Federal Project Grant Award Summary

Indiana University received an $831,656 Project Grant from the National Institute of Mental Health (NIMH) under the Mental Health Research Grants program (CFDA 93.242), awarded May 18, 2026, with completion targeted for February 28, 2031. The project develops advanced statistical models that integrate simplicial complex structures to analyze brain connectivity and its association with human health outcomes. The research extends scalar-on-matrix regression methodologies to include generalized linear and mixed models, incorporating higher-order brain region interactions beyond traditional pairwise connectivity measures. These innovations enable more comprehensive analysis of cognitive function and brain-related health conditions by quantifying neuro-activation patterns through upper-adjacency edge matrices and edge community structures.

The project delivers statistical modeling tools and methodological frameworks designed to improve analysis of large neuroimaging datasets. Through two primary research aims, the work produces penalized regression models that leverage higher-order simplicial structures as both predictors and regularizers, incorporating effective resistance measures to quantify network-wide communication robustness among brain regions. The methods encompass kernel-based approaches and lifted-graph Laplacian techniques that provide new interpretable ways to understand the complexities of brain connectivity. These developments generate computational tools and analytical approaches that advance the field's capacity to identify health-associated connectivity patterns and support evidence-based understanding of mental health conditions through rigorous neuroimaging analysis.