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In neurosurgery, an exact delineation of functional areas is of great interest to spare important regions to ensure the best possible outcome for the patient, i.e. maximum removal while maintaining the highest possible quality of life. Preoperative imaging is routinely performed, including the visualization of not only structural but also functional information. During surgery however, brain shift can occur, leading to an offset between the previously defined and the real position. Real-time imaging during the procedure is therefore desired to obtain this information while performing surgery. In this study 15 patients suffering from glioblastoma multiforme were included. These patients underwent structural and perfusion imaging using Arterial Spin Labeling during the procedure. The latter has been used for gathering information about tumor residual perfusion. However, special post-processing of this data allows for additional mapping of resting state networks and is intended to be used to gather deeper insights to aid the surgeon in planning the further procedure. The data of each patient could be successfully post-processed and used to map different resting state networks alongside the default mode network. Based on this study, it is feasible to use the information obtained from perfusion imaging to not only visualize vascular signal but also functional activation of resting state networks without acquiring any additional data besides the already available information. This may help guide the neurosurgeon in real time to adjust the surgical plan.

Purpose Previous studies have indicated that the global topology of the brain functional network in patients with major depressive disorder (MDD) differs from that of those with normal controls (NCs). However, the relationship between an altered global topology and the response to antidepressants remains unclear. Here, we investigated whether differences in global topology affect the efficacy of antidepressants in MDD patients. Methods 108 MDD patients and 61 NCs were recruited. A magnetic resonance imaging (MRI) scan was performed at the baseline, and the Hamilton Depression Scale-24 (HAMD-24) was assessed at baseline and after 2 and 8 weeks of antidepressant treatment. Seven global topological parameters of the brain functional network were measured and compared between groups. A correlation analysis was performed to identify the relationships between global topological parameters and antidepressant efficacy. Results The brain networks of MDD patients and NCs were both small-world networks. The clustering coefficient (Cp) and local efficiency (Eloc) were significantly smaller in MDD patients compared with those in NCs. The characteristic path length (Lp) were negatively correlated with the 8-week reductive rate of HAMD-24 in the MDD group. Conclusion The present research found that the brain functional network of MDD patients still had a small-world organization but with a lower Cp and Eloc than the NCs. In addition, the brain network global topology might have an impact on the antidepressant response and thus had the potential to become a treatment predictor of MDD.

Background and objectives Higher mental functions depend on global functional coordination of the brain. Our aim was to study the baseline condition and modulation of functional networks in a previously unevaluated clinical population, compare the results with a population from another country, and analyze their relationship with cognitive functioning. Methods We evaluated the functioning of brain networks by EEG in 24 patients with schizophrenia and 32 healthy Ecuadorian controls. EEG recordings were made at rest and while performing a P300 task. Small world (SW), Path Length (PL), clustering coefficient (CLC) and connective strength (CS) values were calculated in both conditions. The values obtained were compared between groups, with the results of Spanish patients, and the relationship between the connective parameters and the cognitive performance of the participants was analyzed. Results Higher PL, CLC and CS values were identified in patients diagnosed with schizophrenia compared to controls (in basal condition) and lower SW values in this same condition. Ecuadorian patients obtained higher values than Spanish patients in the PL and CLC parameters and lower values for the SW parameter, despite these differences, the pattern of alteration in both samples followed the same trend. Finally, the alteration of CS, SW, CLC and PL parameters at baseline was related to cognitive performance. Conclusion The connective alterations identified in Ecuadorian schizophrenic patients are consistent with those found in another sample with different genetic, environmental and cultural conditions. In addition, these alterations were associated with worse performance in different cognitive domains.

Background Bipolar disorder is a common psychiatric disorder characterized by insufficient or ineffective connections associated with white-matter (WM) abnormalities. Previous studies have detected the structural attributes of WM using magnetic resonance imaging (MRI) or diffusion tensor imaging, however, they failed to disentangle the dysfunctional organization within the WM. Methods This study aimed to uncover the WM functional connectivity (FC) in 45 bipolar disorder patients during depressive episodes (BDD) and 45 healthy controls based on resting-state functional MRI. Eight WM functional networks were identified by using a clustering analysis of voxel-based correlation profiles, which were further classified into superficial, middle and deep layers of networks. Results Group comparisons on the FCs among 8 WM networks showed that the superficial tempofrontal network (TFN) in BDD patients had increased FC with the superficial cerebellar network (CN) and with the superficial pre/post-central network (PCN). Further, support vector regression prediction analysis results revealed that the increased FCs of CN-TFN and PCN-TFN could be served as features to predict the numbers of depressive episode in BDD patients. Conclusions The current study extended our knowledge about the impaired WM functional connections associated with emotional and sensory-motor perception processing in BDD, which may facilitate the interpretation of the pathophysiology mechanisms underlying BDD.

