Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Magnetic resonance imaging studies of maltreated children with posttraumatic stress disorder (PTSD) suggest that maltreatment-related PTSD is associated with adverse brain development. Maltreated youth resilient to chronic PTSD were not previously investigated and may elucidate neuromechanisms of the stress diathesis that leads to resilience to chronic PTSD. In this cross-sectional study, anatomical volumetric and corpus callosum diffusion tensor imaging measures were examined using magnetic resonance imaging in maltreated youth with chronic PTSD (N = 38), without PTSD (N = 35), and nonmaltreated participants (n = 59). Groups were sociodemographically similar. Participants underwent assessments for strict inclusion/exclusion criteria and psychopathology. Maltreated youth with PTSD were psychobiologically different from maltreated youth without PTSD and nonmaltreated controls. Maltreated youth with PTSD had smaller posterior cerebral and cerebellar gray matter volumes than did maltreated youth without PTSD and nonmaltreated participants. Cerebral and cerebellar gray matter volumes inversely correlated with PTSD symptoms. Posterior corpus callosum microstructure in pediatric maltreatment-related PTSD differed compared to maltreated youth without PTSD and controls. The group differences remained significant when controlling for psychopathology, numbers of Axis I disorders, and trauma load. Alterations of these posterior brain structures may result from a shared trauma-related mechanism or an inherent vulnerability that mediates the pathway from chronic PTSD to comorbidity.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

BackgroundChildren exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD.MethodsWe studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC.ResultsGraphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model’s performance.ConclusionsGraphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.

Previous neuroimaging studies have suggested that cerebral changes over normal aging are not simply characterized by regional alterations, but rather by the reorganization of cortical connectivity patterns. The investigation of structural covariance networks (SCNs) using voxel-based morphometry is an advanced approach to examining the pattern of covariance in gray matter (GM) volumes among different regions of the human cortex. To date, how the organization of critical SCNs change during normal aging remains largely unknown. In this study, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18–89 years. These participants were subdivided into young (18–23 years), middle aged (30–58 years), and older (61–89 years) subjects. Eight seed regions were chosen from widely reported functional intrinsic connectivity networks. The voxels showing significant positive associations with these seed regions were used to describe the topological organization of an SCN. All of these networks exhibited non-linear patterns in their spatial extent that were associated with normal aging. These networks, except the primary motor network, had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. The structural covariance derived using the primary motor cortex was limited to the ipsilateral motor regions in the young and older participants, but included contralateral homologous regions in the middle aged participants. In addition, there were significant between-group differences in the structural networks associated with language-related speech and semantics processing, executive control, and the default-mode network (DMN). Taken together, the results of this study demonstrate age-related changes in the topological organization of SCNs, and provide insights into normal aging of the human brain.

Predicting cognitive aging through brain structural covariance networks: A decade of longitudinal insights using source-based morphometry.

BackgroundBrain structural alterations play an important role in patients with cervical spondylotic myelopathy (CSM). However, while there have been studies on regional brain structural alterations, only few studies have focused on the topological organization of the brain structural covariance network. This work aimed to describe the structural covariance network architecture alterations that are possibly linked to cortex reorganization in patients with CSM.MethodsHigh-resolution anatomical images of 31 CSM patients and 31 healthy controls (HCs) were included in the study. The images were acquired using a sagittal three-dimensional T1-weighted BRAVO sequence. Firstly, the gray matter volume of 90 brain regions of automated anatomical labeling atlas were computed using a VBM toolbox based on the DARTEL algorithm. Then, the brain structural covariance network was constructed by thresholding the gray matter volume correlation matrices. Subsequently, the network measures and nodal property were calculated based on graph theory. Finally, the differences in the network metrics and nodal property between groups were compared using a non-parametric test.ResultsPatients with CSM showed larger global efficiency and smaller local efficiency, clustering coefficient, characteristic path length, and sigma values than HCs. Patients with CSM had greater betweenness in the left superior parietal gyrus (SPG.L) and the left supplementary motor area (SMA.L) than HCs. Besides, patients with CSM had smaller betweenness in right middle occipital gyrus. The brain structural covariance networks of CSM patients exhibited equal resilience to random failure as those of HCs. However, the maximum relative size of giant connected components was approximately 10% larger in HCs than in CSM patients, upon removal of 44 nodes in targeted attack.ConclusionThese observed alternations in global network measures in CSM patients reflect that the brain structural covariance network in CSM exhibits the less optimal small-world model compared to that in HCs. Increased betweenness in SPG.L and SMA.L seems to be related to cortex reorganization to recover multiple sensory functions after spinal cord injury in CSM patients. The network resilience of patients with CSM exhibiting a relative mild vulnerability, compared to HCs, is probably attributable to the balance and interplay between cortex reorganization and ongoing degeneration.

