Participation Coefficient Research Articles

Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function

BackgroundCocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. MethodsResting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22–55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics – betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) – were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. ResultsParticipants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. ConclusionsWidespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.

Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model.

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

ObjectiveTo explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks. MethodsResting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed. ResultsCompared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale. ConclusionsPatients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

Aberrant modular segregation of brain networks in female patients with bulimia nervosa.

Bulimia nervosa (BN) is an eating disorder associated with the dysfunction of intrinsic brain networks. However, whether the network disruptions in BN patients manifest as dysconnectivity or imbalances of network modular segregation remains unclear. We collected data from 41 women with BN and 41 matched healthy control (HC) women. We performed graph theory analysis based on resting-state functional magnetic resonance imaging (RS-fMRI) data; then, we computed the participation coefficient (PC) among brain modules to characterize the modular segregation for the BN and HC groups. The number of intra- and inter-modular connections was calculated to explain the PC changes. Additionally, we examined the potential associations of the measures mentioned above with clinical variables within the BN group. Compared with the HC group, the BN group showed significantly decreased PC in the fronto-parietal network (FPN), cingulo-opercular network (CON), and cerebellum (Cere). Additionally, the number of intra-modular connections of the default mode network (DMN) and the number of the inter-modular connections between the DMN and CON, FPN and Cere, and CON and Cere in the BN group were lower than those in the HC group. The nodal level analysis showed that the BN group had a decreased PC of the anterior prefrontal cortex (aPFC), dorsal frontal cortex (dFC), inferior parietal lobule (IPL), thalamus, and angular gyrus. Further, these metrics were significantly correlated with clinical variables in the BN group. These findings may provide novel insights to capture atypical topologies associated with pathophysiology mechanisms and clinical symptoms underlying BN.

Structural Modularity is a Feature of C9orf72 Intrinsic Network Degeneration

AbstractBackgroundGenetic forms of frontotemporal degeneration (FTD) and amyotrophic lateral sclerosis (ALS) provide an important target for early therapeutic interventions. However, pre‐symptomatic biomarkers to monitor disease progression are lacking. Prior neuroimaging studies suggest disruption of functional and structural networks are an early feature of neurodegenerative disease. Network modularity quantifies the degree of segregation between sub‐networks. We hypothesize that structural network modularity is a marker of neurodegeneration in C9orf72 carriers.MethodWe evaluated diffusion magnetic resonance imaging (dMRI) in two independent cohorts of C9orf72 carriers and controls: University of Pennsylvania Prodromal Study (Penn‐C9) including symptomatic C9orf72 carriers (SC9; N=27; FTD=20; ALS=7), asymptomatic C9orf72 carriers (AC9; N=24), and healthy controls (HC; N=29); and Human Connectome Project‐FTD (HCP‐FTD) also including SC9 (N=12), AC9 (N=12), and HC (N=20) individuals. We quantified modularity in 7 intrinsic networks of 400 nodes from the Schaefer Atlas using participation coefficient (PC), where lower PC corresponds to higher modularity, and also investigated its constituent measures: within‐network connectivity for each network and pairwise between‐network connectivity. Modularity metrics were residualized in the Penn‐C9 dataset using linear regression to adjust for age. Statistical analyses on residualized scores were first performed in Penn‐C9 and cross‐validated in the HCP‐FTD dataset.ResultANCOVAs on residualized scores in Penn‐C9 revealed group differences in PC for limbic network with SC9 lower than HC (partial‐η2=0.12, p=0.011). We also observed group differences in within‐module connectivity for limbic (partial‐η2=0.16, p=0.0016) and dorsal‐attention (partial‐η2=0.1, p=0.019) networks with SC9 lower than HC. Cross‐validation in HCP‐FTD revealed group differences in modularity metrics for limbic network (PC: partial‐η2=0.29, p=0.0012 and within module connectivity:partial‐η2=0.28, p=0.0016) with both metrics lower in SC9 relative to HC. The group differences were significant between AC9 and HC in Penn‐C9 and the means of aforementioned metrics for AC9 were between HC and SC9 in both datasets, suggesting an intermediate state towards modularity.ConclusionWe observed cross‐validated evidence of greater network modularity, in limbic network, for SC9 relative to HC and intermediate evidence of modularity in AC9. Limbic network is plausible as earliest loci of TDP‐43 pathology in FTD. We suggest that structural network modularity may be an early feature of C9orf72 disease progression.

Disrupted network integration and segregation involving the default mode network in autism spectrum disorder

Changes in the brain's default mode network (DMN) in the resting state are closely related to autism spectrum disorder (ASD). Module segmentation can effectively elucidate the neural mechanism of ASD and explore intra- and inter-network connections by means of the participation coefficient (PC). We used that resting-state fMRI data from 269 ASD patients and 340 healthy controls (HCs) in the current study. From the results, ASD subjects showed a significantly higher PC of the DMN than HC subjects. This difference was related to lower intra-module connections within the DMN and higher inter-network connections between the DMN and other networks. When the subjects were split into age groups, the results were verified in the 7–12- and 12–18-year-old age groups but not in the young adult group (18–25 years). When the subjects were divided according to different subtypes of ASD, the results were also observed in the classic autism and pervasive developmental disorder groups, but not in the Asperger disorder group. In conclusions, less developed network segregation in the DMN could be a valid biomarker for ASD. This provides network scientists with new insights into the intermodular connectivity configurations of complex networks from different dimensions in a systematic and comprehensive manner.

Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.

Longitudinal alterations of cortical structural-functional coupling in temporal lobe epilepsy.

To investigate the longitudinal alterations of cortical structural-functional coupling (SF coupling) in patients with temporal lobe epilepsy (TLE) over a 2-year follow-up, thereby exploring the neuropathophysiological mechanisms of TLE. Twenty-eight TLE patients and 42 age- and gender-matched healthy controls (HCs) were recruited. We used resting-state functional MRI and diffusion-weighted imaging to estimate and compare SF coupling at the multiscale network level (whole-brain, modular, and regional levels). Then, we analyzed the relationships between the spatial patterns of SF coupling, the principal functional connectivity (FC) gradient, and the functional participation coefficient (PC). Finally, we related regional SF coupling changes between baseline and follow-up to the expression of regional TLE-specific genes. Compared with HCs, TLE patients showed higher baseline SF couplings within the whole-brain, limbic, and default-mode modules. SF couplings within visual and dorsal attention modules were increased at follow-up compared to baseline. In all three groups, the spatial patterns of SF coupling aligned with the principal FC gradient and the functional PC. The longitudinal change in regional SF coupling in TLE patients was significantly positively correlated with the expression of the CUX2 gene. Aberrant SF coupling was revealed in TLE and related to macroscale cortical hierarchies, functional segregation, and TLE-specific gene expression; these data help increase our understanding of the neuropathophysiological mechanisms underlying TLE.

Decreased modular segregation of the frontal-parietal network in major depressive disorder.

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant large-scale distributed brain networks. However, it is unclear how the network dysfunction in MDD patients is characterized by imbalance or derangement of network modular segregation. Fifty-one MDD patients and forty-three matched healthy controls (HC) were recruited in the present study. We analyzed intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI) and then examined brain network segregation by computing the participation coefficient (PC). Further intra- and inter-modular connections analysis were preformed to explain atypical PC. Besides, we explored the potential relationship between the above graph theory measures and symptom severity in MDD. Lower modular segregation of the frontal–parietal network (FPN) was found in MDD compared with the HC group. The MDD group exhibited increased inter-module connections between the FPN and cingulo-opercular network (CON), between the FPN and cerebellum (Cere), between the CON and Cere. At the nodal level, the PC of the anterior prefrontal cortex, anterior cingulate cortex, inferior parietal lobule (IPL), and intraparietal sulcus showed larger in MDD. Additionally, the inter-module connections between the FPN and CON and the PC values of the IPL were negatively correlated with depression symptom in the MDD group. These findings might give evidence about abnormal FPN in MDD from the perspective of modular segregation in brain networks.

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain.

Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

An improved two-stage label propagation algorithm based on LeaderRank.

To solve the problems of poor stability and low modularity (Q) of community division results caused by the randomness of node selection and label update in the traditional label propagation algorithm, an improved two-stage label propagation algorithm based on LeaderRank was proposed in this study. In the first stage, the order of node updating was determined by the participation coefficient (PC). Then, a new similarity measure was defined to improve the label selection mechanism so as to solve the problem of label oscillation caused by multiple labels of the node with the most similarity to the node. Moreover, the influence of the nodes was comprehensively used to find the initial community structure. In the second stage, the rough communities obtained in the first stage were regarded as nodes, and their merging sequence was determined by the PC. Next, the non-weak community and the community with the largest number of connected edges were combined. Finally, the community structure was further optimized to improve the modularity so as to obtain the final partition result. Experiments were performed on nine classic realistic networks and 19 artificial datasets with different scales, complexities, and densities. The modularity and normalized mutual information (NMI) were used as evaluation indexes for comparing the improved algorithm with dozens of relevant classic algorithms. The results showed that the proposed algorithm yields superior performance, and the results of community partitioning obtained using the improved algorithm were stable and more accurate than those obtained using other algorithms. In addition, the proposed algorithm always performs well in nine large-scale artificial data sets with 6,000 to 50,000 nodes and three large realistic network datasets, which verifies its computational performance and utility in community detection for large-scale networks.