Background Individuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown. Methods Using multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling. Results Compared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients. Limitations The sample size of the current study is small and the cross-sectional nature can not infer causal relationship. Conclusions Our findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

As Alzheimer’s disease (AD) pathology accumulates, resting-state functional connectivity (rs-fc) within and between brain networks decreases, and fluctuations in cognitive performance known as intraindividual variability (IIV) increase. Here, we assessed the relationship between IIV and anticorrelations in rs-fc between the default mode network (DMN)-dorsal attention network (DAN) in cognitively normal older adults and symptomatic AD participants. We also evaluated the relationship between cerebrospinal fluid (CSF) biomarkers of AD (amyloid-beta [Aβ42] and tau) and IIV-anticorrelation in rs-fc. We observed that cognitive IIV and anticorrelations between DMN × DAN were higher in individuals with AD compared with cognitively normal participants. As DMN × DAN relationship became more positive, cognitive IIV increased, indicating that stronger anticorrelations between networks support more consistent cognitive performance. Moderation analyses indicated that continuous CSF Aβ42, but not CSF total tau, moderated the relationship between cognitive IIV and DMN × DAN, collectively demonstrating that greater amyloid burden and alterations in functional network dynamics are associated with cognitive changes seen in AD. These findings are valuable, as they suggest that amyloid affects cognitive functioning during the early stages of AD.

Epilepsy is a common pediatric disease that often leads to cognitive and intellectual impairments. Here, we explore the reorganized functional networks in children and adolescents with focal epilepsy (CAFE) and analyze the relationship between network reorganization and intellectual deficits to reveal the underlying link between them.Fifty-four CAFE (6-16 years old; right-handed) and 42 well-matched healthy controls were recruited. Subjects underwent resting-state functional magnetic resonance imaging, and functional networks were analyzed by graph analysis. Intelligence testing (Wechsler Intelligence Scale for Children-Chinese revision) included measures for verbal IQ (VIQ), performance IQ, and full-scale IQ.(1) In the CAFE compared with the healthy controls, (a) the local efficiency, clustering coefficient and standardized clustering coefficient were significantly decreased (p0.05); (b) the degree centrality and nodal efficiency of the left precentral gyrus (LPG) were significantly increased (p0.05, Bonferroni correction), and the nodal shortest path length was significantly decreased (p0.05, Bonferroni correction); and (c) functional connectivity of the LPG with the bilateral inferior frontal ventral gyrus, right lateral superior occipital gyrus, left middle occipital gyrus, bilateral superior parietal lobule, right anterior prefrontal cortex, and bilateral cerebellum was enhanced (p0.05,GRF correction), while functional connectivity with the bilateral superior temporal gyrus was decreased (p0.05, GRF correction). (2) The nodal shortest path length of the LPG in CAFE was associated with full-scale IQ, performance IQ, and VIQ, and local efficiency was associated with VIQ.Our results showed that the middle LPG in CAFE undergoes network reorganization that positively influences intelligence. Differences in local efficiency of functional networks in children and early adolescents have a significant effect on intelligence.

Background Fingolimod, an oral drug, has been reported to reduce relapse rate in multiple sclerosis (MS). However disease progression may still occur in spite of control of inflammation. Functional imbalances within and between cerebral networks associated with disruption of structural and functional network integrity, have been reported in MS. An effective therapy is expected to stabilize such functional network integrity. Objective The purpose of this study was to investigate changes in structural and resting-state functional connectivity of motor and cognitive networks, and associated changes in neurologic scores in MS, during 2 years of fingolimod therapy. Methods Twenty five subjects with MS were recruited for this study. Subjects were scanned with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at 3 T with 6-month interval over a period of 2 years. Neurologic performance scores of motor and cognitive performances were also obtained. Results DTI measures worsened during the 1st year and then stabilized; any trend of stabilization of fcMRI was delayed until the 2nd year. While motor performance did not change, cognitive performance showed improvement. Several baseline DTI measures correlated with relevant neurologic scores. Conclusion Initial worsening of motor and cognitive network was reported after 1 year of treatment, but seems DTI and fcMRI measures seem to stabilize after around one year fingolimod therapy.

Objective To characterize cortical networks activated by patterned visual stimuli in infants, and to evaluate their potential for assessment of visual processing and their associations with neurocognitive development. Methods Three visual stimuli, orientation reversal (OR), global form (GF), and global motion (GM), were presented to cohort of five-month-old infants (N = 26). Eye tracker was used to guide the stimulation and to choose epochs for analysis. Visual responses were recorded with electroencephalography and analysed in source space using weighted phase lag index as the connectivity measure. The networks were quantified using several metrics that were compared between stimuli and correlated to cognitive outcomes. Results Responses to OR/GF/GM stimuli were observed in nearly all (96/100/100%) recordings. All stimuli recruited cortical networks that were partly condition-specific in their characteristics. The more complex GF and GM conditions recruited wider global networks than OR. Additionally, strength of the GF network showed positive association with later cognitive performance. Conclusions Network analysis suggests that visual stimulation recruits large-scale cortical networks that extend far beyond the conventional visual streams and that differ between stimulation conditions. Significance The method allows controlled recruitment of wide cortical networks, which holds promise for the early assessment of visual processing and its related higher-order cognitive processes.

Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.

A hallmark of the aging process is increased connectivity between networks and decreased connectivity within networks, which to some extent reflects the reorganization of the brain networks during normal aging. Considering the brain as a complex dynamic system, emerging evidence suggests the time-varying connectivity patterns to be more informative of brain functions. However, the age effect on the dynamic reconfiguration of intrinsic resting state networks is still elusive. By tracking the ongoing formation and dissipation of putative functional modules across time and space, we explored the age-related changes of segregation and integration and further elucidated the underlying brain network dynamics mechanism during normal aging. Results showed that aging strongly weakened dynamic global segregation while enhanced dynamic global integration across the whole brain. Aging was associated with decreasing dynamic segregation of most networks (except the cerebellum) while increasing dynamic integration of only a few networks at the large-scale network level. Notably, the fronto-parietal network, the default mode network, the visual network, and a small group of nodes from these networks, whose dynamic segregation and integration, were both modulated by age. These findings provide direct evidence that there are remarkable changes of dynamic network architecture across the human adult lifespan and suggest the age-related modulations of dynamic segregation and integration intuitively reflect the adaptive changes of the functional dedifferentiation and compensation in older adults.

Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. Neuroimaging studies have implicated altered connectivity among the functional networks of the cerebral cortex in the pathophysiology of OCD. However, there has been no comprehensive investigation of the cross-talk between the cerebellum and functional networks in the cerebral cortex.This functional neuroimaging study was completed by 44 adult participants with OCD and 43 healthy control participants. We performed large-scale data-driven brain network analysis to identify functional connectivity patterns using resting-state functional magnetic resonance imaging data.Participants with OCD showed lower functional connectivity within the somatomotor network and greater functional connectivity among the somatomotor network, cerebellum, and subcortical network (e.g., thalamus and pallidum; all p.005). Network-based statistics analyses demonstrated one component comprising connectivity within the somatomotor network that showed lower connectivity and a second component comprising connectivity among the somatomotor network, and motor regions in particular, and the cerebellum that showed greater connectivity in participants with OCD relative to healthy control participants. In participants with OCD, abnormal connectivity across both network-based statistics-derived components positively correlated with OCD symptom severity (p = .006).To our knowledge, this study is the first comprehensive investigation of large-scale network alteration across the cerebral cortex, subcortical regions, and cerebellum in OCD. Our findings highlight a critical role of the cerebellum in the pathophysiology of OCD.

Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed.

Functional connectivity pattern altered of default mode network (DMN) is gaining more attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. However, the changed functional connectivity except for DMN, the longitudinal changes in executive control network (ECN) and frontoparietal network (FPN) also has attracted wide interest. Moreover, AD-related functional connectivity abnormalities within the DMN are well replicated research, but the (increased/decreased and reduced) functional connectivity in ECN and FPN weren't receive adequate attention. To address the above issues, in this paper, we adopt sparse inverse covariance estimation (SICE) approach to investigate the changed functional connectivity of ECN and FPN on the ADNI2 dataset. Our experimental results indicate the left superior frontal gyrus (SFGmed.L) and left thalamus (THA.L) regions of ECN has shown increased functional connectivity, the left anterior cingulate (ACG.L) region of ECN has shown decreased functional connectivity. The Superior Parietal Gyrus (SPG) regions and left paracentral lobule (PCL.L) of FPN has shown increased functional connectivity, the left supramarginal gyrus (SMG.L) regions has shown decreased functional connectivity in AD patients. On the other hand, the ACG.L regions in ECN, SMG.L and left inferior parietal (IPL.L) in FPN have shown significantly reduced functional connectivity. These results demonstrate that increased/decreased functional connectivity and reduced functional connectivity not only within DMN, but also associated with ECN and FPN. It also suggest that AD is associated with the characteristics of large-scale functional networks, and these changed functional connectivity possibly as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease.

Background Post-traumatic stress disorder (PTSD) is associated with disruption of the brain network topology; however, little is known about the topological changes and sex effects in PTSD patients following a unique trauma, the loss of an only child, in China. Methods Fifty-one lost-only-child parents with PTSD, 93 lost-only-child non-PTSD parents (NPTSD), and 50 healthy subjects underwent resting-state functional MRI. The whole-brain functional network was constructed by thresholding partial correlation matrices of 90 brain regions. Group differences in the topological properties, the diagnosis-by-sex interaction, and the relationships between topological metrics and clinical variables were investigated. Results Compared with healthy subjects, PTSD and NPTSD groups exhibited significantly shorter path lengths and higher nodal centralities in many brain regions across sexes; however, no significant difference was found between the PTSD and NPTSD groups. Additionally, the global topological metrics did not show any sex difference, whereas the nodal centralities in the left insula, right inferior frontal gyrus, and right posterior cingulate cortex differed significantly only in women, and the nodal centralities in the bilateral anterior cingulate cortices and left hippocampus were significantly different only in men. Furthermore, the nodal centralities of the right parahippocampus demonstrated significant diagnosis-by-sex interaction. Limitation Cross-sectional design of this study could not demonstrate the causality. Conclusions The parents who lost their only child exhibited a shift toward randomization and significant nodal topological alterations independent of PTSD effects. Additionally, sex differences were observed primarily in the topological properties at the nodal level, which may indicate a neurobiological contribution to the greater incidence of PTSD in females.

