Reduced local segregation of single-subject gray matter networks in adult PTSD

Toward individualized connectomes of brain morphology

The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.

Characterizing Typhoon-related Posttraumatic Stress Disorder Based on Multimodal Fusion of Structural, Diffusion, and Functional Magnetic Resonance Imaging

Diagnosing posttraumatic stress disorder (PTSD) using only single-modality images is controversial. We aimed to use multimodal magnetic resonance imaging (MRI) combining structural, diffusion, and functional MRI to possibly provide a more comprehensive viewpoint on the decisive characteristics of PTSD patients. Typhoon-exposed individuals with (n = 26) and without PTSD (n = 32) and healthy volunteers (n = 30) were enrolled. Five MRI features from three modalities, including two resting-state functional MRI (rs-fMRI) features (amplitude of low-frequency fluctuation, ALFF; and regional homogeneity, ReHo), one structural MRI feature (gray matter density, GM), and two diffusion tensor imaging (DTI) features (fractional anisotropy, FA; and mean diffusivity, MD) were investigated simultaneously with a multimodal canonical correlation analysis + joint independent component analysis model to identify abnormalities in the PTSD brain. We identified statistical differences between PTSD patients and healthy controls in terms of 1 rs-fMRI (ALFF, ReHo) alterations in the superior frontal gyrus, precuneus, inferior parietal lobule (IPL), anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC), 2 DTI (FA, MD) changes in the pons, genu, and splenium of the corpus callosum, and 3 Structural MRI abnormalities in the precuneus, IPL, ACC, and PCC. A novel ReHo component was found to distinguish PTSD and trauma-exposed controls, including the precuneus, IPL, middle frontal gyrus, middle occipital gyrus, and cerebellum. This study reveals that PTSD individuals exhibit intertwined functional and structural anomalies within the default mode network. Some alterations within this network may serve as a potential marker to distinguish between PTSD patients and trauma-exposed controls.

Causal effects of nonalcoholic fatty liver disease on cerebral cortical structure: a Mendelian randomization analysis

Background

Previous studies have highlighted changes in the cerebral cortical structure and cognitive function among nonalcoholic fatty liver disease (NAFLD) patients. However, the impact of NAFLD on cerebral cortical structure and specific affected brain regions remains unclear. Therefore, we aimed to explore the potential causal relationship between NAFLD and cerebral cortical structure.

Methods

We conducted a Mendelian randomization (MR) study using genetic predictors of alanine aminotransferase (ALT), NAFLD, and percent liver fat (PLF) and combined them with genome-wide association study (GWAS) summary statistics from the ENIGMA Consortium. Several methods were used to assess the effect of NAFLD on full cortex and specific brain regions, along with sensitivity analyses.

Results

At the global level, PLF nominally decreased SA of full cortex; at the functional level, ALT presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars orbitalis, and TH of pericalcarine cortex. Besides, NAFLD presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars triangularis and TH of pericalcarine cortex, but increased TH of entorhinal cortex, lateral orbitofrontal cortex and temporal pole. Furthermore, PLF presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of cuneus and lingual gyrus, but increased TH of entorhinal cortex.

Conclusion

NAFLD is suggestively associated with atrophy in specific functional regions of the human brain.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Altered single-subject gray matter structural networks in social anxiety disorder

Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.

Multilayer Network Analysis of Dynamic Network Reconfiguration in Adults With Posttraumatic Stress Disorder

BACKGROUND

Brain functional network abnormalities are reported in posttraumatic stress disorder (PTSD). Most resting-state functional magnetic resonance imaging studies have assumed that the functional networks remain static during the scans. How these might change dynamically in PTSD remains unclear.

METHODS

Resting-state functional magnetic resonance imaging data were collected from 71 noncomorbid, treatment-naïve patients with PTSD and 70 demographically matched, trauma-exposed non-PTSD control subjects. Network switching rate was used to characterize dynamic changes of individual resting-state functional networks. Results were analyzed by comparing switching rates between the PTSD and trauma-exposed non-PTSD groups, testing for diagnosis × sex interactions, and examining correlations with PTSD symptom severity.

RESULTS

At the global level, the PTSD group showed significantly lower network switching rates than the trauma-exposed non-PTSD group. These were observed mainly in the frontoparietal, default mode, and limbic networks at the subnetwork level and in the frontal and temporal regions at the nodal level. These network switching rate alterations were correlated with PTSD symptom severity. There were no significant effects of sex.

CONCLUSIONS

These disruptions of dynamic functional network stability, reflected by lower network switching rates in the resting state, are a feature of PTSD and suggest that the frontoparietal, default mode, and limbic networks may play a critical role in the underlying neural mechanisms.

