PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

BACKGROUND

One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia.

METHODS

Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S).

RESULTS

Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728).

DISCUSSION

We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

Detecting schizophrenia with 3D structural brain MRI using deep learning

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.

Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium

Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called "B-SNIP1" with 711 psychosis and 274 healthy persons, and the "replication sample" with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r's from .96-.99) and temporally stable (r's from .76-.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%-89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.

A Diagnosis and Biotype Comparison Across the Psychosis Spectrum: Investigating Volume and Shape Amygdala-Hippocampal Differences from the B-SNIP Study

OBJECTIVE

Brain-based Biotypes for psychotic disorders have been developed as part of the B-SNIP consortium to create neurobiologically distinct subgroups within idiopathic psychosis, independent from traditional phenomenological diagnostic methods. In the current study, we aimed to validate the Biotype model by assessing differences in volume and shape of the amygdala and hippocampus contrasting traditional clinical diagnoses with Biotype classification.

METHODS

A total of 811 participants from 6 sites were included: probands with schizophrenia (n = 199), schizoaffective disorder (n = 122), psychotic bipolar disorder with psychosis (n = 160), and healthy controls (n = 330). Biotype classification, previously developed using cognitive and electrophysiological data and K-means clustering, was used to categorize psychosis probands into 3 Biotypes, with Biotype-1 (B-1) showing reduced neural salience and severe cognitive impairment. MAGeT-Brain segmentation was used to determine amygdala and hippocampal volumetric data and shape deformations.

RESULTS

When using Biotype classification, B-1 showed the strongest reductions in amygdala-hippocampal volume and the most widespread shape abnormalities. Using clinical diagnosis, probands with schizophrenia and schizoaffective disorder showed the most significant reductions of amygdala and hippocampal volumes and the most abnormal hippocampal shape compared with healthy controls. Biotype classification provided the strongest neuroanatomical differences compared with conventional DSM diagnoses, with the best discrimination seen using bilateral amygdala and right hippocampal volumes in B-1.

CONCLUSION

These findings characterize amygdala and hippocampal volumetric and shape abnormalities across the psychosis spectrum. Grouping individuals by Biotype showed greater between-group discrimination, suggesting a promising approach and a favorable target for characterizing biological heterogeneity across the psychosis spectrum.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

EEG microstate features for schizophrenia classification

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

Cognitive Subtyping in Schizophrenia: A Latent Profile Analysis

Cognitive dysfunction is a core feature of schizophrenia. The subtyping of cognitive performance in schizophrenia may aid the refinement of disease heterogeneity. The literature on cognitive subtyping in schizophrenia, however, is limited by variable methodologies and neuropsychological tasks, lack of validation, and paucity of studies examining longitudinal stability of profiles. It is also unclear if cognitive profiles represent a single linear severity continuum or unique cognitive subtypes. Cognitive performance measured with the Brief Assessment of Cognition in Schizophrenia was analyzed in schizophrenia patients (n = 767). Healthy controls (n = 1012) were included as reference group. Latent profile analysis was performed in a schizophrenia discovery cohort (n = 659) and replicated in an independent cohort (n = 108). Longitudinal stability of cognitive profiles was evaluated with latent transition analysis in a 10-week follow-up cohort. Confirmatory factor analysis (CFA) was carried out to investigate if cognitive profiles represent a unidimensional structure. A 4-profile solution was obtained from the discovery cohort and replicated in an independent cohort. It comprised of a "less-impaired" cognitive subtype, 2 subtypes with "intermediate cognitive impairment" differentiated by executive function performance, and a "globally impaired" cognitive subtype. This solution showed relative stability across time. CFA revealed that cognitive profiles are better explained by distinct meaningful profiles than a severity linear continuum. Associations between profiles and negative symptoms were observed. The subtyping of schizophrenia patients based on cognitive performance and its associations with symptomatology may aid phenotype refinement, mapping of specific biological mechanisms, and tailored clinical treatments.

Exploring schizophrenia spectrum psychopathology in borderline personality disorder

We have previously argued that the current borderline personality disorder (BPD) diagnosis is over-inclusive and clinically and conceptually impossible to distinguish from the schizophrenia spectrum disorders. This study involves 30 patients clinically diagnosed with BPD as their main diagnosis by three BPD dedicated outpatient treatment facilities in Denmark. The patients underwent a careful and time-consuming psychiatric evaluation involving several senior level clinical psychiatrists and researchers and a comprehensive battery of psychopathological scales. The study found that the vast majority of patients (67% in DSM-5 and 77% in ICD-10) in fact met the criteria for a schizophrenia spectrum disorder, i.e., schizophrenia (20%) or schizotypal (personality) disorder (SPD). The schizophrenia spectrum group scored significantly higher on the level of disorders of core self as measured by the Examination of Anomalous Self-Experiences Scale (EASE). The BPD criterion of "identity disturbance" was significantly correlated with the mean total score of EASE. These findings are discussed in the light of changes from prototypical to polythetic diagnostic systems. We argue that the original prototypes/gestalts informing the creation of BPD and SPD have gone into oblivion during the evolution of polythetic criteria.

