Toward Personalized Neuroscience: Evaluating Individual-Level Information in Neural Mass Models.

ABSTRACT: Macroscale brain modeling using neural mass models (NMMs) offers a framework for simulating human whole-brain dynamics. These models are pivotal for investigating the brain as a complex dynamic system, exploring phenomena like bifurcations, oscillatory patterns, and responses to stimuli. While connectome-based NMMs allow for the creation of personalized NMMs, their utility in capturing individual-specific neural characteristics remains underexplored, with current studies constrained by small sample sizes and computational inefficiencies. To address these limitations, we employed an algorithmically differentiable version of the reduced Wong Wang (RWW) model, enabling efficient optimization for large datasets. Applying this to resting-state fMRI data from 1444 samples, we optimized models wi

SUBMITTER: Barkhau CBC 

PROVIDER: S-EPMC12620565 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature