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Title

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

Authors
Tarun, Anjali
Armondo, Marco
Nelson, Barnaby
Amminger, Paul G
Yuen, Hok Pan
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Published in eLife. 2021, vol. 10, e59811
Abstract 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.
Keywords 22q11.2 deletion syndromeAffective pathwayHumanMedicineNetwork analysisSchizophreniaAdultFemaleHumansLongitudinal StudiesMalePrecision MedicinePsychotic Disorders / physiopathology
Identifiers
PMID: 34569937
PMCID: PMC8476129
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Research groups Laboratoire d'imagerie et de psychopathologie développementale (693)
Traitement d'images médicales (893)
NCCR Synapsy
Projects
Stanley Medical Research Institute: 07TGF-1102
National Health and Medical Research Council (NHMRC): 1027532
National Health and Medical Research Council (NHMRC): 566529
National Health and Medical Research Council (NHMRC): 1060996
National Health and Medical Research Council (NHMRC): 1080963
National Health and Medical Research Council (NHMRC): 566593
National Health and Medical Research Council (NHMRC): 1027532
Swiss National Science Foundation: 320030_179404
Swiss National Science Foundation: 324730_144260
Swiss National Science Foundation: PZ00P1_174206
Swiss National Science Foundation: 51NF40-158776
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 https://archive-ouverte.unige.ch/unige:161520

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Deposited on : 2022-06-17

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