Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes

PRONIA Consortium; Paris Alexandros Lalousis; Lianne Schmaal; Stephen Wood; Renate Reniers; Nicholas Barnes; Katharine Chisholm; Lowri Griffiths; Alexandra Stainton; Junhao Wen; Gyujoon  Hwang; Christos Davatzikos; Julian Wenzel; Lana Kambeitz-Ilankovic; Christina Andreou; Carolina Bonivento; Udo Dannlowski; Adele Ferro; Theresa Liechtenstein; Anita Riecher-Rössler; Georg Romer; Marlene Rosen; Alessandro  Bertolino; Stefan Borgwardt; Paolo Brambilla; Joseph  Kambeitz; Rebekka Lencer; Christos Pantelis; Stephan Ruhrmann; Raimo K.r. Salokangas; Frauke Schultze-Lutter; André Schmidt; Eva M Meisenzahl; Nikolaos Koutsouleris; Dominic Dwyer; Rachel Upthegrove

doi:10.1016/j.biopsych.2022.03.021

Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes

PRONIA Consortium, Paris Alexandros Lalousis, Lianne Schmaal, Stephen Wood, Renate Reniers, Nicholas Barnes, Katharine Chisholm, Lowri Griffiths, Alexandra Stainton, Junhao Wen, Gyujoon Hwang, Christos Davatzikos, Julian Wenzel, Lana Kambeitz-Ilankovic, Christina Andreou, Carolina Bonivento, Udo Dannlowski, Adele Ferro, Theresa Liechtenstein, Anita Riecher-RösslerGeorg Romer, Marlene Rosen, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Joseph Kambeitz, Rebekka Lencer, Christos Pantelis, Stephan Ruhrmann, Raimo K.r. Salokangas, Frauke Schultze-Lutter, André Schmidt, Eva M Meisenzahl, Nikolaos Koutsouleris, Dominic Dwyer, Rachel Upthegrove

Research output: Contribution to journal › Article › peer-review

Abstract

Background
Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. We aimed to identify clusters across patients with recent onset depression (ROD) and recent onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.

Methods
HYDRA (HeterogeneitY through DiscRiminant Analysis) was trained on whole brain volumetric measures from 577 participants from the discovery sample of the multi-site PRONIA study to identify neurobiologically driven clusters which were then externally validated in the PRONIA replication sample (n=404) and three datasets of chronic samples (COBRE, n=146; MCIC, n=202; MUC, n=470).

Results
The optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, more positive, negative, and functional deficits (impaired cluster) and one cluster revealed a more preserved neuroanatomical signature and more ‘core’ depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission -outperforming traditional diagnostic structures.

Conclusions
We identified two transdiagnostic neuroanatomically informed clusters which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Original language	English
Journal	Biological Psychiatry
DOIs	https://doi.org/10.1016/j.biopsych.2022.03.021
Publication status	Accepted/In press - 1 Mar 2022

Bibliographical note

Journal pre-proof currently online. Final version of record not yet available as of 06/06/2022.

Keywords

transdiagnostic
psychosis
depression
clustering
nosology
machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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PRONIA Consortium, Lalousis, P. A., Schmaal, L., Wood, S., Reniers, R., Barnes, N., Chisholm, K., Griffiths, L., Stainton, A., Wen, J., Hwang, G., Davatzikos, C., Wenzel, J., Kambeitz-Ilankovic, L., Andreou, C., Bonivento, C., Dannlowski, U., Ferro, A., Liechtenstein, T., ... Upthegrove, R. (Accepted/In press). Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2022.03.021

PRONIA Consortium ; Lalousis, Paris Alexandros ; Schmaal, Lianne ; Wood, Stephen ; Reniers, Renate ; Barnes, Nicholas ; Chisholm, Katharine ; Griffiths, Lowri ; Stainton, Alexandra ; Wen, Junhao ; Hwang, Gyujoon ; Davatzikos, Christos ; Wenzel, Julian ; Kambeitz-Ilankovic, Lana ; Andreou, Christina ; Bonivento, Carolina ; Dannlowski, Udo ; Ferro, Adele ; Liechtenstein, Theresa ; Riecher-Rössler, Anita ; Romer, Georg ; Rosen, Marlene ; Bertolino, Alessandro ; Borgwardt, Stefan ; Brambilla, Paolo ; Kambeitz, Joseph ; Lencer, Rebekka ; Pantelis, Christos ; Ruhrmann, Stephan ; Salokangas, Raimo K.r. ; Schultze-Lutter, Frauke ; Schmidt, André ; Meisenzahl, Eva M ; Koutsouleris, Nikolaos ; Dwyer, Dominic ; Upthegrove, Rachel. / Neurobiologically based stratification of recent onset depression and psychosis : identification of two distinct transdiagnostic phenotypes. In: Biological Psychiatry. 2022.

