Alteration of Brain Functional Networks in Early-Stage Parkinson's Disease: A Resting-State fMRI Study.
ABSTRACT: Although alterations of topological organization have previously been reported in the brain functional network of Parkinson's disease (PD) patients, the topological properties of the brain network in early-stage PD patients who received antiparkinson treatment are largely unknown. This study sought to determine the topological characteristics of the large-scale functional network in early-stage PD patients. First, 26early-stage PD patients (Hoehn and Yahr stage:1-2) and 30 age-matched normal controls were scanned using resting-state functional MRI. Subsequently, graph theoretical analysis was employed to investigate the abnormal topological configuration of the brain network in early-stage PD patients. We found that both the PD patient and control groups showed small-world properties in their functional brain networks. However, compared with the controls, the early-stage PD patients exhibited abnormal global properties, characterized by lower global efficiency. Moreover, the modular structure and the hub distribution were markedly altered in early-stage PD patients. Furthermore, PD patients exhibited increased nodal centrality, primarily in the bilateral pallidum, the inferior parietal lobule, and the medial superior frontal gyrus, and decreased nodal centrality in the caudate nucleus, the supplementary motor areas, the precentral gyrus, and the middle frontal gyrus. There were significant negative correlations between the Unified Parkinson Disease Rating Scale motor scores and nodal centralities of superior parietal gyrus. These results suggest that the topological organization of the brain functional network was altered in early-stage PD patients who received antiparkinson treatment, and we speculated that the antiparkinson treatment may affect the efficiency of the brain network to effectively relieve clinical symptoms of PD.
Project description:Objective: This study aimed to explore alterations in the topological properties of the functional brain network in primary Parkinson's disease (PD) patients with freezing of gait (PD-FOG). Methods: Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 23 PD-FOG patients, 33 PD patients without FOG (PD-nFOG), and 24 healthy control (HC) participants. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 90 brain regions, and topological properties were analyzed by using graph theory approaches. The network-based statistics (NBS) method was used to determine the suprathreshold connected edges (P < 0.05; threshold T = 2.725), and statistical significance was estimated by using the non-parametric permutation method (5,000 permutations). Statistically significant topological properties were further evaluated for their relationship with clinical neurological scales. Results: The topological properties of the functional brain network in PD-FOG and PD-nFOG showed no abnormalities at the global level. However, compared with HCs, PD-FOG patients showed decreased nodal local efficiency in several brain regions, including the bilateral striatum, frontoparietal areas, visual cortex, and bilateral superior temporal gyrus, increased nodal local efficiency in the left gyrus rectus. When compared with PD-nFOG patients and HCs, PD-FOG showed increased betweenness centrality in the left hippocampus. Moreover, compared to HCs, both PD-FOG and PD-nFOG patients displayed reduced network connections by using the NBS method, mainly involving the sensorimotor cortex (SM), visual network (VN), default mode network (DMN), auditory network (AN), dorsal attention network (DAN), subcortical regions, and limbic network (LIM). The local node efficiency of the right temporal pole: superior temporal gyrus in PD-FOG patients was positively correlated with the Freezing of Gait Questionnaire (FOGQ) scores. Conclusions: This study demonstrates the disrupted regional topological organization in PD-FOG patients, especially associated with damage to the subcortical regions and multiple cortical regions. Our results provide insights into the dysfunctional mechanisms of the relevant networks and indicate potential neuroimaging biomarkers of PD-FOG.
Project description:Previous studies reported that integrated information in the brain ultimately determines the subjective experience of patients with chronic pain, but how the information is integrated in the brain connectome of functional dyspepsia (FD) patients remains largely unclear. The study aimed to quantify the topological changes of the brain network in FD patients.Small-world properties, network efficiency and nodal centrality were utilized to measure the changes in topological architecture in 25 FD patients and 25 healthy controls based on functional magnetic resonance imaging. Pearson's correlation assessed the relationship of each topological property with clinical symptoms.FD patients showed an increase of clustering coefficients and local efficiency relative to controls from the perspective of a whole network as well as elevated nodal centrality in the right orbital part of the inferior frontal gyrus, left anterior cingulate gyrus and left hippocampus, and decreased nodal centrality in the right posterior cingulate gyrus, left cuneus, right putamen, left middle occipital gyrus and right inferior occipital gyrus. Moreover, the centrality in the anterior cingulate gyrus was significantly associated with symptom severity and duration in FD patients. Nevertheless, the inclusion of anxiety and depression scores as covariates erased the group differences in nodal centralities in the orbital part of the inferior frontal gyrus and hippocampus.The results suggest topological disruption of the functional brain networks in FD patients, presumably in response to disturbances of sensory information integrated with emotion, memory, pain modulation, and selective attention in patients.
