Whole-brain White Matter Network Research Articles

Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities

ABSTRACT We examined how the thalamocortical connectivity structure reflects children’s reading performance. Diffusion-weighted MRI at 3 T and a series of reading measures were collected from 64 children (33 girls) ages 8–14 years with and without dyslexia. The topological properties of the left and right thalamus were computed based on the whole-brain white matter network and a hub-attached reading network and were correlated with scores on several tests of children’s reading and reading-related abilities. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Local efficiency was negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of efficiency scores; follow-up analyses further demonstrated that this effect was specific to the pulvinar and mediodorsal nuclei of the left thalamus. We also sought to validate these results using an independent dataset and demonstrated that that the relationship between thalamic connectivity and phonemic decoding was specifically robust. Overall, the results highlight the role of the left thalamus and thalamocortical network in understanding the neurocognitive bases of skilled reading and dyslexia in children.

White matter structural changes before and after microvascular decompression for hemifacial spasm.

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

AGE EFFECTS ON WHITE MATTER TOPOLOGY IN OLDER ADULTS AT HIGH RISK OF ALZHEIMER’S DISEASE

Abstract White matter integrity shows age-related declines in later life. Studies have found altered topological changes of white matter networks in healthy aging populations. We investigated cross-sectional association between white matter network topology, age, education, and cognition in older adults at high-risk for Alzheimer’s disease (AD). A total of 153 cognitively healthy participants (age=67.78±5.03, 111 female, 54 APOE-4 carriers) who have at least one close family member diagnosed with AD from the Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer’s Disease (PREVENT-AD) cohort were included in the study. Whole brain diffusion images were obtained and submitted to preprocessing, fiber tracking and connectome creation in MRtrix. Test scores from the Trail Making task, Stroop task, and Rey Auditory Verbal Learning Test (RAVLT) were included as cognitive function measures. We found that older age was significantly associated with greater modularity (r2=0.48, p corrected&amp;lt; 0.001), lower density (r2=0.32, p corrected&amp;lt; 0.001), greater betweenness centrality (r2=0.39, p corrected=0.003), and greater clustering coefficient (r2=0.05, p corrected=0.042). Older age was also significantly associated with longer Trail Making time series B (r2=0.11, p corrected=0.012) and fewer recalled words (r2=0.18, p corrected=0.007). We further found that participants with higher educational attainment showed less age-related changes in modularity (t=2.42, p=0.017) and density (t=2.40, p=0.018). However, these graph theory measures were not associated with cognitive performance. In conclusion, age related to whole-brain white matter network topology in high-risk healthy older adults, and education may attenuate this relationship. The current study deepens our understanding of white matter changes in high-risk aging.

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.

BackgroundDetection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.MethodsOur study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.Results(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.ConclusionFeature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

White matter, cognition and psychotic-like experiences in UK Biobank.

Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs. We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data. The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD. We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.

Whole-brain white matter network reorganization in HIV.

The human immunodeficiency virus (HIV) causes an infectious disease with a high viral tropism toward CD4 T-lymphocytes and macrophage. Since the advent of combined antiretroviral therapy (CART), the number of opportunistic infectious disease has diminished, turning HIV into a chronic condition. Nevertheless, HIV-infected patients suffer from several life-long symptoms, including the HIV-associated neurocognitive disorder (HAND), whose biological substrates remain unclear. HAND includes a range of cognitive impairments which have a huge impact on daily patient life. The aim of this study was to examine putative structural brain network changes in HIV-infected patient to test whether diffusion-imaging-related biomarkers could be used to discover and characterize subtle neurological alterations in HIV infection. To this end, we employed multi-shell, multi-tissue constrained spherical deconvolution in conjunction with probabilistic tractography and graph-theoretical analyses. We found several statistically significant effects in both local (right postcentral gyrus, right precuneus, right inferior parietal lobule, right transverse temporal gyrus, right inferior temporal gyrus, right putamen and right pallidum) and global graph-theoretical measures (global clustering coefficient, global efficiency and transitivity). Our study highlights a global and local reorganization of the structural connectome which support the possible application of graph theory to detect subtle alteration of brain regions in HIV patients.Clinical Relevance-Brain measures able to detect subtle alteration in HIV patients could also be used in e.g. evaluating therapeutic responses, hence empowering clinical trials.

