طبقه‌بندی بیماری پارکینسون بر مبنای شاخص‌های درون-ناحیه‌ای و بین-ناحیه‌ای شبکه حرکتی مغز با استفاده از دادگان fMRI حالت استراحت

Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movement. Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. In this paper, we used amplitude of low frequency fluctuation (ALFF) for the study of intra-regional characteristics and cross-correlation analysis for the relationship between anatomical regions. According to the results of CCA, we presented functional connectivity network in healthy and PD. Comparing two networks showed that, firstly the activity of cerebellum and basal ganglia areas had a significant negative correlation in PD patients, while this relationship is weak and non-significant in healthy. We also used mean values of ALFF and ReHo as intra-region biomarkers in addition with inter-region characteristics in discriminative analysis to classify PD from healthy. This showed 85% accuracy in clustering. In addition, the score index is 89% and Jaccard coefficient of this clustering is 75%. We found that inter-regional feature (CCA) was more significant compared to the intra-regional feature (ALFF) and functional connectivity between left cerebellum and left putamen was the best discriminator between PD and control