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M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES

BACKGROUND: Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. METHODS: PRONIA (‘Personalized Prognos...

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Autores principales: Haidl, Theresa, Hedderich, Dennis, Rosen, Marlene, Lichtenstein, Thorsten, Kaiser, Nathalie, Seves, Mauro, Ruef, Anne, Schultze-Lutter, Frauke, Upthegrove, Rachel, Salokangas, Raimo K R, Pantelis, Christos, Meisenzahl, Eva, Wood, Stephen J, Brambilla, Paolo, Borgwardt, Stefan, Lencer, Rebekka, Ruhrmann, Stephan, Kambeitz, Joseph, Koutsouleris, Nikolaos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233827/
http://dx.doi.org/10.1093/schbul/sbaa030.313
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author Haidl, Theresa
Hedderich, Dennis
Rosen, Marlene
Lichtenstein, Thorsten
Kaiser, Nathalie
Seves, Mauro
Ruef, Anne
Schultze-Lutter, Frauke
Upthegrove, Rachel
Salokangas, Raimo K R
Pantelis, Christos
Meisenzahl, Eva
Wood, Stephen J
Brambilla, Paolo
Borgwardt, Stefan
Lencer, Rebekka
Ruhrmann, Stephan
Kambeitz, Joseph
Koutsouleris, Nikolaos
author_facet Haidl, Theresa
Hedderich, Dennis
Rosen, Marlene
Lichtenstein, Thorsten
Kaiser, Nathalie
Seves, Mauro
Ruef, Anne
Schultze-Lutter, Frauke
Upthegrove, Rachel
Salokangas, Raimo K R
Pantelis, Christos
Meisenzahl, Eva
Wood, Stephen J
Brambilla, Paolo
Borgwardt, Stefan
Lencer, Rebekka
Ruhrmann, Stephan
Kambeitz, Joseph
Koutsouleris, Nikolaos
author_sort Haidl, Theresa
collection PubMed
description BACKGROUND: Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. METHODS: PRONIA (‘Personalized Prognostic Tools for Early Psychosis Mangement’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis (CHR), patients with a recent onset psychosis (ROP) and patients with a recent onset depression (ROD)) as well as healthy controls (HC). To investigate the high-dimensional patterns of CT experience, measured by the childhood trauma questionnaire (CTQ), in HC and our three patient groups (PAT) (n=643), we used a Support Vector Machine (SVM). Furthermore, we tested whether patient-specific CT exposure is associated with structural brain changes by VBM analyses. RESULTS: We found that patients and HC could be separated very well by their CTQ pattern, whereas the different patient groups showed no specific CTQ pattern. Furthermore, an association with extensive grey matter changes suggests an impact on brain maturation which may put individuals at increased risk for mental disease. DISCUSSION: We have demonstrated in this large multi-center cohort that adverse experiences in childhood contribute transdiagnostically to the riskr for developing a psychiatric disease. The observed association between CTQ scores and structural changes suggests an impact of adverse childhood experiences on brain development. Resulting alterations may add to a neurobiological vulnerability for depression and psychosis. A role of both features for other mental disorders could be assumed and warrants further investigation.
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spelling pubmed-72338272020-05-23 M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES Haidl, Theresa Hedderich, Dennis Rosen, Marlene Lichtenstein, Thorsten Kaiser, Nathalie Seves, Mauro Ruef, Anne Schultze-Lutter, Frauke Upthegrove, Rachel Salokangas, Raimo K R Pantelis, Christos Meisenzahl, Eva Wood, Stephen J Brambilla, Paolo Borgwardt, Stefan Lencer, Rebekka Ruhrmann, Stephan Kambeitz, Joseph Koutsouleris, Nikolaos Schizophr Bull Poster Session II BACKGROUND: Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. METHODS: PRONIA (‘Personalized Prognostic Tools for Early Psychosis Mangement’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis (CHR), patients with a recent onset psychosis (ROP) and patients with a recent onset depression (ROD)) as well as healthy controls (HC). To investigate the high-dimensional patterns of CT experience, measured by the childhood trauma questionnaire (CTQ), in HC and our three patient groups (PAT) (n=643), we used a Support Vector Machine (SVM). Furthermore, we tested whether patient-specific CT exposure is associated with structural brain changes by VBM analyses. RESULTS: We found that patients and HC could be separated very well by their CTQ pattern, whereas the different patient groups showed no specific CTQ pattern. Furthermore, an association with extensive grey matter changes suggests an impact on brain maturation which may put individuals at increased risk for mental disease. DISCUSSION: We have demonstrated in this large multi-center cohort that adverse experiences in childhood contribute transdiagnostically to the riskr for developing a psychiatric disease. The observed association between CTQ scores and structural changes suggests an impact of adverse childhood experiences on brain development. Resulting alterations may add to a neurobiological vulnerability for depression and psychosis. A role of both features for other mental disorders could be assumed and warrants further investigation. Oxford University Press 2020-05 2020-05-18 /pmc/articles/PMC7233827/ http://dx.doi.org/10.1093/schbul/sbaa030.313 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Poster Session II
Haidl, Theresa
Hedderich, Dennis
Rosen, Marlene
Lichtenstein, Thorsten
Kaiser, Nathalie
Seves, Mauro
Ruef, Anne
Schultze-Lutter, Frauke
Upthegrove, Rachel
Salokangas, Raimo K R
Pantelis, Christos
Meisenzahl, Eva
Wood, Stephen J
Brambilla, Paolo
Borgwardt, Stefan
Lencer, Rebekka
Ruhrmann, Stephan
Kambeitz, Joseph
Koutsouleris, Nikolaos
M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title_full M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title_fullStr M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title_full_unstemmed M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title_short M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES
title_sort m1. investigating the relationship between childhood trauma and psychiatric disease using machine learning techniques
topic Poster Session II
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233827/
http://dx.doi.org/10.1093/schbul/sbaa030.313
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