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

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Sandini, Corrado, Zöller, Daniela, Schneider, Maude, Tarun, Anjali, Armondo, Marco, Nelson, Barnaby, Amminger, Paul G, Yuen, Hok Pan, Markulev, Connie, Schäffer, Monica R. et al.
(2021) Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. eLife, 10 . doi:10.7554/elife.59811

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Official URL: https://doi.org/10.7554/elife.59811

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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.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Medicine > Warwick Medical School > Health Sciences
Faculty of Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing
Faculty of Medicine > Warwick Medical School
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Mental illness , Psychology, Pathological, Psychoses , Mental illness -- Diagnosis , Mental illness -- Treatment, Diagnostic imaging -- Digital techniques
Journal or Publication Title: eLife
Publisher: eLife Sciences Publications Ltd.
ISSN: 2050-084X
Official Date: 27 September 2021
Dates:
DateEvent
27 September 2021Published
9 September 2021Accepted
Volume: 10
DOI: 10.7554/elife.59811
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
07TGF-1102Stanley Medical Research Institutehttp://dx.doi.org/10.13039/100007123
566529National Health and Medical Research Councilhttp://dx.doi.org/10.13039/501100000925
1060996National Health and Medical Research Councilhttp://dx.doi.org/10.13039/501100000925
1080963National Health and Medical Research Councilhttp://dx.doi.org/10.13039/501100000925
566593National Health and Medical Research Councilhttp://dx.doi.org/10.13039/501100000925
1027532National Health and Medical Research Councilhttp://dx.doi.org/10.13039/501100000925
FNS 320030_179404Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
FNS 324730_144260Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
PZ00P1_174206Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
51NF40-158776Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711

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