Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Prague, Czech Republic.
Neuroimage Clin. 2023;38:103396. doi: 10.1016/j.nicl.2023.103396. Epub 2023 Apr 7.
In functional magnetic imaging (fMRI) in Parkinson's disease (PD), a paradigm consisting of blocks of finger tapping and rest along with a corresponding general linear model (GLM) is often used to assess motor activity. However, this method has three limitations: (i) Due to the strong magnetic field and the confined environment of the cylindrical bore, it is troublesome to accurately monitor motor output and, therefore, variability in the performed movement is typically ignored. (ii) Given the loss of dopaminergic neurons and ongoing compensatory brain mechanisms, motor control is abnormal in PD. Therefore, modeling of patients' tapping with a constant amplitude (using a boxcar function) and the expected Parkinsonian motor output are prone to mismatch. (iii) The motor loop involves structures with distinct hemodynamic responses, for which only one type of modeling (e.g., modeling the whole block of finger tapping) may not suffice to capture these structure's temporal activation. The first two limitations call for considering results from online recordings of the real motor output that may lead to significant sensitivity improvements. This was shown in previous work using a non-magnetic glove to capture details of the patients' finger movements in a so-called kinematic approach. For the third limitation, modeling motion initiation instead of the whole tapping block has been suggested to account for different temporal activation signatures of the motor loop's structures. In the present study we propose improvements to the GLM as a tool to study motor disorders. For this, we test the robustness of the kinematic approach in an expanded cohort (n = 31), apply more conservative statistics than in previous work, and evaluate the benefits of an event-related model function. Our findings suggest that the integration of the kinematic approach offers a general improvement in detecting activations in subcortical structures, such as the basal ganglia. Additionally, modeling motion initiation using an event-related design yielded superior performance in capturing medication-related effects in the putamen. Our results may guide adaptations in analysis strategies for functional motor studies related to PD and also in more general applications.
在帕金森病（PD）的功能磁共振成像（fMRI）中，常采用由手指敲击和休息块组成的范式以及相应的广义线性模型（GLM）来评估运动活动。然而，这种方法有三个局限性：（i）由于强磁场和圆柱形孔的受限环境，精确监测运动输出很麻烦，因此通常忽略了所执行运动的可变性。（ii）由于多巴胺能神经元的丧失和持续的代偿性大脑机制，PD 患者的运动控制异常。因此，使用恒定幅度（使用方波函数）对患者的敲击进行建模，以及对预期的帕金森运动输出进行建模，容易出现不匹配。（iii）运动回路涉及具有不同血液动力学反应的结构，对于这些结构，仅使用一种类型的建模（例如，对整个手指敲击块进行建模）可能不足以捕获这些结构的时间激活。前两个限制要求考虑真实运动输出的在线记录结果，这可能会显著提高敏感性。以前的工作使用非磁性手套来捕获患者手指运动的细节，在所谓的运动学方法中证明了这一点。对于第三个限制，有人建议对运动起始进行建模而不是对整个敲击块进行建模，以解释运动回路结构的不同时间激活特征。在本研究中，我们提出了对 GLM 的改进，作为研究运动障碍的工具。为此，我们在扩展队列（n=31）中测试了运动学方法的稳健性，使用比以前工作更保守的统计学方法，并评估了事件相关模型函数的好处。我们的发现表明，运动学方法的整合提供了一种检测基底节等皮质下结构激活的通用改进方法。此外，使用事件相关设计对运动起始进行建模可以更好地捕获纹状体中与药物相关的效应。我们的结果可能会指导与 PD 相关的功能性运动研究以及更一般的应用中的分析策略的调整。
