Interpretable and integrative deep learning for discovering brain-behaviour associations
Abstract Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and var...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-85032-5 |
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| author | Corentin Ambroise Antoine Grigis Josselin Houenou Vincent Frouin |
| author_facet | Corentin Ambroise Antoine Grigis Josselin Houenou Vincent Frouin |
| author_sort | Corentin Ambroise |
| collection | DOAJ |
| description | Abstract Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and variability of these conditions. Recent research underlines the importance of considering multiple dimensions that span across different psychiatric syndromes. These developments have led to more comprehensive approaches to studying psychiatric conditions that incorporate diverse data sources such as imaging, genetics, and symptom reports. Multi-view unsupervised learning frameworks, particularly deep learning models, present promising solutions for integrating and analysing complex datasets. Such models contain generative capabilities which facilitate the exploration of relationships between different data views. In this study, we propose a robust framework for interpreting these models that combines digital avatars with stability selection to assess these relationships. We apply this framework to the Healthy Brain Network cohort which includes clinical behavioural scores and brain imaging features, uncovering a consistent set of brain-behaviour interactions. These associations link cortical measurements obtained from structural MRI with clinical reports evaluating psychiatric symptoms. Our framework effectively identifies relevant and stable associations, even with incomplete datasets, while isolating variability of interest from confounding factors. |
| format | Article |
| id | doaj-art-fc4e72b34333419689655fe82b14e7ea |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fc4e72b34333419689655fe82b14e7ea2025-01-19T12:20:24ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-85032-5Interpretable and integrative deep learning for discovering brain-behaviour associationsCorentin Ambroise0Antoine Grigis1Josselin Houenou2Vincent Frouin3University Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027University Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027University Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027University Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027Abstract Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and variability of these conditions. Recent research underlines the importance of considering multiple dimensions that span across different psychiatric syndromes. These developments have led to more comprehensive approaches to studying psychiatric conditions that incorporate diverse data sources such as imaging, genetics, and symptom reports. Multi-view unsupervised learning frameworks, particularly deep learning models, present promising solutions for integrating and analysing complex datasets. Such models contain generative capabilities which facilitate the exploration of relationships between different data views. In this study, we propose a robust framework for interpreting these models that combines digital avatars with stability selection to assess these relationships. We apply this framework to the Healthy Brain Network cohort which includes clinical behavioural scores and brain imaging features, uncovering a consistent set of brain-behaviour interactions. These associations link cortical measurements obtained from structural MRI with clinical reports evaluating psychiatric symptoms. Our framework effectively identifies relevant and stable associations, even with incomplete datasets, while isolating variability of interest from confounding factors.https://doi.org/10.1038/s41598-024-85032-5 |
| spellingShingle | Corentin Ambroise Antoine Grigis Josselin Houenou Vincent Frouin Interpretable and integrative deep learning for discovering brain-behaviour associations Scientific Reports |
| title | Interpretable and integrative deep learning for discovering brain-behaviour associations |
| title_full | Interpretable and integrative deep learning for discovering brain-behaviour associations |
| title_fullStr | Interpretable and integrative deep learning for discovering brain-behaviour associations |
| title_full_unstemmed | Interpretable and integrative deep learning for discovering brain-behaviour associations |
| title_short | Interpretable and integrative deep learning for discovering brain-behaviour associations |
| title_sort | interpretable and integrative deep learning for discovering brain behaviour associations |
| url | https://doi.org/10.1038/s41598-024-85032-5 |
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