Project description:Background:Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson's disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods:Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027?Hz; slow-4: 0.027-0.073?Hz; slow-3: 0.073-0.198?Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results:Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions:Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.
Project description:Recently, graph theoretical approaches applied to neuroimaging data have advanced understanding of the human brain connectome and its abnormalities in psychiatric disorders. However, little is known about the topological organization of brain white matter networks in posttraumatic stress disorder (PTSD). Seventy-six patients with PTSD and 76 age, gender, and years of education-matched trauma-exposed controls were studied after the 2008 Sichuan earthquake using diffusion tensor imaging and graph theoretical approaches. Topological properties of brain networks including global and nodal measurements and modularity were analyzed. At the global level, patients showed lower clustering coefficient (p = .016) and normalized characteristic path length (p = .035) compared with controls. At the nodal level, increased nodal centralities in left middle frontal gyrus, superior and inferior temporal gyrus and right inferior occipital gyrus were observed (p?<?.05, corrected for false-discovery rate). Modularity analysis revealed that PTSD patients had significantly increased inter-modular connections in the fronto-parietal module, fronto-striato-temporal module, and visual and default mode modules. These findings indicate a PTSD-related shift of white matter network topology toward randomization. This pattern was characterized by an increased global network integration, reflected by increased inter-modular connections with increased nodal centralities involving fronto-temporo-occipital regions. This study suggests that extremely stressful life experiences, when they lead to PTSD, are associated with large-scale brain white matter network topological reconfiguration at global, nodal, and modular levels.
Project description:The present study was aimed to evaluate resting-state functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls. Resting-state fMRI was performed in 26 adult narcolepsy patients and 30 matched healthy controls. MRI data were first analyzed by group independent component analysis, then a graph theoretical method was applied to evaluate the topological properties in the whole brain. Small-world network parameters and nodal topological properties were measured. Altered topological properties in brain areas between groups were selected as region-of-interest seeds, then the functional connectivity among these seeds was compared between groups. Partial correlation analysis was performed to evaluate the relationship between the severity of sleepiness and functional connectivity or topological properties in the narcolepsy patients. Twenty-one independent components out of 48 were obtained. Compared with healthy controls, the narcolepsy patients exhibited significantly decreased functional connectivity within the executive and salience networks, along with increased functional connectivity in the bilateral frontal lobes within the executive network. There were no differences in small-world network properties between patients and controls. The altered brain areas in nodal topological properties between groups were mainly in the inferior frontal cortex, basal ganglia, anterior cingulate, sensory cortex, supplementary motor cortex, and visual cortex. In the partial correlation analysis, nodal topological properties in the putamen, anterior cingulate, and sensory cortex as well as functional connectivity between these regions were correlated with the severity of sleepiness (sleep latency, REM sleep latency, and Epworth sleepiness score) among narcolepsy patients. Altered connectivity within the executive and salience networks was found in narcolepsy patients. Functional connection changes between the left frontal cortex and left caudate nucleus may be one of the parameters describing the severity of narcolepsy. Changes in the nodal topological properties in the left putamen and left posterior cingulate, changes in functional connectivity between the left supplementary motor area and right occipital as well as in functional connectivity between the left anterior cingulate gyrus and bilateral postcentral gyrus can be considered as a specific indicator for evaluating the severity of narcolepsy.