A preliminary study of the effects of mindfulness-based cognitive therapy on structural brain networks in mood-dysregulated youth with a familial risk for bipolar disorder

BACKGROUND

Mindfulness-based cognitive therapy for children (MBCT-C), as a psychotherapeutic intervention, has been shown to be effective for treating mood dysregulation (MD). While previous neuroimaging studies of MD have reported both pre-treatment structural and functional alterations, the effects of MBCT-C on brain morphological network organisation has not been investigated.

METHODS

We investigated brain morphological network organisation in 10 mood-dysregulated youth with familial risk for bipolar disorder and 15 matched healthy comparison youth (HC). Effects of 12 weeks of MBCT-C were examined in the mood-dysregulated youth. Topological properties of brain networks used for analyses were constructed based on morphological similarities in regional grey matter using a graph-theory approach using MRI data.

RESULTS

At baseline, compared with the HC group, the mood-dysregulated group exhibited increased global efficiency (E_glob ), decreased path length (L_p ), and abnormal nodal properties, mainly in the limbic system. Right temporal pole alterations at baseline predicted change in Child and Adolescent Mindfulness Measure scores after treatment. The mood-dysregulated group showed significant decreases in both the E_glob and L_p metrics after MBCT-C, suggesting an improved capacity for optimal information processing. Changes in L_p were correlated with changes in Emotion Regulation Checklist scores. Our results show significant topological alterations in the mood-dysregulated group as compared to controls at baseline. After MBCT-C, disrupted topological properties in the mood-dysregulated group were significantly reduced.

CONCLUSION

MBCT-C may facilitate clinically meaningful changes in the brain structural network in mood-dysregulated individuals.

Altered Structural Brain Network Topology in Patients With Primary Craniocervical Dystonia

Purpose

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

Methods

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

Results

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

Conclusions

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

Altered single-subject gray matter structural networks in drug-naïve attention deficit hyperactivity disorder children

Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug-naïve children with ADHD and 83 age-matched healthy controls. Single-subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug-naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

Disrupted brain gray matter networks in drug-naïve participants with essential tremor

PURPOSE

To use structural magnetic resonance imaging and graph theory approaches to investigate the topological organization of the brain morphological network based on gray matter in essential tremor, and its potential relation to disease severity.

METHODS

In this prospective study conducted from November 2018 to November 2019, 36 participants with essential tremor and 37 matched healthy controls underwent magnetic resonance imaging. Brain networks based on the morphological similarity of gray matter across regions were analyzed using graph theory. Nonparametric permutation testing was used to assess group differences in topological metrics. Support vector machine was applied to the gray matter morphological matrices to classify participants with essential tremor vs. healthy controls.

RESULTS

Compared with healthy controls, participants with essential tremor showed increased global efficiency (p < 0.01) and decreased path length (p < 0.01); abnormal nodal properties in frontal, parietal, and cerebellar lobes; and disconnectivity in cerebello-thalamo-cortical network. The abnormal brain nodal centralities (left superior cerebellum gyrus; right caudate nucleus) correlated with clinical measures, both motor (Fahn-Tolosa-Marìn tremor rating, p = 0.017, r = - 0.41) and nonmotor (Hamilton depression scale, p = 0.040, r = - 0.36; Hamilton anxiety scale, p = 0.008, r = - 0.436). Gray matter morphological matrices classified individuals with high accuracy of 80.0%.

CONCLUSION

Participants with essential tremor showed randomization in global properties and dysconnectivity in the cerebello-thalamo-cortical network. Participants with essential tremor could be distinguished from healthy controls by gray matter morphological matrices.

Large-scale network topology reveals brain functional abnormality in Chinese dyslexic children

It has been revealed that dyslexic children learning alphabetic languages are characterized by aberrant topological organization of brain networks. However, little is known about the functional organization and the reconfiguration pattern of brain networks in Chinese dyslexic children. Using graph theoretical analysis and functional magnetic resonance images (fMRI), we examined this issue specifically from the perspective of functional integration and segregation. We first compared large-scale topological organizations between dyslexic children and typically developing children during a Chinese phonological rhyming task, and found that dyslexic children showed increased local efficiency and clustering coefficient compared with typically developing children, which were negatively correlated with task performance. Furthermore, dyslexic children and typically developing children could be accurately distinguished at the individual-subject level based on the nodal local efficiency or clustering coefficient. Second, we studied the group difference of network reconfiguration and found that dyslexic children showed more difficulty when shifting from the resting state to the phonological task. Our results suggest an over-segregated brain functional organization and deficits in brain network reconfiguration in Chinese dyslexic children, which helps to advance our knowledge on the neural mechanisms underlying dyslexia.

