GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Topological functional network analysis of cortical blood flow in hyperacute ischemic rats

Acute cerebral ischemia alters brain network connectivity, leading to notable increases in both anatomical and functional connectivity while observing a reduction in metabolic connectivity. However, alterations of the cerebral blood flow (CBF) based functional connectivity remain unclear. We collected continuous CBF images using laser speckle contrast imaging (LSCI) technology to monitor ischemic occlusion-reperfusion progression through occlusion of the left carotid artery. We also used a dense cortical grid atlas to construct CBF-based functional connectivity networks for hyperacute ischemic rodents. Graph theoretical analysis was used to measure network topological characteristics and construct topological connection graphs. Coactivation pattern (CAP) analysis was utilized to examine the spatiotemporal characteristics of the global network. Additionally, we measured evoked functional hyperemia and correlated it with network topologies. Network analysis indicated a significant increase in functional connectivity, global efficiency, local efficiency, small-worldness, clustering coefficient, and regional degree centrality primarily within the left ischemic intra-hemisphere, accompanied by weaker changes in the right intra-hemisphere. Inter-hemisphere networks exhibited reduced homologous connections, global efficiency, and small-worldness. CAP analysis revealed increased strength of the left negative activation brain network's state fraction of time and transition probability from equilibrium-to-imbalance states. Left network metrics declined following blood flow reperfusion. Furthermore, positive/negative correlations between barrel-evoked intensity and regional network topologies were reversed as negative/positive correlations after cerebral ischemia. These findings suggest a damaged CBF functional network mechanism following acute cerebral ischemia and a disrupted association between resting state and evoked hyperemia.

Aberrant brain structural-functional coupling and structural/functional network topology explain developmental delays in pediatric Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder characterized by dysplasia in early life. Psychoradiology studies have suggested that mental and behavioral deficits in individuals with PWS are linked to abnormalities in brain structural and functional networks. However, little is known about changes in network-based structural-functional coupling and structural/functional topological properties and their correlations with developmental scales in children with PWS. Here, we acquired diffusion tensor imaging and resting-state functional magnetic resonance imaging data from 25 children with PWS and 28 age- and sex-matched healthy controls, constructed structural and functional networks, examined intergroup differences in structural-functional coupling and structural/functional topological properties (both global and nodal), and tested their partial correlations with developmental scales. We found that children with PWS exhibited (1) decreased structural-functional coupling, (2) a higher characteristic path length and lower global efficiency in the structural network in terms of global properties, (3) alterations in classical cortical and subcortical networks in terms of nodal properties, with the structural network dominated by decreases and the functional network dominated by increases, and (4) partial correlation with developmental scales, especially for functional networks. These findings suggest that structural-functional decoupling and abundant structural/functional network topological properties may reveal the mechanism of early neurodevelopmental delays in PWS from a neuroimaging perspective and might serve as potential markers to assess early neurodevelopmental backwardness in PWS.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Background

and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients.

Methods

We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs.

Results

All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937.

Conclusion

The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

Altered default-mode and frontal-parietal network pattern underlie adaptiveness of emotion regulation flexibility following task-switch training

Emotion regulation flexibility (ERF) refers to one's ability to respond flexibly in complex environments. Adaptiveness of ERF has been associated with cognitive flexibility, which can be improved by task-switching training. However, the impact of task-switching training on ERF and its underlying neural mechanisms remain unclear. To address this issue, we examined the effects of training on individuals' adaptiveness of ERF by assessing altered brain network patterns. Two groups of participants completed behavioral experiments and resting-state fMRI before and after training. Behavioral results showed higher adaptiveness scores and network analysis observed a higher number of connectivity edges, in the training group compared to the control group. Moreover, we found decreased connectivity strength within the default mode network (DMN) and increased connectivity strength within the frontoparietal network (FPN) in the training group. Furthermore, the task-switch training also led to decreased DMN-FPN interconnectivity, which was significantly correlated to increased adaptiveness of ERF scores. These findings suggest that the adaptiveness of ERF can be supported by altered patterns with the brain network through task-switch training, especially the increased network segregation between the DMN and FPN.

The impact of sleep disorders on brain network connectivity in postpartum women: a functional near-infrared spectroscopy-based study

Sleep disorder is an important risk factor for postpartum depression. Although previous research has explored brain activity in postpartum depression, it has not fully revealed how insomnia affect mood by altering interactions between brain regions. This study aim to investigate the relationship between insomnia and depressive status in postpartum women, utilizing functional near-infrared spectroscopy (fNIRS) to explore brain network topological properties. Among 143 postpartum women, 40 were diagnosed with insomnia and 103 without. The results indicated that the Edinburgh Postnatal Depression Scale (EPDS) scores were significantly higher in the insomnia group compared to the control group. Compared with the control group, the insomnia group showed significantly increased connection strength of triangularis Broca's between middle and superior temporal gyrus and left between right dorsolateral prefrontal cortex (p < 0.001). Brain network topological analysis revealed that the small-world properties, clustering coefficient (p = 0.009), and local efficiency (p = 0.009) were significantly lower in the insomnia group compared to the control group. Notably, the local efficiency and clustering coefficient of the left temporal pole were significantly reduced and negatively correlated with EPDS scores. These findings elucidate how insomnia may exacerbate postpartum depression through changes in brain network properties. While the observed alterations in connectivity suggest a correlation, causation cannot be definitively established. Improving sleep quality remains a promising intervention, but further research is needed to clarify causal links and therapeutic targets.

CoCoNest: A continuous structural connectivity-based nested family of parcellations of the human cerebral cortex

Despite the widespread exploration and availability of parcellations for the functional connectome, parcellations designed for the structural connectome are comparatively limited. Current research suggests that there may be no single "correct" parcellation and that the human brain is intrinsically a multiresolution entity. In this work, we propose the Continuous Structural Connectivitity-based, Nested (CoCoNest) family of parcellations-a fully data-driven, multiresolution family of parcellations derived from structural connectome data. The CoCoNest family is created using agglomerative (bottom-up) clustering and error-complexity pruning, which strikes a balance between the complexity of each parcellation and how well it preserves patterns in vertex-level, high-resolution connectivity data. We draw on a comprehensive battery of internal and external evaluation metrics to show that the CoCoNest family is competitive with or outperforms widely used parcellations in the literature. Additionally, we show how the CoCoNest family can serve as an exploratory tool for researchers to investigate the multiresolution organization of the structural connectome.

Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder

White-matter tracts play a pivotal role in transmitting sensory and motor information, facilitating interhemispheric communication and integrating different brain regions. Meanwhile, sensorimotor disturbance is a common symptom in patients with major depressive disorder (MDD). However, the role of aberrant sensorimotor white-matter system in MDD remains largely unknown. Herein, we investigated the topological structure alterations of white-matter morphological brain networks in 233 MDD patients versus 257 matched healthy controls (HCs) from the DIRECT consortium. White-matter networks were derived from magnetic resonance imaging (MRI) data by combining voxel-based morphometry (VBM) and three-dimensional discrete wavelet transform (3D-DWT) approaches. Support vector machine (SVM) analysis was performed to discriminate MDD patients from HCs. The results indicated that the network topological changes in node degree, node efficiency, and node betweenness were mainly located in the sensorimotor superficial white-matter system in MDD. Using network nodal topological properties as classification features, the SVM model could effectively distinguish MDD patients from HCs. These findings provide new evidence to highlight the importance of the sensorimotor system in brain mechanisms underlying MDD from a new perspective of white-matter morphological network.

Decreased Functional and Structural Connectivity is Associated with Core Symptom Improvement in Children with Autism Spectrum Disorder After Mini-basketball Training Program

Exercise intervention has been proven helpful to ameliorate core symptoms of Autism Spectrum Disorder (ASD). However, the underlying mechanisms are not fully understood. In this study, we carried out a 12-week mini-basketball training program (MBTP) on ASD children and examined the changes of brain functional and structural networks before and after exercise intervention. We applied individual-based method to construct functional network and structural morphological network, and investigated their alterations following MBTP as well as their associations with the change in core symptom. Structural MRI and resting-state functional MRI data were obtained from 58 ASD children aged 3-12 years (experiment group: n = 32, control group: n = 26). ASD children who received MBTP intervention showed several distinguishable alternations compared to the control without special intervention. These included decreased functional connectivity within the sensorimotor network (SM) and between SM and the salience network, decreased morphological connectivity strength in a cortical-cortical network centered on the left inferior temporal gyrus, and a subcortical-cortical network centered on the left caudate. Particularly, the aforementioned functional and structural changes induced by MBTP were associated with core symptoms of ASD. Our findings suggested that MBTP intervention could be an effective approach to improve core symptoms in ASD children, decrease connectivity in both structure and function networks, and may drive the brain change towards normal-like neuroanatomy.

Insomnia and stress: the mediating roles of frontoparietal network

Insomnia is a widespread health problem among adults, and it impairs cognitive control and emotional regulation functions. Stress and insomnia are positively correlated, and their vicious cycle has been widely reported. In this study, we explore the neural biomarkers of insomnia from the perspective of whole-brain functional connectivity and investigate the neural mechanisms underlying the association between stress and insomnia. The current study was conducted on a cross-sectional sample (N = 430). First, we investigated the correlation between perceived stress and insomnia. Second, we applied connectome-based predictive modeling (CPM) to determine the neuromarkers of insomnia. Finally, we explored the neural basis underlying the association between perceived stress and insomnia. A significant positive correlation was found between perceived stress and insomnia in the present research. Results of CPM revealed the following as the neural substrates supporting insomnia: the emotion regulation circuit involving repetitive negative thinking and the cognitive control circuit involving attention control. According to further results from mediation analysis, the frontoparietal network supporting cognitive emotion regulation is an important neural mechanism that maintains the correlation between stress and insomnia. The present study offers a profound insight into the alterations of brain activity related to insomnia, and it further investigates the neural underpinnings of the robust association between stress and insomnia. This study also opens new avenues for neural interventions to alleviate stress-related insomnia.

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.

The neural basis underlying the association between parents' socioeconomic status and depressive symptoms among college students

Objective

Depression is increasingly prevalent among adolescents, with parents' socioeconomic status (SES) serving as significant predictors. Understanding the link between parents' SES and college students' depressive symptoms is of paramount concern. However, the neural basis linking the association between parents' SES and students' depressive symptoms still remains to be explored. In order to address this issue, this study aims to investigate the relationship between parents' SES and students' depressive symptoms, and the role of brain functional connectivity (FC) pattern in this relationship.

Methods

In this study, a total of 363 college students without a history of mental or neurological disorders underwent depressive symptoms assessment and resting-state functional magnetic resonance imaging scans. We used a connectome-based predictive modeling (CPM) approach to identify neural biomarkers of depressive symptoms.

Results

The results indicate that there is a negative correlation between parents' SES and students' depression tendencies (Father's education level and SDS: r  = -0.119, p < 0.05; Mother's education level and SDS: r  = -0.117, p < 0.05), suggesting that students whose parents have a higher educational level are less likely to suffer from depression. Furthermore, a FC pattern that can significantly predict depressive symptoms outside of the body was identified (r  = 0.13, p < 0.005), with most of the FCs belonging to the default mode network (DMN) and ventral attention network (VAN). Additionally, the FC pattern associated with depressive symptoms mediate the relationship between parents' SES and depressive symptoms.

Conclusion

Therefore, we believe that improving the education levels of parents may have a practical effect in reducing depressive symptoms among adolescents.

Abnormal topological organization of functional brain networks in the patients with anterior segment ischemic optic neuropathy

Objective

An increasing amount of neuroimaging evidence indicates that patients with anterior segment ischemic optic neuropathy (AION) exhibit abnormal brain function and structural architecture. Some studies have shown that there are abnormal functional and structural changes in the brain visual area of AION patients. Nevertheless, the alterations in the topological properties of brain functional connectivity among patients with AION remain unclear. This study aimed to investigate the topological organization of brain functional connectivity in a group of AION patients using graph theory methods.

Methods

Resting-state magnetic resonance imaging was conducted on 30 AION patients and 24 healthy controls (HCs) matched for age, gender, and education level. For each participant, a high-resolution brain functional network was constructed using time series correlation and quantified through graph theory analysis.

Results

Both the AION and HC groups presented high-efficiency small-world networks in their brain functional networks. In comparison to the HCs, the AION group exhibited notable reductions in clustering coefficient (Cp) and local efficiency (Eloc). Specifically, significant decreases in Nodal local efficiency were observed in the right Amygdala of the AION group. Moreover, the NBS method detected a significantly modified network (15 nodes, 15 connections) in the AION group compared to the HCs (p < 0.05).

Conclusion

Patients with AION exhibited topological abnormalities in the human brain connectivity group. Particularly, there was a decrease in Cp and Eloc in the AION group compared to the HC group. The anomalous node centers and functional connections in AION patients were predominantly situated in the prefrontal lobe, temporal lobe, and parietal lobe. These discoveries offer valuable perspectives into the neural mechanisms associated with visual loss, disrupted emotion regulation, and cognitive impairments in individuals with AION.

Dyadic Similarity in Social Value Orientation Modulates Hyper-Brain Network Dynamics During Interpersonal Coordination: An fNIRS-Based Hyperscanning Study

As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode.

How Freely Moving Mind Wandering Relates to Creativity: Behavioral and Neural Evidence

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = -0.11, pFDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c' = 0.01, [-0.0181, -0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship.

Morphological similarity and white matter structural mapping of new daily persistent headache: a structural connectivity and tract-specific study

BACKGROUND

New daily persistent headache (NDPH) is a rare primary headache disorder characterized by daily and persistent sudden onset headaches. Specific abnormalities in gray matter and white matter structure are associated with pain, but have not been well studied in NDPH. The objective of this work is to explore the fiber tracts and structural connectivity, which can help reveal unique gray and white matter structural abnormalities in NDPH.

METHODS

The regional radiomics similarity networks were calculated from T1 weighted (T1w) MRI to depict the gray matter structure. The fiber connectivity matrices weighted by diffusion metrics like fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were built, meanwhile the fiber tracts were segmented by anatomically-guided superficial fiber segmentation (Anat-SFSeg) method to explore the white matter structure from diffusion MRI. The considerable different neuroimaging features between NDPH and healthy controls (HC) were extracted from the connectivity and tract-based analyses. Finally, decision tree regression was used to predict the clinical scores (i.e. pain intensity) from the above neuroimaging features.

RESULTS

T1w and diffusion MRI data were available in 51 participants after quality control: 22 patients with NDPH and 29 HCs. Significantly decreased morphological similarity was found between the right superior frontal gyrus and right hippocampus. The superficial white matter (SWM) showed significantly decreased FA in fiber tracts including the right superficial-frontal, left superficial-occipital, bilateral superficial-occipital-temporal (Sup-OT) and right superficial-temporal, meanwhile significant increased RD was found in the left Sup-OT. For the fiber connectivity, NDPH showed significantly decreased FA in the bilateral basal ganglion and temporal lobe, increased MD in the right frontal lobe, and increased RD in the right frontal lobe and left temporal-occipital lobe. Clinical scores could be predicted dominantly by the above significantly different neuroimaging features through decision tree regression.

CONCLUSIONS

Our research indicates the structural abnormalities of SWM and the neural pathways projected between regions like right hippocampus and left caudate nucleus, along with morphological similarity changes between the right superior frontal gyrus and right hippocampus, constitute the pathological features of NDPH. The decision tree regression demonstrates correlations between these structural changes and clinical scores.

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.

Network efficiency of functional brain connectomes altered in type 2 diabetes patients with and without mild cognitive impairment

AIM

To explore the topological organization alterations of functional connectomes in type 2 diabetes (T2DM) patients with and without mild cognitive impairment (MCI), and compare these with structural connectomes changes.

METHODS

Twenty-six T2DM patients with MCI (DM-MCI), 26 without cognitive impairment (DM-NC), and 28 healthy controls were included. Diffusion tensor imaging (DTI) and resting-state functional MRI images were acquired. Networks were constructed and graph-theory based network measurements were calculated. The global network parameters and nodal efficiencies were compared across the three groups using one-way ANOVA and a false-discovery rate correction was applied for multiple comparisons. Partial correlation analyses were performed to investigate relationships between network parameters, cognitive performance and clinical variables.

RESULTS

In the structural connectome, the DM-MCI group exhibited significantly decreased global efficiency (Eglob) and local efficiency (Eloc) compared to the DM-NC and control groups. In the functional connectome, the DM-MCI group exhibited increased Eloc and clustering coefficient (Cp) compared to the controls. No significant differences were found in Eglob, Eloc, or Cp between the DM-NC and the control group, both in structural and functional connectomes. Nodal efficiencies decreased in some brain regions of structural and functional networks in the DM-MCI and DM-NC groups, but increased in five regions in functional network, some of which were involved in the default-mode network.

CONCLUSION

Unlike the consistently decreased global properties and nodal efficiencies in the structural connectome of T2DM patients, increases in Eloc, Cp, and nodal efficiencies in the functional connectome may be viewed as a compensatory mechanism due to functional plasticity and reorganization. Altered nodal efficiency can hint at cognitive decrements at an early stage in T2DM patients.

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.

Increased functional integration of emotional control network in late adulthood

Across the adult lifespan, emotion regulation ability remains stable or even improves. The corresponding effects, however, in the emotion regulation networks in the brain remain underexplored. By utilizing large-scale datasets such as the Human Connectome Project (HCP-Aging, N=621, 349 females) and Cambridge Centre for Ageing and Neuroscience (Cam-CAN, N=333, 155 females), we were able to investigate how emotion regulation networks' functional topography differs across the entire adult lifespan. Based on previous meta-analytic work that identified four large-scale functional brain networks involved in emotion generation and regulation, we investigated the association between the integration of these emotion regulation networks and measures of mental wellbeing with age in the HCP-Aging dataset. We found an increase in the functional integration of the emotional control network among older adults, which was replicated using the Cam-CAN data set. Further we found that the network that is mediating emotion generative and regulative processes, and carries our introspective and reflective functions, is less integrated in higher age. Our study highlights the importance of identifying topological changes in the functional emotion network architecture across the lifespan, as it allows for a better understanding of functional brain network changes that accompany emotional aging.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

High-altitude exposure leads to increased modularity of brain functional network with the increased occupation of attention resources in early processing of visual working memory

Working memory is a complex cognitive system that temporarily maintains purpose-relevant information during human cognition performance. Working memory performance has also been found to be sensitive to high-altitude exposure. This study used a multilevel change detection task combined with Electroencephalogram data to explore the mechanism of working memory change from high-altitude exposure. When compared with the sea-level population, the performance of the change detection task with 5 memory load levels was measured in the Han population living in high-altitude areas, using the event-related potential analysis and task-related connectivity network analysis. The topological analysis of the brain functional network showed that the normalized modularity of the high-altitude group was higher in the memory maintenance phase. Event-related Potential analysis showed that the peak latencies of P1 and N1 components of the high-altitude group were significantly shorter in the occipital region, which represents a greater attentional bias in visual early processing. Under the condition of high memory loads, the high-altitude group had a larger negative peak in N2 amplitude compared to the low-altitude group, which may imply more conscious processing in visual working memory. The above results revealed that the visual working memory change from high-altitude exposure might be derived from the attentional bias and the more conscious processing in the early processing stage of visual input, which is accompanied by the increase of the modularity of the brain functional network. This may imply that the attentional bias in the early processing stages have been influenced by the increased modularity of the functional brain networks induced by high-altitude exposure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10091-3.

Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features

Background

Irritable bowel syndrome (IBS) is a disorder characterized by signaling dysregulation between the brain and gut, leading to gastrointestinal dysfunction. Symptoms such as abdominal pain and constipation can manifest periodically or persistently, and negative emotions may exacerbate the symptoms. Previous studies have shown that the pathogenesis of IBS is closely related to the brain-gut axis and brain function, but there are still difficulties in disease diagnosis. Therefore, this study applied a machine-learning approach based on resting-state functional magnetic resonance imaging (rs-fMRI) whole-brain functional connectivity (FC) to distinguish IBS patients from healthy controls (HCs).

Methods

A total of 176 subjects, comprising 88 consecutive patients with IBS and 88 age-, sex- and education-matched HCs, were enrolled in this study between January 2020 and January 2024 at the First Affiliated Hospital of Shantou University Medical College. All the subjects underwent rs-fMRI and high-resolution anatomical T1-weighted imaging (T1WI) examinations. Following the preprocessing of the rs-fMRI image data, FC matrices between all regions of interest (ROIs) were extracted using automated anatomical labeling (AAL). Subsequently, supervised machine learning was performed using whole-brain FC for classification features to identify the best-performing model. Finally, weights of the optimal model's features were exported to confirm the neuroanatomical regions significantly influencing model establishment.

Results

Compared with other supervised learning models, the support vector machine (SVM) model had significantly higher classification accuracy and performed significantly better than the other models (P<0.05) with a classification accuracy of 75% and an area under the curve (AUC) of 0.7788 (95% confidence interval [CI]: 0.6861-0.8715) (P<0.01). In addition, the FC features from the Rolandic operculum (ROL) to the anterior cingulate gyrus (ACG), the calcarine sulcus (CAL) to the triangular part of the inferior frontal gyrus (IFG), the gyrus rectus (REC) to the inferior occipital gyrus (IOG), the lingual gyrus (LING) to the putamen (PUT), and the IOG to the angular gyrus (ANG) were relatively important in the construction of the machine-learning models.

Conclusions

The SVM was the optimal machine-learning model for effectively classifying IBS patients and HCs based on whole-brain resting-state FC matrices. The FC features between the emotion-related brain regions significantly affected the construction of the machine-learning models. As a classification feature in machine learning, whole-brain resting-state FC holds the potential to achieve precision medicine in IBS and enhance disease diagnostic efficacy.

The moderating role of information processing speed in the relationship between brain remodeling and episodic memory in amnestic mild cognitive impairment

INTRODUCTION

The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated.

METHODS

Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single-region, connectivity, and network levels.

RESULTS

IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex.

DISCUSSION

IPS functioned as a moderator of associations between recall and neuroimaging metrics at the "single region-connectivity-network" level, providing new insights for cognitive rehabilitation in aMCI patients.

HIGHLIGHTS

aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.

Rehabilitation Response in Tremor- and Non-Tremor-Dominant Parkinson Disease: A Task-fMRI Study

BACKGROUND

Tremor-dominant (TD) and nontremor-dominant (NTD) Parkinson's disease (PD) showed different responses to rehabilitation. However, the neural mechanism behind this remains unclear.

METHODS

This cohort study explores changes in motor function, brain activation, and functional connectivity following 2 weeks of rehabilitation in TD-PD and NTD-PD patients, respectively. A total of 11 TD-PD patients, 24 NTD-PD patients, and 21 age-matched healthy controls (HCs) were included. At baseline, all participants underwent functional magnetic resonance imaging (fMRI) while performing the foot tapping task. Motor symptoms, gait, balance, and task-based fMRI were then evaluated in patients before and after rehabilitation.

RESULTS

Compared to HCs, TD-PD patients showed increased activity in the left inferior frontal gyrus and the right insula, and NTD-PD patients showed increased activations in the left postcentral gyrus and decreased within-cerebellar connectivity at baseline. Rehabilitation improved motor function in PD patients regardless of motor subtype. TD-PD patients showed increased recruitments of the sensorimotor cortex and the bilateral thalamus after rehabilitation, and NTD-PD patients showed increased cerebellar activation and within-cerebellar connectivity that was associated with better motor performance.

CONCLUSIONS

This study demonstrated that rehabilitation-induced brain functional reorganization varied by motor subtypes in PD, which may have important implications for making individualized rehabilitation programs.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: ChiCTR1900020771.

Thalamocortical dysconnectivity is associated with pain in patients with knee osteoarthritis

Previous studies have suggested that the morphology and function of the thalamus and cortex are abnormal in patients with knee osteoarthritis (KOA). However, whether the thalamocortical network is differentially affected in this disorder is unknown. In this study, we examined functional and effective connectivity between the thalamus and major divisions of the cortex in 27 healthy controls and 27 KOA patients using functional magnetic resonance imaging. We also explored the topological features of the brain via graph theory analysis. The results suggested that patients with KOA had significantly reduced resting-state functional connectivity (rsFC) of the thalamo-sensorimotor pathway; enhanced rsFC of the thalamo-medial/lateral frontal cortex (mFC/LFC), parietal, temporal and occipital pathways; reduced effective connectivity of the left sensorimotor-to-thalamus pathway; and enhanced effective connectivity of the right thalamus-to-sensorimotor pathway compared with healthy controls. The functional connectivity of the thalamo-sensorimotor and thalamo-mFC pathways was enhanced when patients performed the multisource interference task. Moreover, patients with KOA presented altered nodal properties associated with thalamocortical circuits, including the thalamus, amygdala, and regions in default mode networks, compared with healthy controls. The correlation analysis suggested a significant negative correlation between thalamo-mFC rsFC and pain intensity, between thalamo-sensorimotor task-related connectivity and disease duration/depression scores, and a positive correlation between right frontal nodal properties and pain intensity in KOA patients. Taken together, these findings establish abnormal and differential alterations in the thalamocortical network associated with pain characteristics in KOA patients, which extends our understanding of their role in the pathophysiology of KOA.

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.

Transcutaneous Auricular Vagus Nerve Stimulation Modulating the Brain Topological Architecture of Functional Network in Major Depressive Disorder: An fMRI Study

BACKGROUND

Transcutaneous auricular vagus nerve stimulation (taVNS) is effective in regulating mood and high-level cognition in patients with major depressive disorder (MDD). This study aimed to investigate the efficacy of taVNS treatment in patients with MDD and an altered brain topological organization of functional networks.

METHODS

Nineteen patients with MDD were enrolled in this study. Patients with MDD underwent 4 weeks of taVNS treatments; resting-state functional magnetic resonance imaging (rs-fMRI) data of the patients were collected before and after taVNS treatment. The graph theory method and network-based statistics (NBS) analysis were used to detect abnormal topological organizations of functional networks in patients with MDD before and after taVNS treatment. A correlation analysis was performed to characterize the relationship between altered network properties and neuropsychological scores.

RESULTS

After 4 weeks of taVNS treatment, patients with MDD had increased global efficiency and decreased characteristic path length (Lp). Additionally, patients with MDD exhibited increased nodal efficiency (NE) and degree centrality (DC) in the left angular gyrus. NBS results showed that patients with MDD exhibited reduced connectivity between default mode network (DMN)-frontoparietal network (FPN), DMN-cingulo-opercular network (CON), and FPN-CON. Furthermore, changes in Lp and DC were correlated with changes in Hamilton depression scores.

CONCLUSIONS

These findings demonstrated that taVNS may be an effective method for reducing the severity of depressive symptoms in patients with MDD, mainly through modulating the brain's topological organization. Our study may offer insights into the underlying neural mechanism of taVNS treatment in patients with MDD.

Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance

Background

Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD.

Methods

Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD.

Results

In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = -0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (r = -0.435, p = 0.006). Topological parameters, including characteristic path length (L p), global efficiency (E g), normalised shortest path length (λ) and nodal local efficiency (N le), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively.

Conclusion

DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.

Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer

Background

Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used.

Objectives

This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.

Methods

We performed a PubMed search for “FreeSurfer AND Dementia” and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations).

Results

In the studies identified, the main diseases and cohorts represented were Alzheimer’s disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson’s disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed.

Conclusion

Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research.

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis

BACKGROUND

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis.

METHODS

Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome.

RESULTS

Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001).

CONCLUSIONS

Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

Classifying Learning Speed Using Brain Networks and Psychological States: Unraveling the Interdependence Between Learning Performance, Psychological States, and Brain Functions

Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis. Methods A total of 20 healthy individuals participated in a choice reaction time task, and brain function was measured using near-infrared spectroscopy (NIRS). Participants were divided into high-PL and low-PL groups based on the median difference in correct responses. Results Random forest analysis identified the left orbitofrontal area, right premotor cortex, right frontal pole, left frontal pole, left dorsolateral prefrontal cortex, and depression and anxiety as key factors. Covariance structure analysis revealed that depression and anxiety affected PL through the frontal pole and prefrontal cortex, suggesting a complex interplay between psychological state, brain function, and learning. Conclusions These findings suggest that psychological states influence brain networks, thereby affecting learning performance. Tailoring rehabilitation programs to address psychological states and providing targeted feedback may improve learning outcomes. The study provides insights into the theoretical and practical applications of understanding the brain's role in PL, as well as the importance of addressing psychological factors to optimize learning and rehabilitation strategies.

Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder

Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

Abnormal Topological Organization of Human Brain Connectome in Primary Dysmenorrhea Patients Using Graph Theoretical Analysis

Background

Accumulating studies have revealed altered brain function and structure in regions linked to sensory, pain and emotion in individuals with primary dysmenorrhea (PD). However, the changes in the topological properties of the brain's functional connectome in patients with PD experiencing chronic pain remain poorly understood.

Purpose

Our study aimed to explore the mechanism of functional brain network impairment in individuals withPD through a graph-theoretic analysis.

Material and Methods

This study was a randomized controlled trial that included individuals with PD and healthy controls (HC) from June 2021 to June 2022. The experiment took place in the magnetic resonance imaging facility at Jiangxi Provincial People's Hospital. Static MRI scans were conducted on 23 female patients with PD and 23 healthy female controls. A two-sample t-test was conducted to compare the global and nodal indices between the two groups, while the Network-Based Statistics (NBS) method was utilized to explore the functional connectivity alterations between the groups.

Results

In the global index, The PD group exhibited decreased Sigma (p = 0.0432) and Gamma (p = 0.0470) compared to the HC group among the small-world network properties.(p<0.05) In the nodal index, the PD group displayed reduced betweenness centrality and increased degree centrality in the default mode network (DMN), along with decreased nodal efficiency and increased degree centrality in the visual network (VN). (P < 0.05, Bonferroni-corrected) Furthermore, in the connection analysis, PD patients showed altered functional connectivity in the basal ganglia network (BGN), VN, and DMN.(NBS corrected).

Conclusion

Our results indicate that individuals with PD showed abnormal brain network efficiency and abnormal connection within DMN, VN and BGN related to pain matrix. These findings have important references for understanding the neural mechanism of pain in PD.

Altered individual-level morphological similarity network in children with growth hormone deficiency

BACKGROUND

Accumulating evidences indicate regional grey matter (GM) morphology alterations in pediatric growth hormone deficiency (GHD); however, large-scale morphological brain networks (MBNs) undergo these patients remains unclear.

OBJECTIVE

To investigate the topological organization of individual-level MBNs in pediatric GHD.

METHODS

Sixty-one GHD and 42 typically developing controls (TDs) were enrolled. Inter-regional morphological similarity of GM was taken to construct individual-level MBNs. Between-group differences of topological parameters and network-based statistics analysis were compared. Finally, association relationship between network properties and clinical variables was analyzed.

RESULTS

Compared to TDs, GHD indicated a disturbance in the normal small-world organization, reflected by increased Lp, γ, λ, σ and decreased Cp, Eglob (all PFDR < 0.017). Regarding nodal properties, GHD exhibited increased nodal profiles at cerebellum 4-5, central executive network-related left inferior frontal gyrus, limbic regions-related right posterior cingulate gyrus, left hippocampus, and bilateral pallidum, thalamus (all PFDR < 0.05). Meanwhile, GHD exhibited decreased nodal profiles at sensorimotor network -related bilateral paracentral lobule, default-mode network-related left superior frontal gyrus, visual network -related right lingual gyrus, auditory network-related right superior temporal gyrus and bilateral amygdala, right cerebellum 3, bilateral cerebellum 10, vermis 1-2, 3, 4-5, 6 (all PFDR < 0.05). Furthermore, serum markers and behavior scores in GHD group were correlated with altered nodal profiles (P ≤ 0.046, uncorrected).

CONCLUSION

GHD undergo an extensive reorganization in large-scale individual-level MBNs, probably due to abnormal cortico-striatal-thalamo-cerebellum loops, cortico-limbic-cerebellum, dorsal visual-sensorimotor-striatal, and auditory-cerebellum circuitry. This study highlights the crucial role of abnormal morphological connectivity underlying GHD, which might result in their relatively slower development in motor, cognitive, and linguistic functional within behavior problem performance.

Functional brain network properties correlate with individual risk tolerance in young adults

Background

Individuals differ substantially in their degree of acceptance of risks, referred to as risk tolerance, and these differences are associated with real-life outcomes such as risky health-related behaviors. While previous studies have identified brain regions that are functionally associated with individual risk tolerance, little is known about the relationship between individual risk tolerance and whole-brain functional organization.

Methods

This study investigated whether the topological properties of individual functional brain networks in healthy young adults (n = 67) are associated with individual risk tolerance using resting-state fMRI data in conjunction with a graph theoretical analysis approach.

Results

The analysis revealed that individual risk tolerance was positively associated with global topological properties, including the normalized clustering coefficient and small-worldness, which represent the degree of information segregation and the balance between information segregation and integration in a network, respectively. Additionally, individuals with higher risk tolerance exhibited greater centrality in the ventromedial prefrontal cortex (vmPFC), which is associated with the subjective value of the available options.

Conclusion

These results extend our understanding of how individual differences in risk tolerance, especially in young adults, are associated with functional brain organization, particularly regarding the balance between segregation and integration in functional networks, and highlight the important role of the connections between the vmPFC and the rest of the brain in the functional networks in relation to risk tolerance.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models

BACKGROUD

Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database.

RESULTS

Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function.

CONCLUSIONS

This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

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

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

Comparison of diffusion tensor imaging (DTI) tissue characterization parameters in white matter tracts of patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD)

OBJECTIVE

To investigate the microstructural properties of T2 lesion and normal-appearing white matter (NAWM) in 20 white matter tracts between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and correlations between the tissue damage and clinical variables.

METHODS

The white matter (WM) compartment of the brain was segmented for 56 healthy controls (HC), 48 patients with MS, and 38 patients with NMOSD, and for the patients further subdivided into T2 lesion and NAWM. Subsequently, the diffusion tensor imaging (DTI) tissue characterization parameters of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared for 20 principal white matter tracts. The correlation between tissue damage and clinical variables was also investigated.

RESULTS

The higher T2 lesion volumes of 14 fibers were shown in MS compared to NMOSD. MS showed more microstructure damage in 13 fibers of T2 lesion, but similar microstructure in seven fibers compared to NMOSD. MS and NMOSD had microstructure damage of NAWM in 20 fibers compared to WM in HC, with more damage in 20 fibers in MS compared to NMOSD. MS patients showed higher correlation between the microstructure of T2 lesion areas and NAWM. The T2 lesion microstructure damage was correlated with duration and impaired cognition in MS.

CONCLUSIONS

Patients with MS and NMOSD show different patterns of microstructural damage in T2 lesion and NAWM areas. The prolonged disease course of MS may aggravate the microstructural damage, and the degree of microstructural damage is further related to cognitive impairment.

CLINICAL RELEVANCE STATEMENT

Microstructure differences between T2 lesion areas and normal-appearing white matter help distinguish multiple sclerosis and neuromyelitis optica spectrum disorder. In multiple sclerosis, lesions rather than normal-appearing white matter should be a concern, because the degree of lesion severity correlated both with normal-appearing white matter damage and cognitive impairment.

KEY POINTS

• Multiple sclerosis and neuromyelitis optica spectrum disorder have different damage patterns in T2 lesion and normal-appearing white matter areas. • The microstructure damage of normal-appearing white matter is correlated with the microstructure of T2 lesion in multiple sclerosis and neuromyelitis optica spectrum disorder. • The microstructure damage of T2 lesion in multiple sclerosis is correlated with duration and cognitive impairment.

Association study of brain structure-function coupling and glymphatic system function in patients with mild cognitive impairment due to Alzheimer's disease

Background

Mild cognitive impairment (MCI) is a critical transitional phase from healthy cognitive aging to dementia, offering a unique opportunity for early intervention. However, few studies focus on the correlation of brain structure and functional activity in patients with MCI due to Alzheimer's disease (AD). Elucidating the complex interactions between structural-functional (SC-FC) brain connectivity and glymphatic system function is crucial for understanding this condition.

Method

The aims of this study were to explore the relationship among SC-FC coupling values, glymphatic system function and cognitive function. 23 MCI patients and 18 healthy controls (HC) underwent diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). DTI analysis along the perivascular space (DTI-ALPS) index and SC-FC coupling values were calculated using DTI and fMRI. Correlation analysis was conducted to assess the relationship between Mini-Mental State Examination (MMSE) scores, DTI-ALPS index, and coupling values. Receiver operating characteristic (ROC) curves was conducted on the SC-FC coupling between the whole brain and subnetworks. The correlation of coupling values with MMSE scores was also analyzed.

Result

MCI patients (67.74 ± 6.99 years of age) exhibited significantly lower coupling in the whole-brain network and subnetworks, such as the somatomotor network (SMN) and ventral attention network (VAN), than HCs (63.44 ± 6.92 years of age). Whole-brain network coupling was positively correlated with dorsal attention network (DAN), SMN, and visual network (VN) coupling. MMSE scores were significantly positively correlated with whole-brain coupling and SMN coupling. In MCI, whole-brain network demonstrated the highest performance, followed by the SMN and VAN, with the VN, DAN, limbic network (LN), frontoparietal network (FPN), and default mode network (DMN). Compared to HCs, lower DTI-ALPS index was observed in individuals with MCI. Additionally, the left DTI-ALPS index showed a significant positive correlation with MMSE scores and coupling values in the whole-brain network and SMN.

Conclusion

These findings reveal the critical role of SC-FC coupling values and the ALPS index in cognitive function of MCI. The positive correlations observed in the left DTI-ALPS and whole-brain and SMN coupling values provide a new insight for investigating the asymmetrical nature of cognitive impairments.

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

BACKGROUND

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.

METHODS

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).

RESULTS

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).

CONCLUSIONS

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.

Cerebellar output neurons impair non-motor behaviors by altering development of extracerebellar connectivity

The capacity of the brain to compensate for insults during development depends on the type of cell loss, whereas the consequences of genetic mutations in the same neurons are difficult to predict. We reveal powerful compensation from outside the cerebellum when the excitatory cerebellar output neurons are ablated embryonically and demonstrate that the minimum requirement for these neurons is for motor coordination and not learning and social behaviors. In contrast, loss of the homeobox transcription factors Engrailed1/2 (EN1/2) in the cerebellar excitatory lineage leads to additional deficits in adult learning and spatial working memory, despite half of the excitatory output neurons being intact. Diffusion MRI indicates increased thalamo-cortico-striatal connectivity in En1/2 mutants, showing that the remaining excitatory neurons lacking En1/2 exert adverse effects on extracerebellar circuits regulating motor learning and select non-motor behaviors. Thus, an absence of cerebellar output neurons is less disruptive than having cerebellar genetic mutations.