Subtyping schizophrenia based on symptomatology and cognition using a data driven approach

Schizophrenia is a highly heterogeneous disorder, not only in its phenomenology but in its clinical course. This limits the usefulness of the diagnosis as a basis for both research and clinical management. Methods of reducing this heterogeneity may inform the diagnostic classification. With this in mind, we performed k-means clustering with symptom and cognitive measures to generate groups in a machine-driven way. We found that our data was best organised in three clusters: high cognitive performance, high positive symptomatology, low positive symptomatology. We hypothesized that these clusters represented biological categories, which we tested by comparing these groups in terms of brain volumetric information. We included all the groups in an ANCOVA analysis with post hoc tests, where brain volume areas were modelled as dependent variables, controlling for age and estimated intracranial volume. We found six brain volumes significantly differed between the clusters: left caudate, left cuneus, left lateral occipital, left inferior temporal, right lateral, and right pars opercularis. The k-means clustering provides a way of subtyping schizophrenia which appears to have a biological basis, though one that requires both replication and confirmation of its clinical significance.

Deconstructing Negative Symptoms in Individuals at Clinical High-Risk for Psychosis: Evidence for Volitional and Diminished Emotionality Subgroups That Predict Clinical Presentation and Functional Outcome

Negative symptoms are characteristic of schizophrenia and closely linked to numerous outcomes. A body of work has sought to identify homogenous negative symptom subgroups-a strategy that can promote mechanistic understanding and precision medicine. However, our knowledge of negative symptom subgroups among individuals at clinical high-risk (CHR) for psychosis is limited. Here, we investigated distinct negative symptom profiles in a large CHR sample (N = 244) using a cluster analysis approach. Subgroups were compared on external validators that are (1) commonly observed in the schizophrenia literature and/or (2) may be particularly relevant for CHR individuals, informing early prevention and prediction. We observed 4 distinct negative symptom subgroups, including individuals with (1) lower symptom severity, (2) deficits in emotion, (3) impairments in volition, and (4) global elevations. Analyses of external validators suggested a pattern in which individuals with global impairments and volitional deficits exhibited more clinical pathology. Furthermore, the Volition group endorsed more disorganized, anxious, and depressive symptoms and impairments in functioning compared to the Emotion group. These data suggest there are unique negative symptom profiles in CHR individuals, converging with studies in schizophrenia indicating motivational deficits may be central to this symptom dimension. Furthermore, observed differences in CHR relevant external validators may help to inform early identification and treatment efforts.

Main Symptomatic Treatment Targets in Suspected and Early Psychosis: New Insights From Network Analysis

The early detection and intervention in psychoses prior to their first episode are presently based on the symptomatic ultra-high-risk and the basic symptom criteria. Current models of symptom development assume that basic symptoms develop first, followed by attenuated and, finally, frank psychotic symptoms, though interrelations of these symptoms are yet unknown. Therefore, we studied for the first time their interrelations using a network approach in 460 patients of an early detection service (mean age = 26.3 y, SD = 6.4; 65% male; n = 203 clinical high-risk [CHR], n = 153 first-episode psychosis, and n = 104 depression). Basic, attenuated, and frank psychotic symptoms were assessed using the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS). Using the R package qgraph, network analysis of the altogether 86 symptoms revealed a single dense network of highly interrelated symptoms with 5 discernible symptom subgroups. Disorganized communication was the most central symptom, followed by delusions and hallucinations. In line with current models of symptom development, the network was distinguished by symptom severity running from SPI-A via SIPS to PANSS assessments. This suggests that positive symptoms developed from cognitive and perceptual disturbances included basic symptom criteria. Possibly conveying important insight for clinical practice, central symptoms, and symptoms "bridging" the association between symptom subgroups may be regarded as the main treatment targets, in order to prevent symptomatology from spreading or increasing across the whole network.

Safety and tolerability of olanzapine compared with other antipsychotics in the treatment of elderly patients with schizophrenia: a naturalistic study

Objective

To evaluate the safety and tolerability of olanzapine in the treatment of elderly patients with schizophrenia.

Methods

A total of 135 outpatients with schizophrenia ≥60 years of age were treated with olanzapine (n = 105) or another antipsychotic (n = 30) and followed up for 6 months. Safety measures included the recording of spontaneous adverse events and extrapyramidal symptoms (EPS). Clinical status and effectiveness of the medications were measured using the Clinical Global Impressions-Severity of Illness and the Global Assessment of Function (GAF) scales. Quality of life was assessed by means of the Spanish version of the EuroQol. The Awad scale was applied to evaluate patients’ subjective attitude towards medication.

Results

The incidence of overall adverse events and EPS was non-significantly lower in patients treated with olanzapine than in patients treated with other antipsychotics. The use of anticholinergic drugs was significantly lower (P = 0.04) in patients treated with olanzapine. Both groups of patients experienced similar improvements in Clinical Global Impressions-Severity and GAF scores. Non-significantly greater improvement in the acceptance of medication occurred at endpoint in olanzapine-treated patients than in control patients as measured by the Awad scale. The improvement in the EuroQol quality of life scale achieved at the end of study did not differ between both treatment groups.

Conclusions

Results from this naturalistic study showed that olanzapine was as safe and effective as other antipsychotic drugs in the treatment of elderly patients with schizophrenia.

