Elliot S. Gershon

Psychosis exhibits marked neurobiological heterogeneity that is not adequately captured by symptom-based diagnoses. Although prior efforts have identified subgroups, it remains unclear how latent disease variability manifests within the brain’s multiscale intrinsic functional organization and whether these patterns can be leveraged to systematically identify biologically driven subtypes of psychosis. Here, we aim to identify Psychosis Imaging Neurosubtypes (PINs)—homogeneous subgroups defined solely from neurobiological features of brain function, independent of clinical or cognitive definitions, and whether these subgroups show meaningful phenotypic differentiation. Resting-state fMRI data from 2103 B-SNIP 1&2 participants were used to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity, which captured system-wide interconnected multiscale information across three components. Projections of probands msFNC onto this subspace revealed three PINs through unsupervised learning that spanned DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective Disorder). Although defined purely from intrinsic neurobiology, PINs exhibited distinct cognitive, clinical, and connectivity profiles. PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognition, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Although covariate-adjusted analyses did not reveal significant clinical differences among PINs, exploratory analyses identified selective differences in YMDR, PANSS, and Age of Onset. Notably, 55% of relatives aligned with the same neurosubtype as their probands, a rate significantly higher than chance and than DSM-based alignment, suggesting potential heritability. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs partially converged with previously reported BSNIP Biotypes with some correspondence (e.g. PIN1 with Biotype 1) but not with the other Biotypes, which were determined using integrated electrophysiological, cognitive, and oculomotor data, supporting convergent validity, while also indicating that intrinsic rsfMRI derived multiscale connectivity partitions psychosis heterogeneity somewhat differently from multimodal biomarker approaches. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

ERPs average neural responses to stimuli across trials. This accentuates consistent activity over time but obscures differences in response strength and phase consistency from trial to trial. The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) identified three transdiagnostic psychosis Biotypes (BT1, BT2, BT3) that differ in cortical excitability and temporal coherence of neural responses. Single-trial power (STP) and inter-trial coherence (ITC) were measured across 3–55 Hz in auditory paired-stimuli and oddball tasks using a large multi-site cohort (n = 2 373). One-way ANOVAs over a continuous time-frequency epoch with FDR and cluster correction identified group differences, resulting in 15 significant STP and ITC features. Linear discriminant analysis of these features identified the dimensions that maximally separated Biotypes. The STP differences covered the entire trial epoch, showing the strongest alterations in the beta band (18–32 Hz), graded BT2 > > BT3 > HC > > BT1. The ITC differences were seen at specific time points primarily in low-frequency bands (3–17 Hz), graded HC > BT3 > BT1 > BT2. These dimensions were orthogonal and did not mirror ERP amplitudes across Biotypes. Biotypes were better distinguished by STP and ITC (Euclidean distances 0.9–2.2) than by DSM diagnosis (0.10–0.45). DSM groups alone did not benefit from this breakdown. Cortical excitability and phase precision are unique neurophysiological targets for the etiological investigation of psychosis Biotypes, which traditional ERP averaging obscures.

Tobacco and cannabis use are highly prevalent among individuals with psychotic disorders. Tobacco use has been consistently linked to poorer clinical and cognitive outcomes, whereas findings regarding cannabis use remain mixed. The present study leveraged a large multi-site psychosis spectrum sample with comprehensive cognitive and clinical assessments to more precisely characterize how tobacco or cannabis use, co-use, and non-use differentially relate to function. The final sample included 323 healthy controls and 884 individuals with psychotic disorders. Individuals with psychosis, regardless of substance use status, exhibited significantly worse cognitive performance across cognitive domains compared to healthy individuals. Among individuals with psychosis, exclusive tobacco use was associated with the highest symptom burden and the greatest cognitive impairment. In contrast, cannabis use showed more nuanced associations. Greater cannabis use was tied to worse general psychosis symptoms for current tobacco users but fewer general symptoms for those who never used tobacco. Further, more cannabis use was linked to better cognitive performance for current tobacco users but was not linked to working memory for prior users of tobacco. Taken together, these findings highlight tobacco use as a potential marker of greater disease severity and cognitive impairment among individuals with psychosis, while also demonstrating the importance of considering patterns of co-occurring use of substances such as cannabis.

