Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium

Impact of Endocannabinoid System Manipulation on Neurodevelopmental Processes Relevant to Schizophrenia

The neurodevelopmental hypothesis of schizophrenia has received much support from epidemiological and neuropathological studies and provides a framework to explain how early developmental abnormalities might manifest as psychosis in early adulthood. According to this theory, the onset of schizophrenia is likely the result of a complex interplay between a genetic predisposition and environmental factors whose respective influence might contribute to the etiology and progression of the disorder. The two most sensitive windows for neurodevelopment are the prenatal/perinatal and the adolescent windows, both of which are characterized by specific processes impinging upon brain structure and functionality, whose alterations may contribute to the onset of schizophrenia. An increasing number of articles suggest the involvement of the endocannabinoid system in the modulation of at least some of these processes, especially in the prenatal/perinatal window. Thus, it is not surprising that disturbing the physiological role of endocannabinoid signaling in these sensitive windows might alter the correct formation of neuronal networks, eventually predisposing to neuropsychiatric diseases later in life. We review the most recent preclinical studies that evaluated the impact of endocannabinoid system modulation in the two sensitive developmental windows on neurodevelopmental processes that possess a specific relevance to schizophrenia.

Neuroimaging Heterogeneity in Psychosis: Neurobiological Underpinnings and Opportunities for Prognostic and Therapeutic Innovation

Heterogeneity in symptom presentation, outcomes, and treatment response has long been problematic for researchers aiming to identify biological markers of schizophrenia or psychosis. However, there is increasing recognition that there may likely be no such general illness markers, which is consistent with the notion of a group of schizophrenia(s) that may have both shared and unique neurobiological pathways. Instead, strategies aiming to capitalize on or leverage such heterogeneity may help uncover neurobiological pathways that may then be used to stratify groups of patients for prognostic purposes or for therapeutic trials. A shift toward larger sample sizes with adequate statistical power to overcome small effect sizes and disentangle the shared variance among different brain-imaging or behavioral variables has become a priority for the field. In addition, recognition that two individuals with the same clinical diagnosis may be more different from each other (at brain, genetic, and behavioral levels) than from another individual in a different disorder or nonclinical control group-coupled with computational advances-has catapulted data-driven efforts forward. Emerging challenges for this new approach include longitudinal stability of new subgroups, demonstration of validity, and replicability. The "litmus test" will be whether computational approaches that are successfully identifying groups of patients who share features in common, more than current DSM diagnostic constructs, also provide better prognostic accuracy over time and in addition lead to enhancements in treatment response and outcomes. These are the factors that matter most to patients, families, providers, and payers.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium

BACKGROUND

Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.

METHODS

We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.

RESULTS

In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.

CONCLUSIONS

The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.

Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of ENIGMA

Reproducibility is one of the most important issues for generalizing the results of clinical research; however, low reproducibility in neuroimaging studies is well known. To overcome this problem, the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) consortium, an international neuroimaging consortium, established standard protocols for imaging analysis and employs either meta‐ and mega‐analyses of psychiatric disorders with large sample sizes. The Cognitive Genetics Collaborative Research Organization (COCORO) in Japan promotes neurobiological studies in psychiatry and has successfully replicated and extended works of ENIGMA especially for neuroimaging studies. For example, (a) the ENIGMA consortium showed subcortical regional volume alterations in patients with schizophrenia (n = 2,028) compared to controls (n = 2,540) across 15 cohorts using meta‐analysis. COCORO replicated the volumetric changes in patients with schizophrenia (n = 884) compared to controls (n = 1,680) using the ENIGMA imaging analysis protocol and mega‐analysis. Furthermore, a schizophrenia‐specific leftward asymmetry for the pallidum volume was demonstrated; and (b) the ENIGMA consortium identified white matter microstructural alterations in patients with schizophrenia (n = 1,963) compared to controls (n = 2,359) across 29 cohorts. Using the ENIGMA protocol, a study from COCORO showed similar results in patients with schizophrenia (n = 696) compared to controls (n = 1,506) from 12 sites using mega‐analysis. Moreover, the COCORO study found that schizophrenia, bipolar disorder (n = 211) and autism spectrum disorder (n = 126), but not major depressive disorder (n = 398), share similar white matter microstructural alterations, compared to controls. Further replication and harmonization of the ENIGMA consortium and COCORO will contribute to the generalization of their research findings.