Alteration of single-subject gray matter networks in major depressed patients with suicidality

While regional brain alterations and functional connectivity in depressed suicidal patients have previously been reported, knowledge about gray matter (GM) structural networks is limited. The aim of this study was to explore the GM of depressed suicidal brains from the single-subject structural network level. This was a cross-sectional study, in which 50 healthy controls (HC, 31 ± 9 years), 50 major depressed patients without suicidality (NSD, 29 ± 10 years), and 50 major depressed patients with suicidality (SU, 29 ± 12 years) were enrolled. T₁ -weighted images (T₁ WI) were acquired with three-dimensional-magnetization prepared rapid gradient echo sequence in 3.0 T magnetic resonance. The analysis was performed using the automated Computational Anatomy Toolbox (CAT12) within Statistical Parametric Mapping while running MATLAB. The T₁ images were segmented into GM, white matter, and cerebrospinal fluid. Then single-subject structural networks were constructed based on the morphological similarity of GM regions. Global network topological properties, including clustering coefficient (C_p ), characterpath length (L_p ), normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), global efficiency (E_glob ), local efficiency (E_loc ), and nodal network topological properties, including nodal efficiency, degree, and betweenness centrality, were measured using graph theory analysis. Statistical tests performed were analysis of variance, Pearson correlation analysis, and multiple linear regression analysis. Decreased E_glob and increased shortest L_p were observed in SU group compared to HC and NSD groups (p < 0.05). The NSD and SU groups had an increased λ and decreased E_loc compared to the HC group (p < 0.05). Altered nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in the SU group compared with the HC and NSD groups (all p < 0.05). The GM network in the SU group showed decreased segregation and weaker integration, that is weaker small-worldness, compared to the NSD and HC groups. Abnormal nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in suicidal brains. This study provides new evidence for therapeutic targets for patients with depression and suicidality. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity

Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10-15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.

Associations between resting-state functional connectivity and treatment response in a randomized clinical trial for posttraumatic stress disorder

BACKGROUND

Alterations in resting-state functional connectivity (rsFC) have been reported in posttraumatic stress disorder (PTSD). Here, we examined pre- and post-treatment rsFC during a randomized clinical trial to characterize alterations and examine predictors of treatment response.

METHODS

Sixty-four combat veterans with PTSD were randomly assigned to prolonged exposure (PE) plus placebo, sertraline plus enhanced medication management, or PE plus sertraline. Symptom assessment and resting-state functional magnetic resonance imaging (fMRI) scans occurred before and after treatment. Twenty-nine trauma-exposed combat veterans without PTSD served as a control group at intake. Seed-based and region of interest (ROI)-to-ROI connectivities, as well as an exploratory connectome-based approach were used to analyze rsFC patterns. Based on previously reported findings, analyses focused on Salience Network (SN) and Default-Mode Network (DMN).

RESULTS

At intake, patients with PTSD showed greater DMN-dorsal attention network (DAN) connectivity (between ventromedial prefrontal cortex and superior parietal lobule; family-wise error corrected p = .011), greater SN-DAN connectivity (between insula and middle frontal gyrus; corrected p = .003), and a negative correlation between re-experiencing symptoms and within-DMN connectivity (between posterior cingulate cortex (PCC) and middle temporal gyrus; corrected p < .001). We also found preliminary evidence for associations between rsFC and treatment response. Specifically, high responders (≥50% PTSD symptom improvement), compared with low responders, had greater SN-DMN segregation (i.e., less pre-treatment amygdala-PCC connectivity; p = .011) and lower pre-treatment global centrality (p = .042).

CONCLUSIONS

Our findings suggest neural abnormalities in PTSD and may inform future research examining neural biomarkers of PTSD treatment response.

Aberrant Gray Matter Networks in Non-comorbid Medication-Naive Patients With Major Depressive Disorder and Those With Social Anxiety Disorder

Major depressive disorder (MDD) and social anxiety disorder (SAD) are among the most prevalent and frequently co-occurring psychiatric disorders in adults and may have, at least in part, a common etiology. However, the unique and the shared neuroanatomical characteristics of the two disorders have not been fully identified. The aim of this study was to compare the topological organization of gray matter networks between non-comorbid medication-naive MDD patients and SAD patients. High-resolution T1-weighted images were acquired from 37 non-comorbid medication-naive MDD patients, 24 non-comorbid medication-naive SAD patients, and 41 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the three groups. The relationships between brain network measures and clinical characteristics were analyzed. Relative to healthy controls, both the MDD and the SAD patients showed global decreases in clustering coefficient, normalized clustering coefficient, and small-worldness and locally decreased nodal centralities and morphological connections in the left insular, lingual, and calcarine cortices. Compared with healthy controls, the SAD patients exhibited increased nodal centralities and morphological connections mainly involving the prefrontal cortex and the sensorimotor network. Furthermore, compared to the SAD patients, the MDD patients showed increased characteristic path length, reduced global efficiency, and decreased nodal centralities and morphological connections in the right middle occipital gyrus and the right postcentral gyrus. Our findings provide new evidence for shared and specific similarity-based gray matter network alterations in MDD and SAD and emphasize that the psychopathological changes in the right middle occipital gyrus and the right postcentral gyrus might be different between the two disorders.