Objective: We examined the clinical utility of resting state electroencephalography (rsEEG) by evaluating its temporal stability, discriminant validity for B-SNIP psychosis Biotypes, and suitability as a treatment target for brain stimulation. Methods: We collected 5 minutes of eyes-open rsEEG from 1401 participants with psychosis and 750 healthy persons. A subset of participants was re-tested after 6 months and 12 months (N=109). In a pilot target engagement study (n=5) we collected rsEEG before and after 2 high-definition transcranial direct current stimulation (HD-tDCS) interventions targeting the left dorsolateral prefrontal cortex (dlPFC) and temporoparietal junction (TPJ). Data were reduced with principal component analyses to delta/theta, alpha, beta, and gamma frequency bands, and compared between groups and timepoints. Results: rsEEG frequency bands displayed good-to-excellent stability and significantly distinguished psychosis Biotypes with large effect sizes. Compared to healthy, Biotype-1 had low activity (average ES=-.58), Biotype-2 had high activity (ES=1.07), and Biotype-3 had slightly elevated activity (ES=.33). There were no rsEEG differences between DSM psychosis groups. After anodal dlPFC stimulation, alpha and gamma power slightly increased while positive symptoms and verbal fluency improved. After cathodal TPJ stimulation, delta/theta power slightly increased while psychoticism and digit sequencing improved. Conclusions: Resting state brain activity is a trait-like marker that differentiates B-SNIP psychosis Biotypes, suggesting differing underlying neurophysiology. The pilot intervention supports the feasibility of targeting this underlying neurophysiology with HD-tDCS. Integrating rsEEG in diagnostic procedures and stratified intervention selection may be beneficial for psychosis patients.

Idiopathic psychosis shows considerable biological heterogeneity across cases. The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) used psychosis-relevant biomarkers to identify psychosis Biotypes, which will aid etiological and targeted treatment investigations. Here, our previous approach (Clementz et al. 2022) is updated, which supports the development of an efficient psychosis Biotype diagnostic procedure called ADEPT. Psychosis probands (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognitive performance, saccades, and auditory EEG/ERP measurements. EEG and ERP quantifications were modified from previous Biotypes iterations. Multivariate integration using multiple approaches reduced biomarker outcomes to 11 "bio-factors." Twenty-four different approaches indicated bio-factor data among probands were best described by three subgroups. Numerical taxonomy with k-means clustering yielded psychosis Biotypes; Rand Indices evaluated individual-case 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 individual-case consistency of Biotype membership when samples included more than 1000 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons: "Pattern-2" (high antisaccade errors, low BACS, high ongoing EEG) captured Biotype-2, "Pattern-1" (low ERP amplitudes, low intrinsic EEG) captured Biotype-1, and "Pattern-3" (low frontal P3 complex, accentuated S2 ERP, faster saccadic reaction times) captured Biotype-3. First-degree relatives had patterns like their proband for general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. These outcomes refine and extend operations for characterizing biologically distinct psychosis Biotypes. They also show that over 1000 observations are useful for achieving consistent individual-case diagnostic assignments. First-degree relative data implicate specific bio-factors as familial within idiopathic psychosis which may inform genetic studies.

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. 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. 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. 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.

Recent genetic studies have linked numerous loci to psychiatric disorders. However, the biological pathways that connect these genetic associations to psychiatric disorders’ specific pathophysiological processes are largely unclear. Endophenotypes, first defined over five decades ago, are heritable traits, independent of disease state that are associated with a disease, encompassing a broad range of neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, and neuropsychological characteristics. Considering the advancements in genetics and genomics over recent decades, we propose a revised definition of endophenotypes as ‘genetically influenced phenotypes linked to disease or treatment characteristics and their related events.’ We also updated endophenotype criteria to include (1) reliable measurement, (2) association with the disease or its related events, and (3) genetic mediation. ‘Genetic mediation’ is introduced to differentiate between causality and pleiotropic effects and allows non-linear relationships. Furthermore, this updated Endophenotype 2.0 framework expands to encompass genetically regulated responses to disease-related factors, including environmental risks, illness progression, treatment responses, and resilience phenotypes, which may be state-dependent. This broadened definition paves the way for developing new endophenotypes crucial for genetic analyses in psychiatric disorders. Integrating genetics, genomics, and diverse endophenotypes into multi-dimensional mechanistic models is vital for advancing our understanding of psychiatric disorders. Crucially, elucidating the biological underpinnings of endophenotypes will enhance our grasp of psychiatric genetics, thereby improving disease risk prediction and treatment approaches.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140 , ARTN , C1orf115 , CYREN , and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

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.