The topological organization of brain networks can be depicted and characterized by the concept of graph theory ( Bullmore and Sporns, 2009 Park and Friston, 2013). The spontaneous activity of human brain is highly structured in which anatomical regions interact within a network ( Schnitzler and Gross, 2005 Bullmore and Sporns, 2012). This study sheds lights on network disorganization in PD with tremor and helps for understanding the neural basis underlying this type of PD. More importantly, functional and morphological brain networks were highly associated in terms of network local efficiency in PD patients. We further found that the global and local network efficiency both worked well on PD classifications (i.e., using MVPA) and clinical performance descriptions (i.e., using MLRM).
Notably, the global and local efficiency were both significantly increased in the morphological brain network of PD patients. However, these observations were not identified in the network global efficiency. Network local efficiency could effectively discriminate PD patients from the NCs using multivariate pattern analysis, and could also describe the variability of tremor based on a multiple linear regression model (MLRM). Compared with the NCs, the network local efficiency was decreased and the network global efficiency was increased in PD patients. Graph-based network analysis indicated that the information translation efficiency in the functional brain network was disrupted within the wavelet scale 2 (i.e., 0.063–0.125 Hz) in PD patients. This study collected magnetic resonance imaging data from 36 participants and constructed wavelet-based functional and morphological brain networks for individual participants. 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.
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