# A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC)

## TL;DR

BRAINIAC is a new model that improves cognitive predictions by integrating brain data and annotations, without assuming sparsity in brain-behavior links.

## Contribution

BRAINIAC introduces a novel Bayesian method for integrative analysis of cognition with annotation-informed variance estimation.

## Key findings

- BRAINIAC was validated through Monte Carlo simulations and real data from the ABCD Study.
- Incorporating annotations improved out-of-study predictive power when applied to HCP-D data.
- The model provides a principled assessment of annotation impact on feature enrichment.

## Abstract

We present the novel Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of predictive features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain–behavior associations. We validate BRAINIAC in Monte Carlo simulation studies. In real data analyses, we train the BRAINIAC model on resting state functional magnetic resonance imaging (rsMRI) and neuropsychiatric data from the Adolescent Brain Cognitive Development (ABCD) Study and use the trained model in an out-of-study application to harmonized resting-state data from the Human Connectome Project Development (HCP-D), demonstrating a substantial improvement in out-of-study predictive power by incorporating relevant annotations into the BRAINIAC model.