Properties Of Brain Networks Research Articles

Developmental Trajectories and Differences in Functional Brain Network Properties of Preterm and At-Term Neonates.

Premature infants, born before 37 weeks of gestation can have alterations in neurodevelopment and cognition, even when no anatomical lesions are evident. Resting-state functional neuroimaging of naturally sleeping babies has shown altered connectivity patterns, but there is limited evidence on the developmental trajectories of functional organization in preterm neonates. By using a large dataset from the developing Human Connectome Project, we explored the differences in graph theory properties between at-term (n = 332) and preterm (n = 115) neonates at term-equivalent age, considering the age subgroups proposed by the World Health Organization for premature birth. Leveraging the longitudinal follow-up for some preterm participants, we characterized the developmental trajectories for preterm and at-term neonates, for this purpose linear, quadratic, and log-linear mixed models were constructed with gestational age at scan as an independent fixed-effect variable and random effects were added for the intercept and subject ID. Significance was defined at p < 0.05, and the model with the lowest Akaike Information Criterion (AIC) was selected as the best model. We found significant differences between groups in connectivity strength, clustering coefficient, characteristic path length and global efficiency. Specifically, at term-equivalent ages, higher connectivity, clustering coefficient and efficiency are identified for neonates born at later postmenstrual ages. Similarly, the characteristic path length showed the inverse pattern. These results were consistent for a variety of connectivity thresholds at both the global (whole brain) and local level (brain regions). The brain regions with the greatest differences between groups include primary sensory and motor regions and the precuneus which may relate to the risk factors for sensorimotor and behavioral deficits associated with premature birth. Our results also show non-linear developmental trajectories for premature neonates, but decreased integration and segregation even at term-equivalent age. Overall, our results confirm altered functional connectivity, integration and segregation properties of the premature brain despite showing rapid maturation after birth.

Simulated brain networks reflecting progression of Parkinson's disease.

The neurodegenerative progression of Parkinson's disease affects brain structure and function and, concomitantly, alters the topological properties of brain networks. The network alteration accompanied by motor impairment and the duration of the disease has not yet been clearly demonstrated in the disease progression. In this study, we aim to resolve this problem with a modeling approach using the reduced Jansen-Rit model applied to large-scale brain networks derived from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that the simulated brain networks exhibit significant differences between healthy controls (n = 51) and patients with Parkinson's disease (n = 60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing the simulated brain networks provides an enhanced view of network alterations in the progression of motor impairment and identifies potential biomarkers for clinical indices.

Reduced Functional Connectivity in the Default Mode Network in EEGs without Other Abnormalities in Early Creutzfeldt-Jacob Disease.

Creutzfeldt-Jakob disease (CJD) is a rare, neurodegenerative disorder that is incurable, always fatal, and transmissible. EEG is an integral part of the diagnostic workup with typical periodic sharp wave complexes indicative of CJD, but early in the disease EEG is often unaltered. Accordingly, we aimed at evaluating disruption of brain network functional connectivity (FC) in regions belonging to the default mode network (DMN) as a potential early marker in CJD when EEG is considered visually normal. EEGs considered visually normal obtained from 7 CJD patients were compared to EEGs of 7 patients with subjective cognitive impairment (SCI) using Brainstorm application for MATLAB. FC was calculated using the phase locking value separately for the delta-, theta-, alpha-, and beta-frequency band. The global efficacy of the DMN was calculated as the inverse characteristic path length with brain regions belonging to the DMN as nodes. CJD versus SCI had significantly lower global efficacy of the DMN in the gamma band. FC was decreased between bilateral orbitofrontal regions and the right parahippocampal gyrus and between bilateral orbitofrontal gyrus and the right anterior cingulate gyrus in CJD. Our findings provide evidence of disruption of the DMN in the gamma band and alterations of FC between specific brain regions in early CJD patients with visually normal EEGs. EEG brain network properties bear potential as a diagnostic tool for CJD early in the disease course in addition to established criteria. These findings call for further studies evaluating the diagnostic value of FC in early CJD.

