Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Adolescence is marked by a sharp increase in the incidence of depression, especially in females. Identification of risk for depressive disorders (DD) in this key developmental stage can help prevention efforts, mitigating the clinical and public burden of DD. While frequently used in diagnosis, nonverbal behaviors are relatively understudied as risk markers for DD. Digital technology, such as facial recognition, may provide objective, fast, efficient, and cost-effective means of measuring nonverbal behavior.

Here, we analyzed video-recorded clinical interviews of 359 never-depressed adolescents females via commercially available facial emotion recognition software.

We found that average head and facial movements forecast future first onset of depression (AUC = 0.70) beyond the effects of other established self-report and physiological markers of DD risk.

Overall, these findings suggest that digital assessment of nonverbal behaviors may provide a promising risk marker for DD, which could aid in early identification and intervention efforts.

Although risk markers for depressive disorders (DD) are dynamic, especially during adolescence, few studies have examined how change in risk levels during adolescence predict DD onset during transition to adulthood. We compared two competing hypotheses of the dynamic effects of risk. The risk escalation hypothesis posits that worsening of risk predicts DD onset beyond risk level. The chronic risk hypothesis posits that persistently elevated risk level, rather than risk change, predicts DD onset.

Our sample included 393 girls (baseline age 13.5–15.5 years) from the adolescent development of emotions and personality traits project. Participants underwent five diagnostic interviews and assessments of risk markers for DD at 9-month intervals and were re-interviewed at a 6-year follow-up. We focused on 17 well-established risk markers. For each risk marker, we examined the prospective effects of risk level and change on first DD onset at wave six, estimated by growth curve modeling using data from the first five waves.

For 13 of the 17 depression risk markers, elevated levels of risk during adolescence, but not change in risk, predicted first DD onset during transition to adulthood, supporting the chronic risk hypothesis. Minimal evidence was found for the risk escalation hypothesis.

Participants who had a first DD onset during transition to adulthood have exhibited elevated levels of risk throughout adolescence. Researchers and practitioners should administer multiple assessments and focus on persistently elevated levels of risk to identify individuals who are most likely to develop DD and to provide targeted DD prevention.

Mismatch negativity (MMN) amplitude is reduced in psychotic disorders and associated with symptoms and functioning. Due to these robust associations, it is often considered a biomarker for psychotic illness. The relationship between MMN and clinical outcomes has been examined well in early onset psychotic illness; however, its stability and predictive utility in chronic samples are not clear.

We examined the five-year stability of MMN amplitude over two timepoints in individuals with established psychotic disorders (cases; N = 132) and never-psychotic participants (NP; N = 170), as well as longitudinal associations with clinical symptoms and functioning.

MMN amplitude exhibited good temporal stability (cases, r = 0.53; never-psychotic, r = 0.52). In cases, structural equation models revealed MMN amplitude to be a significant predictor of worsening auditory hallucinations (β = 0.19), everyday functioning (β = −0.13), and illness severity (β = −0.12) at follow-up. Meanwhile, initial IQ (β = −0.24), negative symptoms (β = 0.23), and illness severity (β = −0.16) were significant predictors of worsening MMN amplitude five years later.

These results imply that MMN measures a neural deficit that is reasonably stable up to five years. Results support disordered cognition and negative symptoms as preceding reduced MMN, which then may operate as a mechanism driving reductions in everyday functioning and the worsening of auditory hallucinations in chronic psychotic disorders. This pattern may inform models of illness course, clarifying the relationships amongst biological mechanisms of predictive processing and clinical deficits in chronic psychosis and allowing us to better understand the mechanisms driving such impairments over time.

Preschool psychiatric symptoms significantly increase the risk for long-term negative outcomes. Transdiagnostic hierarchical approaches that capture general (‘p’) and specific psychopathology dimensions are promising for understanding risk and predicting outcomes, but their predictive utility in young children is not well established. We delineated a hierarchical structure of preschool psychopathology dimensions and tested their ability to predict psychiatric disorders and functional impairment in preadolescence.

Data for 1253 preschool children (mean age = 4.17, s.d. = 0.81) were drawn from three longitudinal studies using a similar methodology (one community sample, two psychopathology-enriched samples) and followed up into preadolescence, yielding a large and diverse sample. Exploratory factor models derived a hierarchical structure of general and specific factors using symptoms from the Preschool Age Psychiatric Assessment interview. Longitudinal analyses examined the prospective associations of preschool p and specific factors with preadolescent psychiatric disorders and functional impairment.

A hierarchical dimensional structure with a p factor at the top and up to six specific factors (distress, fear, separation anxiety, social anxiety, inattention-hyperactivity, oppositionality) emerged at preschool age. The p factor predicted all preadolescent disorders (ΔR2 = 0.04–0.15) and functional impairment (ΔR2 = 0.01–0.07) to a significantly greater extent than preschool psychiatric diagnoses and functioning. Specific dimensions provided additional predictive power for the majority of preadolescent outcomes (disorders: ΔR2 = 0.06–0.15; functional impairment: ΔR2 = 0.05–0.12).

Both general and specific dimensions of preschool psychopathology are useful for predicting clinical and functional outcomes almost a decade later. These findings highlight the value of transdiagnostic dimensions for predicting prognosis and as potential targets for early intervention and prevention.

One of the challenges in human neuroscience is to uncover associations between brain organization and psychopathology in order to better understand the biological underpinnings of mental disorders. Here, we aimed to characterize the neural correlates of psychopathology dimensions obtained using two conceptually different data-driven approaches.

Dimensions of psychopathology that were either maximally dissociable or correlated were respectively extracted by independent component analysis (ICA) and exploratory factor analysis (EFA) applied to the Childhood Behavior Checklist items from 9- to 10-year-olds (n = 9983; 47.8% female, 50.8% white) participating in the Adolescent Brain Cognitive Development study. The patterns of brain morphometry, white matter integrity and resting-state connectivity associated with each dimension were identified using kernel-based regularized least squares and compared between dimensions using Spearman’s correlation coefficient.

ICA identified three psychopathology dimensions, representing opposition–disinhibition, cognitive dyscontrol, and negative affect, with distinct brain correlates. Opposition–disinhibition was negatively associated with cortical surface area, cognitive dyscontrol was negatively associated with anatomical and functional dysconnectivity while negative affect did not show discernable associations with any neuroimaging measure. EFA identified three dimensions representing broad externalizing, neurodevelopmental, and broad Internalizing problems with partially overlapping brain correlates. All EFA-derived dimensions were negatively associated with cortical surface area, whereas measures of functional and structural connectivity were associated only with the neurodevelopmental dimension.

This study highlights the importance of cortical surface area and global connectivity for psychopathology in preadolescents and provides evidence for dissociable psychopathology dimensions with distinct brain correlates.

Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The ‘Risk Escalation hypothesis’ posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the ‘Chronic Risk hypothesis’ posits that the average level rather than change predicts first-onset DD.

We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5–15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset.

Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors.

Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology.

The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS.

Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040–0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062).

PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.

Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the ability of AI-based language assessments to predict PTSD symptom trajectories among responders.

Participants were 124 responders whose health was monitored at the Stony Brook WTC Health and Wellness Program who completed oral history interviews about their initial WTC experiences. PTSD symptom severity was measured longitudinally using the PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were computed for depression, anxiety, neuroticism, and extraversion along with dictionary-based measures of linguistic and interpersonal style. Linear regression and multilevel models estimated associations of AI indicators with concurrent and subsequent PTSD symptom severity (significance adjusted by false discovery rate).

Cross-sectionally, greater depressive language (β = 0.32; p = 0.049) and first-person singular usage (β = 0.31; p = 0.049) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (β = 0.30; p = 0.049), whereas first-person plural usage (β = −0.36; p = 0.014) and longer words usage (β = −0.35; p = 0.014) predicted improvement.

