Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?

Memory deficits in children and adolescents with a psychotic disorder: a systematic review and meta-analysis

Early-onset psychosis (EOP) is a severe disorder which takes place before 18 years. It entails diverse clinical and functional implications, and it may lead to critical impairments in neurocognitive functions. Although deficits in memory are well described in adult populations and they appear to be clinically related with psychosis, impairments in memory in EOP show inconsistencies between studies. This study aimed to gain insight into the relationship between EOP and memory impairments, studying the potential contribution of moderators (storage source and memory content) on the observed memory deficits. This systematic review and meta-analysis was conducted following the PRISMA-2020 guidelines. Search was conducted in English and Spanish in five databases. Case-control studies which met all requirements were selected. Overall effect size was calculated under the random-effects model and Z-based tests were used. Heterogeneity was analysed by the I2 statistic. Mixed-effects meta-regression analysis was used to study the influence of methodological quality of studies, mean age, proportion of female participants within sample, mean diagnosis, memory storage type, memory content as moderators on individual effect size variability. As a result, 32 articles were finally selected, pooling data from 2636 participants (49.29% EOP participants). Overall effect size was Hedges' g =  - 1.01, CI95 = [ - 1.35,  - 0.67], p < .01, indicating lower memory performance in the EOP group in comparison to healthy controls. Diagnosis and memory storage were found as significant moderators in the memory performance variance: larger deficits were found in children with psychosis and in working memory tasks.

Ormerod MB, Aukrust P, Melle I, Steen NE, Andreassen OA, Ueland T. Longitudinal course of inflammatory-cognitive subgroups across first treatment severe mental illness and healthy controls

BACKGROUND

While inflammation is associated with cognitive impairment in severe mental illnesses (SMI), there is substantial heterogeneity and evidence of transdiagnostic subgroups across schizophrenia (SZ) and bipolar (BD) spectrum disorders. There is however, limited knowledge about the longitudinal course of this relationship.

METHODS

Systemic inflammation (C-Reactive Protein, CRP) and cognition (nine cognitive domains) was measured from baseline to 1 year follow-up in first treatment SZ and BD (n = 221), and healthy controls (HC, n = 220). Linear mixed models were used to evaluate longitudinal changes separately in CRP and cognitive domains specific to diagnostic status (SZ, BD, HC). Hierarchical clustering was applied on the entire sample to investigate the longitudinal course of transdiagnostic inflammatory-cognitive subgroups.

RESULTS

There were no case-control differences or change in CRP from baseline to follow-up. We confirm previous observations of case-control differences in cognition at both time-points and domain specific stability/improvement over time regardless of diagnostic status. We identified transdiagnostic inflammatory-cognitive subgroups at baseline with differing demographics and clinical severity. Despite improvement in cognition, symptoms and functioning, the higher inflammation - lower cognition subgroup (75% SZ; 48% BD; 38% HC) had sustained inflammation and lower cognition, more symptoms, and lower functioning (SMI only) at follow-up. This was in comparison to a lower inflammation - higher cognition subgroup (25% SZ, 52% BD, 62% HC), where SMI participants showed cognitive functioning at HC level with a positive clinical course.

CONCLUSIONS

Our findings support heterogenous and transdiagnostic inflammatory-cognitive subgroups that are stable over time, and may benefit from targeted interventions.

Cognitive profiles across the psychosis continuum

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial variability in cognitive functioning within diagnostic groups, along with considerable overlap with healthy controls, hampers the translation of research findings into personalized treatment planning. Aligned with precision medicine, we employed a data driven machine learning method, self-organizing maps, to conduct transdiagnostic clustering based on cognitive functions in a sample comprising 228 healthy controls, 200 individuals at ultra-high risk for psychosis, and 98 antipsychotic-naïve patients with first-episode psychosis. The self-organizing maps revealed six clinically distinct cognitive profiles that significantly predicted baseline functional level and changes in functional level after one year. Cognitive flexibility in particular, as well as specific executive functions emerged as cardinal in differentiating the profiles. The application of self-organizing maps appears to be a promising approach to inform clinical decision-making based on individualized cognitive profiles, including patient allocation to different interventions. Moreover, this method has the potential to enable cross-diagnostic stratification in research trials, utilizing data-driven subgrouping informed by categories from underlying dimensions of cognition rather than from clinical diagnoses. Finally, the method enables cross-diagnostic profiling across other data modalities, such as brain networks or metabolic subtypes.

Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks

Cognitive deficits are prevalent in individuals with psychosis and are associated with neurobiological changes, potentially serving as an endophenotype for psychosis. Using the HCP-Early-Psychosis-dataset (n = 226), we aimed to investigate cognitive subtypes (deficit/intermediate/spared) through data-driven clustering in affective (AP) and non-affective psychosis patients (NAP) and controls (HC). We explored differences between three clusters in symptoms, cognition, medication, and grey matter volume. Applying principal component analysis, we selected features for clustering. Features that explained most variance were scores for intelligence, verbal recognition and comprehension, auditory attention, working memory, reasoning and executive functioning. Fuzzy K-Means clustering on those features revealed that the subgroups significantly varied in cognitive impairment, clinical symptoms, and, importantly, also in medication and grey matter volume in fronto-parietal and subcortical networks. The spared cluster (86%HC, 37%AP, 17%NAP) exhibited unimpaired cognition, lowest symptoms/medication, and grey matter comparable to controls. The deficit cluster (4%HC, 10%AP, 47%NAP) had impairments across all domains, highest symptoms scores/medication dosage, and pronounced grey matter alterations. The intermediate deficit cluster (11%HC, 54%AP, 36%NAP) showed fewer deficits than the second cluster, but similar symptoms/medication/grey matter to the spared cluster. Controlling for medication, cognitive scores correlated with grey matter changes and negative symptoms across all patients. Our findings generally emphasize the interplay between cognition, brain structure, symptoms, and medication in AP and NAP, and specifically suggest a possible mediating role of cognition, highlighting the potential of screening cognitive changes to aid tailoring treatments and interventions.

Cognitive subtypes in youth at clinical high risk for psychosis

Introduction

Schizophrenia is a mental health condition that severely impacts well-being. Cognitive impairment is among its core features, often presenting well before the onset of overt psychosis, underscoring a critical need to study it in the psychosis proneness (clinical high risk; CHR) stage, to maximize the benefits of interventions and to improve clinical outcomes. However, given the heterogeneity of cognitive impairment in this population, a one-size-fits-all approach to therapeutic interventions would likely be insufficient. Thus, identifying cognitive subtypes in this population is crucial for tailored and successful therapeutic interventions. Here we identify, validate, and characterize cognitive subtypes in large CHR samples and delineate their baseline and longitudinal cognitive and functional trajectories.

Methods

Using machine learning, we performed cluster analysis on cognitive measures in a large sample of CHR youth (n = 764), and demographically comparable controls (HC; n = 280) from the North American Prodrome Longitudinal Study (NAPLS) 2, and independently validated our findings with an equally large sample (NAPLS 3; n = 628 CHR, 84 HC). By utilizing several statistical approaches, we compared the clusters on cognition and functioning at baseline, and over 24 months of followup. We further delineate the conversion status within those clusters.

Results

Two main cognitive clusters were identified, "impaired" and "intact" across all cognitive domains in CHR compared to HC. Baseline differences between the cognitively intact cluster and HC were found in the verbal abilities and attention and working memory domains. Longitudinally, those in the cognitively impaired cluster group demonstrated an overall floor effect and did not deteriorate further over time. However, a "catch up" trajectory was observed in the attention and working memory domain. This group had higher instances of conversion overall, with these converters having significantly more non-affective psychotic disorder diagnosis versus bipolar disorder, than those with intact cognition. In the cognitively intact group, we observed differences in trajectory based on conversion status, where those who start with intact cognition and later convert demonstrate a sharp decline in attention and functioning. Functioning was significantly better in the cognitively intact than in the impaired group at baseline. Most of the cognitive trajectories demonstrate a positive relationship with functional ones.

Conclusion

Our findings provide evidence for intact and impaired cognitive subtypes in youth at CHR, independent of conversion status. They further indicate that attention and working memory are important to distinguish between the CHR with intact cognition and controls. The cognitively intact CHR group becomes less attentive after conversion, while the cognitively impaired one demonstrates a catch up trajectory on both attention and working memory. Overall, early evaluation, covering several cognitive domains, is crucial for identifying trajectories of improvement and deterioration for the purpose of tailoring intervention for improving outcomes in individuals at CHR for psychosis.

Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? Results From the EU-GEI Study

BACKGROUND AND HYPOTHESIS

Cognition has been associated with socio-occupational functioning in individuals at Clinical High Risk for Psychosis (CHR-P). The present study hypothesized that clustering CHR-P participants based on cognitive data could reveal clinically meaningful subtypes.

STUDY DESIGN

A cohort of 291 CHR-P subjects was recruited through the multicentre EU-GEI high-risk study. We explored whether an underlying cluster structure was present in the cognition data. Clustering of cognition data was performed using k-means clustering and density-based spatial clustering of applications with noise. Cognitive subtypes were validated by comparing differences in functioning, psychosis symptoms, transition outcome, and grey matter volume between clusters. Network analysis was used to further examine relationships between cognition scores and clinical symptoms.

STUDY RESULTS

No underlying cluster structure was found in the cognitive data. K-means clustering produced "spared" and "impaired" cognition clusters similar to those reported in previous studies. However, these clusters were not associated with differences in functioning, symptomatology, outcome, or grey matter volume. Network analysis identified cognition and symptoms/functioning measures that formed separate subnetworks of associations.

CONCLUSIONS

Stratifying patients according to cognitive performance has the potential to inform clinical care. However, we did not find evidence of cognitive clusters in this CHR-P sample. We suggest that care needs to be taken in inferring the existence of distinct cognitive subtypes from unsupervised learning studies. Future research in CHR-P samples could explore the existence of cognitive subtypes across a wider range of cognitive domains.

Characterizing cognitive subtypes in schizophrenia using cortical curvature

Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.

Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness

Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (NROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. CLINICAL TRIAL REGISTRY NAME: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042.

Prevalence of cognitive impairments and strengths in the early course of psychosis and depression

BACKGROUND

Studies investigating cognitive impairments in psychosis and depression have typically compared the average performance of the clinical group against healthy controls (HC), and do not report on the actual prevalence of cognitive impairments or strengths within these clinical groups. This information is essential so that clinical services can provide adequate resources to supporting cognitive functioning. Thus, we investigated this prevalence in individuals in the early course of psychosis or depression.

METHODS

A comprehensive cognitive test battery comprising 12 tests was completed by 1286 individuals aged 15-41 (mean age 25.07, s.d. 5.88) from the PRONIA study at baseline: HC (N = 454), clinical high risk for psychosis (CHR; N = 270), recent-onset depression (ROD; N = 267), and recent-onset psychosis (ROP; N = 295). Z-scores were calculated to estimate the prevalence of moderate or severe deficits or strengths (>2 s.d. or 1-2 s.d. below or above HC, respectively) for each cognitive test.

RESULTS

Impairment in at least two cognitive tests was as follows: ROP (88.3% moderately, 45.1% severely impaired), CHR (71.2% moderately, 22.4% severely impaired), ROD (61.6% moderately, 16.2% severely impaired). Across clinical groups, impairments were most prevalent in tests of working memory, processing speed, and verbal learning. Above average performance (>1 s.d.) in at least two tests was present for 40.5% ROD, 36.1% CHR, 16.1% ROP, and was >2 SDs in 1.8% ROD, 1.4% CHR, and 0% ROP.

CONCLUSIONS

These findings suggest that interventions should be tailored to the individual, with working memory, processing speed, and verbal learning likely to be important transdiagnostic targets.

The relationship between cognitive clusters and telomere length in bipolar-schizophrenia spectrum disorders

BACKGROUND

Schizophrenia and bipolar disorder are complex mental illnesses that are associated with cognitive deficits. There is considerable cognitive heterogeneity that exists within both disorders. Studies that cluster schizophrenia and bipolar patients into subgroups based on their cognitive profile increasingly demonstrate that, relative to healthy controls, there is a severely compromised subgroup and a relatively intact subgroup. There is emerging evidence that telomere shortening, a marker of cellular senescence, may be associated with cognitive impairments. The aim of this study was to explore the relationship between cognitive subgroups in bipolar-schizophrenia spectrum disorders and telomere length against a healthy control sample.

METHODS

Participants included a transdiagnostic group diagnosed with bipolar, schizophrenia or schizoaffective disorder (n = 73) and healthy controls (n = 113). Cognitive clusters within the transdiagnostic patient group, were determined using K-means cluster analysis based on current cognitive functioning (MATRICS Consensus Cognitive Battery scores). Telomere length was determined using quantitative PCRs genomic DNA extracted from whole blood. Emergent clusters were then compared to the healthy control group on telomere length.

