Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest

Rabuffo, Giovanni; Fousek, J.; Bernard, Christophe; Jirsa, Viktor K.

doi:10.1523/eneuro.0283-21.2021

Cited by 47 publications

(68 citation statements)

References 97 publications

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“…Recent years are witnessing a growing interest in modelling and understanding the dynamics of rsfMRI activity, not only in the human brain but also across species. This work has prominently included measuring and modelling the rsfMRI of the mouse brain [35][36][37][38]. The mouse has the unique advantage of the availability of a precise measure of the whole-brain axonal connectivity and its directionality [21][22][23]42].…”

Section: Discussionmentioning

confidence: 99%

“…Because of the growing importance of rsfMRI studies in the mouse, theoretical research has begun to extend to network models of large-scale rodent brain activity [35][36][37][38]. These models of the mouse brain can help resolving many of the still unknown complex neurophysiological processes and principles that shape spontaneous rsfMRI activity in the mammalian brain.…”

Section: Introductionmentioning

confidence: 99%

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A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

Fasoli

Coletta

Gutierrez-Barragan

et al. 2022

Preprint

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Neural network models have been instrumental in revealing the foundational principles of whole-brain dynamics. Here we describe a new whole-cortex model of mouse resting-state fMRI (rsfMRI) activity. Our model implements neural input-output nonlinearities and excitatory-inhibitory interactions within areas, as well as a directed connectome obtained with viral tracing to model interareal connections. Our model makes novel predictions about the dynamic organization of rsfMRI activity on a fast scale of seconds, and explains its relationship with the underlying axonal connectivity. Specifically, the simulated rsfMRI activity exhibits rich attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we find that empirical mouse rsfMRI time series exhibit analogous signatures of attractor dynamics, and that model attractors recapitulate the topographical organization and temporal structure of empirical rsfMRI co-activation patterns (CAPs). The richness and complexity of attractor dynamics, as well as its ability to explain CAPs, are lost when the directionality of underlying axonal connectivity is neglected. Finally, complexity of fast dynamics on the scale of seconds was maximal for the values of inter-hemispheric axonal connectivity strength and of inter-areal connectivity sparsity measured in real anatomical mouse data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

Fasoli

Coletta

Gutierrez-Barragan

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…SI is an indicator for the metastability or switching behavior of functional brain networks and can be used as a measurement for the realistic evaluation of brain function. It has been analyzed in various studies related to both, whole-brain modeling (Deco et al, n.d.; Hansen et al, 2015; Rabuffo et al, 2021) and aging (Battaglia et al, 2020). In fact, previous results indicate simulated data which was created by the enhancement of a metastable or switching behavior and tuning model parameters have been considered to provide more realistic dynamics than those minimizing the distance between the empirical FC and the simulated one (Courtiol et al, 2020; Deco et al, n.d.; Hansen et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

Lavanga

Stumme

Yalçınkaya

et al. 2022

Preprint

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Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

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“…This neural mass model was adapted for a study on the role which neuronal cascades play in the causation of whole-brain functional dynamics at rest (Rabuffo et al, 2021). Causality is established by linking structural defined features of a brain network model to neural activation patterns and their variability.…”

Section: Use Case: the Montbrió Modelmentioning

confidence: 99%

RateML: A Code Generation Tool for Brain Network Models

Vlag

Woodman

Fousek

et al. 2022

Front. Netw. Physiol.

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Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML’s Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model’s mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.

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Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest

Abstract: ANR-20-NEUC-0005-01 -Brainstim, by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 945539 (SGA3) Human Brain Project and VirtualBrainCloud No. 826421.

Cited by 47 publications

References 97 publications

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

RateML: A Code Generation Tool for Brain Network Models

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