This is the first study to demonstrate the value of AI in understanding PTSD in a vulnerable population. Future studies should extend this application to other trauma exposures and to other demographic groups, especially under-represented minorities.

Life events (LEs) are a risk factor for first onset and relapse of psychotic disorders. However, the impact of LEs on specific symptoms – namely reality distortion, disorganization, negative symptoms, depression, and mania – remains unclear. Moreover, the differential effects of negative v. positive LEs are poorly understood.

The present study utilizes an epidemiologic cohort of patients (N = 428) ascertained at first-admission for psychosis and followed for a decade thereafter. Symptoms were assessed at 6-, 24-, 48-, and 120-month follow-ups.

We examined symptom change within-person and found that negative events in the previous 6 months predicted an increase in reality distortion (β = 0.07), disorganized (β = 0.07), manic (β = 0.08), and depressive symptoms (β = 0.06), and a decrease in negative symptoms (β = −0.08). Conversely, positive LEs predicted fewer reality distortion (β = −0.04), disorganized (β = −0.04), and negative (β = −0.13) symptoms, and were unrelated to mood symptoms. A between-person approach to the same hypotheses confirmed that negative LEs predicted change in all symptoms, while positive LEs predicted change only in negative symptoms. In contrast, symptoms rarely predicted future LEs.

These findings confirm that LEs have an effect on symptoms, and thus contribute to the burden of psychotic disorders. That LEs increase positive symptoms and decrease negative symptoms suggest at least two different mechanisms underlying the relationship between LEs and symptoms. Our findings underscore the need for increased symptom monitoring following negative LEs, as symptoms may worsen during that time.

Genetics hold promise of predicting long-term post-traumatic stress disorder (PTSD) outcomes following trauma. The aim of the current study was to test whether six hypothesized polygenic risk scores (PRSs) developed to capture genetic vulnerability to psychiatric conditions prospectively predict PTSD onset, severity, and 18-year course after trauma exposure.

Participants were 1490 responders to the World Trade Center (WTC) disaster (mean age at 9/11 = 38.81 years, s.d. = 8.20; 93.5% male; 23.8% lifetime WTC-related PTSD diagnosis). Prospective longitudinal data on WTC-related PTSD symptoms were obtained from electronic medical records and modelled as PTSD trajectories using growth mixture model analysis. Independent regression models tested whether six hypothesized psychiatric PRSs (PTSD-PRS, Re-experiencing-PRS, Generalized Anxiety-PRS, Schizophrenia-PRS, Depression-PRS, and Neuroticism-PRS) are predictive of WTC-PTSD outcomes: lifetime diagnoses, average symptom severity, and 18-year symptom trajectory. All analyses were adjusted for population stratification, 9/11 exposure severity, and multiple testing.

Depression-PRS predicted PTSD diagnostic status (OR 1.37, CI 1.17–1.61, adjusted p = 0.001). All PRSs, except PTSD-PRS, significantly predicted average PTSD symptoms (β = 0.06–0.10, adjusted p < 0.05). Re-experiencing-PRS, Generalized Anxiety-PRS and Schizophrenia-PRS predicted the high severity PTSD trajectory class (ORs 1.21–1.28, adjusted p < 0.05). Finally, PRSs prediction was independent of 9/11 exposure severity and jointly accounted for 3.7 times more variance in PTSD symptoms than the exposure severity.

Psychiatric PRSs prospectively predicted WTC-related PTSD lifetime diagnosis, average symptom severity, and 18-year trajectory in responders to 9/11 disaster. Jointly, PRSs were more predictive of subsequent PTSD than the exposure severity. In the future, PRSs may help identify at-risk responders who might benefit from targeted prevention approaches.

