Cortical intercorrelations of frontal area volumes in schizophrenia

Preterm Birth Alters the Regional Development and Structural Covariance of Cerebellum at Term-Equivalent Age

Preterm birth is associated with increased risk for a spectrum of neurodevelopmental disabilities. The cerebellum is implicated in a wide range of cognitive functions extending beyond sensorimotor control and plays an increasingly recognized role in brain development. Morphometric studies based on volume analyses have revealed impaired cerebellar development in preterm infants. However, the structural covariance between the cerebellum and cerebral cortex has not been studied during the neonatal period, and the extent to which structural covariance is affected by preterm birth remains unknown. In this study, using the structural MR images of 52 preterm infants scanned at term-equivalent age and 312 full-term controls from the Developing Human Connectome Project, we compared volumetric growth, local cerebellum shape development and cerebello-cerebral structural covariance between the two groups. We found that although there was no significant difference in the overall volume measurements between preterm and full-term infants, the shape measurements were different. Compared with the control infants, preterm infants had significantly larger thickness in the vermis and lower thickness in the lateral portions of the bilateral cerebral hemispheres. The structural covariance between the cerebellum and frontal and parietal lobes was significantly greater in preterm infants than in full-term controls. The findings in this study suggested that cerebellar development and cerebello-cerebral structural covariance may be affected by premature birth.

Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis

Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.

Low-dose lithium adjunct to atypical antipsychotic treatment nearly improved cognitive impairment, deteriorated the gray-matter volume, and decreased the interleukin-6 level in drug-naive patients with first schizophrenia symptoms: a follow-up pilot study

This study was conducted to investigate the effects of long-term low-dose lithium adjunct to antipsychotic agent use on the cognitive performance, whole-brain gray-matter volume (GMV), and interleukin-6 (IL-6) level in drug-naive patients with first-episode schizophrenia, and to examine relationships among these factors. In this double-blind randomized controlled study, 50 drug-naive patients with first-episode schizophrenia each took low-dose (250 mg/day) lithium and placebo (of the same shape and taste) adjunct to antipsychotic agents (mean, 644.70 ± 105.58 and 677.00 ± 143.33 mg/day chlorpromazine equivalent, respectively) for 24 weeks. At baseline and after treatment completion, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive performance, 3-T magnetic resonance imaging was performed to assess structural brain alterations, and serum IL-6 levels were quantified by immunoassay. Treatment effects were assessed within and between patient groups. Relationships among cognitive performance, whole-brain GMVs, and the IL-6 level were investigated by partial correlation analysis. Relative to baseline, patients in the lithium group showed improved working memory, verbal learning, processing speed, and reasoning/problem solving after 24 weeks of treatment; those in the placebo group showed only improved working memory and verbal learning. The composite MCCB score did not differ significantly between groups. The whole-brain GMV reduction was significantly lesser in the lithium group than in the placebo group (0.46% vs. 1.03%; P < 0.001). The GMV and IL-6 reduction ratios correlated with each other in both groups (r = -0.17, P = 0.025). In the lithium group, the whole-brain GMV reduction ratio correlated with the working memory improvement ratio (r = -0.15, P = 0.030) and processing speed (r = -0.14, P = 0.036); the IL-6 reduction ratio correlated with the working memory (r = -0.21, P = 0.043) and verbal learning (r = -0.30, P = 0.031) improvement ratios. In the placebo group, the whole-brain GMV reduction ratio correlated only with the working memory improvement ratio (r = -0.24, P = 0.019); the IL-6 reduction ratio correlated with the working memory (r = -0.17, P = 0.022) and verbal learning (r = -0.15, P = 0.011) improvement ratios. Both treatments implemented in this study nearly improved the cognitive performance of patients with schizophrenia; relative to placebo, low-dose lithium had slightly greater effects on several aspects of cognition. The patterns of correlation among GMV reduction, IL-6 reduction, and cognitive performance improvement differed between groups.

Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease

This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.

Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers

Introduction

Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers.

Methods

A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing.

Results

As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks.

Discussion

These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis

BACKGROUND

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

METHODS

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

RESULTS

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

CONCLUSIONS

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

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.

Structural covariance networks in schizophrenia: A systematic review Part I

BACKGROUND

Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations.

METHODS

A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria.

RESULTS

Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported.

CONCLUSIONS

Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.

Abnormal Brain Structure Morphology in Early-Onset Schizophrenia

With less exposure to environmental and medication influences, individuals with early-onset schizophrenia (EOS) may provide valuable evidence to study the pathogenesis and phenotypic pattern of schizophrenia.T1-weighted magnetic resonance images were collected in 60 individuals with EOS and 40 healthy controls. Voxel-based morphometry and surface-based morphometry analyzes were performed. Gray matter volume, cortical thickness and cortical surface area were compared between the EOS and healthy controls and among schizophrenia subgroups (with or without family history of schizophrenia). Compared with healthy controls, the EOS group had reduced gray matter volume in the bilateral middle temporal gyrus and reduced cortical thickness in several brain regions. The sporadic early onset schizophrenia and the familial early onset schizophrenia showed different brain structure morphology. These findings suggest that abnormal brain structure morphology, especially in the temporal and frontal lobes, may be an important pathophysiological feature of EOS.

Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis

Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.

TMS-EEG: An emerging tool to study the neurophysiologic biomarkers of psychiatric disorders

The etiology of psychiatric disorders remains largely unknown. The exploration of the neurobiological mechanisms of mental illness helps improve diagnostic efficacy and develop new therapies. This review focuses on the application of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in various mental diseases, including major depressive disorder, bipolar disorder, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, substance use disorder, and insomnia. First, we summarize the commonly used protocols and output measures of TMS-EEG; then, we review the literature exploring the alterations in neural patterns, particularly cortical excitability, plasticity, and connectivity alterations, and studies that predict treatment responses and clinical states in mental disorders using TMS-EEG. Finally, we discuss the potential mechanisms underlying TMS-EEG in establishing biomarkers for psychiatric disorders and future research directions.

Reliability and comparability of human brain structural covariance networks

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.

Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition

Background

An anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated.

New method

Proposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains.

Results

The volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group.

Comparison with Existing Method

The proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected.

Conclusion

The eACA has potential to evaluate alterations of the anatomical network related to pathological processes.

Impact of sex and reproductive status on memory circuitry structure and function in early midlife using structural covariance analysis

Research on age-related memory alterations traditionally targets individuals aged ≥65 years. However, recent studies emphasize the importance of early aging processes. We therefore aimed to characterize variation in brain gray matter structure in early midlife as a function of sex and menopausal status. Subjects included 94 women (33 premenopausal, 29 perimenopausal, and 32 postmenopausal) and 99 demographically comparable men from the New England Family Study. Subjects were scanned with a high-resolution T1 sequence on a 3 T whole body scanner. Sex and reproductive-dependent structural differences were evaluated using Box's M test and analysis of covariances (ANCOVAs) for gray matter volumes. Brain regions of interest included dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (iPAR), anterior cingulate cortex (ACC), hippocampus (HIPP), and parahippocampus. While we observed expected significant sex differences in volume of hippocampus with women of all groups having higher volumes than men relative to cerebrum size, we also found significant differences in the covariance matrices of perimenopausal women compared with postmenopausal women. Associations between ACC and HIPP/iPAR/DLPFC were higher in postmenopausal women and correlated with better memory performance. Findings in this study underscore the importance of sex and reproductive status in early midlife for understanding memory function with aging.

Diametrical relationship between gray and white matter volumes in autism spectrum disorder and schizophrenia

Autism spectrum disorders and schizophrenia have been variously characterized as separate nosological entities with overlapping deficits in social cognition or diametrical extremes of a phenotypic continuum. This study aimed to determine how these models apply to comparative morphometric data. MRI scans of the brain were obtained in 49 subjects with schizophrenia, 20 subjects with autism and 39 healthy controls. Images were parcellated into 40 Brodmann areas and entered into repeated-measures ANOVA for between-group comparison of global and localized gray and white matter volumes. A pattern of lower gray mater volumes and greater white matter volumes was found in subjects with schizophrenia in comparison to subjects with autism. For both gray and white matter, this pattern was most pronounced in regions associated with motor-premotor and anterior frontal cortex, anterior cingulate, fusiform, superior and middle temporal gyri. Patient groups tended to diverge from healthy controls in opposite directions, with greater-than-normal gray matter volumes and lower-than-normal white matter volumes in subjects with autism and reversed patterns in subjects with schizophrenia. White matter reductions in subjects with autism were seen in posterior frontal lobe and along the cingulate arch. Normal hemispheric asymmetry in the temporal lobe was effaced in subjects with autism and schizophrenia, especially in the latter. Nearly identical distribution of changes and diametrically divergent volumetry suggest that autism and schizophrenia may occupy opposite extremes of the same cognitive continuum.

Development of Structural Covariance From Childhood to Adolescence: A Longitudinal Study in 22q11.2DS

Background: Schizophrenia is currently considered a neurodevelopmental disorder of connectivity. Still few studies have investigated how brain networks develop in children and adolescents who are at risk for developing psychosis. 22q11.2 Deletion Syndrome (22q11DS) offers a unique opportunity to investigate the pathogenesis of schizophrenia from a neurodevelopmental perspective. Structural covariance (SC) is a powerful approach to explore morphometric relations between brain regions that can furthermore detect biomarkers of psychosis, both in 22q11DS and in the general population.

Methods: Here we implement a state-of-the-art sliding-window approach to characterize maturation of SC network architecture in a large longitudinal cohort of patients with 22q11DS (110 with 221 visits) and healthy controls (117 with 211 visits). We furthermore propose a new clustering-based approach to group regions according to trajectories of structural connectivity maturation. We correlate measures of SC with development of working memory, a core executive function that is highly affected in both idiopathic psychosis and 22q11DS. Finally, in 22q11DS we explore correlations between SC dysconnectivity and severity of internalizing psychopathology.

Results: In HCs network architecture underwent a quadratic developmental trajectory maturing up to mid-adolescence. Late-childhood maturation was particularly evident for fronto-parietal cortices, while Default-Mode-Network-related regions showed a more protracted linear development. Working memory performance was positively correlated with network segregation and fronto-parietal connectivity. In 22q11DS, we demonstrate aberrant maturation of SC with disturbed architecture selectively emerging during adolescence and correlating more severe internalizing psychopathology. Patients also presented a lack of typical network development during late-childhood, that was particularly prominent for frontal connectivity.

Conclusions: Our results suggest that SC maturation may underlie critical cognitive development occurring during late-childhood in healthy controls. Aberrant trajectories of SC maturation may reflect core developmental features of 22q11DS, including disturbed cognitive maturation during childhood and predisposition to internalizing psychopathology and psychosis during adolescence.

Cortical Dysconnectivity Measured by Structural Covariance Is Associated With the Presence of Psychotic Symptoms in 22q11.2 Deletion Syndrome

BACKGROUND

22q11.2 deletion syndrome (22q11DS) is the third-largest known genetic risk factor for the development of psychosis. Dysconnectivity has consistently been implicated in the physiopathology of psychosis. Structural covariance of cortical morphology is a method of exploring connectivity among brain regions that to date has not been employed in 22q11DS.

