Evidence suggests extracellular free-water (FW) is a potential marker of neuroinflammation. In psychosis spectrum disorders (PSD), neuroinflammation has been associated with cognition deficits, but findings are heterogeneous. Thus, we derived FW and cognition-based subgroups in PSD. PSD (n = 166) and healthy (n = 50) individuals underwent diffusion magnetic resonance imaging, cognitive testing, and clinical assessments. Canonical correlation analysis (CCA) was performed using statistically significant measures, including 8 FW regions (anterior corona radiata, body, genu, and splenium of corpus callosum, fornix/stria terminalis, inferior and superior fronto-occipital fasciculus, and superior longitudinal fasciculus) and 13 cognitive measures. Permutation testing and cross validation quantified CCA significance and reliability. PSD subgroups were identified by hierarchical clustering of CCA projections. Random sampling and bootstrapping assessed clustering significance and reliability. Analysis of variance, general linear models, and Cohen's effect sizes tested for differences between groups. The false discovery rate corrected for multiple comparisons. The first latent variate (r = 0.45) identified two clusters: cluster 1 demonstrated lower FW (n = 110, 66%) while cluster 2 showed higher FW (n = 56, 34%). Both displayed cognitive deficits compared to controls. Cluster 2 showed greater FW in all regions tested (p < 0.05), exhibited cognitive impairment across multiple cognitive domains (p < 0.01), and higher depressive, manic, general and total psychosis symptoms (p < 0.05) compared to cluster 1. However, they did not differ with regard to medication, functioning, or potential inflammatory confounds. Results support previous research indicating that a high neuroinflammatory subgroup of PSD exists and is related to cognition and brain structure, but the translational impact remains to be determined.

BACKGROUND:Early-life psychosocial stress (ELS) has lasting effects on physical and mental health, yet the biological mechanisms linking ELS to midlife cognitive function remain incompletely understood. Chronic, low-grade inflammation has been proposed as a key pathway, but prior studies have examined only a narrow set of markers. METHODS:We analyzed data from 326 participants (mean age = 39.4 years; 56.4% African American; 57.4% female) in two longitudinal cohorts with prospectively collected measures of psychosocial stress before age 25. Plasma concentrations of 1,034 proteins (including 537 inflammatory markers) were quantified using the Olink® Reveal platform. Cognitive function in midlife was assessed with the NIH Toolbox Cognition Battery. A composite ELS score integrated exposures across individual, family, and neighborhood domains. Weighted Quantile Sum (WQS) regression was used to estimate the joint effect of multiple proteins on cognitive function, adjusting for demographic and socioeconomic covariates. RESULTS:Higher ELS was significantly associated with poorer midlife cognitive performance (p = 0.008). Forty-five proteins were related to ELS (p < 0.01), with the strongest associations observed for CXCL17, ISM1, and ADM. Nine (e.g., LAMP3, SASH3, LPL, TNFRSF11A) out of these 45 proteins were associated with cognitive function, and their combined WQS index significantly mediated 35% of the ELS-cognition association (p = 0.013). CONCLUSIONS:This study integrates prospective psychosocial, proteomic, and cognitive data to demonstrate that systemic inflammation partially mediates the long-term impact of early-life stress on midlife cognitive function. These findings highlight inflammation as a potential biological pathway linking early adversity to adult brain health.