Increasing evidence suggests that systemic inflammation adversely affects social experiences and behaviors of older adults by changing the functional state of the brain. In this study, we investigated the relationships among systemic inflammation, functional network connectivity (FNC) of the whole brain, and social-network size using complete social-network data of older adults residing in a Korean village. Sixty-one participants were recruited from the Korean Social Life, Health, and Aging Project (KSHAP). Participants underwent a resting-state functional magnetic resonance imaging scan. High sensitivity C-reactive protein (hs-CRP) levels were measured as an inflammation marker. In-degree and out-degree network sizes were calculated based on the total number of intimate social relationships per participant. We demonstrated that hs-CRP levels were associated with decreased frontotemporal FNC. Stronger frontotemporal FNC was significantly correlated with a larger out-degree network size, suggesting that impaired frontotemporal communication in older adults decreases perceived social connectedness with other people. An exploratory mediation analysis supported the observation that increased systemic inflammation contributes to reduced out-degree social-network size among older adults by changing frontotemporal FNC. The present findings provide meaningful insight into the complex relationship between systemic inflammation and social quality of life.

Background Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as predefined atoms was proposed to improve ODL for functional networks separation. However, SDL cannot estimate the real time courses underlying the brain networks and cannot be applied to the inter-network connectivity analysis. This study aimed at investigating how to add the temporal prior information to ODL to extract the accurate task-related brain networks and the corresponding time courses. New method To improve the performance of ODL, we propose a semi-blind ODL (semi-ODL) method that incorporates temporal prior information of the task paradigm into the dictionary updating process and optimizes the direction of one or more specific atoms "close" to the task time courses. Results Results of the simulated and real fMRI experiment revealed that semi-ODL extracted more accurate task-related component and time courses than ODL and SDL. For one-task fMRI data, semi-ODL and Infomax-ICA showed similar detection power in most cases. Comparison with existing methods The semi-ODL outperformed ODL, SDL in robustness to noise, spatial detection power and time course estimation. Moreover, semi-ODL showed comparable performance to Infomax-ICA for one-task fMRI data and outperformed Infomax-ICA in extracting the components related to each task from multi-task fMRI data. Conclusions The semi-ODL method is potentially useful to reveal brain networks underlying various cognitive tasks and the interactions between task-related brain networks.

Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.

Background Recent studies showed that major depressive disorder (MDD) has been involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited. Methods Functional network (FN) was constructed from resting-state functional MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN. Results The losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients. Limitations The samples size is small and most of the MDD patients take different antidepressant medications. Conclusions Alterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.

Long-term professional sport training may cause the brain functional network of high-level athletes to differ significantly from that of non-athletes. To test this hypothesis, electroencephalograms (EEGs) from 20 high-level shooting athletes and 20 age- and gender-matched non-athletes are collected in an eyes-closed resting state. The frequency spectrum was divided into four bands according to the individual alpha frequency of each participant: delta, theta, alpha1, and alpha2. The phase-locking values of the EEG in each frequency band are calculated and graph theory is used to analyze the topology of the EEG brain functional network based on the phase-locking-value connection. The results show that, compared with non-athletes, high-level shooters have higher connectivity in the left-temporal region, left-posterior temporal region, left-frontal region, left-central region, and right-parietal region. The network-clustering coefficients and small-world characteristics of athletes in the theta and alpha1 bands are significantly greater than that of non-athletes. These results support the hypothesis that brain function coupling in high-level shooting athletes is more connected than that in non-athletes, and the brain networks of high-level athlete have stronger small-world characteristics than those of non-athletes.

Temporal processing is essential for speech perception and directional hearing. However, the number and locations of cortical sources involved in auditory temporal processing are still a matter of debate. Using source reconstruction of human EEG responses, we show that, in addition to primary sources in the auditory cortices, sources outside the auditory cortex, designated as non-primary sources, are involved in auditory temporal processing. Non-primary sources within the left and right motor areas, the superior parietal lobe and the right occipital lobe were activated by amplitude-modulated stimuli, and were involved in the functional network. The robustness of these findings was checked for different stimulation conditions. The non-primary sources showed weaker phase-locking and lower activity than primary sources. These findings suggest that the non-primary sources belong to the non-primary auditory pathway. This pathway and non-primary sources detected in motor area explain how, in temporal prediction of upcoming stimuli and motor theory of speech perception, the motor area receives auditory inputs.