Meso-scale network analysis of resting state-fMRI brain network connectivity performs poorly as a prognostic tool in critically ill traumatic brain injury patients

Prognostication is important in the management of critically ill traumatic brain injury (TBI) patients, as it may guide decisions on treatment intensity. Changes in functional connectivity that arise due to TBI have been observed in several brain networks using resting-state functional magnetic resonance imaging (rs-fMRI). Studies suggest that cognitive dysfunction following TBI can, in part, be explained by changes in functional connectivity. The overall aim of this study was to investigate whether easy-to-calculate graph theory-based summaries of brain network functional connectivity can discriminate between patients with favorable and unfavorable outcomes. To further substantiate these results, we used the Louvain algorithm to detect the community structure of each TBI patient, to assess whether it differs from that of healthy individuals.We performed fMRI on 44 TBI patients who were admitted to the neurocritical care unit. Long-term functional outcome was determined at 6–12 months using the Glasgow outcome scale (GOS), with GOS 1–3 being defined as an unfavorable outcome. Functional connectivity was measured using Pearson’s correlation coefficient. We derived topographical summaries of brain network connectivity using graph theory. We computed the participation coefficient (PC) and the module degree z-score (MDZ) for each parcel in the cortical template of Thomas Yeo et al. (2011). As topographical summaries of the network parameters we used the median, maximum and standard deviation (SD) of PC and MDZ in each of the seven functional networks that are included in the cortical parcellation of Yeo et al. Age and sedative medications were used as nuisance regressors.Using multivariate logistic regression, we observed no statistically significant association between the topographical summaries of brain connectivity and the dichotomized GOS. Thus, although graph theoretical measures applied to brain network connectivity patterns have previously been reported to be associated with cognitive dysfunction, they appear to be unable to predict long-term functional outcomes in critically ill TBI patients. Similarly, the extent to which TBI patient’s community structures differed from that of healthy individuals, measured using adjusted mutual information, was also not associated with the dichotomized GOS.In summary, our results suggest that Meso-scale graph theoretical analyses of rs-fMRI brain network connectivity patterns can be expected to perform poorly as a prognostic tool in critically ill TBI patients.

Aberrant large-scale brain modules in deficit and non-deficit schizophrenia

ObjectiveSchizophrenia is a heterogenous psychiatric disease, and deficit schizophrenia (DS) is a clinical subgroup with primary and enduring negative symptoms. Although previous neuroimaging studies have identified functional connectome alterations in schizophrenia, the modular organizations in DS and nondeficit schizophrenia (NDS) remain poorly understood. Therefore, this study aimed to investigate the modular-level alterations in DS patients compared with the NDS and healthy control (HC) groups. MethodsA previously collected dataset was re-analyzed, in which 74 chronic male schizophrenia patients (33 DS and 41 NDS) and 40 HC underwent resting-state functional magnetic resonance imaging with eyes closed in a Siemens 3 T scanner (scanning duration = 8 min). Modular- (intramodule and intermodule connectivity) and nodal- [normalized within-module degree (Zi) and participation coefficient (PCi)] level graph theory properties were computed and compared among the three groups. Receiver operating characteristic curve (ROC) analyses were performed to examine the classification ability of these measures, and partial correlations were conducted between network measures and symptom severity. Validation analyses on head motion, network sparsity, and parcellation scheme were also performed. ResultsBoth schizophrenia subgroups showed decreased intramodule connectivity in salience network (SN), somatosensory-motor network (SMN), and visual network (VN), and increased intermodule connectivity in SMN-default mode network (DMN) and SMN-frontoparietal network (FPN). Compared with NDS patients, DS patients showed weaker intramodule connectivity in SN and stronger intermodule connectivity in SMN-FPN and SMN-VN. At the nodal level, the schizophrenia-related alterations were distributed in SN, SMN, VN, and DMN, and 7 DS-specific nodal alterations were identified. Intramodule connectivity of SN, intermodule connectivity of SMN-VN, and Zi of left precuneus successfully distinguished the three groups. Partial correlational analyses revealed that these measures were related to negative symptoms, general psychiatric symptoms, and neurocognitive function. ConclusionOur findings suggest that functional connectomes, especially SN, SMN, and VN, may capture the distinct and common disruptions of DS and NDS. These findings may help to understand the neuropathology of negative symptoms of schizophrenia and inform targets for treating different schizophrenia subtypes.

