Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the d...
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| Published in: | Nature communications Vol. 16; no. 1; pp. 2724 - 12 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
19.03.2025
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2041-1723, 2041-1723 |
| Online Access: | Get full text |
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