Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

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

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.

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.

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

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.

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.

Numerous neuroimaging studies investigating the neural substrates of obsessive-compulsive disorder (OCD) have yielded inconsistent findings, and growing evidence suggests that psychiatric disorders are more accurately localized to brain networks rather than discrete brain regions. We sought to identify brain network localization in OCD. We initially examined brain locations of structural and functional alterations among patients with OCD and healthy controls using neuroimaging studies. Employing a novel technique called functional connectivity network mapping (FCNM) and large-scale human brain connectome data, we mapped these damaged brain regions to 2 brain impairment networks in OCD. We included 62 neuroimaging studies involving 2578 patients with OCD and 2502 healthy controls. For FCNM, we used data from 556 healthy adults. Among patients with OCD, the grey matter volume (GMV) and resting-state activity impairment networks encompassed a broad range of brain regions, primarily involving the default mode, sensorimotor, and limbic networks, as well as the bilateral middle frontal gyrus and bilateral middle temporal gyrus. Additionally, the GMV impairment network specifically involved bilateral inferior frontal gyrus. We used large-scale human brain connectome data from healthy people, rather than the samples clinically and demographically matched to the original study participants, to examine brain networks in OCD. Our study integrated an FCNM method with large-scale human brain connectome data to map heterogeneous abnormal brain locations of OCD to structural and functional impairment networks. Our findings deepen our understanding of the neuropathological mechanisms of OCD from a network perspective and may inform future neuromodulation treatment.

BackgroundThe extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood.MethodsUsing magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small‐worldness index, were evaluated.ResultsCompared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs (p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties (all p > 0.05). The real‐worldness SCN of ULAs showed a small‐world topology ranging from 2% to 34%, and the area under the curve of the small‐worldness index in ULAs was significantly different compared to HCs (p < 0.001).ConclusionThese results suggest that the topological organization of human CNS functional networks is altered after amputation of the upper limb, providing further support for the cortical remapping theory of PLP.

Introduction:Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD).Method:We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14–50, IQ=92–132), 16 AS (age=7–58, IQ=93–133) and 28 HC (age=9–39, IQ=95–144).Result:The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD.Conclusion:This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

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.

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.

Carpal tunnel syndrome (CTS) is a common peripheral entrapment neuropathy. However, CTS-related changes of brain structural covariance and structural covariance networks (SCNs) patterns have not been clearly studied. To explore CTS-related brain changes from perspectives of structural connectivity and SCNs. Brain structural magnetic resonance images were acquired from 27 CTS patients and 19 healthy controls (HCs). Structural covariance and SCNs were constructed based on gray matter volume. The global network properties including clustering coefficient (Cp), characteristic path length (Lp), small-worldness index, global efficiency (Eglob), and local efficiency (Eloc) and regional network properties including degree, betweenness centrality (BC), and Eloc of a given node were calculated with graph theoretical analysis. Compared with HCs, the strength of structural connectivity between the dorsal anterior insula and medial prefrontal thalamus decreased (P <.001) in CTS patients. There was no intergroup difference of area under the curve for Cp, Lp¸ Eglob, and Eloc (all P >.05). The real-world SCN of CTS patients showed a small-world topology ranging from 2% to 32%. CTS patients showed lower nodal degrees of the dorsal anterior insula and medial prefrontal thalamus, and higher Eloc of a given node and BC in the lateral occipital cortex (P <.001) and the dorsolateral middle temporal gyrus (P <.001) than HCs, respectively. CTS had a profound impact on brain structures from perspectives of structural connectivity and SCNs.

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

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

Morphological alterations of brain structure are generally assumed to be involved in the pathophysiology of obsessive–compulsive disorder (OCD). Yet, little is known about the morphological connectivity properties of structural brain networks in OCD or about the heritability of those morphological connectivity properties. To better understand these properties, we conducted a study that defined three different groups: OCD group with 30 subjects, siblings group with 19 subjects, and matched controls group with 30 subjects. A structural brain network was constructed using 68 cortical regions of each subject within their respective group (i.e., one brain network for each group). Both small-worldness and modularity were measured to reflect the morphological connectivity properties of each constructed structural brain network. When compared to the matched controls, the structural brain networks of patients with OCD indeed exhibited atypical small-worldness and modularity. Specifically, small-worldness showed decreased local efficiency, and modularity showed reduced intra-connectivity in Module III (default mode network) and increased interconnectivity between Module I (executive function) and Module II (cognitive control/spatial). Intriguingly, the structured brain networks of the unaffected siblings showed similar small-worldness and modularity as OCD patients. Based on the atypical structural brain networks observed in OCD patients and their unaffected siblings, abnormal small-worldness and modularity may indicate a candidate endophenotype for OCD.