Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research

Mass univariate testing on functional connectivity MRI (fcMRI) data is limited by difficulties achieving experiment-wide significance. Recent work addressing this problem has used enrichment analysis, which aggregates univariate screening statistics for a set of variables into a single enrichment statistic. There have been promising results using this method to explore fcMRI-behavior associations. However, there has not yet been a rigorous examination of the statistical properties of enrichment analysis when applied to fcMRI data. Establishing power for fcMRI enrichment analysis will be important for future neuropsychiatric and cognitive neuroscience study designs that plan to include this method. Here, we use realistic simulation methods, which mimic the covariance structure of fcMRI data, to examine the false positive rate and statistical power of one technique for enrichment analysis, over-representation analysis. We find it can attain high power even for moderate effects and sample sizes, and it strongly outperforms univariate analysis. The false positive rate associated with permutation testing is robust.

Authors

Institutions

Publication Details

Journal
Developmental Cognitive Neuroscience
Published
2025-02-22
DOI
https://doi.org/10.1016/j.dcn.2025.101534
Citations
2
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
2.86
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research

Guido Gerig, Alan C. Evans, Jessica B. Girault, Natasha Marrus et al.
2 citations
Developmental Cognitive Neuroscience
Functional Brain Connectivity Studies
2.86
article

Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research

Guido Gerig, Alan C. Evans, Jessica B. Girault, Natasha Marrus, Juhi Pandey, Santiago Torres-Gomez, Stephen R. Dager, Lonnie Zwaigenbaum, Alishia Ferguson, Tomoyuki Nishino, Robert T. Schultz, Joseph Piven, Tanya St. John, Heather C. Hazlett, Martin Andreas Styner, Annette Estes, John R. Pruett, Alexandre A. Todorov
article en
2 citations

Abstract

Mass univariate testing on functional connectivity MRI (fcMRI) data is limited by difficulties achieving experiment-wide significance. Recent work addressing this problem has used enrichment analysis, which aggregates univariate screening statistics for a set of variables into a single enrichment statistic. There have been promising results using this method to explore fcMRI-behavior associations. However, there has not yet been a rigorous examination of the statistical properties of enrichment analysis when applied to fcMRI data. Establishing power for fcMRI enrichment analysis will be important for future neuropsychiatric and cognitive neuroscience study designs that plan to include this method. Here, we use realistic simulation methods, which mimic the covariance structure of fcMRI data, to examine the false positive rate and statistical power of one technique for enrichment analysis, over-representation analysis. We find it can attain high power even for moderate effects and sample sizes, and it strongly outperforms univariate analysis. The false positive rate associated with permutation testing is robust.

Developmental Cognitive NeuroscienceVol. 72
University of North Carolina at Chapel Hill (US), Children's Hospital of Philadelphia (US), University of Alberta (CA), University of Washington (US), Washington University in St. Louis (US), McGill University Health Centre (CA), University of Alberta Hospital (CA), Piedmont International University (US), McGill University (CA), New York University (US)
Openalex Percentile: Top 12%
Functional Brain Connectivity Studies
2.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.