Subthreshold depression may exist on a spectrum with major depressive disorder: Evidence from gray matter volume and morphological brain network

BACKGROUND

Subthreshold depression (StD) is a prevalent condition that may increase the risk of incident major depressive disorder (MDD). However, the relationship between StD and MDD remains unclear.

METHODS

A total of 153 adult subjects, including 53 drug-naive MDD, 50 StD and 50 healthy control (HC) subjects, underwent a T1-weighted magnetic resonance imaging scan, and the gray matter volume (GMV) alterations among the three groups were quantitatively analyzed using voxel-based morphometry (VBM). Then, to capture the whole-brain connectivity characteristics, we constructed morphological brain networks (MBN) based on the similarity among brain regions of individual VBM images and compared the network connection strengths among the three groups.

RESULTS

The StD and MDD subjects had similar patterns of GMV reductions in the orbitofrontal cortex and left temporal gyrus, although the magnitude of the reductions was smaller in StD subjects. Moreover, a total of 21 morphological connections were significantly different among the three groups. For the majority of the different connections (15/21), the connection strength of the StD group took an intermediate position between that of the MDD and HC groups.

LIMITATIONS

There is still a lack of a consistent definition of StD, and the age range of the subjects in this study was wide. Meanwhile the mechanisms and biological significance of the MBN remains to be clarified.

CONCLUSIONS

These results may support the hypothesis that depression is better expressed as a spectrum and that StD exists on a spectrum with MDD.

Topological Regularization of Networks in Adult Patients with Moderate-to-Severe Obstructive Sleep Apnea-Hypopnea Syndrome: A Structural MRI Study

OBJECTIVE

Patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) exhibit neurocognitive impairments; however, the neuroimaging mechanism of neurocognitive impairments remains unclear. The aim of this study was to understand the neuroimaging mechanism in adult patients with moderate-to-severe OSAHS, from the perspective of the connectome.

PATIENTS AND METHODS

Thirty-one untreated patients with moderate-to-severe OSAHS (mean age: 41.23±8.22) were compared with 26 good sleepers (GS) (mean age: 39.50±7.92) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the skull using a 3.0T MRI. Then, a large-scale structural covariance network was constructed based on the gray matter volume extracted from the structural MRI. Graph theory was then used to determine the topological changes in the structural covariance network of OSAHS patients.

RESULTS

Although small-world networks were retained,the structural covariance network exhibited topological irregularities in regular architecture as evidenced by an increase in the clustering coefficient (p=0.009), transfer coefficient (p=0.029) and local efficiency (p=0.031), and a local increase in the shortest path length (p<0.05) compared with the GS group. Locally, OSAHS patients showed a decrease in nodal betweenness and degree in the left inferior parietal gyrus, left angular gyrus and right anterior cingulate cortex compared with the GS group (p<0.05, uncorrected). In addition, the resistance of structural covariance networks in OSAHS patients to random fault is significantly lower than that of the GS group (p=0.044).

CONCLUSION

Structural covariance networks are abnormal in terms of multiple network parameters, which provide network-level insight into the neuroimaging mechanism of cognitive impairments in adult OSAHS patients.

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.

Reduced local segregation of single-subject gray matter networks in adult PTSD

To psychoradiologically investigate the topological organization of single-subject gray matter networks in patients with PTSD. Eighty-nine adult PTSD patients and 88 trauma-exposed controls (TEC) underwent a structural T1 magnetic resonance imaging scan. The single-subject brain structural networks were constructed based on gray matter similarity of 90 brain regions. The area under the curve (AUC) of each network metric was calculated and both global and nodal network properties were measured in graph theory analysis. We used nonparametric permutation tests to identify group differences in topological metrics. Relationships between brain network measures and clinical symptom severity were analyzed in the PTSD group. Compared with TEC, brain networks of PTSD patients were characterized by decreased clustering coefficient (C_p ) (p = .04) and local efficiency (E_loc ) (p = .04). Locally, patients with PTSD exhibited altered nodal centrality involving medial superior frontal (mSFG), inferior orbital frontal (iOFG), superior parietal (SPG), middle frontal (MFG), angular, and para-hippocampal gyri (p < .05, corrected). A negative correlation between the segregation (C_p ) of gray matter and functional networks was found in PTSD patients but not the TEC group. Analyses of topological brain gray matter networks indicate a more randomly organized brain network in PTSD. The reduced segregation in gray matter networks and its negative relation with increased segregation in the functional network indicate an inverse relation between gray matter and functional changes. The present psychoradiological findings may reflect a compensatory increase in functional network segregation following a loss of segregation in gray matter networks.