The efficacy of topological properties of functional brain networks in identifying major depressive disorder

Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identifying MDD patients, revealing variational brain regions with efficient TPs. Functional connectivity (FC) networks were constructed from resting-state functional magnetic resonance imaging (rs-fMRI). Small-worldness did not exhibit significant variations in MDD patients. Subsequently, two-sample t-tests were employed to screen FC and reconstruct the network. The discriminative ability of TPs between MDD patients and healthy controls was analyzed using receiver operating characteristic (ROC), ROC analysis showed the small-worldness of binary reconstructed FC network (p < 0.05) was reduced in MDD patients, with area under the curve (AUC) of local efficiency (Le) and clustering coefficient (Cp) as sample features having AUC of 0.6351 and 0.6347 respectively being optimal. The AUC of Le and Cp for retained brain regions by T-test (p < 0.05) were 0.6795 and 0.6956 respectively. Further, support vector machine (SVM) model assessed the effectiveness of TPs in identifying MDD patients, and it identified the Le and Cp in brain regions selected by the least absolute shrinkage and selection operator (LASSO), with average accuracy from leave-one-site-out cross-validation being 62.03% and 61.44%. Additionally, shapley additive explanations (SHAP) was employed to elucidate variations in TPs across brain regions, revealing that predominant variations among MDD patients occurred within the default mode network. These results reveal efficient TPs that can provide empirical evidence for utilizing nodal TPs as effective inputs for deep learning on graph structures, contributing to understanding the pathological mechanisms of MDD.

Disrupted topologic efficiency of white matter structural connectome in migraine: a graph-based connectomics study

ObjectiveTo delineate the structural connectome alterations in patients with chronic migraine (CM), episodic migraine (EM), and healthy controls (HCs).BackgroundThe pathogenesis of migraine chronification remains elusive, with structural brain network changes potentially playing a key role. However, there is a paucity of research employing graph theory analysis to explore changes in the whole brain structural networks in patients with CM and EM.MethodsThe individual structural brain connectome of 60 patients with CM, 34 patients with EM, and 39 healthy control participants were constructed by using deterministic diffusion-tensor tractography. Graph metrics including global efficiency, characteristic path length, local efficiency, clustering coefficient, and small-world parameters were evaluated to describe the topologic organization of the white matter structural networks. Additionally, nodal clustering coefficient and efficiency were considered to assess the regional characteristics of the brain connectome. A graph-based statistic was used to assess brain network properties across the groups.ResultsGraph theory analysis revealed significant disruptions in the structural brain networks of CM patients, characterized by reduced global efficiency, local efficiency, and increased characteristic path length compared to HCs. Additionally, CM patients exhibited significantly lower local efficiency than EM patients. Notably, the CM group demonstrated marked reductions in local clustering coefficient and nodal local efficiency in the frontal and temporal regions compared with the healthy control group and EM group. Nodal local efficiency can effectively distinguish CM from EM and HCs. Moreover, the disrupted topologic efficiency was significantly associated with attack frequency and MIDAS score in patients with migraine after Bonferroni correction.ConclusionDecreased structural connectivity in the frontal and temporal regions may serve as a neuroimaging marker for migraine chronification and disease progression, providing valuable insights into the pathophysiology of chronic migraine.

Neural mechanisms of fear responses to emotional stimuli: a preliminary study combining early posterior negativity and electroencephalogram source network analysis

Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.

Impact of moderate aerobic exercise on small-world topology and characteristics of brain networks after sport-related concussion: an exploratory study

Sport-related concussion (SRC) is a complex brain injury. By applying graph-theoretical analysis to networks derived from neuroimaging techniques, studies have shown that despite an overall retention of small-world topology, changes in small-world properties occur after brain injury. Less is known about how exercise during athletes’ return to sport (RTS) influences these brain network properties. Therefore, in the present study dense electroencephalography (EEG) datasets were collected pre- and post-moderate aerobic exercise. Small-world properties of whole brain (WB) and the default mode network (DMN) were extracted from the EEG datasets of 21 concussed athletes and 21 healthy matched controls. More specifically, path length (LP), clustering coefficient (CP), and small-world index (SWI) in binary and weighted graphs were calculated in the alpha frequency band (7–13 Hz). Pre-exercise, SRC athletes had higher DMN-CP values compared to controls, while post-exercise SRC athletes had higher WB-LP compared to controls. Weighted WB analysis revealed a significant association between SRC and the absence of small-world topology (SWI ≤ 1) post-exercise. This explorative study provides preliminary evidence that moderate aerobic exercise during athletes’ RTS induces an altered network response. Furthermore, this altered response may be related to the clinical characteristics of the SRC athlete.

