Disrupted morphological grey matter networks in early-stage Parkinson’s disease

Abstract

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

“…Many studies indicated that the lateral parietal cortex (including SPL) plays an important role in PD with movement dysfunction (Tian et al, 2020), and the precuneus is located in SPL and involved in visuospatial processing, episodic memory, self-reflection, and consciousness (Guo et al, 2020). Abnormal spontaneous brain activities in right PCL (Chen B. et al, 2017;Guo et al, 2020;Sheng et al, 2021;Suo et al, 2021), left ITG (Jiang et al, 2016;Chen B. et al, 2017;Guo et al, 2020;Tian et al, 2020), and STG (Chen B. et al, 2017;Lin et al, 2017) in patients with PD had also been reported. Those are consistent with the previous results.…”

Morphologic brain network predicts levodopa responsiveness in Parkinson disease

“…High-resolution (3.0 T or 7.0 T) MRI can provide information on pathologic changes in the brain of PD patients ( Sclocco et al, 2018 ). Previous brain network-related studies in PD patients have mainly focused on diagnosis; analyzing the relationship between GM network topology and the GM network determined from imaging data and disease severity can provide greater resolution for early diagnosis ( Suo et al, 2021b ). PD patients have higher E global and E local than normal subjects, which are unrelated to their clinical features ( Zhang et al, 2015 ).…”

“…The distribution divergence-based method has been used in morphologic brain network investigations including in PD ( Yoo et al, 2017 ; Fiorenzato et al, 2019 ; Yang et al, 2021 ). Specifically, Kullback–Leibler (KL) divergence ( Suo et al, 2021b ) was applied to construct the network according to the following formula:…”

Topologically convergent and divergent morphological gray matter networks in early‐stage Parkinson's disease with and without mild cognitive impairment

“…Twenty‐nine HC matched for age, gender, and years of education, with no history of neuropsychiatric disease, were recruited for comparative purposes. Details of inclusion and exclusion criteria are described in our previous paper (Suo et al, 2017; Suo et al, 2019; Suo et al, 2021a).…”