Diffusion Tensor Imaging Reveals Altered Topological Efficiency of Structural Networks in Type-2 Diabetes Patients With and Without Mild Cognitive Impairment.

Some patients with type 2 diabetes mellitus (T2DM) progress towards mild cognitive impairment (MCI), while some patients can always maintain normal cognitive function. Network topologic alterations at global and nodal levels between T2DM individuals with and without cognitive impairment may underlie the difference. To investigate the topological alterations of the whole-brain white matter (WM) structural connectome in T2DM patients with and without MCI and characterize its relationship with disease severity. Cross-sectional and prospective study. Forty-four (63.6% females) T2DM patients, 22 with mild cognitive impairment (DM-MCI) and 22 with normal cognition (DM-NC), and 34 (58.8% females) healthy controls (HC). 3 T/diffusion tensor imaging. Graph theoretical analysis was used to investigate the topological organization of the structural networks. The global topological properties and nodal efficiency were investigated and compared. Relationship between network metrics and clinical measurements was characterized. Student's t-test, chi-square test, ANOVA, partial correlation analyses, and multiple comparisons correction. The global topological organization of WM networks was significantly disrupted in T2DM patients with cognitive impairment (reduced global and local efficiency and increased shortest path length) but not in those with normal cognition, compared with controls. The DM-MCI group had significantly decreased network efficiency compared with the DM-NC group. Compared with controls, decreased nodal efficiency was detected in three regions in DM-NC group. More regions with decreased nodal efficiency were found in the DM-MCI group. Altered global network properties and nodal efficiency of some regions were correlated with diabetic duration, HbA1c levels, and cognitive assessment scores. The more disrupted WM connections and weaker organized network are found in DM-MCI patients relative to DM-NC patients and controls. Network analyses provide information for the neuropathology of cognitive decline in T2DM patients. Altered nodal efficiency may act as potential markers for early detection of T2DM-related MCI. 1 TECHNICAL EFFICACY: Stage 2.

The relationships between the topological properties of the whole-brain white matter network and the severity of autism spectrum disorder: A study from monozygotic twins

Twins provide a valuable perspective for exploring the pathological mechanism of autism spectrum disorder (ASD). We aim to analyze differences in the topological properties of the white matter (WM) network between monozygotic twins with ASD (MZCo-ASD) and children with typical development (TD). We enrolled 67 subjects aged 2–9 years. Twenty-three pairs of MZCo-ASD and 21 singleton children with TD completed clinical assessments and diffusion tensor imaging (DTI). Graph theory was used to compare the topological properties of the WM network between the two groups, and analyzed their correlations with the severity of clinical symptoms. We found that the global efficiency (Eg) of MZCo-ASD is weaker than that of TD children, while the shortest path length (Lp) of MZCo-ASD is longer than that of TD children, and MZCo-ASD have three unique hubs (the bilateral dorsolateral superior frontal gyrus and right insula). Eg and Lp were both correlated with the repetitive behavior scores of the Autism Diagnostic Interview-Revised (ADI-R) in the MZCo-ASD group, and the nodal efficiency of the dorsal superior frontal gyrus (SFGdor) was correlated with the ADI-R scores of repetitive behaviors. Left SFGdor nodal efficiency was correlated with Repetitive Behavior and Communication, two core symptoms of autism. The results implicated that MZCo-ASD had atypical brain structural network attributes and node distributions. Using MZCo-ASD, we found that the WM topological properties that correlate with the severity of ASD core symptoms were Eg, Lp, and the nodal efficiency of the SFGdor.

The effects of cognitive behavioral therapy on the whole brain structural connectome in unmedicated patients with obsessive-compulsive disorder

