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Background and HypothesisSchizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks.Study DesignWe used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom.Study ResultsGCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.ConclusionsThe present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), share clinical and neurobiological features. Because previous investigations of functional dysconnectivity have mainly focused on single disorders, the transdiagnostic alterations in the functional connectome architecture of the brain remain poorly understood. We collected resting-state functional magnetic resonance imaging data from 512 participants, including 121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls. Individual functional brain connectomes were constructed in a voxelwise manner, and the modular architectures were examined at different scales, including (1) global modularity, (2) module-specific segregation and intra- and intermodular connections, and (3) nodal participation coefficients. The correlation of these modular measures with clinical scores was also examined. We reliably identify common alterations in modular organization in patients compared to controls, including (1) lower global modularity; (2) lower modular segregation in the frontoparietal, subcortical, visual, and sensorimotor modules driven by more intermodular connections; and (3) higher participation coefficients in several network connectors (the dorsolateral prefrontal cortex and angular gyrus) and the thalamus. Furthermore, the alterations in the SCZ group are more widespread than those of the BD and MDD groups and involve more intermodular connections, lower modular segregation and higher connector integrity. These alterations in modular organization significantly correlate with clinical scores in patients. This study demonstrates common hyper-integrated modular architectures of functional brain networks among patients with SCZ, BD, and MDD. These findings reveal a transdiagnostic mechanism of network dysfunction across psychiatric disorders from a connectomic perspective.

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

Neuroimaging studies of schizophrenia have indicated that the development of auditory verbal hallucinations (AVHs) is associated with altered structural and functional connectivity within the perisylvian language network. However, these studies focussed mainly on either structural or functional alterations in patients with chronic schizophrenia. Therefore, they were unable to examine the relationship between the 2 types of measures and could not establish whether the observed alterations would be expressed in the early stage of the illness. We used diffusion tensor imaging and functional magnetic resonance imaging to examine white matter integrity and functional connectivity within the left perisylvian language network of 46 individuals with an at risk mental state for psychosis or a first episode of the illness, including 28 who had developed AVH group and 18 who had not (nonauditory verbal hallucination [nAVH] group), and 22 healthy controls. Inferences were made at P < .05 (corrected). The nAVH group relative to healthy controls showed a reduction of both white matter integrity and functional connectivity as well as a disruption of the normal structure−function relationship along the fronto-temporal pathway. For all measures, the AVH group showed intermediate values between healthy controls and the nAVH group. These findings seem to suggest that, in the early stage of the disorder, a significant impairment of fronto-temporal connectivity is evident in patients who do not experience AVHs. This is consistent with the hypothesis that, whilst mild disruption of connectivity might still enable the emergence of AVHs, more severe alterations may prevent the occurrence of the hallucinatory experience.

Background: Functional neuroimaging studies of schizophrenia have identified abnormal activations in many brain regions. In an effort to interpret these findings from a network perspective, we carried out a meta-analysis of this literature, mapping anatomical locations of under- and over-activation to the topology of a normative human functional connectome. Methods: We included 314 task-based functional neuroimaging studies including more than 5000 patients with schizophrenia and over 5000 controls. Coordinates of significant under- or over-activations in patients relative to controls were mapped to nodes of a normative connectome defined by a prior meta-analysis of 1641 functional neuroimaging studies of task-related activation in healthy volunteers. Results: Under-activations and over-activations were reported in a wide diversity of brain regions. Both under- and over-activations were significantly more likely to be located in hub nodes that constitute the “rich club” or core of the normative connectome. In a subset of 121 studies that reported both under- and over-activations in the same patients, we found that, in network terms, these abnormalities were located in close topological proximity to each other. Under-activation in a peripheral node was more frequently associated specifically with over-activation of core nodes than with over-activation of another peripheral node. Conclusions: Although schizophrenia is associated with altered brain functional activation in a wide variety of regions, abnormal responses are concentrated in hubs of the normative connectome. Task-specific under-activation in schizophrenia is accompanied by over-activation of topologically central, less functionally specialized network nodes, which may represent a compensatory response.

