Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

PRONIA-consortium

doi:10.1016/j.bpsc.2023.06.001

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

PRONIA-consortium

Clinic of Psychiatry and Psychotherapy

5 Citations (Scopus)

Abstract

Background: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. Methods: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. Results: Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BAC_slow-5 = 73.2%, BAC_slow-4 = 72.9%, BAC_slow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%. Conclusions: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

Original language	English
Journal	Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume	8
Issue number	12
Pages (from-to)	1207-1217
Number of pages	11
ISSN	2451-9022
DOIs	https://doi.org/10.1016/j.bpsc.2023.06.001
Publication status	Published - 12.2023

Funding

PRONIA (Personalized Prognostic Tools for Early Psychosis Management) is a Collaboration Project funded by the European Union under the Seventh Framework Programme (Grant agreement No. 602152). OFO and DP were supported by the Else-Kröner-Fresenius-Foundation through the Clinician Scientist Program Else-Kröner-Fresenius-Stiftung-Translational Psychiatry. SSH has received support from the National Institutes of Health, National Institute of Mental Health (Grant No. T32MH122394 ). NK, SR, and AR-R report grants from European Union over the duration of the study. AB reports speaker fees from Otsuka, Lundbeck, Angelini, and Rovi outside the submitted work, unrelated to the submitted work. JH reports personal fees from Orion Ltd., personal fees from Lundbeck, personal fees from Otsuka, and others from Takeda during the conduct of the study. EM and NK hold patent US20160192889A1 (Adaptive pattern recognition for psychosis risk modeling). NK reports speaker fees from Otsuka, Roche, and Angelini outside of the submitted work. CP reports grants from the Australian National Health and Medical Research Council during the study and personal fees from Lundbeck, Australia Pty Ltd. outside the submitted work. RU reports speaker fees from Sunovion, Otsuka, and Vitaris outside the submitted work as well as unpaid officership with the British Association for Pharmacology - Honorary General Secretary 2021–2024. She serves as the Deputy Editor for The British Journal of Psychiatry. PF reports that he has received research support/honoraria for lectures or advisory activities from Boehringer Ingelheim, Janssen, Lundbeck, Otsuka, Recordati, and Richter outside the submitted work. CP was supported by an Australian National Health and Medical Research Council L3 Investigator Grant (Grant No. 1196508) outside the submitted work. LK-I reports receiving a NARSAD Young Investigator Award of the Brain and Behavior Research Foundation (Award No. 28474) outside the submitted work. All other authors report no biomedical financial interests or potential conflicts of interest. PRONIA (Personalized Prognostic Tools for Early Psychosis Management) is a Collaboration Project funded by the European Union under the Seventh Framework Programme (Grant agreement No. 602152). OFO and DP were supported by the Else-Kröner-Fresenius-Foundation through the Clinician Scientist Program Else-Kröner-Fresenius-Stiftung-Translational Psychiatry. SSH has received support from the National Institutes of Health, National Institute of Mental Health (Grant No. T32MH122394). NK, SR, and AR-R report grants from European Union over the duration of the study. The PRONIA Consortium: The following collaborators participated in the screening, recruitment, rating, examination, and follow-up of the study participants used in the current analyses. Individuals are listed in alphabetical order according to the institution of affiliation. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany: Shalaila S. Haas, Alkomiet Hasan, Claudius Hoff, Ifrah Khanyaree, Yanis Köhler, Camilla Krämer, Aylin Melo, Susanna Muckenhuber-Sternbauer, Oemer Faruk Oeztuerk, Nora Penzel, David Popovic, Adrian Rangnick, Sebastian von Saldern, Rachele Sanfelici, Moritz Spangemacher, Ana Tupac, Maria Fernanda Urquijo, Johanna Weiske, Antonia Wosgien. Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany: Karsten Blume, Dennis Hedderich, Dominika Julkowski, Nathalie Kaiser, Thorsten Lichtenstein, Ruth Milz, Alexandra Nikolaides, Tanja Pilgram, Mauro Seves, Martina Wassen. Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Switzerland: Christina Andreou, Laura Egloff, Fabienne Harrisberger, Ulrike Heitz, Letizia Leanza, Claudia Lenz, Amatya Mackintosh, Renata Smieskova, Erich Studerus, Anna Walter, Sonja Widmayer. Institute of Mental Health & School of Psychology, University of Birmingham, United Kingdom: Chris Day, Sian Lowri Griffiths, Mariam Iqbal, Ashleigh Lin, Pavan Mallikarjun, Mirabel Pelton, Alexandra Stainton. Department of Psychiatry, University of Turku, Finland: Alexander Denissoff, Anu Ellilä, Tiina From, Markus Heinimaa, Tuula Ilonen, Päivi Jalo, Heikki Laurikainen, Antti Luutonen, Akseli Mäkela, Janina Paju, Henri Pesonen, Reetta-Liina Säilä, Anna Toivonen, Otto Turtonen. Department of Psychiatry (Psychiatric University Hospital Landschaftsverband Rheinland/Heinrich Heine University Düsseldorf), University of Düsseldorf, Germany: Gerald Antoch, Sonja Botterweck, Julian Caspers, Norman Kluthausen, Hans-Jörg Wittsack. Department of Basic Medical Science, Neuroscience and Sense Organs—University of Bari Aldo Moro: Ileana Andriola, Marina Barone, Giuseppe Blasi, Grazia Caforio, Andrea Falsetti, Leonardo Fazio, Barbara Gelao, Roberta Passiatore, Giulio Pergola, Tiziana Quarto, Raffaella Romano, Marina SanGiuliano. Department of Psychiatry and Psychotherapy of the University of Münster, Germany: Olga Bienek, Udo Dannlowski, Marian Surmann. General Electric Global Research Inc. United States: Manuela Abraham, Timo Schirmer, Ana Beatriz Solana. Workgroup of PB, University of Milan, Italy: Department of Neuroscience and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy: Carlo Altamura, Marika Belleri, Francesca Bottinelli, Adele Ferro, Marta Re; Programma2000, Niguarda Hospital, Milan: Emiliano Monzani, Maurizio Sberna; San Paolo Hospital, Milan: Armando D'Agostino, Lorenzo Del Fabro; Villa San Benedetto Menni, Albese con Cassano (CO): Alessandra Alciati, Maria Nobile, Giampaolo Perna. Workgroup of PB at the University of Udine, Italy: Department of Medical Area, University of Udine, Udine, Italy: Matteo Balestrieri, Carolina Bonivento, Giuseppe Cabras, Franco Fabbro; Istituto di Ricovero e Cura a Carattere Scientifico Scientific Institute “E. Medea,” Polo Friuli-Venezia Giulia, Udine: Marco Garzitto, Sara Piccin. AB reports speaker fees from Otsuka, Lundbeck, Angelini, and Rovi outside the submitted work, unrelated to the submitted work. JH reports personal fees from Orion Ltd. personal fees from Lundbeck, personal fees from Otsuka, and others from Takeda during the conduct of the study. EM and NK hold patent US20160192889A1 (Adaptive pattern recognition for psychosis risk modeling). NK reports speaker fees from Otsuka, Roche, and Angelini outside of the submitted work. CP reports grants from the Australian National Health and Medical Research Council during the study and personal fees from Lundbeck, Australia Pty Ltd. outside the submitted work. RU reports speaker fees from Sunovion, Otsuka, and Vitaris outside the submitted work as well as unpaid officership with the British Association for Pharmacology - Honorary General Secretary 2021–2024. She serves as the Deputy Editor for The British Journal of Psychiatry. PF reports that he has received research support/honoraria for lectures or advisory activities from Boehringer Ingelheim, Janssen, Lundbeck, Otsuka, Recordati, and Richter outside the submitted work. CP was supported by an Australian National Health and Medical Research Council L3 Investigator Grant (Grant No. 1196508) outside the submitted work. LK-I reports receiving a NARSAD Young Investigator Award of the Brain and Behavior Research Foundation (Award No. 28474) outside the submitted work. All other authors report no biomedical financial interests or potential conflicts of interest.