Project description:Highlights • MELAS patients showed topological reorganization of brain functional network.• Network abnormalities in MELAS patients may be affected by stroke-like lesions.• Graph theory based on rs-fMRI may be used for monitoring the status of MELAS. Mitochondrial encephalomyopathy with lactic acidosis and stroke?like episodes (MELAS) is a rare maternally inherited genetic disease; however, little is known about its underlying brain basis. Furthermore, the topological organization of brain functional network in MELAS has not been explored. Here, 45 patients with MELAS (22 at acute stage, 23 at chronic stage) and 22 normal controls were studied using resting- state functional magnetic resonance imaging and graph theory analysis approaches. Topological properties of brain functional networks including global and nodal metrics, rich club organization and modularity were analyzed. At the global level, MELAS patients exhibited reduced clustering coefficient, normalized clustering coefficient, normalized characteristic path length and local network efficiency compared with the controls. At the nodal level, several nodes with abnormal degree centrality and nodal efficiency were detected in MELAS patients, and the distribution of these nodes was partly consistent with the stroke-like lesions. For rich club organization, rich club nodes were reorganized and the connections among them were decreased in MELAS patients. Modularity analysis revealed that MELAS patents had altered intra- or inter-modular connections in default mode network, fronto-parietal network, sensorimotor network, occipital network and cerebellum network. Notably, the patients at acute stage showed more obvious changes in these topological properties than the patients at chronic stage. These findings indicated that MELAS patients, particularly those at acute stage, exhibited topological reorganization of the whole-brain functional network. This study may help us to understand the neuropathological mechanisms of MELAS.
Project description:Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting-state functional magnetic resonance imaging was performed on 51 drug-naïve children with ADHD and 55 age-matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q?<?0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p?<0?.05). Alterations in network topologies in drug-naïve ADHD patients indicate weaker small-worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo-fronto-parietal attention network were associated with inhibitory control deficits.
Project description:The 'human connectome' concept has been proposed to significantly increase our understanding of how functional brain states emerge from their underlying structural substrates. Especially, the network hub has been considered one of the most important topological properties to interpret a network as a complex system. However, previous structural brain connectome studies have reported network hub regions based on various nodal resolutions. We hypothesized that brain network hubs should be determined considering various nodal scales in a certain range. We tested our hypothesis using the hub strength determined by the mean of the "hubness" values over a range of nodal scales. Some regions of the precuneus, superior occipital gyrus, and superior parietal gyrus in a bilaterally symmetric fashion had a relatively higher level of hub strength than other regions. These regions had a tendency of increasing contributions to local efficiency than other regions. We proposed a methodological framework to detect network hubs considering various nodal scales in a certain range. This framework might provide a benefit in the detection of important brain regions in the network.
Project description:Parkinson's disease (PD) is a neurodegenerative disease characterized by dysfunction in distributed functional brain networks. Previous studies have reported abnormal changes in static functional connectivity using resting-state functional magnetic resonance imaging (fMRI). However, the dynamic characteristics of brain networks in PD is still poorly understood. This study aimed to quantify the characteristics of dynamic functional connectivity in PD patients at nodal, intra- and inter-subnetwork levels. Resting-state fMRI data of a total of 42 PD patients and 40 normal controls (NCs) were investigated from the perspective of the temporal variability on the connectivity profiles across sliding windows. The results revealed that PD patients had greater nodal variability in precentral and postcentral area (in sensorimotor network, SMN), middle occipital gyrus (in visual network), putamen (in subcortical network) and cerebellum, compared with NCs. Furthermore, at the subnetwork level, PD patients had greater intra-network variability for the subcortical network, salience network and visual network, and distributed changes of inter-network variability across several subnetwork pairs. Specifically, the temporal variability within and between subcortical network and other cortical subnetworks involving SMN, visual, ventral and dorsal attention networks as well as cerebellum was positively associated with the severity of clinical symptoms in PD patients. Additionally, the increased inter-network variability of cerebellum-auditory pair was also correlated with clinical severity of symptoms in PD patients. These observations indicate that temporal variability can detect the distributed abnormalities of dynamic functional network of PD patients at nodal, intra- and inter-subnetwork scales, and may provide new insights into understanding PD.
Project description:Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n?=?42 treatment-responsive depression (RD) and n?=?40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (Dnodal) in the left anterior cingulate gyrus; as for RD, the Dnodal of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P?<?0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the ? and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication.