Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. / Sandini, Corrado; Zöller, Daniela; Schneider, Maude; Tarun, Anjali; Armondo, Marco; Nelson, Barnaby; Amminger, Paul G.; Yuen, Hok Pan; Markulev, Connie; Schäffer, Monica R.; Mossaheb, Nilufar; Schlögelhofer, Monika; Smesny, Stefan; Hickie, Ian B.; Berger, Gregor Emanuel; Chen, Eric YH; de Haan, Lieuwe; Nieman, Dorien H.; Nordentoft, Merete; Riecher-Rössler, Anita; Verma, Swapna; Thompson, Andrew; Yung, Alison Ruth; McGorry, Patrick D; Van de Ville, Dimitri; Eliez, Stephan.
I: eLife, Bind 10, e59811, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing
AU - Sandini, Corrado
AU - Zöller, Daniela
AU - Schneider, Maude
AU - Tarun, Anjali
AU - Armondo, Marco
AU - Nelson, Barnaby
AU - Amminger, Paul G.
AU - Yuen, Hok Pan
AU - Markulev, Connie
AU - Schäffer, Monica R.
AU - Mossaheb, Nilufar
AU - Schlögelhofer, Monika
AU - Smesny, Stefan
AU - Hickie, Ian B.
AU - Berger, Gregor Emanuel
AU - Chen, Eric YH
AU - de Haan, Lieuwe
AU - Nieman, Dorien H.
AU - Nordentoft, Merete
AU - Riecher-Rössler, Anita
AU - Verma, Swapna
AU - Thompson, Andrew
AU - Yung, Alison Ruth
AU - McGorry, Patrick D
AU - Van de Ville, Dimitri
AU - Eliez, Stephan
N1 - Publisher Copyright: © 2021, eLife Sciences Publications Ltd. All rights reserved.
PY - 2021
Y1 - 2021
N2 - There is a growing recognition that psychiatric symptoms have the potential to causally interact with one another. Particularly in the earliest stages of psychopathology dynamic interactions between symptoms could contribute heterogeneous and cross-diagnostic clinical evolutions. Current clinical approaches attempt to merge clinical manifestations that co-occur across subjects and could therefore significantly hinder our understanding of clinical pathways connecting individual symptoms. Network approaches have the potential to shed light on the complex dynamics of early psychopathology. In the present manuscript we attempt to address 2 main limitations that have in our opinion hindered the application of network approaches in the clinical setting. The first limitation is that network analyses have mostly been applied to cross-sectional data, yielding results that often lack the intuitive interpretability of simpler categorical or dimensional approaches. Here we propose an approach based on multi-layer network analysis that offers an intuitive low-dimensional characterization of longitudinal pathways involved in the evolution of psychopathology, while conserving high-dimensional information on the role of specific symptoms. The second limitation is that network analyses typically characterize symptom connectivity at the level of a population, whereas clinical practice deals with symptom severity at the level of the individual. Here we propose an approach based on graph signal processing that exploits knowledge of network interactions between symptoms to predict longitudinal clinical evolution at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis.
AB - There is a growing recognition that psychiatric symptoms have the potential to causally interact with one another. Particularly in the earliest stages of psychopathology dynamic interactions between symptoms could contribute heterogeneous and cross-diagnostic clinical evolutions. Current clinical approaches attempt to merge clinical manifestations that co-occur across subjects and could therefore significantly hinder our understanding of clinical pathways connecting individual symptoms. Network approaches have the potential to shed light on the complex dynamics of early psychopathology. In the present manuscript we attempt to address 2 main limitations that have in our opinion hindered the application of network approaches in the clinical setting. The first limitation is that network analyses have mostly been applied to cross-sectional data, yielding results that often lack the intuitive interpretability of simpler categorical or dimensional approaches. Here we propose an approach based on multi-layer network analysis that offers an intuitive low-dimensional characterization of longitudinal pathways involved in the evolution of psychopathology, while conserving high-dimensional information on the role of specific symptoms. The second limitation is that network analyses typically characterize symptom connectivity at the level of a population, whereas clinical practice deals with symptom severity at the level of the individual. Here we propose an approach based on graph signal processing that exploits knowledge of network interactions between symptoms to predict longitudinal clinical evolution at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis.
U2 - 10.7554/eLife.59811
DO - 10.7554/eLife.59811
M3 - Journal article
C2 - 34569937
AN - SCOPUS:85116856872
VL - 10
JO - eLife
JF - eLife
SN - 2050-084X
M1 - e59811
ER -
ID: 301451421