METHODS

In the present study we employed structural covariance of cortical thickness to explore connectivity alterations in a group of 108 patients with 22q11DS compared with 96 control subjects. We subsequently divided patients into two subgroups of 31 subjects each according to the presence of attenuated psychotic symptoms. FreeSurfer software was used to obtain the mean cortical thickness in 148 brain regions from T1-weighted 3T images. For each population we reconstructed a brain graph using Pearson correlation between the average thickness of each couple of brain regions, which we characterized in terms of mean correlation strength and in terms of network architecture using graph theory.

RESULTS

Patients with 22q11DS presented increased mean correlation strength, but there was no difference in global architecture compared with control subjects. However, symptomatic patients presented increased mean correlation strength coupled with increased segregation and decreased integration compared with both control subjects and nonsymptomatic patients. They also presented increased centrality for a cluster of anterior cingulate and dorsomedial prefrontal regions.

CONCLUSIONS

These results confirm the importance of cortical dysconnectivity in the physiopathology of psychosis. Moreover they support the significance of aberrant anterior cingulate connectivity.

Modular Development of Cortical Gray Matter Across Childhood and Adolescence

Brain maturation across childhood and adolescence is characterized by cortical thickness (CT) and volume contraction, and early expansion of surface area (SA). These processes occur asynchronously across the cortical surface, with functional, topographic, and network-based organizing principles proposed to account for developmental patterns. Characterizing regions undergoing synchronized development can help determine whether "maturational networks" overlap with well-described functional networks, and whether they are targeted by neurodevelopmental and psychiatric disorders. In the present study, we modeled changes with age in CT, SA, and volume from 335 typically developing subjects in the NIH MRI study of normal brain development, with 262 followed longitudinally for a total of 724 scans. Vertices showing similar maturation between 5 and 22 years were grouped together using data-driven clustering. Patterns of CT development distinguished sensory and motor regions from association regions, and were vastly different from SA patterns, which separated anterior from posterior regions. Developmental modules showed little similarity to networks derived from resting-state functional connectivity. Our findings present a novel perspective on maturational changes across the cortex, showing that several proposed organizing principles of cortical development co-exist, albeit in different structural parameters, and enable visualization of developmental trends occurring in parallel at remote cortical sites.

Alterations in Anatomical Covariance in the Prematurely Born

Preterm (PT) birth results in long-term alterations in functional and structural connectivity, but the related changes in anatomical covariance are just beginning to be explored. To test the hypothesis that PT birth alters patterns of anatomical covariance, we investigated brain volumes of 25 PTs and 22 terms at young adulthood using magnetic resonance imaging. Using regional volumetrics, seed-based analyses, and whole brain graphs, we show that PT birth is associated with reduced volume in bilateral temporal and inferior frontal lobes, left caudate, left fusiform, and posterior cingulate for prematurely born subjects at young adulthood. Seed-based analyses demonstrate altered patterns of anatomical covariance for PTs compared with terms. PTs exhibit reduced covariance with R Brodmann area (BA) 47, Broca's area, and L BA 21, Wernicke's area, and white matter volume in the left prefrontal lobe, but increased covariance with R BA 47 and left cerebellum. Graph theory analyses demonstrate that measures of network complexity are significantly less robust in PTs compared with term controls. Volumes in regions showing group differences are significantly correlated with phonological awareness, the fundamental basis for reading acquisition, for the PTs. These data suggest both long-lasting and clinically significant alterations in the covariance in the PTs at young adulthood.

Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder

There is significant clinical heterogeneity in language and communication abilities of individuals with Autism Spectrum Disorders (ASD). However, no consistent pathology regarding the relationship of these abilities to brain structure has emerged. Recent developments in anatomical correlation-based approaches to map structural covariance networks (SCNs), combined with detailed behavioral characterization, offer an alternative for studying these relationships. In this study, such an approach was used to study the integrity of SCNs of cortical thickness and surface area associated with language and communication, in 46 high-functioning, school-age children with ASD compared with 50 matched, typically developing controls (all males) with IQ > 75. Findings showed that there was alteration of cortical structure and disruption of fronto-temporal cortical covariance in ASD compared with controls. Furthermore, in an analysis of a subset of ASD participants, alterations in both cortical structure and covariance were modulated by structural language ability of the participants, but not communicative function. These findings indicate that structural language abilities are related to altered fronto-temporal cortical covariance in ASD, much more than symptom severity or cognitive ability. They also support the importance of better characterizing ASD samples while studying brain structure and for better understanding individual differences in language and communication abilities in ASD.

Anomalous gray matter structural networks in recent onset post-traumatic stress disorder

Alterations of the topological organization of abnormal regions or network-level structural aberrations are still poorly understood for post-traumatic stress disorder (PTSD). Herein, we investigated brain structural networks in recent-onset PTSD patients, all affected by the coalmine-flood disaster. Cortical networks were studied in recent onset PTSD patients (n = 15) and matched healthy controls (n = 25). Cortical networks were constructed by thresholding correlation matrices of 150 regions and quantified using graph theoretical approaches. Contributions of high-degree nodes, and regional and global network measures, including degree and betweenness, were studied. Compared with healthy controls, PTSD patients showed altered quantitative values in global network properties, characterized by shorter path length and higher clustering. Moreover, PTSD patients exhibited decreased connectivity in the right lingual gyrus, parahippocampal gyrus, left supramarginal gyrus, parahippocampal gyrus, bilateral superior and inferior frontal gyrus, superior frontal gyrus, and posterior cingulate gyrus. Nodal centrality decreased predominantly in the occipital regions (lingual gyrus) and default-mode regions, while increased correlations and centralities were observed in the medial temporal lobe and posterior cingulate cortex. PTSD-related networks exhibited a less efficient organization and regional connectivity. According to these findings, we conclude that regional connections involving fear-processing and re-experiential-processing cortex may play a role in maintaining or adapting to PTSD pathology.