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 10 Reduced Inequalities

Research Areas and Centers

Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)

Access to Document

10.1016/j.bpsc.2023.06.001

Cite this

@article{7c31afa133184561b9c6448e23ddb3e3,

title = "Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study",

abstract = "Background: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. Methods: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. Results: Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8\%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2\%, BACslow-4 = 72.9\%, BACslow-3 = 68.0\%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7\%), and smaller frontal white matter volume (BAC = 73.1\%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77\%. Conclusions: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.",

author = "PRONIA-consortium and Buciuman, \{Madalina Octavia\} and Oeztuerk, \{Oemer Faruk\} and David Popovic and Paolo Enrico and Anne Ruef and Nadia Bieler and Elif Sarisik and Johanna Weiske and Dong, \{Mark Sen\} and Dwyer, \{Dominic B.\} and Lana Kambeitz-Ilankovic and Haas, \{Shalaila S.\} and Alexandra Stainton and Stephan Ruhrmann and Katharine Chisholm and Joseph Kambeitz and Anita Riecher-R{\"o}ssler and Rachel Upthegrove and Frauke Schultze-Lutter and Salokangas, \{Raimo K.R.\} and Jarmo Hietala and Christos Pantelis and Rebekka Lencer and Eva Meisenzahl and Wood, \{Stephen J.\} and Paolo Brambilla and Stefan Borgwardt and Peter Falkai and Antonucci, \{Linda A.\} and Alessandro Bertolino and Peter Liddle and Nikolaos Koutsouleris",

note = "Publisher Copyright: {\textcopyright} 2023 Society of Biological Psychiatry",

year = "2023",

month = dec,

doi = "10.1016/j.bpsc.2023.06.001",

language = "English",

volume = "8",

pages = "1207--1217",

journal = "Biological Psychiatry: Cognitive Neuroscience and Neuroimaging",

issn = "2451-9022",

publisher = "Elsevier Inc.",

number = "12",

}

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study. / PRONIA-consortium.
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol. 8, No. 12, 12.2023, p. 1207-1217.

Research output: Journal Articles › Journal articles › Research › peer-review

TY - JOUR

T1 - Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis

T2 - Results From the PRONIA Study

AU - PRONIA-consortium

AU - Buciuman, Madalina Octavia

AU - Oeztuerk, Oemer Faruk

AU - Popovic, David

AU - Enrico, Paolo

AU - Ruef, Anne

AU - Bieler, Nadia

AU - Sarisik, Elif

AU - Weiske, Johanna

AU - Dong, Mark Sen

AU - Dwyer, Dominic B.

AU - Kambeitz-Ilankovic, Lana

AU - Haas, Shalaila S.

AU - Stainton, Alexandra

AU - Ruhrmann, Stephan

AU - Chisholm, Katharine

AU - Kambeitz, Joseph

AU - Riecher-Rössler, Anita

AU - Upthegrove, Rachel

AU - Schultze-Lutter, Frauke

AU - Salokangas, Raimo K.R.

AU - Hietala, Jarmo

AU - Pantelis, Christos

AU - Lencer, Rebekka

AU - Meisenzahl, Eva

AU - Wood, Stephen J.

AU - Brambilla, Paolo

AU - Borgwardt, Stefan

AU - Falkai, Peter

AU - Antonucci, Linda A.

AU - Bertolino, Alessandro

AU - Liddle, Peter

AU - Koutsouleris, Nikolaos

PY - 2023/12

Y1 - 2023/12

N2 - Background: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. Methods: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. Results: Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%. Conclusions: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

AB - Background: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. Methods: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. Results: Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%. Conclusions: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

UR - https://www.scopus.com/pages/publications/85171130078

U2 - 10.1016/j.bpsc.2023.06.001

DO - 10.1016/j.bpsc.2023.06.001

M3 - Journal articles

C2 - 37343661

AN - SCOPUS:85171130078

SN - 2451-9022

VL - 8

SP - 1207

EP - 1217

JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

IS - 12

ER -

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

Abstract

Funding

UN SDGs

Research Areas and Centers

Access to Document

Other files and links

Fingerprint

Cite this