Trio Haploinsufficiency Causes Neurodevelopmental Disease-Associated Deficits

Heterozygous coding mutations in TRIO are associated with neurodevelopmental disorders, including autism, schizophrenia, bipolar disorder, and epilepsy, and impair TRIO's biochemical activities. To model mutant alleles, we ablated one or both Trio alleles from excitatory neurons in the cortex and hippocampus of mice. Trio haploinsufficiency increases anxiety and impairs social preference and motor coordination. Trio loss reduces forebrain size and dendritic arborization but increases dendritic spine densities. Cortical synapses in Trio haploinsufficient mice are small, exhibit pre- and postsynaptic deficits, and cannot undergo long-term potentiation. Similar phenotypes are observed in Trio knockout mice. Overall, Trio haploinsufficiency causes severe disease-relevant deficits in behavior and neuronal structure and function. Interestingly, phosphodiesterase 4A5 (PDE4A5) levels are reduced and protein kinase A (PKA) signaling is increased when TRIO levels are reduced. Elevation of PDE4A5 and drug-based attenuation of PKA signaling rescue Trio haploinsufficiency-related dendritic spine defects, suggesting an avenue for therapeutic intervention for TRIO-related neurodevelopmental disorders.

Abnormal Effective Connectivity Underlying Auditory Mismatch Negativity Impairments in Schizophrenia

BACKGROUND

Auditory mismatch negativity (MMN) is a translatable event-related potential biomarker, and its reduction in schizophrenia is associated with the severity of clinical symptoms. While MMN recorded at the scalp is generated by a distributed network of temporofrontal neural sources, the primary contributing sources and the dynamic interactions among sources underlying MMN impairments in schizophrenia have not been previously characterized.

METHODS

A novel data-driven analytic framework was applied to large cohorts of healthy comparison subjects (n = 449) and patients with schizophrenia (n = 589) to identify the independent contributing sources of MMN, characterize the patterns of effective connectivity underlying reduced MMN in patients, and explore the clinical significance of these abnormal source dynamics in schizophrenia.

RESULTS

A network of 11 independent contributing sources underlying MMN distributed across temporofrontal cortices was identified. Orderly shifts in peak source activity were detected in a steplike manner, starting at temporal structures and progressing across frontal brain regions. MMN reduction in patients was predominantly associated with reduced contributions from 3 frontal midline sources: orbitofrontal, anterior cingulate, and middle cingulate cortices. Patients showed increased connectivity from temporal to prefrontal regions in conjunction with decreased cross-hemispheric connectivity to prefrontal regions. The decreased connectivity strength of precentral to prefrontal regions in patients with schizophrenia was associated with greater severity of negative symptoms.

CONCLUSIONS

Alterations in the dynamic interactions among temporofrontal sources underlie MMN abnormalities in schizophrenia. These results advance our understanding of the neural substrates and temporal dynamics of normal and impaired information processing with novel applications for translatable biomarkers of neuropsychiatric disorders.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium

OBJECTIVE

Schizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants.

METHODS

The meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition.

RESULTS

Meta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44).

CONCLUSIONS

This study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

A Prospective Longitudinal Investigation of Cortical Thickness and Gyrification in Schizophrenia

BACKGROUND

Cortical thickness (CT) and gyrification are complementary indices that assess different aspects of gray matter structural integrity. Both neurodevelopment insults and acute tissue response to antipsychotic medication could underlie the known heterogeneity of treatment response and are well-suited for interrogation into the relationship between gray matter morphometry and clinical outcomes in schizophrenia (SZ).

METHODS

Using a prospective design, we enrolled 34 unmedicated patients with SZ and 23 healthy controls. Patients were scanned at baseline and after a 6-week trial with risperidone. CT and local gyrification index (LGI) values were quantified from structural MRI scans using FreeSurfer 5.3.

RESULTS

We found reduced CT and LGI in patients compared to controls. Vertex-wise analyses demonstrated that hypogyrification was most prominent in the inferior frontal cortex, temporal cortex, insula, pre/postcentral gyri, temporoparietal junction, and the supramarginal gyrus. Baseline CT was predictive of subsequent response to antipsychotic treatment, and increase in CT after 6 weeks was correlated with greater symptom reductions.

CONCLUSIONS

In summary, we report evidence of reduced CT and LGI in unmedicated patients compared to controls, suggesting involvement of different aspects of gray matter morphometry in the pathophysiology of SZ. Importantly, we found that lower CT at baseline and greater increase of CT following 6 weeks of treatment with risperidone were associated with better clinical response. Our results suggest that cortical thinning may normalize as a result of a good response to antipsychotic medication, possibly by alleviating potential neurotoxic processes underlying gray matter deterioration.

Visual Cortical Alterations and their Association with Negative Symptoms in Antipsychotic-Naïve First Episode Psychosis

Visual perceptual and processing deficits are common in schizophrenia and possibly point towards visual pathway alterations. However, no studies have examined visual cortical morphology in first-episode psychosis (FEP). In an antipsychotic-naïve FEP population, we investigated primary visual (V1), association area (V2), and motion perception (V5/MT) morphology compared to controls. We found reductions in the V1 and V2 areas, greater MT area and lower MT thickness in the FEP-schizophrenia group when compared to controls. Also, lower MT thickness was associated with worse negative symptoms. Our results shed light on this poorly studied area of visual cortex morphology in FEP.

