Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10-4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10-3; p = 7.67 × 10-4) and right insula (p = 0.02; p = 8.99 × 10-6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10-5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = -0.36, p = 8.45 × 10-3) and small-worldness (r = -0.35, p = 9.89 × 10-3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Individualized Texture Similarity Network in Schizophrenia

BACKGROUND

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

METHODS

The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants).

RESULTS

The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes.

CONCLUSIONS

These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Cumulative Effect of Psychosis and Aging on Cognitive Function in Patients Diagnosed With Schizophrenia Spectrum Disorders: A Cognitive Domain Approach

BACKGROUND

Schizophrenia spectrum disorders are characterized by cognitive decline, which is evident even in the prodromal phase. Aging is a complex gradual procedure that affects, among other organs, the central nervous system, resulting in age-related cognitive decline.

OBJECTIVE

The objective of this study is to assess the cognitive function of patients diagnosed with psychotic disorders, in comparison with healthy controls, along the age spectrum.

METHODS

Sixty patients diagnosed with schizophrenia spectrum disorders in remission, 20-59 years old, and 60 healthy controls, matched by age and educational level, from the region of Thessaly in Central Greece, were evaluated, with respect to their cognitive performance, using the Greek version of the Montreal Cognitive Assessment (MoCA). Correlations between age and MoCA total and cognitive domains' scores, as well as statistical analysis of variance (ANOVA) and t-test among age groups, were performed using Statistical Product and Service Solutions (SPSS, version 23; IBM SPSS Statistics for Windows, Armonk, NY).

RESULTS

The MoCA score was negatively correlated with age, both in the patients' group (p<0.001) and in the control group (p=0.001). A significant statistical difference in mean MoCA scores between patients and healthy controls was observed, not only in the total sample (p<0.001) but also in all age groups (20-29: p=0.006, 40-49: p=0.024, 50-59: p<0.001), except for age group 30-39 (30-39: p=0.356). Statistically significant differences were also found between patients and healthy controls in the total sample, regarding specific cognitive domains, in the visuospatial and executive function domain (p=0.01), attention domain (p<0.001), language domain (p<0.001), and orientation domain (p<0.005). Interestingly, different deterioration patterns in cognitive domains were observed in each age group. Specifically, in the age group 20-29, statistically significant differences were found between patients and healthy controls in the language domain (p<0.014) and orientation domain (p<0.041). No difference was found in the age group 30-39, while statistically significant differences were found between patients and healthy controls in the age group 40-49 in the attention domain (p<0.001) and language domain (p<0.001). Finally, in the age group 50-59, such differences were found in the visuospatial and executive function domain (p=0.041), attention domain (p=0.006), and language domain (p=0.001). Statistically significant cognitive decline occurs in a shorter period in the patients' group, suggesting an accelerated cognitive decline in psychotic patients after middle age.

CONCLUSIONS

Age-related cognitive decline in psychotic patients occurs at an accelerated rate in relation to the control sample, with age-specific cognitive domain decline patterns, due to the cumulative effect of aging and psychosis on cognition. Further, larger, multicenter research should focus on establishing these results and designing relevant procognitive interventions.

Similarity between structural and proxy estimates of brain connectivity

Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.

Abnormal Topological Organization of Structural Covariance Networks in Patients with Temporal Lobe Epilepsy Comorbid Sleep Disorder

OBJECTIVE

The structural covariance network (SCN) alterations in patients with temporal lobe epilepsy and comorbid sleep disorder (PWSD) remain poorly understood. This study aimed to investigate changes in SCNs using structural magnetic resonance imaging.

METHODS

Thirty-four PWSD patients, thirty-three patients with temporal lobe epilepsy without sleep disorder (PWoSD), and seventeen healthy controls underwent high-resolution structural MRI imaging. Subsequently, SCNs were constructed based on gray matter volume and analyzed via graph-theoretical approaches.

RESULTS

PWSD exhibited significantly increased clustering coefficients, shortest path lengths, transitivity, and local efficiency. In addition, various distributions and numbers of SCN hubs were identified in PWSD. Furthermore, PWSD networks were less robust to random and target attacks than those of healthy controls and PWoSD patients.