Social class and type of schizophrenia

Objective

Current and past research strongly indicates a high prevalence of schizophrenia in the lower class in the USA and other stratified societies. To date, no study has tested for a connection between type of schizophrenia and socioeconomic status (SES). We tested for an interrelationship between schizophrenic subtype, SES and race.

Methods

Positive and negative symptom scales were used to evaluate 436 schizophrenic patients at a state hospital in the USA. All patients were also diagnosed by DSM standards. Social class of origin was assessed by the Occupational Classification Distributions of the U.S. Bureau of the Census. Multivariate analysis was conducted with the likelihood ratio chi-square.

Results

We uncovered a distinct propensity for deficit schizophrenia to be elevated among the poor. The finding presents as a pure SES effect since the likelihood of deficit schizophrenia does not vary by race when social class is held constant.

Conclusion

The finding is potentially an important new insight into the epidemiology of schizophrenia. It offers a better understanding for poor outcome among lower class patients in stratified societies such as the United States. It is also consistent with longitudinal research by European investigators.

A Non-D2-Receptor-Binding Drug for the Treatment of Schizophrenia

BACKGROUND

An oral compound, SEP-363856, that does not act on dopamine D2 receptors but has agonist activity at trace amine-associated receptor 1 (TAAR1) and 5-hydroxytryptamine type 1A (5-HT_1A) receptors, may represent a new class of psychotropic agent for the treatment of psychosis in schizophrenia.

METHODS

We performed a randomized, controlled trial to evaluate the efficacy and safety of SEP-363856 in adults with an acute exacerbation of schizophrenia. The patients were randomly assigned in a 1:1 ratio to receive once-daily treatment with SEP-363856 (50 mg or 75 mg) or placebo for 4 weeks. The primary end point was the change from baseline in the total score on the Positive and Negative Symptom Scale (PANSS; range, 30 to 210; higher scores indicate more severe psychotic symptoms) at week 4. There were eight secondary end points, including the changes from baseline in the scores on the Clinical Global Impressions Severity (CGI-S) scale and the Brief Negative Symptom Scale (BNSS).

RESULTS

A total of 120 patients were assigned to the SEP-363856 group and 125 to the placebo group. The mean total score on the PANSS at baseline was 101.4 in the SEP-363856 group and 99.7 in the placebo group, and the mean change at week 4 was -17.2 points and -9.7 points, respectively (least-squares mean difference, -7.5 points; 95% confidence interval, -11.9 to -3.0; P = 0.001). The reductions in the CGI-S and BNSS scores at week 4 were generally in the same direction as those for the primary outcome, but the results were not adjusted for multiple comparisons. Adverse events with SEP-363856 included somnolence and gastrointestinal symptoms; one sudden cardiac death occurred in the SEP-363856 group. The incidence of extrapyramidal symptoms and changes in the levels of lipids, glycated hemoglobin, and prolactin were similar in the trial groups.

CONCLUSIONS

In this 4-week trial involving patients with an acute exacerbation of schizophrenia, SEP-363856, a non-D2-receptor-binding antipsychotic drug, resulted in a greater reduction from baseline in the PANSS total score than placebo. Longer and larger trials are necessary to confirm the effects and side effects of SEP-363856, as well as its efficacy relative to existing drug treatments for patients with schizophrenia. (Funded by Sunovion Pharmaceuticals; ClinicalTrials.gov number, NCT02969382.).

Towards an ICF Core Set for functioning assessment in severe mental disorders: Commonalities in bipolar disorder, depression and schizophrenia

BACKGROUND

The International Classification of Functioning, Disability and Health (ICF) offers an internationally accepted standard for describing and assessing functioning and disability in any health condition. A specific list of ICF categories, an ICF Core Set (CS), has been developed for bipolar disorder, depression and schizophrenia. The aim of this study was to determine commonalities in the ICF-CSs for these three disorders, and to identify relevant categories for the development of tentative ICF-CSs for severe mental disorders in general.

METHODS

The ICF categories of all three mental health conditions were examined and compared.

RESULTS

Comparison of the Comprehensive ICF-CSs for the three mental health conditions revealed a set of 34 common categories (i.e., 10 from the Body functions component, 14 from the Activities and participation component, and 10 Environmental factors ). These categories formed the proposed Comprehensive ICF-CS for severe mental disorders. A total of 11 categories were common to the Brief ICF-CSs of the three mental health conditions, and these formed the Brief ICF-CS for severe mental disorders (i.e., 3 from the Body functions component, 6 from the Activities and participation component, and 2 Environmental factors ). All the categories included refer to key aspects of functioning for severe mental disorders.

CONCLUSIONS

The proposed ICF-CSs for severe mental disorders may be applicable across a number of psychotic and affective disorders and they should prove useful for mental health services whose care remit covers a range of conditions.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

BACKGROUND

A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.

METHODS

Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.

RESULTS

Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.

CONCLUSIONS

In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)

Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.

Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification

BACKGROUND

Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices.

METHODS

Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis.

