Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

IMAGEN Consortium

doi:10.1016/j.jad.2024.05.060

Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

IMAGEN Consortium

Center for Neurogenomics and Cognitive Research

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = −0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = −0.10), and mOFC was negatively associated with anhedonia (partial cor. = −0.05). Limitations: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Original language	English
Pages (from-to)	140-144
Number of pages	5
Journal	Journal of Affective Disorders
Volume	359
Early online date	14 May 2024
DOIs	https://doi.org/10.1016/j.jad.2024.05.060
Publication status	Published - 15 Aug 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Funding

This work received support from the following sources: the European Union -funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the Horizon 2020 funded ERC Advanced Grant \u2018STRATIFY\u2019 (Brain network based stratification of reinforcement-related disorders) ( 695313 ), Human Brain Project (HBP SGA 2, 785907 , and HBP SGA 3, 945539 ), the Medical Research Council Grant \u2018c-VEDA\u2019 (Consortium on Vulnerability to Externalizing Disorders and Addictions) ( MR/N000390/1 ), the National Institute of Health (NIH) ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the Bundesministeriumf\u00FCr Bildung und Forschung (BMBF grants 01GS08152 ; 01EV0711 ; Forschungsnetz AERIAL 01EE1406A , 01EE1406B ; Forschungsnetz IMAC-Mind 01GL1745B ), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2 , SFB 940 , TRR 265 , NE 1383/14-1 ), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01 ), NSFC grant 82150710554 and European Union funded project \u2018environMENTAL\u2019, grant no: 101057429 . Further support was provided by grants from: - the ANR ( ANR-12-SAMA-0004 , AAPG2019 - GeBra), the Eranet Neuron ( AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the Fondation de France ( 00081242 ), the Fondation pour la Recherche M\u00E9dicale ( DPA20140629802 ), the Mission Interminist\u00E9rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H\u00F4pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l'Avenir (grant AP-RM-17-013 ), the F\u00E9d\u00E9ration pour la Recherche sur le Cerveau ; the National Institutes of Health , Science Foundation Ireland ( 16/ERCD/3797 ), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1 ) and by NIH Consortium grant U54 EB020403 , supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. This study is part of the project \u2018New Science of Mental Disorders\u2019 ( www.nsmd.eu ), supported by the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.004.016 ). T.F.B. was supported by an Amsterdam Brain and Cognition Project Grant. This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant \u2018STRATIFY\u2019 (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the Medical Research Council Grant \u2018c-VEDA\u2019 (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumf\u00FCr Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01), NSFC grant 82150710554 and European Union funded project \u2018environMENTAL\u2019, grant no: 101057429. Further support was provided by grants from: - the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the Fondation de France (00081242), the Fondation pour la Recherche M\u00E9dicale (DPA20140629802), the Mission Interminist\u00E9rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H\u00F4pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l'Avenir (grant AP-RM-17-013), the F\u00E9d\u00E9ration pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1) and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. This study was part of the project \u2018New Science of Mental Disorders\u2019 (www.nsmd.eu), supported by the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.004.016). T.F.B. was supported by an Amsterdam Brain and Cognition Project Grant.

