade biophysical fidelity to phenomenological validity. Such models have provided insights into some of the inherent key characteristics of the brain as a dynamic system; for instance, the importance of stable patterns, so-called "attractor states", in governing brain dynamics Golos et al, 2015;Hansen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease

Englert,

Kincses,

Kotikalapudi

et al. 2023

Preprint

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Understanding large-scale brain dynamics is a grand challenge in neuroscience. We propose functional connectome-based Hopfield Neural Networks (fcHNNs) as a model of macro-scale brain dynamics, arising from recurrent activity flow among brain regions. AnfcHNNis neither optimized to mimic certain brain characteristics, nor trained to solve specific tasks; its weights are simply initialized with empirical functional connectivity values. In thefcHNNframework, brain dynamics are understood in relation to so-called attractor states, i.e. neurobiologically meaningful low-energy activity configurations. Analyses of 7 distinct datasets demonstrate thatfcHNNs can accurately reconstruct and predict brain dynamics under a wide range of conditions, including resting and task states and brain disorders. By establishing a mechanistic link between connectivity and activity,fcHNNs offers a simple and interpretable computational alternative to conventional descriptive analyses of brain function. Being a generative framework,fcHNNs can yield mechanistic insights and hold potential to uncover novel treatment targets.Key PointsWe present a simple yet powerful computational model for large-scale brain dynamicsThe model uses a functional connectome-based Hopfield artificial neural network (fcHNN) architecture to compute recurrent “activity flow” through the functional brain connectomeFcHNNattractor dynamics provide a mechanistic account for gradient- and state-dynamics in the brain in resting conditionsFcHNNs conceptualize both task-induced and pathological changes in brain activity as a non-linear shift in these dynamicsOur approach is validated using large-scale neuroimaging data from seven studiesFcHNNs offers a simple and interpretable computational alternative to conventional descriptive analyses of brain functionProject website (with interactive manuscript):https://pni-lab.github.io/connattractor

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“…To address these shortcoming, we propose an alternative framework for estimating network level neural communication dynamics from LFP recordings, by combining a biologically plausible network diffusion process 35,36 with the autoregressive framework 37 . The resulting graph diffusion autoregressive (GDAR) model naturally gives rise to a communication signal with millisecond temporal resolution between nodes of a predefined graph, therefore incorporating the spatial information of the recording array and describing highly transient communication events.…”

Section: Introductionmentioning

confidence: 99%

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Schwock,

Bloch,

Khateeb

et al. 2024

Preprint

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Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

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Brain network communication: concepts, models and applications

Cited by 87 publications

References 168 publications

The controllosphere: The neural origin of cognitive effort.

The controllosphere: The neural origin of cognitive effort.

Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

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