Time-varying measures of cerebral network centrality correlate with visual saliency during movie watching.

The extensive development of graph‐theoretic analysis for functional connectivity has revealed the multifaceted characteristics of brain networks. Network centralities identify the principal functional regions, individual differences, and hub structure in brain networks. Neuroimaging studies using movie‐watching have investigated brain function under naturalistic stimuli. Visual saliency is one of the promising measures for revealing cognition and emotions driven by naturalistic stimuli. This study investigated whether the visual saliency in movies was associated with network centrality. The study examined eigenvector centrality (EC), which is a measure of a region's influence in the brain network, and the participation coefficient (PC), which reflects the hub structure in the brain, was used for comparison. Static and time‐varying EC and PC were analyzed by a parcel‐based technique. While EC was correlated with brain activity in parcels in the visual and auditory areas during movie‐watching, it was only correlated with parcels in the visual areas in the retinotopy task. In addition, high PC was consistently observed in parcels in the putative hub both during the tasks and the resting‐state condition. Time‐varying EC in the parietal parcels and time‐varying PC in the primary sensory parcels significantly correlated with visual saliency in the movies. These results suggest that time‐varying centralities in brain networks are distinctively associated with perceptual processing and subsequent higher processing of visual saliency.

Selective Functional Network Changes Following tDCS-Augmented Language Treatment in Primary Progressive Aphasia.

ObjectiveTranscranial direct current stimulation (tDCS) has shown promising results when used as an adjunct to behavioral training in neurodegenerative diseases. However, the underlying neural mechanisms are not understood and neuroimaging evidence from pre/post treatment has been sparse. In this study, we examined tDCS-induced neural changes in a language intervention study for primary progressive aphasia (PPA), a neurodegenerative syndrome with language impairment as the primary clinical presentation. Anodal tDCS was applied to the left inferior frontal gyrus (LIFG). To evaluate the hypothesis that tDCS promotes system segregation, analysis focused on understanding tDCS-induced changes in the brain-wide functional network connectivity of the targeted LIFG.MethodsResting-state fMRI data were obtained from 32 participants with PPA before and after receiving a written naming therapy, accompanied either by tDCS or sham stimulation. We focused on evaluating changes in the global connectivity of the stimulated LIFG-triangularis (LIFG-tri) region given its important role in lexical processing. Global connectivity was indexed by the graph-theoretic measure participation coefficient (PC) which quantifies a region’s level of system segregation. The values before and after treatment were compared for each condition (tDCS or Sham) as well as with age-matched healthy controls (n = 19).ResultsHigher global connectivity of the LIFG-tri before treatment was associated with greater dementia severity. After treatment, the tDCS group showed a significant decrease in global connectivity whereas the Sham group’s did not change, suggesting specific neural effects induced by tDCS. Further examination revealed that the decrease was driven by reduced connectivity between the LIFG-tri and regions outside the perisylvian language area, consistent with the hypothesis that tDCS enhances the segregation of the language system and improves processing efficiency. Additionally, we found that these effects were specific to the LIFG-tri and not observed in other control regions.ConclusionTDCS-augmented language therapy in PPA increased the functional segregation of the language system, a normalization of the hyper-connectivity observed before treatment. These findings add to our understanding of the nature of tDCS-induced neural changes in disease treatment and have applications for validating treatment efficacy and designing future tDCS and other non-invasive brain stimulation (NIBS) treatments.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control

Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region’s inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD.

Community-informed connectomics of cortical intrinsic organization in subjective cognitive decline