Performance monitoring entails rapid error detection to maintain task performance. Impaired performance monitoring is a candidate pathophysiological process in psychotic disorders, which may explain the broader deficit in executive function and its known associations with negative symptoms and poor functioning. The current study models cross-sectional pathways bridging neurophysiological measures of performance monitoring with executive function, symptoms, and functioning.

Data were from the 20-year assessment of the Suffolk County Mental Health Project. Individuals with psychotic disorders (N = 181) were originally recruited from inpatient psychiatric facilities. Data were also collected from a geographically and demographically matched group with no psychosis history (N = 242). Neural measures were the error-related negativity (ERN) and error positivity (Pe). Structural equation modeling tested mediation pathways.

Blunted ERN and Pe in the clinical cohort related to impaired executive function (r = 0.26–0.35), negative symptom severity (r = 0.17–0.25), and poor real-world functioning (r = 0.17–0.19). Associations with executive function were consistent across groups. Multiple potential pathways were identified in the clinical cohort: reduced ERN to inexpressivity was mediated by executive function (β = 0.10); reduced Pe to global functioning was mediated by executive function and avolition (β = 0.10).

This supports a transdiagnostic model of psychotic disorders by which poor performance monitoring contributes to impaired executive function, which contributes to negative symptoms and poor real-world functioning. If supported by future longitudinal research, these pathways could inform the development of targeted interventions to address cognitive and functional deficits that are central to psychotic disorders.

Classic conceptual frameworks explaining the relationship of personality traits to depression include the precursor and predisposition models. The former hypothesizes that depression is predicted by traits alone whereas the latter hypothesizes that stress, together with personality, predicts depression. Dynamic vulnerability models (DVM) expand on these perspectives by incorporating fluctuations in personality over time. The stress generation model provides an alternative view, positing that depression generates stress, creating a self-perpetuating cycle. However, these conceptual models are rarely directly compared.

We tested these models, focusing on neuroticism and stressful life events that the participant may have contributed to, using path analysis in a sample of 550 never-depressed, adolescent females assessed five times over 3 years.

A dynamic precursor model with stress generation was best supported. For the precursor component, neuroticism predicted subsequent depression across four assessment intervals. For the dynamic trait component, stressful life events predicted subsequent neuroticism at three of four intervals. Finally, in line with stress generation, depression consistently predicted subsequent stressful life events, and life events then predicted depression.

Finding support for the DVM is noteworthy, as this is the first comprehensive test of this model. Moreover, results supported integrating stress generation with trait vulnerability. Continued use of integrated approaches and refining the statistical implementation of these theories is necessary to advance understanding of the development of depression.

Individual differences in neuroticism, extraversion, and conscientiousness are associated with, and may predict onset of, internalizing disorders. These general traits can be parsed into facets, but there is a surprising paucity of research on facet risk for internalizing disorders. We examined general traits and facets of neuroticism, extraversion, and conscientiousness in predicting first onsets of depressive and anxiety disorders.

A community sample of 550 adolescent females completed general and facet-level personality measures and diagnostic interviews. Interviews were re-administered 18 months later.

First onsets of depressive disorders were predicted by neuroticism, extraversion, and conscientiousness. Facets predicting first onset of depression included depressivity (neuroticism facet) and lower positive emotionality and sociability (extraversion facets). First onsets of generalized anxiety disorder (GAD) were predicted by neuroticism, and particularly the facet of anxiousness. First onsets of social phobia were predicted at the facet level by anxiousness. First onsets of specific phobia were predicted by neuroticism, low conscientiousness, and all neuroticism facets. In multivariate analyses, first onsets of depression were uniquely predicted by depressivity, and onsets of GAD and social phobia were uniquely predicted by anxiousness over and above the general trait of neuroticism.

General traits predict first onsets of depressive and anxiety disorders. In addition, more specific associations are evident at the facet level. Facets can refine our understanding of the links between personality and psychopathology risk, and provide finer-grained targets for personality-informed interventions.