Chen Yingchuan, Zhu Guanyu, Liu Defeng, Liu Yuye, Yuan Tianshuo, Zhang Xin, Jiang Yin, Du Tingting, Zhang Jianguo
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China.
J Neurol Sci. 2020 Apr 15;411:116721. doi: 10.1016/j.jns.2020.116721. Epub 2020 Feb 3.
In Parkinson's disease (PD), the thalamus plays an important role in pathogenesis and disease symptoms; however, the morphological changes in thalamic subnuclei have not been clearly investigated. And there are still many challenges in individual PD diagnosis, especially clinical condition evaluations. Structural magnetic resonance imaging (MRI) was performed on 131 PD patients and 69 healthy controls (HC), and the volumes of 25 thalamic subnuclei were evaluated by FreeSurfer and a newly developed thalamus segment algorithm. Then, the individual PD diagnosis and clinical condition prediction were conducted on support vector machines (SVM) classification or regression. The bilateral thalami were enlarged; the volumes of 21 of 25 left thalamic subnuclei and 20 of 25 right thalamic subnuclei were increased, accompanied by 2 left nuclei atrophy. An accuracy of 95% with sensitivity of 97.44%, and specificity of 90.48% was achieved in PD diagnosis. United Parkinson's disease Rating Scale (UPDRS) III, limb bradykinesia, and axial akinetic symptoms score prediction were obtained with Pearson correlation coefficient of 0.5497, 0.5382, and 0.5911, respectively; however, the results of tremor, rigidity, and speech prediction were limited. Finally, accuracies of 76.92% were achieved in the UPDRS III improvement prediction. These findings confirmed that numerous left and right thalamic subnuclei were enlarged, accompanied by a few atrophies. The individual PD diagnosis, symptom, and clinical improvement prediction could be achieved based on morphology of thalamic subnuclei via machine learning.
在帕金森病（PD）中，丘脑在发病机制和疾病症状中起重要作用；然而，丘脑亚核的形态变化尚未得到明确研究。而且，个体PD诊断仍存在许多挑战，尤其是临床状况评估。对131例PD患者和69名健康对照者（HC）进行了结构磁共振成像（MRI），并通过FreeSurfer和一种新开发的丘脑分割算法评估了25个丘脑亚核的体积。然后，在支持向量机（SVM）分类或回归的基础上进行个体PD诊断和临床状况预测。双侧丘脑增大；25个左侧丘脑亚核中的21个以及25个右侧丘脑亚核中的20个体积增加，同时有2个左侧核萎缩。PD诊断的准确率为95%，灵敏度为97.44%，特异性为90.48%。统一帕金森病评定量表（UPDRS）III、肢体运动迟缓及轴性运动不能症状评分预测的Pearson相关系数分别为0.5497、0.5382和0.5911；然而，震颤、强直和言语预测的结果有限。最后，UPDRS III改善预测的准确率为76.92%。这些发现证实，左右丘脑的许多亚核增大，同时伴有少数萎缩。通过机器学习，基于丘脑亚核的形态可实现个体PD诊断、症状及临床改善预测。
