AIM:Current estimates suggest that about 1.7% of the general population meets criteria for clinical high-risk for psychosis (CHR), but Latinx individuals are more likely to be at higher risk due to factors that could inflate rates of symptomatology. Disparities in screening, access to services, and intervention for Latinx individuals at CHR persist despite calls to action. METHODS:Through the lens of service utilisation models, the present narrative review builds on prior calls to action by summarising the literature and providing culturally sensitive recommendations for clinicians and researchers. RESULTS:Disparities include challenges in the identification of need for services by individuals and their social networks, the lack of culturally appropriate tools and interventions across community and specialty settings, and barriers to service retention and engagement. DISCUSSION:In response, recommendations include disseminating psychoeducational materials and programs to Latinx communities, creating norms for Latinx individuals for standard screeners and clinical interviews, increasing specialised trainings for providers in different settings, and making psychosis-related research in Latinx populations more accessible through open science practices.

Abstract Negative symptoms of schizophrenia are associated with deficits in representing the value of actions, observed in reinforcement learning (RL) tasks as impaired learning from gains but intact learning from losses. This RL profile contrasts with depression, where enhanced loss sensitivity is common. Whether a schizophrenia-like RL pattern characterizes youth at clinical high-risk (CHR) for psychosis – who show modest psychosis transition rates but high rates of co-occurring depression – remains unclear. We tested whether CHR youth show a schizophrenia-like RL profile and whether RL parameters relate differentially to negative vs. depressive symptoms by estimating RL in CHR ( n = 292), clinical controls (CC; n = 241) with other psychopathologies, and healthy controls ( n = 175). Participants completed symptom interviews, neuropsychological assessments, a dimensional psychosis risk calculator, and an RL task in which participants could seek gains or avoid losses. A computational gain-loss Q -learning model decomposed task behavior into components indexing value updating and value-guided choice. Although overall RL performance was similar across groups, in both CHR and CC youth, higher negative, but not depressive symptoms, were selectively associated with reduced win-stay behavior following gains. Computational parameters suggested this behavioral pattern reflected disrupted updating and expression of positive value representations. Lower win-stay rates were also linked to lower premorbid intelligence and higher psychosis risk calculator scores. Here, a schizophrenia-like RL profile was unrelated to depression but associated with multiple indicators of psychosis risk across clinical groups, suggesting a transdiagnostic psychosis risk mechanism distinct from affective disturbance.

Background and Hypotheses Research suggests that stress contributes to psychosis risk through an affective pathway, where heightened emotional responses to stressors lead to increased vulnerability. Specifically, individuals at clinical high-risk for psychosis (CHR-p) display more intense negative affect (NA) intensity, which is thought to exacerbate psychosis risk. The present study explored temporal dynamics between momentary NA intensity and delusional severity in CHR-p using ecological momentary assessment (EMA). Aims were to: (1) examine group differences in NA intensity and variability and (2) explore bidirectional associations between NA and attenuated delusions in the context of daily life. Study Design A sample of 120 CHR participants and 59 healthy controls completed 1 week of EMA surveys examining NA and attenuated delusions. Multilevel models examined time-lagged effects of NA reactivity on attenuated delusions and vice versa. Results Consistent with previous research, individuals at CHR displayed greater levels of NA intensity compared to healthy controls. Additionally, the CHR group exhibited variability affective changes throughout the day, suggesting a disrupted return to emotional homeostasis. Contrary to predictions, NA intensity did not predict subsequent delusional severity, highlighting potential complexities in the association that may be revealed by EMA methodologies. Instead, the study demonstrated that heightened attenuated delusional severity predicted subsequent increases in NA intensity. Conclusions These results demonstrated attenuated delusions exacerbate momentary elevations in NA intensity. Together with evidence from previous literature, findings underscore this relationship may be sensitive to the timescale of measurement, indicating the need for elucidating mechanisms underlying these associations to improve outcomes.