RESULTS

Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p < .001] and decreased FT in a highly-connected state [p < .001]. SZ further showed decreased number of transitions between states [p < .001], decreased FT in a widely-connected state [p < .001], increased dwell time (DT) in the weakly-connected state [p < .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates.

CONCLUSIONS

Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.

Classification of schizophrenia by intersubject correlation in functional connectome

Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.

Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study

BACKGROUND

The value of the nosological distinction between non-affective and affective psychosis has frequently been challenged. We aimed to investigate the transdiagnostic dimensional structure and associated characteristics of psychopathology at First Episode Psychosis (FEP). Regardless of diagnostic categories, we expected that positive symptoms occurred more frequently in ethnic minority groups and in more densely populated environments, and that negative symptoms were associated with indices of neurodevelopmental impairment.

METHOD

This study included 2182 FEP individuals recruited across six countries, as part of the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study. Symptom ratings were analysed using multidimensional item response modelling in Mplus to estimate five theory-based models of psychosis. We used multiple regression models to examine demographic and context factors associated with symptom dimensions.

RESULTS

A bifactor model, composed of one general factor and five specific dimensions of positive, negative, disorganization, manic and depressive symptoms, best-represented associations among ratings of psychotic symptoms. Positive symptoms were more common in ethnic minority groups. Urbanicity was associated with a higher score on the general factor. Men presented with more negative and less depressive symptoms than women. Early age-at-first-contact with psychiatric services was associated with higher scores on negative, disorganized, and manic symptom dimensions.

CONCLUSIONS

Our results suggest that the bifactor model of psychopathology holds across diagnostic categories of non-affective and affective psychosis at FEP, and demographic and context determinants map onto general and specific symptom dimensions. These findings have implications for tailoring symptom-specific treatments and inform research into the mood-psychosis spectrum.

DLPFC transcriptome defines two molecular subtypes of schizophrenia

Little is known about the molecular pathogenesis of schizophrenia, possibly because of unrecognized heterogeneity in diagnosed patient populations. We analyzed gene expression data collected from the dorsolateral prefrontal cortex (DLPFC) of post-mortem frozen brains of 189 adult diagnosed schizophrenics and 206 matched controls. Transcripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to controls at Bonferroni-corrected significance levels. Seventeen of those genes are differentially expressed at very high significance levels (<10-8 after Bonferroni correction). The findings were closely replicated in a dataset from an entirely unrelated source. The statistical significance of this differential gene expression is being driven by about half of the schizophrenic DLPFC samples, and importantly, it is the same half of the samples that is driving the significance for almost all of the differentially expressed transcripts. Weighted gene co-expression network analysis (WGCNA) of the schizophrenic subjects, based on the transcripts differentially expressed in the schizophrenics as compared to controls, divides them into two groups. "Type 1" schizophrenics have a DLPFC transcriptome similar to that of controls with only four differentially expressed genes identified. "Type 2" schizophrenics have a DLPFC transcriptome dramatically different from that of controls, with 3529 expression array probes to 3092 genes detecting transcripts that are differentially expressed at very high significance levels. These findings were re-tested and replicated in a separate independent cohort, using the RNAseq data from the DLPFC of an independent set of schizophrenics and control subjects. We suggest the hypothesis that these striking differences in DLPFC transcriptomes, identified and replicated in two populations, imply a fundamental biologic difference between these two groups of diagnosed schizophrenics, and we propose specific paths for further testing and expanding the hypothesis.

Convergent and divergent altered patterns of default mode network in deficit and non-deficit schizophrenia

OBJECTIVE

Previous studies suggested likely mechanisms underlying the dysfunction of the default mode network (DMN) in schizophrenia. However, altered patterns of the intrinsic activity of the DMN in both deficit schizophrenia (DS) and non-deficit schizophrenia (NDS) patients, as well as the neurocognitive relationships among them, remain unknown. This study explores the resting-state characteristics of the DMN activity in both DS and NDS patients, and further investigates correlations with neurocognitive features.

METHODS

Demographic, resting-state functional MRI, and neurocognitive data were collected in 37 DS and 38 NDS patients, as well as in 38 matched healthy control subjects (HCs). Independent component analysis was conducted to investigate the characteristics of DMN activity and to further distinguish between common and specific altered regions. In addition, partial correlation analysis was conducted to examine associations between the activity of altered regions and neurocognitive assessments.

RESULTS

Overlapping altered brain activity was observed in both DS and NDS patients in the left middle frontal gyrus (MFG), the left angular gyrus (ANG), and the calcarine sulcus (CAL) region. Furthermore, compared to HCs, DS patients showed less activity in the right inferior temporal gyrus, the right para-hippocampal gyrus / hippocampus (PHP / HIP), and the left precuneus (PCUN), while they showed increased activity in the posterior cingulate cortex (PCC). Notably, NDS patients showed less activity in the bilateral middle occipital gyrus. Correlation analysis indicated that, in the DS group, both Trail Making Test (TMT)-B and spatial processing scores were positively associated with the activities of the left PCUN and the right PHP / HIP, while the Stroop color scores were negatively associated with PCC activity. In the NDS group, the TMT-B scores were associated with activities of the left MFG and CAL regions, while the scores of the Wechsler adult intelligence scale (Chinese revision) were negatively associated with CAL region activity.

CONCLUSION

The present study demonstrates convergent and divergent altered patterns of the DMN in both DS and NDS patients. Importantly, the specific altered regions of the DMN in DS patients may be associated with extensive deficient neurocognition, indicating novel insights into the pathogenesis of cognitive impairment in schizophrenia.

Long-term cognitive trajectories and heterogeneity in patients with schizophrenia and their unaffected siblings

OBJECTIVE

This study aimed to assess the heterogeneity and stability of cognition in patients with a non-affective psychotic disorder and their unaffected siblings. In addition, we aimed to predict the cognitive subtypes of siblings by their probands.

METHOD

Assessments were conducted at baseline, 3 and 6 years in 1119 patients, 1059 siblings and 586 controls from the Genetic Risk and Outcome of Psychosis (GROUP) study. Group-based trajectory modeling was applied to identify trajectories and clustered multinomial logistic regression analysis was used for prediction modeling. A composite score of eight neurocognitive tests was used to measure cognitive performance.

RESULTS

Five stable cognitive trajectories ranging from severely altered to high cognitive performance were identified in patients. Likewise, four stable trajectories ranging from moderately altered to high performance were found in siblings. Siblings had a higher risk of cognitive alteration when patients' alteration was mild (OR = 2.21), moderate (OR = 5.70), and severe (OR = 10.07) compared with patients with intact cognitive function. The familial correlation coefficient between pairs of index patients and their siblings was 0.27 (P = 0.003).

CONCLUSIONS

The cognitive profiles identified in the current study might be suitable as endophenotypes and could be used in future genetic studies and predicting functional and clinical outcomes.

Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.

TNF-α and IL-6 are associated with the deficit syndrome and negative symptoms in patients with chronic schizophrenia

INTRODUCTION

Increased inflammatory markers have been found in patients with chronic schizophrenia, and have been associated with negative symptoms. The deficit syndrome is a distinct subtype of schizophrenia, characterized by primary and enduring negative symptoms.

METHOD

We measured inflammatory markers in patients with and without deficit schizophrenia and controls.

RESULTS

Using multivariate analyses, tumor necrosis factor (TNF)-α and interleukin-6 were associated with the deficit syndrome, and TNF-α predicted blunted affect, alogia, and total negative symptoms.

CONCLUSIONS

Findings suggest that deficit schizophrenia subtype is associated with increased inflammation and immunotherapies may be a novel target for negative symptoms.

The Latent Taxonicity of Schizotypy in Biological Siblings of Probands With Schizophrenia

If schizotypy is a taxonic liability for schizophrenia with a general population prevalence of ~10%, it should also be taxonic among biological siblings of probands with schizophrenia. Moreover, assuming this is so, siblings' schizotypy class membership should be predicted by probands' familial load for psychotic disorder and clinical severity, consistent with a multifactorial polygenic threshold model of schizophrenia. We tested these hypotheses in the Genetic Risk and Outcome of Psychosis (GROUP) Study where siblings of probands (n = 792) and unaffected controls (n = 559) provided self-report ratings on the Community Assessment of Psychic Experiences (CAPE). Maximum covariance analyses of control group ratings led to the identification of CAPE items sensitive to nonredundant positive and negative schizotypy classes in the control group (prevalence = 7.9% and 11.1%, respectively). When the same taxonic solution was applied to siblings' CAPE rating, taxometric analyses yielded evidence for larger positive and negative schizotypy classes among siblings (prevalence = 14.1% and 21.8%, respectively). Whereas probands' familial loads for bipolar disorder or drug use disorders did not predict siblings' membership in the schizotypy classes, probands' familial load for psychotic disorder did. Siblings were more likely to be members of the positive schizotypy class where their probands were more severely affected. The pattern of findings is consistent with Meehl's argument that schizotypy reflects liability for schizophrenia.

Autism: a transdiagnostic, dimensional, construct of reasoning?

The concept of autism has changed across time, from the Bleulerian concept, which defined it as one of several symptoms of dementia praecox, to the present-day concept representing a pervasive development disorder. The present theoretical contribution to this special issue of EJN on autism introduces new theoretical ideas and discusses them in light of selected prior theories, clinical examples, and recent empirical evidence. The overall aim is to identify some present challenges of diagnostic practice and autism research and to suggest new pathways that may help direct future research. Future research must agree on the definitions of core concepts such as autism and psychosis. A possible redefinition of the concept of autism may be a condition in which the rationale of an individual's behaviour differs qualitatively from that of the social environment due to characteristic cognitive impairments affecting reasoning. A broad concept of psychosis could focus on deviances in the experience of reality resulting from impairments of reasoning. In this light and consistent with recent empirical evidence, it may be appropriate to redefine dementia praecox as a developmental disorder of reasoning. A future challenge of autism research may be to develop theoretical models that can account for the impact of complex processes acting at the social level in addition to complex neurobiological and psychological processes. Such models could profit from a distinction among processes related to (i) basic susceptibility, (ii) adaptive processes and (iii) decompensating factors involved in the development of manifest illness.