@article{7ff658a945284648839d50a2a6cc3cb5,

title = "Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes",

abstract = "BackgroundIdentifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. We aimed to identify clusters across patients with recent onset depression (ROD) and recent onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.MethodsHYDRA (HeterogeneitY through DiscRiminant Analysis) was trained on whole brain volumetric measures from 577 participants from the discovery sample of the multi-site PRONIA study to identify neurobiologically driven clusters which were then externally validated in the PRONIA replication sample (n=404) and three datasets of chronic samples (COBRE, n=146; MCIC, n=202; MUC, n=470).ResultsThe optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, more positive, negative, and functional deficits (impaired cluster) and one cluster revealed a more preserved neuroanatomical signature and more {\textquoteleft}core{\textquoteright} depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission -outperforming traditional diagnostic structures.ConclusionsWe identified two transdiagnostic neuroanatomically informed clusters which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments.",

keywords = "transdiagnostic, psychosis, depression, clustering, nosology, machine learning",

author = "{PRONIA Consortium} and Lalousis, {Paris Alexandros} and Lianne Schmaal and Stephen Wood and Renate Reniers and Nicholas Barnes and Katharine Chisholm and Lowri Griffiths and Alexandra Stainton and Junhao Wen and Gyujoon Hwang and Christos Davatzikos and Julian Wenzel and Lana Kambeitz-Ilankovic and Christina Andreou and Carolina Bonivento and Udo Dannlowski and Adele Ferro and Theresa Liechtenstein and Anita Riecher-R{\"o}ssler and Georg Romer and Marlene Rosen and Alessandro Bertolino and Stefan Borgwardt and Paolo Brambilla and Joseph Kambeitz and Rebekka Lencer and Christos Pantelis and Stephan Ruhrmann and Salokangas, {Raimo K.r.} and Frauke Schultze-Lutter and Andr{\'e} Schmidt and Meisenzahl, {Eva M} and Nikolaos Koutsouleris and Dominic Dwyer and Rachel Upthegrove",

note = "Journal pre-proof currently online. Final version of record not yet available as of 06/06/2022. ",

year = "2022",

month = mar,

day = "1",

doi = "10.1016/j.biopsych.2022.03.021",

language = "English",

journal = "Biological Psychiatry",

issn = "0006-3223",

publisher = "Elsevier",

}

PRONIA Consortium, Lalousis, PA, Schmaal, L, Wood, S , Reniers, R , Barnes, N, Chisholm, K, Griffiths, L, Stainton, A, Wen, J, Hwang, G, Davatzikos, C, Wenzel, J, Kambeitz-Ilankovic, L, Andreou, C, Bonivento, C, Dannlowski, U, Ferro, A, Liechtenstein, T, Riecher-Rössler, A, Romer, G, Rosen, M, Bertolino, A, Borgwardt, S, Brambilla, P, Kambeitz, J, Lencer, R, Pantelis, C, Ruhrmann, S, Salokangas, RKR, Schultze-Lutter, F, Schmidt, A, Meisenzahl, EM, Koutsouleris, N, Dwyer, D & Upthegrove, R 2022, 'Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes', Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2022.03.021

