Fully Automated Habenula Segmentation Provides Robust and Reliable Volume Estimation Across Large Magnetic Resonance Imaging Datasets, Suggesting Intriguing Developmental Trajectories in Psychiatric Disease

Jürgen Germann, Flavia Venetucci Gouveia, Raquel C R Martinez, Marcus Vinicius Zanetti, Fábio Luís de Souza Duran, Tiffany M Chaim-Avancini, Mauricio H Serpa, M Mallar Chakravarty, Gabriel A Devenyi

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Studies of habenula (Hb) function and structure provided evidence of its involvement in psychiatric disorders, including schizophrenia and bipolar disorder. Previous studies using magnetic resonance imaging (manual/semiautomated segmentation) have reported conflicting results. Aiming to improve Hb segmentation reliability and the study of large datasets, we describe a fully automated protocol that was validated against manual segmentations and applied to 3 datasets (childhood/adolescence and adult bipolar disorder and schizophrenia). It achieved reliable Hb segmentation, providing robust volume estimations across a large age range and varying image acquisition parameters. Applying it to clinically relevant datasets, we found smaller Hb volumes in the adult bipolar disorder dataset and larger volumes in the adult schizophrenia dataset compared with healthy control subjects. There are indications that Hb volume in both groups shows deviating developmental trajectories early in life. This technique sets a precedent for future studies, as it allows for fast and reliable Hb segmentation and will be publicly available.

Original languageEnglish
Pages (from-to)923-929
Number of pages7
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume5
Issue number9
DOIs
Publication statusPublished - Sept 2020

Keywords

  • Automatic segmentation
  • Bipolar disorder
  • Habenula
  • MAGeTbrain
  • Schizophrenia
  • Volume

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