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.

Translating ENIGMA schizophrenia findings using the regional vulnerability index: Association with cognition, symptoms, and disease trajectory

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta-Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia-specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6-20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10^-6 ). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease-duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns.

Common gray and white matter abnormalities in schizophrenia and bipolar disorder

This study aimed to investigate abnormalities in the gray matter and white matter (GM and WM, respectively) that are shared between schizophrenia (SZ) and bipolar disorder (BD). We used 3T-magnetic resonance imaging to examine patients with SZ, BD, or healthy control (HC) subjects (aged 20–50 years, N = 65 in each group). We generated modulated GM maps through voxel-based morphometry (VBM) for T1-weighted images and skeletonized fractional anisotropy, mean diffusion, and radial diffusivity maps through tract-based special statistics (TBSS) methods for diffusion tensor imaging (DTI) data. These data were analyzed using a generalized linear model with pairwise comparisons between groups with a family-wise error corrected P < 0.017. The VBM analysis revealed widespread decreases in GM volume in SZ compared to HC, but patients with BD showed GM volume deficits limited to the right thalamus and left insular lobe. The TBSS analysis showed alterations of DTI parameters in widespread WM tracts both in SZ and BD patients compared to HC. The two disorders had WM alterations in the corpus callosum, superior longitudinal fasciculus, internal capsule, external capsule, posterior thalamic radiation, and fornix. However, we observed no differences in GM volume or WM integrity between SZ and BD. The study results suggest that GM volume deficits in the thalamus and insular lobe along with widespread disruptions of WM integrity might be the common neural mechanisms underlying the pathologies of SZ and BD.

Psychotic-like experiences, polygenic risk scores for schizophrenia, and structural properties of the salience, default mode, and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank, N = 3875) including information on PLEs (obtained from the Mental Health Questionnaire (MHQ); UKBiobank Category: 144; N auditory hallucinations = 55, N visual hallucinations = 79, N persecutory delusions = 16, N delusions of reference = 13), polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode, and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations, both negative and positive, with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus, and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. Generally, these results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and may predispose to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

White Matter Microstructure across the Psychosis Spectrum

Diffusion-weighted imaging (DWI) is a neuroimaging technique that has allowed us an unprecedented look at the role that white matter microstructure may play in mental illnesses, such as psychosis. Psychosis-related illnesses, including schizophrenia, are increasingly viewed as existing along a spectrum; spectrums may be defined based on factors such as stage of illness, symptom severity, or genetic liability. This review first focuses on an overview of some of the recent findings from DWI studies. Then, it examines the ways in which DWI analyses have been extended across the broader psychosis spectrum, or spectrums, and what we have learned from such approaches.

Functioning in schizophrenia: a Delphi study covering the perspective of physiotherapists

PURPOSE

This qualitative study explores the barriers, personal characteristics/resources, and environmental factors that experienced physiotherapists identify as relevant in the assessment and treatment of persons living with schizophrenia, and whether the identified aspects are represented in the International Classification of Functioning, Disability and Health Core Sets for schizophrenia.

METHODS

A three-round Delphi study with physiotherapists was conducted between April and July 2018. In the first round, participants had to list all the aspects they considered to be relevant when assessing and/or treating individuals with schizophrenia, and they were asked six open-ended questions. Their responses were linked to categories. In the second and third rounds, physiotherapists had to judge whether each category/personal factor was relevant for describing functioning in schizophrenia.

RESULTS

Thirteen of 22 eligible physiotherapists from eight countries responded to the first round, and 10 completed all three rounds. Eighty-two (84.5%) of the 97 categories in the Comprehensive Core Set for schizophrenia and all 25 categories in the Brief Core Set were considered relevant. A total of five categories were additionally identified.

CONCLUSIONS

The barriers, personal characteristics/resources, and environmental factors from the physiotherapists' perspective have been identified. The results largely confirm the content validity of the Core Sets for schizophrenia.Implications for rehabilitationThis study shows which barriers, personal characteristics/resources, and environmental factors in persons with schizophrenia are relevant from physiotherapists' perspective and should be integrated in the rehabilitation process.The content validity of the Comprehensive and Brief Core Sets for schizophrenia is largely supported from the physiotherapists' perspective and therefore could be used in the assessment of functioning in persons with schizophrenia.The Comprehensive and Brief ICF Core Sets for schizophrenia could be used to plan and assess multidisciplinary rehabilitation interventions.

Spatial Patterning of Tissue Volume Loss in Schizophrenia Reflects Brain Network Architecture

BACKGROUND

There is growing recognition that connectome architecture shapes cortical and subcortical gray matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.