Progressive disability and prefrontal shrinkage in schizophrenia patients with poor outcome: A 3-year longitudinal study

INTRODUCTION

Schizophrenia is a severe disabling disorder with heterogeneous illness courses. In this longitudinal study we characterized schizophrenia patients with poor and good outcome (POS, GOS), using functional and imaging metrics. Patients were defined in accordance to Keefe's criteria (i.e. Kraepelinian and non-Kraepelinian patients).

METHODS

35 POS patients, 35 GOS patients and 76 healthy controls (H) underwent clinical, functioning and magnetic resonance imaging (MRI) assessments twice over three years of follow-up. Information on psychopathology, treatment, disability (using the World Health Organization Disability Assessment Scale II, WHO-DAS-2) and prefrontal morphology was collected. Dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC) were manually traced.

RESULTS

At baseline, subjects with POS showed significantly decreased right dorsolateral prefrontal cortex (DLPFC) white matter volumes (WM) compared to healthy controls and GOS patients (POS VS HC, p<0.001; POS vs GOS, p=0.03), with shrinkage of left DLPFC WM volumes at follow up (t=2.66, p=0.01). Also, POS patients had higher disability in respect to GOS subjects both at baseline and after 3years at the WHO-DAS-2 (p<0.05).

DISCUSSION

Our study supports the hypothesis that POS is characterized by progressive deficits in brain structure and in "real-life" functioning. These are particularly notable in the DLPFC.

Increased density of DISC1-immunoreactive oligodendroglial cells in fronto-parietal white matter of patients with paranoid schizophrenia

Profound white matter abnormalities have repeatedly been described in schizophrenia, which involve the altered expression of numerous oligodendrocyte-associated genes. Transcripts of the disrupted-in-schizophrenia 1 (DISC1) gene, a key susceptibility factor in schizophrenia, have recently been shown to be expressed by oligodendroglial cells and to negatively regulate oligodendrocyte differentiation and maturation. To learn more about the putative role(s) of oligodendroglia-associated DISC1 in schizophrenia, we analyzed the density of DISC1-immunoreactive oligodendrocytes in the fronto-parietal white matter in postmortem brains of patients with schizophrenia. Compared with controls (N = 12) and cases with undifferentiated/residual schizophrenia (N = 6), there was a significantly increased density of DISC1-expressing glial cells in paranoid schizophrenia (N = 12), which unlikely resulted from neuroleptic treatment. Pathophysiologically, over-expression of DISC1 protein(s) in white matter oligodendrocytes might add to the reduced levels of two myelin markers, 2',3'-cyclic-nucleotide 3'-phosphodiesterase and myelin basic protein in schizophrenia. Moreover, it might significantly contribute to cell cycle abnormalities as well as to deficits in oligodendroglial cell differentiation and maturation found in schizophrenia.

Multimodal MRI reveals structural connectivity differences in 22q11 deletion syndrome related to impaired spatial working memory

INTRODUCTION

Impaired spatial working memory is a core cognitive deficit observed in people with 22q11 Deletion syndrome (22q11DS) and has been suggested as a candidate endophenotype for schizophrenia. However, to date, the neuroanatomical mechanisms describing its structural and functional underpinnings in 22q11DS remain unclear. We quantitatively investigate the cognitive processes and associated neuroanatomy of spatial working memory in people with 22q11DS compared to matched controls. We examine whether there are significant between-group differences in spatial working memory using task related fMRI, Voxel based morphometry and white matter fiber tractography.

MATERIALS AND METHODS

Multimodal magnetic resonance imaging employing functional, diffusion and volumetric techniques were used to quantitatively assess the cognitive and neuroanatomical features of spatial working memory processes in 22q11DS. Twenty-six participants with genetically confirmed 22q11DS aged between 9 and 52 years and 26 controls aged between 8 and 46 years, matched for age, gender, and handedness were recruited.

RESULTS

People with 22q11DS have significant differences in spatial working memory functioning accompanied by a gray matter volume reduction in the right precuneus. Gray matter volume was significantly correlated with task performance scores in these areas. Tractography revealed extensive differences along fibers between task-related cortical activations with pronounced differences localized to interhemispheric commissural fibers within the parietal section of the corpus callosum.

CONCLUSIONS

Abnormal spatial working memory in 22q11DS is associated with aberrant functional activity in conjunction with gray and white matter structural abnormalities. These anomalies in discrete brain regions may increase susceptibility to the development of psychiatric disorders such as schizophrenia. Hum Brain Mapp 37:4689-4705, 2016. © 2016 Wiley Periodicals, Inc.

