Prefrontal cortical hemodynamics and functional network organization during Tai Chi standing meditation: an fNIRS study

Introduction

Tai Chi standing meditation (Zhan Zhuang, also called pile standing) is characterized by meditation, deep breathing, and mental focus based on theories of traditional Chinese medicine. The purpose of the present study was to explore prefrontal cortical hemodynamics and the functional network organization associated with Tai Chi standing meditation by using functional near-infrared spectroscopy (fNIRS).

Methods

Twenty-four channel fNIRS signals were recorded from 24 male Tai Chi Quan practitioners (54.71 ± 8.04 years) while standing at rest and standing during Tai Chi meditation. The general linear model and the SPM method were used to analyze the fNIRS signals. Pearson correlation was calculated to determine the functional connectivity between the prefrontal cortical sub-regions. The small world properties of the FC networks were then further analyzed based on graph theory.

Results

During Tai Chi standing meditation, significantly higher concentrations of oxygenated hemoglobin were observed in bilateral dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), frontal eye field (FEF), and pre-motor cortex (PMC) compared with the values measured during standing rest (p < 0.05). Simultaneously, significant decreases in deoxygenated hemoglobin concentration were observed in left VLPFC, right PMC and DLPFC during Tai Chi standing meditation than during standing rest (p < 0.05). Functional connectivity between the left and right PFC was also significantly stronger during the Tai Chi standing meditation (p < 0.05). The functional brain networks exhibited small-world architecture, and more network hubs located in DLPFC and VLPFC were identified during Tai Chi standing meditation than during standing rest.

Discussion

These findings suggest that Tai Chi standing meditation introduces significant changes in the cortical blood flow and the brain functional network organization.

Fire and Rhizosphere Effects on Bacterial Co-Occurrence Patterns

Fires are common in Mediterranean soils and constitute an important driver of their evolution. Although fire effects on vegetation dynamics are widely studied, their influence on the assembly rules of soil prokaryotes in a small-scale environment has attracted limited attention. In the present study, we reanalyzed the data from Aponte et al. (2022) to test whether the direct and/or indirect effects of fire are reflected in the network of relationships among soil prokaryotes in a Chilean sclerophyllous ecosystem. We focused on bacterial (genus and species level) co-occurrence patterns in the rhizospheres and bulk soils in burned and unburned plots. Four soils were considered: bulk-burnt (BB), bulk-unburnt (BU), rhizosphere-burnt (RB), and rhizosphere-unburnt (RU). The largest differences in network parameters were recorded between RU and BB soils, while RB and BU networks exhibited similar values. The network in the BB soil was the most compact and centralized, while the RU network was the least connected, with no central nodes. The robustness of bacterial communities was enhanced in burnt soils, but this was more pronounced in BB soil. The mechanisms mainly responsible for bacterial community structure were stochastic in all soils, whether burnt or unburnt; however, communities in RB were much more stochastic than in RU.

Disorganization of Semantic Brain Networks in Schizophrenia Revealed by fMRI

OBJECTIVES

Schizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts since the term "schizophrenia" was first coined by Bleuler. However, a mechanistic account of this cardinal disturbance in terms of functional dysconnection has been lacking. To evaluate how aberrant semantic connections are expressed through brain activity, we characterized large-scale network structures of concept representations using functional magnetic resonance imaging (fMRI).

STUDY DESIGN

We quantified various concept representations in patients' brains from fMRI activity evoked by movie scenes using encoding modeling. We then constructed semantic brain networks by evaluating the similarity of these semantic representations and conducted graph theory-based network analyses.

STUDY RESULTS

Neurotypical networks had small-world properties similar to those of natural languages, suggesting small-worldness as a universal property in semantic knowledge networks. Conversely, small-worldness was significantly reduced in networks of schizophrenia patients and was correlated with psychological measures of delusions. Patients' semantic networks were partitioned into more distinct categories and had more random within-category structures than those of controls.

CONCLUSIONS

The differences in conceptual representations manifest altered semantic clustering and associative intrusions that underlie thought disorders. This is the first study to provide pathophysiological evidence for the loosening of associations as reflected in randomization of semantic networks in schizophrenia. Our method provides a promising approach for understanding the neural basis of altered or creative inner experiences of individuals with mental illness or exceptional abilities, respectively.