METHODS

Here, we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Gray matter deformation was estimated in a sample of 133 individuals with chronic schizophrenia (48 women, mean age 34.7 ± 12.9 years) and 113 control subjects (64 women, mean age 23.5 ± 8.4 years). Deformation-based morphometry was used to estimate cortical and subcortical gray matter deformation from T1-weighted magnetic resonance images. Structural and functional connectivity patterns were derived from an independent sample of 70 healthy participants using diffusion spectrum imaging and resting-state functional magnetic resonance imaging.

RESULTS

We found that regional deformation is correlated with the deformation of structurally and functionally connected neighbors. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.

CONCLUSIONS

Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25% to 35% of regional variance in deformation.

Normalizing the Abnormal: Do Antipsychotic Drugs Push the Cortex Into an Unsustainable Metabolic Envelope?

The use of antipsychotic medication to manage psychosis, principally in those with a diagnosis of schizophrenia or bipolar disorder, is well established. Antipsychotics are effective in normalizing positive symptoms of psychosis in the short term (delusions, hallucinations and disordered thought). Their long-term use is, however, associated with side effects, including several types of movement (extrapyramidal syndrome, dyskinesia, akathisia), metabolic and cardiac disorders. Furthermore, higher lifetime antipsychotic dose-years may be associated with poorer cognitive performance and blunted affect, although the mechanisms driving the latter associations are not well understood. In this article, we propose a novel model of the long-term effects of antipsychotic administration focusing on the changes in brain metabolic homeostasis induced by the medication. We propose here that the brain metabolic normalization, that occurs in parallel to the normalization of psychotic symptoms following antipsychotic treatment, may not ultimately be sustainable by the cerebral tissue of some patients; these patients may be characterized by already reduced oxidative metabolic capacity and this may push the brain into an unsustainable metabolic envelope resulting in tissue remodeling. To support this perspective, we will review the existing data on the brain metabolic trajectories of patients with a diagnosis of schizophrenia as indexed using available neuroimaging tools before and after use of medication. We will also consider data from pre-clinical studies to provide mechanistic support for our model.

Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features

The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person’s brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.

A Meta-analysis of Structural and Functional Brain Abnormalities in Early-Onset Schizophrenia

Early-onset schizophrenia (EOS) patients demonstrate brain changes that are similar to severe cases of adult-onset schizophrenia. Neuroimaging research in EOS is limited due to the rarity of the disorder. The present meta-analysis aims to consolidate MRI and functional MRI findings in EOS. Seven voxel-based morphometry (VBM) and 8 functional MRI studies met the inclusion criteria, reporting whole-brain analyses of EOS vs healthy controls. Activation likelihood estimation (ALE) was conducted to identify aberrant anatomical or functional clusters across the included studies. Separate ALE analyses were performed, first for all task-dependent studies (Cognition ALE) and then only for working memory ones (WM ALE). The VBM ALE revealed no significant clusters for gray matter volume reductions in EOS. Significant hypoactivations peaking in the right anterior cingulate cortex (rACC) and the right temporoparietal junction (rTPJ) were detected in the Cognition ALE. In the WM ALE, consistent hypoactivations were found in the left precuneus (lPreC), the right inferior parietal lobule (rIPL) and the rTPJ. These hypoactivated areas show strong associations with language, memory, attention, spatial, and social cognition. The functional co-activated networks of each suprathreshold ALE cluster, identified using the BrainMap database, revealed a core co-activation network with similar topography to the salience network. Our results add support to posterior parietal, ACC and rTPJ dysfunction in EOS, areas implicated in the cognitive impairments characterizing EOS. The salience network lies at the core of these cognitive processes, co-activating with the hypoactivating regions, and thus highlighting the importance of salience dysfunction in EOS.

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Diagnosing schizophrenia with network analysis and a machine learning method

OBJECTIVE

Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research.

METHODS

We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls.

RESULTS

In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns.

CONCLUSION

In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.

[Neuroanatomical brain profile of juvenile shiftlike schizophrenia: morphometry of grey matter in the prefrontal cortex and subcortical structures]

AIM

To determine neuroanatomical peculiarities of grey matter in some regions of the prefrontal cortex and several subcortical structures in patients with juvenile shift like schizophrenia (F20 ICD-10).

MATERIAL AND METHODS

Forty-three young male patients and 54 mentally healthy men without family history of mental diseases underwent structural MRI with T1 high resolution images.

RESULTS

As compared to mentally healthy subjects, there was a decrease of grey matter thickness in all tested regions of the prefrontal cortex in patients. No between-group differences in subcortical structures volumes were found. No correlations between structural changes and psychopathological symptoms were observed.

CONCLUSION

Structural abnormalities of the frontal lobes in juvenile shift like schizophrenia are not associated with severity of psychopathological symptoms.