Background and Hypothesis The brain age gap (BAG) quantifies the difference between predicted brain age and chronological age. Prior research implicates higher BAG in psychotic disorders, suggesting accelerated brain aging. We hypothesized distinct brain aging profiles among biological subtypes of psychosis and intermediate BAG in their relatives. Study Design Brain age gap values were quantified in 348 healthy controls (HCs), 950 psychosis probands classified by both DSM diagnoses of psychotic bipolar disorder, type I (BP, n = 247), schizoaffective disorder (SAD, n = 313), and schizophrenia (SZ, n = 390), and Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) Biotypes (301 Biotype 1, 304 Biotype 2, and 345 Biotype 3), and 491 of their non-psychotic first-degree relatives. We calculated brain age values from structural T1-weighted images using the pre-trained, open-source brain age package, brainageR. In probands, we assessed associations between BAG and clinical characteristics, comorbid disorders, medications, and polygenic risk scores for SZ (PRS-SZ). Study Results All DSM diagnosis and Biotype groups had higher BAG than HC. While no significant differences were observed between BP, SAD, or SZ, Biotypes 1 and 2 had significantly higher BAG compared to Biotype 3. Relatives exhibited intermediate BAG values between HC and probands, with the highest BAG in relatives of those with SAD. Brain age gap was not linked to comorbid disorders or PRS-SZ, but was associated with symptom severity, cognition, functioning, and psychotropic medication use. Conclusions Bipolar-Schizophrenia Network for Intermediate Phenotypes Biotypes better captured age-related brain structural differences in psychosis than DSM diagnoses. Associations between BAG and medication underscore the potential influence of pharmacotherapy on brain aging in psychosis.

Cognitive dysfunction is a prominent feature of psychotic spectrum disorders. Identifying neurocognitive subgroups and their neural underpinnings may help elucidate distinct pathophysiological mechanisms and inform targeted interventions. This study aimed to derive cognitive subtypes using latent profile analysis (LPA) of the Brief Assessment of Cognition in Schizophrenia (BACS) and investigate associated variations in resting-state functional connectivity among these cognitive profiles and biologically derived Biotypes. The BACS was administered to 1807 psychosis patients from the B-SNIP1 and 2 cohorts to perform LPA and identify cognitive subgroups. Regional homogeneity (ReHo), a measure of local functional connectivity, was computed from resting-state fMRI data in a subset (717 patients, 427 controls). Multivariate regression models examined associations between ReHo and cognitive LPA, Biotypes, and DSM diagnostic categories. LPA identified four cognitive profiles: cognitively comparable to controls (CCC), intermediate-1, intermediate-2, and severely impaired. These profiles showed unique dysconnectivity patterns, particularly within the striatal, default mode, salience, and executive control networks. The severely impaired group exhibited hyperconnectivity in basal ganglia and executive control networks. The intermediate groups showed default mode and salience network connectivity disruptions. The CCC group was the least impaired, with hyperconnectivity in sensory and auditory networks. Compared to Biotypes, LPA subgroups presented more domain-specific connectivity fingerprints. Psychosis patients exhibit heterogeneous cognitive profiles with divergent intrinsic functional dysconnectivity patterns. Cognitive LPA subgroups demonstrated more domain-localized neural signatures than DSM subtypes, potentially allowing for more targeted interventions. This approach highlights the utility of cognitive subtyping using standardized cognitive assessments in elucidating pathophysiological mechanisms in psychosis.

Functional magnetic resonance imaging (fMRI) data based on blood oxygenation level dependent (BOLD) signal have become widely available, leading to exponential growth in the number of published studies reporting on human brain function. fMRI data have also posed challenges, including a low signal to noise ratio, various noise sources, correlation between observations, and size of the data set. Also, researchers are interested in drawing conclusions from a sample of subjects to a relevant population, and in comparing the performance between groups of people. Our motivating fMRI data involve both block and event-related runs, multiple tasks, scanning sessions, and groups of subjects. The objective of this study is to identify brain regions associated with performance of cognitive tasks and to observe the effects of practice as measured by BOLD signal across different tasks and contexts. To accomplish the goal, we develop a suite of reliable and robust statistical tools, called BrainPack, that is composed of aggregation, decorrelation, data volume reduction, cluster analysis, and comparison of group clustered maps. The proposed approach does not require a specific model, can detect signals from noisy data, and take temporal correlations into account compared to model-based analysis. Through use of the BrainPack suite, we find practice-induced BOLD signal attenuation across groups and tasks in regions associated with sensorimotor and cognitive control processes. The BrainPack application improves existing between-group analysis methods to solve persistent problems in fMRI data analysis using the following advancements: (i) robust, effective, and powerful analyses for identifying neural circuits across any group using statistical learning methods and (ii) optimized multiple group analysis methods using simultaneous comparison of group maps.

