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Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network

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Abstract

This work aims to exploit a novel graph neural network to predict the sex of the brain topological network, and to find the sex differences in the cerebrum and cerebellum. A two-branch multi-scale graph convolutional network (TMGCN) is designed to analyze the sex differences of the brain. Two complementary templates are used to construct cerebrum and cerebellum networks, respectively, followed by a two-branch sub-network with multi-scale filters and a trainable weighted fusion strategy for the final prediction. Finally, a trainable graph topk-pooling layer is utilized in our model to visualize key brain regions relevant to the prediction. The proposed TMGCN achieves a prediction accuracy of 84.48%. In the cerebellum, the bilateral Crus I–II, lobule VI and VIIb, and the posterior vermis (VI–X) are discriminative for this task. As for the cerebrum, the discriminative brain regions consist of the bilateral inferior temporal gyrus, the bilateral fusiform gyrus, the bilateral parahippocampal gyrus, the bilateral cingulate gyrus, the bilateral medial ventral occipital cortex, the bilateral lateral occipital cortex, the bilateral amygdala, and the bilateral hippocampus. This study tackles the sex prediction problem from a more comprehensive view, and may provide the resting-state fMRI evidence for further study of sex differences in the cerebellum and cerebrum.

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Data Availability

We used the rs-fMRI of the Southwest University Longitudinal Imaging Multimodal (SLIM) dataset in the experiment. This dataset is provided by the International Data-sharing Initiative (INDI, http://fcon_1000.projects.nitrc.org/).

Code Availability

Our work was finished by the custom code.

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Acknowledgements

This work was supported in part by the High Performance Computing Center of Central South University. The author would like to thank the 2020 Key Project of Research on Postgraduate Education and Teaching Reform of Central South University [grant numbers 2020JGA011], the 2020 Hunan Province Degree and Postgraduate Education Reform Research Project [grant number 2020JGZD014] and the Research Fund of the Guangxi Key Lab of Multi-source Information Mining and Security [grant number MIMS20-08] for their supports.

Funding

This work was supported by the 2020 Key Project of Research on Postgraduate Education and Teaching Reform of Central South University [grant numbers 2020JGA011], the 2020 Hunan Province Degree and Postgraduate Education Reform Research Project [grant number 2020JGZD014] and the Research Fund of the Guangxi Key Lab of Multi-source Information Mining and Security [grant number MIMS20-08].

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Yang Gao, Yan Tang, Hao Zhang, Tingting Dong & Qiaolan Jia

  2. Stephenson School of Biomedical Engineering, The University of Oklahoma, Tulsa, USA

    Yuan Yang

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  1. Yang Gao
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Correspondence to Yan Tang or Hao Zhang.

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Gao, Y., Tang, Y., Zhang, H. et al. Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network. Interdiscip Sci Comput Life Sci 14, 532–544 (2022). https://doi.org/10.1007/s12539-021-00498-5

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