RESULTS

Two clusters emerged within the patient group that were deemed to reflect a relatively intact cognitive group and a cognitively impaired subgroup. Telomere length was significantly shorter in the severely impaired cognitive subgroup compared to the healthy control group.

CONCLUSIONS

This study replicates previous findings of transdiagnostic cognitive subgroups and associates shorter telomere length with the severely impaired cognitive subgroup. These findings support emerging literature associating cognitive impairments in psychiatric disorders to accelerated cellular aging as indexed by telomere length.

Cognitive subgroups and the relationships with symptoms, psychosocial functioning and quality of life in first-episode non-affective psychosis: a cluster-analysis approach

Introduction

Prior research examining cognitive heterogeneity in psychotic disorders primarily focused on chronic schizophrenia, with limited data on first-episode psychosis (FEP). We aimed to identify distinct cognitive subgroups in adult FEP patients using data-driven cluster-analytic approach, and examine relationships between cognitive subgroups and a comprehensive array of illness-related variables.

Methods

Two-hundred-eighty-nine Chinese patients aged 26-55 years presenting with FEP to an early intervention program in Hong Kong were recruited. Assessments encompassing premorbid adjustment, illness-onset profile, symptom severity, psychosocial functioning, subjective quality-of-life, and a battery of cognitive tests were conducted. Hierarchical cluster-analysis was employed, optimized with k-means clustering and internally-validated by discriminant-functional analysis. Cognitive subgroup comparisons in illness-related variables, followed by multivariable multinominal-regression analyzes were performed to identify factors independently predictive of cluster membership.

Results

Three clusters were identified including patients with globally-impaired (n = 101, 34.9%), intermediately-impaired (n = 112, 38.8%) and relatively-intact (n = 76, 26.3%) cognition (GIC, IIC and RIC subgroups) compared to demographically-matched healthy-controls' performance (n = 50). GIC-subgroup was older, had lower educational attainment, greater positive, negative and disorganization symptom severity, poorer insight and quality-of-life than IIC- and RIC-subgroups, and higher antipsychotic-dose than RIC-subgroup. IIC-subgroup had lower education levels and more severe negative symptoms than RIC-subgroup, which had better psychosocial functioning than two cognitively-impaired subgroups. Educational attainment and disorganization symptoms were found to independently predict cluster membership.

Discussion

Our results affirmed cognitive heterogeneity in FEP and identified three subgroups, which were differentially associated with demographic and illness-related variables. Further research should clarify longitudinal relationships of cognitive subgroups with clinical and functional outcomes in FEP.

Neurocognitive subgroups among newly diagnosed patients with schizophrenia spectrum or bipolar disorders: A hierarchical cluster analysis

Studies across schizophrenia (SZ) and bipolar disorder (BD) indicate common transdiagnostic neurocognitive subgroups. However, existing studies of patients with long-term illness precludes insight into whether impairments result from effects of chronic illness, medication or other factors. This study aimed to investigate whether neurocognitive subgroups across SZ and BD can be demonstrated during early illness stages. Data from overlapping neuropsychological tests were pooled from cohort studies of antipsychotic-naïve patients with first-episode SZ spectrum disorders (n = 150), recently diagnosed BD (n = 189) or healthy controls (HC) (n = 280). Hierarchical cluster analysis was conducted to examine if transdiagnostic subgroups could be identified based on the neurocognitive profile. Patterns of cognitive impairments and patient characteristics across subgroups were examined. Patients could be clustered into two, three and four subgroups, of which the three-cluster solution (with 83% accuracy) was selected for posthoc analyses. This solution revealed a subgroup covering 39% of patients (predominantly BD) who were cognitively relatively intact, a subgroup of 33% of patients (more equal distributions of SZ and BD) displaying selective deficits, particularly in working memory and processing speed, and a subgroup of 28% (mainly SZ) with global impairments. The globally impaired group exhibited lower estimated premorbid intelligence than the other subgroups. Globally impaired BD patients also showed more functional disability than cognitively relatively intact patients. No differences were observed across subgroups in symptoms or medications. Neurocognitive results can be understood by clustering analysis with similar clustering solutions occurring across diagnoses. The subgroups were not explained by clinical symptoms or medication, suggesting neurodevelopmental origins.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Similarities and differences between multivariate patterns of cognitive and socio-cognitive deficits in schizophrenia, bipolar disorder and related risk

