Wein, Simon and Riebel, Marco and Brunner, Lisa-Marie and Nothdurfter, Caroline and Rupprecht, Rainer and Schwarzbach, Jens (2025) Data integration with Fusion Searchlight: Classifying brain states from resting-state fMRI. NEUROIMAGE, 315: 121263. ISSN 1053-8119, 1095-9572
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MAJOR DEPRESSIVE DISORDER; LOW-FREQUENCY FLUCTUATION; FUNCTIONAL CONNECTIVITY; DYSFUNCTION; AMPLITUDE; CORTEX; MRI; Resting-state fMRI; MVPA; Searchlight; Data fusion |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Jul 2026 06:05 |
| Last Modified: | 28 Jul 2026 06:05 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66040 |
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