[Schizophrenia is a clinically and biologically heterogeneous syndrome]

Currently psychiatric diagnoses are solely based on clinical psychopathological criteria, and do not involve objective biological markers. However, a wealth of neuropsychiatric research supports, that schizophrenia is both clinically and biologically a heterogeneous syndrome. Perspectives for neurobiological subgrouping of patients with schizophrenia, targeted treatment regimens as well as clarification of early causal risk factors envision, that in the coming years objective neuropsychiatric paradigms will be implemented in clinical practice.

The slow death of the concept of schizophrenia and the painful birth of the psychosis spectrum

The concept of schizophrenia only covers the 30% poor outcome fraction of a much broader multidimensional psychotic syndrome, yet paradoxically has become the dominant prism through which everything 'psychotic' is observed, even affective states with mild psychosis labelled 'ultra-high risk' (for schizophrenia). The inability of psychiatry to frame psychosis as multidimensional syndromal variation of largely unpredictable course and outcome - within and between individuals - hampers research and recovery-oriented practice. 'Psychosis' remains firmly associated with 'schizophrenia', as evidenced by a vigorous stream of high-impact but non-replicable attempts to 'reverse-engineer' the hypothesized biological disease entity, using case-control paradigms that cannot distinguish between risk for illness onset and risk for poor outcome. In this paper, the main issues surrounding the concept of schizophrenia are described. We tentatively conclude that with the advent of broad spectrum phenotypes covering autism and addiction in DSM5, the prospect for introducing a psychosis spectrum disorder - and modernizing psychiatry - appears to be within reach.

Alive but not well: the limited validity but continued utility of the concept of schizophrenia

Guloksuz & van Os boldly challenge the status quo as pertains to schizophrenia. In 'The Slow Death of the Concept of Schizophrenia, and the Painful Birth of the Psychosis Spectrum' (Guloksuz & van Os, 2017) they thoughtfully review long-standing concerns about this diagnostic category and present a new conceptualization. The authors question the validity of the schizophrenia concept citing variable clinical outcomes, transdiagnostic manifestations of psychosis, and the difficulty in identifying biomarkers, among other concerns. They also point toward the over-representation of schizophrenia in the psychosis literature and lament that patients and clinicians have come to associate this illness with predominantly poor outcomes. Finally, they propose removing the diagnosis of schizophrenia from the diagnostic nomenclature and instituting a broad new classification system, 'psychosis spectrum disorder' (PSD), to capture the many manifestations of psychosis. In this commentary, we advise against the institution of a psychosis spectrum due to the potential negative effects this framework would have on clinical care and progress in biological research.

Defining functioning levels in patients with schizophrenia: A combination of a novel clustering method and brain SPECT analysis

This study aims to define functioning levels of patients with schizophrenia by using a method of interpretable clustering based on a specific functioning scale, the Functional Remission Of General Schizophrenia (FROGS) scale, and to test their validity regarding clinical and neuroimaging characterization. In this observational study, patients with schizophrenia have been classified using a hierarchical top-down method called clustering using unsupervised binary trees (CUBT). Socio-demographic, clinical, and neuroimaging SPECT perfusion data were compared between the different clusters to ensure their clinical relevance. A total of 242 patients were analyzed. A four-group functioning level structure has been identified: 54 are classified as "minimal", 81 as "low", 64 as "moderate", and 43 as "high". The clustering shows satisfactory statistical properties, including reproducibility and discriminancy. The 4 clusters consistently differentiate patients. "High" functioning level patients reported significantly the lowest scores on the PANSS and the CDSS, and the highest scores on the GAF, the MARS and S-QoL 18. Functioning levels were significantly associated with cerebral perfusion of two relevant areas: the left inferior parietal cortex and the anterior cingulate. Our study provides relevant functioning levels in schizophrenia, and may enhance the use of functioning scale.

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

Very Late-Onset Schizophrenia-Like Psychosis: A Clinical Update

Psychosis symptoms (delusions and hallucinations) are multifactorial in origin and, in later life, occur in the context of schizophrenia, delirium, dementia, delusional and schizophrenia-like disorders, mood disorders, and alcohol or substance abuse. The current article provides a clinical overview of very late-onset (after age 60) schizophrenia-like psychosis (VLOSLP), summarizing the literature on treatment options and reflecting on the role of psychiatric-mental health nurses (PMHNs). Increased awareness of the clinical presentation, key features, and evidence-based treatment options will assist PMHNs to confidently recognize this often under-diagnosed disorder and adopt a more assertive role in terms of engagement and follow up. Pragmatic research involving individuals with VLOSLP is required to increase the evidence base for treatment and improve outcomes of care. [Journal of Psychosocial Nursing and Mental Health Services, 56(1), 37-47.].

Classification of Schizophrenia Based on Individual Hierarchical Brain Networks Constructed From Structural MRI Images

With structural magnetic resonance imaging (MRI) images, conventional methods for the classification of schizophrenia (SCZ) and healthy control (HC) extract cortical thickness independently at different regions of interest (ROIs) without considering the correlation between these regions. In this paper, we proposed an improved method for the classification of SCZ and HC based on individual hierarchical brain networks constructed from structural MRI images. Our method involves constructing individual hierarchical networks where each node and each edge in these networks represents a ROI and the correlation between a pair of ROIs, respectively. We demonstrate that edge features make significant improvement in performance of SCZ/HC classification, when compared with only node features. Classification performance is further investigated by combining edge features with node features via a multiple kernel learning framework. The experimental results show that our proposed method achieves an accuracy of 88.72% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.9521 for SCZ/HC classification, which demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of SCZ via structural MRI images. Therefore, this paper provides an alternative method for extracting high-order cortical thickness features from structural MRI images for classification of neurodegenerative diseases such as SCZ.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum

BACKGROUND

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored.

