Aberrant brain network efficiency in Parkinson’s disease patients with tremor: a multi-modality study

Abstract

Read online

The coordination of spontaneous brain activity is widely enhanced relative to compensation activity in Parkinson’s disease (PD) with tremor; however, the associated topological organization remains unclear. Here, we collected magnetic resonance imaging (MRI) data from 16 patients and 20 matched normal controls (NCs) and constructed wavelet-based functional and morphological brain networks for individual participants. Graph-based network analysis indicated that the information translation efficiency in the functional brain network was disrupted within the wavelet scale 2 (i.e., .063–.125 Hz) in PD patients. Compared with the NCs, the network local efficiency was decreased and the network global efficiency was increased in PD patients. Network local efficiency could effectively discriminate PD patients from the NCs using multivariate pattern analysis (MVPA), and could also describe the variability of tremor based on a multiple linear regression model (MLRM). However, these observations were not identified in the network global efficiency. Notably, the global and local efficiency were both significantly increased in the morphological brain network of PD patients. Further analysis showed that the global and local network efficiency both performed well in PD classifications (i.e., using MVPA) and clinical performance descriptions (i.e., using MLRM). More importantly, functional and morphological brain networks were highly associated in terms of network local efficiency in PD patients. These findings provide a comprehensive view of network disorganization in PD with tremor and have important implications for understanding the neural substrates underlying this specific type of PD.

Keywords

• Tremor
• Parkinson’s disease
• multivariate analysis
• resting-state network
• individual morphological network