Structural and functional alterations in the brain gray matter among first-degree relatives of schizophrenia patients: A multimodal meta-analysis of fMRI and VBM studies

OBJECTIVE

We conducted a multimodal coordinate-based meta-analysis (CBMA) to investigate structural and functional brain alterations in first-degree relatives of schizophrenia patients (FRs).

METHODS

We conducted a systematic literature search from electronic databases to find studies that examined differences between FRs and healthy controls using whole-brain functional magnetic resonance imaging (fMRI) or voxel-based morphometry (VBM). A CBMA of 30 fMRI (754 FRs; 959 controls) and 11 VBM (885 FRs; 775 controls) datasets were conducted using the anisotropic effect-size version of signed differential mapping. Further, we conducted separate meta-analyses about functional alterations in different cognitive tasks: social cognition, executive functioning, working memory, and inhibitory control.

RESULTS

FRs showed higher fMRI activation in the right frontal gyrus during cognitive tasks than healthy controls. In VBM studies, there were no differences in gray matter density between FRs and healthy controls. Furthermore, multi-modal meta-analysis obtained no differences between FRs and healthy controls. By utilizing the BrainMap database, we showed that the brain region which showed functional alterations in FRs (i) overlapped only slightly with the brain regions that were affected in the meta-analysis of schizophrenia patients and (ii) correlated positively with the brain regions that exhibited increased activity during cognitive tasks in healthy individuals.

CONCLUSIONS

Based on this meta-analysis, FRs may exhibit only minor functional alterations in the brain during cognitive tasks, and the alterations are much more restricted and only slightly overlapping with the regions that are affected in schizophrenia patients. The familial risk did not relate to structural alterations in the gray matter.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Effect of aerobic exercise combined with cognitive remediation on cortical thickness and prediction of social adaptation in patients with schizophrenia

Aerobic exercise is a promising intervention for patients with schizophrenia, but structural neuroplastic effects on brain regions relevant to the pathophysiology of the disease remain unclear. This study aimed to elucidate longitudinal changes in cortical thickness after aerobic exercise intervention in schizophrenia patients and the relationship of these changes to clinical correlates. We investigated 21 schizophrenia patients and 23 healthy controls who performed aerobic exercise and 21 schizophrenia patients who played table soccer. The 12-week exercise intervention was combined with computer-assisted cognitive remediation training from week 6 to week 12. Magnetic resonance imaging (MRI) scans were acquired at baseline and weeks 6, 12, and 24. The thickness of the entorhinal, parahippocampal, and lateral and medial prefrontal cortices was assessed with FreeSurfer 6.0. The schizophrenia aerobic exercise group showed a significant increase of cortical thickness in the right entorhinal cortex at week 6, and we found a significant correlation between the cortical thickness of the right lateral prefrontal cortex at baseline and improvement of social adaptation at week 12. In the schizophrenia table soccer and healthy control groups, we found no significant longitudinal change in cortical thickness through the intervention and follow-up period and no correlation of cortical thickness at baseline with clinical measures. Our results suggest that aerobic exercise in schizophrenia modulates the thickness of the entorhinal cortex, a structure adjacent to the hippocampus. Greater cortical thickness of the right lateral prefrontal cortex appears to predict better clinical response to an aerobic exercise intervention in patients with schizophrenia.

Regional brain volumes in brief psychotic disorder

Brief psychotic disorder (BPD) is a relatively rare representative of psychotic disorders. Structural brain abnormalities in BPD are not known. We compared 30 patients with BPD and 30 matched healthy controls using high-resolution structural T1-weighted magnetic resonance imaging (MRI). We performed cortical/subcortical reconstruction and volumetric segmentation using FreeSurfer v6.0. Results revealed that the caudal/rostral middle frontal cortex, superior frontal cortex, and the frontal pole were significantly smaller in patients with BPD compared to controls. The number of lifetime psychotic episodes negatively correlated with caudal middle frontal and frontal pole volumes. These results indicate structural abnormalities of the frontal cortex in BPD, which are associated with the number of psychotic relapses.

Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.

[Brain imaging in schizophrenia : A review of current trends and developments]

Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.