CONCLUSION

This study identifies aberrant SCN changes in PWSD that may be related to the susceptibility of patients with epilepsy to sleep disorders.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes

INTRODUCTION

In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).

METHODS

We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.

RESULTS

As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.

DISCUSSION

Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Network biomarkers in recovered psychosis patients who discontinued antipsychotics

There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal "attacks" (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Increased cortical structural covariance correlates with anhedonia in schizophrenia

Anhedonia is a common symptom in schizophrenia and is closely related to poor functional outcomes. Several lines of evidence reveal that the orbitofrontal cortex plays an important role in anhedonia. In the present study, we aimed to investigate abnormalities in structural covariance within the orbitofrontal subregions, and to further study their role in anticipatory and consummatory anhedonia in schizophrenia. T1 images of 35 schizophrenia patients and 45 healthy controls were obtained. The cortical thickness of 68 cerebral regions parcellated by the Desikan-Killiany (DK) atlas was calculated. The structural covariance within the orbitofrontal subregions was calculated in both schizophrenia and healthy control groups. Stepwise linear regression was performed to examine the relationship between structural covariance and anhedonia in schizophrenia patients. Patients with schizophrenia exhibited higher structural covariance between the left and right medial orbitofrontal thickness, the left lateral orbitofrontal thickness and left pars orbitalis thickness compared to healthy controls (p < 0.05, FDR corrected). This results imply that the increased structural covariance in orbitofrontal thickness may be involved in the process of developing anhedonia in schizophrenia. The result indicated that the increased structural covariance between the left and right medial orbitofrontal thickness might be a protective factor for anticipatory pleasure (B' = 0.420, p = 0.012).

Structural covariance predictors of clinical improvement at 2-year follow-up in first-episode psychosis

The relationship between structural brain alterations and prediction of clinical improvement in first-episode psychosis (FEP) has been scarcely studied. We investigated whether structural covariance, a well-established approach to identify abnormal patterns of volumetric correlation across distant brain regions, which allows incorporating network-level information to structural assessments, is associated with longitudinal clinical course. We assessed a sample of 74 individuals from a multicenter study. Magnetic resonance imaging scans were acquired at baseline, and clinical assessments at baseline and at a 2-year follow-up. Participants were split in two groups as a function of their clinical improvement after 2 years (i.e., ≥ < 40% reduction in psychotic symptom severity, (n = 29, n = 45)). We performed a seed-based approach and focused our analyses on 3 cortical and 4 subcortical regions of interest to identify alterations in cortical and cortico-subcortical networks. Improvers presented an increased correlation between the volumes of the right posterior cingulate cortex (PCC) and the left precentral gyrus, and between the left PCC and the left middle occipital gyrus. They also showed an increased correlation between right posterior hippocampus and left angular gyrus volumes. Our study provides a novel mean to identify structural correlates of clinical improvement in FEP, describing clinically-relevant anatomical differences in terms of large-scale brain networks, which is better aligned with prevailing neurobiological models of psychosis. The results involve brain regions considered to participate in the multisensory processing of bodily signals and the construction of bodily self-consciousness, which resonates with recent theoretical accounts in psychosis research.

Progressive brain abnormalities in schizophrenia across different illness periods: a structural and functional MRI study

Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12-18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.

A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.

Altered Surface Area Covariance in the Mentalizing Network in Schizophrenia: Insight Into Theory of Mind Processing

BACKGROUND

Theory of mind (ToM), the cognitive capacity to attribute mental states to self and others, is robustly affected in schizophrenia. The neural substrates of ToM impairment have been largely studied with functional imaging, but little is known about structural abnormalities. We compared structural covariance (between-subjects correlations of brain regional measures) of magnetic resonance imaging-based cortical surface area between patients with schizophrenia and healthy control subjects and between schizophrenia subgroups based on the patients' ToM ability to examine ToM-specific effects on structural covariance in schizophrenia.

METHODS

T1-weighted structural images were acquired on a 3T magnetic resonance imaging scanner, and ToM was assessed with the Hinting Task for 104 patients with schizophrenia and 69 healthy control subjects. The sum of surface area was computed for 12 regions of interest selected and compared between groups to examine structural covariance within the often reported mentalizing network: rostral and caudal middle frontal gyrus, inferior parietal lobule, precuneus, and middle and superior temporal gyrus. High and low ToM groups were defined using a median split on the Hinting Task.

