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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

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

Harvard

Sandini, C, Zöller, D, Schneider, M, Tarun, A, Armondo, M, Nelson, B, Amminger, PG, Yuen, HP, Markulev, C, Schäffer, MR, Mossaheb, N, Schlögelhofer, M, Smesny, S, Hickie, IB, Berger, GE, Chen, EYH, de Haan, L, Nieman, DH, Nordentoft, M, Riecher-Rössler, A, Verma, S, Thompson, A, Yung, AR, McGorry, PD, Van de Ville, D & Eliez, S 2021, 'Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing', eLife, bind 10, e59811. https://doi.org/10.7554/eLife.59811

APA

Sandini, C., Zöller, D., Schneider, M., Tarun, A., Armondo, M., Nelson, B., Amminger, P. G., Yuen, H. P., Markulev, C., Schäffer, M. R., Mossaheb, N., Schlögelhofer, M., Smesny, S., Hickie, I. B., Berger, G. E., Chen, E. YH., de Haan, L., Nieman, D. H., Nordentoft, M., ... Eliez, S. (2021). Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. eLife, 10, [e59811]. https://doi.org/10.7554/eLife.59811

Vancouver

Sandini C, Zöller D, Schneider M, Tarun A, Armondo M, Nelson B o.a. Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. eLife. 2021;10. e59811. https://doi.org/10.7554/eLife.59811

Author

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. / Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. I: eLife. 2021 ; Bind 10.

Bibtex

@article{940cfc53f98f4fe2bf2c6f42e20d663e,

title = "Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing",

abstract = "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.",

author = "Corrado Sandini and Daniela Z{\"o}ller and Maude Schneider and Anjali Tarun and Marco Armondo and Barnaby Nelson and Amminger, {Paul G.} and Yuen, {Hok Pan} and Connie Markulev and Sch{\"a}ffer, {Monica R.} and Nilufar Mossaheb and Monika Schl{\"o}gelhofer and Stefan Smesny and Hickie, {Ian B.} and Berger, {Gregor Emanuel} and Chen, {Eric YH} and {de Haan}, Lieuwe and Nieman, {Dorien H.} and Merete Nordentoft and Anita Riecher-R{\"o}ssler and Swapna Verma and Andrew Thompson and Yung, {Alison Ruth} and McGorry, {Patrick D} and {Van de Ville}, Dimitri and Stephan Eliez",

year = "2021",

doi = "10.7554/eLife.59811",

language = "English",

volume = "10",

journal = "eLife",

issn = "2050-084X",

publisher = "eLife Sciences Publications Ltd.",

}

RIS

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

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