The Complex Latent Structure of Attenuated Psychotic Symptoms: Hierarchical and Bifactor Models of SIPS Symptoms Replicated in Two Large Samples at Clinical High Risk for Psychosis.

BACKGROUND AND HYPOTHESIS: The Structured Interview for Psychosis-Risk Syndromes (SIPS) and other assessments of psychosis risk define clinical high risk for psychosis (CHR) by the presence of attenuated psychotic symptoms. Despite extensive research on attenuated psychotic symptoms, substantial questions remain about their internal psychometric structure and relationships to comorbid non-psychotic symptoms. STUDY DESIGN: Hierarchical and bifactor models were developed for the SIPS in a large CHR sample (NAPLS-3, N = 787) and confirmed through preregistered replication in an independent sample (NAPLS-2, N = 1043). Criterion validity was tested through relationships with CHR status, comorbid symptoms/diagnoses, functional impairment, demographics, neurocognition, and conversion to psychotic disorders. STUDY RESULTS: Most variance in SIPS items (75%-77%) was attributable to a general factor. Hierarchical and bifactor models included a general factor and five specific/lower-order factors (positive symptoms, eccentricity, avolition, lack of emotion, and deteriorated thought process). CHR participants were elevated on the general factor and the positive symptoms factor. The general factor was associated with depressive symptoms; functional impairment; and mood, anxiety, and schizotypal personality diagnoses. The general factor was the best predictor of psychotic disorders (d ≥ 0.50). Positive symptoms and eccentricity had specific effects on conversion outcomes. The deteriorated thought process was least meaningful/replicable. CONCLUSIONS: Attenuated psychotic symptoms, measured by the SIPS, have a complex hierarchical structure with a strong general factor. The general factor relates to internalizing symptoms and functional impairment, emphasizing the roles of general psychopathological distress/impairment in psychosis risk. Shared symptom variance complicates the interpretation of raw symptom scores. Broad transdiagnostic assessment is warranted to model psychosis risk accurately.

Longitudinal Trajectories of Premorbid Social and Academic Adjustment in Youth at Clinical High Risk for Psychosis: Implications for Conversion.

BACKGROUND AND HYPOTHESIS: Social and academic adjustment deteriorate in the years preceding a psychotic disorder diagnosis. Analyses of premorbid adjustment have recently been extended into the clinical high risk for psychosis (CHR) syndrome to identify risk factors and developmental pathways toward psychotic disorders. Work so far has been at the between-person level, which has constrained analyses of premorbid adjustment, clinical covariates, and conversion to psychosis. STUDY DESIGN: Growth-curve models examined longitudinal trajectories in retrospective reports of premorbid social and academic adjustment from youth at CHR (n = 498). Interaction models tested whether known covariates of premorbid adjustment problems (attenuated negative symptoms, cognition, and childhood trauma) were associated with different premorbid adjustment trajectories in converters vs non-converters (ie, participants who did/did not develop psychotic disorders within 2-year follow-up). STUDY RESULTS: Converters reported poorer social adjustment throughout the premorbid period. Converters who developed psychosis with an affective component reported poorer academic adjustment throughout the premorbid period than those who developed non-affective psychosis. Tentatively, baseline attenuated negative symptoms may have been associated with worsening social adjustment in the premorbid period for non-converters only. Childhood trauma impact was associated with fewer academic functioning problems among converters. Cognition effects did not differ based on conversion status. CONCLUSIONS: Premorbid social function is an important factor in risk for conversion to psychosis. Negative symptoms and childhood trauma had different relationships to premorbid functioning in converters vs non-converters. Mechanisms linking symptoms and trauma to functional impairment may be different in converters vs non-converters, suggesting possible new avenues for risk assessment.

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI.

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

The hierarchical taxonomy of psychopathology in clinical high risk for psychosis: Validation and extension.

The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium's transdiagnostic dimensional model of psychopathology has considerable support; however, this model has been underresearched in individuals at clinical high risk for psychosis (CHR-P), a population that may advance the model. CHR-P individuals not only have attenuated psychotic symptoms that vary in severity, but also have many comorbid diagnoses and varied clinical outcomes, including disorders with uncertain relations to HiTOP (e.g., obsessive-compulsive disorder). The present study used self-report and interview data from North American Prodrome Longitudinal Study-3 (710 CHR, 96 controls) to replicate the HiTOP model and test specific hypotheses regarding disorders with uncertain relations to its dimensions. Additionally, the present study examined the HiTOP model in relation to childhood trauma, declines in social functioning, and development of full psychosis. Confirmatory factor analysis indicated that the HiTOP model's fit was nearly adequate (e.g., comparative fit index = .89), though several theory-relevant modifications were indicated. Additionally, specific tests were conducted to gain a more fine-grained perspective on how disorders with less clear prior evidence were related to the HiTOP model. Notable findings from these analyses include bipolar spectrum disorders relating to the psychosis super spectrum (i.e., .39 loading), and obsessive-compulsive disorder showing a complex pattern of loadings (e.g., internalizing and psychosis). The final model parsimoniously accounted for childhood trauma (e.g., super spectra rs = .22-.32), associations with current functioning, and predicted future conversion to a psychotic disorder (e.g., super spectra R² = .13). Overall, these results inform the HiTOP model and suggest its promise for CHR-P research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Plasma complement and coagulation proteins as prognostic factors of negative symptoms: An analysis of the NAPLS 2 and 3 studies.

