Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping. Academic Article uri icon

Overview

abstract

  • Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.

publication date

  • March 15, 2022

Identity

PubMed Central ID

  • PMC8964629

Scopus Document Identifier

  • 85127473783

Digital Object Identifier (DOI)

  • 10.3389/fnins.2022.810111

PubMed ID

  • 35368264

Additional Document Info

volume

  • 16