The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis

Effects of persistent cannabis use on depression, psychosis, and suicidality following cannabis-induced psychosis: A longitudinal study

BACKGROUND AND OBJECTIVES

Cannabis use is associated with psychotic disorder onset and exacerbation. This study examines how continued cannabis use affects depressive symptoms, psychotic symptoms, and suicidal behaviors following cannabis-induced first-episode psychosis (FEP).

METHODS

Sixty-five participants (aged 16-50 years) with FEP were recruited from psychiatric inpatient facilities in northern Italy. Participants were categorized into two groups: non-cannabis users (NCU) and cannabis users (CU), based on substance use during the 9-month follow-up. Twenty-one participants (32.3%) were lost to follow-up, with a final sample of 44 subjects (NCU = 22, CU = 22). Assessments were conducted at baseline, 3 months, and 9 months using PANSS, CDSS, SSI, and GAF scales).

RESULTS

CU exhibited persistently higher depression (CDSS) and suicidality (SSI) scores than NCU throughout follow-up, with significant differences at both 3 months (CDSS: p = .000006; SSI: p < .001) and 9 months (CDSS: p = .0000001; SSI: p < .001). Positive psychotic symptoms improved in both groups, though CU showed slower recovery and higher relapse rates (59.9% vs. 18.8%). PANSS positive subscale scores remained significantly higher in CU at 3 months (p = .001) and 9 months (p < .0002). GAF scores improved significantly only in NCU (p = .024 at 9 months).

DISCUSSION AND CONCLUSIONS

Continued cannabis use adversely affects depressive and psychotic symptoms and suicidality in FEP patients, while cannabis cessation is associated with improved clinical outcomes.

SCIENTIFIC SIGNIFICANCE

This study tracks depressive symptoms, suicidality, and psychotic manifestations in cannabis-induced FEP, demonstrating that continued use is associated with treatment-resistant depressive symptoms even when psychotic symptoms improve, highlighting the need for integrated clinical approaches.

Do help-seeking adolescents report more psychotic-like experiences than young adults on the 16-item version of the prodromal questionnaire (PQ-16)?

AIM

To compare psychotic-like experiences (PLEs) in adolescents and young adults referred to the Mental Health Services (MHSs).

METHODS

Participants scored the 16-item Prodromal Questionnaire (PQ-16) as part of the intake procedure. Data on the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification and demographic data were collected.

RESULTS

The PQ-16 was completed by 13 783 respondents (mean age 24.63 years, SD = 6.09; 62.6% female). Overall, the scores on the PQ-16 were not higher for adolescents (11-17 years; m = 4.84, SD = 3.62) than for young adults (18-35 years; m = 5.47, SD = 3.85). On PQ-16 item level, adolescents reported seeing and hearing things more than adults did. Across all age groups, males scored lower on the PQ-16 than females. Specifically, adolescent males scored lower than other participants. For adolescents and young adults alike, PQ-16 scores were higher for participants with borderline personality disorder, PTSD, and mood disorder than for those with other DSM classifications.

CONCLUSIONS

Although help-seeking adolescents did not score higher on the PQ-16 than help-seeking young adults, more of them reported perceptual anomalies. Irrespective of age, participants with borderline personality disorder, PTSD and mood disorder scored higher on the PQ-16 than those with other DSM classifications.

Differences of regional homogeneity and cognitive function between psychotic depression and drug-naïve schizophrenia

BACKGROUND

Psychotic depression (PD) and schizophrenia (SCZ) share overlapping symptoms yet differ in etiology, progression, and treatment approaches. Differentiating these disorders through symptom-based diagnosis is challenging, emphasizing the need for a clearer understanding of their distinct cognitive and neural mechanisms.

AIM

This study aims to compare cognitive impairments and brain functional activities in PD and SCZ to pinpoint distinguishing characteristics of each disorder.

