Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

TL;DR

This study uses persistent homology to analyze fMRI and MEG data, revealing that topological features of brain networks are conserved across cognitive states and covary with MEG theta-alpha activity, indicating shared functional constraints.

Contribution

It introduces a novel topological analysis of fMRI connectomes and demonstrates their covariation with MEG aperiodic dynamics, advancing multimodal neuroimaging understanding.

Findings

fMRI-based topological scaffolds reconfigure from rest to task

Cortical contributions to functional cycles are constrained and conserved

Covariation between topological features and MEG theta-alpha activity

Abstract

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning.