Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
Nat Med. 2024 Aug 15. doi: 10.1038/s41591-024-03144-x. Online ahead of print.
ABSTRACT
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.
PMID:39147830 | DOI:10.1038/s41591-024-03144-x
![Google](https://www.google.com/images/branding/googlelogo/2x/googlelogo_light_color_92x30dp.png)
![Google Keep](https://www.gstatic.com/images/branding/product/1x/keep_48dp.png)
![Share on Linkedin](https://psychiatryai.com/wp-content/uploads/2023/10/linkedin-logo-png-2048-1.png)
Estimated reading time: 4 minute(s)
Latest: Psychiatryai.com #RAISR4D Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Cool Evidence: Engaging Young People and Students in Real-World Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Real-Time Evidence Search [Psychiatry]
![](/wp-content/uploads/2024/04/pubmed.png)
AI Research
![](/wp-content/uploads/2024/05/Il5nR_nf_400x400-modified-1.png)
Brain aging patterns in a large and diverse cohort of 49,482 individuals
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
🌐 90 Days
Evidence Blueprint
Brain aging patterns in a large and diverse cohort of 49,482 individuals
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)