Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis
ObjectiveAccurate diagnosis of neurodevelopmental disorders relies on understanding the complex interactions and high-order relationships between brain regions. This work aims to model the subtle, disease-specific high-order relationships among brain regions that have been overlooked in existing wor...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1654199/full |
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| author | Xiangmin Han Junchang Li |
| author_facet | Xiangmin Han Junchang Li |
| author_sort | Xiangmin Han |
| collection | DOAJ |
| description | ObjectiveAccurate diagnosis of neurodevelopmental disorders relies on understanding the complex interactions and high-order relationships between brain regions. This work aims to model the subtle, disease-specific high-order relationships among brain regions that have been overlooked in existing works.MethodThis paper proposes a Unified Multi-View Hypergraph Learning framework that combines knowledge-driven and data-driven strategies for a more precise and comprehensive representation of the adolescent brain network. The knowledge-driven branch leverages prior knowledge of functional brain subnetworks to guide feature learning and uncover structured, high-order functional associations. Meanwhile, the data-driven branch consists of two complementary modules: at the global level, a nearest-neighbor-based strategy captures large-scale associations involving overlapping brain regions; at the local level, a granularity-adaptive approach identifies finer, region-specific high-order relationships, allowing for a more nuanced understanding of brain network interactions.ResultsExperimental results on the ABIDE and ADHD datasets demonstrate that our method outperforms existing methods in diagnostic accuracy and robustness. Additionally, visualizing the high-order associations learned from both branches reveals new insights into the pathogenic mechanisms of these disorders.ConclusionThe proposed method combines knowledge-driven and data-driven strategies for high-order brain network modeling, advancing the understanding of brain networks in neurodevelopmental diseases. |
| format | Article |
| id | doaj-art-37fe8cf3efbc453083e85be59de08eb8 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-37fe8cf3efbc453083e85be59de08eb82025-08-20T03:55:54ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.16541991654199Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosisXiangmin Han0Junchang Li1School of Software, Tsinghua University, Beijing, ChinaShenzhen Clinical Research Center for Mental Disorders, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, ChinaObjectiveAccurate diagnosis of neurodevelopmental disorders relies on understanding the complex interactions and high-order relationships between brain regions. This work aims to model the subtle, disease-specific high-order relationships among brain regions that have been overlooked in existing works.MethodThis paper proposes a Unified Multi-View Hypergraph Learning framework that combines knowledge-driven and data-driven strategies for a more precise and comprehensive representation of the adolescent brain network. The knowledge-driven branch leverages prior knowledge of functional brain subnetworks to guide feature learning and uncover structured, high-order functional associations. Meanwhile, the data-driven branch consists of two complementary modules: at the global level, a nearest-neighbor-based strategy captures large-scale associations involving overlapping brain regions; at the local level, a granularity-adaptive approach identifies finer, region-specific high-order relationships, allowing for a more nuanced understanding of brain network interactions.ResultsExperimental results on the ABIDE and ADHD datasets demonstrate that our method outperforms existing methods in diagnostic accuracy and robustness. Additionally, visualizing the high-order associations learned from both branches reveals new insights into the pathogenic mechanisms of these disorders.ConclusionThe proposed method combines knowledge-driven and data-driven strategies for high-order brain network modeling, advancing the understanding of brain networks in neurodevelopmental diseases.https://www.frontiersin.org/articles/10.3389/fmed.2025.1654199/fullneurodevelopmental disordershypergraph learninghigh-order correlationbrain diseaseknowledge and data dual-driven |
| spellingShingle | Xiangmin Han Junchang Li Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis Frontiers in Medicine neurodevelopmental disorders hypergraph learning high-order correlation brain disease knowledge and data dual-driven |
| title | Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis |
| title_full | Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis |
| title_fullStr | Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis |
| title_full_unstemmed | Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis |
| title_short | Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis |
| title_sort | leveraging unified multi view hypergraph learning for neurodevelopmental disorders diagnosis |
| topic | neurodevelopmental disorders hypergraph learning high-order correlation brain disease knowledge and data dual-driven |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1654199/full |
| work_keys_str_mv | AT xiangminhan leveragingunifiedmultiviewhypergraphlearningforneurodevelopmentaldisordersdiagnosis AT junchangli leveragingunifiedmultiviewhypergraphlearningforneurodevelopmentaldisordersdiagnosis |