Mass univariate aggregation methods for machine learning in neuroscience
Machine learning is a ubiquitous part of the modern neuroimaging toolkit, particularly for research aimed towards precision medicine goals of improving individual-level diagnosis and treatment. However, the high dimensionality of neuroimaging data poses significant challenges for constructing interpretable predictive models. Several established methods, such as Connectome-based Predictive Modeling (CPM), Polyneuro Risk Scores (PNRS, inspired by Polygenic Risk Scores), and Polyconnectomic Scoring (PCS), offer an interpretable approach, which we term “Mass Univariate Aggregation” (MUA). MUA approaches evaluate each feature independently and then use a linear combination of weighted features to derive predictions, providing directly interpretable weights for individual features. Despite the existence and widespread usage of various MUA approaches in neuroimaging, tools for their application are still fragmented, and there does not yet exist an open-access unified tool for implementing, evaluating, or comparing these models within a standardized machine learning workflow. Here, we present a unified, flexible, and accessible configurable pipeline that can be used for implementing CPM, PNRS, PCS, and many new MUA configurations facilitated by user-specified parameters. Built in Python as an add-on for scikit-learn, our configurable pipeline enables researchers to leverage the functionality and standards provided by a widely used open-access machine learning tool. We validated the configurable pipeline by replicating the outcomes achieved by existing CPM and PNRS implementations, utilizing resting-state functional connectivity data from the Human Connectome Project to predict fluid intelligence (n = 1067). We further validated the pipeline’s PCS implementation, confirming PCS computation with external connectome summary statistics (CSS) matrices using the same data, and CSS derivation using simulated data. Although designed to fill a gap in neuroimaging, our open-source, configurable pipeline provides a standardized platform for applying the MUA methods to any machine learning setting that features high-dimensional data.