Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagnosed. This heterogeneity is often neglected and difficult to study. Therefore, innovative data-driven approaches utilizing substantial autopsy cohorts are needed to address this complexity and improve diagnosis, prognosis and fundamental research. We present clinical disease trajectories from 3,042 Netherlands Brain Bank donors, encompassing 84 neuropsychiatric signs and symptoms identified through natural language processing. This unique resource provides valuable new insights into neurodegenerative disorder symptomatology. To illustrate, we identified signs and symptoms that differed between frequently misdiagnosed disorders. In addition, we performed predictive modeling and identified clinical subtypes of various brain disorders, indicative of neural substructures being differently affected. Finally, integrating clinical diagnosis information revealed a substantial proportion of inaccurately diagnosed donors that masquerade as another disorder. The unique datasets allow researchers to study the clinical manifestation of signs and symptoms across neurodegenerative disorders, and identify associated molecular and cellular features.

神经退行性疾病表现出相当大的临床异质性，并且经常被误诊。这种异质性常常被忽视并且难以研究。因此，需要利用大量尸检队列的创新数据驱动方法来解决这种复杂性并改善诊断、预后和基础研究。我们展示了 3,042 名荷兰脑库捐赠者的临床疾病轨迹，包括通过自然语言处理识别的 84 种神经精神体征和症状。这种独特的资源为神经退行性疾病症状学提供了宝贵的新见解。为了说明这一点，我们确定了经常被误诊的疾病之间不同的体征和症状。此外，我们还进行了预测建模并确定了各种脑部疾病的临床亚型，表明神经亚结构受到不同的影响。最后，整合临床诊断信息揭示了很大一部分被错误诊断的捐赠者，这些捐赠者伪装成另一种疾病。独特的数据集使研究人员能够研究神经退行性疾病的体征和症状的临床表现，并识别相关的分子和细胞特征。