A novel method linking neural connectivity to behavioral fluctuations: Behavior-Regressed Connectivity

TL;DR

This paper introduces behavior-regressed connectivity (BRC), a novel method that links neural connectivity fluctuations to behavioral performance within an experimental session, providing dynamic insights beyond traditional static connectivity analyses.

Contribution

The paper presents BRC, a new approach that captures transient brain-behavior relationships by analyzing connectivity variations aligned with behavioral fluctuations during tasks.

Findings

BRC reveals subject-specific connectivity-behavior relationships.

BRC extends understanding of dynamic neural connectivity.

Conventional methods assume static connectivity, unlike BRC.

Abstract

Background: During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant. New Method: We introduce a novel method, behavior-regressed connectivity (BRC), to directly examine behavioral fluctuations within an experimental session and capture their relationship to changes in functional connectivity. This method employs the weighted phase lag index (WPLI) applied to a window of trials with a weighting function. Using two datasets, the BRC results are compared to conventional connectivity results during two time windows: the one second before stimulus onset to identify predictive relationships, and the one second after onset to capture task-dependent relationships. Results: In both tasks, we replicate the expected results for the conventional connectivity analysis, and extend our understanding of the brain-behavior relationship using the BRC analysis, demonstrating subject-specific connectivity patterns that correspond to both positive and negative relationships with behavior. Comparison with Existing Method(s): Conventional connectivity analyses assume a consistent relationship between behaviors and functional connectivity, but the BRC method examines performance variability within an experimental session to understand dynamic connectivity and transient behavior. Conclusion: The BRC approach examines connectivity as it covaries with behavior to complement the knowledge of underlying neural activity derived from conventional connectivity analyses. Within this framework, BRC may be implemented for the purpose of understanding performance variability both within and between participants.