RESULTS

Cortical surface contraction was observed in the schizophrenia group, predominantly in temporoparietal regions. Patients with schizophrenia also exhibited significantly stronger covariance between the right rostral middle frontal gyrus and the right superior temporal gyrus than control subjects (r = 4.015; p < .001). Direct comparisons between high and low ToM subgroups revealed stronger contralateral frontotemporal covariances in the low ToM group.

CONCLUSIONS

Our results provide evidence for structural changes underlying ToM impairments in schizophrenia that need to be confirmed to develop new therapeutic perspectives.

Structural covariance networks in schizophrenia: A systematic review Part II

BACKGROUND

Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.

METHODS

A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.

RESULTS

A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.

CONCLUSIONS

Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties

It has been proposed recently that major depressive disorder (MDD) could represent an adaptation to conserve energy after the perceived loss of an investment in a vital source, such as group identity, personal assets, or relationships. Energy conserving behaviors associated with MDD may form a persistent marker in brain regions and networks involved in cognition and emotion regulation. In this study, we examined whether subcortical regions and volume-based structural covariance networks (SCNs) have state-independent alterations (trait markers). First-episode drug-naïve currently depressed (cMDD) patients (N = 131), remitted MDD (RD) patients (N = 67), and healthy controls (HCs, N = 235) underwent structural magnetic resonance imaging (MRI). Subcortical gray matter volumes (GMVs) were calculated in FreeSurfer software, and group differences in GMVs and SCN were analyzed. Compared to HCs, major findings were decreased GMVs of left pallidum and pulvinar anterior of thalamus in the cMDD and RD groups, indicative of a trait marker. Relative to HCs, subcortical SCNs of both cMDD and RD patients were found to have reduced small-world-ness and path length, which together may represent a trait-like topological feature of depression. In sum, the left pallidum, left pulvinar anterior of thalamus volumetric alterations may represent trait marker and reduced small-world-ness, path length may represent trait-like topological feature of MDD.

Antipsychotics Effects on Network-Level Reconfiguration of Cortical Morphometry in First-Episode Schizophrenia

Cortical thickness reductions are evident in schizophrenia (SZ). Associations between antipsychotic medications (APMs) and cortical morphometry have been explored in SZ patients. This raises the question of whether the reconfiguration of morphological architecture by APM plays potential compensatory roles for abnormalities in the cerebral cortex. Structural magnetic resonance imaging was obtained from 127 medication-naive first-episode SZ patients and 133 matched healthy controls. Patients received 12 weeks of APM and were categorized as responders (n = 75) or nonresponders (NRs, n = 52) at follow-up. Using surface-based morphometry and structural covariance (SC) analysis, this study investigated the short-term effects of antipsychotics on cortical thickness and cortico-cortical covariance. Global efficiency was computed to characterize network integration of the large-scale structural connectome. The relationship between covariance and cortical thinning was examined by SC analysis among the top-n regions with thickness reduction. Widespread cortical thickness reductions were observed in pre-APM patients. Post-APM patients showed more reductions in cortical thickness, even in the frontotemporal regions without baseline reductions. Covariance analysis revealed strong cortico-cortical covariance and higher network integration in responders than in NRs. For the NRs, some of the prefrontal and temporal nodes were not covariant between the top-n regions with cortical thickness reduction. Antipsychotic effects are not restricted to a single brain region but rather exhibit a network-level covariance pattern. Neuroimaging connectomics highlights the positive effects of antipsychotics on the reconfiguration of brain architecture, suggesting that abnormalities in regional morphology may be compensated by increasing interregional covariance when symptoms are controlled by antipsychotics.

Insight does not come at random: Individual gray matter networks relate to clinical and cognitive insight in schizophrenia

BACKGROUND

Impaired clinical and cognitive insight are prevalent in schizophrenia and relate to poorer outcome. Good insight has been suggested to depend on social cognitive and metacognitive abilities requiring global integration of brain signals. Impaired insight has been related to numerous focal gray matter (GM) abnormalities distributed across the brain suggesting dysconnectivity at the global level. In this study, we test whether global integration deficiencies reflected in gray matter network connectivity underlie individual variations in insight.