Funders	Funder number
Agence Nationale de la Recherche
National Institutes of Health, Science Foundation Ireland
Institut national de la santé et de la recherche médicale
National Institute for Health and Care Research
Fédération pour la Recherche sur le Cerveau
South London and Maudsley NHS Foundation Trust
Université Paris-Sud
King's College London
Ministerie van Onderwijs, Cultuur en Wetenschap
Horizon 2020
National Institutes of Health	5U54EB020403-05, R01DA049238, 1R56AG058854-01
National Institutes of Health
National Natural Science Foundation of China	82150710554, 101057429
National Natural Science Foundation of China
Fondation de France	00081242
Fondation de France
Fondation pour la Recherche Médicale	DPA20140629802
Fondation pour la Recherche Médicale
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	024.004.016
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
European Union-funded FP6	LSHM-CT-2007-037286
Bundesministerium für Bildung und Forschung	01GL1745B, 01GS08152, 01EV0711, AERIAL 01EE1406A, 01EE1406B
Bundesministerium für Bildung und Forschung
Sixth Framework Programme	LSHM-CT-2007-037286
Sixth Framework Programme
Fondation de l'Avenir pour la Recherche Médicale Appliquée	AP-RM-17-013
Fondation de l'Avenir pour la Recherche Médicale Appliquée
European Commission	101057429
European Commission
Era-Net Neuron	ANR-18-NEUR00002-01 - ADORe, AF12-NEUR0008-01 - WM2NA
Era-Net Neuron
Medical Research Foundation and Medical research council	MR/R00465X/1, MR/S020306/1
European Research Council	695313, 785907, 945539
European Research Council
Medical Research Council	MR/N000390/1
Medical Research Council
ANR-12-SAMA-0004	AAPG2019 - GeBra
National Institutes of Health, Science Foundation Ireland	16/ERCD/3797
Deutsche Forschungsgemeinschaft	SM 80/7-2, NE 1383/14-1, SFB 940, TRR 265
Deutsche Forschungsgemeinschaft
Testosterone and Mental Health	RO1 MH085772-01A1

Keywords

Depression symptoms
Heterogeneity
Network analysis
Neural markers

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10.1016/j.jad.2024.05.060

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Cite this

@article{7b76ccd604904115965d59194b57f14e,

title = "Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity",

abstract = "Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = −0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = −0.10), and mOFC was negatively associated with anhedonia (partial cor. = −0.05). Limitations: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.",

keywords = "Depression symptoms, Heterogeneity, Network analysis, Neural markers",

author = "Ren{\'e} Freichel and Agatha Lenartowicz and Linda Douw and Kruschwitz, {Johann D.} and Tobias Banaschewski and Barker, {Gareth J.} and Bokde, {Arun L.W.} and Sylvane Desrivi{\`e}res and Herta Flor and Antoine Grigis and Hugh Garavan and Andreas Heinz and R{\"u}diger Br{\"u}hl and Martinot, {Jean Luc} and Martinot, {Marie Laure Paill{\`e}re} and Eric Artiges and Frauke Nees and Orfanos, {Dimitri Papadopoulos} and Tom{\'a}{\v s} Paus and Luise Poustka and Nathalie Holz and Christian Baeuchl and Smolka, {Michael N.} and Nilakshi Vaidya and Robert Whelan and Vincent Frouin and Gunter Schumann and Henrik Walter and Blanken, {Tessa F.} and {IMAGEN Consortium}",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2024",

month = aug,

day = "15",

doi = "10.1016/j.jad.2024.05.060",

language = "English",

volume = "359",

pages = "140--144",

journal = "Journal of Affective Disorders",

issn = "0165-0327",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

AU - Freichel, René

AU - Lenartowicz, Agatha

AU - Douw, Linda

AU - Kruschwitz, Johann D.

AU - Banaschewski, Tobias

AU - Barker, Gareth J.

AU - Bokde, Arun L.W.

AU - Desrivières, Sylvane

AU - Flor, Herta

AU - Grigis, Antoine

AU - Garavan, Hugh

AU - Heinz, Andreas

AU - Brühl, Rüdiger

AU - Martinot, Jean Luc

AU - Martinot, Marie Laure Paillère

AU - Artiges, Eric

AU - Nees, Frauke

AU - Orfanos, Dimitri Papadopoulos

AU - Paus, Tomáš

AU - Poustka, Luise

AU - Holz, Nathalie

AU - Baeuchl, Christian

AU - Smolka, Michael N.

AU - Vaidya, Nilakshi

AU - Whelan, Robert

AU - Frouin, Vincent

AU - Schumann, Gunter

AU - Walter, Henrik

AU - Blanken, Tessa F.

AU - IMAGEN Consortium

PY - 2024/8/15

Y1 - 2024/8/15

N2 - Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = −0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = −0.10), and mOFC was negatively associated with anhedonia (partial cor. = −0.05). Limitations: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

AB - Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = −0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = −0.10), and mOFC was negatively associated with anhedonia (partial cor. = −0.05). Limitations: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

KW - Depression symptoms

KW - Heterogeneity

KW - Network analysis

KW - Neural markers

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Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

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