Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI
Journal:
arXiv
Published Date:
Dec 13, 2024
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.
