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Exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks
- Source :
- Machine Learning in Medical Imaging ISBN: 9783030875886, MLMI@MICCAI
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- One of the most prominent anatomical characteristics of the human brain lies in its highly folded cortical surface into convex gyri and concave sulci. Previous studies have demonstrated that gyri and sulci exhibit fundamental differences in terms of genetic influences, morphology and structural connectivity as well as function. Recent studies have demonstrated time-frequency differences in neural activity between gyri and sulci. However, the functional connectivity between gyri and sulci is currently unclear. Moreover, the regularity/variability of the gyro-sulcal functional connectivity across different task domains remains unknown. To address these two questions, we developed a novel anatomy-guided spatio-temporal graph convolutional network (AG-STGCN) to classify task-based fMRI (t-fMRI) and resting state fMRI (rs-fMRI) data, and to further investigate gyro-sulcal functional connectivity differences across different task domains. By performing seven independent classifications based on seven t-fMRI and one rs-fMRI datasets of 800 subjects from the Human Connectome Project, we found that the constructed gyro-sulcal functional connectivity features could satisfactorily differentiate the t-fMRI and rs-fMRI data. For those functional connectivity features contributing to the classifications, gyri played a more crucial role than sulci in both ipsilateral and contralateral neural communications across task domains. Our study provides novel insights into unveiling the functional differentiation between gyri and sulci as well as for understanding anatomo-functional relationships in the brain.
Details
- ISBN :
- 978-3-030-87588-6
- ISBNs :
- 9783030875886
- Database :
- OpenAIRE
- Journal :
- Machine Learning in Medical Imaging ISBN: 9783030875886, MLMI@MICCAI
- Accession number :
- edsair.doi...........931bf43537cd7f261caeb50101141c01
- Full Text :
- https://doi.org/10.1007/978-3-030-87589-3_14