Corrigendum: Altered Topological Organization of Functional Brain Networks in Betel Quid Dependence: A Resting-State Functional MRI Study

[This corrects the article DOI: 10.3389/fpsyt.2021.779878.].

Alterations in brain networks in children with sub-threshold autism spectrum disorder: A magnetoencephalography study

Individuals with sub-threshold autism spectrum disorder (ASD) are those who have social communication difficulties but do not meet the full ASD diagnostic criteria. ASD is associated with an atypical brain network; however, no studies have focused on sub-threshold ASD. Here, we used the graph approach to investigate alterations in the brain networks of children with sub-threshold ASD, independent of a clinical diagnosis. Graph theory is an effective approach for characterizing the properties of complex networks on a large scale. Forty-six children with ASD and 31 typically developing children were divided into three groups (i.e., ASD-Unlikely, ASD-Possible, and ASD-Probable groups) according to their Social Responsiveness Scale scores. We quantified magnetoencephalographic signals using a graph-theoretic index, the phase lag index, for every frequency band. Resultantly, the ASD-Probable group had significantly lower small-worldness (SW) in the delta, theta, and beta bands than the ASD-Unlikely group. Notably, the ASD-Possible group exhibited significantly higher SW than the ASD-Probable group and significantly lower SW than the ASD-Unlikely group in the delta band only. To our knowledge, this was the first report of the atypical brain network associated with sub-threshold ASD. Our findings indicate that magnetoencephalographic signals using graph theory may be useful in detecting sub-threshold ASD.

Altered Brain Functional Network Topology in Lung Cancer Patients After Chemotherapy

Purpose: This study aimed to explore the topological features of brain functional network in lung cancer patients before and after chemotherapy using graph theory.

Methods: Resting-state functional magnetic resonance imaging scans were obtained from 44 post-chemotherapy and 46 non-chemotherapy patients as well as 49 healthy controls (HCs). All groups were age- and gender-matched. Then, the topological features of brain functional network were assessed using graph theory analysis.

Results: At the global level, compared with the HCs, both the non-chemotherapy group and the post-chemotherapy group showed significantly increased values in sigma (p < 0.05), gamma (p < 0.05), and local efficiency, E_loc (p < 0.05). The post-chemotherapy group and the non-chemotherapy group did not differ significantly in the above-mentioned parameters. At the nodal level, when non-chemotherapy or post-chemotherapy patients were compared with the HCs, abnormal nodal centralities were mainly observed in widespread brain regions. However, when the post-chemotherapy group was compared with the non-chemotherapy group, significantly decreased nodal centralities were observed primarily in the prefrontal–subcortical regions.

Conclusions: These results indicate that lung cancer and chemotherapy can disrupt the topological features of functional networks, and chemotherapy may cause a pattern of prefrontal–subcortical brain network abnormality. As far as we know, this is the first study to report that altered functional brain networks are related to lung cancer and chemotherapy.

Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss

Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Brain Small-Worldness Properties and Perceived Fatigue in Mild Cognitive Impairment

BACKGROUND

Perceived fatigue is among the most common complaints in older adults and is substantially influenced by diminished resources or impaired structure of widespread cortical and subcortical regions. Alzheimer's disease and its preclinical stage - mild cognitive impairment (MCI) - is considered a brain network disease. It is unknown, however, whether those with MCI will therefore perceive worse fatigue, and whether an impaired global brain network will worsen their experience of fatigue.

METHODS

In this pilot case-control study of age-, sex-, and education-matched MCI and their cognitively healthy counterparts (HC), perceived fatigue was measured using Multidimensional Fatigue Inventory, and diffusion tensor imaging (DTI) tractography data was analyzed using graph theory methods to explore small-worldness properties: segregation and integration.

RESULTS

Perceived fatigue was more severe in MCI than HC. Despite a trend for greater network alterations in MCI, there were no significant group differences in integration or segregation. Greater perceived fatigue was related to higher segregation across groups; more perceived fatigue was related to higher segregation and lower integration in MCI but not HC.