Cognitive behavioral therapy (CBT) is considered a first-line treatment for patients with obsessive-compulsive disorder (OCD), and it possesses advantages over pharmacological treatments in stronger tolerance to distress, lower rates of drop out and relapse, and no physical “side-effects”. Previous studies have reported CBT-related alterations in focal brain regions and connections. However, the effects of CBT on whole-brain structural networks have not yet been elucidated. Here, we collected diffusion MRI data from 34 unmedicated OCD patients before and after 12 weeks of CBT. Fifty healthy controls (HCs) were also scanned twice at matched intervals. We constructed individual brain white matter connectome and performed a graph-theoretical network analysis to investigate the effects of CBT on whole-brain structural topology. We observed significant group-by-time interactions on the global network clustering coefficient and the nodal clustering of the left lingual gyrus, the left middle temporal gyrus, the left precuneus, and the left fusiform gyrus of 26 CBT responders in OCD patients. Further analysis revealed that these CBT responders showed prominently higher global and nodal clustering compared to HCs at baseline and reduced to normal levels after CBT. Such significant changes in the nodal clustering of the left lingual gyrus were also found in 8 CBT non-responders. The pre-to-post decreases in nodal clustering of the left lingual gyrus and the left fusiform gyrus positively correlated with the improvements in obsessive-compulsive symptoms in the CBT-responding patients. These findings indicated that the network segregation of the whole-brain white matter network in OCD patients was abnormally higher and might recover to normal after CBT, which provides mechanistic insights into the CBT response in OCD and potential imaging biomarkers for clinical practice.

Unique white matter structural connectivity in early-stage drug-naive Parkinson disease.

To investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD). We employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance. PD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network. By minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.

Effects of brain network construction on individual differences of topological properties

Advances in diffusion MRI techniques have made it possible to virtually reconstruct white matter tracts, which further allows the modeling of the human brain as a complex network in vivo . Graph theoretical approaches have been applied to characterize topological architectures of whole-brain white matter networks. By using these techniques, many important topological properties for the whole-brain white matter network of human brain have been revealed in many previous works, while the construction method for white matter networks differs across studies. At present, a few studies have assessed the effects of network construction methods on specific white matter network properties and substantial influences of the construction methods were observed. However, the previous research mainly focuses on the effects of the constructed methods on the same subjects for their network properties. Up to now, how the topological properties are affected by construction methods for the individual differences is still unknown, even it is the key for the investigation of white matter networks in a cross-sectional manner, including group comparison, correlational analysis, etc. Therefore, it is necessary but still insufficient to make a systematic evaluation for the network construction methods on the individual differences. In the present work, the influence of different network construction methods on the network properties is desired to be assessed individual differences. Thirty-two methods were adopted to construct the whole-brain white matter networks of 80 healthy adult subjects, which includes two node definitions (low-resolution and high-resolution), four deterministic tractography algorithms (Fiber Assignment by Continuous Tracking, Tensorline Tracking, Interpolated streamline method and 2nd-order Runge-Kutta method) and four weighted edge definitions (binary, FA weighted, fiber-length weighted, and fiber-number weighted). For these white matter networks, individual differences were analyzed by graph theory and hierarchical clustering in three topological parameters including the global efficiency, the local efficiency and the nodal efficiency of the network. The results can be summarized that, firstly, the nodal efficiency with the same nodal definition exhibits convergent individual differences, which indicated that the individual differences of network nodal properties are divergent across network resolutions. Secondly, the individual differences of global and local efficiency of white matter networks constructed by different deterministic fiber tracking algorithms do not differ significantly. However, the constructed networks based on the same deterministic fiber tracking algorithm exhibited the same individual differences with the different node efficiency, which indicated that the effect of fiber tracking algorithm should be emphasized in the comparison and analysis of nodal properties in different studies. Finally, the method clustering analysis for the network global efficiency and local efficiency indicates that the networks with both low-resolution and high-resolution will compose to be the same module with the definition of the same edges, which indicated the high convergent of individual differences for the network efficiencies with different resolutions. It can be concluded that the difference of network construction methods depends on the network properties when it is used to reflexing the individual difference. These findings may provide valuable implications for understanding the intersubject variability of white matter networks and comparing results across different studies.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc.

Characteristics of the whole-brain white-matter structural network in 2 to 3-year-old children with autism spectrum disorder

