Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI

TL;DR

The paper introduces Fusion Searchlight, a framework that integrates multiple resting-state fMRI metrics to improve brain state classification and treatment prediction, enhancing spatial specificity and interpretability.

Contribution

Fusion Searchlight is a novel data fusion framework that combines diverse fMRI metrics, improving classification accuracy and interpretability in brain state analysis.

Findings

Enhanced pharmacological treatment prediction accuracy

Identified additional brain regions affected by sedation

Improved spatial specificity through explainable AI

Abstract

Resting-state fMRI captures spontaneous neural activity characterized by complex spatiotemporal dynamics. Various metrics, such as local and global brain connectivity and low-frequency amplitude fluctuations, quantify distinct aspects of these dynamics. However, these measures are typically analyzed independently, overlooking their interrelations and potentially limiting analytical sensitivity. Here, we introduce the Fusion Searchlight (FuSL) framework, which integrates complementary information from multiple resting-state fMRI metrics. We demonstrate that combining these metrics enhances the accuracy of pharmacological treatment prediction from rs-fMRI data, enabling the identification of additional brain regions affected by sedation with alprazolam. Furthermore, we leverage explainable AI to delineate the differential contributions of each metric, which additionally improves spatial specificity of the searchlight analysis. Moreover, this framework can be adapted to combine information across imaging modalities or experimental conditions, providing a versatile and interpretable tool for data fusion in neuroimaging.