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Pré-Publication, Document De Travail Année : 2023

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications

René Freichel
Agatha Lenartowicz
  • Fonction : Auteur
Linda Douw
  • Fonction : Auteur
Johann Kruschwitz
  • Fonction : Auteur
Tobias Banaschewski
  • Fonction : Auteur
Gareth Barker
  • Fonction : Auteur
Arun Bokde
  • Fonction : Auteur
Sylvane Desrivières
  • Fonction : Auteur
Herta Flor
  • Fonction : Auteur
Antoine Grigis
  • Fonction : Auteur
Hugh Garavan
  • Fonction : Auteur
Andreas Heinz
  • Fonction : Auteur
Rüdiger Brühl
  • Fonction : Auteur
Frauke Nees
  • Fonction : Auteur
Dimitri Papadopoulos Orfanos
  • Fonction : Auteur
Tomáš Paus
  • Fonction : Auteur
Luise Poustka
  • Fonction : Auteur
Nathalie Holz
  • Fonction : Auteur
Christian Baeuchl
  • Fonction : Auteur
Michael Smolka
Nilakshi Vaidya
  • Fonction : Auteur
Robert Whelan
  • Fonction : Auteur
Vincent Frouin
  • Fonction : Auteur
Gunter Schumann
  • Fonction : Auteur
Henrik Walter
  • Fonction : Auteur
Tessa Blanken
  • Fonction : Auteur
Arun L.W. Bokde
  • Fonction : Auteur

Résumé

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 networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Methods We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). 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 symptom/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 symptom scores revealed associations between cortical thickness measures and specific symptoms, 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 included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms. Conclusions This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD’s phenotypic heterogeneity.

Dates et versions

hal-04471117 , version 1 (21-02-2024)

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René Freichel, Agatha Lenartowicz, Linda Douw, Johann Kruschwitz, Tobias Banaschewski, et al.. Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications. 2024. ⟨hal-04471117⟩
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