Rich-club Connectivity Research Articles

Effects of second language acquisition on brain functional networks at different developmental stages.

Previous studies have shown that language acquisition influences both the structure and function of the brain. However, whether the acquisition of a second language at different periods of life alters functional network organization in different ways remains unclear. Here, functional magnetic resonance imaging data from 27 English-speaking monolingual controls and 52 Spanish-English bilingual individuals, including 22 early bilinguals who began learning a second language before the age of ten and 30 late bilinguals who started learning a second language at age fourteen or later, were collected from the OpenNeuro database. Topological metrics of resting-state functional networks, including small-world attributes, network efficiency, and rich- and diverse-club regions, that characterize functional integration and segregation of the networks were computed via a graph theoretical approach. The results showed obvious increases in network efficiency in early bilinguals and late bilinguals relative to the monolingual controls; for example, the global efficiency of late bilinguals and early bilinguals was improved relative to that of monolingual controls, and the local efficiency of early bilinguals occupied an intermediate position between that of late bilinguals and monolingual controls. Obvious increases in rich-club and diverse-club functional connectivity were observed in the bilinguals relative to the monolingual controls. Three network metrics were positively correlated with Spanish proficiency test scores. These findings demonstrated that early and late acquisition of a second language had different impacts on the functional networks of the brain.

Increasing variance of rich-club nodes distribution in early onset depression according to dynamic network.

Early onset depression (EOD) and late onset depression (LOD) are thought to have different pathogeneses, but lack of pathological evidence. In the current study we describe the dynamic rich-club properties of patients with EOD and LOD to address this question indirectly. We recruited 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. Memory, executive function and processing speed were measured, and resting-stage functional MRI was performed with all participants. We constructed a dynamic functional connectivity network and carried out rich-club and modularity analyses. Normalized mutual information (NMI) was applied to describe the variance in rich-club nodes distribution and partitioning. The NMI coefficient of rich club nodes distribution among the three groups was the lowest in the EOD patients (F = 4.298; P = 0.0151, FDR = 0.0231), which was positively correlated with rich-club connectivity (R = 0.886, P < 0.001) and negatively correlated with memory (R = -0.347, P = 0.038) in the EOD group. In the LOD patients, non-rich-club connectivity was positively correlated with memory (R = 0.353, P = 0.030 and R = 0.420, P = 0.009). Furthermore, local connectivity was positively correlated with processing speed in the LOD patients (R = 0.374, P = 0.021). The modular partition was different between the EOD patients and the HCs (P = 0.0013 < 0.05/3). The temporal instability of rich-club nodes was found in the EOD patients, but not the LOD patients, supporting the hypothesis that EOD and LOD result from different pathogenesis, and showing that the instability of the rich-club nodes across time might disrupt rich-club connectivity.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors.

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease.

Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD. In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis. We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia. Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.

Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study.

It has been suggested that the rich club organization in major depressive disorder (MDD) was altered. However, it remained unclear whether the rich club organization could be served as a biomarker that predicted the improvement of clinical symptoms in MDD. The current study included 29 mild or moderate patients with MDD, who were grouped into a treatment group (receiving cognitive behavioral therapy or real-time fMRI feedback treatment) and a no-treatment group. Resting-state MRI scans were obtained for all participants. Graph theory was employed to investigate the treatment-related changes in network properties and rich club organization. We found that patients in the treatment group had decreased depressive symptom scores and enhanced rich club connectivity following the nonpharmacological treatment. Moreover, the changes in rich club connectivity were significantly correlated with the changes in depressive symptom scores. In addition, the nonpharmacological treatment on patients with MDD increased functional connectivity mainly among the salience network, default mode network, frontoparietal network, and subcortical network. Patients in the no-treatment group did not show significant changes in depressive symptom scores and rich club organization. Those results suggested that the remission of depressive symptoms after nonpharmacological treatment in MDD patients was associated with the increased efficiency of global information processing.

Functional and spatial rewiring principles jointly regulate context-sensitive computation.

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

White matter structural topologic efficiency predicts individual resistance to sleep deprivation.

Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD. The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD. DTI and graph theory approaches were used to investigate the topologic organization differences of the brain structural connectome between SD-vulnerable and -resistant individuals. We measured the level of global efficiency and clustering in rich club and non-rich club organizations. We demonstrated that participants vulnerable to SD had less global efficiency, network strength, and local efficiency but longer shortest path length compared with participants resistant to SD. Lower efficiency was mainly distributed in the right insula, bilateral thalamus, bilateral frontal, temporal, and temporal lobes. Furthermore, a disrupted subnetwork was observed that consisted of widespread connections. Moreover, the vulnerable group showed significantly decreased strength of the rich club compared with the resistant group. The strength of rich club connectivity was found to be correlated negatively with PVT performance (r = -0.395, p = 0.005). We further tested the reliability of the results. The findings revealed that individual differences in resistance to SD are related to disrupted topologic efficiency connectome pattern, and our study may provide potential connectome-based biomarkers for the early detection of the vulnerable degree to SD.

Impaired rich-club connectivity in childhood absence epilepsy.

Childhood absence epilepsy (CAE) is a well-known pediatric epilepsy syndrome. Recent evidence has shown the presence of a disrupted structural brain network in CAE. However, little is known about the rich-club topology. This study aimed to explore the rich-club alterations in CAE and their association with clinical characteristics. Diffusion tensor imaging (DTI) datasets were acquired in a sample of 30 CAE patients and 31 healthy controls. A structural network was derived from DTI data for each participant using probabilistic tractography. Then, the rich-club organization was examined, and the network connections were divided into rich-club connections, feeder connections, and local connections. Our results confirmed a less dense whole-brain structural network in CAE with lower network strength and global efficiency. In addition, the optimal organization of small-worldness was also damaged. A small number of highly connected and central brain regions were identified to form the rich-club organization in both patients and controls. However, patients exhibited a significantly reduced rich-club connectivity, while the other class of feeder and local connections was relatively spared. Moreover, the lower levels of rich-club connectivity strength were statistically correlated with disease duration. Our reports suggest that CAE is characterized by abnormal connectivity concentrated to rich-club organizations and might contribute to understanding the pathophysiological mechanism of CAE.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia.

