Evaluation of Second-Level Inference in fMRI Analysis

Abstract EN

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results.

Second-level analysis based on a mass univariate approach typically consists of 3 phases.

First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects.

We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability.

Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference.

Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size.

Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction.

The FDR results in most variable results, for both permutation-based inference and parametrical inference.

Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.