Identifying rapid changes in the hemodynamic response in event-related functional magnetic resonance imaging

TL;DR

This paper introduces two novel statistical procedures to detect rapid, nonstationary changes in the hemodynamic response during event-related fMRI, accounting for variability across subjects, conditions, and brain regions.

Contribution

The work presents new methods for identifying rapid HR changes in fMRI data, including procedures that handle variable change point locations and account for estimation uncertainty.

Findings

Procedures effectively detect rapid HR changes in simulated data.

Application to real fMRI data reveals insights into brain activity during learning.

Methods outperform existing approaches in sensitivity to rapid changes.

Abstract

The hemodynamic response (HR) in event-related functional magnetic resonance imaging is typically assumed to be stationary. While there are some approaches in the literature to model nonstationary HRs, few focus on rapid changes. In this work, we propose two procedures to investigate rapid changes in the HR. Both procedures make inference on the existence of rapid changes for multi-subject data. We allow the change point locations to vary between subjects, conditions and brain regions. The first procedure utilizes available information about the change point locations to compare multiple shape parameters of the HR over time. In the second procedure, the change point locations are determined for each subject separately. To account for the estimation of the change point locations, we propose the notion of post selection variance. The power of the proposed procedures is assessed in simulation studies. We apply the procedure for pre-specified change point locations to data from a category learning experiment.