Distinguishing bipolar and major depressive disorders by brain structural morphometry: A pilot study

G. Fung, Y. Deng, Q. Zhao, Z. Li, M. Qu, K. Li, Y.-W. Zeng, Z. Jin, Y.-T. Ma, X. Yu, Z.-R. Wang, Ho Keung David Shum, R.C.K. Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)

Abstract

© 2015 Fung et al. Background: The clinical presentation of common symptoms during depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) poses challenges for accurate diagnosis. Disorder-specific neuroanatomical features may aid the development of reliable discrimination between these two clinical conditions. Methods: For our sample of 16 BD patients, 19 MDD patients and 29 healthy volunteers, we adopted vertex-wise cortical based brain imaging techniques to examine cortical thickness and surface area, two components of cortical volume with distinct genetic determinants. Based on specific characteristics of neuroanatomical features, we then used support vector machine (SVM) algorithm to discriminate between patients with BD and MDD. Results: Compared to MDD patients, BD patients showed significantly larger cortical surface area in the left bankssts, precuneus, precentral, inferior parietal, superior parietal and the right middle temporal gyri. In addition, larger volumes of subcortical regions were found in BD patients. In SVM discriminative analyses, the overall accuracy was 74.3 %, with a sensitivity of 62.5 % and a specificity of 84.2 % (p = 0.028). Compared to controls, larger surface area in the temporo-parietal regions were observed in BD patients, and thinner cortices in fronto-temporal regions were observed in MDD patients, especially in the medial orbito-frontal area. Conclusions: These findings have demonstrated distinct spatially distributed variations in cortical thickness and surface area in patients with BD and MDD, suggesting potentially varying etiological and neuropathological processes in these two conditions. The employment of multimodal classification on disorder-specific biological features has shed light to the development of potential classification tools that could aid diagnostic decisions.
Original languageEnglish
Article number298
JournalBMC Psychiatry
Volume15
Issue number1
DOIs
Publication statusPublished - 21 Nov 2015
Externally publishedYes

Keywords

  • Bipolar disorder
  • Cortical Thickness
  • Depression
  • Support vector machine
  • Surface area

ASJC Scopus subject areas

  • Psychiatry and Mental health

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