Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing
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 clinic...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2021-09-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/59811 |
| _version_ | 1818026726778732544 |
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| author | Corrado Sandini Daniela Zöller Maude Schneider Anjali Tarun Marco Armando Barnaby Nelson Paul G Amminger Hok Pan Yuen Connie Markulev Monica R Schäffer Nilufar Mossaheb Monika Schlögelhofer Stefan Smesny Ian B Hickie Gregor Emanuel Berger Eric YH Chen Lieuwe de Haan Dorien H Nieman Merete Nordentoft Anita Riecher-Rössler Swapna Verma Andrew Thompson Alison Ruth Yung Patrick D McGorry Dimitri Van De Ville Stephan Eliez |
| author_facet | Corrado Sandini Daniela Zöller Maude Schneider Anjali Tarun Marco Armando Barnaby Nelson Paul G Amminger Hok Pan Yuen Connie Markulev Monica R Schäffer Nilufar Mossaheb Monika Schlögelhofer Stefan Smesny Ian B Hickie Gregor Emanuel Berger Eric YH Chen Lieuwe de Haan Dorien H Nieman Merete Nordentoft Anita Riecher-Rössler Swapna Verma Andrew Thompson Alison Ruth Yung Patrick D McGorry Dimitri Van De Ville Stephan Eliez |
| author_sort | Corrado Sandini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T04:36:35Z |
| format | Article |
| id | doaj.art-a5a6f78b8748495e9632964859498719 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-12-10T04:36:35Z |
| publishDate | 2021-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-a5a6f78b8748495e96329648594987192022-12-22T02:01:59ZengeLife Sciences Publications LtdeLife2050-084X2021-09-011010.7554/eLife.59811Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processingCorrado Sandini0https://orcid.org/0000-0003-2933-1607Daniela Zöller1https://orcid.org/0000-0002-7049-0696Maude Schneider2https://orcid.org/0000-0001-7147-8915Anjali Tarun3Marco Armando4Barnaby Nelson5Paul G Amminger6Hok Pan Yuen7Connie Markulev8Monica R Schäffer9Nilufar Mossaheb10Monika Schlögelhofer11Stefan Smesny12Ian B Hickie13Gregor Emanuel Berger14Eric YH Chen15Lieuwe de Haan16Dorien H Nieman17Merete Nordentoft18Anita Riecher-Rössler19Swapna Verma20Andrew Thompson21Alison Ruth Yung22Patrick D McGorry23Dimitri Van De Ville24https://orcid.org/0000-0002-2879-3861Stephan Eliez25Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, SwitzerlandDevelopmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandDevelopmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Center for Contextual Psychiatry, Research Group Psychiatry, Department of Neuroscience, KU Leuven, Leuven, BelgiumInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandDevelopmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, SwitzerlandOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, AustraliaOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, AustriaOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, AustraliaOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, AustraliaThe Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, AustriaDepartment of Psychiatry, University Hospital Jena, Jena, GermanyBrain and Mind Centre, University of Sydney, Sydney, AustraliaChild and Adolescent Psychiatric Service of the Canton of Zurich, Zurich, SwitzerlandDepartment of Psychiatry, University of Hong Kong, Hong Kong, ChinaDepartment of Psychiatry, Amsterdam University Medical Centers, Amsterdam, NetherlandsPsychiatric Centre Bispebjerg, Copenhagen, DenmarkUniversity of Basel, Basel, SwitzerlandInstitute of Mental Health, Singapore, SingaporeOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, United Kingdom; North Warwickshire Early Intervention in Psychosis Service, Conventry and Warwickshire National Health Service Partnership Trust, Coventry, United KingdomOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom; Greater Manchester Mental Health NHS Foundation Trust, Manchester, United KingdomOrygen, Parkville, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, AustraliaInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, SwitzerlandDevelopmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, SwitzerlandCausal 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.https://elifesciences.org/articles/59811schizophrenianetwork analysis22q11.2 deletion syndromeaffective pathway |
| spellingShingle | Corrado Sandini Daniela Zöller Maude Schneider Anjali Tarun Marco Armando Barnaby Nelson Paul G Amminger Hok Pan Yuen Connie Markulev Monica R Schäffer Nilufar Mossaheb Monika Schlögelhofer Stefan Smesny Ian B Hickie Gregor Emanuel Berger Eric YH Chen Lieuwe de Haan Dorien H Nieman Merete Nordentoft Anita Riecher-Rössler Swapna Verma Andrew Thompson Alison Ruth Yung Patrick D McGorry Dimitri Van De Ville Stephan Eliez Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing eLife schizophrenia network analysis 22q11.2 deletion syndrome affective pathway |
| title | Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| title_full | Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| title_fullStr | Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| title_full_unstemmed | Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| title_short | Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| title_sort | characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing |
| topic | schizophrenia network analysis 22q11.2 deletion syndrome affective pathway |
| url | https://elifesciences.org/articles/59811 |
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