Scientific article
Open access

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Published ineLife, vol. 10, e59811
Publication date2021-09-27
First online date2021-09-27

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care.

  • 22q11.2 deletion syndrome
  • Affective pathway
  • Human
  • Medicine
  • Network analysis
  • Schizophrenia
  • Adult
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Precision Medicine
  • Psychotic Disorders / physiopathology
Citation (ISO format)
SANDINI, Corrado et al. Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. In: eLife, 2021, vol. 10, p. e59811. doi: 10.7554/eLife.59811
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Article (Published version)
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ISSN of the journal2050-084X

Technical informations

Creation12/22/2021 10:07:00 AM
First validation12/22/2021 10:07:00 AM
Update time03/16/2023 6:47:40 AM
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