Where Top-Down Meets Bottom-Up: Cell-Type Specific Connectivity Map of the Whisker System

Sensorimotor computation integrates bottom-up world state information with top-down knowledge and task goals to form action plans. In the rodent whisker system, a prime model of active sensing, evidence shows neuromodulatory neurotransmitters shape whisker control, affecting whisking frequency and amplitude. Since neuromodulatory neurotransmitters are mostly released from subcortical nuclei and have long-range projections that reach the rest of the central nervous system, mapping the circuits of top-down neuromodulatory control of sensorimotor nuclei will help to systematically address the mechanisms of active sensing. Therefore, we developed a neuroinformatic target discovery pipeline to mine the Allen Institute's Mouse Brain Connectivity Atlas. Using network connectivity analysis, we identified new putative connections along the whisker system and anatomically confirmed the existence of 42 previously unknown monosynaptic connections. Using this data, we updated the sensorimotor connectivity map of the mouse whisker system and developed the first cell-type-specific map of the network. The map includes 157 projections across 18 principal nuclei of the whisker system and neuromodulatory neurotransmitter-releasing. Performing a graph network analysis of this connectome, we identified cell-type specific hubs, sources, and sinks, provided anatomical evidence for monosynaptic inhibitory projections into all stages of the ascending pathway, and showed that neuromodulatory projections improve network-wide connectivity. These results argue that beyond the modulatory chemical contributions to information processing and transfer in the whisker system, the circuit connectivity features of the neuromodulatory networks position them as nodes of sensory and motor integration.

Association of neighborhood disadvantage with cognitive function and cortical disorganization in an unimpaired cohort

Objective

Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture intricate changes in cortical patterns related to neighborhood disadvantage and cognitive function, potentially explaining some of the risk for later life cognitive impairment among individuals who live in disadvantaged contexts.

Methods

This cross-sectional study included cognitively unimpaired participants (n=524, age=62.96±8.377, gender (M:F)=181:343, ADI(L:H) =450,74) from the Wisconsin Alzheimer's Disease Research Center or Wisconsin Registry for Alzheimer's Prevention. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed through six tests evaluating memory, executive functioning, and the modified preclinical Alzheimer's cognitive composite (mPACC). Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. We used linear regression to examine ADI associations with cognitive scores and MSN features. The mediating effect of MSN features on the relationship between ADI and cognitive performance was statistically assessed.

Results

Neighborhood disadvantage showed negative association with category fluency, implicit learning speed, story recall and mPACC scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal brain regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance.

Conclusion

Our findings suggest differences in local cortical organization by neighborhood disadvantage, which also partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods. Future work will examine the exposure to neighborhood disadvantage on structural organization of the brain.

Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback

EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09939-x.

Epilepsy alters brain networks in patients with insular glioma

AIMS

We intend to elucidate the alterations of cerebral networks in patients with insular glioma-related epilepsy (GRE) based on resting-state functional magnetic resonance images.

METHODS

We collected 62 insular glioma patients, who were subsequently categorized into glioma-related epilepsy (GRE) and glioma with no epilepsy (GnE) groups, and recruited 16 healthy individuals matched to the patient's age and gender to form the healthy control (HC) group. Graph theoretical analysis was applied to reveal differences in sensorimotor, default mode, visual, and executive networks among different subgroups.

RESULTS

No significant alterations in functional connectivity were found in either hemisphere insular glioma. Using graph theoretical analysis, differences were found in visual, sensorimotor, and default mode networks (p < 0.05). When the glioma located in the left hemisphere, the degree centrality was reduced in the GE group compared to the GnE group. When the glioma located in the right insula, the degree centrality, nodal efficiency, nodal local efficiency, and nodal clustering coefficient of the GE group were lower than those of the GnE group.

CONCLUSION

The impact of insular glioma itself and GRE on the brain network is widespread. The networks altered by insular GRE differ depending on the hemisphere location. GRE reduces the nodal properties of brain networks than that in insular glioma.

Increased functional connectivity within the salience network in patients with insomnia

BACKGROUND

Insomnia is a common sleep disorder with significant negative impacts on emotional states; however, the underlying mechanism of insomnia with comorbid emotional dysregulation remains largely unknown. The salience network (SN) plays an important role in both sleep and emotional regulation. The study aimed to explore the specific alterations in functional connectivity (FC) within the SN in insomnia patients.

METHODS

A total of 30 eligible patients with insomnia disorder (ID group) and 30 healthy controls (HC group) underwent resting-state functional magnetic resonance imaging (fMRI) scanning and psychometric assessments. Differences in FC within the SN were examined using seed-based region-to-region connectivity analysis.

RESULTS

Compared with healthy controls, patients with insomnia showed increased FC within the SN, mainly between the anterior cingulate cortex (ACC) and right superior frontal gyrus (SFG), the right SFG and right supramarginal gyrus (SMG), and between the right insular (INS) and left SMG (P<0.05). Additionally, significant correlations were observed between increased FC and the Hamilton Depression Rating Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), and Hamilton Anxiety Rating Scale (HAMA) scores (P<0.05, after Bonferroni correction).

CONCLUSIONS

These results suggest that increased FC within the SN may be related to poor sleep quality and negative emotions, highlighting the importance of the SN in the pathophysiological mechanisms of insomnia with comorbid emotional dysregulation.

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

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

Topological alterations in white matter anatomical networks in cervical dystonia

BACKGROUND

Accumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients' WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations.

METHODS

Diffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed.

RESULTS

Compared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD.

CONCLUSION

Our findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.