Brain White Matter Networks Research Articles

MACHINE LEARNING CLASSIFICATION OF VERIFIED HEAD IMPACT EXPOSURE REVEALS ASSOCIATIONS WITH LONGITUDINAL WHITE MATTER CHANGES IN FEMALE HIGH SCHOOL SOCCER PLAYERS

Background:Longitudinal changes in white matter (WM) integrity have been reported following cumulative exposure to sub-concussive head impacts (SCI) incurred during sports. SCI exposure is typically quantified using accelerometers that use an arbitrary g-force threshold for impact detection; however, this approach does not differentiate true impacts from non-physiological events, such as high-frequency cutaneous vibrations. This leads to a high false positive rate and the tendency to overestimate SCI exposure.Hypothesis/Purpose:To examine whether machine learning classification trained on video-verified impacts can produce more accurate quantification of SCI exposure and whether this can reveal associations between cumulative exposure and diffusion tensor imaging (DTI)-measured longitudinal WM changes in athletes.Methods:Pre- and post-season brain MRI scans were collected from 46 female high school soccer athletes. Athletes’ SCI exposure was recorded during their competitive season using head-mounted accelerometers. 24 athletes were assigned to a “Treatment Group” (TG) and were also video recorded during 17 of their games, while the remaining 22 athletes were assigned to a “Control Group” (CG) and were unrecorded. The TG video was used to verify whether the sensor-recorded impacts during those games were true head impacts or false positives. This dataset, along with the corresponding true/false labels, was used to train a machine learning classifier to learn a mapping between the accelerometer data and their respective labels. The trained model was then used to classify and filter the impacts recorded for the CG athletes by removing their predicted false positives. The CG athletes’ SCI exposure in both the unfiltered and filtered conditions was correlated with their pre- to post-season changes in DTI measures of mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA).Results:During the TG athletes’ 17 video-recorded games, 5146 impacts were recorded and 1128 were confirmed as true positives (22.0% accuracy). After training the machine learning classifier, the model was able to achieve 83.5% accuracy on this dataset. Associating the CG athletes’ unfiltered SCI exposure to pre- to post-season WM changes revealed no significant associations based on voxel-wise analysis over the whole brain WM network; however, after removing predicted false positives, the filtered SCI exposure revealed significant associations with changes in MD, RD, and FA (all p < 0.05).Conclusion:Machine learning classification of sensor-recorded SCI exposure exhibits superior accuracy and sensitivity to threshold-based detection used in standard accelerometry. Accurate quantification of SCI exposure also reveals associations with longitudinal WM changes.Figure 1:Season-long head impact exposure for the Treatment (TG) and Control (CG) groups. The unfiltered exposure (dark blue) represents the impacts recorded using standard accelerometry (using threshold-based detection). A subset of the TG impacts was video-recorded (red), with 22.0% of them being verified as true positives (orange). The CG impacts were predicted using a machine learning classifier trained on the TG video-verified subset as either true (green) or false positives (light blue).Figure 2:WM regions with significant longitudinal changes associated with filtered SCI exposure. Pre- to post-season reductions were found in mean diffusivity (red; A) and radial diffusivity (red; B), and significant increases were found in fractional anisotropy (blue; C) in the 22 CG athletes. The significant regions were overlaid onto the white matter skeleton (green) and standard T1-weighted image (gray).

White Matter Structural and Network Topological Changes Underlying the Behavioral Phenotype of MECP2 Mutant Monkeys.

To explore the brain structural basis underlying the behavioral abnormalities associated with Rett syndrome (RTT), we carried out detailed longitudinal noninvasive magnetic resonance imaging analyses of RTT monkey models created by gene-editing, from weaning, through adolescence, till sexual maturation. Here, we report abnormal developmental dynamics of brain white matter (WM) microstructures and network topological organizations via diffusion tensor imaging. Specifically, disrupted WM microstructural integrity was observed at 9months, but recovered thereafter, whereas WM network topological properties showed persistent abnormal dynamics from 9 to 37months. Changes in the WM microstructure and WM network topology were correlated well with RTT-associated behavioral abnormalities including sleep latency, environmental exploration, and conflict encounters. Deleterious and protracted early WM myelination process likely lead to abnormal synaptic pruning, resulting in poor functional segregations. Together, this study provides initial evidence for changes in WM microstructure and network topological organization, which may underlie the neuro-patho-etilogy of RTT.

