Meta-analysis of psychophysiological interactions: Revisiting cluster-level thresholding and sample sizes

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Brain Connectivity Analysis

Background:

• Coordinate-based meta-analyses (CBMAs) are standard for aggregating neuroimaging findings on brain region activation.
• Previous CBMA applications primarily focused on brain activation, not connectivity.
• The study by Smith et al. (2016) extended CBMA to analyze brain connectivity modulated by psychological processes.

Purpose of the Study:

• To re-evaluate findings from a CBMA of brain connectivity studies (n=284) using psychophysiological interactions (PPI).
• To address an implementation error in the GingerALE v2.3.3 software used for the original analysis.
• To provide updated recommendations for sample sizes in CBMAs and emphasize user-developer communication.

Main Methods:

• Conducted a coordinate-based meta-analysis (CBMA) on 284 studies investigating brain connectivity via psychophysiological interactions (PPI).
• Reanalyzed the data after identifying an implementation error in the GingerALE v2.3.3 software.
• Evaluated the robustness of the original findings against the software error and proposed new sample size guidelines.

Main Results:

• The reanalysis confirmed that psychophysiological interaction (PPI) data yield consistent and psychologically specific brain connectivity patterns across studies.
• The identified software error led to more liberal results than intended, but key findings remained robust upon correction.
• The study provides new recommendations for sample sizes in coordinate-based meta-analyses.

Conclusions:

• The original conclusions regarding the reliability and specificity of PPI-based connectivity patterns in CBMA are supported by the reanalysis.
• Addressing software implementation errors is crucial for the integrity of neuroimaging meta-analyses.
• Clear communication channels between software users and developers are essential for advancing the field.