CONCLUSIONS

Findings of the present study support the notion that altered whole-brain small-worldness properties in brain aging or neurodegeneration may underpin perceived fatigue.

Abnormal Brain Connectivity in Carpal Tunnel Syndrome Assessed by Graph Theory

Introduction

Numerous resting-state functional magnetic resonance imaging (fMRI) researches have indicated that large-scale functional and structural remodeling occurs in the whole brain despite an intact sensorimotor network after carpal tunnel syndrome (CTS). Investigators aimed to explore alterations of the global and nodal properties that occur in the whole brain network of patients with CTS based on topographic theory.

Methods

Standard-compliant fMRI data were collected from 27 patients with CTS in bilateral hands and 19 healthy control subjects in this cross-sectional study. The statistics based on brain networks were calculated the differences between the patients and the healthy. Several topological properties were computed, such as the small-worldness, nodal clustering coefficient, characteristic path length, and degree centrality.

Results

Compared to those of the healthy controls, the global properties of the CTS group exhibited a decreased characteristic path length. Changes in the local-level properties included a decreased nodal clustering coefficient in 6 separate brain regions and significantly different degree centrality in several brain regions that were related to sensorimotor function and pain.

Discussion

The study suggested that CTS reinforces global connections and makes their networks more random. The changed nodal properties were affiliated with basal ganglia-thalamo-cortical circuits and the pain matrix. These results provided new insights for improving our understanding of abnormal topological theory in relation to the functional brain networks of CTS patients.

Perspective

This article presents that the CTS patients’ brain with a higher global efficiency. And the significant alterations in several brain regions which are more related to pain and motor processes. The results provided effective complements to the neural mechanisms underlying CTS.

Event-related network changes unfold the dynamics of cortical integration during face processing

Face perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite the wide acknowledgment that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time-varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph-theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from the early stages of visual processing and reaches a climax in the same time-window in which the face-sensitive N170 is observed. Furthermore, using Granger causality, we found that the time-evolving centrality of these nodes is associated with ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small-worldness and modularity, we found that face processing requires a functional network with a strong small-world organization that maximizes integration, at the cost of segregated subdivisions. Interestingly, we found that this architecture is not static, but instead, it is implemented by the network from stimulus onset to ~200 ms. Altogether, this study reveals the event-related changes underlying face processing at the network level, suggesting that a distributed processing mechanism operates through dynamically weighting the contribution of the cortical regions involved.

Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach

Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60-89 months old) and for 25 typically developing (TD) control children (10 girls, 60-91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan-Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD.

Altered Topological Organization of Functional Brain Networks in Betel Quid Dependence: A Resting-State Functional MRI Study

Background: Betel quid dependence (BQD) is associated with abnormalities in the widespread inter-regional functional connectivity of the brain. However, no studies focused on the abnormalities in the topological organization of brain functional networks in chewers in Mainland China. Methods: In the current study, resting-state functional magnetic resonance images were acquired from 53 BQD individuals and 37 gender- and age-matched healthy controls (HCs). A functional network was constructed by calculating the Pearson correlation coefficients among 90 subregions in the human Brainnetome Atlas. The topological parameters were compared between BQD individuals and HCs. Results: The results showed that BQD individuals presented a small-world topology, but the normalized characteristic path length (λ) increased compared with HCs (0.563 ± 0.030 vs. 0.550 ± 0.027). Compared to HCs, BQ chewers showed increased betweenness centrality (Be) in the right supplementary motor area, right medial superior frontal gyrus, right paracentral lobule, right insula, left posterior cingulate gyrus, right hippocampus, right post-central gyrus, right superior parietal gyrus, and right supramarginal gyrus, while decreased Be was found in the orbitofrontal area and temporal area, which is associated with reward network, cognitive system, and default mode network. The area under the curve (AUC) value of λ displayed a positive correlation with the duration of BQ chewing (r = 0.410, p = 0.002). Conclusions: The present study revealed the disruption of functional connectome in brain areas of BQD individuals. The findings may improve our understanding of the neural mechanism of BQD from a brain functional network topological organization perspective.

Gray Matter Structural Network Disruptions in Survivors of Acute Lymphoblastic Leukemia with Chemotherapy Treatment

OBJECTIVES

Neuroimaging studies of acute lymphoblastic leukemia (ALL) during chemotherapy treatment have shown alterations in structure, function, and connectivity in several brain regions, suggesting neurobiological impairment that might influence the large-scale brain network. This study aimed to detect the alterations in the topological organization of structural covariance networks of ALL patients.

METHODS

This study included 28 ALL patients undergoing chemotherapy and 20 matched healthy controls. We calculated the gray matter volume of 90 brain regions based on an automated anatomical labeling template and applied graph theoretical analysis to compare the topological parameters of the gray matter structural networks between the two groups.

RESULTS

The results demonstrated that both the ALL and healthy control groups exhibited a small-world topology across the range of densities. Compared to healthy controls, ALL patients had less highly interactive nodes and a reduced degree/betweenness in temporal regions, which may contribute to impaired memory and executive functions in these patients.

CONCLUSION

These results reveal that ALL patients undergoing chemotherapy treatment may have decreased regional connectivity and reduced efficiency of their structural covariance network. This is the first report of anomalous large-scale gray matter structural networks in ALL patients undergoing chemotherapy treatment and provides new insights regarding the neurobiological mechanisms underlying the chemo-brain network.

Does Scale-Free Syntactic Network Emerge in Second Language Learning?

Language is a complex system during whose operation many properties may emerge spontaneously. Using complex network approach, existing studies have found that, in first language (L1) acquisition, syntactic complex network featuring the scale-free and the small-world properties, will emerge at the age of 24 months. For foreign language (L2) learning, however, researchers have not reached a consensus on whether syntactic network with these two properties will emerge. Therefore, this study adopts complex network approach in L2 learning study, attempting to answer this question. In this study, nine networks are constructed on the basis of English compositions by Chinese students. Properties of these networks reveal that the syntactic network featuring these two properties, instead of emerging suddenly at a certain point, has existed at the very beginning of the L2 learning of Chinese students, and persists throughout the entire process of L2 learning, which is different from what has been found in L1 acquisition. The reason is probably that the already established L1 syntactic system provides foundation for L2 syntactic learning, and L2 learners tend to use the entrenched L1 syntactic network to generate L2 syntactic structures. L2 syntactic learning thus is not characterized by a sudden emergence of syntactic system, but a gradual approximation to the target language, with its own unique properties. For the first time, this study provides a tentative answer to L2 syntactic emergence from the perspective of complex network, and provides a macroscopic description of L2 syntactic developmental trajectory.

Multi-Granularity Whole-Brain Segmentation Based Functional Network Analysis Using Resting-State fMRI

In this work, we systematically analyzed the effects of various nodal definitions, as determined by a multi-granularity whole-brain segmentation scheme, upon the topological architecture of the human brain functional network using the resting-state functional magnetic resonance imaging data of 19 healthy, young subjects. A number of functional networks were created with their nodes defined according to two types of anatomical definitions (Type I and Type II) each of which consists of five granularity levels of whole brain segmentations with each level linked through ontology-based, hierarchical, structural relationships. Topological properties were computed for each network and then compared across levels within the same segmentation type as well as between Type I and Type II. Certain network architecture patterns were observed in our study: (1) As the granularity changes, the absolute values of each node's nodal degree and nodal betweenness change accordingly but the relative values within a single network do not change considerably; (2) The average nodal degree is generally affected by the sparsity level of the network whereas the other topological properties are more specifically affected by the nodal definitions; (3) Within the same ontology relationship type, as the granularity decreases, the network becomes more efficient at information propagation; (4) The small-worldness that we observe is an intrinsic property of the brain's resting-state functional network, independent of the ontology type and the granularity level. Furthermore, we validated the aforementioned conclusions and measured the reproducibility of this multi-granularity network analysis pipeline using another dataset of 49 healthy young subjects that had been scanned twice.

Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models

Recently, functional connectome studies based on resting-state functional magnetic resonance imaging (R-fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R-fMRI data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R-fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole-brain functional networks. At the global level, we first found that these three factors affected not only the quantitative values but also the individual variability profile in small-world related metrics and modularity, wherein global signal regression exhibited the predominant influence. Moreover, strategies without global signal regression and with topological randomization null model enhanced the sensitivity of the detection of differences between AD and control groups in small-worldness and modularity. At the nodal level, strategies of global signal regression dominantly influenced the spatial distribution of both hubs and between-group differences in terms of nodal degree centrality. Together, we highlight the remarkable joint influence of global signal regression, regional parcellation schemes and null network models on functional connectome analyses in both health and diseases, which may provide guidance for the choice of analysis strategies in future functional network studies.

Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients

Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.

Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study

Children exposed to natural disasters are vulnerable to the development of posttraumatic stress disorder (PTSD). Recent studies of other neuropsychiatric disorders have used graph-based theoretical analysis to investigate the topological properties of the functional brain connectome. However, little is known about this connectome in pediatric PTSD. Twenty-eight pediatric PTSD patients and 26 trauma-exposed non-PTSD patients were recruited from 4,200 screened subjects after the 2008 Sichuan earthquake to undergo a resting-state functional magnetic resonance imaging scan. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was then used to examine the group-specific topological properties of the two functional connectomes. Both the PTSD and non-PTSD control groups exhibited "small-world" brain network topology. However, the functional connectome of the PTSD group showed a significant increase in the clustering coefficient and a normalized characteristic path length and local efficiency, suggesting a shift toward regular networks. Furthermore, the PTSD connectomes showed both enhanced nodal centralities, mainly in the default mode- and salience-related regions, and reduced nodal centralities, mainly in the central-executive network regions. The clustering coefficient and nodal efficiency of the left superior frontal gyrus were positively correlated with the Clinician-Administered PTSD Scale. These disrupted topological properties of the functional connectome help to clarify the pathogenesis of pediatric PTSD and could be potential biomarkers of brain abnormalities.

Structural connectivity abnormality in children with acute mild traumatic brain injury using graph theoretical analysis

The traumatic biomechanical forces associated with mild traumatic brain injury (mTBI) typically impart diffuse, as opposed to focal, brain injury potentially disrupting the structural connectivity between neural networks. Graph theoretical analysis using diffusion tensor imaging was used to assess injury-related differences in structural connectivity between 23 children (age 11-16 years) with mTBI and 20 age-matched children with isolated orthopedic injuries (OI) scanned within 96 h postinjury. The distribution of hub regions and the associations between alterations in regional network measures and symptom burden, as assessed by the postconcussion symptom scale score (PCSS), were also examined. In comparison to the OI group, the mTBI group was found to have significantly higher small-worldness (P < 0.0001), higher normalized clustering coefficients (P < 0.0001), higher normalized characteristic path length (P = 0.007), higher modularity (P = 0.0005), and lower global efficiency (P < 0.0001). A series of hub regions in the mTBI group were found to have significant alterations in regional network measures including nodal degree, nodal clustering coefficient, and nodal between-ness centrality. Correlation analysis showed that PCSS total score acquired at the time of imaging was significantly associated with the nodal degree of two hubs, the superior frontal gyrus at orbital section and the middle frontal gyrus. These findings provide new evidence of acute white matter alteration at both global and regional network level following mTBI in children furthering our understanding of underlying mechanisms of acute neurological insult associated with mTBI.

Disparity between dorsal and ventral networks in patients with obsessive-compulsive disorder: evidence revealed by graph theoretical analysis based on cortical thickness from MRI

As one of the most widely accepted neuroanatomical models on obsessive-compulsive disorder (OCD), it has been hypothesized that imbalance between an excitatory direct (ventral) pathway and an inhibitory indirect (dorsal) pathway in cortico-striato-thalamic circuit underlies the emergence of OCD. Here we examine the structural network in drug-free patients with OCD in terms of graph theoretical measures for the first time. We used a measure called efficiency which quantifies how a node transfers information efficiently. To construct brain networks, cortical thickness was automatically estimated using T1-weighted magnetic resonance imaging. We found that the network of the OCD patients was as efficient as that of healthy controls so that the both networks were in the small-world regime. More importantly, however, disparity between the dorsal and the ventral networks in the OCD patients was found in terms of graph theoretical measures, suggesting a positive evidence to the imbalance theory on the underlying pathophysiology of OCD.