Objective To investigate the topological property alterations of the whole-brain white-matter (WM) structural network between 2 to 3-year-old children with autism spectrum disorder (ASD) children. Methods This research used diffusion tensor imaging(DTI) tractography to construct the human brain WM networks of 45 ASD children ranging from 2 to 3-year-old and 45 age- and sex-matched DD controls, followed by a graph theoretical analysis to establish topological architecture of the cerebral anatomical network, so as to further show the relationship between topological properties and clinical symptoms of the ASD group. Results The ASD children exhibited abnormal global topological properties, as indicated by decreased global efficiency(P=0.006), and increased characteristic path length (P=0.010). There were 11 hub nodes of the ASD group, 9 of them were the same as the DD controls, while the other two were right middle temporal gyrus and right dorsolateral superior frontal gyrus.Compared with DD controls, regional efficiency in several brain areas of ASD was significantly reduced(P<0.001), including left inferior frontal gyrus(orbital part), left superior frontal gyrus(medial orbital), right posterior cingulate gyrus, left inferior occipital gyrus, right precuneus, left paracentral lobule, bilateral caudate nucleus, left temporal pole and superior temporal gyrus(TPOsup). However, it also exhibited increased regional efficiency (P<0.001)in a few brain regions of ASD such as left supramarginal gyrus, right angular gyrus, and left heschl gyrus.Furthermore, no significant relationship was found between topological properties of the whole-brain WM structural network and clinical symptoms in ASD children. Conclusions The ASD toddlers exhibitedsmall-worldcharacter of the whole-brain WM structural network; the whole brain information transfer and interactions between brain regions are slower and less efficient than DD controls; the ASD group exhibited the phenomenon of coexistence of decreased and increased structural connectivity between different regions. Key words: Autism spectrum disorder; Diffusion tensor imaging; Brain network; Graph theoretical

Aberrant white matter networks mediate cognitive impairment in patients with silent lacunar infarcts in basal ganglia territory.

Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency-behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.

Altered whole-brain white matter networks in preclinical Alzheimer's disease.

Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined.12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume).We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects.Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.

Probabilistic Diffusion Tractography and Graph Theory Analysis Reveal Abnormal White Matter Structural Connectivity Networks in Drug-Naive Boys with Attention Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD), which is characterized by core symptoms of inattention and hyperactivity/impulsivity, is one of the most common neurodevelopmental disorders of childhood. Neuroimaging studies have suggested that these behavioral disturbances are associated with abnormal functional connectivity among brain regions. However, the alterations in the structural connections that underlie these behavioral and functional deficits remain poorly understood. Here, we used diffusion magnetic resonance imaging and probabilistic tractography method to examine whole-brain white matter (WM) structural connectivity in 30 drug-naive boys with ADHD and 30 healthy controls. The WM networks of the human brain were constructed by estimating inter-regional connectivity probability. The topological properties of the resultant networks (e.g., small-world and network efficiency) were then analyzed using graph theoretical approaches. Nonparametric permutation tests were applied for between-group comparisons of these graphic metrics. We found that both the ADHD and control groups showed an efficient small-world organization in the whole-brain WM networks, suggesting a balance between structurally segregated and integrated connectivity patterns. However, relative to controls, patients with ADHD exhibited decreased global efficiency and increased shortest path length, with the most pronounced efficiency decreases in the left parietal, frontal, and occipital cortices. Intriguingly, the ADHD group showed decreased structural connectivity in the prefrontal-dominant circuitry and increased connectivity in the orbitofrontal-striatal circuitry, and these changes significantly correlated with the inattention and hyperactivity/impulsivity symptoms, respectively. The present study shows disrupted topological organization of large-scale WM networks in ADHD, extending our understanding of how structural disruptions of neuronal circuits underlie behavioral disturbances in patients with ADHD.

Altered Topological Organization of White Matter Structural Networks in Patients with Neuromyelitis Optica

ObjectiveTo investigate the topological alterations of the whole-brain white-matter (WM) structural networks in patients with neuromyelitis optica (NMO).MethodsThe present study involved 26 NMO patients and 26 age- and sex-matched healthy controls. WM structural connectivity in each participant was imaged with diffusion-weighted MRI and represented in terms of a connectivity matrix using deterministic tractography method. Graph theory-based analyses were then performed for the characterization of brain network properties. A multiple linear regression analysis was performed on each network metric between the NMO and control groups.ResultsThe NMO patients exhibited abnormal small-world network properties, as indicated by increased normalized characteristic path length, increased normalized clustering and increased small-worldness. Furthermore, largely similar hub distributions of the WM structural networks were observed between NMO patients and healthy controls. However, regional efficiency in several brain areas of NMO patients was significantly reduced, which were mainly distributed in the default-mode, sensorimotor and visual systems. Furthermore, we have observed increased regional efficiency in a few brain regions such as the orbital parts of the superior and middle frontal and fusiform gyri.ConclusionAlthough the NMO patients in this study had no discernible white matter T2 lesions in the brain, we hypothesize that the disrupted topological organization of WM networks provides additional evidence for subtle, widespread cerebral WM pathology in NMO.