Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction.
Authors
Affiliations (12)
Affiliations (12)
- Department of International Medical, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
- Institute on Aging and Brain Disorders, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
- Neurodegenerative Disorder Research Center, School of Life Sciences, Division of Life Sciences and Medicine, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China.
- Department of Nuclear Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China.
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. [email protected].
- Institute on Aging and Brain Disorders, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. [email protected].
- Neurodegenerative Disorder Research Center, School of Life Sciences, Division of Life Sciences and Medicine, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China. [email protected].
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. [email protected].
- Institute on Aging and Brain Disorders, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. [email protected].
- Neurodegenerative Disorder Research Center, School of Life Sciences, Division of Life Sciences and Medicine, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China. [email protected].
Abstract
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. 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. 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. 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.