Neurobiologically based stratification of recent onset depression and psychosis : identification of two distinct transdiagnostic phenotypes. / PRONIA Consortium; Lalousis, Paris Alexandros; Schmaal, Lianne; Wood, Stephen ; Reniers, Renate ; Barnes, Nicholas; Chisholm, Katharine; Griffiths, Lowri; Stainton, Alexandra; Wen, Junhao; Hwang, Gyujoon ; Davatzikos, Christos; Wenzel, Julian; Kambeitz-Ilankovic, Lana; Andreou, Christina; Bonivento, Carolina; Dannlowski, Udo; Ferro, Adele; Liechtenstein, Theresa; Riecher-Rössler, Anita; Romer, Georg; Rosen, Marlene; Bertolino, Alessandro ; Borgwardt, Stefan; Brambilla, Paolo; Kambeitz, Joseph ; Lencer, Rebekka; Pantelis, Christos; Ruhrmann, Stephan; Salokangas, Raimo K.r.; Schultze-Lutter, Frauke; Schmidt, André; Meisenzahl, Eva M; Koutsouleris, Nikolaos; Dwyer, Dominic; Upthegrove, Rachel.

In: Biological Psychiatry, 01.03.2022.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Neurobiologically based stratification of recent onset depression and psychosis

T2 - identification of two distinct transdiagnostic phenotypes

AU - PRONIA Consortium

AU - Lalousis, Paris Alexandros

AU - Schmaal, Lianne

AU - Wood, Stephen

AU - Reniers, Renate

AU - Barnes, Nicholas

AU - Chisholm, Katharine

AU - Griffiths, Lowri

AU - Stainton, Alexandra

AU - Wen, Junhao

AU - Hwang, Gyujoon

AU - Davatzikos, Christos

AU - Wenzel, Julian

AU - Kambeitz-Ilankovic, Lana

AU - Andreou, Christina

AU - Bonivento, Carolina

AU - Dannlowski, Udo

AU - Ferro, Adele

AU - Liechtenstein, Theresa

AU - Riecher-Rössler, Anita

AU - Romer, Georg

AU - Rosen, Marlene

AU - Bertolino, Alessandro

AU - Borgwardt, Stefan

AU - Brambilla, Paolo

AU - Kambeitz, Joseph

AU - Lencer, Rebekka

AU - Pantelis, Christos

AU - Ruhrmann, Stephan

AU - Salokangas, Raimo K.r.

AU - Schultze-Lutter, Frauke

AU - Schmidt, André

AU - Meisenzahl, Eva M

AU - Koutsouleris, Nikolaos

AU - Dwyer, Dominic

AU - Upthegrove, Rachel

N1 - Journal pre-proof currently online. Final version of record not yet available as of 06/06/2022.

PY - 2022/3/1

Y1 - 2022/3/1

N2 - BackgroundIdentifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. We aimed to identify clusters across patients with recent onset depression (ROD) and recent onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.MethodsHYDRA (HeterogeneitY through DiscRiminant Analysis) was trained on whole brain volumetric measures from 577 participants from the discovery sample of the multi-site PRONIA study to identify neurobiologically driven clusters which were then externally validated in the PRONIA replication sample (n=404) and three datasets of chronic samples (COBRE, n=146; MCIC, n=202; MUC, n=470).ResultsThe optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, more positive, negative, and functional deficits (impaired cluster) and one cluster revealed a more preserved neuroanatomical signature and more ‘core’ depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission -outperforming traditional diagnostic structures.ConclusionsWe identified two transdiagnostic neuroanatomically informed clusters which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

AB - BackgroundIdentifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. We aimed to identify clusters across patients with recent onset depression (ROD) and recent onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.MethodsHYDRA (HeterogeneitY through DiscRiminant Analysis) was trained on whole brain volumetric measures from 577 participants from the discovery sample of the multi-site PRONIA study to identify neurobiologically driven clusters which were then externally validated in the PRONIA replication sample (n=404) and three datasets of chronic samples (COBRE, n=146; MCIC, n=202; MUC, n=470).ResultsThe optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, more positive, negative, and functional deficits (impaired cluster) and one cluster revealed a more preserved neuroanatomical signature and more ‘core’ depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission -outperforming traditional diagnostic structures.ConclusionsWe identified two transdiagnostic neuroanatomically informed clusters which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

KW - transdiagnostic

KW - psychosis

KW - depression

KW - clustering

KW - nosology

KW - machine learning

U2 - 10.1016/j.biopsych.2022.03.021

DO - 10.1016/j.biopsych.2022.03.021

M3 - Article

JO - Biological Psychiatry

JF - Biological Psychiatry

SN - 0006-3223

ER -

Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes

Abstract

Bibliographical note

Keywords

UN SDGs

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