Using human brain imaging studies as a guide toward animal models of schizophrenia

Schizophrenia is a heterogeneous and poorly understood mental disorder that is presently defined solely by its behavioral symptoms. Advances in genetic, epidemiological and brain imaging techniques in the past half century, however, have significantly advanced our understanding of the underlying biology of the disorder. In spite of these advances clinical research remains limited in its power to establish the causal relationships that link etiology with pathophysiology and symptoms. In this context, animal models provide an important tool for causally testing hypotheses about biological processes postulated to be disrupted in the disorder. While animal models can exploit a variety of entry points toward the study of schizophrenia, here we describe an approach that seeks to closely approximate functional alterations observed with brain imaging techniques in patients. By modeling these intermediate pathophysiological alterations in animals, this approach offers an opportunity to (1) tightly link a single functional brain abnormality with its behavioral consequences, and (2) to determine whether a single pathophysiology can causally produce alterations in other brain areas that have been described in patients. In this review we first summarize a selection of well-replicated biological abnormalities described in the schizophrenia literature. We then provide examples of animal models that were studied in the context of patient imaging findings describing enhanced striatal dopamine D2 receptor function, alterations in thalamo-prefrontal circuit function, and metabolic hyperfunction of the hippocampus. Lastly, we discuss the implications of findings from these animal models for our present understanding of schizophrenia, and consider key unanswered questions for future research in animal models and human patients.

White Matter Microstructure in Early-Onset Schizophrenia: A Systematic Review of Diffusion Tensor Imaging Studies

OBJECTIVE

Neurodevelopmental processes and neural connectivity are thought to play pivotal roles in schizophrenia. This article reviews diffusion tensor imaging (DTI) studies of brain white matter connections and microstructure and their development in patients with early-onset schizophrenia (EOS), that is, schizophrenia with an age of onset before 18 years.

METHOD

A systematic literature search revealed 21 original case-control DTI studies of children and/or adolescents with EOS.

RESULTS

Nearly all studies report significantly lower regional fractional anisotropy (FA) in patients with EOS than in healthy control participants. However, the anatomical locations and extent of these differences are highly variable across studies. Furthermore, consistent evidence for associations between DTI indices and age of onset, medication variables, and measures of symptomatology and cognition in EOS is lacking. Only 3 available studies have investigated cross-sectional age-related differences or longitudinal changes in DTI measures in adolescents with EOS. The results are mixed, with different studies indicating diverging, converging, or parallel developmental FA trajectories between patients and controls.

CONCLUSION

The study of brain structural connectivity, as inferred from DTI, and its development in EOS may inform us on the origin and ontogeny of schizophrenia. We suggest some directions for future research in this field and argue for increased focus on developmental questions. Specifically, further investigations of age of onset effects and multimethod longitudinal studies of structural and functional connectivity development before, at, and after onset of schizophrenia and related syndromes in children and adolescents are called for.

Corpus callosum size and diffusion tensor anisotropy in adolescents and adults with schizophrenia

The corpus callosum has been implicated as a region of dysfunctional connectivity in schizophrenia, but the association between age and callosal pathology is unclear. Magnetic resonance imaging (MRI) and diffusion-tensor imaging (DTI) were performed on adults (n=34) and adolescents (n=17) with schizophrenia and adult (n=33) and adolescent (n=15) age- and sex-matched healthy controls. The corpus callosum was manually traced on each participant׳s MRI, and the DTI scan was co-registered to the MRI. The corpus callosum was divided into five anteroposterior segments. Area and anisotropy were calculated for each segment. Both patient groups demonstrated reduced callosal anisotropy; however, the adolescents exhibited reductions mostly in anterior regions while the reductions were more prominent in posterior regions of the adults. The adolescent patients showed greater decreases in absolute area as compared with the adult patients, particularly in the anterior segments. However, the adults showed greater reductions when area was considered relative to whole brain white matter volume. Our results suggest that the initial stages of the illness are characterized by deficiencies in frontal connections, and the chronic phase is characterized by deficits in the posterior corpus callosum; or, alternatively, adolescent-onset schizophrenia may represent a different or more severe form of the illness.

Temporal lobe cortical thickness correlations differentiate the migraine brain from the healthy brain

Background

Interregional cortical thickness correlations reflect underlying brain structural connectivity and functional connectivity. A few prior studies have shown that migraine is associated with atypical cortical brain structure and atypical functional connectivity amongst cortical regions that participate in sensory processing. However, the specific brain regions that most accurately differentiate the migraine brain from the healthy brain have yet to be determined. The aim of this study was to identify the brain regions that comprised interregional cortical thickness correlations that most differed between migraineurs and healthy controls.

Methods

This was a cross-sectional brain magnetic resonance imaging (MRI) investigation of 64 adults with migraine and 39 healthy control subjects recruited from tertiary-care medical centers and their surrounding communities. All subjects underwent structural brain MRI imaging on a 3T scanner. Cortical thickness was determined for 70 brain regions that cover the cerebral cortex and cortical thickness correlations amongst these regions were calculated. Cortical thickness correlations that best differentiated groups of six migraineurs from controls and vice versa were identified.

Results

A model containing 15 interregional cortical thickness correlations differentiated groups of migraineurs from healthy controls with high accuracy. The right temporal pole was involved in 13 of the 15 interregional correlations while the right middle temporal cortex was involved in the other two.

Conclusions

A model consisting of 15 interregional cortical thickness correlations accurately differentiates the brains of small groups of migraineurs from those of healthy controls. Correlations with the right temporal pole were highly represented in this classifier, suggesting that this region plays an important role in migraine pathophysiology.

Structural abnormalities in schizophrenia: further evidence on the key role of the anterior cingulate cortex

OBJECTIVE

The present study examined whole-brain structural abnormalities in schizophrenia, with a special focus on the anterior and posterior cingulate cortex (ACC, PCC) as this is an understudied issue in schizophrenia.

METHOD

Whole-brain voxel-based morphometry analyses of gray matter (GM) and white matter (WM) were performed to detect volumetric differences between 14 patients with schizophrenia and 14 healthy controls matched for age, sex, educational level and parents' educational level. We examined within-group GM and WM correlations and completed the analysis with measurements of sulci in medial cortical areas.