Alterations of White Matter Network Properties in Patients With Functional Constipation.

Background: The abnormalities in brain function and structure of patients with functional constipation (FC) have been identified using multiple neuroimaging studies and have confirmed the abnormal processing of visceral sensation at the level of the central nervous system (CNS) as an important reason for FC. As an important basis for central information transfer, the role of the white matter (WM) networks in the pathophysiology of FC has not been investigated. This study aimed to explore the topological organization of WM networks in patients with FC and its correlation with clinical variables.Methods and Analysis: In this study, 70 patients with FC and 45 age- and gender-matched healthy subjects (HS) were recruited. Diffusion tensor imaging (DTI) data and clinical variables were acquired from each participant. WM networks were constructed using the deterministic fiber tracking approach, and the global and nodal properties of the WM networks were compared using graph theory analysis between patients with FC and HS. The relationship between the representative nodal characteristics–nodal betweenness and clinical parameters was assessed using partial correlation analysis.Results: Patients with FC showed increased nodal characteristics in the left superior frontal gyrus (orbital part), right middle frontal gyrus (orbital part), and right anterior cingulate and paracingulate (P < 0.05, corrected for false discovery rate) and decreased nodal characteristics in the left caudate and left thalamus (P < 0.05, corrected for false discovery rate) compared with HS. The duration of FC was negatively correlated with the nodal betweenness of the left thalamus (r = −0.354, P = 0.04, corrected for false discovery rate).Conclusion: The results indicated the alternations in WM networks of patients with FC and suggested the abnormal visceral sensation processing in the CNS from the perspective of large-scale brain WM network.

Impaired frontal-parietal control network in chronic prostatitis/chronic pelvic pain syndrome revealed by graph theoretical analysis: A DTI study.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is characterized by chronic pain in pelvic area and lower urinary tract symptoms (LUTS). Previous neuroimaging studies demonstrated that chronic pain was associated with the altered brain activity. However, the pathological mechanisms associated with altered brain control of CP/CPPS are not well-understood. Therefore, we sought to investigate the topological properties of white matter brain networks in patients with CP/CPPS and whether the topological configuration of frontal-parietal control network was disrupted. We collected 19 patients with CP/CPPS and 32 matched healthy controls (HCs). Diffusion tensor imaging data of all participates were used to map the white matter structural networks. Graph theoretical method was applied to investigate the alterations of topological properties of brain network in patients. Moreover, we also investigated whether the alerted brain regions might be correlated with any clinical features of patients by the method of Pearson correlation analysis. Both CP/CPPS patients and HCs exhibited a 'small-world' behavior or economical small-world architecture of the white matter brain networks. In addition, CP/CPPS had a lower global efficiency in the right middle frontal gyrus (orbital part) and a higher global efficiency in the left middle cingulate and paracingulate gyri. CP/CPPS also showed increased local efficiency in the left middle cingulate and paracingulate gyri and paracentral lobule. Moreover, the local efficiency of the left middle cingulate gyrus was positively correlated with the scores of the influence of symptoms on the quality of life. The local efficiency of the left precuneus and right supplementary motor area were positively correlated with the total scores of NIH-CPSI and the scores of pain and discomfort symptoms, respectively. Together, we found that patients with CP/CPPS had alterations of connections within the frontal-parietal control network, which suggested that the altered connectivity involved in the executive control processing procedures might contribute to the pathogenesis of the pelvic pain and LUTS in CP/CPPS. Thus these results provided new insights into the understanding of CP/CPPS.

