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

Aberrant salience network functional connectivity in auditory verbal hallucinations::a first episode psychosis sample

Auditory verbal hallucinations (AVH) often lead to distress and functional disability, and are frequently associated with psychotic illness. Previously both state and trait magnetic resonance imaging (MRI) studies of AVH have identified activity in brain regions involving auditory processing, language, memory and areas of default mode network (DMN) and salience network (SN). Current evidence is clouded by research mainly in participants on long-term medication, with chronic illness and by choice of seed regions made 'a priori'. Thus, the aim of this study was to elucidate the intrinsic functional connectivity in patients presenting with first episode psychosis (FEP). Resting state functional MRI data were available from 18 FEP patients, 9 of whom also experienced AVH of sufficient duration in the scanner and had symptom capture functional MRI (sc fMRI), together with 18 healthy controls. Symptom capture results were used to accurately identify specific brain regions active during AVH; including the superior temporal cortex, insula, precuneus, posterior cingulate and parahippocampal complex. Using these as seed regions, patients with FEP and AVH showed increased resting sb-FC between parts of the SN and the DMN and between the SN and the cerebellum, but reduced sb-FC between the claustrum and the insula, compared to healthy controls.It is possible that aberrant activity within the DMN and SN complex may be directly linked to impaired salience appraisal of internal activity and AVH generation. Furthermore, decreased intrinsic functional connectivity between the claustrum and the insula may lead to compensatory over activity in parts of the auditory network including areas involved in DMN, auditory processing, language and memory, potentially related to the complex and individual content of AVH when they occur

Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness

Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (NROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. Clinical trial registry name: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/. Clinical trial registry number: DRKS00005042

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 modelling. 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 multi-site 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 (COBRE, n=146; MCIC, n=202; MUC, n=470). Results The optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, 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 which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments

Using machine learning to disentangle heterogeneity within and between psychosis and depression: Improving pathways for precision medicine in psychiatry

The aim of this PhD was to examine the clinical and biological -primarily structural brain- heterogeneity within and between depression and psychosis and provide tools for the improvement of diagnosis and targeted treatment. In chapter 3, a systematic review of structural neuroimaging studies in depression and psychosis identified potential transdiagnostic patterns of gray matter volume (GMV) and white matter volume (WMV) reductions in areas including the middle frontal gyrus, hippocampus, and left-sided posterior subgenual prefrontal cortex. In chapter 4, clinical/neurocognitive and neuroanatomical support vector machine (SVM) learning models demonstrated separability of prototypic depression from psychosis. Psychosis patients with affective comorbidity aligned more strongly to depressive rather than psychotic disease processes. In chapter 5, we identified two transdiagnostic neuroanatomically informed clusters which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. In chapter 6, five clusters of schizophrenia with distinct immune signatures, associated with differing GMV and neurocognitive function were identified, with potential to inform the development of novel, targeted treatments. Overall, machine learning was utilised to elucidate and reduce heterogeneity within and between psychosis and depression, and identify biologically relevant and transdiagnostic subtypes that could become potential candidates for targeted treatment. The results are promising and challenge the current nosological system

Trait related aberrant connectivity in clinically stable patients with schizophrenia:A seed based resting state fMRI study

Aberrant resting-state connectivity within and between the Default Mode Network, the Executive Control Network, and the Salience Network is well-established in schizophrenia. Meta-analyses have identified that bilateral lingual gyrus is as the only region showing hyperactivity in schizophrenia and there are reports of increased connectivity between the lingual gyrus and other brain regions in schizophrenia. It is not clear whether these abnormalities represent state or trait markers of the illness, i.e., if they are only present during the acute phase of the illness (state) or if they reflect a predisposition to schizophrenia (trait). In this study, we used a seed-based functional connectivity analysis to investigate brain networks in schizophrenia patients who are in the stable phase of their illness and assess functional connectivity using seeds in the lingual gyrus, the posterior cingulate, the right dorsolateral prefrontal cortex (dlPFC), the right anterior insula (rAI) and the right orbital frontoinsula. Twenty patients with schizophrenia in a stable phase of their illness (as defined by the course of illness and Signs and Symptoms of Psychotic Illness (SSPI) scores) and 20 age and sex-matched healthy controls underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Data was analysed using the Data Processing Assistant for Resting-State fMRI Advanced Edition (DPARSFA) V3.1 (http://rfmri.org/DPARSF) and the statistical parametric mapping software 8 (SPM8). Compared with healthy controls, patients with schizophrenia showed increased connectivity between the left lingual gyrus and the middle frontal gyrus, and the cingulate cortex. Lingual gyrus hyper-connectivity may be a stable trait neuroimaging marker for schizophrenia. Our findings suggest that aberrant connectivity in major resting-state networks may not be present after the acute illness has stabilised. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11682-022-00731-9

Exploring causal mechanisms of psychosis risk

Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors and developing preventive interventions targeting risk and protective factors