Neurodegenerative diseases manifest different motor and cognitive signs and symptoms that are highly heterogeneous. Parsing these heterogeneities may lead to an improved understanding of underlying disease mechanisms; however, current methods are dependent on clinical assessments and an arbitrary choice of behavioural tests. Here we present a data-driven subtyping approach using video-captured human motion and brain functional connectivity from resting-state functional magnetic resonance imaging. We applied our framework to a cohort of individuals at different stages of Parkinson’s disease. The process mapped the data to low-dimensional measures by projecting them onto a canonical correlation space that identified three Parkinson’s disease subtypes: subtype I was characterized by motor difficulties and poor visuospatial abilities; subtype II exhibited difficulties in non-motor components of activities of daily living and motor complications (dyskinesias and motor fluctuations) and subtype III was characterized by predominant tremor symptoms. We conducted a convergent validity analysis by comparing our approach to existing and widely used approaches. The compared approaches yielded subtypes that were adequately well-clustered in the motion-brain representation space we created to delineate subtypes. Our data-driven approach, contrary to other forms of subtyping, derived biomarkers predictive of motion impairment and subtype memberships that were captured objectively by digital videos.

神经退行性疾病表现出高度异质性的不同运动和认知体征和症状。解析这些异质性可能会加深对潜在疾病机制的理解；然而，目前的方法依赖于临床评估和行为测试的任意选择。在这里，我们提出了一种数据驱动的子类型方法，使用视频捕获的人体运动和静息态功能磁共振成像的大脑功能连接。我们将我们的框架应用于一组处于帕金森病不同阶段的个体。该过程通过将数据投影到规范相关空间上将数据映射到低维测量，该空间确定了帕金森病的三种亚型：亚型 I 的特点是运动困难和视觉空间能力差；亚型 I 的特点是运动困难和视觉空间能力差； II 型在日常生活活动的非运动部分和运动并发症（运动障碍和运动波动）方面表现出困难，而 III 型则以震颤症状为主。我们通过将我们的方法与现有和广泛使用的方法进行比较来进行收敛有效性分析。比较方法产生的亚型在我们创建的用于描述亚型的运动脑表示空间中充分聚类。与其他形式的亚型划分相反，我们的数据驱动方法派生出预测运动障碍的生物标志物和通过数字视频客观捕获的亚型成员资格。