Computational meta-analysis of statistical parametric maps in major depression

Danilo Arnone, Dominic Job, Sudhakar Selvaraj, Osamu Abe, Francesco Amico, Yuqi Cheng, Sean J. Colloby, John T. O'Brien, Thomas Frodl, Ian H. Gotlib, Byung-Joo Ham, M. Justin Kim, P. Cédric Mp Koolschijn, Cintia A M Périco, Giacomo Salvadore, Alan J. Thomas, Marie José Van Tol, Nic J A van der Wee, Dick J. Veltman, Gerd WagnerAndrew M. Mcintosh

Research output: Contribution to journalArticle

56 Citations (Scopus)

Abstract

Objective: Several neuroimaging meta-analyses have summarized structural brain changes in major depression using coordinate-based methods. These methods might be biased toward brain regions where significant differences were found in the original studies. In this study, a novel voxel-based technique is implemented that estimates and meta-analyses between-group differences in grey matter from individual MRI studies, which are then applied to the study of major depression. Methods: A systematic review and meta-analysis of voxel-based morphometry studies were conducted comparing participants with major depression and healthy controls by using statistical parametric maps. Summary effect sizes were computed correcting for multiple comparisons at the voxel level. Publication bias and heterogeneity were also estimated and the excess of heterogeneity was investigated with metaregression analyses. Results: Patients with major depression were characterized by diffuse bilateral grey matter loss in ventrolateral and ventromedial frontal systems extending into temporal gyri compared to healthy controls. Grey matter reduction was also detected in the right parahippocampal and fusiform gyri, hippocampus, and bilateral thalamus. Other areas included parietal lobes and cerebellum. There was no evidence of statistically significant publication bias or heterogeneity. Conclusions: The novel computational meta-analytic approach used in this study identified extensive grey matter loss in key brain regions implicated in emotion generation and regulation. Results are not biased toward the findings of the original studies because they include all available imaging data, irrespective of statistically significant regions, resulting in enhanced detection of additional areas of grey matter loss.

Original languageEnglish
Pages (from-to)1393-1404
Number of pages12
JournalHuman Brain Mapping
Volume37
Issue number4
DOIs
Publication statusPublished - 2016 Apr 1

Keywords

  • Affective disorders
  • Depression
  • Magnetic resonance imaging
  • Meta-analysis

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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  • Cite this

    Arnone, D., Job, D., Selvaraj, S., Abe, O., Amico, F., Cheng, Y., Colloby, S. J., O'Brien, J. T., Frodl, T., Gotlib, I. H., Ham, B-J., Kim, M. J., Koolschijn, P. C. M., Périco, C. A. M., Salvadore, G., Thomas, A. J., Van Tol, M. J., van der Wee, N. J. A., Veltman, D. J., ... Mcintosh, A. M. (2016). Computational meta-analysis of statistical parametric maps in major depression. Human Brain Mapping, 37(4), 1393-1404. https://doi.org/10.1002/hbm.23108