METHODS

We used graph theory to examine whether individual GM-network metrics relate to insight in patients with a psychotic disorder (n = 114). Clinical insight was measured with the Schedule for the Assessment of Insight-Expanded and item G12 of the Positive and Negative Syndrome Scale, and cognitive insight with the Beck Cognitive Insight Scale. Individual GM-similarity networks were created from GM-segmentations of T1-weighted MRI-scans. Graph metrics were calculated using the Brain Connectivity Toolbox.

RESULTS

Networks of schizophrenia patients with poorer clinical insight showed less segregation (i.e. clustering coefficient) into specialized subnetworks at the global level. Schizophrenia patients with poorer cognitive insight showed both less segregation and higher connectedness (i.e. lower path length) of their brain networks, making their network topology more "random".

CONCLUSIONS

Our findings suggest less segregated processing of information in patients with poorer cognitive and clinical insight, in addition to higher connectedness in patients with poorer cognitive insight. The ability to take a critical perspective on one's symptoms (clinical insight) or views (cognitive insight) might depend especially on segregated specialized processing within distinct subnetworks.

"First-episode psychosis: Structural covariance deficits in salience network correlate with symptoms severity"

BACKGROUND

Patterns of coordinated variations of gray matter (GM) morphology across individuals are promising indicators of disease. However, it remains unclear if they can help characterize first-episode psychosis (FEP) and symptoms' severity.

METHODS

Sixty-seven FEP and 67 matched healthy controls (HC) were assessed with structural MRI to evaluate the existence of distributed GM structural covariance patterns associated to brain areas belonging to salience network. Voxel-based morphometry (VBM) and structural covariance differences, investigated with salience network seed-based Partial Least Square, were applied to explore differences between groups. GM density associations with Raven's intelligent quotient (IQ) and Positive and Negative Syndrome Scale (PANSS) scores were investigated.

RESULTS

Univariate VBM results gave trend without significant GM differences across groups. GM and IQ correlated positively in both groups: in FEP, mostly in hippocampus, insula, and fronto-temporal structures, while in HC mostly in amygdala, thalamus and fronto-temporal regions. GM and PANSS scores correlated negatively in FEP, with widespread clusters located in limbic regions. Multivariate analysis showed strong and opposite structural GM covariance with salience network for FEP and HC. Moreover, structural covariance of the salience network in FEP correlated negatively with severity of clinical symptoms.

CONCLUSION

Our study provides evidence supporting the insular dysfunction model of psychosis. Reduced structural GM covariance of the salience network, with its association to symptom's severity, appears a promising morphometry feature for FEP detection.

Divergent Alterations of Structural-Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients

Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural-functional connectivity couplings (SC-FC couplings) have been previously reported in schizophrenia patients, the alterations in SC-FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC-FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC-FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC-FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.

Neural Abnormalities in Fear Generalization in Schizophrenia and Associations With Negative Symptoms

BACKGROUND

Associative learning and memory processes, including the generalization of previously learned associations, may be altered in schizophrenia. Deficits in schizophrenia in stimulus generalization, one of the simplest forms of memory, could interfere with the ability to efficiently categorize related, similar information, potentially leading to impairments in daily functioning.

METHODS

To measure generalization in schizophrenia, 37 individuals with a nonaffective psychotic disorder and 32 demographically matched healthy control subjects underwent a Pavlovian fear conditioning and generalization procedure, which accounted for variation in perceptual ability across participants, while undergoing functional magnetic resonance imaging. Skin conductance and neural responses to conditioned (CS+), neutral (CS-), and generalization stimuli were measured. Explicit memory ratings reflecting successful generalization were also collected after the scanning, as well as measures of symptom severity.

RESULTS

Compared with healthy control subjects, individuals with nonaffective psychotic disorders showed significant deficits in fear generalization across multiple measurements, with impairments in memory ratings and reductions in activation and deactivation of the salience and default networks, respectively, during fear generalization. Moreover, in the psychotic disorder group, greater behavioral and neural abnormalities in generalization were associated with higher levels of negative symptoms.

CONCLUSIONS

Fear generalization is impaired in psychotic illness. Given that successful generalization relies on a dynamic balance between excitatory and inhibitory neurotransmission, these results reveal a potentially quantifiable mechanism linked to negative symptoms that can be investigated further in future human and experimental animal studies.

Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

Progression of neuroanatomical abnormalities after first-episode of psychosis: A 3-year longitudinal sMRI study

The location, extent and progression of longitudinal morphometric changes after first-episode of psychosis (FEP) remains unclear. We investigated ventricular and cortico-subcortical regions over a 3-year period in FEP patients compared with healthy controls. High resolution 1.5T T1-weighted MR images were obtained at baseline from 28 FEP patients at presentation and 28 controls, and again after 3-years. The longitudinal FreeSurfer pipeline (v.5.3.0) was used for regional volumetric and cortical reconstruction image analyses. Repeated-measures ANCOVA and vertex-wise linear regression analyses compared progressive changes between groups in subcortical structures and cortical thickness respectively. Compared with controls, patients displayed progressively reduced volume of the caudate [F (1,51)=5.86, p=0.02, Hedges' g=0.66], putamen [F (1,51)=6.06, p=0.02, g=0.67], thalamus [F (1,51)=6.99, p=0.01, g=0.72] and increased right lateral ventricular volume [F (1, 51)=4.03, p=0.05], and significantly increased rate of cortical thinning [F (1,52)=5.11, p=0.028)] at a mean difference of 0.84% [95% CI (0.10, 1.59)] in the left lateral orbitofrontal region over the 3-year period. In patients, greater reduction in putamen volume over time was associated with lower cumulative antipsychotic medication dose (r=0.49, p=0.01), and increasing lateral ventricular volume over time was associated with worsening negative symptoms (r=0.41, p=0.04) and poorer global functioning (r= -0.41, p=0.04). This study demonstrates localised progressive structural abnormalities in the cortico-striato-thalamo-cortical circuit after the onset of psychosis, with increasing ventricular volume noted as a neuroanatomical marker of poorer clinical and functional outcome.

Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder

A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.

Grey matter volume and structural covariance associated with schizotypy

In this study, we applied brain grey matter volume and structural covariance methods on T1 weighted images to delineate potential structural brain changes in individuals with high schizotypy, who were defined as healthy individuals scoring in the top tenth percentile of the Schizotypal Personality Questionnaire (SPQ). Eighty-seven college students with high schizotypy and 122 controls were recruited in China. Differences in grey matter volume and volume covariance between the two groups, and correlations of grey matter volume with SPQ scores in the high schizotypy group were examined. We found that individuals with high schizotypy had decreased grey matter volume at the left medial superior frontal gyrus (medsFG) extending towards the superior frontal gyrus, decreased structural covariance within the right medsFG, between the right superior frontal gyrus (sFG), the right superior temporal gyrus and the right anterior insula; and increased structural covariance between the caudate and the right inferior temporal gyrus. Correlation analysis revealed that grey matter volume of the left middle temporal pole and the right sFG correlated positively with the SPQ total scores, volume of the bilateral cerebellum 9 sub-region correlated negatively with the SPQ cognitive-perceptual sub-scale scores, volume of the bilateral striatum correlated positively with the SPQ interpersonal sub-scale scores, and volume of the bilateral superior temporal pole correlated positively with the SPQ disorganization sub-scale scores in the high schizotypy group. These results highlight important grey matter structural changes in the medsFG in individuals with high schizotypy.

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

Depth-dependent abnormal cortical myelination in first-episode treatment-naïve schizophrenia

Myelination is key to effective message passing in the central nervous system and is likely linked to the pathogenesis of schizophrenia (SZ). Emerging evidence indicates that a large portion of intracortical myelin insulates inhibitory interneurons that are highly relevant to pathogenesis of schizophrenia. Here for the first time, we characterized intracortical myelination across the entire cortical surface in first‐episode treatment‐naïve patients with schizophrenia (FES) using T1w/T2w ratio of structural MRI, FES patients exhibited significantly higher myelin content in the left inferior parietal lobe, supramarginal gyrus, and superior temporal gyrus in the superficial layer, as well as left IPL in the middle layer, but significantly lower myelin content in the left middle insula and posterior cingulate gyrus. Years of education, a proxy for onset of functional decline, significantly altered the relationship between abnormal parietal and posterior cingulate myelination and clinical symptoms, indicating that the pathoplastic role of myelination hinges on the age of onset of functional decline. In addition, higher myelination generally related to better cognitive function in younger subjects but worse cognitive function in older subjects. We conclude that FES is characterized by increased myelination of the superficial layers of the parietal–temporal association cortex, but reduced myelination of the cingulo‐insular midcortical layer cortex. Intracortical myelin content affects both cognitive functioning and symptom burden in FES, with the effect conditional upon age and timing of onset of functional decline. These results suggest myelination might be a critical biological target for procognitive interventions in SZ.

