BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhang, Jing Yu, Xiaowei Chen, Tong Cao, Chao Chen, Mingheng Zhuang, Yan Lyu, Yanjun Zhang, Lu Su, Li Liu, Tianming Zhu, Dajiang |
| author_facet | Zhang, Jing Yu, Xiaowei Chen, Tong Cao, Chao Chen, Mingheng Zhuang, Yan Lyu, Yanjun Zhang, Lu Su, Li Liu, Tianming Zhu, Dajiang |
| contents | The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD and LBD is crucial because they require different treatment approaches, but this is challenging due to significant clinical overlap, heterogeneity, complex pathogenesis, and the rarity of LBD. While recent advances in artificial intelligence (AI) demonstrate powerful learning capabilities and offer new hope for accurate diagnosis, existing methods primarily focus on designing "neural-level networks". Our work represents a pioneering effort in modeling system-level artificial neural network called BrainNet-MoE for brain modeling and diagnosing. Inspired by the brain's hierarchical organization of bottom-up sensory integration and top-down control, we design a set of disease-specific expert groups to process brain sub-network under different condition, A disease gate mechanism guides the specializa-tion of expert groups, while a transformer layer enables communication be-tween all sub-networks, generating a comprehensive whole-brain represen-tation for downstream disease classification. Experimental results show superior classification accuracy with interpretable insights into how brain sub-networks contribute to different neurodegenerative conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification Zhang, Jing Yu, Xiaowei Chen, Tong Cao, Chao Chen, Mingheng Zhuang, Yan Lyu, Yanjun Zhang, Lu Su, Li Liu, Tianming Zhu, Dajiang Machine Learning Artificial Intelligence Neurons and Cognition The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD and LBD is crucial because they require different treatment approaches, but this is challenging due to significant clinical overlap, heterogeneity, complex pathogenesis, and the rarity of LBD. While recent advances in artificial intelligence (AI) demonstrate powerful learning capabilities and offer new hope for accurate diagnosis, existing methods primarily focus on designing "neural-level networks". Our work represents a pioneering effort in modeling system-level artificial neural network called BrainNet-MoE for brain modeling and diagnosing. Inspired by the brain's hierarchical organization of bottom-up sensory integration and top-down control, we design a set of disease-specific expert groups to process brain sub-network under different condition, A disease gate mechanism guides the specializa-tion of expert groups, while a transformer layer enables communication be-tween all sub-networks, generating a comprehensive whole-brain represen-tation for downstream disease classification. Experimental results show superior classification accuracy with interpretable insights into how brain sub-networks contribute to different neurodegenerative conditions. |
| title | BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification |
| topic | Machine Learning Artificial Intelligence Neurons and Cognition |
| url | https://arxiv.org/abs/2503.07640 |