Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records

Maite Arribas; Joseph M Barnby; Rashmi Patel; Robert A McCutcheon; Daisy Kornblum; Hitesh Shetty; Kamil Krakowski; Daniel Stahl; Nikolaos Koutsouleris; Philip McGuire; Paolo Fusar-Poli; Dominic Oliver

doi:10.1038/s41380-025-02896-3

Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records

Mol Psychiatry. 2025 Jan 22. doi: 10.1038/s41380-025-02896-3. Online ahead of print.

Authors

Maite Arribas¹, Joseph M Barnby^{2

3

4}, Rashmi Patel⁵, Robert A McCutcheon^{6

7

8}, Daisy Kornblum⁹, Hitesh Shetty⁹, Kamil Krakowski^{10

11}, Daniel Stahl^{9

12}, Nikolaos Koutsouleris^{8

13

14}, Philip McGuire^{6

7

15}, Paolo Fusar-Poli^{10

11

13

16}, Dominic Oliver^{10

6

7

15}

Affiliations

¹ Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. [email protected].
² Social Computation and Cognitive Representation (SoCCR) Lab, Department of Psychology, Royal Holloway, University of London, London, UK.
³ Cultural and Social Neuroscience Group, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, University of London, London, UK.
⁴ School of Psychiatry and Clinical Neuroscience, The University of Western Australia, Perth, Australia.
⁵ Department of Psychiatry, University of Cambridge, Cambridge, UK.
⁶ Department of Psychiatry, University of Oxford, Oxford, UK.
⁷ NIHR Oxford Health Biomedical Research Centre, Oxford, UK.
⁸ Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
⁹ NIHR Maudsley Biomedical Research Centre, London, UK.
¹⁰ Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
¹¹ Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
¹² Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
¹³ Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
¹⁴ Max-Planck Institute of Psychiatry, Munich, Germany.
¹⁵ OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, UK.
¹⁶ Outreach and Support in South-London (OASIS) service, South London and Maudsley (SLaM) NHS Foundation Trust, London, UK.

PMID: 39843546
DOI: 10.1038/s41380-025-02896-3

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_out) or entering (in-centrality, c_in) a node. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis, and community analysis was performed using Spinglass. The SMD network revealed strong autocorrelations (0.04 ≤ z ≤ 0.10), predominantly positive connections, and identified aggression (c_out = 0.103) and tearfulness (c_in = 0.134) as the most central features. Sub-networks for UMD, BMD, and PSY showed minimal differences, with 3.5% of edges differing between UMD and PSY, 0.8% between UMD and BMD, and 0.4% between BMD and PSY. Community analysis identified one positive psychotic community (delusional thinking-hallucinations-paranoia) and two behavioural communities (aggression-cannabis use-cocaine use-hostility, aggression-agitation-hostility) as the most common. This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. The findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets.

Abstract

Grants and funding