Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA.
Gait Posture. 2022 Sep;97:122-129. doi: 10.1016/j.gaitpost.2022.07.003. Epub 2022 Jul 8.
Parkinson's disease (PD) is a neurodegenerative disorder causing postural control impairments. Postural control involves multiple domains, such as control of postural sway in stance, automatic postural responses (APRs) and anticipatory postural adjustments (APAs). We hypothesize that impairments in each postural domain is associated with resting-state functional connectivity (rsFC), accounted by predictive modeling and that cortical and cerebellar networks would predict postural control in people with PD (PwPD).
To determine whether rsFC can predict three domains of postural control independently in PwPD and older adults (OA) based on predictive accuracy of models.
The cohort consisted of 65 PwPD (67.7 +8.1 age) tested in their OFF-state and 42 OA (69.7 +8.2 age). Six body-worn, inertial sensors measured postural sway area while standing on foam, step length of APRs to a backward push-and-release perturbation, and magnitude of lateral APAs prior to voluntary gait initiation. Resting state-fMRI data was reported on 384 regions of interest that were grouped into 13 functional brain networks. Associations between rsFC and postural metrics were characterized using predictive modeling, with an independent training (n = 67) and validation (n = 40) dataset. Models were trained in the training sample and performance of the best model was validated in the independent test dataset.
rsFC of different brain networks predicted each domain of postural control in PD: Frontoparietal and Ventral Attention rsFC for APAs; Cerebellar-Subcortical and Visual rsFC and Auditory and Cerebellar-Subcortical rsFC for APRs; Ventral Attention and Ventral Multimodal rsFC for postural sway. In OA, CinguloOpercular and Somatomotor rsFC predicted APAs.
Our findings suggest that cortical networks predict postural control in PD and there is little overlap in brain network connectivities that predict different domains of postural control, given the rsFC methodology used. PwPD use different cortical networks for APAs compared to OA.
帕金森病（PD）是一种神经退行性疾病，导致姿势控制障碍。姿势控制涉及多个领域，例如站立时姿势摆动的控制、自动姿势反应（APR）和预期姿势调整（APA）。我们假设每个姿势域的障碍与静息状态功能连接（rsFC）相关，可通过预测模型来解释，并且皮质和小脑网络将预测 PD 患者（PwPD）的姿势控制。
基于模型的预测准确性，确定 rsFC 是否可以独立预测 PwPD 和老年人（OA）的三个姿势控制领域。
该队列包括 65 名处于 OFF 状态的 PwPD（67.7+8.1 岁）和 42 名 OA（69.7+8.2 岁）。使用六个穿戴式惯性传感器测量站立在泡沫上时的姿势摆动面积、向后推释放干扰时 APR 的步长以及自愿步态启动前的横向 APA 幅度。报告了 384 个感兴趣区域的静息状态 fMRI 数据，这些区域被分为 13 个功能大脑网络。使用预测模型来描述 rsFC 与姿势指标之间的关系，使用独立的训练（n=67）和验证（n=40）数据集。在训练样本中训练模型，并在独立测试数据集验证最佳模型的性能。
不同大脑网络的 rsFC 预测了 PD 中的每个姿势控制领域：额顶叶和腹侧注意 rsFC 预测 APA；小脑-皮质下和视觉 rsFC 和听觉和小脑-皮质下 rsFC 预测 APR；腹侧注意和腹侧多模态 rsFC 预测姿势摆动。在 OA 中，扣带回和躯体运动 rsFC 预测 APA。
我们的发现表明，皮质网络预测 PD 中的姿势控制，并且在使用 rsFC 方法时，预测不同姿势控制领域的大脑网络连通性几乎没有重叠。与 OA 相比，PwPD 对 APA 使用不同的皮质网络。