Introduction The aim of the study was to identify abnormalities of whole-brain network functional organization and their relation to clinical measures in a well-characterized, multi-site cohort of very early-stage, drug-naive Parkinson's Disease (PD) patients. Methods Functional-MRI data for 16 healthy controls and 20 very early-stage, drug-naive patients with PD were obtained from the Parkinson's Progression Markers Initiative database after controlling for strict inclusion/exclusion imaging criteria. Connectivity between regions of interest was estimated using Pearson's correlation between averaged time-series, and subsequently a connectivity matrix was obtained for each subject. These connectivity matrices were then used in an unbiased, whole-brain graph theoretical approach to investigate the functional connectome and its correlation with disease severity in very early PD. Results The current study revealed altered network topology which correlated with multiple clinical measures in very early drug-naive PD. Decreased functional segregation and integration (both globally and locally) were evident in PD. Importantly, our results demonstrated that most of the cortical regions hypothesized to be involved early in PD manifested decreased graph theoretical measures, despite utilizing a whole-brain analytic approach that is free from prior assumptions regarding cortical region involvement. Conclusion Graph theoretical investigation of very early drug-naive PD revealed disrupted topological organization. These findings are evident in a stringently homogeneous group of very early-stage, medication-naive, and non-tremor dominant PD patients by using a whole-brain unbiased approach. These results provide an important unbiased and rigorously controlled baseline for understanding further studies of PD functional connectivity investigating response to treatment, symptom development, and disease progression.

Resting-state functional MRI (rsfMRI) is a useful technique for investigating the functional organization of human gray-matter in neuroscience and neuropsychiatry. Nevertheless, most studies have demonstrated the functional connectivity and/or task-related functional activity in the gray-matter. White-matter functional networks have been investigated in healthy subjects. Schizophrenia has been hypothesized to be a brain disorder involving insufficient or ineffective communication associated with white-matter abnormalities. However, previous studies have mainly examined the structural architecture of white-matter using MRI or diffusion tensor imaging and failed to uncover any dysfunctional connectivity within the white-matter on rsfMRI. The current study used rsfMRI to evaluate white-matter functional connectivity in a large cohort of ninety-seven schizophrenia patients and 126 healthy controls. Ten large-scale white-matter networks were identified by a cluster analysis of voxel-based white-matter functional connectivity and classified into superficial, middle and deep layers of networks. Evaluation of the spontaneous oscillation of white-matter networks and the functional connectivity between them showed that patients with schizophrenia had decreased amplitudes of low-frequency oscillation and increased functional connectivity in the superficial perception-motor networks. Additionally, we examined the interactions between white-matter and gray-matter networks. The superficial perception-motor white-matter network had decreased functional connectivity with the cortical perception-motor gray-matter networks. In contrast, the middle and deep white-matter networks had increased functional connectivity with the superficial perception-motor white-matter network and the cortical perception-motor gray-matter network. Thus, we presumed that the disrupted association between the gray-matter and white-matter networks in the perception-motor system may be compensated for through the middle-deep white-matter networks, which may be the foundation of the extensively disrupted connections in schizophrenia.

Neuroimaging studies have revealed brain regions involved in social cognition, which reportedly show functional alterations in schizophrenia. However, the social neural network has not been investigated with regards to language perception and social interactions in daily life. Here we developed and validated an integrative fMRI task to explore the neural basis of social cognition with regards to language perception in schizophrenia. The task comprised listening to film extracts and inferring mental states to characters. We first identified the functional network activated during the task in 28 healthy controls (HC). Next, we evaluated the reproducibility of Blood-Oxygen-Level Dependent (BOLD) variations in 14 HC participants. Finally, we investigated network impairment in 20 patients with schizophrenia (SZ) compared to HC. The HC group exhibited bilateral activation in the superior and middle temporal gyri (including the poles and the temporo-parietal junction). Overall, our novel integrative task induced activation of a functional network with good reproducibility and involved in language conveying social information. Compared to the HC group, the SZ group showed decreased recruitment of the right temporo-parietal junction. These findings may be useful for testing the impact of remediation on the brain, particularly on the network of language conveying social information.

Background Pain is an unpleasant sensory and emotional experience followed by anxiety, depression, and frustration. Functional Near-Infrared Spectroscopy (fNIRS) as an optical technique identifies the brain functional networks by investigating connectivity between functionally linked of different anatomical regions in response to pain stimulation. Methods In this research, fNIRS was performed in order to study the difference in effective functional connectivity of the brain prefrontal cortex between the two modes of pain and rest based on the dynamic causal modeling (DCM) method. Effective functional connectivity changes in the prefrontal cortex between pain and rest states were calculated using DCM approach to investigate (1) areas known for pain sensation and (2) to analyze inter-network functional connectivity strength (FCS) by selecting several brain functional networks based on the analysis findings. All analyses were performed using toolboxes SPM-fNIRS and SPM8, Matlab software. Results Regional hemodynamics changes caused deoxyhemoglobin concentration to decrease in the prefrontal cortex of both hemispheres, particularly on the right side. We found a simultaneous increase in the concentration of oxyhemoglobin in the prefrontal cortex of the left hemisphere in comparison to the right hemisphere, that there was a trend toward reduction in oxyhemoglobin concentration. The results indicate that during the cold pain stimulation, the connectivities between prefrontal cortex regions were significantly changed. Specifically, a significantly consistent increase in the RPFC to MPFC connectivity was found while a significant consistent decrease was observed in the both MPFC to LPFC and LPFC to MPFC connectivities. Conclusion This study contributes to the pain research field to identify the directionality and causality of neuronal connections in the prefrontal cortex by applying DCM to fNIRS data. The results suggest that the proposed method infers directional interactions between hidden neuronal states in the brain under neuronal dynamic conditions based on optical density changes measurement.