OBJECTIVE:The authors sought to determine whether genetic predispositions to cognitive ability or psychiatric conditions interact with anticholinergic burden (AChB) to impact cognition and brain structure in individuals with psychotic disorders. METHODS:Participants with psychosis spectrum disorders (N=1,704) from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium, 18-65 years of age and representing diverse ancestries, underwent cognitive assessments, structural neuroimaging, genotyping, and a comprehensive medication review. The primary cognitive outcome was the Brief Assessment of Cognition in Schizophrenia (BACS) composite score, and the primary brain structural phenotype was total gray matter volume. AChB scores for scheduled medications were quantified using the CRIDECO Anticholinergic Load Scale. Polygenic scores (PGSs) for cognition, schizophrenia, bipolar disorder, and depression were constructed, and a composite psychiatric PGS was subsequently generated. Linear regression models were used to examine AChB-PGS interactions and their associations with cognitive and brain structure outcomes, adjusting for clinical covariates and multiple testing with false discovery rate. Hypothesis-driven moderated mediation models were used to explore potential association pathways. RESULTS:Higher AChB was significantly associated with lower BACS performance and reduced gray matter volume. Individuals with higher cognitive PGS values exhibited greater adverse effects of AChB on BACS, while those with lower composite psychiatric PGS values showed more pronounced gray matter volume reductions from AChB. AChB associations with cognitive impairment were partially mediated by reduced gray matter volume and were moderated by composite psychiatric PGS. CONCLUSIONS:Anticholinergic-polygenic interactions significantly impact cognition and brain structure in individuals with psychotic disorders, highlighting a novel gene-by-environment interaction that advances our mechanistic understanding of cognitive impairments in this population.

Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.

Background: Investigations of causal pathways for psychosis can be guided by the identification of environmental risk factors. A recently developed composite risk tool, the exposome score for schizophrenia (ES-SCZ), which controls for intercorrelations between risk factors, has shown fair to good performance. We tested the transdiagnostic psychosis classifier performance of the ES-SCZ with the Bipolar-Schizophrenia Network for Intermedial Phenotypes data and examined its relationship with clinical-level outcomes. Study Design: We computed the case-control classifier performance for the ES-SCZ from cross-sectional data on 1055 volunteers with psychotic diagnoses (schizophrenia, schizoaffective, bipolar psychosis) and 510 controls. Multivariate regression models were used to control for the correlations between outcomes and to correct for the effects of age, sex, and family socioeconomic status across outcomes. We estimated association for the ES-SCZ with psychosis and mood symptom severity, the 5-factor model of personality, and function across biologically defined biotypes, traditional diagnostic categories, and controls. Study Results: ES-SCZ classifier performance for psychosis was fair to good. ES-SCZ associations with personality factor scores were qualitatively similar between psychosis groups and controls with decreased conscientiousness and agreeableness and increased neuroticism. The patterns of associations between ES-SCZ and symptoms differed across biotypes and diagnoses. Biotype 3 and bipolar disorder had consistent within-group associations where greater exposome score predicted more severe symptoms and worse function. Conclusions: ES-SCZ performance was consistent with previous reports in this transdiagnostic psychosis sample (adjusted odds ratio: 3.331 [2.834, 3.915], P < .001; area under the curve: 0.762 [0.735, 0.789]). Individual differences in ES-SCZ magnitude may be useful for investigating causal pathways between developmentally relevant exposures and symptomatic expression of psychosis.

BACKGROUND:Past studies associating personality with psychosis have been limited by small nonclinical samples and a focus on general symptom burden. This study uses a large clinical sample to examine personality's relationship with psychosis-specific features and compare personality dimensions across clinically and neurobiologically defined categories of psychoses. METHODS:A total of 1352 participants with schizophrenia, schizoaffective disorder, and bipolar with psychosis, as well as 623 healthy controls (HC), drawn from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (BSNIP-2) study, were included. Three biomarker-derived biotypes were used to separately categorize the probands. Mean personality factors (openness, conscientiousness, extraversion, agreeableness, and neuroticism) were compared between HC and proband subgroups using a generalized linear model. A robust linear regression was utilized to determine personality differences across biotypes and diagnostic subgroups. Associations between personality factors and cognition were determined through Pearson's correlation. A canonical correlation was run between the personality factors and general functioning, positive symptoms, and negative symptoms to delineate the relationship between personality and clinical outcomes of psychosis. RESULTS:There were significant personality differences between the proband and HC groups across all five personality factors. Overall, the probands had higher neuroticism and lower extraversion, agreeableness, conscientiousness, and openness. Openness showed the greatest difference across the diagnostic subgroups and biotypes, and greatest correlation with cognition. Openness, agreeableness, and extraversion had the strongest associations with symptom severity. CONCLUSIONS:Individuals with psychotic disorders have different personality traits compared to HC. In particular, openness may be relevant in distinguishing psychosis-specific phenotypes and experiences, and associated with biological underpinnings of psychosis, including cognition. Further studies should identify potential causal factors and mediators of this relationship.

BackgroundApproximately 50% of individuals with psychosis spectrum disorders (PSD) experience visual hallucinations and deficits in visual processing. Cerebral blood flow (CBF) alterations have been identified in the occipital lobe (OL) and fusiform gyrus (FG) in PSD. However, prior studies neither report on cytoarchitectonic subregions of the OL or FG, nor their correlations with cognition. Moreover, perfusion differences across neurobiologically defined psychosis Biotypes in these regions are not investigated yet.MethodsExploreASL and FreeSurfer were used to extract perfusion measures from pseudo-continuous arterial spin labeling scans of visual (hOc1-hOc3v, middle temporal area (MT)) and fusiform (FG2-FG4) subregions in 122 bipolar disorder with psychosis (BP), 179 schizoaffective disorder (SAD), 203 schizophrenia (SZ), and 350 healthy controls (NC), as well as psychosis Biotypes (BT1-3). The data was adjusted for scanner effects using ComBat. Analyses were co-varied for total gray matter CBF. We used R to perform statistical comparisons across PSD and NC and across Biotypes. Partial Spearman correlation was performed between CBF and cognitive measures. Benjamini & Hochberg correction was used to correct for multiple comparisons.ResultsPSD exhibited greater perfusion in MT and FG2 compared to NC. Perfusion significantly differed across psychosis Biotypes in hOc1 but not across diagnostic groups. Higher MT and FG4 perfusion in PSD were associated with worse overall cognitive performance.ConclusionsVisual and fusiform subregions demonstrate significant perfusion alterations which may indicate neurovascular deficits in PSD. Moreover, these perfusion alterations may contribute to cognitive impairments and visual abnormalities in psychosis.

Psychiatry lags in adopting etiological approaches to diagnosis, prognosis, and outcome prediction compared to the rest of medicine. Etiological factors such as childhood trauma (CHT), substance use (SU), and socioeconomic status (SES) significantly affect psychotic disorder symptoms. This study applied an agnostic clustering approach to identify exposome clusters "Exposotypes (ETs)" and examine their relationship with clinical, cognitive, and functional outcomes. Using data from individuals with psychotic disorders (n=1,350), and controls (n=623), we assessed the relationship between the exposotypes and outcomes. Four exposotypes were identified: ET1 characterized by high CHT and SU; ET2, high CHT; ET3, high SU; ET4, low exposure. Compared to ET4, ET1 demonstrated higher positive and general symptoms, anxiety, depression, impulsivity, and mania; ET2 had higher anxiety, depression, and impulsivity; ET3 had better cognitive and functional outcomes with lower negative symptoms. Intracranial volume was largest in ET3, and smallest in ET2. No group differences in schizophrenia polygenic risk scores were found. The age of onset was 5 years earlier in ET1 than in ET4. These findings provide insight into the complex etiological interplay between trauma, and SU, as well as their unique effects on clinical symptoms, cognition, neurobiology, genetic risk, and functioning.