Objective: To investigate the intrinsic organization of cortical circuitry in individuals with subjective cognitive decline (SCD) via resting-state functional magnetic resonance imaging (rs-fMRI) connectome analysis and its correlation with cognitive level. Methods: From June 2017 to November 2019, thirty-six middle-aged and elderly individuals with complaints of memory decline and 32 normal controls (NC) were enrolled from communities in Nanjing. We collected cognitive scale performance,T1-weighted imaging (T1WI) and rs-fMRI data of all subjects. There were 5 males and 31 females in the SCD group, with an average age of (64±5) years. In the NC group, there were 8 males and 24 females, with an average age of (65±5) years. Preprocessing of rs-fMRI data was conducted, then the cerebral cortex was divided into 333 cortical parcels (nodes) and 10 predefined communities according to the prior template. Further, we established full connection matrices between cortical parcels and calculated the within-module degree (WMD) and participation coefficient (PC) of each node based on the matrices. The WMD and PC values were compared between the SCD and NC groups,and their correlations with cognitive scale performance were analyzed. Results: Compared to the NC group,the SCD group showed increased WMD in the dorsolateral prefrontal cortex (DLPFC)(P<0.05,FDR corrected) and the middle frontal gyrus (P<0.005,uncorrected) of the right frontoparietal network (FPN). The SCD group also showed decreased WMD(P<0.05,FDR corrected) in the superior occipital gyrus of the left visual network (VN) and decreased PC (P<0.005,uncorrected) in the supramarginal gyrus of the left dorsal attention network (DAN). The WMD values in the DLPFC showed significant positive correlations with the auditory verbal learning test (AVLT)short-delayed memory (r=0.364,P=0.029),recognition memory (r=0.364, P=0.029) and the Boston naming test scores (BNT, r=0.356, P=0.033)in the SCD group. The PC values in the supramarginal gyrus were significantly positively correlated with the BNT scores (r=0.413, P=0.012) in the SCD group. Conclusion: Cortical network imbalance and reconstruction characterized by decreased intra-module connectivity of VN and inter-module connectivity of DAN exist in SCD subjects,while increased intra-module connectivity of FPN may serve in a compensatory way for the early cognitive decline.

Altered modular segregation of brain networks during the cue-craving task contributes to the disrupted executive functions in internet gaming disorder

BackgroundPrevious studies have shown that gaming-related cues could induce gaming cravings and bring about changes in brain activities in subjects with Internet gaming disorder (IGD). However, little is known about the brain network organizations in IGD subjects during a cue-craving task and the relationship between this network organization and IGD severity. MethodsSixty-one IGD subjects and 61 matched recreational game users (RGUs) were scanned while performing a cue-craving task. We calculated and compared the participation coefficient (PC) among brain network modules between IGD subjects and RGUs. Based on the results, further group comparison analyses were performed to explain the PC changes and to explore the relationship between PCs and IGD severity. ResultsWhile performing a cue-craving task, compared with RGUs, IGD subjects showed significantly decreased PCs in the default-mode network (DMN) and the frontal-parietal network (FPN). Specifically, the number of connections between nodes in the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex and other nodes in the DMN of IGD subjects was much larger than that in RGUs. Correlation results showed that the number of DMN intra-modular connections was positively correlated with addiction severity and craving degree. ConclusionsThese results provide neural evidence that can explain why cognitive control, emotion, attention and other functions are impaired in IGD subjects in the face of gaming cues, which leads to compulsive behavior toward games. These findings extend our understanding of the neural mechanism of IGD and have important implications for developing effective interventions to treat IGD subjects.

Reducing the influence of intramodular connectivity in participation coefficient.

Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient (PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.

Global network community and non-uniform cell density in the macaque brain

The important question, how the global network architecture connecting cortical regions keeps balances between integration and segregation of information processes, have been asked to understand the design of the brain [1,2]. This study aimed to clarify how topological characteristics of such global network architecture relate with physiological characteristics inside of segmented cortical regions in the monkey brain [3]. Especially, I focused on cell densities (densities of neurons or non-neurons) as the representative characteristics of segmented cortical regions [4], and compared the cell densities with network topologies of cortico-cortical fiber tracts [Figure1-A]. To reduce biases in comparisons, I surveyed many topological measures as wide as possible. Total number of evaluated network measures was 27. Figure1 (A) is cell densities at segmented 69 cortical regions. Denser color at each region indicates higher density of neurons. (B) is the network organization of white matter fibers. (C) Community (Modular) structures of monkey cortical networks. The locations ... As the result, surprisingly, only participation coefficient (PCs) showed significant correlations with cell densities [3]. Although a previous study reported that cell densities significantly change on the anterior-posterior coordinate [5], spatial coordinates did not correlate significantly with participation coefficients. Participation coefficient is the topological measure evaluating how often each node (segmented brain region) connects to other nodes locating different communities (modules). The modules, which detected based on a computational criterion [3], corresponded with visual, somatosensory, auditory, and two associative modules [Figure1-C]. The associative modules simultaneously showed low neuron density and high participation coefficient, which means there are diversive connections with different modules. These findings led us to the conclusion that the brain is designed for achieving integrative information process at associative brain regions by sacrificing number of elements (neurons).