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Dynamic Connectivity Reveals Transformative Power of Neurofeedback in Brain Functional Networks

Authors :

Kasra Momeni¹ Gholam- Ali Hossein-Zadeh²

1- دانشگاه تهران 2- دانشگاه تهران

Keywords :

ICA،brain networks،dFNC،reality monitoring،neurofeedback

Abstract :

The unknown procedure of neurofeedback interaction on brain networks is a critical drawback of this method. In this study, a dynamic functional connectivity (dFC) framework, using spatially constrained independent component analysis, was used to capture transient changes of brain networks due to reality monitoring neurofeedback. As a result of applying the analysis on fMRI data, four recurring connectivity states involving the default mode (DMN), cognitive control (CC), and sensorimotor (SM) networks were identified. After training, participants showed a significant increase (p<0.05) in time spent in a DMN-integrated state (State 2/Cluster 2), occurring 37% of the time and marked by strong within-DMN coupling, reflecting enhanced internal processing. Conversely, dwell time decreased (p<0.05) in a CC–SM dominated state (State 1/Cluster 1), suggesting reduced reliance on externally driven control or sensorimotor interactions. Transition analyses supported these effects, with increased shifts toward the DMN-integrated state (from 1.5% to 4.5%) and fewer transitions to Cluster 1 (from 1.8% to 0.2%). Overall, this dFC framework effectively captured neurofeedback-induced reorganization, offering a promising tool for optimizing interventions. Its ability to detect subtle, time-varying network changes highlights its potential clinical utility for monitoring and personalizing treatments in some brain disorders.