Discriminative Analysis of Parkinson’s Disease Based on Whole-Brain Functional Connectivity

Abstract

Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity‐based single‐subject classification

“…When comparing classification performance to previous work based on whole-brain functional connectomes [cf. Chen et al, 2015;Long et al, 2012;Meier et al, 2012;Su et al, 2013;Tang et al, 2012;Vergun et al, 2013;Yu et al, 2013], we note that our approach yielded higher functional specificity, allowing inference on the amount of diseasespecific information in well-defined functional systems. We acknowledge that even though most of the classifications well exceeded chance level, the achieved networkbased classification accuracies are not strong enough for successful connectivity-based single-subject diagnosis.…”

“…Arbabshirani et al, 2016;Kambeitz et al, 2015;Wolfers et al, 2015] or PD patients [cf. Chen et al, 2015;Long et al, 2012] from HCs, as well as aged from young subjects (NA) [cf. Meier et al, 2012;Vergun et al, 2013].…”

Exploring the reproducibility of functional connectivity alterations in Parkinson’s disease

“…A comprehensive review and analysis of the literature related to resting-state fMRI studies of Parkinson's disease is out of the scope of the present paper [Gottlich et al, 2013;Long et al, 2012;Skidmore et al, 2013;Baudrexel et al, 2011;Wu et al, 2011;Kwak et al, 2010;Kwak et al, 2012;Helmich et al, 2010;Helmich et al, 2011;Luo et al, 2014;Yu et al, 2013;Hacker et al, 2012;Kurani et al, 2015;Baggio et al, 2015;Esposito et al, 2013;Szewczyk-Krolikowski et al, 2014;Tessitore et al, 2012;Sharman et al, 2013;Wu et al, 2012;Chen et al, 2015;Wen et al, 2013;Zhang et al, 2015] ( Supplementary Table 1; see also the review by [Tahmasian et al, 2015]). We only mention some important inconsistencies of reported functional connectivity changes in PD.…”

Neurodegeneration and the Cerebellum

“…Most abnormal connections localize to the default mode, cingulo-opercular, and frontoparietal networks of the cerebellum [69]. Despite the regions of hypoactivity, when patients are evaluated in the off-levodopa state, levels of connectivity in both cerebellar-cerebellar and whole brain-cerebellar circuits actually increased, and thus their behavioral performances in several tasks improved.…”