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Article Dans Une Revue Science Advances Année : 2023

Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics

Viktor Sip
Meysam Hashemi
Timo Dickscheid
Katrin Amunts
Viktor Jirsa

Résumé

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region-and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.

Domaines

Neurosciences
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Licence : CC BY - Paternité

Dates et versions

hal-04092057 , version 1 (09-05-2023)

Licence

Paternité

Identifiants

Citer

Viktor Sip, Meysam Hashemi, Timo Dickscheid, Katrin Amunts, Spase Petkoski, et al.. Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics. Science Advances , 2023, 9 (11), ⟨10.1126/sciadv.abq7547⟩. ⟨hal-04092057⟩
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