METHODS

We evaluated cognitive function in 42 PD and 30 SCZ patients using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) values were derived from rs-fMRI data, and group differences in RBANS scores were analyzed. Additionally, Pearson correlation analysis was performed to assess the relationship between cognitive domains and brain functional metrics.

RESULTS

(1) The SCZ group showed significantly lower RBANS scores than the PD group across all cognitive domains, particularly in visuospatial/constructional ability and delayed memory (p < 0.05); (2) The SCZ group exhibited a significantly higher ReHo value in the left precuneus compared to the PD group (p < 0.05); (3) A negative correlation was observed between visuospatial construction, delayed memory scores, and the ReHo value of the left precuneus.

CONCLUSION

Cognitive impairment is more pronounced in SCZ than in PD, with marked deficits in visuospatial and memory domains. Enhanced left precuneus activity further differentiates SCZ from PD and correlates with cognitive impairments in both disorders, providing neuroimaging-based evidence to aid differential diagnosis and insights into cognitive dysfunction mechanisms, while also paving a clearer path for psychiatric research.

Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Antidepressants for the prevention of depression following first-episode psychosis (ADEPP): study protocol for a multi-centre, double-blind, randomised controlled trial

BACKGROUND

Depressive episodes are common after first-episode psychosis (FEP), affecting more than 40% of people, adding to individual burden, poor outcomes, and healthcare costs. If the risks of developing depression were lower, this could have a beneficial effect on morbidity and mortality, as well as improving outcomes. Sertraline is a selective serotonin reuptake inhibitor and a common first-line medication for the treatment of depression in adults. It has been shown to be safe when co-prescribed with antipsychotic medication, and there is evidence that it is an effective treatment for depression in established schizophrenia. We present a protocol for a multi-centre, double-blind, randomised, placebo-controlled clinical trial called ADEPP that aims to investigate the efficacy and cost-effectiveness of sertraline in preventing depression after FEP.

METHODS

The recruitment target is 452 participants between the ages of 18 and 65 years who are within 12 months of treatment initiation for FEP. Having provided informed consent, participants will be randomised to receive either 50 mg of sertraline daily or matched placebo for 6 months, in addition to treatment as usual. The primary outcome measure will be a comparison of the number of new cases of depression between the treatment and placebo arms over the 6-month intervention phase. Secondary outcomes include suicidal behaviour, anxiety, rates of relapse, functional outcome, quality of life, and resource use.

DISCUSSION

The ADEPP trial will test whether the addition of sertraline following FEP is a clinically useful, acceptable, and cost-effective way of improving outcomes following FEP.

TRIAL REGISTRATION

ISRCTN12682719 registration date 24/11/2020.

Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks

BACKGROUND

Psychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called 'symptom network'. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk.

METHODS

To explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity (N = 7242). We sought to identify 'network phenotypes' by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol.

RESULTS

Sex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse and domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation.

CONCLUSION

Symptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all?

The heterogeneity in recovery outcomes for individuals with First Episode Psychosis (FEP) calls for a strong evidence base to inform practice at an individual level. Between 19-89% of young people with FEP have an incomplete recovery despite gold-standard evidence-based treatments, suggesting current service models, which adopt a 'one-size fits all' approach, may not be addressing the needs of many young people with psychosis. The lack of consistent terminology to define key concepts such as recovery and treatment resistance, the multidimensional nature of these concepts, and common comorbid symptoms are some of the challenges faced by the field in delineating heterogeneity in recovery outcomes. The lack of robust markers for incomplete recovery also results in potential delay in delivering prompt, and effective treatments to individuals at greatest risk. There is a clear need to adopt a stratified approach to care where interventions are targeted at subgroups of patients, and ultimately at the individual level. Novel machine learning, using large, representative data from a range of modalities, may aid in the parsing of heterogeneity, and provide greater precision and sophistication in identifying those on a pathway to incomplete recovery.