Growing work underscores the importance of understanding disturbances in positive valence or emotional processes in psychopathology. Despite evidence that substance use disorders, such as cannabis misuse, are associated with positive emotion processes, few studies have examined associations between cannabis use and clinically relevant disorders that centrally feature positive emotions (such as bipolar spectrum disorders) and associated positive emotion processes. The present study investigates associations between self-reported cannabis use and bipolar spectrum disorder (BSD) risk and mood severity, as well as three well-studied positive valence processes (i.e., positive emotion experience, reward responsiveness, and positive emotion valuation). Emerging adult college students who endorsed cannabis use (N = 968) were recruited from nine North American universities. Higher self-reported BSD risk was associated with greater cannabis-related interference with daily life, but not cannabis use frequency or difficulty stopping. Furthermore, higher positive emotion experience was associated with lower cannabis frequency, interference with life, and difficulty stopping. Greater reward responsiveness was associated with decreased cannabis interference in daily life. These findings highlight the importance of clinically relevant and basic positive emotion-relevant processes in understanding cannabis use.

Despite advances in the assessment of negative symptoms in schizophrenia (SZ), little is known about how patients and their relatives/caregivers (REL/CARE) subjectively perceive these symptoms and the tools used to measure them. The current study used a combined qualitative and quantitative approach to obtain subjective perceptions on negative symptoms. Participants included 52 outpatients with SZ and 22 REL/CARE who completed structured online surveys with quantitative and qualitative questions evaluating agreement with standard definitions of 6 negative symptom domains (anhedonia, avolition, asociality, blunted affect, alogia, lack of normal distress), the perceived importance of each of the domains, clarity of interview probes used on the Brief Negative Symptom Scale (BNSS), and the level of symptom improvement required for meaningful change on the BNSS. Clinician-rated BNSS scores were compared with SZ and REL/CARE subjective perceptions of negative symptom severity. A subset of participants completed follow-up live, one-on-one qualitative interviews to probe responses further. Participants showed high agreement with BNSS definitions for the five core domains, but less agreement for lack of normal distress. Both groups rated anhedonia, avolition, and asociality as most important to improve. Most BNSS items were considered clear, though lack of normal distress was sometimes misunderstood. Participants rated negative symptom severity higher than clinicians and reported that a 1-2 point reduction on BNSS anchors would reflect meaningful change. Findings provide important insight into subjective perceptions of negative symptoms from stakeholders and suggest that the BNSS assessment approach captures the construct in ways that are meaningful to people with SZ and their REL/CARE.

Developing digital technologies for quantifying symptoms of serious mental illness (SMI) has been a focus of research for over 7 decades. Recent efforts have focused on multimodal feature integration, which captures conceptually distinct behavioral domains. We evaluated links between nonverbal digital features and human-rated negative symptoms, with the expectation that their relationship would depend on verbal information (i.e., be moderated by language emotional tone). We believe this better approximates how humans integrate verbal and nonverbal information when making clinical ratings. Mobile video diaries were evaluated for people with SMI, those at clinical high risk for developing SMI, and control groups (Ns = 48, 20, 36, and 21, respectively; K videos = 902, 440, 602, and 399, respectively). We identified six features (i.e., capturing positive and neutral facial expressions, vocal intonation and emphasis, and speaking pause length and articulation rate) that showed acceptable reliability (final N and K for video/audio analysis = 109/912 and 120/2,200, respectively), but most features failed to show acceptable reliability. Videos were behaviorally rated for alogia and blunted vocal and facial affect. Relationships between nonverbal features and human ratings were dependent on verbal tone, but only when verbal tone was positive in valence. For example, behaviorally rated alogia was associated with longer pauses, but primarily when language had a positive tone. These results support a relatively novel approach to multimodal digital phenotyping, one that emphasizes using multimodal features to provide "context" in the way that humans likely interpret and integrate multimodal streams of information. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background Cultural contexts, such as whether one’s immediate environment is ethnoracially congruent, are known to influence emotional expression, emotional experience, motivation, and social behavior in healthy individuals. However, it is unclear whether such cultural factors play a role in state exacerbations in negative symptoms that occur in schizophrenia (SZ). Aims The current study combined GPS data, environmental geocoding, and ecological momentary assessment (EMA) to test the hypothesis that ethnoracial incongruence encountered in daily-life situations predicts state increases in negative symptoms in SZ. Method Participants included outpatients with SZ (n = 37) and healthy controls (CN: n = 41) with marginalized ethnoracial identities who completed EMA and passive digital phenotyping recordings. Geolocation was used to pair participant GPS location at the time of completing EMA symptom surveys with geocoded measures of that location’s ethnoracial density based on government census records. Ethnoracial congruence was determined in relation to the match between a participant’s identified ethnoracial identity and the ethnoracial density of their location at the time of EMA survey. Results Results indicated that ethnoracially incongruent contexts were associated with state increases in negative symptoms in individuals with SZ, but not CN. Conclusions These findings suggest that interactions between one's own ethnoracial identity and the ethnoracial context of the current environment contributes to negative symptom exacerbations in SZ. Identity factors are not typically considered in the assessment and treatment of negative symptoms in SZ, but it would be beneficial to do so.

Several factor-analytic studies of the Scale for the Assessment of Negative Symptoms (SANS) and Positive and Negative Syndrome Scale (PANSS) have identified two dimensions. These dimensions reflect expressive (EXP) and experiential or Motivation and Pleasure (MAP) negative symptoms. However, the extent to which these factor solutions yield similar dimension scores across the two scales remains unclear. This study compared equipercentile linking and linear regression data harmonization methods for cross-scale EXP and MAP conversions and evaluated their accuracy and reliability. While conversions between methods were similar, regression provided a slight edge over equipercentile linking in most comparisons. Most conversions had moderate or better reliability (ICC > 0.6, p < 0.01). EXP conversions generally had higher ICCs than MAP, with EXP >60% and MAP <60% posterior probability of exceeding ICCs>0.75. Some factor score conversions yielded higher reliability than others. However, when considering both EXP and MAP together, Ahmed's factor scores for SANS and Jang's or Khan's for PANSS performed the best. This study is the first to compare equipercentile linking and regression frameworks for scale harmonization as well as to examine the conversion reliability and accuracy for the EXP and MAP dimensions across SANS and PANSS. It is also the first to employ a Bayesian approach, which provides additional information through probability distributions for choosing the most favorable conversion. The conversion formulae from this study and their reported metrics lay the groundwork for analyses of the EXP and MAP even when assessed with different clinical scales.

Negative symptoms of psychotic disorders are best represented within a hierarchical structure comprising two broad dimensions—Motivation and Pleasure (MAP) and Diminished Expressivity (EXP)—and five lower-level domains. The validity of these two dimensions and five domains is supported by associations with cognitive, psychological, and clinical outcomes. However, few studies have examined whether they are differentiated by distinct neural mechanisms. The current study examined the specificity of associations between the two dimensions and five domains and resting-state functional connectivity (RS-FC) within five large-scale brain networks critical for social behavior. Participants included 125 early psychosis (EP) patients and 58 healthy controls (CN) from the Human Connectome Project-Early Psychosis who completed resting-state functional magnetic resonance imaging (rsfMRI) scans. RS-FC was quantified in five social brain networks: affiliation network, aversion network, perception network, mentalizing network, and mirror network. Early psychosis patients exhibited significantly reduced RS-FC in social brain networks compared to CN, but no specific network was responsible for this effect. Reduced RS-FC in the mirror network was significantly associated with greater asociality, anhedonia, and avolition, while reduced RS-FC in the mirror and mentalizing networks was associated with more severe blunted affect. Findings suggest that some RS-FC networks align with the broader higher-order dimensions, while others align with the lower-level domains. Overall, the pattern of findings suggests that abnormal patterns of resting-state social brain network activation are broadly associated with the MAP dimension and not more selectively related to anhedonia, avolition, or asociality. Thus, findings suggest that the neurobiology of negative symptoms in EP is best captured at the level of the broader higher-order dimensions than the specific lower-level domains that make them up.

OBJECTIVE:The dissemination of inexpensive computerized behavioral tasks indexing amotivation may enhance the assessment of clinical high risk (CHR) across settings. However, the impact of varying reward value in such tasks is unclear. If point-based rewards engage participants, this could improve the scalability of computerized assessments. We tested how point-based rewards versus money impacted effort-cost decision-making in CHR individuals. We further assessed how negative symptom severity and household income interacted with reward-type to impact behavior. METHODS:Participants completed the Effort Expenditure for Reward Task (EEfRT). Participants were randomly assigned to receive either money or points for their performance during the EEfRT. Data from a large sample of CHR (N = 233) individuals and healthy controls (HC; N = 157) were collected. RESULTS:Across diagnostic groups, we observed heightened effort expenditure when money was used as a reward (b = 0.13, p = 0.018). We did not find an interaction of CHR status (b = 0.07, p = 0.845) or negative symptoms (b = 0.01, p = 0.429) with reward-type. Within CHR individuals, heightened negative symptom severity was associated with reduced expended effort (b = -0.03, p = 0.016), regardless of reward type. In an exploratory analysis, we found that individuals in the money condition with relatively high household income expended less effort during high reward, high probability trials (b = -0.24, p = 0.046). CONCLUSIONS:Across CHR and HC individuals, individuals pursuing money expended greater effort. While we did not find a group by reward type interaction, CHR individuals with heightened negative symptom severity expended less effort across trials, replicating prior work. Present findings support further study of point-based rewards in tasks indexing amotivation.

Introduction Negative symptoms are a core feature of schizophrenia and severely impact functioning and quality of life. The five symptom domains (anhedonia, asociality, avolition, blunted affect, and alogia) have emerged as the preferred structure of negative symptoms; however, it is unclear whether these dimensions demonstrate measurement bias across sociodemographic variables. We used regularized moderated nonlinear factor analysis (MNLFA) to evaluate potential sociodemographic bias in the measurement of negative symptoms among individuals with schizophrenia. Methods The study included two samples of people with schizophrenia (total n = 1175). Negative symptoms were assessed with the Brief Negative Symptom Scale. Regularized MNLFA was conducted to assess whether the five-factors of negative symptoms showed measurement bias at the item and latent levels as a function of age, education level, and sex. Results At the item level, the asociality behavior item showed differential item functioning, such that older participants were more likely to endorse more severe deficits on this item after accounting for their latent level of asociality. In addition, those with higher education levels were more likely to endorse more severe deficits on the avolition internal experience item after accounting for their latent level of avolition. Measurement bias was also present across all five of the latent negative symptom variables. Conclusion As negative symptoms increasingly serve as treatment targets and surrogate outcomes, ensuring unbiased measurement is essential for advancing clinical precision. The presence of measurement bias underscores the need for careful consideration of age, education, and sex when interpreting symptom scores in both research and clinical settings.

Limited progress in treating negative symptoms in schizophrenia (SZ) may stem from an imprecise mechanistic understanding of these symptoms. This study tests a newly proposed hypothesis about a novel psychological mechanism-that negative symptoms result from discrepancies between ideal and actual affect. Greater trait discrepancies between how one wants to feel versus how one actually feels have previously been associated with greater negative symptom severity. This study uses Ecological Momentary Assessment (EMA) to determine whether these effects extend to daily life contexts. Thirty-nine outpatients with SZ and 33 healthy controls (CN) completed 7 days of EMA surveys assessing negative symptoms and ideal-actual affect discrepancies for positive and negative affect. SZ had greater ideal-actual negative but not positive affect discrepancies in daily life, suggesting SZ feel more negative affect than they desire to than CN. A greater discrepancy between one's ideal and actual negative and positive affect was associated with greater concurrent anhedonia and avolition. Time-lagged analyses showed a greater discrepancy between ideal-actual positive and negative affect was uniquely tied to higher levels of prospective anhedonia in SZ. The strength of the discrepancy between ideal and actual affect also varied across activity, social partner, and location contexts. The greater discrepancy between how positive and negative a person with SZ wants to feel may result in feeling defeated and demotivated, contributing to reduced engagement in goal-directed and pleasurable activities. This discrepancy between ideal and actual affect is a novel mechanistic target for negative symptoms that could be targeted with just-in-time digital therapeutics.

