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

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

Abnormal structural covariance network in major depressive disorder: Evidence from the REST-meta-MDD project

Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder

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

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

Major depressive disorder (MDD) is a devastating mental disorder characterized by considerable clinical and biological heterogeneity. While comparable clinical symptoms may represent a common pathological endpoint, it is conceivable that distinct neurophysiological mechanisms underlie their manifestation. In this study, both static and model-based dynamic functional connectivity were employed as predictive variables in the normative model to map multilevel functional developmental trajectories and determined clusters of distinguishable MDD subgroups in a large multi-site resting fMRI dataset of 2428 participants (healthy controls: N = 1128; MDD: N = 1300). An independent cohort of 72 participants (healthy controls: N = 35; MDD: N = 37) with both resting fMRI and task-based fMRI data was utilized to validate the identified MDD subtypes and explore subtype-specific task-based neural representations. Our findings indicated brain-wide, interpatient heterogeneous multilevel brain functional deviations in MDD. We identified two distinct and reproducible MDD subtypes, exhibiting comparable severity of clinical symptoms but opposing patterns of multilevel functional deviations. Specifically, MDD subtype 1 displayed positive deviations in the frontoparietal and default mode networks, coupled with negative deviations in the occipital and sensorimotor networks. Conversely, MDD subtype 2 exhibited a significantly contrasting deviation pattern. Additionally, we found that these two identified MDD subtypes exhibited different neural representations during empathic processing, while the subtypes did not differ during implicit face processing. These findings underscore the neurobiological complexity of MDD and highlights the need for a multifaceted approach to diagnosis and treatment that can be tailored specifically to individual subtypes, facilitating personalized and more effective interventions for individuals with MDD.

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.

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.

Reward dysfunction in major depression: Multimodal neuroimaging evidence for refining the melancholic phenotype

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

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

Depression, also known as major depressive disorder (MDD), is characterized by significant and persistent MDD. The pathogenesis of MDD is complex; some molecular markers have been repeatedly reported in MDD studies, hopefully making them reliable indicators of MDD. This study aimed to explore the correlation between each subtype of MDD. MDD data sets and the corresponding clinical data were collected using GEO database. Consensus cluster analysis was used to classify the MDD cases, using the WGCNA (Weighted gene co-expression network analysis) modules were identified using the dynamic tree cutting segmentation module and clinical traits, respectively. Based on the relationship between the color differentiation module and clinical characteristics, GSEA (Gene Set Enrichment Analysis) was used to screen the genes with a higher degree of enrichment in the differentially expressed upregulated genes between each subgroup and the normal control, and to analyze their molecular biological function and the related pathways. We divided the MDD cases into three subgroups, the WGCNA analysis based on the subtype-specific characteristics showed that six WGCNA modules were correlated with the clinical characteristics. GSEA was used to screen the expression of MARCKS, TAAR1, and ITGB1, among other molecular markers. The main metabolic pathways in which these molecular markers participate include axon guidance, bacterial invasion of epithelial cells, prion infection, and pyrimidine metabolism. Cases from different subgroups may have their specific or dominant gene expression patterns, and that the biomarker levels can help assess the severity of MDD, predict outcomes, or guide clinical treatment.

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.

Partially distinct combinations of psychological, metabolic and inflammatory risk factors are prospectively associated with the onset of the subtypes of Major Depressive Disorder in midlife