Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimens...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/15/1/17 |
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| author | Hamed Mohammadi Waldemar Karwowski |
| author_facet | Hamed Mohammadi Waldemar Karwowski |
| author_sort | Hamed Mohammadi |
| collection | DOAJ |
| description | Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications. |
| format | Article |
| id | doaj-art-7c9e5bd9ca054e3f8ec325fd028203bc |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-7c9e5bd9ca054e3f8ec325fd028203bc2025-01-24T13:25:40ZengMDPI AGBrain Sciences2076-34252024-12-011511710.3390/brainsci15010017Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future DirectionsHamed Mohammadi0Waldemar Karwowski1Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USAComputational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USABrain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.https://www.mdpi.com/2076-3425/15/1/17graph neural networks (GNNs)brain connectivityneuroimaginginterpretability multimodal data integrationneurodegenerative diseases |
| spellingShingle | Hamed Mohammadi Waldemar Karwowski Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions Brain Sciences graph neural networks (GNNs) brain connectivity neuroimaging interpretability multimodal data integration neurodegenerative diseases |
| title | Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions |
| title_full | Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions |
| title_fullStr | Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions |
| title_full_unstemmed | Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions |
| title_short | Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions |
| title_sort | graph neural networks in brain connectivity studies methods challenges and future directions |
| topic | graph neural networks (GNNs) brain connectivity neuroimaging interpretability multimodal data integration neurodegenerative diseases |
| url | https://www.mdpi.com/2076-3425/15/1/17 |
| work_keys_str_mv | AT hamedmohammadi graphneuralnetworksinbrainconnectivitystudiesmethodschallengesandfuturedirections AT waldemarkarwowski graphneuralnetworksinbrainconnectivitystudiesmethodschallengesandfuturedirections |