From co-morbidity to transdiagnostic potential and novel immunotherapies for psychosis

One of my first placements in psychiatry training was with the early intervention in psychosis services in Birmingham, in the late 1990's. It was in this context that I became aware of the frequency and importance of affective co- modbidity and lack of diagnostic certianty in early stages of develping severe mental illness. This challenged the established dichotomy between affective and non-affective severe mental illnesses, and has driven my work and thinking ever since- including that embracing the presence of affective symptoms in schizophrenia may open the door for new treatments. Understanding affective dysfunction as a potential intrisic component of developing psychotic disorders has also shown the potential for transdiagnostic symptoms with shared underlying biological processess, including immune dysfunction, related to remission and functional outcomes. Currently my work focuses on targeting the immune system to improve recovery in clinical trials, and further mechanistic studies that reach beyond traditional diagnostic catagories.

Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

BACKGROUND

Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.

METHODS

HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470).

RESULTS

The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures.

CONCLUSIONS

We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis

Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.

The specific phenotype of depression in recent onset schizophrenia spectrum disorders: A symptom profile and network comparison to recent onset major depressive disorder without psychotic features

The specific phenotype of depression in recent-onset schizophrenia spectrum disorders (SSD) and its relation to non-psychotic depression is unknown. Symptom profile and network analysis are complementary statistical techniques that may provide important insights into the presentation and relative importance of individual symptoms that give rise to depression. The aim of the current study was to characterise the profile and network of depressive symptoms in SSD and compare it to individuals with major depressive disorder (MDD) without psychotic features. This study involved analysis of baseline data pertaining to 109 individuals with comorbid SSD and depression and 283 with MDD without psychotic features. Study cohorts were the Psychosis Recent Onset GRoningen Survey (PROGR-S) and Youth Depression Alleviation (YoDA) trials, respectively. Profile and network analyses revealed that SSD and MDD differed in the profile and relative importance of individual depressive symptoms. While reported sadness was the primary hallmark of depression in both SSD and MDD, individuals with depression in SSD were more likely to sleep more, and have lower lassitude and pessimism. While sadness had great importance in MDD and SSD, in SSD but not MDD lassitude, sleep, appetite, concentration difficulties, and inability to feel were important in the network of depressive symptoms. The specific phenotype of depression might be different in SSD compared to MDD. Symptom inequivalence or underlying functional mechanisms in SSD might result in depression in SSD that is similar to MDD with atypical features.

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort

BACKGROUND

Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

METHODS

We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

RESULTS

After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

CONCLUSIONS

Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness

Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.

General psychopathology links burden of recent life events and psychotic symptoms in a network approach

Recent life events have been implicated in the onset and progression of psychosis. However, psychological processes that account for the association are yet to be fully understood. Using a network approach, we aimed to identify pathways linking recent life events and symptoms observed in psychosis. Based on previous literature, we hypothesized that general symptoms would mediate between recent life events and psychotic symptoms. We analyzed baseline data of patients at clinical high risk for psychosis and with recent-onset psychosis (n = 547) from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. In a network analysis, we modeled links between the burden of recent life events and all individual symptoms of the Positive and Negative Syndrome Scale before and after controlling for childhood trauma. To investigate the longitudinal associations between burden of recent life events and symptoms, we analyzed multiwave panel data from seven timepoints up to month 18. Corroborating our hypothesis, burden of recent life events was connected to positive and negative symptoms through general psychopathology, specifically depression, guilt feelings, anxiety and tension, even after controlling for childhood trauma. Longitudinal modeling indicated that on average, burden of recent life events preceded general psychopathology in the individual. In line with the theory of an affective pathway to psychosis, recent life events may lead to psychotic symptoms via heightened emotional distress. Life events may be one driving force of unspecific, general psychopathology described as characteristic of early phases of the psychosis spectrum, offering promising avenues for interventions.

A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care?

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a ‘one-size fits all’ approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards ‘big data’ and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one’s illness may also be key to understanding the factors that drive illness course.