Cognition and social cognition anomalies in patients with bipolar disorder (BD) and schizophrenia (SCZ) have been largely documented, but the degree of overlap between the two disorders remains unclear in this regard. We used machine learning to generate and combine two classifiers based on cognitive and socio-cognitive variables, thus delivering unimodal and multimodal signatures aimed at discriminating BD and SCZ from two independent groups of Healthy Controls (HC1 and HC2 respectively). Multimodal signatures discriminated well between patients and controls in both the HC1-BD and HC2-SCZ cohorts. Although specific disease-related deficits were characterized, the HC1 vs. BD signature successfully discriminated HC2 from SCZ, and vice-versa. Such combined signatures allowed to identify also individuals at First Episode of Psychosis (FEP), but not subjects at Clinical High Risk (CHR), which were classified neither as patients nor as HC. These findings suggest that both trans-diagnostic and disease-specific cognitive and socio-cognitive deficits characterize SCZ and BD. Anomalous patterns in these domains are also relevant to early stages of disease and offer novel insights for personalized rehabilitative programs.

Genetic and Structural Brain Correlates of Cognitive Subtypes Across Youth at Family Risk for Schizophrenia and Bipolar Disorder

OBJECTIVE

Cognitive impairment is an important feature of schizophrenia (SZ) and bipolar disorder (BP) with severity across the two disorders characterized by significant heterogeneity. Youth at family risk for SZ and BP were clustered based on cognitive function and examined in terms of the clinical, genetic, and brain imaging correlates of cluster membership.

METHOD

One hundred sixty participants, 32 offspring of patients with SZ, 59 offspring of patients with BP and 69 offspring of healthy control parents underwent clinical and cognitive assessments, genotyping and structural MRI. K-means clustering was used to group family risk participants based on cognitive measures. Clusters were compared in terms of cortical and subcortical brain measures as well as polygenic risk scores.

RESULTS

Participants were grouped in 3 clusters with intact, intermediate, and impaired cognitive performance. The intermediate and impaired clusters had lower total brain surface area compared with the intact cluster, with prominent localization in frontal and temporal cortices. No between-cluster differences were identified in cortical thickness and subcortical brain volumes. The impaired cluster also had poorer psychosocial functioning and worse PRS-COG compared with the other 2 clusters and with offspring of healthy control parents, while there was no significant between-cluster difference in terms of PRS-SZ and PRS-BP. PRS-COG predicted psychosocial functioning, yet this effect did not appear to be mediated by an effect of PRS-COG on brain area.

CONCLUSION

Stratification based on cognition may help to elucidate the biological underpinnings of cognitive heterogeneity across SZ and BP risk.

Evidence that a working memory cognitive phenotype within schizophrenia has a unique underlying biology

It is suggested studying phenotypes within the syndrome of schizophrenia will accelerate understanding the complex molecular pathology of the disorder. Supporting this hypothesis, we have identified a sub-group within schizophrenia with impaired working memory (WM) and have used Affymetrix™ Human Exon 1.0 ST Arrays to compare their blood RNA levels (n=16) to a group of with intact WM (n=18). Levels of 72 RNAs were higher in blood from patients with impaired WM, 11 of which have proven links to the maintenance of different aspects of working memory (cognition). Overall, changed gene expression in those with impaired WM could be linked to cognition through glutamatergic activity, olfaction, immunity, inflammation as well as energy and metabolism. Our data gives preliminary support to the hypotheses that there is a working memory deficit phenotype within the syndrome of schizophrenia with has a biological underpinning. In addition, our data raises the possibility that a larger study could show that the specific changes in gene expression we have identified could prove to be the biomarkers needed to develop a blood test to identify those with impaired WM; a significant step toward allowing the use of personalised medicine directed toward improving their impaired working memory.