Capturing psychosis risk before illness onset is an ongoing challenge for psychosis-spectrum studies. Natural language processing (NLP) tools can harness information embedded in the notes generated during clinical interviews to obtain more objective markers of psychosis risk; for this project, we used readily available assessor notes. The following project acts as proof of concept on the usefulness of AI-based tools to capture latent psychosis risk information. We used assessor notes for 2,077 Structured Interview for Psychosis-risk Syndromes interviews as input for different AI-based NLP models to produce metrics for psychosis risk, namely, a trained encoder-only model (ModernBERT), and zero-shot and few-shot instantiation of two decoder-only models (Large Language Model Meta AI Version 4 [LLaMA-4] and GPT-4.1 mini). AI-based risk metrics were compared with gold-standard ratings of clinical high risk for psychosis (CHR). The AI-based risk metrics' relationship to traditional psychosis risk scores (Shanghai-At-Risk-for-Psychosis [SHARP] and North American Prodrome Longitudinal Study [NAPLS]) was also assessed. Finally, we explored the added benefit of adding AI-based risk metrics to models predicting future participant conversion. All models performed above chance in classifying interview notes for the presence or absence of CHR syndromes. The trained encoder model performed the best out of all models determining the presence of CHR syndromes (accuracy = 82.67%, κ = .63). Positive CHR classification by the encoder model resulted in a 0.69 and 0.85 standard deviation increase in SHARP and NAPLS risk scores (p < .001). A 1 standard deviation increase in decoder-generated risk scores increased traditional risk scores between 0.24 and 0.47 standard deviations (all ps < .001). In exploratory analyses, decoder-generated risk scores incrementally improved models predicting conversion including SHARP but not NAPLS risk scores. Although our results need to be considered in the context of one consortium, AI-based NLPs show potential as an aid for the diagnosis of CHR syndromes, evaluating psychosis risk, and even predicting future conversion, even with suboptimal but readily available inputs (i.e., assessor notes). Future projects could use AI-based tools' potential in augmenting psychosis risk screenings and risk predictors. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background Social anhedonia is an essential part of schizophrenia spectrum disorders (SSD), as well as a part of the negative symptom domain. Ecological momentary assessment (EMA) has emerged as a valuable method for assessing momentary emotional experiences in daily life of patients with negative symptoms, while minimizing retrospective reporting bias. However, few EMA studies have examined how patients with SSD and negative symptoms experience social contexts, and findings are inconsistent. The present study aimed to extend this line of research by examining associations between social context and positive affect, negative affect, and pleasure in patients with SSD and prominent negative symptoms. Methods Twenty-six participants with SSD completed one week of EMA assessments. Repeated momentary ratings of social context, affect, and pleasure were collected, and multilevel analyses were conducted to examine within-person associations between social context and emotional experience. Results Participants reported significantly greater positive affect in social contexts compared to non-social contexts. In contrast, social context was not significantly associated with negative affect or consummatory pleasure. Time lagged analyses of experienced affect following a social context were not significant, and social context did not moderate the association between anticipatory and consummatory pleasure. Conclusion The findings suggest that patients with SSD and prominent negative symptoms experience greater positive affect in social contexts compared to being alone, without corresponding increases in consummatory pleasure, highlighting a potential dissociation between affective and hedonic processes in daily social experiences.

Background and Hypothesis:Humans are capable of detecting faces and speech in noisy environments. While such detection is generally adaptive, we hypothesize that hallucinations and perceptual distortions arise from overdetection of such features under uncertainty. Study Design:To test this hypothesis, we measured face and speech detection rates in over 800 adolescents and young adults, oversampled for individuals at risk of developing psychotic disorders. Study Results:Individuals who reported seeing more faces in degraded 2-tone images tended to report hearing more speech in degraded filtered audio clips (r(765) = 0.32, P < .001). Recognition rates from both tasks independently predicted clinician-rated positive symptoms (face detection: r(784) = 0.19, P < .001, 95% CI, 0.12-0.26; speech detection: r(818) = 0.16, P < .001, 95% CI, 0.1-0.23). However, a composite score exhibited a stronger association than either task individually (r(760) = 0.23, P < .001, 95% CI, 0.16-0.29). This composite score was uniquely predictive of perceptual abnormalities over-and-above other types of positive symptoms such as unusual thought content, suspiciousness, grandiose ideas, and disorganized communication (b = 0.066, t(781) = 3.01, P = .003, partial r 2 = 0.011). Conclusions:Our results suggest that a supramodal tendency to report the presence of socially salient content under uncertainty may contribute to positive perceptual symptoms and index clinical risk for psychosis. We place these findings within the broader literature and discuss the potential utility of these highly scalable behavioral measures for better identifying individuals at risk for developing psychosis.