RESULTS

Compared with the healthy controls, the schizophrenic patients showed significant decreases in GM volumes in the ACC and PCC, and in neighboring WM regions such as the corpus callosum and the fimbriae of the fornix. Moreover, the patient group also displayed a negative correlation between volumes of GM and WM in the ACC. Finally, the patients showed significantly reduced volumes in the right cingulate sulci and left inferior frontal sulci.

CONCLUSION

Our results replicate typical brain-structural abnormalities with new findings in the medial prefrontal cortex, suggested to be a key region in this disorder.

Early visual deprivation changes cortical anatomical covariance in dorsal-stream structures

Early blind individuals possess thicker occipital cortex compared to sighted ones. Occipital cortical thickness is also predictive of performance on several auditory discrimination tasks in the blind, which suggests that it can serve as a neuroanatomical marker of auditory behavioural abilities. In light of this atypical relationship between occipital thickness and auditory function, we sought to investigate here the covariation of occipital cortical morphology in occipital areas with that of all other areas across the cortical surface, to assess whether the anatomical covariance with the occipital cortex differs between early blind and sighted individuals. We observed a reduction in anatomical covariance between the right occipital cortex and several areas of the visual dorsal stream in a group of early blind individuals relative to sighted controls. In a separate analysis, we show that the performance of the early blind in a transposed melody discrimination task was strongly predicted by the strength of the cortical covariance between the occipital cortex and intraparietal sulcus, a region for which cortical thickness in the sighted was previously shown to predict performance in the same task. These findings therefore constitute the first evidence linking altered anatomical covariance to early sensory deprivation. Moreover, since covariation of cortical morphology could potentially be related to anatomical connectivity or driven by experience-dependent plasticity, it could consequently help guide future functional connectivity and diffusion tractography studies.

A review of structural neuroimaging in schizophrenia: from connectivity to connectomics

In patients with schizophrenia neuroimaging studies have revealed global differences with some brain regions showing focal abnormalities. Examining neurocircuitry, diffusion-weighted imaging studies have identified altered structural integrity of white matter in frontal and temporal brain regions and tracts such as the cingulum bundles, uncinate fasciculi, internal capsules and corpus callosum associated with the illness. Furthermore, structural co-variance analyses have revealed altered structural relationships among regional morphology in the thalamus, frontal, temporal and parietal cortices in schizophrenia patients. The distributed nature of these abnormalities in schizophrenia suggests that multiple brain circuits are impaired, a neural feature that may be better addressed with network level analyses. However, even with the advent of these newer analyses, a large amount of variability in findings remains, likely partially due to the considerable heterogeneity present in this disorder.

Disrupted prefrontal interhemispheric structural coupling in schizophrenia related to working memory performance

BACKGROUND

Prominent regional cortical thickness reductions have been shown in schizophrenia. In contrast, little is known regarding alterations of structural coupling between regions in schizophrenia and how these alterations may be related to cognitive impairments in this disorder.

METHODS

T1-weighted magnetic resonance images were acquired in 54 patients with schizophrenia and 68 healthy control subjects aged 18-55 years. Cortical thickness was compared between groups using a vertex-wise approach. To assess structural coupling, seeds were selected within regions of reduced thickness, and brain-wide cortical thickness correlations were compared between groups. The relationships between identified patterns of circuit structure disruption and cognitive task performance were then explored.

RESULTS

Prominent cortical thickness reductions were found in patients compared with controls at a 5% false discovery rate in a predominantly frontal and temporal pattern. Correlations of the left dorsolateral prefrontal cortex (DLPFC) with right prefrontal regions were significantly different in patients and controls. The difference remained significant in a subset of 20 first-episode patients. Participants with stronger frontal interhemispheric thickness correlations had poorer working memory performance.

CONCLUSIONS

We identified structural impairment in a left-right DLPFC circuit in patients with schizophrenia independent of illness stage or medication exposure. The relationship between left-right DLPFC thickness correlations and working memory performance implicates prefrontal interhemispheric circuit impairment as a vulnerability pathway for poor working memory performance. Our findings could guide the development of novel therapeutic interventions aimed at improving working memory performance in patients with schizophrenia.

Disrupted thalamo-cortical connectivity in schizophrenia: a morphometric correlation analysis

Increasing studies have implicated the thalamus in schizophrenia, supporting the view that this structure has an important role in this disorder. Given that extensive reciprocal connections exist between the thalamus and the cerebral cortex, it is believed that disruptions of the thalamo-cortical connections may underlie the multiplicity of schizophrenic symptoms. Therefore, assessing the relationship between the thalamus and the neocortex may provide new insights into the pathophysiology of schizophrenia. We analyzed magnetic resonance images from a sample of 101 schizophrenic patients and 101 healthy controls. By assessing the correlation between the thalamic volume and cortical thickness at each vertex on the cortical surface, a thalamo-cortical network was obtained for each group. We compared the patterns of thalamo-cortical connectivity between the two groups. Compared with healthy controls, less distributed cortical regions were identified in the thalamo-cortical network in patients with schizophrenia. Vertex-wise comparison revealed decreased thalamo-cortical connectivity in bilateral inferior frontal gyrus, the left superior temporal gyrus and the right parieto-occipital region in schizophrenia. The observed disruptions in thalamo-cortical connectivity might be the substrate underlying the wide range of schizophrenic symptoms and provide further evidence to support the notion of schizophrenia as a disorder of brain dysconnectivity.

Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

BACKGROUND

Although previous studies on late life depression (LLD) have shown morphological abnormalities in frontal-striatal-temporal areas, alterations in coordinated patterns of structural brain networks in LLD are still poorly understood. The aim of this study was to investigate differences in gray matter structural brain network between LLD and healthy controls.

METHODS

We used gray matter volume measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 37 LLD patients and 40 normal controls. Brain networks were constructed by thresholding gray matter volume correlation matrices of 90 regions and analyzed using graph theoretical approaches.

RESULTS

Although both LLD and control groups showed a small-world organization of group networks, there were no differences in the clustering coefficient, the path length, and the small-world index across a wide range of network density. Compared with controls, LLD patients showed decreased nodal betweenness in the medial orbitofrontal and angular gyrus regions. In addition, LLD patients showed hub regions in superior temporal gyrus and middle cingulate gyrus, and putamen. On the other hand, the control group showed hub regions in the medial orbitofrontal gyrus, middle cingulate gyrus, and cuneus.

CONCLUSION

Our findings suggest that the gray matter structural networks are not globally but regionally altered in LLD patients. This multivariate structural analysis using graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of LLD.

Cortical grey matter and subcortical white matter brain microstructural changes in schizophrenia are localised and age independent: a case-control diffusion tensor imaging study

It is still unknown whether the structural brain impairments that characterize schizophrenia (SZ) worsen during the lifetime. Here, we aimed to describe age-related microstructural brain changes in cortical grey matter and subcortical white matter of patients affected by SZ. In this diffusion tensor imaging study, we included 69 patients diagnosed with SZ and 69 healthy control (HC) subjects, age and gender matched. We carried out analyses of covariance, with diagnosis as fixed factor and brain diffusion-related parameters as dependent variables, and controlled for the effect of education. White matter fractional anisotropy decreased in the entire age range spanned (18–65 years) in both SZ and HC and was significantly lower in younger patients with SZ, with no interaction (age by diagnosis) effect in fiber tracts including corpus callosum, corona radiata, thalamic radiations and external capsule. Also, grey matter mean diffusivity increased in the entire age range in both SZ and HC and was significantly higher in younger patients, with no age by diagnosis interaction in the left frontal operculum cortex, left insula and left planum polare and in the right temporal pole and right intracalcarine cortex. In individuals with SZ we found that localized brain cortical and white matter subcortical microstructural impairments appear early in life but do not worsen in the 18–65 year age range.

Reduced dorso-lateral prefrontal cortex in treatment resistant schizophrenia

BACKGROUND

Treatment resistance affects up to one third of patients with schizophrenia (SCZ). A better understanding of its biological underlying processes could improve treatment. The aim of this study was to compare cortical thickness between non-resistant SCZ (NR-SCZ), treatment-resistant SCZ (TR-SCZ) patients and healthy controls (HC).

METHODOLOGY

Structural MRI scans were obtained from 3 groups of individuals: 61 treatment resistant SCZ individuals, 67 non-resistant SCZ and 80 healthy controls. Images were analyzed using cortical surface modelling (implemented in freesurfer package) to identify group differences in cortical thickness. Statistical significant differences were identified using Monte-Carlo simulation method with a corrected p-cluster<0.01.

RESULTS

Patients in the TR-SCZ group showed a widespread reduction in cortical thickness in frontal, parietal, temporal and occipital regions bilaterally. NR-SCZ group had reduced cortex in two regions (left superior frontal cortex and left caudal middle frontal cortex). TR-SCZ group also showed decreased thickness in the left dorsolateral prefrontal cortex (DLPFC) when compared with patients from NR-SCZ group.

CONCLUSIONS

The reduction in cortical thickness in DLPFC indicates a more severe form of the disease or a specific finding for this group. Alterations in this region should be explored as a putative marker for treatment resistance. Prospective studies, with individuals being followed from first episode psychosis until refractoriness is diagnosed, are needed to clarify these hypotheses.

Selective disruption of sociocognitive structural brain networks in autism and alexithymia

Autism spectrum conditions (ASC) are neurodevelopmental disorders characterized by abnormal social cognition. A core feature of ASC is disrupted Theory of Mind (ToM), our ability to take the mental perspective of others. ASC is also associated with alexithymia, a trait characterized by altered emotional interoception and empathy. Here, we applied structural MRI covariance analysis to assess whether ASC and alexithymia differentially affect structural brain networks associated with sociocognitive and socioaffective functions. Based on previous functional MRI findings, we expected disrupted ToM networks (centered on dorsomedial prefontal cortex [dmPFC], and temporo-parietal junction [TPJ]) in ASC, while alexithymia would affect networks centered on fronto-insular cortex (FI), regions associated with interoception of emotion and empathy. Relative to controls, ASC indeed showed reduced covariance in networks centered on dmPFC and TPJ, but not within FI networks. Irrespective of ASC, covariance was negatively modulated by alexithymia in networks extending from FI to posterior regions. Network findings were complemented by self-reports, indicating decreased perspective taking but normal empathic concern in ASC. Our results show divergent effects of ASC and alexithymia on inter-regional structural networks, suggesting that networks mediating socioaffective processes may be separable from networks mediating sociocognitive processing.