Prenatal stress and its association with amygdala-related structural covariance patterns in youth

Brain structural covariance networks (SCNs) based on pairwise statistical associations of cortical thickness data across brain areas reflect underlying physical and functional connections between them. SCNs capture the complexity of human brain cortex structure and are disrupted in neurodegenerative conditions. However, the longitudinal assessment of SCN dynamics has not yet been explored, despite its potential to unveil mechanisms underlying neurodegeneration. Here, we evaluated the changes of SCNs over 12 months in patients with a first inflammatory-demyelinating attack of the Central Nervous System and assessed their clinical relevance by comparing SCN dynamics of patients with and without conversion to multiple sclerosis (MS) over one year. All subjects underwent clinical and brain MRI assessments over one year. Brain cortical thicknesses for each subject and time point were used to obtain group-level between-area correlation matrices from which nodal connectivity metrics were obtained. Robust bootstrap-based statistical approaches (allowing sampling with replacement) assessed the significance of longitudinal changes. Patients who converted to MS exhibited significantly greater network connectivity at baseline than non-converters (p = 0.02) and a subsequent connectivity loss over time (p = 0.001–0.02), not observed in non-converters’ network. These findings suggest SCN analysis is sensitive to brain tissue changes in early MS, reflecting clinically relevant aspects of the condition. However, this is preliminary work, indicated by the low sample sizes, and its results and conclusions should be treated with caution and confirmed with larger cohorts.

Cognitive impairment (CI) is common in Parkinson's disease (PD). Multiple brain regions and their interactions are involved in PD associated CI. Magnetic resonance imaging (MRI) technology is a non-invasive method in investigating brain structure and inter-regional connections. In this study, by comparing cortical thickness, subcortical volume, and brain network topology properties in PD patients with and without CI, we aimed to understand the changes of brain structure and structural covariance network properties in PD associated CI. A total of 18 PD patients with CI and 33 PD patients without CI were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hoehn and Yahr stage, Mini Mental State Examination Scale, Non-motor Symptom Rating Scale, Hamilton Anxiety Scale, and Hamilton Depression Scale were assessed. All participants underwent structural 3T MRI. Cortical thickness, subcortical volume, global and nodal network topology properties were measured. Compared with PD patients without CI, the volumes of white matter, thalamus and hippocampus were lower in PD patients with CI. And decreased whole-brain local efficiency is associated with CI in PD patients. While the cortical thickness and nodal network topology properties were comparable between PD patients with and without CI. Our findings support the alterations of brain structure and disruption of structural covariance network are involved in PD associated CI, providing a new insight into the association between graph properties and PD associated CI.

Amygdala, Hippocampus, and Ventral Medial Prefrontal Cortex Volumes Differ in Maltreated Youth with and without Chronic Posttraumatic Stress Disorder.

Radiation-induced brain injury (RBI) is a common complication in patients with nasopharyngeal carcinoma (NPC) who have undergone radiotherapy (RT), which is characterized by significant cognitive and psychological impairments. Although radiation-induced regional structural abnormalities have been well-reported, the effects of RT on the whole brain structural covariance networks are mostly unknown. Here, we performed a source-based morphometry (SBM) study to solve this issue. In this cross-sectional study, 131 NPC patients with pre- and post-RT were stratified into pre-RT (n=47) and post-RT (n=84) groups. The SBM method was adopted to investigate the radiation-induced alterations in structural covariance networks in patients with NPC. Compared to the pre-RT group, our SBM analyses revealed increased z-scores in the independent component 05 (IC05; mainly located in the posterior cingulate, precuneus areas, and superior parietal lobe) (P=0.040) and decreased z-scores in the temporal-occipital network (P=0.015) and cerebellar network (P=0.023) in post-RT NPC patients. Compared to the pre-RT group, voxel-based morphometry (VBM) revealed reduced gray matter volume in the left temporal lobe, cerebellum, bilateral thalamus, left insular, and occipital lobe in the post-RT group. Notably, a significant negative correlation was observed between the mean radiation doses of the right temporal lobe and the z-score of the cerebellar network (r=-0.349, P=0.027). This present study revealed radiation-induced changes in structural covariance networks and cortical volume in patients with NPC. These findings shed some light on the neural basis of symptom patterns in RBI and may support the development of new intervention strategies to prevent progression to radiation-induced brain necrosis.

This study investigated brain structural covariance network (SCN) topological changes and alertness in temporal lobe epilepsy (TLE) with and without focal to bilateral tonic-clonic seizures (FBTCS). Seventy-eight subjects, including 32 TLE patients with FBTCS (TLE-FBTCS), 46 TLE patients without FBTCS (TLE-FS), and 42 healthy controls (HCs), underwent the Attention Network Test to assess alertness and volumetric MRI scans. SCNs were constructed and analyzed using graph theory. Results showed that TLE-FS patients had lower total cerebral volume than HCs, and the lowest volume was observed in the TLE-FBTCS group. Compared to HCs and TLE-FBTCS patients, TLE-FS patients exhibited increased small-worldness, normalized clustering coefficient, global efficiency, and modularity, but decreased normalized characteristic shortest path length and assortativity. Specific brain regions, such as the hippocampus, thalamus, and superior temporal sulcus, showed changes in nodal clustering coefficients and efficiency in TLE-FS patients. Further analysis revealed decreased intrinsic/phasic alertness in TLE-FBTCS patients. Correlation analysis indicated that SCN topological properties were associated with alertness in TLE-FS patients but not in TLE-FBTCS patients. These findings suggest that TLE-FS and TLE-FBTCS patients show different changes in SCN integration and segregation, with TLE-FS alertness linked to SCN topological properties, providing insights into TLE's neuropathological mechanisms.