The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
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Alzheimer’s disease and the mathematical mind
Throughout the 19th and 20th centuries, aided by advances in medical imaging, discoveries in physiology and medicine have added nearly 25 years to the average life expectancy. This resounding success brings with it a need to understand a broad range of age-related health conditions, such as dementia. Today, mathematics, neuroimaging and scientific computing are being combined with fresh insights, from animal models, to study the brain and to better understand the etiology and progression of Alzheimer's disease, the most common cause of age-related dementia in humans. In this manuscript, we offer a brief primer to the reader interested in engaging with the exciting field of mathematical modeling and scientific computing to advance the study of the brain and, in particular, human AD research.
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- Award ID(s):
- 2325276
- PAR ID:
- 10504146
- Editor(s):
- Goriely, Alain; Jerusalem, Antoine
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Brain Multiphysics
- Volume:
- 6
- Issue:
- C
- ISSN:
- 2666-5220
- Page Range / eLocation ID:
- 100094
- Subject(s) / Keyword(s):
- Alzheimer’s disease Mathematical modeling Scientific computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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