Individual structural features constrain the mouse functional connectome

Melozzi, Francesca; Bergmann, Eyal; Harris, Julie A.; Kahn, Itamar; Jirsa, Viktor K.; Bernard, Christophe

doi:10.1073/pnas.1906694116

Cited by 74 publications

(42 citation statements)

References 63 publications

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“…Using this NMM, we simulate whole-brain dynamics in a mouse brain avatar (Melozzi et al, 2019, 2017), which detailed connectome is imported from the tracer experiments of the Allen Institute and is parcellated into anatomic regions of interest, associated with the network nodes (Figure 1c) (Oh et al, 2014). The communication between regions is weighted by the structural links W nm of the structural connectivity (Figure 1d).…”

Section: Resultsmentioning

confidence: 99%

Neuronal cascades shape whole-brain functional dynamics at rest

Rabuffo

Fousek

Bernard

et al. 2020

Preprint

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At rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

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Section: Resultsmentioning

confidence: 99%

Neuronal cascades shape whole-brain functional dynamics at rest

Rabuffo

Fousek

Bernard

et al. 2020

Preprint

Self Cite

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show abstract

“…Using this NMM, we simulate whole-brain dynamics in a mouse brain avatar (Melozzi et al, 2019(Melozzi et al, , 2017, the detailed connectivity of which is imported from the tracer experiments of the Allen Institute and is parcellated into anatomic regions of interest, associated with the network nodes (Figure 1c) (Oh et al, 2014). The communication between regions is weighted by the structural links W nm of the structural connectivity (Figure 1d).…”

Section: Rules Of Single and Coupled Nodes Dynamicsmentioning

confidence: 99%

Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest

Rabuffo

Fousek

Bernard

et al. 2021

eNeuro

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“…In addition to the brain's anatomical information which structurally impose a constraint upon network dynamics [80][81][82], the individual variability can be further constrained by EZ hypothesis formulation in order to produce more specific pathological patterns of brain activity. The hypothesis on the location of EZ allows refining the network pathology to better predict seizure onset and propagation for a given patient.…”

Section: Epileptogenicity Hypothesis Formulationmentioning

confidence: 99%

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

Hashemi

Vattikonda²,

Sip³

et al. 2021

PLoS Comput Biol

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Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.

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Individual structural features constrain the mouse functional connectome

Cited by 74 publications

References 63 publications

Neuronal cascades shape whole-brain functional dynamics at rest

Neuronal cascades shape whole-brain functional dynamics at rest

Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

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