AIM:Cognition varies across people with psychosis, including within a specific diagnosis. An important issue is identifying psychosis-specific neuro-cognitive dysfunctions. We addressed this issue by studying patterns of relationships between cognition and multiple other measures in persons with psychosis, their first-degree biological relatives, and healthy individuals (largest possible n = 2826). METHODS:Brief Assessment of Cognition and Wide Range Achievement Test estimated cognitive performance. Neuroanatomical measures were FreeSurfer parcellations of 3T MRI structural brain scans. Brain functioning measures included saccades, smooth pursuit eye movements, stop signal, EEG, ERPs, resting state fMRI, plus clinical characteristics. Overall associations between 452 measures of brain structure-function and clinical characteristics (predictors) with cognitive performance (criterion) were estimated using the High Dimensional Empirical Bayes Screening algorithm. RESULTS:The model yielded a common slope of predictors on cognitive performance (slope = 0.18, r = 0.33, P < 0.001). The majority (85%) of predictors fit this function, called the BAsic NeuroCognitive Continuum (BANCC). This relationship was stronger for psychosis probands (slope = 0.20, r = 0.38) than for relatives (slope = 0.09, r = 0.17) and healthy persons (slope = 0.11, r = 0.22). There were predictor-specific deviations from the common slope. Variables more strongly associated with cognitive performance (frontal-temporal-parietal lobe volumes, hippocampal regions, antisaccade performance) may tap neural architecture common to primary psychosis pathology. Variables unrelated to cognitive performance (intrinsic neural activity, volumes of lateral thalamic nuclei) distinguish specific neurophysiologically defined B-SNIP psychosis Biotypes and may capture signatures of psychosis pathophysiology. DISCUSSION:BANCC is identifiable across humans, but deviations from that common attribute identify features of brain structure-function perhaps most central and specific to psychosis-related pathophysiology.

AIM:This study aimed to evaluate associations in bipolar disorder (BD) across multimodal measures of white matter microstructure (using diffusion tensor imaging; DTI), cognitive, behavioral, and brain electrophysiological measures (using electroencephalography; EEG). METHODS:Subjects were recruited through the Psychosis and Affective Research Domains and Intermediate Phenotypes Consortium (n = 45 bipolar with psychosis, n = 40 bipolar without psychosis, n = 66 healthy subjects). DTI data were used to quantify the white matter variables, fractional anisotropy (FA) and radial diffusivity (RD). The Brief Assessment of Cognition in Schizophrenia (BACS), Stop Signal Task (SST), pro- and anti-saccades, auditory event-related potentials (ERPs), and intrinsic brain activity were used as estimates of brain function. RESULTS:The combined BD group differed from healthy controls, but no differences between BD with and without psychosis were observed. BD-related white matter abnormalities were seen across multiple tracts: right cingulum-cingulate gyrus, bilateral anterior thalamic radiation, bilateral superior longitudinal fasciculus, right inferior longitudinal fasciculus, and forceps major. Results also showed modestly compromised cognitive performance and elevated intrinsic EEG activity associated with BD. CONCLUSIONS:Further analysis indicated worse white matter integrity related to higher intrinsic EEG and modestly higher ERPs. These multimodal analyses are likely to aid in creating future informative diagnostic, etiological, and treatment targets for BD.

BACKGROUND:Studies using functional magnetic resonance imaging (fMRI) broadly require a method of parcellating the brain into regions of interest (ROIs). Parcellations can be based on standardized brain anatomy, such as the Montreal Neurological Institute's (MNI) 152 atlas, or an individual's functional activity patterns, such as the Personode software. NEW METHOD:This work outlines and tests the independent component analysis (ICA)-based parcellation algorithm (IPA) when applied to a hypertension study (n = 48) that uses the independent components (ICs) output from group ICA (gICA) to build ROIs which are ideally spatially consistent and functionally homogeneous. After regression of ICs to all subjects, the IPA builds individualized parcellations while simultaneously obtaining a gICA-derived parcellation. RESULTS:ROI spatial consistency quantified by dice similarity coefficients (DSCs) show individualized parcellations exhibit mean DSCs of 0.69 ± 0.14. Functional homogeneity, calculated as mean Pearson correlation value of all voxels comprising a ROI, shows individualized parcellations with a mean of 0.30 ± 0.14 and gICA-derived parcellations' mean of 0.38 ± 0.15. COMPARISON WITH EXISTING METHOD(S):Individualized Personode parcellations show decreased mean DSCs (0.43 ± 0.11) with the individualized parcellations, gICA-derived parcellations, and the MNI atlas having decreased homogeneity values of 0.28 ± 0.14, 0.31 ± 0.15, and 0.20 ± 0.11 respectively. CONCLUSIONS:Results show that the IPA can more reliably define a ROI and does so with higher functional homogeneity. Given these findings, the IPA shows promise as a novel parcellation technique that could aid the analysis of fMRI data.