Imaging structural co-variance between human brain regions

Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults

In recent years, coordinated variations in brain morphology (e.g. volume, thickness, surface area) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks (SCNs). However, it remains unclear how morphometric correlations relate to functional connectivity between brain regions. Resting-state networks (RSNs), derived from coordinated variations in neural activity at rest, have been shown to reflect connectivity between functionally related regions as well as, to some extent, anatomical connectivity between brain regions. Therefore, it is intriguing to investigate similarities between SCN and RSN to help identify how morphometric correlations relate to connections defined by resting-state connectivity. We investigated the similarities in connectivity patterns and small-world organization between SCN, derived from correlations of regional gray matter volume across individuals, and RSN in 36 healthy individuals. The results showed a significant similarity between SCN and RSN (60% for positive connections and 40% for negative connections) that might be explained by shared experience-related functional connectivity underlying both SCN and RSN. Conversely, the small-world parameters of the networks were significantly different, suggesting that SCN topological parameters cannot be regarded as a substitute for topological organization in resting-state networks. While our data suggest that using structural correlation networks can be useful in understanding alterations in structural associations in various brain disorders, it should be noted that a portion of the observed alterations might be explained by factors other than those reflecting resting-state connectivity.

Disturbed grey matter coupling in schizophrenia

In schizophrenia, grey matter deficits have been shown for many regions throughout the brain. These regions do not operate in isolation. Rather, they form a structural network of interconnected grey matter regions. To examine the mutual dependence of brain regions, this study investigated interregional coupling in lobar and regional grey matter volumes obtained from 146 schizophrenia patients and 122 healthy comparison subjects. Compared to healthy controls, schizophrenia patients showed both decreased (e.g. between left frontal and bilateral subcortical, p≤0.005) and increased (e.g. between left temporal and bilateral subcortical, p≤0.001) coupling between lobar grey matter volumes. On a regional scale, decreased coupling was most pronounced between fronto-parietal cortical regions and subcortical structures, and between frontal and occipital regions. In addition, an increased association was found among frontal and limbic regions, and for temporo-occipital connexions. Consistent with dysconnectivity theories of schizophrenia, impaired grey matter coupling may be reflective of reduced integrity of the brain's network. Furthermore, as cross-sectional volumetric coupling is indicative of maturational coupling, aberrant grey matter coupling may be a marker of neurodevelopmental abnormalities in schizophrenia.

Abnormal topological organization of structural brain networks in schizophrenia

Schizophrenia is a debilitating mental disorder characterized by disturbances of thought and emotion as well as neurocognitive deficits. It is hypothesized that the core symptoms of schizophrenia arise from the inability to integrate neural processes segregated across distributed brain regions. Graph theory allows us to verify this hypothesis at large-scale structural network level. In this study, a sample of 101 schizophrenic patients and 101 healthy controls was included. We sought to investigate the abnormality of network topological organization in patients with schizophrenia by using the cortical thickness measurement from magnetic resonance imaging. Brain networks were constructed by thresholding cortical thickness correlation matrices of 78 regions and analyzed using graph theoretical approaches. Compared to healthy controls, patients showed increased characteristic path length and clustering coefficient in the structural cortical networks. Moreover, schizophrenia patients were associated with reduced nodal centrality in several regions of the default network and increased nodal centrality mainly in primary cortex and paralimbic cortex regions. These findings suggest that the structural networks of schizophrenic patients have a less optimal topological organization, resulting in reduced capacity to integrate information across brain regions.

Anterior limb of the internal capsule in schizotypal personality disorder: fiber-tract counting, volume, and anisotropy

Mounting evidence suggests that white matter abnormalities and altered subcortical-cortical connectivity may be central to the pathology of schizophrenia (SZ). The anterior limb of the internal capsule (ALIC) is an important thalamo-frontal white-matter tract shown to have volume reductions in SZ and to a lesser degree in schizotypal personality disorder (SPD). While fractional anisotropy (FA) and connectivity abnormalities in the ALIC have been reported in SZ, they have not been examined in SPD. In the current study, magnetic resonance (MRI) and diffusion tensor imaging (DTI) were obtained in age- and sex-matched individuals with SPD (n=33) and healthy controls (HCs; n=38). The ALIC was traced bilaterally on five equally spaced dorsal-to-ventral axial slices from each participant's MRI scan and co-registered to DTI for the calculation of FA. Tractography was used to examine tracts between the ALIC and two key Brodmann areas (BAs; BA10, BA45) within the dorsolateral prefrontal cortex (DLPFC). Compared with HCs, the SPD participants exhibited (a) smaller relative volume at the mid-ventral ALIC slice level but not the other levels; (b) normal FA within the ALIC; (c) fewer relative number of tracts between the most-dorsal ALIC levels and BA10 but not BA45 and (d) fewer dorsal ALIC-DLPFC tracts were associated with greater symptom severity in SPD. In contrast to prior SZ studies that report lower FA, individuals with SPD show sparing. Our findings are consistent with a pattern of milder thalamo-frontal dysconnectivity in SPD than schizophrenia.

Reduced white matter fractional anisotropy and clinical symptoms in schizophrenia: a voxel-based diffusion tensor imaging study

Although not consistently replicated, diffusion tensor imaging (DTI) studies in schizophrenia have revealed lower fractional anisotropy (FA) in various white matter regions, a finding consistent with the disruption of white matter integrity. In this study, we used voxel-based DTI to investigate possible whole-brain differences in the white matter FA values between 58 schizophrenia patients and 58 healthy controls. We also explored the association between FA values and clinical symptoms in schizophrenia. Compared with the controls, the schizophrenia patients showed significant FA reductions in bilateral superior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus, and genu of right internal capsule. Furthermore, in the patient group, the FA value of the anterior part of the corpus callosum was negatively correlated with the avolition score on the Scale for the Assessment of Negative Symptoms. These findings suggest widespread disruption of white matter integrity in schizophrenia, which could partly explain the severity of negative symptomatology.