Abnormal topological characteristics of brain white matter network relate to cognitive and emotional deficits of non-small cell lung cancer (NSCLC) patients prior to chemotherapy

Introduction Cancer and chemotherapy are correlated with brain functional and structural changes in cancer patients, which may lead to cognitive dysfunction. However, little is known about the structural abnormalities of brain in patients with non-small cell lung cancer (NSCLC). The aim of this study was to explore the topological properties within the brain white matter network of NSCLC patients prior to chemotherapy. Methods To explore the neurobiological biomarkers of NSCLC, brain magnetic resonance imaging (MRI) data were acquired in 24 non-nervous system metastatic NSCLC patients and 25 matched healthy controls. The topological properties of the brain structural networks of NSCLC were measured by the parameters of local and global efficiency. Results Treatment-naïve NSCLC patients showed cognitive and emotional deficits. In addition, NSCLC patients also exhibited decreased global efficacy in the left inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), right rolandic operculum, right gyrus rectus, right lenticular nucleus (putamen), left superior temporal gyrus and right inferior temporal gyrus. Decreased local efficacy were found in the left middle frontal gyrus (orbital part) and left superior temporal gyrus in NSCLC patients. Moreover, the aberrant brain regions were associated with the impaired cognitiion and emotion of NSCLC patients. Conclusion Overall our results suggested that altered local and global efficiency of brain white matter network were associated with cancer-induced cognitive and emotional deficits of NSCLC patients. These findings demonstrated that disrupted topological characteristics of the brain network might underlie the impaired cognition and emotion in NSCLC.

Reduced anxiety and changes in amygdala network properties in adolescents with training for awareness, resilience, and action (TARA)

Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen’s d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen’s d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and depression.

Specific white matter connectomic changes in schizophrenia compared with psychotic bipolar disorder.

Schizophrenia (SZ) and bipolar disorder with psychosis (BDP) can be clinically confusing. The specific connectomic changes in SZ compared with BDP may lead to a deeper comprehension of the pathophysiological core of SZ. Therefore, this study explored the common and distinct white matter (WM) structural connectomic alterations between these two diseases. Diffusion tensor imaging data were collected from 19 drug-naïve patients with first episode SZ, 19 drug-naïve patients with BDP, and 19 healthy controls (HC). A graph theoretical approach was used to assess the brain WM network properties. Except for the clustering coefficients, no significant differences in the global parameters was found between SZ and BDP. Five brain regions, the right precentral, right post-cingulum, right insula, left superior occipital, and left inferior temporal gyri, showed specific differences in the nodal parameters in SZ compared with BDP and HC. Nine brain regions, the left rectus, left lingual, right inferior parietal, left superior temporal, right precentral, right postcentral, bilateral middle frontal, and right post-cingulum gyri, showed specific differences in the nodal parameters in BDP. Significant correlations between clinical symptoms and connectomic changes were detected in the right insula and left superior occipital gyrus in patients with SZ but in the left lingual gyrus in patients with BDP. Identifying shared and distinct WM structural networks between SZ and BDP may improve the understanding of the neuroanatomy of mental diseases. Specifically, the insula, the inferior temporal, superior temporal, and the lingual gyri may help to distinguish between SZ and BDP.

Altered Brain Structural Networks in Patients with Brain Arteriovenous Malformations Located in Broca's Area.

Focal brain lesions, such as stroke and tumors, can lead to remote structural alterations across the whole-brain networks. Brain arteriovenous malformations (AVMs), usually presumed to be congenital, often result in tissue degeneration and functional displacement of the perifocal areas, but it remains unclear whether AVMs may produce long-range effects upon the whole-brain white matter organization. In this study, we used diffusion tensor imaging and graph theory methods to investigate the alterations of brain structural networks in 14 patients with AVMs in the presumed Broca's area, compared to 27 normal controls. Weighted brain structural networks were constructed based on deterministic tractography. We compared the topological properties and network connectivity between patients and normal controls. Functional magnetic resonance imaging revealed contralateral reorganization of Broca's area in five (35.7%) patients. Compared to normal controls, the patients exhibited preserved small-worldness of brain structural networks. However, AVM patients exhibited significantly decreased global efficiency (p = 0.004) and clustering coefficient (p = 0.014), along with decreased corresponding nodal properties in some remote brain regions (p < 0.05, family-wise error corrected). Furthermore, structural connectivity was reduced in the right perisylvian regions but enhanced in the perifocal areas (p < 0.05). The vulnerability of the left supramarginal gyrus was significantly increased (p = 0.039, corrected), and the bilateral putamina were added as hubs in the AVM patients. These alterations provide evidence for the long-range effects of AVMs on brain white matter networks. Our preliminary findings contribute extra insights into the understanding of brain plasticity and pathological state in patients with AVMs.