Functional networks (FNs) are extensions of neural networks (NNs). Unlike NNs, FNs considers general functional models instead of sigmoid-like models. Additionally, in FNs, there are no weights associated with the links that connect neurons. In this paper, we review the research progress and applications of FNs models in recent years. First, we introduce FNs architecture, three typical functional models and the learning process, and we explain the differences between NNs and FNs. Second, we discuss recent applications of FNs that have been introduced in many fields, such as time series prediction, differential and functional equations, pattern classification, detection and prediction, approximation computation, complex system modeling, computer-aided design (CAD), and linear and nonlinear regression. Finally, we present some remarks on future research directions for FNs.

BACKGROUND: Late Life Depression (LLD) has been associated with alterations in intrinsic functional networks, best characterized in the Default Mode Network (DMN), the Cognitive Control Network (CCN), and the Salience Network (SN). However these findings often derive from small samples and it is not well understood how network findings relate to clinical and cognitive symptomatology. METHODS: We studied 100 older adults (n=79 with LLD, n=21 nondepressed) and collected resting state functional MRI, clinical measures of depression, and performance on cognitive tests. We selected canonical network regions for each intrinsic functional network (DMN, CCN, and SN) as seeds in seed-to-voxel analysis. We compared connectivity between depressed and non-depressed groups and correlated connectivity with depression severity among depressed subjects. We then investigated whether the observed connectivity findings were associated with greater severity of common neuropsychiatric symptoms or poorer cognitive performance. RESULTS: LLD was characterized by decreased DMN connectivity to the frontal pole, a CCN region (Wald χ(2)=22.33, P20.14, p4.65, p4.29, p

The past decades witnessed a surge of interest in neuroimaging study of normal and abnormal early brain development. Structural and functional studies of normal early brain development revealed massive structural maturation as well as sequential, coordinated, and hierarchical emergence of functional networks during the infancy period, providing a great foundation for the investigation of abnormal early brain development mechanisms. Indeed, studies of altered brain development associated with either genetic or environmental risks emerged and thrived. In this paper, we will review selected studies of genetic and environmental risks that have been relatively more extensively investigated-familial risks, candidate risk genes, and genome-wide association studies (GWAS) on the genetic side; maternal mood disorders and prenatal drug exposures on the environmental side. Emerging studies on environment-gene interactions will also be reviewed. Our goal was not to perform an exhaustive review of all studies in the field but to leverage some representative ones to summarize the current state, point out potential limitations, and elicit discussions on important future directions.

The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture. Nanyang Technological University Published version This work was supported by the Medical University of Vienna, the Austrian Research Fund (FWF) [grants P 35189, P 34198, and I 3925-B27] in collaboration with the French National Research Agency (ANR), the Vienna Science and Technology Fund (WWTF) [LS20-065], the European Research Council Grant [866533-CORTIGRAD], the National Natural Science Foundation of China [Grant No. 81790652, No.81790650] and the NAM Advanced Biomedical Imaging Program [FY2016] between Nanyang Technological University, Singapore and Medical University of Vienna, Austria.

Korean has a large number of markers signaling diverse notions involved in comparative constructions. This paper analyzes Korean comparatives from the grammaticalization perspective, thus identifying the source semantics of the comparative markers in contemporary as well as historical data, analyzing the grammaticalization mechanisms behind their development, creating the conceptual and functional networks among the comparative markers, and comparing the grammaticalization sources with those attested in other languages. It is found that the standard markers are small in number, whereas degree markers diverge greatly: superiors, inferiors and superlatives make use of a small number of markers, whereas elatives make use of a large number of intensifying adverbials. We discuss the development of the comparatives with respect to grammaticalization mechansims, i.e. Desemanticization, Extension, Decategorialization, and Erosion, and conclude that their operation is variable depending on the functional category item as well as the individual lexical form. From a crosslinguistic comparison, we argue that the concepts and schemas found in Korean are largely consonant with those attested in crosslinguistic literature.