Magnitude and heterogeneity of brain structural abnormalities in 22q11.2 deletion syndrome: a meta-analysis

The 22q11.2 deletion syndrome (22q11.2DS) is a neurodevelopmental disorder associated with a number of volumetric brain abnormalities. The syndrome is also associated with an increased risk for neuropsychiatric disorders including schizophrenia and autism spectrum disorder. An earlier meta-analysis showed reduced grey and white matter volumes in individuals with 22q11.2DS. Since this analysis was conducted, the number of studies has increased markedly, permitting more precise estimates of effects and more regions to be examined. Although 22q11.2DS is clinically heterogeneous, it is not known to what extent this heterogeneity is mirrored in neuroanatomy. The aim of this study was thus to investigate differences in mean brain volume and structural variability within regions, between 22q11.2DS and typically developing controls. We examined studies that reported measures of brain volume using MRI in PubMed, Web of Science, Scopus and PsycINFO from inception to 1 May 2019. Data were extracted from studies in order to calculate effect sizes representing case-control difference in mean volume, and in the variability of volume (as measured using the log variability ratio (lnVR) and coefficient of variation ratio (CVR)). We found significant overall decreases in mean volume in 22q11.2DS compared with control for: total brain (g = -0.96; p < 0.001); total grey matter (g = -0.81, p < 0.001); and total white matter (g = -0.81; p < 0.001). There was also a significant overall reduction of mean volume in 22q11.2DS subjects compared with controls in frontal lobe (g = -0.47; p < 0.001), temporal lobe (g = -0.84; p < 0.001), parietal lobe (g = -0.73; p = 0.053), cerebellum (g = -1.25; p < 0.001) and hippocampus (g = -0.90; p < 0.001). Significantly increased variability in 22q11.2DS individuals compared with controls was found only for the hippocampus (VR, 1.14; p = 0.036; CVR, 1.30; p < 0.001), and lateral ventricles (VR, 1.56; p = 0.004). The results support the notion that structural abnormalities in 22q11.2DS and schizophrenia are convergent, and also to some degree with findings in autism spectrum disorder. Finally, the increased variability seen in the hippocampus in 22q11.2DS may underlie some of the heterogeneity observed in the neuropsychiatric phenotype.

Amygdala subnucleus volumes in psychosis high-risk state and first-episode psychosis

Structural and functional abnormalities of the amygdala in schizophrenia have been well documented. Post-mortem studies suggest that the lateral nucleus is particularly affected in schizophrenia. It is not known whether the amygdala subnuclei are differently affected at the time of the first-episode psychosis or already at high-risk state. 75 first-episode psychosis patients (FEP), 45 clinical high-risk patients (CHR) and 76 population controls participated in this cross-sectional case-control study. Participants underwent T1-weighted 3T MRI scans, from which the amygdala was segmented using a newly developed automated algorithm. Because early adverse events increase risk for psychosis and affect the amygdala, we also tested whether experiences of childhood maltreatment associate with the putative amygdala subnuclei abnormalities. Compared to the population controls, FEP had smaller volumes of the lateral, and basal nuclei. In CHR, only the lateral nucleus was significantly smaller compared to the control subjects. Experience of childhood maltreatment was inversely associated with lateral nucleus volumes in FEP but not in CHR. These results show that the lateral and basal nuclei of the amygdala are already affected in FEP. These volumetric changes may reflect specific cellular abnormalities that have been observed in post-mortem studies in schizophrenia in the same subnuclei. Decreased volume of the lateral nucleus in CHR suggest that a smaller lateral nucleus could serve as a potential biomarker for psychosis risk. Finally, we found that the lateral nucleus volumes in FEP may be sensitive to the effects of childhood maltreatment.

White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar cross-disorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Microstructural White Matter and Links With Subcortical Structures in Chronic Schizophrenia: A Free-Water Imaging Approach

Schizophrenia is a severe mental disorder with often a chronic course. Neuroimaging studies report brain abnormalities in both white and gray matter structures. However, the relationship between microstructural white matter differences and volumetric subcortical structures is not known. We investigated 30 long-term treated patients with schizophrenia and schizoaffective disorder (mean age 51.1 ± 7.9 years, mean illness duration 27.6 ± 8.0 years) and 42 healthy controls (mean age 54.1 ± 9.1 years) using 3 T diffusion and structural magnetic resonance imaging. The free-water imaging method was used to model the diffusion signal, and subcortical volumes were obtained from FreeSurfer. We applied multiple linear regression to investigate associations between (i) patient status and regional white matter microstructure, (ii) medication dose or clinical symptoms on white matter microstructure in patients, and (iii) for interactions between subcortical volumes and diagnosis on microstructural white matter regions showing significant patient-control differences. The patients had significantly decreased free-water corrected fractional anisotropy (FA_t), explained by decreased axial diffusivity and increased radial diffusivity (RD_t) bilaterally in the anterior corona radiata (ACR) and the left anterior limb of the internal capsule (ALIC) compared to controls. In the fornix, the patients had significantly increased RD_t. In patients, positive symptoms were associated with localized increased free-water and negative symptoms with localized decreased FA_t and increased RD_t. There were significant interactions between patient status and several subcortical structures on white matter microstructure and the free-water compartment for left ACR and fornix, and limited to the free-water compartment for right ACR and left ALIC. The Cohen's d effect sizes were medium to large (0.61 to 1.20, absolute values). The results suggest a specific pattern of frontal white matter axonal degeneration and demyelination and fornix demyelination that is attenuated in the presence of larger structures of the limbic system in patients with chronic schizophrenia and schizoaffective disorder. Findings warrant replication in larger samples.