It remains unclear whether brain structural abnormalities observed before the onset of psychosis are specific to schizophrenia or are common to all psychotic disorders. This study aimed to measure regional gray matter volume prior to the onset of schizophreniform and of affective psychoses. We investigated 102 subjects at ultrahigh risk (UHR) of developing psychosis recruited from the Personal Assessment and Crisis Evaluation Clinic in Melbourne, Australia. Twenty-eight of these subjects developed psychosis subsequent to scanning: 19 schizophrenia, 7 affective psychoses, and 2 other psychoses. We examined regional gray matter volume using 1.5 mm thick, coronal, 1.5 Tesla magnetic resonance imaging and voxel-based morphometry methods of image analysis. Subjects were scanned at presentation and were followed up clinically for a minimum of 12 months, to detect later transition to psychosis. We found that both groups of subjects who subsequently developed psychosis (schizophrenia and affective psychosis) showed reductions in the frontal cortex relative to UHR subjects who did not develop psychosis. The subgroup that subsequently developed schizophrenia also showed smaller volumes in the parietal cortex and, at trend level, in the temporal cortex, whereas those who developed an affective psychosis had significantly smaller subgenual cingulate volumes. These preliminary findings suggest that volumetric abnormalities in UHR individuals developing schizophrenia vs affective psychoses comprise a combination of features that predate both disorders and others that may be specific to the nature of the subsequent disorder.

Background Structural abnormalities in first episode psychosis (FEP) tend to be subtle and widespread. Most studies investigating structural abnormalities in this clinical population have used small samples, and therefore may be under-powered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, findings from existing studies have often been heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP relative to healthy controls that are expressed consistently across five independent datasets. Methods Structural Magnetic Resonance Imaging data were acquired from a total of 572 patients with FEP and 502 age and gender comparable healthy controls (HC) at five sites - London (UK), Utrecht (Netherlands), Chengdu (China) and two independent sites at Santander (Spain). Voxel-based morphometry (VBM) as implemented in Statistical Parametric Mapping software (SPM12) was used to investigate differences in gray matter volume (GMV) between the two groups. The statistical analysis was carried out using an analysis of variance (ANCOVA), with diagnostic group and scanning site as factors, and age and gender as covariates of no interest. Neuroanatomical alterations in patients with FEP relative to HC common to the five datasets were identified by comparing the total FEP group against the total HC group, and then using the inclusive masking option (at p

Background Long-term outcomes for individuals at risk of developing psychosis are heterogeneous; some develop a psychotic disorder, others continue to experience attenuated psychotic symptoms (APS) and some experience clinical remission and functional recovery. Existing UHR literature is primarily vulnerability- and disease-focused. In recent years, there has been a gradual shift in research to focus on more favourable outcomes, yet despite positive findings, very few UHR studies have directly investigated or even reported good outcomes in this population. Perhaps one major obstacle for this research is the lack of a sound definition of what constitutes a ‘good’ outcome for UHR individuals. The current study uses the Delphi method to systematically reach a consensus definition, amongst UHR clinical and research experts, of good outcome in this clinical population. To our knowledge, this is the first UHR-focused study to utilize the Delphi method. Methods A three-round online Delphi study was conducted and n=135 UHR-expert clinicians and researchers drawn from multiple continents were invited to take part. In Round 1, participants were asked to: i) select from a list of items those which they considered most important to the definition of good outcome in UHR and ii) make suggestions on ways in which good outcome could be determined in a standardised way. In Round 2, participants were asked rate the importance of each item to good outcome in UHR individuals, on a 5-point Likert scale. According to the proportion of participants who rated the items as ‘essential’ or ‘important’, items were: i) accepted as part of the consensus and included as a standard if rated by ≥80% or more of the group, ii) re-introduced in the third round and participants were given the opportunity to re-rate them if rated by between 50–79% of the group or iii) excluded if rated by less than 50% of participants. Results Forty-six (34.1%) participants responded to the first round of the Delphi process, 39 (84.7% retention rate) responded to Round 2, and 30 (76.9% retention rate) to Round 3. Of the 46 UHR-experts, 20 were psychiatrists, 17 were psychologists, 8 were researchers/lecturers and 1 was a social worker. Fifteen items were endorsed by ≥80% of the expert-participants as ‘essential’ or ‘important’ for defining UHR-specific good outcome at one-year follow-up. Items fell into one of the following categories: functioning, symptoms, other clinically relevant factors or personal wellbeing. ‘Daily functional capacity’ and and ‘self-reported improvement in mental health’ were rated as ‘essential’ to defining good outcome for an UHR individual at one-year, by 100% of the Delphi sample. A reduction in the distress associated with APS was deemed ‘important’ by 92.1% of the sample, more so than the complete remission of APS. Many similar items were rated with the same level of endorsement for the question on outcome at five-year follow-up. Twenty-one protective factors reached ≥80% endorsement for being essential or important for good outcome in UHR individuals and fell into at least one of the following categories: community support; mental health services support; cognitive factors; personal wellbeing; social network/support; substance use/abuse; daily living factors; and premorbid factors. Discussion This three-phase Delphi study achieved consensus on the core features of good outcome at one-year and five-years in the UHR population. The items that form this definition could be used in future research and clinically, to evaluate treatment and outcome of UHR individuals. They can also be of value to the development of intervention frameworks. Further studies involving other stakeholder groups, particularly individuals considered to be at risk of developing psychosis, are needed.