INTRODUCTION: Negative symptoms impact the quality of life of individuals with psychosis and current treatment options for negative symptoms have limited effectiveness. Previous studies have demonstrated that complement and coagulation pathway protein levels are related to later psychotic experiences, psychotic disorder, and functioning. However, the prognostic relationship between complement and coagulation proteins and negative symptoms is poorly characterised. METHODS: In the North American Prodrome Longitudinal Studies 2 and 3, negative symptoms in 431 individuals at clinical high-risk for psychosis (mean age: 18.2, SD 3.6; 42.5 % female) were measured at multiple visits over 2 years using the Scale of Psychosis-Risk Symptoms. Plasma proteins were quantified at baseline using mass spectrometry. Four factors were derived to represent levels of proteins involved in the activation or regulation of the complement or coagulation systems. The relationships between standardised protein group factors and serial measurements of negative symptoms over time were modelled using generalised least squares regression. Analyses were adjusted for baseline candidate prognostic factors: negative symptoms, positive symptoms, functioning, depressive symptoms, suicidal ideation, cannabis use, tobacco use, antipsychotic use, antidepressant use, age, and sex. RESULTS: Clinical and demographic prognostic factors of follow-up negative symptoms included negative, positive, and depressive symptoms, functioning, and age. Adjusting for all candidate prognostic factors, the complement regulators group and the coagulation regulators group were identified as prognostic factors of follow-up negative symptoms (ß: 0.501, 95 % CI: 0.160, 0.842; ß: 0.430, 95 % CI: 0.080, 0.780 respectively. The relationship between complement regulator levels and negative symptoms was also observed in NAPLS2 alone (ß: 0.501, 95 % CI: -0.037, 1.039) and NAPLS3 alone, additionally adjusting for BMI (ß: 0.442, 95 % CI: 0.127, 0.757). CONCLUSION: The results indicate that plasma complement and coagulation regulator levels are prognostic factors of negative symptoms, independent of clinical and demographic prognostic factors. These results suggest complement and coagulation regulator levels could have potential utility in informing treatment decisions for negative symptoms in individuals at risk.

P300 in schizophrenia: Then and now.

The 1965 discovery of the P300 component of the electroencephalography (EEG)-based event-related potential (ERP), along with the subsequent identification of its alteration in people with schizophrenia, initiated over 50 years of P300 research in schizophrenia. Here, we review what we now know about P300 in schizophrenia after nearly six decades of research. We describe recent efforts to expand our understanding of P300 beyond its sensitivity to schizophrenia itself to its potential role as a biomarker of risk for psychosis or a heritable endophenotype that bridges genetic risk and psychosis phenomenology. We also highlight efforts to move beyond a syndrome-based approach to understand P300 within the context of the clinical, cognitive, and presumed pathophysiological heterogeneity among people diagnosed with schizophrenia. Finally, we describe several recent approaches that extend beyond measuring the traditional P300 ERP component in people with schizophrenia, including time-frequency analyses and pharmacological challenge studies, that may help to clarify specific cognitive mechanisms that are disrupted in schizophrenia. Moreover, we discuss several promising areas for future research, including studies of animal models that can be used for treatment development.

Pons-to-Cerebellum Hypoconnectivity Along the Psychosis Spectrum and Associations With Sensory Prediction and Hallucinations in Schizophrenia.

BACKGROUND: Sensory prediction allows the brain to anticipate and parse incoming self-generated sensory information from externally generated signals. Sensory prediction breakdowns may contribute to perceptual and agency abnormalities in psychosis (hallucinations, delusions). The pons, a central node in a cortico-ponto-cerebellar-thalamo-cortical circuit, is thought to support sensory prediction. Examination of pons connectivity in schizophrenia and its role in sensory prediction abnormalities is lacking. METHODS: We examined these relationships using resting-state functional magnetic resonance imaging and the electroencephalography-based auditory N1 event-related potential in 143 participants with psychotic spectrum disorders (PSPs) (with schizophrenia, schizoaffective disorder, or bipolar disorder); 63 first-degree relatives of individuals with psychosis; 45 people at clinical high risk for psychosis; and 124 unaffected comparison participants. This unique sample allowed examination across the psychosis spectrum and illness trajectory. Seeding from the pons, we extracted average connectivity values from thalamic and cerebellar clusters showing differences between PSPs and unaffected comparison participants. We predicted N1 amplitude attenuation during a vocalization task from pons connectivity and group membership. We correlated participant-level connectivity in PSPs and people at clinical high risk for psychosis with hallucination and delusion severity. RESULTS: Compared to unaffected comparison participants, PSPs showed pons hypoconnectivity to 2 cerebellar clusters, and first-degree relatives of individuals with psychosis showed hypoconnectivity to 1 of these clusters. Pons-to-cerebellum connectivity was positively correlated with N1 attenuation; only PSPs with heightened pons-to-postcentral gyrus connectivity showed this pattern, suggesting a possible compensatory mechanism. Pons-to-cerebellum hypoconnectivity was correlated with greater hallucination severity specifically among PSPs with schizophrenia. CONCLUSIONS: Deficient pons-to-cerebellum connectivity linked sensory prediction network breakdowns with perceptual abnormalities in schizophrenia. Findings highlight shared features and clinical heterogeneity across the psychosis spectrum.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Linking enlarged choroid plexus with plasma analyte and structural phenotypes in clinical high risk for psychosis: A multisite neuroimaging study.