Hippocampal subfield and amygdala nuclei volumes in schizophrenia patients with a history of violence

Schizophrenia (SCZ) is associated with an increased risk of violence compared to the general population. Previous studies have indicated smaller hippocampal and amygdala volumes in violent than non-violent psychotic patients^. However, little is known about volumetric differences at the subdivision level of these structures. In the present study, hippocampal subfields and amygdala nuclei volumes were estimated with FreeSurfer from 3 T MRI of SCZ patients with (SCZ-V, n = 24) and without (SCZ-NV, n = 51) a history of severe violence and 90 healthy controls (HC). Volumetric differences between groups were explored with a general linear model covarying for confounders, in addition to follow-up analyses in patient groups controlling for clinical characteristics such as antipsychotic medication, duration of illness and illicit substance use. SCZ-V had smaller total hippocampal volume and smaller CA1, HATA, fimbria, and molecular layer of DG volumes compared to HC. Total amygdala volume together with basal nucleus, accessory basal nucleus, CTA, and paralaminar nucleus volumes were smaller in SCZ-V compared to HC. In SCZ-NV, compared to HC, the observed smaller volumes were limited to basal and paralaminar nucleus. There were no significant differences in hippocampal subfield and amygdala nuclei volumes between SCZ-V and SCZ-NV. Follow-up analyses showed that the results in patient groups were not affected by clinical characteristics. The results suggest that smaller hippocampal subfield and amygdala nuclei volumes may be relevant to violence risk in SCZ. However, the neurobiological signature of violence in SCZ should be further investigated in larger cohorts.

Total gray matter volume is reduced in individuals with bipolar disorder currently treated with atypical antipsychotics

BACKGROUND/AIMS

Recent evidence indicates that the intake of atypical antipsychotics (AAP) is associated with gray matter abnormalities in patients with psychiatric disorders. We explored if patients with bipolar disorder (BD) who are medicated with AAP exhibit total gray matter volume (TGV) reduction compared to BD individuals not medicated with AAP and healthy controls (HC).

METHODS

In a cross-sectional design, 124 individuals with BD and 86 HC underwent 3T-MRI of the brain and clinical assessment as part of our BIPFAT-study. The TGV was estimated using Freesurfer. We used univariate covariance analysis (ANCOVA) to test for normalized TGV differences and controlled for covariates.

RESULTS

ANCOVA results indicated that 75 BD individuals taking AAP had significantly reduced normalized TGV as compared to 49 BD not taking AAP (F = 9.995, p = .002., Eta = 0.084) and 86 HC (F = 7.577, p = .007, Eta = 0.046).

LIMITATIONS

Our cross-sectional results are not suited to draw conclusions about causality. We have no clear information on treatment time and baseline volumes before drug treatment in the studied subjects. We cannot exclude that patients received different psychopharmacologic medications prior to the study point. We did not included dosages into the calculation. Many BD individuals received combinations of psychopharmacotherapy across drug classes. We did not have records displaying quantitative alcohol consumption and drug abuse in our sample.

CONCLUSIONS

Our data provide further evidence for the impact of AAP on brain structure in BD. Longitudinal studies are needed to investigate the causal directions of the proposed relationships.

Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1-2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.

Structural abnormality in schizophrenia versus bipolar disorder: A whole brain cortical thickness, surface area, volume and gyrification analyses

OBJECTIVES

The profiles of cortical abnormalities in schizophrenia and bipolar disorder, and how far they resemble each other, have only been studied to a limited extent. The aim of this study was to identify and compare the changes in cortical morphology associated with these pathologies.

METHODS

A total of 384 subjects, including 128 patients with schizophrenia, 128 patients with bipolar disorder and 127 sex-age-matched healthy subjects, were examined using cortical surface-based morphology. Four cortical structural measures were studied: cortical volume (CV), cortical thickness (CT), surface area (SA) and gyrification index (GI). Group comparisons for each separate cortical measure were conducted.

RESULTS

At a threshold of P = 0.05 corrected, both patient groups showed significant widespread CV and CT reductions in similar areas compared to healthy subjects. However, the changes in schizophrenia were more pronounced. While CV decrease in bipolar disorder was exclusively explained by cortical thinning, in schizophrenia it was driven by changes in CT and partially by SA. Reduced GI was only found in schizophrenia. The direct comparison between both disorders showed significant reductions in all measures in patients with schizophrenia.

CONCLUSIONS

Cortical volume and cortical thickness deficits are shared between patients with schizophrenia and bipolar disorder, suggesting that both pathologies may be affected by similar environmental and neurodegenerative factors. However, the exclusive alteration in schizophrenia of metrics related to the geometry and curvature of the brain cortical surface (SA, GI) suggests that this group is influenced by additional neurodevelopmental and genetic factors.