Although the medial temporal lobes (MTLs) are critical for both item memory (IM) and source memory (SM), the lateral prefrontal cortex and posterior parietal cortex play a greater role during SM than IM. It is unclear, however, how these differences translate into shared and distinct IM versus SM network components and how these network components vary with age. Within a sample of younger adults (YAs; n = 15, Mage = 19.5 years) and older adults (OAs; n = 40, Mage = 68.6 years), we investigated the functional networks underlying IM and SM. Before functional MRI scanning, participants encoded nouns while making either pleasantness or size judgments. During functional MRI scanning, participants completed IM and SM retrieval tasks. We found that MTL nodes were similarly interconnected among each other during both IM and SM (shared network components) but maintained more intermodule connections during SM (distinct network components). Also, during SM, OAs (compared to YAs) had MTL nodes with more widespread connections. These findings provide a novel viewpoint on neural mechanism differences underlying IM versus SM in YAs and OAs.

Extreme weather, such as torrential rain, could lead to severe damage to transportation. Many studies have been proposed focusing on the influence of extreme weather on the traffic flow properties. However, the robustness of whole dynamic traffic networks under extreme weather is rarely addressed. Particularly, little attention has been paid to the question whether and how the local destruction of roads is aggregated into a degradation of global traffic operation. Based on real-time traffic data, here we apply percolation analysis on traffic networks and find that the torrential rain can lead to different effects on different levels: on the network scale, the traffic percolation threshold as an indicator for city traffic reliability is stable against weather perturbation, while a portion of roads at the microscopic level is significantly influenced and forming local cluster isolated from the main functional network. This may be due to the fact that torrential rain and other extreme weather condition will not only generate the damaged roads in the supply end, but also reduce the traffic demand correspondingly. Our research suggests the traffic percolation may reflect the nature of relation between local flow and global flow, which can help to design corresponding management strategies.

Over the past decade, a number of advanced magnetic resonance-based methods have been brought to bear on questions related to early development of the human cerebral cortex. Herein, we describe studies employing analysis of cortical surface folding (cortical cartography), cortical microstructure (diffusion anisotropy), and cortically-based functional networks (resting state-functional connectivity MRI). The fundamentals of each MR method are described, followed by a discussion of application of the method to developing cortex and potential clinical uses. We use premature birth as an exemplar of how these modalities can be used to investigate the effects of medical and environmental variables on early cortical development.

The functional relevance of crossmodal activation (e.g. auditory activation of occipital brain regions) in congenitally blind individuals is still not fully understood. The present study tested whether the occipital cortex of blind individuals is integrated into a challenged functional network. A working memory (WM) training over four sessions was implemented. Congenitally blind and matched sighted participants were adaptively trained with an n-back task employing either voices (auditory training) or tactile stimuli (tactile training). In addition, a minimally demanding 1-back task served as an active control condition. Power and functional connectivity of EEG activity evolving during the maintenance period of an auditory 2-back task were analyzed, run prior to and after the WM training. Modality-specific (following auditory training) and modality-independent WM training effects (following both auditory and tactile training) were assessed. Improvements in auditory WM were observed in all groups, and blind and sighted individuals did not differ in training gains. Auditory and tactile training of sighted participants led, relative to the active control group, to an increase in fronto-parietal theta-band power, suggesting a training-induced strengthening of the existing modality-independent WM network. No power effects were observed in the blind. Rather, after auditory training the blind showed a decrease in theta-band connectivity between central, parietal, and occipital electrodes compared to the blind tactile training and active control groups. Furthermore, in the blind auditory training increased beta-band connectivity between fronto-parietal, central and occipital electrodes. In the congenitally blind, these findings suggest a stronger integration of occipital areas into the auditory WM network.

Hubs in complex networks can greatly influence the integration of network functions, and recognition of hubs helps to better understand the interaction between pairs of network nodes. This paper proposes a new hub recognition method with multiple-feature combination for the brain functional networks constructed by resting-state functional Magnetic Resonance Imaging (fMRI). Three single-feature methods, including degree centrality, betweenness centrality and closeness centrality, are used to calculate hubs of the brain functional network separately. For reordering the nodes, a composite equation is constructed based on the three recognition parameters. Network vulnerability and average shortest path length are used to evaluate the importance of the hubs recognized by above four methods. Experimental result demonstrates that, the hubs recognized by multiple-feature combination have more significant differences from ordinary nodes than those by single-feature methods, and they have an important impact on the global efficiency of brain functional networks.

Summary Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.

