ArticleMolecular psychiatry2025
Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Article in Molecular psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The role of social support in transdiagnostic symptom networks in affective and psychotic disorders: a cross-sectional and longitudinal network analysis.European archives of psychiatry and clinical neuroscience · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Transdiagnostic symptom networks in adolescent psychopathology: A longitudinal panel network analysis.JCPP advances · 2026Article
- Linking spontaneous speech, cognition, and psychopathology across affective and psychotic disorders: A network approach.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Acceptability of cannabidiol as a treatment for people at clinical high risk for psychosis.Frontiers in psychiatry · 2026Article
- Blood immuno-metabolic biomarker signatures of depression and affective symptoms in young adults.Brain, behavior, and immunity · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. This retrospective (2-year) cohort study aimed to develop a global transdiagnostic SMD network of the temporal relationships between prodromal features and to examine within-group differences with sub-networks specific to UMD, BMD and PSY. Electronic health records (EHRs) from South London and Maudsley (SLaM) NHS Foundation Trust were included from 6462 individuals with SMD diagnoses (UMD:2066; BMD:740; PSY:3656). Validated natural language processing algorithms extracted the occurrence of 61 prodromal features every three months from two years to six months before SMD onset. Temporal networks of prodromal features were constructed using generalised vector autoregression panel analysis, adjusting for covariates. Edge weights (partial directed correlation coefficients, z) were reported in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of (non-autoregressive) connections leaving (out-centrality, c
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