GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Multisite Harmonization of Structural DTI Networks in Children: An A-CAP Study

The analysis of large, multisite neuroimaging datasets provides a promising means for robust characterization of brain networks that can reduce false positives and improve reproducibility. However, the use of different MRI scanners introduces variability to the data. Managing those sources of variability is increasingly important for the generation of accurate group-level inferences. ComBat is one of the most promising tools for multisite (multiscanner) harmonization of structural neuroimaging data, but no study has examined its application to graph theory metrics derived from the structural brain connectome. The present work evaluates the use of ComBat for multisite harmonization in the context of structural network analysis of diffusion-weighted scans from the Advancing Concussion Assessment in Pediatrics (A-CAP) study. Scans were acquired on six different scanners from 484 children aged 8.00-16.99 years [Mean = 12.37 ± 2.34 years; 289 (59.7%) Male] ~10 days following mild traumatic brain injury (n = 313) or orthopedic injury (n = 171). Whole brain deterministic diffusion tensor tractography was conducted and used to construct a 90 x 90 weighted (average fractional anisotropy) adjacency matrix for each scan. ComBat harmonization was applied separately at one of two different stages during data processing, either on the (i) weighted adjacency matrices (matrix harmonization) or (ii) global network metrics derived using unharmonized weighted adjacency matrices (parameter harmonization). Global network metrics based on unharmonized adjacency matrices and each harmonization approach were derived. Robust scanner effects were found for unharmonized metrics. Some scanner effects remained significant for matrix harmonized metrics, but effect sizes were less robust. Parameter harmonized metrics did not differ by scanner. Intraclass correlations (ICC) indicated good to excellent within-scanner consistency between metrics calculated before and after both harmonization approaches. Age correlated with unharmonized network metrics, but was more strongly correlated with network metrics based on both harmonization approaches. Parameter harmonization successfully controlled for scanner variability while preserving network topology and connectivity weights, indicating that harmonization of global network parameters based on unharmonized adjacency matrices may provide optimal results. The current work supports the use of ComBat for removing multiscanner effects on global network topology.

Impaired Functional Homotopy and Topological Properties Within the Default Mode Network of Children With Generalized Tonic-Clonic Seizures: A Resting-State fMRI Study

Introduction

The aim of the present study was to examine interhemispheric functional connectivity (FC) and topological organization within the default-mode network (DMN) in children with generalized tonic-clonic seizures (GTCS).

Methods

Resting-state functional MRI was collected in 24 children with GTCS and 34 age-matched typically developing children (TDC). Between-group differences in interhemispheric FC were examined by an automated voxel-mirrored homotopic connectivity (VMHC) method. The topological properties within the DMN were also analyzed using graph theoretical approaches. Consistent results were detected and the VMHC values were extracted as features in machine learning for subject classification.

Results

Children with GTCS showed a significant decrease in VMHC in the DMN, including the hippocampal formation (HF), lateral temporal cortex (LTC), and angular and middle frontal gyrus. Although the patients exhibited efficient small-world properties of the DMN similar to the TDC, significant changes in regional topological organization were found in the patients, involving the areas of the bilateral temporal parietal junction, bilateral LTC, left temporal pole, and HF. Within the DMN, disrupted interhemispheric FC was found between the bilateral HF and LTC, which was consistent with the VMHC results. The VMHC values in bilateral HF and LTC were significantly correlated with clinical information in patients. Support vector machine analysis using average VMHC information in the bilateral HF and LTC as features achieved a correct classification rate of 89.34% for the classification.

Conclusion

These results indicate that decreased homotopic coordination in the DMN can be used as an effective biomarker to reflect seizure effects and to distinguish children with GTCSs from TDC.

Dynamic Inter-Brain Networks Correspond With Specific Communication Behaviors: Using Functional Near-Infrared Spectroscopy Hyperscanning During Creative and Non-creative Communication

Social interaction is a dynamic and variable process. However, most hyperscanning studies implicitly assume that inter-brain synchrony (IBS) is constant and rarely investigate the temporal variability of the multi-brain networks. In this study, we used sliding windows and k-mean clustering to obtain a set of representative inter-brain network states during different group communication tasks. By calculating the network parameters and temporal occurrence of the inter-brain states, we found that dense efficient interbrain states and sparse inefficient interbrain states appeared alternately and periodically, and the occurrence of efficient interbrain states was positively correlated with collaborative behaviors and group performance. Moreover, compared to common communication, the occurrence of efficient interbrain states and state transitions were significantly higher during creative communication, indicating a more active and intertwined neural network. These findings may indicate that there is a close correspondence between inter-brain network states and social behaviors, contributing to the flourishing literature on group communication.

Functional connectivity dynamics as a function of the fluctuation of tension during film watching

To advance the understanding of the dynamic relationship between brain activities and emotional experiences, we examined the neural patterns of tension, a unique emotion that highly depends on how an event unfolds. Specifically, the present study explored the temporal relationship between functional connectivity patterns within and between different brain functional modules and the fluctuation in tension during film watching. Due to the highly contextualized and time-varying nature of tension, we expected that multiple neural networks would be involved in the dynamic tension experience. Using the neuroimaging data of 546 participants, we conducted a dynamic brain analysis to identify the intra- and inter-module functional connectivity patterns that are significantly correlated with the fluctuation of tension over time. The results showed that the inter-module connectivity of cingulo-opercular network, fronto-parietal network, and default mode network is involved in the dynamic experience of tension. These findings demonstrate a close relationship between brain functional connectivity patterns and emotional dynamics, which supports the importance of functional connectivity dynamics in understanding our cognitive and emotional processes.

Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia

Schizophrenia is a chronic mental disorder characterized by continuous or relapsing episodes of psychosis. While previous studies have detected functional network connectivity alterations in patients with schizophrenia, and most have focused on static functional connectivity. However, brain activity is believed to change dynamically over time. Therefore, we computed dynamic functional network connectivity using the sliding window method in 38 patients with schizophrenia and 31 healthy controls. We found that patients with schizophrenia exhibited higher occurrences in the weakly and sparsely connected state (state 3) than healthy controls, positively correlated with negative symptoms. In addition, patients exhibited fewer occurrences in a strongly connected state (state 4) than healthy controls. Lastly, the dynamic functional network connectivity between the right executive-control network and the medial visual network was decreased in schizophrenia patients compared to healthy controls. Our results further prove that brain activity is dynamic, and that alterations of dynamic functional network connectivity features might be a fundamental neural mechanism in schizophrenia.

In vivo structural connectome of arousal and motor brainstem nuclei by 7 Tesla and 3 Tesla MRI

Brainstem nuclei are key participants in the generation and maintenance of arousal, which is a basic function that modulates wakefulness/sleep, autonomic responses, affect, attention, and consciousness. Their mechanism is based on diffuse pathways ascending from the brainstem to the thalamus, hypothalamus, basal forebrain and cortex. Several arousal brainstem nuclei also participate in motor functions that allow humans to respond and interact with the surrounding through a multipathway motor network. Yet, little is known about the structural connectivity of arousal and motor brainstem nuclei in living humans. This is due to the lack of appropriate tools able to accurately visualize brainstem nuclei in conventional imaging. Using a recently developed in vivo probabilistic brainstem nuclei atlas and 7 Tesla diffusion‐weighted images (DWI), we built the structural connectome of 18 arousal and motor brainstem nuclei in living humans (n = 19). Furthermore, to investigate the translatability of our findings to standard clinical MRI, we acquired 3 Tesla DWI on the same subjects, and measured the association of the connectome across scanners. For both arousal and motor circuits, our results showed high connectivity within brainstem nuclei, and with expected subcortical and cortical structures based on animal studies. The association between 3 Tesla and 7 Tesla connectivity values was good, especially within the brainstem. The resulting structural connectome might be used as a baseline to better understand arousal and motor functions in health and disease in humans.

Reorganized Brain Functional Network Topology in Presbycusis

Purpose

Presbycusis is characterized by bilateral sensorineural hearing loss at high frequencies and is often accompanied by cognitive decline. This study aimed to identify the topological reorganization of brain functional network in presbycusis with/without cognitive decline by using graph theory analysis approaches based on resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

Resting-state fMRI scans were obtained from 30 presbycusis patients with cognitive decline, 30 presbycusis patients without cognitive decline, and 50 age-, sex-, and education-matched healthy controls. Graph theory was applied to analyze the topological properties of brain functional networks including global and nodal metrics, modularity, and rich-club organization.

Results

At the global level, the brain functional networks of all participants were found to possess small-world properties. Also, significant group differences in global network metrics were observed among the three groups such as clustering coefficient, characteristic path length, normalized characteristic path length, and small-worldness. At the nodal level, several nodes with abnormal betweenness centrality, degree centrality, nodal efficiency, and nodal local efficiency were detected in presbycusis patients with/without cognitive decline. Changes in intra-modular connections in frontal lobe module and inter-modular connections in prefrontal subcortical lobe module were found in presbycusis patients exposed to modularity analysis. Rich-club nodes were reorganized in presbycusis patients, while the connections among them had no significant group differences.

Conclusion

Presbycusis patients exhibited topological reorganization of the whole-brain functional network, and presbycusis patients with cognitive decline showed more obvious changes in these topological properties than those without cognitive decline. Abnormal changes of these properties in presbycusis patients may compensate for cognitive impairment by mobilizing additional neural resources.

Abnormal Dynamic Functional Connectivity in Patients With End-Stage Renal Disease

Dynamic functional connectivity (FC) analysis can capture time-varying properties of connectivity; however, studies focusing on dynamic FC in patients with end-stage renal disease (ESRD) are very limited. This is the first study to explore the dynamic aspects of whole-brain FC and topological properties in ESRD patients. Resting-state functional magnetic resonance imaging data were acquired from 100 ESRD patients [50 hemodialysis (HD) patients and 50 non-dialysis patients] and 64 healthy controls (HCs). Independent component analysis, a sliding-window approach and graph-theory methods were used to study the dynamic FC properties. The intrinsic brain FC were clustered into four configuration states. Compared with HCs, both patient groups spent longer time in State 3, in which decreased FC between subnetworks of the default mode network (DMN) and between the dorsal DMN and language network was observed in these patients, and a further reduction in FC between the DMN subnetworks was found in HD patients compared to non-dialysis patients. The number of transitions and the variability of global and local efficiency progressively decreased from that in HCs to that of non-dialysis patients to that of HD patients. The completion time of Trail Making Test A and Trail Making Test B positively correlated with the mean dwell time of State 3 and negatively correlated with the number of transitions in ESRD patients. Our findings suggest impaired functional flexibility of network connections and state-specific FC disruptions in patients with ESRD, which may underlie their cognitive deficits. HD may have an adverse effect on time-varying FC.

Aberrant Functional Network of Small-World in Sudden Sensorineural Hearing Loss With Tinnitus

Few researchers investigated the topological properties and relationships with cognitive deficits in sudden sensorineural hearing loss (SNHL) with tinnitus. To explore the topological characteristics of the brain connectome following SNHL from the global level and nodal level, we recruited 36 bilateral SNHL patients with tinnitus and 37 well-matched healthy controls. Every subject underwent pure tone audiometry tests, neuropsychological assessments, and MRI scanning. AAL atlas was employed to divide a brain into 90 cortical and subcortical regions of interest, then investigated the global and nodal properties of “small world” network in SNHL and control groups using a graph-theory analysis. The global characteristics include small worldness, cluster coefficient, characteristic path length, local efficiency, and global efficiency. Node properties include degree centrality, betweenness centrality, nodal efficiency, and nodal clustering coefficient. Interregional connectivity analysis was also computed among 90 nodes. We found that the SNHL group had significantly higher hearing thresholds and cognitive impairments, as well as disrupted internal connections among 90 nodes. SNHL group displayed lower AUC of cluster coefficient and path length lambda, but increased global efficiency. The opercular and triangular parts of the inferior frontal gyrus, rectus gyrus, parahippocampal gyrus, precuneus, and amygdala showed abnormal local features. Some of these connectome alterations were correlated with cognitive ability and the duration of SNHL. This study may prove potential imaging biomarkers and treatment targets for future studies.

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

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

Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality

Background

Sleep disturbances are widespread among patients with essential tremor (ET) and may have adverse effects on patients' quality of life. However, the pathophysiology underlying poor quality of sleep (QoS) in patients with ET remains unclear. Our study aimed to identify gray matter (GM) network alterations in the topological properties of structural MRI related to QoS in patients with ET.

Method

We enrolled 45 ET patients with poor QoS (SleET), 59 ET patients with normal QoS (NorET), and 66 healthy controls (HC), and they all underwent a three-dimensional T1-weighted MRI scan. We used a graph-theoretical approach to investigate the topological organization of GM morphological networks, and individual morphological brain networks were constructed according to the interregional similarity of GM volume distributions. Furthermore, we performed network-based statistics, and partial correlation analyses between topographic features and clinical characteristics were conducted.

Results

Global network organization was disrupted in patients with ET. Compared with the NorET group, the SleET group exhibited disrupted topological GM network organization with a shift toward randomization. Moreover, they showed altered nodal centralities in mainly the frontal, temporal, parietal, and cerebellar lobes. Morphological connection alterations within the default mode network (DMN), salience, and basal ganglia networks were observed in the SleET group and were generally more extensive than those in the NorET and HC groups. Alterations within the cerebello-thalamo-(cortical) network were only detected in the SleET group. The nodal degree of the left thalamus was negatively correlated with the Fahn-Tolosa-Marin Tremor Rating Scale score (r = −0.354, p =0.027).

Conclusion

Our findings suggest that potential complex interactions underlie tremor and sleep disruptions in patients with ET. Disruptions within the DMN and the cerebello-thalamo-(cortical) network may have a broader impact on sleep quality in patients with ET. Our results offer valuable insight into the neural mechanisms underlying poor QoS in patients with ET.

Topologic Reorganization of White Matter Connectivity Networks in Early-Blind Adolescents

Blindness studies are important models for the comprehension of human brain development and reorganization, after visual deprivation early in life. To investigate the global and local topologic alterations and to identify specific reorganized neural patterns in early-blind adolescents (EBAs), we applied diffusion tensor tractography and graph theory to establish and analyze the white matter connectivity networks in 21 EBAs and 22 age- and sex-matched normal-sighted controls (NSCs). The network profiles were compared between the groups using a linear regression model, and the associations between clinical variables and network profiles were analyzed. Graph theory analysis revealed “small-world” attributes in the structural connection networks of both EBA and NSC cohorts. The EBA cohort exhibited significant lower network density and global and local efficiency, as well as significantly elevated shortest path length, compared to the NSC group. The network efficiencies were markedly reduced in the EBA cohort, with the largest alterations in the default-mode, visual, and limbic areas. Moreover, decreased regional efficiency and increased nodal path length in some visual and default-mode areas were strongly associated with the period of blindness in EBA cohort, suggesting that the function of these areas would gradually weaken in the early-blind brains. Additionally, the differences in hub distribution between the two groups were mainly within the occipital and frontal areas, suggesting that neural reorganization occurred in these brain regions after early visual deprivation during adolescence. This study revealed that the EBA brain structural network undergoes both convergent and divergent topologic reorganizations to circumvent early visual deprivation. Our research will add to the growing knowledge of underlying neural mechanisms that govern brain reorganization and development, under conditions of early visual deprivation.

Disrupted Spatiotemporal Complexity of Resting-State Electroencephalogram Dynamics Is Associated With Adaptive and Maladaptive Rumination in Major Depressive Disorder

Patients with major depressive disorder (MDD) exhibit abnormal rumination, including both adaptive and maladaptive forms. However, the neural substrates of rumination in depression remain poorly understood. We hypothesize that divergent spatiotemporal complexity of brain oscillations would be associated with the levels of rumination in MDD. We employed the multi-scale entropy (MSE), power and phase-amplitude coupling (PAC) to estimate the complexity of rhythmic dynamics from the eye-closed high-density electroencephalographic (EEG) data in treatment-naive patients with MDD (n = 24) and healthy controls (n = 22). The depressive, brooding, and reflective subscales of the Ruminative Response Scale were assessed. MDD patients showed higher MSE in timescales finer than 5 (cluster P = 0.038) and gamma power (cluster P = 0.034), as well as lower PAC values between alpha/low beta and gamma bands (cluster P = 0.002- 0.021). Higher reflective rumination in MDD was region-specifically associated with the more localized EEG dynamics, including the greater MSE in scales finer than 8 (cluster P = 0.008), power in gamma (cluster P = 0.018) and PAC in low beta-gamma (cluster P = 0.042), as well as weaker alpha-gamma PAC (cluster P = 0.016- 0.029). Besides, the depressive and brooding rumination in MDD showed the lack of correlations with global long-range EEG variables. Our findings support the disturbed neural communications and point to the spatial reorganization of brain networks in a timescale-dependent migration toward local during adaptive and maladaptive rumination in MDD. These findings may provide potential implications on probing and modulating dynamic neuronal fluctuations during the rumination in depression.

The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification

Objective

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.

Methods

We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.

Results

The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.

Conclusion

This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

Cerebral Metabolic Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis on 18F-FDG PET Imaging

Background

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis (AE), and the prognosis may significantly be improved if identified earlier and immune-related treated more effectively. This study evaluated the brain metabolic network using fluorodeoxyglucose positron emission tomography (FDG PET).

Material and methods

FDG PET imaging of patients with NMDAR encephalitis was used to investigate the metabolic connectivity network, which was analyzed using the graph theory. The results in patients were compared to those in age- and sex-matched healthy controls.

Results

The hub nodes were mainly in the right frontal lobe in patients with NMDAR encephalitis. The global and local efficiencies in most brain regions were significantly reduced, and the shortest characteristic path length was significantly longer, especially in the temporal and occipital lobes. Significant network functions of topology properties were enhanced in the right frontal, caudate nucleus, and cingulate gyrus. In addition, the internal connection integration in the left cerebral hemisphere was poor, and the transmission efficiency of Internet information was low.

Conclusion

The present findings indicate that those characteristic and connections of metabolic network were changed in the brain by graph theory analysis quantitatively, which is helpful to better understand neuropathological and physiological mechanisms in patients with anti-NMDAR encephalitis.

Contralesional Sensorimotor Network Participates in Motor Functional Compensation in Glioma Patients

Background

Some gliomas in sensorimotor areas induce motor deficits, while some do not. Cortical destruction and reorganization contribute to this phenomenon, but detailed reasons remain unclear. This study investigated the differences of the functional connectivity and topological properties in the contralesional sensorimotor network (cSMN) between patients with motor deficit and those with normal motor function.

Methods

We retrospectively reviewed 65 patients (32 men) between 2017 and 2020. The patients were divided into four groups based on tumor laterality and preoperative motor status (deficit or non-deficit). Thirty-three healthy controls (18 men) were enrolled after matching for sex, age, and educational status. Graph theoretical measurement was applied to reveal alterations of the topological properties of the cSMN by analyzing resting-state functional MRI.

Results

The results for patients with different hemispheric gliomas were similar. The clustering coefficient, local efficiency, transitivity, and vulnerability of the cSMN significantly increased in the non-deficit group and decreased in the deficit group compared to the healthy group (p < 0.05). Moreover, the nodes of the motor-related thalamus showed a significantly increased nodal efficiency and nodal local efficiency in the non-deficit group and decreased in the deficit group compared with the healthy group (p < 0.05).

Conclusions

We posited the existence of two stages of alterations of the preoperative motor status. In the compensatory stage, the cSMN sacrificed stability to acquire high efficiency and to compensate for impaired motor function. With the glioma growing and the motor function being totally damaged, the cSMN returned to a stable state and maintained healthy hemispheric motor function, but with low efficiency.

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

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

Altered topological properties of the intrinsic functional brain network in patients with right-sided unilateral hearing loss caused by acoustic neuroma

Neuroimaging studies have identified alterations in functional connectivity between specific brain regions in patients with unilateral hearing loss (UHL) and different influence of the side of UHL on neural plasticity. However, little is known about changes of whole-brain functional networks in patients with UHL and whether differences exist in topological organization between right-sided UHL (RUHL) and left-sided UHL (LUHL). To address this issue, we employed resting-state fMRI (rs-fMRI) and graph-theoretical approaches to investigate the topological alterations of brain functional connectomes in patients with RUHL and LUHL. Data from 44 patients with UHL (including 22 RUHL patients and 22 LUHL patients) and 37 healthy control subjects (HCs) were collected. Functional brain networks were constructed for each participant, following by graph-theoretical network analyses at connectional and global (e.g., small-worldness) levels. The correlations between brain network topologies and clinical variables were further studied. Using network-based analysis, we found a subnetwork in the visual cortex which had significantly lower connectivity strength in patients with RUHL as compared to HCs. At global level, all participants showed small-world architecture in functional brain networks, however, significantly lower normalized clustering coefficient and small-worldness were observed in patients with RUHL than in HCs. Moreover, these abnormal network metrics were demonstrated to be correlated with the clinical variables and cognitive performance of patients with RUHL. Notably, no significant alterations in the functional brain networks were found in patients with LUHL. Our findings demonstrate that RUHL (rather than LUHL) is accompanied with aberrant topological organization of the functional brain connectome, indicating different pathophysiological mechanisms between RUHL and LUHL from a viewpoint of network topology.

Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Most current research on Alzheimer's disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer's disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.

Reduced left lateralized functional connectivity of the thalamic subregions between short-term and chronic insomnia disorder

The purpose of the study was to systematically investigate the structural and functional abnormalities in the subregions of the thalamus and to examine their clinical relevance in patients with short-term and chronic insomnia disorder (ID). Thirty-four patients with short-term ID, 41 patients with chronic ID, and 46 healthy controls (HCs) were recruited. Grey matter volume and seed-based resting-state functional connectivity (RSFC) were compared for each thalamic subregion (bilateral cTtha, lPFtha, mPFtha, mPMtha, Otha, Pptha, rTtha, and Stha) between the three groups. Spearman's correlation was used to estimate the associations between thalamic alterations and clinical variables. Compared with the HCs and chronic ID group, the short-term ID group exhibited lower RSFC of the left cTtha, lPFtha, Otha and Pptha with the bilateral caudate. In addition, the short-term ID group exhibited lower RSFC between the left mPFtha and left caudate in comparison with the other two groups. Convergent RSFC alterations were found in the left cTtha and Otha with the right parahippocampal gyrus in both ID groups. Moreover, a positive correlation was found for the left Otha-caudate RSFC with the Epworth sleepiness scale scores (r = 0.340, P = 0.040). Our findings suggest shared and unique RSFC alterations of certain thalamic subregions with paralimbic regions between short-term and chronic ID. These findings have implications for understanding common and specific pathophysiology of different types of ID.

Effect of Shift Work on Cognitive Function in Chinese Coal Mine Workers: A Resting-State fNIRS Study

Aim: Pilot study to examine the impact of shift work on cognitive function in Chinese coal mine workers. Background: Shift work is commonly used in modern industries such as the coal industry, and there is growing concern over the impact that shift work has on miners’ work performance and personal well-being. Method: A total of 54 miners working three shifts (17 in morning shift, 18 in afternoon, and 19 in night shift) participated in this exploratory study. A resting-state fNIRS functional connectivity method was conducted to assess the cognitive ability before and after the work shift. Results: Results showed significant differences in cognitive ability between before and after the work shifts among the three-shift workers. The brain functional connectivity was reduced ranking as the night, afternoon, and morning shifts. Decreased brain functional connectivity at the end of the working shift was found compared with before in the morning and afternoon shifts. Opposite results were obtained during the night shift. The resting-state functional brain networks in the prefrontal cortex of all groups exhibited small-world properties. Significant differences in betweenness centrality and nodal local efficiency were found in the prefrontal cortex in the morning and night shifts. Conclusions: The current findings provide new insights regarding the effect of shift work on the cognitive ability of Chinese coal mine workers from the view of brain science.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium's ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Altered Structural Brain Network Topology in Patients With Primary Craniocervical Dystonia

Purpose

Regional cortical thickness or volume analyses based upon structural MRI scans have been employed to study the pathophysiology of primary craniocervical dystonia (CCD). In the present study, brain connectivity network analyses based upon morphological distribution similarities among different brain areas were used to study the network disruption in individuals affected by CCD.

Methods

The T1 MRI scans were completed for 37 patients with CCD and 30 healthy controls, with individual brain structural networks being constructed based upon gray matter (GM) similarities in 90 regions within the brain. Area under the curve (AUC) values for each network parameter were determined, and the GRETNA program was used to conduct a graph theory-based measurement of nodal and global network properties. These properties were then compared between healthy controls and those with CCD. In addition, relationships between nodal properties and the severity of clinical dystonia were assessed through Spearman's correlation analyses.

Results

Relative to individuals in the control group, patients with CCD exhibited decreased local nodal properties in the right globus pallidus, right middle frontal gyrus, and right superior temporal pole. The degree of centrality as well as the node efficiency of the right globus pallidus were found to be significantly correlated with ocular dystonic symptom. The node efficiency of right middle frontal gyrus was significantly related to the total motor severity. No nodal properties were significantly correlated with oral dystonic motor scores. Among CCD patients, the right hemisphere exhibited more widespread decreases in connectivity associated with the motor related brain areas, associative cortex, and limbic system, particularly in the middle frontal gyrus, globus pallidus, and cingulate gyrus.

Conclusions

The assessment of morphological correlations between different areas in the brain may represent a sensitive approach for detecting alterations in brain structures and to understand the mechanistic basis for CCD at the network level. Based on the nodal properties identified in this study, the right middle frontal gyrus and globus pallidus were the most severely affected in patients with CCD. The widespread alterations in morphological connectivity, such as the cortico-cortical and cortico-subcortical networks, further support the network mechanism as a basis for CCD.

Brain Network Topology and Structural–Functional Connectivity Coupling Mediate the Association Between Gut Microbiota and Cognition

Increasing evidence indicates that gut microbiota can influence cognition via the gut–brain axis, and brain networks play a critical role during the process. However, little is known about how brain network topology and structural–functional connectivity (SC–FC) coupling contribute to gut microbiota-related cognition. Fecal samples were collected from 157 healthy young adults, and 16S amplicon sequencing was used to assess gut diversity and enterotypes. Topological properties of brain structural and functional networks were acquired by diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI data), and SC–FC coupling was further calculated. 3-Back, digit span, and Go/No-Go tasks were employed to assess cognition. Then, we tested for potential associations between gut microbiota, complex brain networks, and cognition. The results showed that gut microbiota could affect the global and regional topological properties of structural networks as well as node properties of functional networks. It is worthy of note that causal mediation analysis further validated that gut microbial diversity and enterotypes indirectly influence cognitive performance by mediating the small-worldness (Gamma and Sigma) of structural networks and some nodal metrics of functional networks (mainly distributed in the cingulate gyri and temporal lobe). Moreover, gut microbes could affect the degree of SC–FC coupling in the inferior occipital gyrus, fusiform gyrus, and medial superior frontal gyrus, which in turn influence cognition. Our findings revealed novel insights, which are essential to provide the foundation for previously unexplored network mechanisms in understanding cognitive impairment, particularly with respect to how brain connectivity participates in the complex crosstalk between gut microbiota and cognition.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.

Structural connectivity of autonomic, pain, limbic, and sensory brainstem nuclei in living humans based on 7 Tesla and 3 Tesla MRI

Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions. Yet, the structural connectivity of brainstem nuclei in living humans remains understudied. These tiny structures are difficult to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high‐spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants. To demonstrate clinical translatability, we also acquired 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. Results showed the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area‐parabrachial pigmented, microcellular tegmental–parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus‐alpha part), derived from probabilistic tractography computation. Through graph measure analysis, we identified network hubs and demonstrated high intercommunity communication in these nuclei. We found good (r = .5) translational capability of the 7 Tesla connectome to clinical (i.e., 3 Tesla) datasets. Furthermore, we validated the structural connectome by building diagrams of autonomic/pain/limbic connectivity, vestibular connectivity, and their interactions, and by inspecting the presence of specific links based on human and animal literature. These findings offer a baseline for studies of these brainstem nuclei and their functions in health and disease, including autonomic dysfunction, chronic pain, psychiatric, and vestibular disorders.

Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers

The complexity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data has been applied for exploring cognitive states and occupational neuroplasticity. However, there is little information about the influence of occupational factors on dynamic complexity and topological properties of the connectivity networks. In this paper, we proposed a novel dynamical brain complexity analysis (DBCA) framework to explore the changes in dynamical complexity of brain activity at the voxel level and complexity topology for professional seafarers caused by long-term working experience. The proposed DBCA is made up of dynamical brain entropy mapping analysis and complex network analysis based on brain entropy sequences, which generate the dynamical complexity of local brain areas and the topological complexity across brain areas, respectively. First, the transient complexity of voxel-wise brain map was calculated; compared with non-seafarers, seafarers showed decreased dynamic entropy values in the cerebellum and increased values in the left fusiform gyrus (BA20). Further, the complex network analysis based on brain entropy sequences revealed small-worldness in terms of topological complexity in both seafarers and non-seafarers, indicating that it is an inherent attribute of human the brain. In addition, seafarers showed a higher average path length and lower average clustering coefficient than non-seafarers, suggesting that the information processing ability is reduced in seafarers. Moreover, the reduction in efficiency of seafarers suggests that they have a less efficient processing network. To sum up, the proposed DBCA is effective for exploring the dynamic complexity changes in voxel-wise activity and region-wise connectivity, showing that occupational experience can reshape seafarers’ dynamic brain complexity fingerprints.

Disrupted Brain Functional Network Topology in Essential Tremor Patients With Poor Sleep Quality

Sleep disturbances, especially poor quality of sleep (QoS), are common among essential tremor (ET) patients and may have adverse effects on their quality of life, but the etiology driving the poor QoS in these individuals remains inadequately understood. Few data are available on the neuroimaging alterations of ET with poor QoS. Thirty-eight ET patients with poor QoS (SleET), 48 ET patients with normal QoS (NorET), and 80 healthy controls (HCs) participated in this study. All subjects underwent a 3.0-T magnetic resonance imaging (MRI) scan for resting-state functional MRI data collection. Then, the whole-brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions. Graph theory and network-based statistical analyses were performed. We used a non-parametric permutation test for group comparisons of topological metrics. Partial correlation analyses between the topographical features and clinical characteristics were conducted. The SleET and NorET groups exhibited decreased clustering coefficients, global efficiency, and local efficiency and increased the characteristic path length. Both of these groups also showed reduced nodal degree and nodal efficiency in the left superior dorsolateral frontal gyrus, superior frontal medial gyrus (SFGmed), posterior cingulate gyrus (PCG), lingual gyrus, superior occipital gyrus, right middle occipital gyrus, and right fusiform gyrus. The SleET group additionally presented reduced nodal degrees and nodal efficiency in the right SFGmed relative to the NorET and HC groups, and nodal efficiency in the right SFGmed was negatively correlated with the Pittsburgh Sleep Quality Index score. The observed impaired topographical organizations of functional brain networks within the central executive network (CEN), default mode network (DMN), and visual network serve to further our knowledge of the complex interactions between tremor and sleep, adding to our understanding of the underlying neural mechanisms of ET with poor QoS.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: An event-related electroencephalogram study

BACKGROUND

Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.

METHODS

Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.

RESULTS

Behavioral results suggested that the single motor units (SMU) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.

CONCLUSION

This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Decreasing Shortest Path Length of the Sensorimotor Network Induces Frontal Glioma-Related Epilepsy

Background

Glioma-related epilepsy (GRE) is a common symptom in patients with prefrontal glioma. Epilepsy onset is associated with functional network alterations. This study investigated alterations of functional networks in patients with prefrontal glioma and GRE.

Methods

Sixty-five patients with prefrontal lobe gliomas were retrospectively assessed and classified into GRE and non-GRE groups. Additionally, 25 healthy participants were enrolled after matching for general information. Imaging data were acquired within 72 h in pre-operation. The sensorimotor network was used to delineate alterations in functional connectivity (FC) and topological properties. One-way analysis of variance and post-hoc analysis with Bonferroni correction were used to calculate differences of FC and topological properties.

Results

All significant alterations were solely found in the sensorimotor network. Irrespective of gliomas located in the left or right prefrontal lobes, the edge between medial Brodmann area 6 and caudal ventrolateral Brodmann area 6 decreased FC in the GRE group compared with the non-GRE group [p < 0.0001 (left glioma), p = 0.0002 (right glioma)]. Moreover, the shortest path length decrease was found in the GRE group compared with the non-GRE group [p = 0.0292 (left glioma) and p = 0.0129 (right glioma)].

Conclusions

The reduction of FC between the medial BA 6 (supplementary motor area) and caudal ventrolateral BA 6 in the ipsilateral hemisphere and the shortening of the path length of the sensorimotor network were characteristics alterations in patients with GRE onset. These findings fill in the gap which is the relationship between GRE onset and the alterations of functional networks in patients with prefrontal glioma.

Significance Statement

Glioma related epilepsy is the most common symptom of prefrontal glioma. It is important to identify characteristic alterations in functional networks in patients with GRE. We found that all significant alterations occurred in the sensorimotor network. Moreover, a decreased FC in the supplementary motor area and a shortening of the path’s length are additional characteristics of glioma-related epilepsy. We believe that our findings indicate new directions of research that will contribute to future investigations of glioma-related epilepsy onset.

Topological Abnormalities of Pallido-Thalamo-Cortical Circuit in Functional Brain Network of Patients With Nonchemotherapy With Non-small Cell Lung Cancer

INTRODUCTION

Some previous studies in patients with lung cancer have mainly focused on exploring the cognitive dysfunction and deficits of brain function associated with chemotherapy. However, little is known about functional brain alterations that might occur prior to chemotherapy. Therefore, this study aimed to evaluate brain functional changes in patients with nonchemotherapy before chemotherapy with non-small cell lung cancer (NSCLC).

METHODS

Resting-state functional MRI data of 35 patients with NSCLC and 46 matched healthy controls (HCs) were acquired to construct functional brain networks. Graph theoretical analysis was then applied to investigate the differences of the network and nodal measures between groups. Finally, the receiver operating characteristic (ROC) curve analysis was performed to distinguish between NSCLC and HC.

RESULTS

Decreased nodal strength was found in the left inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part), inferior occipital gyrus, and right inferior frontal gyrus (triangular part) of patients with NSCLC while increased nodal strength was found in the right pallidum and thalamus. NSCLC also showed decreased nodal betweenness in the right superior occipital gyrus. Different hub regions distribution was found between groups, however, no hub regions showed group differences in the nodal measures. Furthermore, the ROC curve analysis showed good performance in distinguishing NSCLC from HC.

CONCLUSION

These results suggested that topological abnormalities of pallido-thalamo-cortical circuit in functional brain network might be related to NSCLC prior to chemotherapy, which provided new insights concerning the pathophysiological mechanisms of NSCLC and could serve as promising biological markers for the identification of patients with NSCLC.

Altered Spontaneous Brain Activity and Network Property in Patients With Congenital Monocular Blindness

Individuals with congenital monocular blindness may have specific brain changes since the brain is prenatally deprived of half the normal visual input. To explore characteristic brain functional changes of congenital monocular blindness, we analyzed resting-state functional MRI (rs-fMRI) data of 16 patients with unilateral congenital microphthalmia and 16 healthy subjects with normal vision to compare intergroup differences of amplitude of low frequency fluctuations (ALFFs), functional connectivity (FC), and network topolgoical properties. Compared with controls, patients with microphthalmia exhibited significantly lower ALFF values in the left inferior occipital and temporal gyri, superior temporal gyrus, inferior parietal lobe and post-central gyrus, whereas higher ALFF in the right middle and inferior temporal gyri, middle and superior frontal gyri, left superior frontal, and temporal gyri, such as angular gyrus. Meanwhile, FC between left medial superior frontal gyrus and angular gyrus, FC between left superior temporal gyrus and inferior parietal lobe and post-central gyrus decreased in the patients with congenital microphthalmia. In addition, a graph theory-analysis revealed increased regional network metrics (degree centrality and nodal efficiency) in the middle and inferior temporal gyri and middle and superior frontal gyri, while decreased values in the inferior occipital and temporal gyri, inferior parietal lobule, post-central gyrus, and angular gyrus. Taken together, patients with congenital microphthalmia had widespread abnormal activities within neural networks involving the vision and language and language-related regions played dominant roles in their brain networks. These findings may provide clues for functional reorganization of vision and language networks induced by the congenital monocular blindness.

Functional connectome fingerprint of holistic-analytic cultural style

Although research in the field of cultural psychology and cultural neuroscience has revealed that culture is an important factor related to the human behaviors and neural activities in various tasks, it remains unclear how different brain regions organize together to construct a topological network for the representation of individual's cultural tendency. In this study, we examined the hypothesis that resting-state brain network properties can reflect individual's cultural background or tendency. By combining the methods of resting-state magnetic resonance imaging and graph theoretical analysis, significant cultural differences between participants from Eastern and Western cultures were found in the degree and global efficiency of regions mainly within the default mode network and subcortical network. Furthermore, the holistic-analytic thinking style, as a cultural value, provided a partial explanation for the cultural differences on various nodal metrics. Validation analyses further confirmed that these network properties effectively predicted the tendency of holistic-analytic cultural style within a group (r = 0.23) and accurately classified cultural groups (65%). The current study establishes a neural connectome representation of holistic-analytic cultural style including the topological brain network properties of regions in the default mode network, the basal ganglia and amygdala, which enable accurate cultural group membership classification.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis

Patients with bipolar disorder have deficits in self-referenced information. The brain functional connectivity during social cognitive processing in bipolar disorder is unclear. Electroencephalogram (EEG) was recorded in 23 patients with bipolar disorder and 19 healthy comparison subjects. We analyzed the time-frequency distribution of EEG power for each electrode associated with self, other, and font reflection conditions and used the phase lag index to characterize the functional connectivity between electrode pairs for 4 frequency bands. Then, the network properties were assessed by graph theoretic analysis. The results showed that bipolar disorder induced a weaker response power and phase lag index values over the whole brain in both self and other reflection conditions. Moreover, the characteristic path length was increased in patients during self-reflection processing, whereas the global efficiency and the node degree were decreased. In addition, when discriminating patients from normal controls, we found that the classification accuracy was high. These results suggest that patients have impeded integration of attention, memory, and other resources of the whole brain, resulting in a deficit of efficiency and ability in self-referential processing.

Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study

Objective

The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons.

Methods

A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP.

Results

The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere.

Conclusion

The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.

Acupuncture Treatment Decreased Temporal Variability of Dynamic Functional Connectivity in Chronic Tinnitus

Acupuncture is recommended for the relief of chronic tinnitus in traditional Chinese medicine, but the underlying neural mechanism remains unclear. The human brain is a dynamic system, and it’s unclear about acupuncture’s effects on the dynamic functional connectivity (DFC) of chronic tinnitus. Therefore, this study based on resting-state functional magnetic resonance imaging (fMRI) investigates abnormal DFC in chronic tinnitus patients and the neural activity change evoked by acupuncture treatment for tinnitus. In this study, 17 chronic tinnitus patients and 22 age- and sex-matched normal subjects were recruited, and their tinnitus-related scales and hearing levels were collected. The fMRI data were measured before and after acupuncture, and then sliding-window and k-means clustering methods were used to calculate DFC and perform clustering analysis, respectively. We found that, compared with the normal subjects, chronic tinnitus patients had higher temporal variability of DFC between the supplementary motor area and medial part of the superior frontal gyrus, and it positively correlated with hearing loss. Clustering analysis showed higher transition probability (TP) between connection states in chronic tinnitus patients, and it was positively correlated with tinnitus severity. Furthermore, the findings showed that acupuncture treatment might improve tinnitus. DFC between the posterior cingulate gyrus and angular gyrus in chronic tinnitus patients after acupuncture showed significantly decreased, and it positively correlated with the improvement of tinnitus. Clustering analysis showed that acupuncture treatment might promote chronic tinnitus patients under lower DFC state, and it also positively correlated with the improvement of tinnitus. This study suggests that acupuncture as an alternative therapy method might decrease the tinnitus severity by decreasing the time variability of DFC in chronic tinnitus patients.

Aberrant Resting-State Functional Connectivity of the Dorsal Attention Network in Tinnitus

Previous functional magnetic resonance imaging (fMRI) analyses have shown that the dorsal attention network (DAN) is involved in the pathophysiological changes of tinnitus, but few relevant studies have been conducted, and the conclusions to date are not uniform. The purpose of this research was to test whether there is a change in intrinsic functional connectivity (FC) patterns between the DAN and other brain regions in tinnitus patients. Thirty-one patients with persistent tinnitus and thirty-three healthy controls were enrolled in this study. A group independent component analysis (ICA), degree centrality (DC) analysis, and seed-based FC analysis were conducted. In the group ICA, the tinnitus patients showed increased connectivity in the left superior parietal gyrus in the DAN compared to the healthy controls. Compared with the healthy controls, the tinnitus patients showed increased DC in the left inferior parietal gyrus and decreased DC in the left precuneus within the DAN. The clusters within the DAN with significant differences in the ICA or DC analysis between the tinnitus patients and the healthy controls were selected as regions of interest (ROIs) for seeds. The tinnitus patients exhibited significantly increased FC from the left superior parietal gyrus to several brain regions, including the left inferior parietal gyrus, the left superior marginal gyrus, and the right superior frontal gyrus, and decreased FC to the right anterior cingulate cortex. The tinnitus patients exhibited decreased FC from the left precuneus to the left inferior occipital gyrus, left calcarine cortex, and left superior frontal gyrus compared with the healthy controls. The findings of this study show that compared with healthy controls, tinnitus patients have altered functional connections not only within the DAN but also between the DAN and other brain regions. These results suggest that it may be possible to improve the disturbance and influence of tinnitus by regulating the DAN.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p &lt; 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p &lt; 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.

Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.

Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall r_s: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (r_s: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.

Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizures

BACKGROUND

Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood.

METHODS

We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed.

RESULTS

We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients.

CONCLUSION

Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

Objectives. Recent resting-state functional magnetic resonance imaging (fMRI) studies have focused on glaucoma-related neuronal degeneration in structural and spontaneous functional brain activity. However, there are limited studies regarding the differences in the topological organization of the functional brain network in patients with glaucoma. In this study, we aimed to assess both potential alterations and the network efficiency in the functional brain networks of patients with primary angle-closure glaucoma (PACG). Methods. We applied resting-state fMRI data to construct the functional connectivity network of 33 patients with PACG ( $54.21\pm 7.21\text{years}$ ) and 33 gender- and age-matched healthy controls ( $52.42\pm 7.80\text{years}$ ). The differences in the global and regional topological brain network properties between the two groups were assessed using graph theoretical analysis. Partial correlations between the altered regional values and clinical parameters were computed for patients with PACG. Results. No significant differences in global topological measures were identified between the two groups. However, significant regional alterations were identified in the patients with PACG, including differences within visual and nonvisual (somatomotor and cognition-emotion) regions. The normalized clustering coefficient and normalized local efficiency of the right superior parietal gyrus were significantly correlated with the retinal fiber layer thickness (RNFLT) and the vertical cup to disk ratio (V C/D). In addition, the normalized node betweenness of the left middle frontal gyrus (orbital portion) was significantly correlated with the V C/D in the patients with PACG. Conclusions. Our results suggest that regional inefficiency with decrease and compensatory increase in local functional properties of visual and nonvisual nodes preserved the brain network of the PACG at the global level.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

Analysis on topological alterations of functional brain networks after acute alcohol intake using resting-state functional magnetic resonance imaging and graph theory

AIMS

Alcohol consumption could lead to a series of health problems and social issues. In the current study, we investigated the resting-state functional brain networks of healthy volunteers before and after drinking through graph-theory analysis, aiming to ascertain the effects of acute alcohol intake on topology and information processing mode of the functional brain networks.

MATERIALS AND METHODS

Thirty-three healthy volunteers were enrolled in this experiment. Each volunteer accepted alcohol breathalyzer tests followed by resting-state magnetic resonance imaging at three time points: before drinking, 0.5 h after drinking, and 1 h after drinking. The data obtained were grouped based on scanning time into control group, 0.5-h group and 1-h group, and post-drinking data were regrouped according to breath alcohol concentration (BrAC) into relative low BrAC group (A group; 0.5-h data, n = 17; 1-h data, n = 16) and relative high BrAC group (B group; 0.5-h data, n = 16; 1-h data, n = 17). The graph-theory approach was adopted to construct whole-brain functional networks and identify the differences of network topological properties among all the groups.

RESULTS

The network topology of most groups was altered after drinking, with the B group presenting the most alterations. For global network measures, B group exhibited increased global efficiency, Synchronization, and decreased local efficiency, clustering coefficient, normalized clustering coefficient, characteristic path length, normalized characteristic path length, as compared to control group. Regarding nodal network measures, nodal clustering coefficient and nodal local efficiency of some nodes were lower in B group than control group. These changes suggested that the network integration ability and synchrony improved, while the segregation ability diminished.

CONCLUSION

This study revealed the effects of acute alcohol intake on the topology and information processing mode of resting-state functional brain networks, providing new perceptions and insights into the effects of alcohol on the brain.

Functional brain rewiring and altered cortical stability in ulcerative colitis

Despite recent advances, there is still a major need to better understand the interactions between brain function and chronic gut inflammation and its clinical implications. Alterations in executive function have previously been identified in several chronic inflammatory conditions, including inflammatory bowel diseases. Inflammation-associated brain alterations can be captured by connectome analysis. Here, we used the resting-state fMRI data from 222 participants comprising three groups (ulcerative colitis (UC), irritable bowel syndrome (IBS), and healthy controls (HC), N = 74 each) to investigate the alterations in functional brain wiring and cortical stability in UC compared to the two control groups and identify possible correlations of these alterations with clinical parameters. Globally, UC participants showed increased functional connectivity and decreased modularity compared to IBS and HC groups. Regionally, UC showed decreased eigenvector centrality in the executive control network (UC < IBS < HC) and increased eigenvector centrality in the visual network (UC > IBS > HC). UC also showed increased connectivity in dorsal attention, somatomotor network, and visual networks, and these enhanced subnetwork connectivities were able to distinguish UC participants from HCs and IBS with high accuracy. Dynamic functional connectome analysis revealed that UC showed enhanced cortical stability in the medial prefrontal cortex (mPFC), which correlated with severe depression and anxiety-related measures. None of the observed brain changes were correlated with disease duration. Together, these findings are consistent with compromised functioning of networks involved in executive function and sensory integration in UC.

Alteration of resting-state functional connectivity network properties in patients with social anxiety disorder after virtual reality-based self-training

Social anxiety disorder (SAD) is a mental disorder characterized by excessive anxiety in social situations. This study aimed to examine the alteration of resting-state functional connectivity in SAD patients related to the virtual reality-based self-training (VRS) which enables exposure to social situations in a controlled environment. Fifty-two SAD patients were randomly assigned to the experimental group who received the VRS, or the control group who did not. Self-report questionnaires and resting-state functional magnetic resonance imaging (fMRI) were performed to assess clinical symptoms and analyze the resting-state network properties, respectively. Significant decrease in social anxiety and an increase in self-esteem was found in the experimental group. From the resting-state fMRI analysis, alteration of local network properties in the left dorsolateral prefrontal gyrus (-10.0%, p = 0.025), left inferior frontal gyrus (-32.3%, p = 0.044), left insula (-17.2%, p = 0.046), left Heschl's gyrus (-21.2%, p = 0.011), bilateral inferior temporal gyrus (right: +122.6%, p = 0.045; left:-46.7%, p = 0.015), and right calcarine sulcus (+17.0%, p = 0.010) were found in the experimental group. Average shortest path length (+8.3%, p = 0.008) and network efficiency (-7.6%, p = 0.011) are found to be altered from the global network property analysis. In addition, the experimental group displayed more positive and more negative changes in the correlation trend of average shortest path length (p = 0.004) and global network efficiency (p = 0.014) with the severity of social anxiety, respectively. These results suggest potential effectiveness of the VRS, which is possibly related to the change of aberrant processing and control of visual and auditory linguistic stimuli and the adaptive change in rumination pattern.

Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder

BACKGROUND

Alterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification.

METHODS

Seventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance.

RESULTS

Experimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification.

CONCLUSION

These findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.

Topological Characteristics Associated with Intraoperative Stimulation Related Epilepsy of Glioma Patients: A DTI Network Study

Background: Awake craniotomy with intraoperative stimulation has been utilized in glioma surgical resection to preserve the quality of life. Epilepsy may occur in 5–20% of cases, leading to severe consequences. This study aimed to discuss the mechanism of intraoperative stimulation-related epilepsy (ISE) using DTI-based graph theoretical analysis. Methods: Twenty patients with motor-area glioma were enrolled and divided into two groups (Ep and nEp) according to the presence of ISE. Additionally, a group of 10 healthy participants matched by age, sex, and years of education was also included. All participants underwent T1, T2, and DTI examinations. Graph theoretical analysis was applied to reveal the topological characteristics of white matter networks. Results: Three connections were found to be significantly lower in at least one weighting in the Ep group. These connections were between A1/2/3truL and A4ulL, A1/2/3truR and A4tR, and A6mL and A6mR. Global efficiency was significantly decreased, while the shortest path length increased in the Ep group in at least one weighting. Ten nodes exhibited significant differences in nodal efficiency and degree centrality analyses. The nodes A6mL and A6mR showed a marked decrease in total four weightings in the Ep group. Conclusions: The hub nodes A6mL and A6mR are disconnected in patients with ISE, causing subsequent lower efficiency of global and regional networks. These findings provide a basis for presurgical assessment of ISE, for which caution should be taken when it involves hub nodes during intraoperative electrical stimulation.