Identifying schizophrenia subgroups using clustering and supervised learning

Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are highly heterogeneous and intimately linked to prognosis. In this study, we present a method to predict individual symptom profiles by first deriving clinical subgroups and then using machine learning methods to perform subject-level classification based on magnetic resonance imaging (MRI) derived neuroanatomical measures. Symptomatic and MRI data of 167 subjects were used. Subgroups were defined using hierarchical clustering of clinical data resulting in 3 stable clusters: 1) high symptom burden, 2) predominantly positive symptom burden, and 3) mild symptom burden. Cortical thickness estimates were obtained in 78 regions of interest and were input, along with demographic data, into three machine learning models (logistic regression, support vector machine, and random forest) to predict subgroups. Random forest performance metrics for predicting the group membership of the high and mild symptom burden groups exceeded those of the baseline comparison of the entire schizophrenia population versus normal controls (AUC: 0.81 and 0.78 vs. 0.75). Additionally, an analysis of the most important features in the random forest classification demonstrated consistencies with previous findings of regional impairments and symptoms of schizophrenia.

Imaging-Based Subtyping for Psychiatric Syndromes

Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.

Neuroimaging in Schizophrenia

Schizophrenia is a chronic psychotic disorder with a lifetime prevalence of about 1%. Onset is typically in adolescence or early adulthood; characteristic symptoms include positive symptoms, negative symptoms, and impairments in cognition. Neuroimaging studies have shown substantive evidence of brain structural, functional, and neurochemical alterations that are more pronounced in the association cortex and subcortical regions. These abnormalities are not sufficiently specific to be of diagnostic value, but there may be a role for imaging techniques to provide predictions of outcome. Incorporating multimodal imaging datasets using machine learning approaches may offer better diagnostic and predictive value in schizophrenia.

Structural Brain Alterations in Youth With Psychosis and Bipolar Spectrum Symptoms

OBJECTIVE

Adults with established diagnoses of serious mental illness (bipolar disorder and schizophrenia) exhibit structural brain abnormalities, yet less is known about how such abnormalities manifest earlier in development.

METHOD

Cross-sectional data publicly available from the Philadelphia Neurodevelopmental Cohort (PNC) were analyzed. Structural magnetic resonance neuroimaging data were collected on a subset of the PNC (N = 989; 9-22 years old). Cortical thickness, surface area (SA), and subcortical volumes were calculated. Study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376), psychosis spectrum (PS; n = 113), bipolar spectrum (BP; n = 117), and BP + PS (n = 109). Group and developmental differences in structural magnetic resonance neuroimaging measures were examined. In addition, the extent to which any structural aberration was related to neurocognition, global functioning, and clinical symptomatology was examined.

RESULTS

Compared with other groups, PS youth exhibited significantly decreased SA in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups. The strongest effects for precentral and posterior cingulate SA decreases were seen during early adolescence (13-15 years old) in PS youth. The strongest effects for decreases in thalamic volume and orbitofrontal and postcentral SA were observed in mid-adolescence (16-18 years) in PS youth. Across groups, better overall functioning was associated with increased lateral orbitofrontal SA. Increased postcentral SA was associated with better executive cognition and less severe negative symptoms in the entire sample.

CONCLUSION

In a community-based sample, decreased cortical SA and thalamic volume were present early in adolescent development in youth with PS symptoms, but not in youth with BP symptoms or with BP and PS symptoms. These findings point to potential biological distinctions between PS and BP conditions, which could suggest additional biomarkers relevant to early identification.

Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen’s d = −0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.

Changes in brain structure asymmetry have been reported in autism spectrum disorder. Here the authors investigate this issue using a large-scale sample consisting of 54 data sets.

Abnormal auditory tonotopy in patients with schizophrenia

Auditory hallucinations are among the most prevalent and most distressing symptoms of schizophrenia. Despite significant progress, it is still unclear whether auditory hallucinations arise from abnormalities in primary sensory processing or whether they represent failures of higher-order functions. To address this knowledge gap, we capitalized on the increased spatial resolution afforded by ultra-high field imaging at 7 Tesla to investigate the tonotopic organization of the auditory cortex in patients with schizophrenia with a history of recurrent hallucinations. Tonotopy is a fundamental feature of the functional organization of the auditory cortex that is established very early in development and predates the onset of symptoms by decades. Compared to healthy participants, patients showed abnormally increased activation and altered tonotopic organization of the auditory cortex during a purely perceptual task, which involved passive listening to tones across a range of frequencies (88–8000 Hz). These findings suggest that the predisposition to auditory hallucinations is likely to be predicated on abnormalities in the functional organization of the auditory cortex and which may serve as a biomarker for the early identification of vulnerable individuals.