Abstract Objective Negative symptoms are frequently experienced among youth at clinical high-risk for psychosis (CHR). While clinical interviews are the most well-established and widely used measurements for assessing negative symptoms in individuals at CHR, several limitations exist. Digital phenotyping methods (e.g., geolocation, ecological momentary assessment [EMA]) have shown promise in measuring negative symptoms across contexts. However, little is known about how CHR and their relatives (REL) perceive these methods or understand EMA questions. Accounting for subjective viewpoints is important before these tools can be utilized at scale and in clinical trials. Method Groups of CHR (n = 45) and REL (n = 18) participants completed structured surveys and qualitative interviews evaluating perceived importance and concerns of using digital phenotyping measures to assess negative symptoms as well as the perceived clarity of ecological momentary assessment (EMA) survey questions. Results Both groups showed low levels of concern regarding digital phenotyping methods. Concerns mainly revolved around privacy/data security and were mitigated after clarifications on protection policies and research procedures were provided. Participants reported that active and passive digital phenotyping measurements were moderately important and can accurately measure their symptoms. They also deemed most EMA questions clear and provided suggestions for revising those they found less clear. Conclusions CHR and REL participants largely supported the use of digital phenotyping to measure negative symptoms, endorsing its perceived relevance and practical feasibility. These findings contribute to ongoing efforts to align clinical assessments tools with lived experience and to provide support for regulatory standards for patient-centered outcome measures.

BACKGROUND:Negative symptoms are a core feature of psychosis and a strong predictor of functional outcome, yet they remain difficult to assess due to conceptual and methodological challenges. Although abnormalities in emotional expressivity and emotional reactivity are documented in individuals at clinical high-risk (CHR) for psychosis, these domains are typically examined independently, and their relationship remains unclear. METHODS:Facial expressions were quantified using automated facial analysis (FaceReader) during clinical interviews in 101 CHR individuals and 41 healthy controls (HCs). Emotional reactivity was assessed using the International Affective Picture System (IAPS). Principal component analyses were conducted on facial expression and emotional reactivity variables within the CHR group. Associations with negative symptom domains, positive symptoms, and social functioning were examined using correlational and two-step regression analyses. RESULTS:CHR participants showed greater disgust expression than HCs (g = 0.40, uncorrected p = .0025, FDR-corrected p = .023). Facial expression and emotional reactivity components showed minimal associations (p > .20). Reduced high-arousal facial expressions were associated with greater emotional expressivity deficits (r = -.22, p = .027), whereas greater happy facial expression was associated with more motivation and pleasure impairment (r = .21, p = .044). Happy facial expression explained additional variance in motivation symptoms beyond emotional reactivity (ΔR2 = .089, p = .008). CONCLUSIONS:Automated facial expression captured variance in some negative symptom domains that was largely independent of emotional reactivity. These findings support the use of multimodal, objective assessments to improve characterization of negative symptoms in psychosis risk.

Effort-based decision-making (EBDM) impairments predict negative symptoms across multiple psychiatric diagnoses. However, it is unclear whether equifinality is present and different disorders reach the same clinical endpoint of negative symptoms via different mechanistic EBDM processes. This study used computational modeling to isolate processes underlying EBDM in a large severe mental illness-spectrum sample. The Effort Expenditure for Rewards Task, negative symptom measures, and neuropsychological tests were administered to 920 participants: schizophrenia (SZ; n = 147), first-episode psychosis (FEP; n = 54), bipolar disorder (n = 53), depressive disorder (n = 37), clinical high-risk for psychosis (CHR; n = 231), other clinical (n = 99), and healthy control groups (HC; n = 299). Computational modeling identified whether participants' EBDM behavior was best fit by models indexing full or partial subjective value (use reward magnitude and/or probability) or bias (failure to use reward magnitude and probability). Best fitting models significantly differed across diagnostic groups. SZ and FEP were best fit by the bias model and less likely to use reward magnitude and probability to guide EBDM. The CHR, other clinical, depressive disorder, and HC groups were best fit by the full subjective value model and were more likely to use reward magnitude and probability, while the bipolar disorder group’s behavior was more variable. Across groups, participants best fit by the bias model had the greatest negative symptoms and cognitive impairments. Results indicate mood and psychosis-spectrum disorders differentially approach EBDM. Equifinality in the pathway to negative symptoms was not supported; those with difficulty utilizing reward and probability information had the greatest negative symptoms, independent of diagnosis.