The brain’s structural connectivity and pre-reading abilities in young children with prenatal alcohol exposure

Children with prenatal alcohol exposure (PAE) may develop a range of neurological and behavioral deficits, including reading and language disorders. Studying the brain’s structural connectivity and its relationship to pre-reading/reading skills in young children with PAE can help understand the roots of reading deficits associated with PAE. 363 diffusion MRI scans from 135 children (114 scans from 53 children with PAE) were collected between ages 3–7 years. Children completed NEPSY-II Phonological Processing and Speeded Naming to assess pre-reading skills at each scan. Structural brain network properties were assessed in 16 regions from both hemispheres using graph theory. Linear mixed models were used to account for repeated measures within participants. Children with PAE had significantly lower pre-reading scores than unexposed children, and significantly lower graph theory metrics across bilateral reading networks. Moreover, PAE significantly moderated the associations between Phonological Processing and global efficiency and nodal degree in the bilateral and left hemisphere reading networks, such that children with PAE had stronger associations than unexposed controls. No significant associations were found for Speeded Naming. Our results suggest that brain alterations may underlie early pre-reading difficulties in children with PAE.

Brain networks in newborns and infants with and without sensorineural hearing loss: A functional near-infrared spectroscopy study.

Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions. While previous studies have looked into the impact of prenatal experiences on language development, there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss (SNHL). To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls. The research involved analyzing the functional brain networks of 65 infants, categorized into three groups: 28 healthy newborns, 22 4-month-old participants with SNHL, and 15 age-matched healthy participants. The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties. Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls, indicating a developmental lag in brain connectivity for those with SNHL. Surprisingly, SNHL participants showed better connectivity development compared to healthy newborns, with connectivity strengths of 0.13 ± 0.04 for SNHL, 0.16 ± 0.08 for controls, and 0.098 ± 0.04 for newborns. Graph theory analysis revealed enhanced global brain network properties for the SNHL group, suggesting higher communication efficiency at 4 months. No significant differences were noted in network properties between 4-month-old SNHL participants and neonates. A unique pattern of central hubs was observed in the SNHL group, with 2 hubs in the left hemisphere compared to 6 in controls. 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.

Patients with temporomandibular disorders and chronic pain of myofascial origin display reduced alpha power density and altered small-world properties of brain networks

BACKGROUND: Chronic pain is one of the most common symptoms of temporomandibular disorders (TMD). Although its pathophysiology is still a challenge, TMD has been associated with changes in central nervous system activity related to pain modulatory capacity. OBJECTIVE: To assess the cortical activity of patients with temporomandibular disorders and chronic pain of myofascial origin using quantitative electroencephalography (qEEG) in different mental states. METHOD: This study consists of a cross-sectional study. Individuals with TMD and chronic pain and healthy controls were evaluated using qEEG in four consecutive conditions, all with closed eyes: 1) initial resting condition; 2) non-painful motor imagery task of hand movement; 3) painful motor imagery task of clenching the teeth; 4) final resting condition. RESULTS: Participants with TMD and chronic pain overall presented decreased alpha power density during baseline at rest, non-painful and painful motor imagery tasks when compared to healthy controls. Furthermore, functional brain connectivity was distinct between groups, with TMD and chronic pain showing lower small-world values for the delta (all conditions), theta (painful and non-painful motor imagery task), and alpha bands (painful motor imagery task), and an increase in the beta band (all conditions). CONCLUSION: These results suggest that TMD and chronic pain could be associated with maladaptive plasticity in the brain, which may correspond to a reduced ability to modify brain activity during different mental tasks, including painful and non-painful motor imagery.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Identifying individual brain development using multimodality brain network

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.

Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism.