BACKGROUND: Choroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. METHODS: Clinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. RESULTS: Compared to controls, CHR (d = 0.23, p = 0.017) and non-converters (d = 0.22, p = 0.03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r = -0.22, p < 0.001), subcortical gray matter (r = -0.21, p < 0.001), and total white matter volume (r = -0.28,p < 0.001), as well as larger lateral ventricle volume (r = 0.63,p < 0.001). Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r = -0.30, p = 0.035), ICAM1 (r = 0.33, p = 0.02)], converters [IL1ß (r = 0.66, p = 0.004)], and non-converters [BMP6 (r = -0.96, p < 0.001), CALB1 (r = -0.98, p < 0.001), ICAM1 (r = 0.80, p = 0.003), SELE (r = 0.59, p = 0.026), SHBG (r = 0.99, p < 0.001), TNFRSF10C (r = 0.78, p = 0.001)]. CONCLUSIONS: CHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key an important biomarker in CHR.

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study.

Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.

AIM: To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). METHODS: The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. RESULTS: Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and modest harmonization for CHR-P criteria. The semi-structured interview, named Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. CONCLUSIONS: Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.

Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator.

BACKGROUND: Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models. METHODS: Participants from both the NAPLS2 and NAPLS3 samples were classified as either 'long' or 'short' symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis. RESULTS: The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78). CONCLUSIONS: These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis.

Importance: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. Objective: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and Participants: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. Main Outcomes and Measures: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. Results: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (ß = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (ß = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate). Conclusions and Relevance: In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk.

BACKGROUND: Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS: We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS: Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.

Relations of Lifetime Perceived Stress and Basal Cortisol With Hippocampal Volume Among Healthy Adolescents and Those at Clinical High Risk for Psychosis: A Structural Equation Modeling Approach.

BACKGROUND: Hippocampal volume (HV) is sensitive to environmental influences. Under normative conditions in humans, HV increases linearly into childhood and asymptotes in early adulthood. Studies of humans and nonhuman animals have provided evidence of inverse relationships between several measures of stress and HV. METHODS: Using structural equation modeling, this study aimed to characterize the relationships of age, basal cortisol, biological sex, and lifetime perceived stress with bilateral HV in a sample of healthy adolescents and adolescents at clinical high risk for psychosis (CHR-P) (N = 571, 43% female; age range = 12-19.9 years). This sample included 469 individuals at CHR-P and 102 healthy comparison participants from the combined baseline cohorts of the second and third NAPLS (North American Prodrome Longitudinal Study). RESULTS: A structural model that constrained the individual effects of basal cortisol and perceived stress to single path coefficients, and freely estimated the effects of age and biological sex in group models, optimized model fit and parsimony relative to other candidate models. Significant inverse relationships between basal cortisol and bilateral HV were observed in adolescents at CHR-P and healthy comparison participants. Significant sex differences in bilateral HV were also observed, with females demonstrating smaller HV than males in both groups. CONCLUSIONS: Multigroup structural equation modeling revealed heterogeneity in the relationships of age and biological sex with basal cortisol, lifetime perceived stress, and bilateral HV in individuals at CHR-P and healthy comparison participants. Moreover, the findings support previous literature indicating that elevated basal cortisol is a nonspecific risk factor for reduced HV.

Hippocampal Connectivity with the Default Mode Network is Linked to Hippocampal Volume in the Clinical High Risk for Psychosis Syndrome and Healthy Individuals.

Reduced hippocampal volume (HV) is an established brain morphological feature of psychiatric conditions. HV is associated with brain connectivity in humans and non-human animals and altered connectivity is associated with risk for psychiatric illness. Associations between HV and connectivity remain poorly characterized in humans, and especially in phases of psychiatric illness that precede disease onset. This study examined associations between HV and hippocampal functional connectivity (FC) during rest in 141 healthy controls and 248 individuals at-risk for psychosis. Significant inverse associations between HV and hippocampal FC with the inferior parietal lobe (IPL) and thalamus were observed. Select associations between hippocampal FC and HV were moderated by diagnostic group. Significant moderation results shifted from implicating the IPL to the temporal pole after excluding participants on antipsychotic medication. Considered together, this work implicates hippocampal FC with the temporoparietal junction, within a specialized subsystem of the default mode network, as sensitive to HV.

Auditory oddball hypoactivation in schizophrenia.

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach.

Importance: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. Objective: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. Design, Setting, and Participants: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. Results: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001). Conclusions and Relevance: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity.

BACKGROUND: Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. METHODS: Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. RESULTS: Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. CONCLUSIONS: Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.