This study explored how diverse factors including neurocognitive disorders, socioeconomic inequalities, pollution and gender disparities influence brain aging in underserved populations (groups with limited access to essential services such as healthcare and education). Using deep learning on EEG and fMRI data, we identified brain-age gaps as key markers of accelerated brain aging and their connections to macrosocial determinants of health.

中文翻译：

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计算大脑模型绘制了衰老和痴呆中嵌入的多样性


本研究探讨了包括神经认知障碍、社会经济不平等、污染和性别差异在内的各种因素如何影响服务不足人群（无法获得医疗保健和教育等基本服务的群体）的大脑衰老。通过对 EEG 和 fMRI 数据进行深度学习，我们确定大脑年龄差距是大脑加速衰老的关键标志，以及它们与健康的宏观社会决定因素的联系。