Transdiagnostic inflammatory subgroups among psychiatric disorders and their relevance to role functioning: a nested case-control study of the ALSPAC cohort

Individuals with psychotic disorders and depressive disorder exhibit altered concentrations of peripheral inflammatory markers. It has been suggested that clinical trials of anti-inflammatory therapies for psychiatric disorders should stratify patients by their inflammatory profile. Hence, we investigated whether different subgroups of individuals exist across psychiatric disorders, based on their inflammatory biomarker signatures. We measured the plasma concentrations of 17 inflammatory markers and receptors in 380 participants with psychotic disorder, depressive disorder or generalised anxiety disorder and 399 controls without psychiatric symptoms from the ALSPAC cohort at age 24. We employed a semi-supervised clustering algorithm, which discriminates multiple clusters of psychiatric disorder cases from controls. The best fit was for a two-cluster model of participants with psychiatric disorders (Adjusted Rand Index (ARI) = 0.52 ± 0.01) based on the inflammatory markers. Permutation analysis indicated the stability of the clustering solution performed better than chance (ARI = 0.43 ± 0.11; p < 0.001), and the clusters explained the inflammatory marker data better than a Gaussian distribution (p = 0.021). Cluster 2 exhibited marked increases in sTNFR1/2, suPAR, sCD93 and sIL-2RA, compared to cluster 1. Participants in the cluster exhibiting higher inflammation were less likely to be in employment, education or training, indicating poorer role functioning. This study found evidence for a novel pattern of inflammatory markers specific to psychiatric disorders and strongly associated with a transdiagnostic measure of illness severity. sTNFR1/2, suPAR, sCD93 and sIL-2RA could be used to stratify clinical trials of anti-inflammatory therapies for psychiatric disorders.

Characterising cognitive heterogeneity in individuals at clinical high-risk for psychosis: a cluster analysis with clinical and functional outcome prediction

Schizophrenia is characterised by cognitive impairments that are already present during early stages, including in the clinical high-risk for psychosis (CHR-P) state and first-episode psychosis (FEP). Moreover, data suggest the presence of distinct cognitive subtypes during early-stage psychosis, with evidence for spared vs. impaired cognitive profiles that may be differentially associated with symptomatic and functional outcomes. Using cluster analysis, we sought to determine whether cognitive subgroups were associated with clinical and functional outcomes in CHR-P individuals. Data were available for 146 CHR-P participants of whom 122 completed a 6- and/or 12-month follow-up; 15 FEP participants; 47 participants not fulfilling CHR-P criteria (CHR-Ns); and 53 healthy controls (HCs). We performed hierarchical cluster analysis on principal components derived from neurocognitive and social cognitive measures. Within the CHR-P group, clusters were compared on clinical and functional variables and examined for associations with global functioning, persistent attenuated psychotic symptoms and transition to psychosis. Two discrete cognitive subgroups emerged across all participants: 45.9% of CHR-P individuals were cognitively impaired compared to 93.3% of FEP, 29.8% of CHR-N and 30.2% of HC participants. Cognitively impaired CHR-P participants also had significantly poorer functioning at baseline and follow-up than their cognitively spared counterparts. Specifically, cluster membership predicted functional but not clinical outcome. Our findings support the existence of distinct cognitive subgroups in CHR-P individuals that are associated with functional outcomes, with implications for early intervention and the understanding of underlying developmental processes.

The neurobiological characterization of distinct cognitive subtypes in early-phase schizophrenia-spectrum disorders

INTRODUCTION

Cognitive deficits are present in some, but not all patients with schizophrenia-spectrum disorders (SSD). We and others have demonstrated three cognitive clusters: cognitively intact patients, patients with deficits in a few domains and those with global cognitive deficits. This study aimed to identify cognitive subtypes of early-phase SSD with matched controls as a reference group, and evaluated cognitive subgroups regarding clinical and brain volumetric measures.

METHODS

Eighty-six early-phase SSD patients were included. Hierarchical cluster analysis was conducted using global performance on the Brief Assessment of Cognition in Schizophrenia (BACS). Cognitive subgroups were subsequently related to clinical and brain volumetric measures (cortical, subcortical and cortical thickness) using ANCOVA.

RESULTS

Three distinct cognitive clusters emerged: relative to controls we found one cluster of patients with preserved cognition (n = 25), one moderately impaired cluster (n = 38) and one severely impaired cluster (n = 23). Cognitive subgroups were characterized by differences in volume of the left postcentral gyrus, left middle caudal frontal gyrus and left insula, while differences in cortical thickness were predominantly found in fronto-parietal regions. No differences were demonstrated in subcortical brain volume.

DISCUSSION

Current results replicate the existence of three distinct cognitive subgroups including one relatively large group with preserved cognitive function. Cognitive subgroups were characterized by differences in cortical regional brain volume and cortical thickness, suggesting associations with cortical, but not subcortical development and cognitive functioning such as attention, executive functions and speed of processing.