Studies investigating the strength and membership of regions within multiple functional networks primarily focus on either resting state or single cognitive tasks. The goals of the current study were to investigate whether task-related functional connectivity changes with task complexity, and whether this connectivity-complexity relationship is age-sensitive. We assessed seed-to-voxel functional connectivity for the default mode network (DMN) and two attentional networks [cingulo-opercular (CO), fronto-parietal (FP)] in three cognitive control tasks of increasing complexity (Single task, Dual task, and Memory Updating), across younger and older adults (N = 52; NYoung = 23; NOld = 29). The three tasks systematically varied in cognitive control demands due to differing maintenance, switching, and updating requirements. Functional connectivity for all networks, resulting from task > rest contrasts, increased with greater task complexity, irrespective of age and gray matter volume. Moreover, between-network connectivity for DMN, CO, and FP regions was greatest for working memory updating, the most complex task. Regarding age-related differences in accuracy, none were observed for Single or Dual tasks, but older adults had poorer accuracy in Memory Updating. More anterior frontal clusters of functional connectivity were observed for older, compared to younger, adults; these were limited to seeds of the two attentional networks. Importantly, increased connectivity in these additional frontal regions in older adults were non-compensatory, because they were associated with detrimental task performance, especially Memory Updating. For the Memory Updating > Rest, the younger > older contrast resulted in greater DMN seed connectivity to regions in the other two attentional networks, implicating increased reliance on between-network connectivity for the DMN seeds during complex cognitive tasks. Our results also implicate functional connectivity between attentional networks and the cerebellum during cognitive control. Reliability of multiple seeds in the seed-to-voxel connectivity is also discussed.

Objective: This paper studies the fatigue caused by prolonged watching of three-dimensional television (3DTV) by applying the amplitude of low frequency fluctuation (ALFF) and the community partitioning algorithm in brain functional network techniques to resting-state functional magnetic resonance imaging (fMRI). In this study, two specific frequency bands (Slow5: 0.01–0.027 Hz and Slow4: 0.027–0.073 Hz) were analyzed to investigate the change of brain spontaneous activity after one-hour watching of 3DTV. Meanwhile, the changes of brain modular organization were analyzed to discuss the physiological mechanism of brain fatigue during long period of watching 3DTV. Methods: Valid data of resting state fMRI from 20 dextromanual subjects were adopted before and after 1 h watching of 3DTV/2DTV. Then, ALFF was processed using SPM8 and REST software to analyze the variation of resting-state fMRI induced by watching 2D/3DTV. Community partitioning algorithm was used to evaluate the change of brain modular organization. Results: While watching 2DTV does not significantly change the brain spontaneous activity, watching 3DTV makes significant differences on middle frontal gyrus (BA9/BA10), right inferior occipital gyrus (BA18/19), right inferior temporal gyrus (BA20/37) and cingulate cortex (BA32). The result shows that dorsal stream was activated after watching 3DTV through analyzed modularity and community, which verifies that watching 3DTV requires the brain visual pathway to process stereoscopic display. Meanwhile, the result further reflects that the neural circuit, including visual conducting pathway, temporal lobe and basal ganglia loop, is closely related to brain fatigue caused by long period of watching 3DTV. The study has shown the potential risk of 3DTV to viewers’ health, especially on vision and recognition functions. The basal ganglia neurons play crucial roles in the physiological mechanism of brain fatigue. Therefore, prolonged 3DTV watching should be avoided particularly for physically developing teenagers. This research suggested that the ALFF technique and brain modular organization is able to reveal intrinsic changes caused by visual fatigue and provided a foundation for 3DTV fatigue assessment.

The ITER CODAC (Control, Data Access and Communication) system is the conventional central control system responsible for operating the ITER facility. CODAC Networks comprise Plant Operation Network (PON), Time Communication Network (TCN), Synchronous Databus Network (SDN) and Data Archiving Network (DAN). CODAC networks connect all plant systems to the central control system through one or more networks depending on the requirements of the plant system I&Cs. The CODAC network infrastructure has been defined including network topology, backbone cable and space allocation network equipment in different ITER plant buildings. This paper presents the design of the current network infrastructure and the four functional networks. This paper also presents the implementation plan of the CODAC network according to the ITER project schedule.

Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.

Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood. Keywords: Attention, Children, Neural networks, Early childhood, Functional connectivity, ICA

Certain deficits of episodic memory among young adults are the delayed consequences of an earlier mild or moderate Traumatic Brain Injury (mTBI). We examined alterations in hippocampal Local Field Oscillations (LFOs) of equivalently-impaired juvenile rodents to identify reliable functional markers of single-incidence mTBI. Two persistent, behavior-dependent, electrophysiological markers of injury were identified in the absence of external physiological symptoms by the analysis of wirelessly-transmitted hippocampal LFOs (3-80Hz) during repeat measures of the Novel Object Recognition (NOR) paradigm. Using a new method for detecting functional network activity at a single recording site, we correlated instantaneous increases in theta frequency and gamma magnitude with injury during periods of functional network organization and dissolution, presented here as Theta Epochs (TEs) and Theta Non-Epochs (TNEs), respectively. We estimated the efficacy of networks across pairs of such sites using new metrics such as the Coherence of Theta Phase (PCOH) and Inter-Epoch Intervals (IEIs) and demonstrated that behavioral deficits observed during the NOR testing stage correspond to electrophysiological deficits recorded during the preceding NOR familiarization stage. Increased theta frequency during TNEs and increased smoothness of PCOH during TEs were found to be robust markers of injury during memory-reliant behaviors.

s: Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.