TY - JOUR
T1 - Sex Differences of Cerebellum and Cerebrum
T2 - Evidence from Graph Convolutional Network
AU - Gao, Yang
AU - Tang, Yan
AU - Zhang, Hao
AU - Yang, Yuan
AU - Dong, Tingting
AU - Jia, Qiaolan
N1 - Funding Information:
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.
Publisher Copyright:
© 2022, International Association of Scientists in the Interdisciplinary Areas.
PY - 2022/6
Y1 - 2022/6
N2 - 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. Graphical Abstract: [Figure not available: see fulltext.]
AB - 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. Graphical Abstract: [Figure not available: see fulltext.]
KW - Cerebellum
KW - Graph topk-pooling
KW - Resting-state fMRI
KW - Sex differences
KW - Two-branch multi-scale graph convolutional network (TMGCN)
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U2 - 10.1007/s12539-021-00498-5
DO - 10.1007/s12539-021-00498-5
M3 - Article
C2 - 35103919
AN - SCOPUS:85124105447
SN - 1913-2751
VL - 14
SP - 532
EP - 544
JO - Interdisciplinary Sciences – Computational Life Sciences
JF - Interdisciplinary Sciences – Computational Life Sciences
IS - 2
ER -