痴呆
失智症
神经影像学
心理学
神经科学
磁共振成像
认知功能衰退
疾病
正电子发射断层摄影术
阿尔茨海默病
生物标志物
脑老化
认知
医学
听力学
病理
放射科
生物
生物化学
作者
Jeyeon Lee,Brian J. Burkett,Hoon‐Ki Min,Matthew L. Senjem,Emily S. Lundt,Hugo Botha,Jonathan Graff‐Radford,Leland R Barnard,Jeffrey L. Gunter,Christopher G. Schwarz,Kejal Kantarci,David S. Knopman,Bradley F. Boeve,Val J. Lowe,Ronald C. Petersen,Clifford R. Jack,David T. Jones
出处
期刊:Nature Aging
日期:2022-05-09
卷期号:2 (5): 412-424
被引量:56
标识
DOI:10.1038/s43587-022-00219-7
摘要
Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.
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