Brain Structural Covariance Networks Research Articles

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

BackgroundAlthough brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood. MethodsUsing a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression. ResultsGroup comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling. ConclusionsThese findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

Changes of brain structure and structural covariance networks in Parkinson's disease associated cognitive impairment.

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.

Brain structural covariance network features are robust markers of early heavy alcohol use.

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. Graph theory metrics of segregation and integration were used to summarize SCN. Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Altered topology of individual brain structural covariance networks in major depressive disorder.

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis.

The 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. Using 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. Compared 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). These 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.

Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis.

ObjectiveIschemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.MethodForty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).ResultsMost of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.ConclusionsBoth global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.

Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities

Synergistic effects of amyloid deposition and cerebral small vessel disease (CSVD) on the systematic disruption of large-scale brain anatomical organization are not well known. We investigated the brain structural covariance network (SCN) in 245 cognitively impaired older adults with the information of amyloid deposition and CSVD represented by white matter hyperintensities (WMH). We stratified the participants into 4 groups based on amyloid burden (A+/A -) and WMH severity (W+/W-). Using source-based morphometry analysis, we selected 13 independent components (ICs) in functional brain networks. SCNs between ICs were defined using Pearson correlations between individual weights; SCNs of the A+W+ group were compared with those of other groups using Fisher's r-to-z transformation. Our results revealed that SCN characteristics related to amyloid burden with CSVD could be represented by decreased intra- and increased cortico-subcortical inter-network connectivity in the salience (SN) and default mode networks (DMN), decreased cortico-subcortical internetwork connectivity in the central executive network (CEN), and altered internetwork connectivity among DMN-SN-CEN. Amyloid deposition and CSVD maybe associated with altered connectivity in structural networks in the brain and should be considered when assessing network disruption.

Subtly Altered Topological Asymmetry of Brain Structural Covariance Networks in Autism Spectrum Disorder Across 43 Datasets From the Enigma Consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture coordinated maturational-trophic networks between different cortical areas at macroscale.

Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents.

Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048). Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium’s ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

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

BackgroundPrenatal stress influences brain development and mood disorder vulnerability. Brain structural covariance network (SCN) properties based on inter-regional volumetric correlations may reflect developmentally-mediated shared plasticity among regions. Childhood trauma is associated with amygdala-centric SCN reorganization patterns, however, the impact of prenatal stress on SCN properties remains unknown. MethodsThe study included participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) with archival prenatal stress data and structural MRI acquired in young adulthood (age 23–24). SCNs were constructed based on Freesurfer-extracted volumes of 7 subcortical and 34 cortical regions. We compared amygdala degree centrality, a measure of hubness, between those exposed to high vs. low (median split) prenatal stress, defined by maternal reports of stressful life events during the first (n = 93, 57% female) and second (n = 125, 54% female) half of pregnancy. Group differences were tested across network density thresholds (5–40%) using 10,000 permutations, with sex and intracranial volume as covariates, followed by sex-specific analyses. Finally, we sought to replicate our results in an independent all-male sample (n = 450, age 18–20) from the Avon Longitudinal Study of Parents and Children (ALSPAC). ResultsThe high-stress during the first half of pregnancy ELSPAC group showed lower amygdala degree particularly in men, who demonstrated this difference at 10 consecutive thresholds, with no significant differences in global network properties. At the lowest significant density threshold, amygdala volume was positively correlated with hippocampus, putamen, rostral anterior and posterior cingulate, transverse temporal, and pericalcarine cortex in the low-stress (p(FDR) < 0.027), but not the high-stress (p(FDR) > 0.882) group. Although amygdala degree was nominally lower across thresholds in the high-stress ALSPAC group, these results were not significant. ConclusionUnlike childhood trauma, prenatal stress may shift SCN towards a less amygdala-centric SCN pattern, particularly in men. These findings did not replicate in an all-male ALSPAC sample, possibly due to the sample’s younger age and lower prenatal stress exposure.

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.

Brain Structural Changes in Carpal Tunnel Syndrome Patients: From the Perspectives of Structural Connectivity and Structural Covariance Network.

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.

Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis

BackgroundPostpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis. MethodsHigh-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork. ResultsThe PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network. ConclusionsThis study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder.

Associations Between Neonatal Brain Structure, the Home Environment, and Childhood Outcomes Following Very Preterm Birth

BackgroundVery preterm birth is associated with an increased risk of childhood psychopathology and cognitive deficits. However, the extent to which these developmental problems associated with preterm birth are amenable to environmental factors or determined by neurobiology at birth remains unclear. MethodsWe derived neonatal brain structural covariance networks using non-negative matrix factorization in 384 very preterm infants (median gestational age [range], 30.29 [23.57–32.86] weeks) who underwent magnetic resonance imaging at term-equivalent age (median postmenstrual age, 42.57 [37.86–44.86] weeks). Principal component analysis was performed on 32 behavioral and cognitive measures assessed at preschool age (n = 206; median age, 4.65 [4.19–7.17] years) to identify components of childhood psychopathology and cognition. The Cognitively Stimulating Parenting Scale assessed the level of cognitively stimulating experiences available to the child at home. ResultsCognitively stimulating parenting was associated with reduced expression of a component reflecting developmental psychopathology and executive dysfunction consistent with the preterm phenotype (inattention-hyperactivity, autism spectrum behaviors, and lower executive function scores). In contrast, a component reflecting better general cognitive abilities was associated with larger neonatal gray matter volume in regions centered on key nodes of the salience network, but not with cognitively stimulating parenting. ConclusionsOur results suggest that while neonatal brain structure likely influences cognitive abilities in very preterm children, the severity of behavioral symptoms that are typically observed in these children is sensitive to a cognitively stimulating home environment. Very preterm children may derive meaningful mental health benefits from access to cognitively stimulating experiences during childhood.

Morphological Development Trajectory and Structural Covariance Network of the Human Fetal Cortical Plate during the Early Second Trimester.

During the early second trimester, the cortical plate, or "the developing cortex", undergoes immensely complex and rapid development to complete its major complement of neurons. However, morphological development of the cortical plate and the precise patterning of brain structural covariance networks during this period remain unexplored. In this study, we used 7.0T high-resolution magnetic resonance images of brain specimens ranging from 14 to 22 gestational weeks to manually segment the cortical plate. Thickness, area expansion, and curvature (i.e., folding) across the cortical plate regions were computed, and correlations of thickness values among different cortical plate regions were measured to analyze fetal cortico-cortical structural covariance throughout development of the early second trimester. The cortical plate displayed significant increases in thickness and expansions in area throughout all regions but changes of curvature in only certain major sulci. The topological architecture and network properties of fetal brain covariance presented immature and inefficient organizations with low degree of integration and high degree of segregation. Altogether, our results provide novel insight on the developmental patterning of cortical plate thickness and the developmental origin of brain network architecture throughout the early second trimester.

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

BackgroundIdentifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. MethodsThis study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. ResultsSource-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. ConclusionsOverall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Altered Single-Subject Gray Matter Structural Networks in Drug-Naïve ADHD 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 may have been overlooked.

BCCT: A GUI Toolkit for Brain Structural Covariance Connectivity Analysis on MATLAB.

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Differences in Brain Structural Covariance Network Characteristics in Children and Adults With Autism Spectrum Disorder.

Systematically describing the structural topological configuration of human brain during development is an essential task. Autism spectrum disorder (ASD) represents a powerful challenge for psychiatry and neuroscience researchers. In this study, we investigated variations in the structural covariance network properties of 441 patients with ASD ranging in age from 7 to 45 years and in 426 age-matched healthy controls (HCs) using structural magnetic resonance neuroimaging from the ABIDE database. We applied a sliding window approach to study topological variation during development using comprehensive graph theoretical analysis. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) the HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed. We used cross-sectional analysis to infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. Our findings are consistent with the notion that adolescence is a sensitive period of brain development with strong potential for brain plasticity, offering opportunities for environmental adaptation and social integration and for increasing vulnerability. ASD may be a product of susceptibility. LAY SUMMARY: We used cross-sectional analysis to preliminarily infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) Network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) The HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed.