Background and PurposeAccording to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress.Materials and MethodsStructural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01).ResultsOur findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas.ConclusionOur results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.

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.

Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.

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

This study investigated brain morphometry changes associated with fatigue severity in fibromyalgia (FM). Clinical profiles and brain-MRI data were collected in patients with FM. Patients were divided into three groups based on their fatigue severity. Using voxel-based morphometry analysis and trend analysis, neural substrates showing volumetric changes associated with fatigue severity across the three groups were identified. Their seed-to-voxel structural covariance (SC) networks with the whole brain were studied in distribution and strength. Among the 138 enrolled patients with FM, 23, 57, and 58 were categorised into the mild, moderate, and severe fatigue groups, respectively. The number of musculoskeletal pain regions and intensity of pain were not associated with fatigue severity, but somatic symptoms and psychiatric distress, including waking unrefreshed, depression, and anxiety, were associated with fatigue severity. After adjusting for anxiety and depression, decreased bilateral thalamic volumes were associated with higher fatigue severity. The SC distributions of the thalamic seed were more widespread to the frontal, parietal, subcortical, and limbic regions in patients with higher fatigue severity. In addition, increased right inferior temporal cortex volumes were associated with higher fatigue severity. The SC distributions of the right inferior temporal seed were more over the temporal cortex and the SC strengths of the seed were higher with the bilateral occipital cortex in patients with higher fatigue severity. The thalamus and the right inferior temporal cortex are implicated in the manifestation of fatigue severity in FM. Future therapeutic strategies targeting these regions are worthy of investigation.

In‐scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion‐affected and low‐motion whole brain T1‐weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel‐based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion‐affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion‐affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in‐scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

ObjectivesThis research amid to elucidate the disease stage-specific spatial patterns and the probable sequences of gray matter (GM) deterioration as well as the causal relationship among structural network components in hepatitis B virus-related cirrhosis (HBV-RC) patients.MethodsTotally 30 HBV-RC patients and 38 healthy controls (HC) were recruited for this study. High-resolution T1-weighted magnetic resonance imaging and psychometric hepatic encephalopathy score (PHES) were evaluated in all participants. Voxel-based morphometry (VBM), structural covariance network (SCN), and causal SCN (CaSCN) were applied to identify the disease stage-specific GM abnormalities in morphology and network, as well as their causal relationship.ResultsCompared to HC (0.443 ± 0.073 cm3), the thalamus swelled significantly in the no minimal hepatic encephalopathy (NMHE) stage (0.607 ± 0.154 cm3, p <0.05, corrected) and further progressed and expanded to the bilateral basal ganglia, the cortices, and the cerebellum in the MHE stage (p < 0.05, corrected). Furthermore, the thalamus swelling had a causal effect on other parts of cortex-basal ganglia-thalamus circuits (p < 0.05, corrected), which was negatively correlated with cognitive performance (r = −0.422, p < 0.05). Moreover, the thalamus-related SCN also displayed progressive deterioration as the disease advanced in HBV-RC patients (p < 0.05, corrected).ConclusionProgressive deterioration of GM morphology and SCN exists in HBV-RC patients during advanced disease, displaying thalamus-related causal effects. These findings indicate that bilateral thalamus morphology as well as the thalamus-related network may serve as an in vivo biomarker for monitoring the progression of the disease in HBV-RC patients.

P657. Cerebellum Structural Covariance Networks in PTSD and Depression

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.

The high heterogeneity of obsessive-compulsive disorder (OCD) denies attempts of traditional case-control studies to derive neuroimaging biomarkers indicative of precision diagnosis and treatment. To handle the heterogeneity, we uncovered subject-level altered structural covariance by adopting individualized differential structural covariance network (IDSCN) analysis. The IDSCN measures how structural covariance edges in a patient deviated from those in matched healthy controls (HCs) yielding subject-level differential edges. One hundred patients with OCD and 106 HCs were recruited and whose T1-weighted anatomical images were acquired. We obtained individualized differential edges and then clustered patients into subtypes based on these edges. Patients presented tremendously low overlapped altered edges while frequently shared altered edges within subcortical-cerebellum network. Two robust neuroanatomical subtypes were identified. Subtype 1 presented distributed altered edges while subtype 2 presented decreased edges between default mode network and motor network compared with HCs. Altered edges in subtype 1 predicted the total Yale-Brown Obsessive Compulsive Scale score while that in subtype 2 could not. We depict individualized structural covariance aberrance and identify that altered connections within subcortical-cerebellum network are shared by most patients with OCD. These 2 subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of OCD.

Schizophrenia originates early in neurodevelopment, underscoring the need to elaborate on anomalies in the still maturing brain of early-onset schizophrenia (EOS). Gray matter (GM) volumes were evaluated in 94 antipsychotic-naïve first-episode EOS patients and 100 typically developing (TD) controls. The anatomical profiles of changing GM deficits in EOS were detected using 2-way analyses of variance with diagnosis and age as factors, and its timing was further charted using stage-specific group comparisons. Interregional relationships of GM alterations were established using structural covariance network analyses. Antagonistic interaction results suggested dynamic GM abnormalities of the left fusiform gyrus, inferior occipital gyrus, and lingual gyrus in EOS. These regions comprise a dominating part of the ventral stream, a ventral occipitotemporal (vOT) network engaged in early social information processing. GM abnormalities were mainly located in the vOT regions in childhood-onset patients, whereas in the rostral prefrontal cortex (rPFC) in adolescent-onset patients. Moreover, compared with TD controls, patients' GM synchronization with the ventral stream was disrupted in widespread high-order social perception regions including the rPFC and salience network. The current findings reveal age-related anatomical abnormalities of the social perception system in pediatric patients with schizophrenia.

Morphometric studies demonstrated wide-ranging distribution of brain structural abnormalities in major depressive disorder (MDD). This study explored the progressive gray matter volume (GMV) changes pattern of structural network in 108 MDD patients throughout the illness duration by using voxel-based morphometric analysis. The causal structural covariance network method was applied to map the causal effects of GMV alterations between the original source of structural changes and other brain regions as the illness duration prolonged in MDD. This was carried out by utilizing the Granger causality analysis to T1-weighted data ranked based on the disease progression information. With greater illness duration, the GMV reduction was originated from the right insula and progressed to the frontal lobe, and then expanded to the occipital lobe, temporal lobe, dorsal striatum (putamen and caudate) and the cerebellum. Importantly, results revealed that the right insula was the prominent node projecting positive causal influences (i.e., GMV decrease) to frontal lobe, temporal lobe, postcentral gyrus, putamen, and precuneus. While opposite causal effects were detected from the right insula to the angular, parahippocampus, supramarginal gyrus and cerebellum. This work may provide further information and vital evidence showing that MDD is associated with progressive brain structural alterations.

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Major depressive disorder (MDD) is a psychiatric disorder with a relatively limited response to treatment. It is necessary to better understand the neuroanatomical mechanisms of structural networks. The current study recruited 181 first-onset, untreated adult MDD patients: slight MDD (SD, N = 23), moderate MDD (MD, N = 77), Heavy MDD (HD, N = 81) groups; along with a healthy control group (HC, N = 81) with matched general clinical data. FreeSurfer was used to preprocess T1 images for gray matter volume (GMV), and the default mode network (DMN) and the execution control network (ECN) were analyzed by structural covariance network (SCN). Present study found that the GMV of brain regions reduced with the severity of the disease. Specifically, the GMV of the left anterior cingulate gyrus (ACC.L) is negatively correlated with MDD severity. In addition, the SCN connectivity of the whole-brain network increases with the increase of severity in MDD. ACC.L is a key brain region with increased connectivity between the left orbitofrontal in DMN and between the right orbitofrontal in ECN, which leads to damage to the balance of neural circuits. Patients with smaller GMV of ACC.L are more likely to develop severe MDD, and as a key region in both networks which have distinct structural network models in DMN and ECN. MDD patients with different severity have different neuroimaging changes in DMN and ECN.

ObjectiveAdult patients with major depressive disorder (MDD) may not actively reveal their suicidal ideation (SI). Therefore, this study is committed to finding the alterations in the cingulo-opercular network (CON) that are closely related to SI with multi-imaging methods, thus providing neuroimaging basis for SI.MethodA total of 198 participants (129 MDD patients and 69 healthy controls) were recruited and evaluated with the Montgomery–Asberg Depression Rating Scale (MADRS). The healthy individuals formed the HC group, while the MDD patients were subdivided into no SI MDD (NSI, n = 32), mild SI MDD (MSI, n = 64), and severe SI MDD (SSI, n = 33) according to their MADRS item 10. We obtained MRI data of all participants and applied regional homogeneity (ReHo) analysis to verify a previous finding that links CON abnormality to SI. In addition, we employed the structural covariance network (SCN) analysis to investigate the correlation between abnormal structural connectivity of CON and SI severity.ResultsCompared to those of the HC group, MDD ReHo values and gray matter volume (GMV) were consistently found abnormal in CON. ReHo values and GMV of the right orbital inferior frontal gyrus (ORBinf.R) in the MDD group decreased with the increase of SI. Compared to the HC group, the MDD patients showed enhanced structural connectivity of three pairs of brain regions in CON [ACC.L–left superior frontal gyrus (SFG.L), SFG.L–left middle temporal gyrus (MTG.L), and the SFG.L–left post-central gyrus (PoCG.L)]. Compared with that of the NSI and MSI groups, the structural connectivity of three pairs of brain regions in CON is enhanced in the SSI groups [ORBinf.L–right ventral posterior cingulate gyrus (VPCC.R), VPCC.R–SFG.R, and SFG.R–PoCG.R].ConclusionOur findings showed the distinctive ReHo, GMV, and SCN pattern of CON in MDD patients with SI; and with the severity of suicide, abnormal brain regions increased. Our finding suggested that MDD patients with different severity of SI have different neuroimaging changes.

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.

The Cortico-basal-cerebellar Neurocircuit is Linked to Personality Trait of Novelty Seeking

Patterns of a structural covariance network associated with dispositional optimism during late adolescence

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices. Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations. VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network. Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

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.