In Parkinson’s disease (PD), concurrent declines in cognitive and motor domain function can severely limit an individual’s ability to conduct daily tasks. Current diagnostic methods, however, lack precision in differentiating domain-specific contributions of cognitive or motor impairments based on a patients’ clinical manifestation. Fear of falling (FOF) is a common clinical manifestation among the elderly, in which both cognitive and motor impairments can lead to significant barriers to a patients’ physical and social activities. The present study evaluated whether a set of analytical and machine-learning approaches could be used to help delineate boundary conditions and separate cognitive and motor contributions to a patient’s own perception of self-efficacy and FOF. Cognitive and motor clinical scores, in conjunction with FOF, were collected from 57 Parkinson’s patients during a multi-center rehabilitation intervention trial. Statistical methodology was used to extract a subset of uncorrelated cognitive and motor components associated with cognitive and motor predictors, which were then used to independently identify and visualize cognitive and motor dimensions associated with FOF. We found that a central cognitive process, extracted from tests of executive, attentional, and visuoperceptive function, was a unique and significant independent cognitive predictor of FOF in PD. In addition, we provide evidence that the approaches described here may be used to computationally discern specific types of FOF based on separable cognitive or motor models. Our results are consistent with a contemporary model that the deterioration of a central cognitive mechanism that modulates self-efficacy also plays a critical role in FOF in PD.

在帕金森病 (PD) 中，认知和运动功能的同时下降会严重限制个人执行日常任务的能力。然而，当前的诊断方法在根据患者的临床表现区分认知或运动障碍的特定领域贡献方面缺乏精确性。害怕跌倒（FOF）是老年人常见的临床表现，认知和运动障碍都会导致患者的身体和社交活动出现严重障碍。本研究评估了是否可以使用一套分析和机器学习方法来帮助描绘边界条件并区分认知和运动对患者自我效能和 FOF 感知的贡献。在一项多中心康复干预试验中，收集了 57 名帕金森病患者的认知和运动临床评分以及 FOF。使用统计方法提取与认知和运动预测因子相关的不相关认知和运动成分的子集，然后将其用于独立识别和可视化与 FOF 相关的认知和运动维度。我们发现，从执行、注意力和视觉感知功能测试中提取的中央认知过程是 PD 中 FOF 的独特且重要的独立认知预测因素。此外，我们提供的证据表明，此处描述的方法可用于基于可分离的认知或运动模型通过计算辨别特定类型的 FOF。我们的结果与当代模型一致，即调节自我效能的中央认知机制的恶化也在 PD 的 FOF 中发挥着关键作用。