Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912369210294272 |
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| author | Wein, Simon Riebel, Marco Brunner, Lisa-Marie Nothdurfter, Caroline Rupprecht, Rainer Schwarzbach, Jens V. |
| author_facet | Wein, Simon Riebel, Marco Brunner, Lisa-Marie Nothdurfter, Caroline Rupprecht, Rainer Schwarzbach, Jens V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI Wein, Simon Riebel, Marco Brunner, Lisa-Marie Nothdurfter, Caroline Rupprecht, Rainer Schwarzbach, Jens V. Neurons and Cognition Machine Learning 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. |
| title | Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2412.10161 |