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Background: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. Objective: This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence–driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. Methods: A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Results: Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Conclusions: Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited. [ABSTRACT FROM AUTHOR] more...

Background: Treatment-resistant schizophrenia (TRS) is associated with high levels of functional impairment, healthcare usage and societal costs. Cross-sectional studies may overestimate TRS rates because of selection bias.Aims: We aimed to quantify TRS rates by using first-episode cohorts to improve resource allocation and clozapine access.Method: We undertook a systematic review of TRS rates among people with first-episode psychosis and schizophrenia, with a minimum follow-up of 8 weeks. We searched PubMed, PsycINFO, EMBASE, CINAHL and the Cochrane Database of Systematic Reviews, and meta-analysed TRS rates from included studies.Results: Twelve studies were included, totalling 11 958 participants; six studies were of high quality. The rate of TRS was 22.8% (95% CI 19.1-27.0%, P < 0.001) among all first-episode cohorts and 24.4% (95% CI 19.5-30.0%, P < 0.001) among first-episode schizophrenia cohorts. Subgroup sensitivity analyses by location of recruitment, TRS definition, study quality, time of data collection and retrospective versus prospective data collection did not lead to statistically significant differences in heterogeneity. In a meta-regression, duration of follow-up and percentage drop-out did not significantly affect the overall TRS rate. Men were 1.57 times more likely to develop TRS than women (95% CI 1.11-2.21, P = 0.010).Conclusions: Almost a quarter of people with first-episode psychosis or schizophrenia will develop TRS in the early stages of treatment. When including people with schizophrenia who relapse despite initial response and continuous treatment, rates of TRS may be as high as a third. These high rates of TRS highlight the need for improved access to clozapine and psychosocial supports. [ABSTRACT FROM AUTHOR] more...

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k‐means clustering, and sub‐network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub‐network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub‐network comprised brain areas implicated in bottom‐up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk. [ABSTRACT FROM AUTHOR] more...

Psychosis has been proposed to develop from dysfunction in a hippocampal-striatal-midbrain circuit, leading to aberrant salience processing. Here, we used functional magnetic resonance imaging (fMRI) during novelty salience processing to investigate this model in people at clinical high risk (CHR) for psychosis according to their subsequent clinical outcomes. Seventy-six CHR participants as defined using the Comprehensive Assessment of At-Risk Mental States (CAARMS) and 31 healthy controls (HC) were studied while performing a novelty salience fMRI task that engaged an a priori hippocampal-striatal-midbrain circuit of interest. The CHR sample was then followed clinically for a mean of 59.7 months (~5 y), when clinical outcomes were assessed in terms of transition (CHR-T) or non-transition (CHR-NT) to psychosis (CAARMS criteria): during this period, 13 individuals (17%) developed a psychotic disorder (CHR-T) and 63 did not. Functional activation and effective connectivity within a hippocampal-striatal-midbrain circuit were compared between groups. In CHR individuals compared to HC, hippocampal response to novel stimuli was significantly attenuated (P =.041 family-wise error corrected). Dynamic Causal Modelling revealed that stimulus novelty modulated effective connectivity from the hippocampus to the striatum, and from the midbrain to the hippocampus, significantly more in CHR participants than in HC. Conversely, stimulus novelty modulated connectivity from the midbrain to the striatum significantly less in CHR participants than in HC, and less in CHR participants who subsequently developed psychosis than in CHR individuals who did not become psychotic. Our findings are consistent with preclinical evidence implicating hippocampal-striatal-midbrain circuit dysfunction in altered salience processing and the onset of psychosis. [ABSTRACT FROM AUTHOR] more...

As internet access and use increase exponentially, pedagogical practice becomes increasingly embedded in online platforms. We report on an online initiative of engaged student learning, the peer-led, staff-assisted e-helpdesk for research methods and statistics, which we evaluated and redeveloped using the lens and guiding principles of the framework for partnership in learning and teaching of the Higher Education Academy (HEA). The aim of the redevelopment was to steer the initiative towards a more integrative and sustainable implementation, as manifest in the applied construct of an online partnership learning community. Our evolving experience of the e-helpdesk highlighted the central role of the facilitator in engineering and maintaining social presence in the online community. We proposean extended model for building an online partnership learning community, whereby partnership encapsulates all the essential elements of student and staff partnership as outlined in the HEA framework, but is also critically defined by similar parameters of partnership between users and facilitators. In this model, the facilitator's role becomes more involved in instructional teaching as disciplinary expertise increases, but descending levels of disciplinary expertise can foster ascending levels of independent learning and shared discovery for both users and facilitators. [ABSTRACT FROM AUTHOR] more...

The missense SNP NC&#95;000004.12:g.102267552C>T (SLC39A8.p.(Ala391Thr), rs13107325) in SLC39A8 , which encodes a zinc transporter, has been linked to schizophrenia and is the likely causal variant for one of the genome-wide association loci associated with the disorder. We tested whether the schizophrenia-risk allele at p.(Ala391Thr) was associated with schizophrenia-related phenotypes, including positive, negative, and disorganised symptoms, cognitive ability, educational attainment, and age of psychosis onset, within three schizophrenia cohorts (combined N=1,232) and, with equivalent phenotypes, in a sample of population controls (UK Biobank, N=355,069). We used regression analyses controlling for age, sex, and population stratification. Within the schizophrenia cohorts, after correction for multiple testing, p.(Ala391Thr) was not significantly associated with any schizophrenia-related phenotypes. In the unaffected participants from the UK Biobank, the schizophrenia-risk allele at p.(Ala391Thr) was associated with significantly poorer cognitive ability and fluid intelligence, a lower probability of obtaining GCSEs or a degree-level qualification, and fewer years in education. There was no association between p.(Ala391Thr) and self-reported psychotic experiences in this cohort. The schizophrenia-risk allele was associated with poorer cognitive ability, but not psychotic experiences, in a volunteer sample drawn from of the general population. To determine whether p.(Ala391Thr) is associated with cognitive phenotypes in people with schizophrenia, and to understand the role of p.(Ala391Thr) in the aetiology of cognitive impairment in schizophrenia, larger independent samples are required. more...

Background: Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia., Methods: The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC)., Results: In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r 2 , for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts., Conclusions: Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended., Competing Interests: Conflict of Interest JTRW, MCO’D and MJO received a research grant to Cardiff University from Takeda Pharmaceuticals that funded this work and GW’s research position. Takeda Pharmaceuticals have not had any input into the study design, analysis, or interpretation of results. JTRW, MCO’D, MJO, IRJ and AFP have received research funding from Akrivia Health for work unrelated to this study. more...