METHOD

Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).

RESULTS

Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently.

CONCLUSIONS

Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Letter to Editor. In search of definition of the term "psychotic process" - thoughts and doubts

no summary.

Could PANSS be a useful tool in the determining of the stages of schizophrenia? A clinically operational approach

Staging in schizophrenia might be an important approach for the better treatment and rehabilitation of patients. The purpose of this study was to empirically devise a staging approach in a sample of stabilized patients with schizophrenia. One hundred and seventy patients aged ≥18 years (mean = 40.7, SD = 11.6) diagnosed by DSM-5 criteria were evaluated with the Positive and Negative Syndrome Scale (PANSS). Principal components analysis (PCA) with varimax rotation was used. The model was examined in the total sample and separately across a hypothesized stage of illness based on three age groups and between the two sexes. The PCA revealed a six factor structure for the total sample: 1) Negative, 2) Positive, 3) Depression and anxiety, 4) Excitement and Hostility, 5) Neurocognition and 6) Disorganization. The separate PCAs by stage of illness and sex revealed different patterns and quality of symptomatology. The Negative and Positive factors were stable across all examined groups. The models corresponding to different stages differed mainly in terms of neurocognition and disorganization and their interplay. Catatonic features appear more prominent in males while in females neurocognition takes two forms; one with disorganization and one with stereotype thinking with delusions. This study suggests that the three arbitrary defined stages of illness (on the basis of age) seem to reflect a progress from a preserved insight and more coherent mental functioning to disorganization and eventually neurocognitive impairment. Sexes differ in terms of the relationship of psychotic features with neurocognition. These results might have significant research and clinical implications.

Unique and Overlapping Symptoms in Schizophrenia Spectrum and Dissociative Disorders in Relation to Models of Psychopathology: A Systematic Review

Schizophrenia spectrum disorders (SSDs) and dissociative disorders (DDs) are described in the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) and tenth edition of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) as 2 categorically distinct diagnostic categories. However, several studies indicate high levels of co-occurrence between these diagnostic groups, which might be explained by overlapping symptoms. The aim of this systematic review is to provide a comprehensive overview of the research concerning overlap and differences in symptoms between schizophrenia spectrum and DDs. For this purpose the PubMed, PsycINFO, and Web of Science databases were searched for relevant literature. The literature contained a large body of evidence showing the presence of symptoms of dissociation in SSDs. Although there are quantitative differences between diagnoses, overlapping symptoms are not limited to certain domains of dissociation, nor to nonpathological forms of dissociation. In addition, dissociation seems to be related to a history of trauma in SSDs, as is also seen in DDs. There is also evidence showing that positive and negative symptoms typically associated with schizophrenia may be present in DD. Implications of these results are discussed with regard to different models of psychopathology and clinical practice.

A Severity-Based Clinical Staging Model for the Psychosis Prodrome: Longitudinal Findings From the New York Recognition and Prevention Program

Clinical staging improved the possibility of intervening during the psychosis prodrome to limit progression of illness. The current study aimed to validate a novel 4-stage severity-based model with a focus on clinical change over time and risk for conversion to psychosis. One hundred seventy-one individuals at clinical high risk (CHR) for psychosis were followed prospectively (3 ± 1.6 y) as part of the Recognition and Prevention (RAP) program and divided into 4 diagnostic stages according to absence/presence and severity of attenuated positive symptoms. Twenty-two percent of the combined sample recovered (no prodromal symptoms) by study outcome. The negative symptoms only subgroup had the highest symptom stability (70%), but the lowest conversion rate at 5.9%. The subgroup with more severe baseline attenuated positive symptom levels had a higher conversion rate (28%) and a more rapid onset when compared to the moderate attenuated positive symptom subgroup (11%). Finally, the Schizophrenia-Like Psychosis (SLP) subgroup showed low stability (3%), with 49% developing a specific psychotic disorder. The proposed stage model provides a more finely grained classification system than the standard diagnostic approach for prodromal individuals. All 4 stages are in need of early intervention because of low recovery rates. The negative symptom only stage is possibly a separate clinical syndrome, with an increased risk of functional disability. Both subgroups with attenuated positive symptoms are appropriate for studying the mechanisms of psychosis risk, however, individuals with more severe baseline positive symptoms appear better suited to clinical trials. Finally, the SLP category represents an intermediate outcome group appropriate for preventative intervention research but questionable for inclusion in prodromal studies of mechanisms.

[Cognitive Functioning and Work Outcome Among People with Schizophrenia Spectrum Disorder: The Contribution of the International Classification of Functioning]

Objectives Cognitive impairment can be a barrier to employment of people with a schizophrenia spectrum disorder (SSD). However, other factors have also been identified as potentially hindering work integration and job tenure. But the links between all these factors remain unknown. The objective of this article is to propound an integrative model, using the International Classification of Functioning (ICF), of how cognitive impairment associated with SSD is related to other factors involved in difficulties in work integration and job tenure.Methods The description of the theoretical framework of the ICF enables to organize these factors in a comprehensive model. Then, a review of recent literature allows us to identify factors associated with employment of people with SSD, and to see the link between cognitive functioning and other factors.Results Most of reviewed studies find moderate correlations or no correlation between cognitive impairments and work integration or job tenure. Stronger correlations were nevertheless found between cognitive factors and work behavior or performance. Considering other factors, like personal or environmental factors, and the framework of the ICF, a comprehensive view of the vocational rehabilitation for people with SSD is developed. Interactions between some personal (e.g. self-efficacy or self esteem) and environmental (e.g. job coach or layout of workstation) factors may influence the translation of cognitive difficulties into work participation restrictions.Conclusion Vocational rehabilitation programs should further consider the complexity of interactions between cognitive, personal and environmental factors, and how they impact work functioning. As defined in ICF, activity limitations may represent an interesting mediator between cognitive variables and work functioning. Future research should be conducted to bring a better understanding of these patterns of interactions.

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI

BACKGROUND

Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples.

NEW METHOD

We revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism.

RESULTS

We apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy.

COMPARISON WITH EXISTING METHODS

We find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls.

CONCLUSIONS

Unambiguous components provide a robust way to estimate important regions of imaging data.

Psychosis and schizophrenia: effects of changes in psychiatric classifications on clinical and theoretical approaches to mental illness

This article discusses changes in the diagnostic classification systems for mental illness, especially the conceptual weakening of the "psychosis" category while schizophrenia became the only psychosis. Current pathological classifications prioritize a physicalist approach. Consequently, conditions that previously were associated with neurosis and subjectivity are being medicalized, conditions previously recognized as psychotic are relocated under the heading of personality disorders, and psychosis has been reduced to schizophrenia and considered a deficit of psychic functions. This article indicates the clinical and operational validity of the notion of "psychosis" as a nosographic category permitting a more complex approach to "schizophrenia", which in psychiatry is the last concept that bears the symbolic weight of madness.

Prevalence and classification of hallucinations in multiple sensory modalities in schizophrenia spectrum disorders

BACKGROUND

Auditory hallucinations are experienced by 60-80% of all patients diagnosed with a schizophrenia spectrum disorder. However, in this patient group, the prevalence of hallucinations in multiple sensory modalities, i.e. multimodal hallucinations (MMHs), is unknown.

AIMS

To assess the prevalence of MMHs in patients diagnosed with a schizophrenia spectrum disorder, data were analyzed from 750 patients who participated in the Dutch Genetic Risk and Outcome of Psychosis (GROUP) study.

METHOD

We drew on the section of the CASH (Comprehensive Assessment of Symptoms and History) that probes into the lifetime presence of auditory, visual, somatic/tactile, and olfactory hallucinations.

RESULTS

A lifetime prevalence of 80% was found in this group for hallucinations in any of these modalities. Within the whole group, 27% of the participants reported unimodal hallucinations and 53% MMHs. There were no significant differences in prevalence rate for Dutch versus migrant participants from Morocco, Turkey, Surinam or the (former) Dutch Antilles.

CONCLUSION

We conclude that MMHs, rather than auditory hallucinations, are the most frequent perceptual symptom of patients diagnosed with a schizophrenia spectrum disorder. Our data also suggest that hallucinations experienced in a single sensory modality (notably auditory ones) stochastically increase the risk for more sensory modalities to join in. We recommend that future studies take into account all 14 sensory modalities in which hallucinations can be experienced. For this we provide a classification of MMHs that allows characterization of their serial versus simultaneous occurrence and their congruent versus incongruent nature.

Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance

Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.

The association between polyunsaturated fatty acid consumption and the transition to psychosis in ultra-high risk individuals

PUFA deficiencies in cellular membranes have been observed in ultra-high risk (HR) individuals and in early schizophrenia. It is uncertain whether dietary PUFA consumption can be associated with the risk of transition to psychosis in HR individuals. The aim of the study was to assess PUFA consumption and confirm whether dietary habits are related to the risk of transition to full-threshold psychosis in HR individuals during a 12-month follow-up. PUFA consumption during the previous year was analyzed in 62 h individuals and 33 healthy controls (HC) at the beginning of the follow-up period using a validated Food-Frequency Questionnaire and the Polish Food Composition Tables. Fifteen HR individuals converted into psychosis (C-HR) during the 12-month follow-up. C-HR individuals reported significantly higher consumption of n-6 fatty acids (linoleic acid, LA and arachidonic acid, AA) in comparison with individuals who did not develop psychosis (NC-HR). The C-HR group reported a significantly higher AA/(EPA+DHA) consumption ratio than the NC-HR group. HC reported significantly higher consumption of most n-3 PUFA and lower consumption of all n-6 PUFA than both groups of HR individuals. The results suggest that dietary patterns of PUFA consumption may play a role in the conversion to psychosis of HR individuals.