ORCID Profile
0000-0001-7147-8915
Current Organisation
Université de Genève
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Publisher: Springer Science and Business Media LLC
Date: 03-02-2020
Publisher: Elsevier BV
Date: 08-2011
Publisher: American Psychiatric Association Publishing
Date: 06-2014
Publisher: Elsevier BV
Date: 03-2013
Publisher: Springer Science and Business Media LLC
Date: 09-11-2020
Publisher: eLife Sciences Publications, Ltd
Date: 27-09-2021
DOI: 10.7554/ELIFE.59811
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 in idual 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 in idual. We test our approaches in two independent s les of in iduals 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.
Publisher: Wiley
Date: 10-2018
DOI: 10.1002/AJMG.A.40359
Publisher: Elsevier BV
Date: 02-2019
Publisher: eLife Sciences Publications, Ltd
Date: 23-07-2021
Publisher: Elsevier BV
Date: 10-2017
Publisher: American Psychiatric Association Publishing
Date: 11-2017
Publisher: American Medical Association (AMA)
Date: 04-2015
Publisher: Cold Spring Harbor Laboratory
Date: 12-06-2020
DOI: 10.1101/2020.06.11.20128769
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 in idual 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 in idual. 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 in idual. We test our approaches in two independent s les of in iduals with genetic and clinical vulnerability for developing psychosis.
Location: United States of America
No related grants have been discovered for Maude Schneider.