Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction
Abstract Background Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer’s disease (AD), have been linked to accelerated br...
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BMC
2025-05-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-025-01773-z |
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| author | Chenxi Wang Weiwei Zhang Ming Ni Qiong Wang Chang Liu Linbin Dai Mengguo Zhang Yong Shen Feng Gao |
| author_facet | Chenxi Wang Weiwei Zhang Ming Ni Qiong Wang Chang Liu Linbin Dai Mengguo Zhang Yong Shen Feng Gao |
| author_sort | Chenxi Wang |
| collection | DOAJ |
| description | Abstract Background Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer’s disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology. Methods In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.We collected over 10,000 T1-weighted MRI scans from more than 7,000 individuals across six cohorts. We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. Our initial model focused on predicting brain age, serving as a foundational model from which we developed separate models for cognition function and amyloid plaque prediction through transfer learning. Results The brain age prediction model achieved the mean absolute error (MAE) for cognitive normal population in the ADNI (test) datasets of 3.302 years. The gap between predicted brain age and chronological age significantly increases while cognition declines. The cognition prediction model exhibited a root mean square error (RMSE) of 0.334 for the Clinical Dementia Rating (CDR) regression task, achieving an area under the curve (AUC) of approximately 0.95 in identifying ing dementia patients. Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. Conclusions These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD. |
| format | Article |
| id | doaj-art-337c7f58b66f4f3b82d099ece72aa7f8 |
| institution | DOAJ |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-337c7f58b66f4f3b82d099ece72aa7f82025-08-20T03:22:11ZengBMCAlzheimer’s Research & Therapy1758-91932025-05-0117111310.1186/s13195-025-01773-zDeep-learning based multi-modal models for brain age, cognition and amyloid pathology predictionChenxi Wang0Weiwei Zhang1Ming Ni2Qiong Wang3Chang Liu4Linbin Dai5Mengguo Zhang6Yong Shen7Feng Gao8Department of International Medical, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of International Medical, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Nuclear Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaDepartment of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaDepartment of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaAbstract Background Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer’s disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology. Methods In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.We collected over 10,000 T1-weighted MRI scans from more than 7,000 individuals across six cohorts. We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. Our initial model focused on predicting brain age, serving as a foundational model from which we developed separate models for cognition function and amyloid plaque prediction through transfer learning. Results The brain age prediction model achieved the mean absolute error (MAE) for cognitive normal population in the ADNI (test) datasets of 3.302 years. The gap between predicted brain age and chronological age significantly increases while cognition declines. The cognition prediction model exhibited a root mean square error (RMSE) of 0.334 for the Clinical Dementia Rating (CDR) regression task, achieving an area under the curve (AUC) of approximately 0.95 in identifying ing dementia patients. Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. Conclusions These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD.https://doi.org/10.1186/s13195-025-01773-zBrain agingAlzheimer’ s diseaseDeep learningMRITransfer learningPattern recognition |
| spellingShingle | Chenxi Wang Weiwei Zhang Ming Ni Qiong Wang Chang Liu Linbin Dai Mengguo Zhang Yong Shen Feng Gao Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction Alzheimer’s Research & Therapy Brain aging Alzheimer’ s disease Deep learning MRI Transfer learning Pattern recognition |
| title | Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction |
| title_full | Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction |
| title_fullStr | Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction |
| title_full_unstemmed | Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction |
| title_short | Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction |
| title_sort | deep learning based multi modal models for brain age cognition and amyloid pathology prediction |
| topic | Brain aging Alzheimer’ s disease Deep learning MRI Transfer learning Pattern recognition |
| url | https://doi.org/10.1186/s13195-025-01773-z |
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