Socioeconomic (SES) and ethno-racial factors may tilt psychosis diagnoses for persons from different backgrounds. Clinical diagnoses depend on patient and informant reports and are suspected of being susceptible to unintended bias. Diagnoses using laboratory tests are thought to be objective. Social disadvantages, however, alter brain functions related to psychosis. Race and class bias in laboratory medical diagnostics is an area of concern. To probe these issues for psychosis diagnoses, we describe relationships of SES, race/ethnicity, and ancestral genetic background to 11 integrated laboratory bio-factors that are associated with DSM categories and distinguish B-SNIP Biotypes. A series of analyses evaluated relationships of social factors and ancestry-related genetic background to those bio-factors: (i) canonical correlation revealed that SES and race (a social construct) are strongly associated (r2=.305) with cognitive performance and measures of brain physiology (prominently ERP magnitudes); genetic background neither significantly added to nor altered the structure of those associations; (ii) regression models illustrated that cognitive performance, intrinsic brain activity, and ERP magnitudes are substantially to modestly predicted by SES/race/genetic background, with SES/race accounting for the most variance on cognitive performance (approximately 25 %); (iii) regardless of including SES/race in differential diagnosis models, group differences between psychosis Biotypes were largely (85 %) preserved on bio-factor scores. For DSM diagnoses, less than 11 % of psychosis group differences were preserved. These outcomes illustrate that social factors are associated with psychosis-related laboratory tests. Nevertheless, SES/race did not substantially modify differential diagnosis of psychosis Biotypes. Using laboratory tests for psychosis differential diagnosis may facilitate the usefulness of stratification approaches, aid investigations of psychosis neurobiology and environmental risk, and improve treatment selections and approaches for all persons suffering with idiopathic psychosis.

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.

Categorical diagnosis, a pillar of the medical model, has not worked well in psychiatry where most diagnoses are still exclusively symptom based. Uncertainty continues about whether categories or dimensions work better for the assessment and treatment of idiopathic psychoses. The Bipolar Schizophrenia Network for Intermediate Phenotypes (B-SNIP) examined multiple cognitive and electrophysiological biomarkers across a large transdiagnostic psychosis data set. None of the variables supported neurobiological distinctiveness for conventional clinical psychosis diagnoses but showed a continuum of severity. Using numerical taxonomy of these data, B-SNIP identified three biological subtypes (Biotypes) agnostic to DSM diagnoses. Biotype-1 is characterized by reduced physiological response to salient stimuli, while Biotype-2 showed accentuated intrinsic (background or ongoing) neural activity and the worst inhibition. Biotype-3 cases are like healthy persons on many laboratory measures. These Biotypes differed in imaging and other electrophysiological measures not included in subgroup creation, illustrating external validation. The Biotypes solution also replicated in an independent sample of psychosis cases. Biotypes are differentiable by clinical characteristics, leading to a feasible algorithm for Biotype estimates. Identifying Biotypes may aid treatment selection and outcome prediction. As an example, preliminary cross-sectional B-SNIP data suggest that Biotype-1 cases may have physiological features that predict a more favorable response to clozapine. While psychosis Biotypes reveal physiological heterogeneity across cases with similar clinical characteristics, data also suggest a dimensional vulnerability for serious psychopathology that cuts across diagnostic boundaries. Both categorical and dimensional diagnostic approaches should be considered within idiopathic psychosis for optimum diagnosis, care, and research.