Background Despite an increasing research and clinical focus on trans-diagnostic approaches to mental health, it remains unclear whether different diagnostic categories share a common neuronatomical basis. The current investigation sought to investigate whether a shared (trans-diagnostic) set of structural alterations characterized schizophrenia, depression, post-traumatic stress disorder and obsessive-compulsive disorder, and determine whether any such alterations reflected markers of psychiatric illness or pre-existing familial vulnerability. Methods A total of 404 patients with a psychiatric diagnosis were recruited (psychosis, n=129; unipolar depression, n=92; post-traumatic stress disorder, n=91; obsessive compulsive disorder, n=92) alongside 201 healthy controls and 20 unaffected first-degree relatives. We collected structural Magnetic Resonance Imaging scans from each participant using the same 3.0 Tesla scanner and acquisition sequence, and tested for trans-diagnostic alterations using Voxel-Based Morphometry. Results We report that the four psychiatric groups were all characterized by significantly greater gray matter volume in the bilateral putamen relative to healthy controls (right putamen: x=32 y=6 z=-2; z-score: 5.97; p-value

BACKGROUND: The prodromal phase of psychosis is characterized by impaired executive function and altered prefrontal activation. The extent to which the severity of these deficits at presentation predicts subsequent clinical outcomes is unclear. METHODS: We employed functional magnetic resonance imaging in a cohort of subjects at clinical risk for psychosis and in healthy controls. Images were acquired at clinical presentation and again after 1 year, using a 1.5-T Signa MRI scanner while subjects were performing a verbal fluency task. SPM5 was used for the analysis of imaging data. Psychopathological assessment of the "at-risk" symptoms was performed by using the Comprehensive Assessment for the At-Risk Mental State (CAARMS) and the Positive and Negative Symptom Scale (PANSS). RESULTS: In the at-risk mental state (ARMS) group, between presentation and follow-up, the CAARMS (perceptual disorder and thought disorder subscales) and the PANSS general scores decreased, while the Global Assessment of Functioning (GAF) score increased. Both the ARMS and control groups performed the verbal fluency task with a high degree of accuracy. The ARMS group showed greater activation in the left inferior frontal gyrus but less activation in the anterior cingulate gyrus than controls. Within the ARMS group, the longitudinal normalization of neurofunctional response in the left inferior frontal gyrus was positively correlated with the improvement in severity of hallucination-like experiences. CONCLUSIONS: The normalization of the abnormal prefrontal response during executive functioning is associated with 12-month psychopathological improvement of prodromal symptoms.

Background: Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Initial applications of deep learning in neuroimaging studies of psychiatric populations, including schizophrenia, have yielded promising results. Convolutional neural networks (ConvNets) is a particularly promising deep leaning model that has yet to be tested in schizophrenia. The aim of this study was to (1) investigate how consistent is the performance of ConvNets when applied to structural MRI data to identify individuals diagnosed with schizophrenia from healthy controls and (2) examine what brain regions best contributed to distinguish the 2 groups.