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n=41) and ESZ (n=70) relative to healthy controls (HC; n=74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps<.024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps<.048). There was also cortical thinning of rich-club regions in ESZ (ps<.013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n=9) had fewer connections among rich-club regions (ps<.037) and greater modularity (ps<.037) compared to CHR-P non-converters (n=19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps<.012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

Altered rich-club organization of brain functional network in autism spectrum disorder.

Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n= 45) and healthy controls (HC; n= 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD=259.20 ± 32.97, HC=244.98 ± 30.09, p= 0.041; degree of local connections: ASD=664.02 ± 39.19, HC=679.89 ± 34.05, p= 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.

The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults.

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036). We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.

Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease.

Background:Though mediotemporal lobe volume changes are well-known features of Alzheimer’s disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN).Objective:To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club.Methods:To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression.Results:Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume.Conclusion:Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.

Targeting disrupted rich-club network organization with neuroplasticity-based computerized cognitive remediation in major depressive disorder patients

Disrupted rich-club organization has been extensively studied in major depressive disorder (MDD) patients. Although data indicate that neuroplasticity-based computerized cognitive remediation (nCCR) can accelerate clinical responses in MDD patients, the mechanisms underlying its antidepressant efficacy are unknown. In this study, all MDD patients underwent two (baseline and week 4) neuropsychological assessments and DTI imaging. Additionally, 17 MDD patients did nCCR for 30 hours spread across 4 weeks. Rich-club organization was calculated with a graph-theoretical approach, and SC-FC coupling was explored. After 4 weeks of treatment, the number of rich-club connections, global efficiency, and SC-FC coupling strength increased significantly and were negatively associated with TMT-B scores. The effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD. Effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD.

Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease.

ObjectiveThe diagnosis of Parkinson’s disease (PD) remains challenging. Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has revealed the metabolic abnormalities associated with PD at systemic levels, the underlying rich-club organization of the metabolic connectome in these patients remains largely unknown.Materials and MethodsThe data of 49 PD patients and 49 well-matched healthy controls (HCs) were retrieved and assessed. An individual metabolic connectome based on the standard uptake value (SUV) was built using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method to compare the rich-club properties between PD patients and HC.ResultsOur results showed the rich-club organization of metabolic networks (normalized rich-club coefficients > 1) in the PD and HC group were within a range of thresholds. Further, patients with PD demonstrated lower strength and degree in rich-club connections compared with HCs (strength: HCs = 55.70 ± 8.52, PDs = 52.03 ± 10.49, p = 0.028; degree: HCs = 56.55 ± 8.60, PDs = 52.85 ± 10.62, p = 0.029), but difference between their feeder and local connections was not significant.ConclusionIndividual metabolic networks combined with rich club analysis indicated that PD patients had decreased rich club connections but similar feeder and local connections compared with HCs, indicating rich club connections as a promising marker for early diagnosis of PD.

A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.

Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads

ObjectiveEven with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads.MethodsIn this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well.ResultsAt the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus.ConclusionThe results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders.

Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline byusing regional morphology from magnetic resonance imaging.

Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.

Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats.

Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (Eloc) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (Enod) of left posterior dorsal hippocampus, and Enod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.

The Local Topological Reconfiguration in the Brain Network After Targeted Hub Dysfunction Attacks in Patients With Juvenile Myoclonic Epilepsy.

The central brain regions of brain networks have been extensively studied in terms of their roles in various diseases. This study provides a direct measure of the brain’s responses to targeted attacks on central regions, revealing the critical role these regions play in patients with juvenile myoclonic epilepsy (JME). The resting-state data of 37 patients with JME and 37 healthy subjects were collected, and brain functional networks were constructed for the two groups of data according to their Pearson correlation coefficients. The left middle cingulate gyrus was defined as the central brain region by the eigenvector centrality algorithm and was attacked by the CLM sequential failure model. The rich-club connection differences between the patients with JME and healthy controls before and after the attacks were compared according to graph theory indices and the number of rich-club connections. We found that the numbers of rich connections in the brain networks of the healthy control group and the group of patients with JME were significantly reduced [p < 0.05, false discovery rate (FDR) correction] before the CLM sequential failure attacks, and no significant differences were observed between the feeder connections and local connections. In the healthy control group, significant rich connection differences were obtained (p < 0.01, FDR correction), and no statistically significant differences were observed regarding the feeder connections and local connections in the brain network before and after CLM failure attacks on the central brain region. No significant differences were obtained between the rich connections, feeder connections, and local connections in patients with JME before and after CLM successive failure attacks on the central brain area. The rich connections, feeder connections, and local connections were not significantly different in the brain networks of the healthy control group and the group of patients with JME after CLM successive failure attacks on the central brain region. We concluded that the damage to the left middle cingulate gyrus is closely linked to various brain disorders, suggesting that this region is of great importance for understanding the pathophysiological basis of myoclonic seizures in patients with JME.

Strengthened and posterior-shifted structural rich-club organization in people who use cocaine

BackgroundPeople with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD. MethodsUsing diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use. ResultsPeople with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity. ConclusionsThese findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.