Carol A. Tamminga

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.

We report a pilot study of deep brain stimulation (DBS) in three individuals with treatment refractory schizophrenia (TRS). DBS target selection was supported by an inpatient brain network mapping paradigm using intracranial electroencephalography. In conjunction with assessing stimulation-dependent symptom improvement, we derived spatiotemporally resolved profiles of psychosis and healthy brain states and identified stimulation targets that best shifted brain networks towards healthy states. Therapeutic stimulation sites were personalized for each participant but converged on salience network nodes including anterior cingulate and anterior insula. No significant adverse events were noted across participants. Moreover, two participants with chronic stimulation and clinical follow-up of at least 4 months reported significant improvement in both positive and negative symptoms, and treatment optimization is underway for the third. These critical pilot data establish the feasibility of personalized DBS guided by concurrent stimulation mapping, behavioral assessment, and biomarker monitoring as a treatment for TRS.

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.

Background/Objectives: Prader–Willi Syndrome (PWS) is a neurodevelopmental disease associated with multiple behavioral features, including a prevalence for psychosis. The genetic causes of PWS are well characterized and involve the silencing or deletion of the paternal copy of a region of chromosome 15q11–13. One gene within this region, Snord116, a non-coding RNA, has been determined to have a determinant role in the manifestation of PWS. However, it remains unclear as to how the deletion of this allele can affect activity in the brain and influence psychosis-like behaviors. Methods: In this study, we assessed the effects of the microdeletion of the paternal copy of Snord116 on regional neural activity in psychosis-associated brain regions and psychosis-like behaviors in mice. Results: The results suggest that Snord116 deletion causes increased c-Fos expression in the hippocampus and anterior cingulate cortex. Snord116 deletion also results in behavioral phenotypes consistent with psychosis, most notably in stressful paradigms, with deficits in sensorimotor gating and augmented contextual as well as cued fear conditioning. Conclusions: These results implicate the targets of Snord116 in the presentation of a psychosis-like state with regional specificity.

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.