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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Vydané v:Nature communications Ročník 16; číslo 1; s. 2724 - 12
Hlavní autori: Govindarajan, Sindhuja Tirumalai, Mamourian, Elizabeth, Erus, Guray, Abdulkadir, Ahmed, Melhem, Randa, Doshi, Jimit, Pomponio, Raymond, Tosun, Duygu, Bilgel, Murat, An, Yang, Sotiras, Aristeidis, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L. S., Espeland, Mark A., Masters, Colin L., Maruff, Paul, Launer, Lenore J., Fripp, Jurgen, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Bryan, R. Nick, Resnick, Susan M., Habes, Mohamad, Shou, Haochang, Wolk, David A., Nasrallah, Ilya M., Davatzikos, Christos
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 19.03.2025
Nature Publishing Group
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ISSN:2041-1723, 2041-1723
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