Disrupted Rich Club Organization of Hemispheric White Matter Networks in Bipolar Disorder.

Neuroimaging studies suggest disrupted connections of the brain white matter (WM) network in bipolar disorder (BD). A group of highly interconnected high-density structures, termed the ‘rich club,’ represents an important network for brain functioning. Recent works have revealed abnormal rich club organization in brain networks in BD. However, little is known regarding changes in the rich club organization of the hemispheric WM network in BD. Forty-nine BD patients and fifty-five age- and sex-matched normal controls (NCs) underwent diffusion tensor imaging (DTI). Graph theory approaches were applied to quantify group-specific rich club organization and nodal degree of hemispheric WM networks. We demonstrated that rich club organization of hemispheric WM networks in BD was disrupted, with disrupted feeder and local connections among hub and peripheral regions located in the default mode network (DMN) and the control execution network (CEN). In addition, BD patients showed abnormal asymmetry in the feeder and local connections, involving the hub and peripheral regions associated with emotion regulation and visuospatial functions. Moreover, the clinical symptoms of BD showed a significant correlation with the aberrant asymmetry in the regional degree of peripheral regions. These findings reveal that BD is closely associated with disrupted feeder and local connections but no alteration in rich-club connections in the rich club organization of hemispheric WM networks and provide novel insight into the changes of brain functions in BD.

Cancer-associated changes of emotional brain network in non-nervous system metastatic non-small cell lung cancer patients: a structural connectomic diffusion tensor imaging study.

BackgroundEmotional distress frequently occur in cancer patients following diagnosis. Previous neuroimaging studies have demonstrated that depression and anxiety are associated with functional and structural brain abnormalities. However, little is known about the cancer-associated changes of emotional brain network in non-small cell lung cancer (NSCLC) patients. The aim of this study was to assess the topological features of brain structural network and emotions in non-nervous system metastatic NSCLC patients prior to chemotherapy.MethodsTwenty-four treatment-naïve patients with non-nervous system metastatic NSCLC and 25 healthy controls (HC) matched for gender, age and education participated in this study. All subjects underwent diffusion tensor imaging (DTI), and were assessed with the 17 item hamilton depression rating scale (HAMD-17) and hamilton anxiety rating scale (HAMA). Properties of brain network were examined by the method of graph-theoretic analysis. The assessments included small-worldness, clustering coefficient and shortest path length.ResultsNSCLC patients had higher scores of HAMD-17 and HAMA when compared with HC. Additionally, we found a small-world topology of brain white matter network in both NSCLC and HC. NSCLC patients had significantly reduced clustering coefficient compared to healthy controls in the left hippocampus. Moreover, increased shortest path length were identified in NSCLC patients, which included the left middle frontal gyrus (orbital part), superior temporal gyrus and right Rolandic operculum, rectus gyrus, lenticular nucleus (putamen). However, no correlations were found between the impaired brain regions and HAMD-17, HAMA scores of NSCLC patients.ConclusionsOur results indicated impaired topological characteristics in the brain structural network of non-nervous system metastatic NSCLC patients prior to chemotherapy, which might account for the cancer-related emotional distress. Our findings demonstrated that NSCLC might affect brain regions involved in the process of emotion, which identified the basis of emotional changes associated with cancer.

Investigating Brain Network Changes and Their Association With Cognitive Recovery After Traumatic Brain Injury: A Longitudinal Analysis.

Traumatic brain injury (TBI) can result in acute cognitive deficits and diffuse axonal injury reflected in white matter brain network alterations, which may, or may not, later recover. Our objective is to first characterize the ways in which brain networks change after TBI and, second, investigate if those changes are associated with recovery of cognitive deficits. We aim to make initial progress in discerning the relationships between brain network changes, and their (dys)functional correlates. We analyze longitudinally acquired MRI from 23 TBI patients (two time points: 6 days, 12 months post-injury) and cross-sectional data from 28 controls to construct white matter brain networks. Cognitive assessment was also performed. Graph theory and regression analysis were applied to identify changed brain network metrics after injury that are associated with subsequent improvements in cognitive function. Sixteen brain network metrics were found to be discriminative of different post-injury phases. Eleven of those explain 90% (adjusted R2) of the variability observed in cognitive recovery following TBI. Brain network metrics that had a high contribution to the explained variance were found in frontal and temporal cortex, additional to the anterior cingulate cortex. Our preliminary study suggests that network reorganization may be related to recovery of impaired cognitive function in the first year after a TBI.

Tai Chi Training Evokes Significant Changes in Brain White Matter Network in Older Women.

Background: Cognitive decline is age relevant and it can start as early as middle age. The decline becomes more obvious among older adults, which is highly associated with increased risk of developing dementia (e.g., Alzheimer’s disease). White matter damage was found to be related to cognitive decline through aging. The purpose of the current study was to compare the effects of Tai Chi (TC) versus walking on the brain white matter network among Chinese elderly women. Methods: A cross-sectional study was conducted where 42 healthy elderly women were included. Tai Chi practitioners (20 females, average age: 62.9 ± 2.38 years, education level 9.05 ± 1.8 years) and the matched walking participants (22 females, average age: 63.27 ± 3.58 years, educational level: 8.86 ± 2.74 years) underwent resting-state functional magnetic resonance imaging (rsfMRI) scans. Diffusion tensor imaging (DTI) and graph theory were employed to study the data, construct the white matter matrix, and compare the brain network attributes between the two groups. Results: Results from graph-based analyses showed that the small-world attributes were higher for the TC group than for the walking group (p < 0.05, Cohen’s d = 1.534). Some effects were significant (p < 0.001) with very large effect sizes. Meanwhile, the aggregation coefficient and local efficiency attributes were also higher for the TC group than for the walking group (p > 0.05). However, no significant difference was found between the two groups in node attributes and edge analysis. Conclusion: Regular TC training is more conducive to optimize the brain functioning and networking of the elderly. The results of the current study help to identify the mechanisms underlying the cognitive protective effects of TC.

Graph analysis of DTI-based connectome: decreased local efficiency of subcortical regions in PE patients with high sympathetic activity.

Hyperactivity of the sympathetic nervous system was considered as one of the factors involved in the pathological mechanisms of premature ejaculation (PE). Sympathetic skin response of the penis (PSSR) was used to evaluate the activity of sympathetic innervations in the penis, which was controlled by the central nervous system (brain). Shorter PSSR was found in PE patients; however, little was known regarding the central neural mechanisms of PE patients with high sympathetic activity. PSSR of PE patients was evaluated, and diffusion tensor images ofparticipants were collected. Graph theoretical analysis was employed to examine the differences of the topological properties of structural brain connectome between PE patients with high sympathetic activity and healthy controls (HCs). Moreover, the relationships between topological characteristics and clinical features in PE patients were also explored. Decreased local efficacy was found in the left amygdala, right pallidum and thalamus in the white matter brain networks of PE when compared with HC (survived false discovery rate (FDR) correction). In addition, PE patients showed decreased global efficacy in the left amygdala and right rolandic operculum, supramarginal gyrus, heschl gyrus, inferior temporal gyrus, pallidum and thalamus; however, these results did not survive FDR correction. Finally, the local efficacy of right thalamus had a positive correlation with the premature ejaculation diagnostic tool (PEDT) scores, while the local efficacy of left amygdala was negatively associated with the state score of the state-trait anxiety inventory (STAI) and penile shaft sensory threshold. The results highlighted the abnormal topological properties of structural brain connectome in PE with high sympathetic activity. We also suggested that the clinical features of PE were related to the abnormality of several brain regions involved in the central control of ejaculation and emotion. This study provided new insights into the central neural mechanisms of PE, which might offer biological markers for understanding the physiopathology of PE.

Large-scale white matter network reorganization in posttraumatic stress disorder.

Recently, graph theoretical approaches applied to neuroimaging data have advanced understanding of the human brain connectome and its abnormalities in psychiatric disorders. However, little is known about the topological organization of brain white matter networks in posttraumatic stress disorder (PTSD). Seventy-six patients with PTSD and 76 age, gender, and years of education-matched trauma-exposed controls were studied after the 2008 Sichuan earthquake using diffusion tensor imaging and graph theoretical approaches. Topological properties of brain networks including global and nodal measurements and modularity were analyzed. At the global level, patients showed lower clustering coefficient (p = .016) and normalized characteristic path length (p = .035) compared with controls. At the nodal level, increased nodal centralities in left middle frontal gyrus, superior and inferior temporal gyrus and right inferior occipital gyrus were observed (p < .05, corrected for false-discovery rate). Modularity analysis revealed that PTSD patients had significantly increased inter-modular connections in the fronto-parietal module, fronto-striato-temporal module, and visual and default mode modules. These findings indicate a PTSD-related shift of white matter network topology toward randomization. This pattern was characterized by an increased global network integration, reflected by increased inter-modular connections with increased nodal centralities involving fronto-temporo-occipital regions. This study suggests that extremely stressful life experiences, when they lead to PTSD, are associated with large-scale brain white matter network topological reconfiguration at global, nodal, and modular levels.

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

BackgroundThe purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic.MethodsA DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject’s DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier.ResultsFor FA, the accuracy of classification between the 0–6, 6–12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0–6, 6–12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced.ConclusionsRBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.

Impaired brain white matter and functional networks in healthy individuals with auditory verbal hallucinations.

Impaired brain white matter and functional networks in healthy individuals with auditory verbal hallucinations.

Impaired brain white matter and functional networks in healthy individuals with auditory verbal hallucinations

Impaired brain white matter and functional networks in healthy individuals with auditory verbal hallucinations

Aberrant development of the asymmetry between hemispheric brain white matter networks in autism spectrum disorder

Atypical brain asymmetry/lateralization has long been hypothesized for autism spectrum disorder (ASD), and this model has been repeatedly supported by various neuroimaging studies. Recently, hemispheric network topologies have been found to be asymmetric, thereby providing a new avenue for investigating brain asymmetries under various conditions. To date, however, how network topological asymmetries are altered in ASD remains largely unexplored. To clarify this, the present study included ASD individuals from the newly released Autism Brain Imaging Data Exchange II database (58 right-handed male ASD individuals aged 5 to 26 years and 70 age- and IQ-matched typically developing (TD) individuals). Diffusion and structural magnetic resonance imaging were used to construct hemispheric white matter networks, and graph-theory approaches were applied to quantify topological efficiencies for hemispheric networks. Statistical analyses revealed a decreased rightward asymmetry of network efficiencies with increasing age in the TD group, but not in the ASD group. More specifically, the TD group did not exhibit an age-related increase in network efficiency in the right hemisphere, but the ASD group did. For the left hemisphere, no difference between the groups was observed for the developmental trajectory of network efficiencies. Intriguingly, within the ASD group, more severe restricted and repetitive behavior in ASD was found to be correlated with less rightward asymmetry of network local efficiency. These findings provide suggestive evidence of atypical network topological asymmetries and offer important insights into the abnormal development of ASD brains.

Research on brain white matter network in cerebral palsy infant

Present study used diffusion tensor image and tractography to construct brain white matter networks of 15 cerebral palsy infants and 30 healthy infants that matched for age and gender. After white matter network analysis, we found that both cerebral palsy and healthy infants had a small-world topology in white matter network, but cerebral palsy infants exhibited abnormal topological organization: increased shortest path length but decreased normalize clustering coefficient, global efficiency and local efficiency. Furthermore, we also found that white matter network hub regions were located in the left cuneus, precuneus, and left posterior cingulate gyrus. However, some abnormal nodes existed in the frontal, temporal, occipital and parietal lobes of cerebral palsy infants. These results indicated that the white matter networks for cerebral palsy infants were disrupted, which was consistent with previous studies about the abnormal brain white matter areas. This work could help us further study the pathogenesis of cerebral palsy infants.

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment.

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.