A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data.

ABSTRACT:

Introduction

Brain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global structural network. Researchers have been interested in using these models to explain measured brain activity, particularly resting state functional magnetic resonance imaging (rs-fMRI). BNMs have shown to produce similar properties as measured data computed over longer periods of time such as average functional connectivity (FC), but it is unclear how well simulated trajectories compare to empirical trajectories on a timepoint-by-timepoint basis. During task fMRI, the relevant processes pertaining to task occur over the time frame of the

SUBMITTER: Kashyap A 

PROVIDER: S-EPMC10390027 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature