Qiyong Gong

Purpose The mutations of the glucocerebrosidase (GBA) gene are the greatest genetic risk factor for Parkinson’s disease (PD). The mechanism underlying the association between GBA mutations and PD has not been fully elucidated. Methods Using resting-state functional magnetic resonance imaging and graph theory analysis to investigate the disrupted topological organization in PD patients with GBA mutation (GBA-PD). Eleven GBA-PD patients, 11 noncarriers with PD, and 18 healthy controls (HCs) with a similar age and sex distribution were recruited. Individual whole-brain functional connectome was constructed, and the global and nodal topological disruptions were calculated among groups. Partial correlation analyses between the clinical features of patients with PD and topological alterations were performed. Results The GBA-PD group showed prominently decreased characteristic path length (L_p) and increased global efficiency (E_g) compared to HCs at the global level; a significantly increased nodal betweenness centrality in the medial prefrontal cortex (mPFC) and precuneus within the default mode network, and precentral gyrus within the sensorimotor network, while a significantly decreased betweenness centrality in nodes within the cingulo-opercular network compared to the noncarrier group at the regional level. The altered nodal betweenness centrality of mPFC was positively correlated with fatigue severity scale scores in all patients with PD. Conclusion The preliminary pilot study found that GBA-PD patients had a higher functional integration at the global level. The nodal result of the mPFC is congruent with the potential fatigue pathology in PD and is suggestive of a profound effect of GBA mutations on the clinical fatigue in patients with PD.

The effective early prediction of clinical outcomes of Parkinson's disease (PD) is of great significance in the implementation of appropriate interventions. We aimed to propose a method based on the use of baseline resting-state functional characteristics (i.e., fractional amplitude of low-frequency fluctuations, fALFF) to predict motor progression in PD patients. Resting-state functional magnetic resonance imaging was performed on 48 newly-diagnosed drug-naive PD patients and 27 age- and sex- matched healthy controls (HCs). Two PD subgroups were defined with different annual increase of Unified PD Rating Scale Part III motor scores. Least absolute shrinkage and selection operator regression analysis was performed to explore the baseline region-functional indicators for PD discrimination as well as the predictors for future motor deficits. Two significant models composed of baseline fALFF values from cerebral subregions were proposed. The classification model that distinguished PD patients from HCs (area under the curve [AUC] = 0.897) showed the most significant imaging characteristics in the putamen and precentral gyrus. The other prediction model that evaluated the degree of future deterioration of motor symptoms in PD patients (AUC = 0.916) showed the most significant imaging characteristics in the superior occipital gyrus and caudate nucleus. Furthermore, the increased regional function in bilateral caudate nuclei was correlated with the lower annual increase in motor deficits in all PD patients. The caudate nucleus might be the core region responsible for future motor deficits in newly-diagnosed PD patients, which may aid the development of disease progression preventive strategies in clinical practice.

Background: Idiopathic blepharospasm (BSP) is a common adult-onset focal dystonia. Neuroimaging technology can be used to visualize functional and microstructural changes of the whole brain.Method: We used resting-state functional MRI (rs-fMRI) and graph theoretical analysis to explore the functional connectome in patients with BSP. Altogether 20 patients with BSP and 20 age- and gender-matched healthy controls (HCs) were included in the study. Measures of network topology were calculated, such as small-world parameters (clustering coefficient [C_p], the shortest path length [L_p]), network efficiency parameters (global efficiency [E_glob], local efficiency [E_loc]), and the nodal parameter (nodal efficiency [E_nod]). In addition, the least absolute shrinkage and selection operator (LASSO) regression was adopted to determine the most critical imaging features, and the classification model using critical imaging features was constructed.Results: Compared with HCs, the BSP group showed significantly decreased E_loc. Imaging features of nodal centrality (E_nod) were entered into the LASSO method, and the classification model was constructed with nine imaging nodes. The area under the curve (AUC) was 0.995 (95% CI: 0.973–1.000), and the sensitivity and specificity were 95% and 100%, respectively. Specifically, four imaging nodes within the sensorimotor network (SMN), cerebellum, and default mode network (DMN) held the prominent information. Compared with HCs, the BSP group showed significantly increased E_nod in the postcentral region within the SMN, decreased E_nod in the precentral region within the SMN, increased E_nod in the medial cerebellum, and increased E_nod in the precuneus within the DMN.Conclusion: The network model in BSP showed reduced local connectivity. Baseline connectomic measures derived from rs-fMRI data may be capable of identifying patients with BSP, and regions from the SMN, cerebellum, and DMN may provide key insights into the underlying pathophysiology of BSP.

Background: Cognitive impairment is a common non-motor symptom in patients with Parkinson’s disease (PD). Mild cognitive impairment (MCI) is also prevalent in nondemented PD patients, even in newly diagnosed PD patients. The possible impacts of MCI on brain function activities for PD patients need more investigation, and the potential of emerging technologies for detecting underlying pathophysiology of cognitive signs in PD can be further improved. Method: Forty-seven newly diagnosed drug-naïve PD patients (28 PD-MCI patients and 19 PD patients with cognitively unimpaired (PD-CU)) and 28 healthy controls (HCs) underwent resting-state functional MRI. The connectivity patterns of specific networks were investigated through the independent component analysis among PD-MCI, PD-CU and HCs groups. Results: The independent component analysis revealed significantly decreased functional connectivity (FC) of the default mode network, visual network and sensorimotor network in the PD-MCI subgroup compared with the HC group. Furthermore, FC of the default mode network was positively correlated with memory scores from the brief visuospatial memory test-revised, and FC of the visual network was positively correlated with visuospatial scores from the clock copying test in the PD-MCI group. In all patients with PD, FC of the sensorimotor network negatively correlated with motor severity scores from the Unified PD Rating Scale (UPDRS) part III. On the other hand, the potential damage was more likely to occur in FC between the sensorimotor network and limbic network, and between the ventral attention network and visual network in all PD patients. Conclusions: Newly diagnosed drug-naïve PD-MCI patients showed characteristic damage of FC within the default mode network, visual network and sensorimotor network, and all PD patients presented impaired FC between the sensorimotor network and limbic network, and FC between the ventral attention network and visual network. These network-wide functional aberrations may underline the pathophysiology of PD.