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.
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Acknowledgements
This work was partly supported by the National Natural Science Foundation of China (61976045), Sichuan Science and Technology Program (2021YJ0247), Key Scientific and Technological Projects of Guangdong Province Government (2018B030335001), National Natural Science Foundation of China (31971288 and 31671005), National Natural Science Foundation of China (62006194), the Fundamental Research Funds for the Central Universities (3102019QD005), High-level researcher start-up projects (06100-20GH020161).
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Jiang, M. et al. (2021). Exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_14
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