A growing understanding of the nature of brain function has led to increased interest in interpreting the properties of large-scale brain networks. Methodological advances in network neuroscience provide means to decompose these networks into smaller functional communities and measure how they reconfigure over time as an index of their dynamic and flexible properties. Recent evidence has identified associations between flexibility and a variety of traits pertaining to complex cognition including creativity and working memory. The present study used measures of dynamic resting-state functional connectivity in data from the Human Connectome Project (n = 994) to test associations with Openness/Intellect, general intelligence, and psychoticism, three traits that involve flexible cognition. Using a machine-learning cross-validation approach, we identified reliable associations of intelligence with cohesive flexibility of parcels in large communities across the cortex, of psychoticism with disjoint flexibility, and of Openness/Intellect with overall flexibility among parcels in smaller communities. These findings are reasonably consistent with previous theories of the neural correlates of these traits and help to expand on previous associations of behavior with dynamic functional connectivity, in the context of broad personality dimensions.

Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale.

Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)-an analytic approach for handling functional misalignment-can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling

Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer’s Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BackgroundInternet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. MethodResting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. ResultsIGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. ConclusionThe dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Exploring the correlation between childhood trauma experiences, inflammation, and brain activity in first-episode, drug-naive major depressive disorder.

Childhood trauma experiences and inflammation are pivotal factors in the onset and perpetuation of major depressive disorder (MDD). However, research on brain mechanisms linking childhood trauma experiences and inflammation to depression remains insufficient and inconclusive. Resting-state fMRI scans were performed on fifty-six first-episode, drug-naive MDD patients and sixty healthy controls (HCs). A whole-brain functional network was constructed by thresholding 246 brain regions, and connectivity and network properties were calculated. Plasma interleukin-6 (IL-6) levels were assessed using enzyme-linked immunosorbent assays in MDD patients, and childhood trauma experiences were evaluated through the Childhood Trauma Questionnaire (CTQ). Negative correlations were observed between CTQ total (r = -0.28, p = 0.047), emotional neglect (r = -0.286, p = 0.042) scores, as well as plasma IL-6 levels (r = -0.294, p = 0.036), with mean decreased functional connectivity (FC) in MDD patients. Additionally, physical abuse exhibited a positive correlation with the nodal clustering coefficient of the left thalamus in patients (r = 0.306, p = 0.029). Exploratory analysis indicated negative correlations between CTQ total and emotional neglect scores and mean decreased FC in MDD patients with lower plasma IL-6 levels (n = 28), while these correlations were nonsignificant in MDD patients with higher plasma IL-6 levels (n = 28). This finding enhances our understanding of the correlation between childhood trauma experiences, inflammation, and brain activity in MDD, suggesting potential variations in their underlying pathophysiological mechanisms.

Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.

EEG alpha band functional brain network correlates of cognitive performance in children after perinatal stroke

Mechanisms underlying cognitive impairment after perinatal stroke could be explained through brain network alterations. With aim to explore this connection, we conducted a matched test-control study to find a correlation between functional brain network properties and cognitive functions in children after perinatal stroke. First, we analyzed resting-state functional connectomes in the alpha frequency band from a 64-channel resting state EEG in 24 children with a history of perinatal stroke (12 with neonatal arterial ischemic stroke and 12 with neonatal hemorrhagic stroke) and compared them to the functional connectomes of 24 healthy controls. Next, all participants underwent cognitive evaluation. We analyzed the differences in functional brain network properties and cognitive abilities between groups and studied the correlation between network characteristics and specific cognitive functions. Functional brain networks after perinatal stroke had lower modularity, higher clustering coefficient, higher interhemispheric strength, higher characteristic path length and higher small world index. Modularity correlated positively with the IQ and processing speed, while clustering coefficient correlated negatively with IQ. Graph metrics, reflecting network segregation (clustering coefficient and small world index) correlated positively with a tendency to impulsive decision making, which also correlated positively with graph metrics, reflecting stronger functional connectivity (characteristic path length and interhemispheric strength). Our study suggests that specific cognitive functions correlate with different brain network properties and that functional network characteristics after perinatal stroke reflect poorer cognitive functioning.

Meso-scale reorganization of local–global brain networks under mild sedation of propofol anesthesia

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local–global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local–global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.