Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
Nat Med. 2024 Oct;30(10):3015-3026. doi: 10.1038/s41591-024-03144-x. Epub 2024 Aug 15.
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.
大脑老化过程受到各种生活方式、环境和遗传因素的影响，以及与年龄相关的、常常并存的病变。磁共振成像和人工智能方法在理解老化过程中发生的神经解剖变化方面发挥了重要作用。大型、多样化的人群研究能够确定由不同但重叠的病理和生物学因素导致的全面和有代表性的大脑变化模式，揭示受影响的大脑区域和临床表型中的交叉和异质性。在这里，我们利用一种最先进的深度表示学习方法 Surreal-GAN，并提出了方法上的进展和广泛的实验结果，阐明了来自 11 项研究的 49482 个人的队列中的大脑老化异质性。通过各自的 R 指数，为每个个体确定和量化了五种主要的大脑萎缩模式。它们与生物医学、生活方式和遗传因素的关联为观察到的差异的病因提供了深入的了解，表明它们作为遗传和生活方式风险的大脑内表型具有潜在的应用价值。此外，基线 R 指数预测疾病进展和死亡率，作为补充预后标志物，捕捉早期变化。这些 R 指数建立了一种衡量老化轨迹和相关大脑变化的维度方法。它们有望用于精确诊断，特别是在临床前阶段，根据特定的大脑内表型表达和预后，促进个性化的患者管理和有针对性的临床试验招募。