Structural Covariance Reveals Alterations in Control and Salience Network Integrity in Chronic Schizophrenia

Schizophrenia (SCZ) is recognized as a disorder of distributed brain dysconnectivity. While progress has been made delineating large-scale functional networks in SCZ, little is known about alterations in grey matter integrity of these networks. We used a multivariate approach to identify the structural covariance of the salience, default, motor, visual, fronto-parietal control, and dorsal attention networks. We derived individual scores reflecting covariance in each structural image for a given network. Seed-based multivariate analyses were conducted on structural images in a discovery (n = 90) and replication (n = 74) sample of SCZ patients and healthy controls. We first validated patterns across all networks, consistent with well-established functional connectivity reports. Next, across two SCZ samples, we found reliable and robust reductions in structural integrity of the fronto-parietal control and salience networks, but not default, dorsal attention, motor and sensory networks. Well-powered exploratory analyses failed to identify relationships with symptoms. These findings provide evidence of selective structural decline in associative networks in SCZ. Such decline may be linked with recently identified functional disturbances in associative networks, providing more sensitive multi-modal network-level probes in SCZ. Absence of symptom effects suggests that identified disturbances may underlie a trait-type marker in SCZ.

Antipsychotic treatment and basal ganglia volumes: Exploring the role of receptor occupancy, dosage and remission status

Antipsychotic treatment may affect brain morphology, and enlargement of the basal ganglia (BG) is a replicated finding. Here we investigated associations between antipsychotic treatment and BG volumes in patients with psychotic and bipolar disorders. We hypothesized that current treatment and, among those medicated, higher dosage, estimated D2R occupancy and being in remission would predict larger BG volumes. Structural covariance analysis was performed to examine if correlations between BG volumes and cortical thickness differed by treatment status. 224 patients treated with antipsychotics; 26 previously treated, 29 never treated and 301 healthy controls (HC) were included from the TOP study cohort (NORMENT, Norway). T1-weighted MR images were processed using FreeSurfer. D2R occupancy was estimated based on serum concentration measurements for patients receiving stable monotherapy. Statistical analyses were adjusted for age, gender and estimated intracranial volume (ICV). We found larger right (p < 0.003) and left putamen (p < 0.02) and right globus pallidus (GP) (p < 0.03) in currently medicated patients compared to HC. Bilateral regional cortical thinning was also observed in currently and previously medicated patients compared to HC. In medicated patients, higher chlorpromazine equivalent dose (CPZ) was associated with larger left GP (p < 0.04). There was no association with estimated D2R occupancy (n = 47) or remission status. Lower positive correlation between left putamen volume and cortical thickness of the left lateral occipital cortex was found in medicated patients compared to HC. We replicated the BG enlargement in medicated patients, but found no association with estimated D2R occupancy. Further studies are needed to clarify the underlying mechanisms.

Neural Correlates of Sensory Abnormalities Across Developmental Disabilities

Abnormalities in sensory processing are a common feature of many developmental disabilities (DDs). Sensory dysfunction can contribute to deficits in brain maturation, as well as many vital functions. Unfortunately, while some patients with DD benefit from the currently available treatments for sensory dysfunction, many do not. Deficiencies in clinical practice surrounding sensory dysfunction may be related to lack of understanding of the neural mechanisms that underlie sensory abnormalities. Evidence of overlap in sensory symptoms between diagnoses suggests that there may be common neural mechanisms that mediate many aspects of sensory dysfunction. Thus, the current manuscript aims to review the extant literature regarding the neural correlates of sensory dysfunction across DD in order to identify patterns of abnormality that span diagnostic categories. Such anomalies in brain structure, function, and connectivity may eventually serve as targets for treatment.