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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05781 |
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| _version_ | 1866908546002583552 |
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| author | Zhou, Shuo Luo, Junhao Jiang, Yaya Wang, Haolin Lu, Haiping Gong, Gaolang |
| author_facet | Zhou, Shuo Luo, Junhao Jiang, Yaya Wang, Haolin Lu, Haiping Gong, Gaolang |
| contents | Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models. To capture sex-specific patterns, we develop the Group-Specific Discriminant Analysis (GSDA) for first-order classification. The evaluation on two public neuroimaging datasets demonstrates the efficacy of GSDA in learning sex-specific models from functional networks, achieving a significant improvement in group specificity over baseline methods. The major sex differences are in the strength of lateralization and the interactions within and between lobes. The GSDA-based method is generic in nature and can be adapted to other group-specific analyses such as handedness-specific or disease-specific analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network Zhou, Shuo Luo, Junhao Jiang, Yaya Wang, Haolin Lu, Haiping Gong, Gaolang Neurons and Cognition Machine Learning Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models. To capture sex-specific patterns, we develop the Group-Specific Discriminant Analysis (GSDA) for first-order classification. The evaluation on two public neuroimaging datasets demonstrates the efficacy of GSDA in learning sex-specific models from functional networks, achieving a significant improvement in group specificity over baseline methods. The major sex differences are in the strength of lateralization and the interactions within and between lobes. The GSDA-based method is generic in nature and can be adapted to other group-specific analyses such as handedness-specific or disease-specific analyses. |
| title | Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2404.05781 |