Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI
This provides a versatile and interpretable tool for data fusion in neuroimaging, addressing a domain-specific problem for researchers analyzing brain states from fMRI data, though it appears incremental as it builds on existing searchlight methods.
The paper tackled the problem of independently analyzing resting-state fMRI metrics by introducing the Fusion Searchlight framework to integrate multiple metrics, resulting in enhanced accuracy for pharmacological treatment prediction and identification of additional brain regions affected by sedation with alprazolam.
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.