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Background:The symptoms and pathogenesis of Parkinson's disease (PD) are complicated and an accurate diagnosis of PD is difficult, particularly in early-stage. Because functional magnetic resonance imaging (fMRI) is non-invasive and is characterized by the integration of different brain areas in terms of functional connectivity (FC), fMRI has been widely used in PD research. Non-motor symptom (NMS) features are also frequently present in PD before the onset of classical motor symptoms with pain as the primary NMS. Considering that PD could affect the pain process at multiple levels, we hypothesized that pain is one of the earliest symptoms in PD and investigated whether FC of the pain network was disrupted in PD without pain. To better understand the pathogenesis of pain in PD, we combined resting state and pain-stimuli-induced task state fMRI to identify alterations in FC related to pain in PD.Methods: Fourteen early drug-naïve PD without pain and 17 age-and sex-matched healthy controls (HC) participated in our testing task. We used independent component analysis to select seven functional networks related to PD and pain. We focused on abnormalities in FC and in functional network connectivity (FNC) in PD compared with HC during the task (51°C heat pain stimuli) and at rest. Results:Compared with HC, PD showed decreased FC in putamen within basal ganglia network (BGN) in task state and decreased FC in putamen of salience network (SN) and mid-cingulate cortex of sensorimotor network in rest state. FNC between the BGN and the SN are reduced during both states in PD compared with HC. In addition, right frontoparietal network (RFPN), which is considered as a bridge between the SN and default-mode network, was significantly disturbed during the task. Conclusion:These findings suggest that BGN plays a role in the pathological mechanisms of pain underlying PD, and RFPN likely contributes greatly to harmonization between intrinsic brain activity and external stimuli.

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by motor and nonmotor signs and symptoms. To date, many studies of PD have focused on its cardinal motor symptoms. To study the nonmotor signs of early PD, we investigated the reactions solicited by heat pain stimuli in early untreated PD patients without pain using fMRI. The activation patterns of contact heat stimuli (51°C) were assessed in 14 patients and 17 age- and sex-matched healthy controls. Patients with PD showed significant decreases in activation of the superior temporal gyrus (STG) and insula compared with controls. In addition, a significant relationship between activation of the insula and STG and the pain scores was observed in healthy controls but not in PD. This study provided further support that the insula and STG are important parts of the somatosensory circuitry recruited during the period of pain. The hypoactivity of the STG and insula in PD implied that functions including affective, cognitive, and sensory-discriminative processes, which are associated with the insula and STG, were disturbed. This finding supports the view that leaving early PD untreated could be tied directly to central nervous system dysfunction.

Integrating information of neuroimaging multi-modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), has become popularly for investigating various types of epilepsy. However, there are also some problems for the analysis of simultaneous EEG-fMRI data in epilepsy: one is the variation of HRFs, and another is low signal-to-noise ratio in the data. Here, we propose a new multimodal unsupervised method, termed local multimodal serial analysis (LMSA), which may compensate for these deficiencies in multimodal integration. A simulation study with comparison to the traditional EEG-informed fMRI analysis which directly implemented the general linear model (GLM) was conducted to confirm the superior performance of LMSA. Then, applied to the simultaneous EEG-fMRI data of familial cortical myoclonic tremor and epilepsy (FCMTE), some meaningful information of BOLD changes related to the EEG discharges, such as the cerebellum and frontal lobe (especially in the inferior frontal gyrus), were found using LMSA. These results demonstrate that LMSA is a promising technique for exploring various data to provide integrated information that will further our understanding of brain dysfunction.