Brain disorders in the early and late life of humans potentially share
pathological alterations in brain functions. However, the key evidence from
neuroimaging data for pathological commonness remains unrevealed. To explore
this hypothesis, we build a deep learning model, using multi-site functional
magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different
brain disorders from healthy controls, with a set of common features. Our model
achieves 62.6(1.9)% overall classification accuracy on data from the 6
investigated sites and detects a set of commonly affected functional
subnetworks at different spatial scales, including default mode, executive
control, visual, and limbic networks. In the deep-layer feature representation
for individual data, we observe young and aging patients with disorders are
continuously distributed, which is in line with the clinical concept of the
"spectrum of disorders". The revealed spectrum underlying early- and late-life
brain disorders promotes the understanding of disorder comorbidities in the
lifespan.

利用多个不同来源的功能性磁共振成像数据（4,410 名受试者，6 个位置）建立深度学习模型，能够识别 5 种脑部疾病，包括默认模式、执行控制、视觉和边缘网络等共同受损的功能子网络，并发现了早期和晚期大脑疾病共同谱系。