Effects of regularization on the stability and behavioral relevance of VAR-based functional connectivity.

Authors

Koki Masaoka, Ruixiang Li, Ayumi Kasagi, Teppei Matsui

Published in

Neuroscience research. Pages 105127. Oct 02, 2026. Epub Oct 02, 2026.

Abstract

Vector autoregressive modeling (VAR) is a powerful method for estimating dynamic functional connectivity (dFC) from resting-state functional magnetic resonance imaging (fMRI) data. However, VAR-based dFC can become unstable when only limited data are available. We compared multiple regularization approaches (PLS, PCA, Ridge, and Lasso) to examine whether they can stabilize VAR-based dFC without substantially compromising behaviorally relevant information. All regularization methods improved stability. Behavioral measure prediction improved and reached a level comparable to Pearson correlation-based FC, except Lasso. These findings suggest that regularized VAR-based dFC can provide behaviorally relevant information about temporal dynamics not represented in Pearson correlation-based FC.

PMID:
42826884
Bibliographic data and abstract were imported from PubMed on 03 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.