Sivaranjini S, Sujatha C M
Department of Electronics and Communication Engineering, College of Engineering (CEG), Anna University, Chennai, India.
Cogn Neurodyn. 2021 Oct;15(5):835-845. doi: 10.1007/s11571-021-09671-4. Epub 2021 Mar 14.
Cognitive impairment in Parkinson's Disease (PD) is the most prevalent non-motor symptom that requires analysis of anatomical associations to cognitive decline in PD. The objective of this study is to analyse the morphological variations of the subcortical structures to assess cognitive dysfunction in PD. In this study, T1 MR images of 58 Healthy Control (HC) and 135 PD subjects categorised as 91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D) subjects, based on cognitive scores are utilised. The 132 anatomical regions are segmented using spatially localized multi-atlas model and volumetric analysis is carried out. The morphological alterations through textural features are captured to differentiate among the HC and PD subjects under different cognitive domains. The volumetric differences in the segmented subcortical structures of accumbens, amygdala, caudate, putamen and thalamus are able to predict cognitive impairment in PD. The volumetric distribution of the subcortical structures in PD-MCI subjects exhibit an overlap with the HC group due to lack of spatial specificity in their atrophy levels. The 3D GLCM features extracted from the significant subcortical structures could discriminate HC, NC-PD, PD-MCI and PD-D subjects with better classification accuracies. The disease related atrophy levels of the subcortical structures captured through morphological analysis provide sensitive evaluation of cognitive impairment in PD.
帕金森病（PD）中的认知障碍是最常见的非运动症状，需要分析其与PD认知衰退的解剖学关联。本研究的目的是分析皮质下结构的形态变化，以评估PD中的认知功能障碍。在本研究中，基于认知分数，使用了58名健康对照（HC）和135名PD患者的T1 MR图像，这些PD患者被分类为91名认知正常的PD（NC-PD）、25名轻度认知障碍的PD（PD-MCI）和19名患有痴呆症的PD（PD-D）患者。使用空间定位多图谱模型对132个解剖区域进行分割，并进行体积分析。通过纹理特征捕捉形态学改变，以区分不同认知领域的HC和PD患者。伏隔核、杏仁核、尾状核、壳核和丘脑的分割皮质下结构的体积差异能够预测PD中的认知障碍。由于PD-MCI患者萎缩水平缺乏空间特异性，其皮质下结构的体积分布与HC组存在重叠。从重要皮质下结构提取的3D灰度共生矩阵（GLCM）特征能够以更好的分类准确率区分HC、NC-PD、PD-MCI和PD-D患者。通过形态学分析捕捉到的皮质下结构的疾病相关萎缩水平为PD中的认知障碍提供了敏感的评估。
