Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

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J Psychiatry Neurosci 2013; 39(2): 78-86

Lihua Qiu, MD;* Xiaoqi Huang, MD, PhD;* Junran Zhang, PhD; Yuqing Wang, PhD; Weihong Kuang, MD; Jing Li, MD; Xiuli Wang, MD; Lijuan Wang, MD; Xun Yang, MD; Su Lui, MD, PhD; Andrea Mechelli, PhD; Qiyong Gong, MD, PhD

Qiu, Huang, Zhang, Y. Wang, X. Yang, Lui, Gong — Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Kuang, Li, X. Wang, L. Wang — Department of Psychiatry, State Key Lab of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Qiu — Department of Radiology, The Second People’s Hospital of Yibin, Yibin, China; Mechelli — Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, UK

*L. Qiu and X. Huang contributed equally to the work.

Abstract

Background: Major depressive disorder (MDD) is one of the most disabling mental illnesses. Previous neuroanatomical studies of MDD have revealed regional alterations in grey matter volume and density. However, owing to the heterogeneous symptomatology and complex etiology, MDD is likely to be associated with multiple morphometric alterations in brain structure. We sought to distinguish first-episode, medication-naive, adult patients with MDD from healthy controls and characterize neuroanatomical differences between the groups using a multiparameter classification approach.

Methods: We recruited medication-naive patients with first-episode depression and healthy controls matched for age, sex, handedness and years of education. High-resolution T1-weighted images were used to extract 7 morphometric parameters, including both volumetric and geometric features, based on the surface data of the entire cerebral cortex. These parameters were used to compare patients and controls using multivariate support vector machine, and the regions that informed the discrimination between the 2 groups were identified based on maximal classification weights.

Results: Thirty-two patients and 32 controls participated in the study. Both volumetric and geometric parameters could discriminate patients with MDD from healthy controls, with cortical thickness in the right hemisphere providing the greatest accuracy (78%, p ≤ 0.001). This discrimination was informed by a bilateral network comprising mainly frontal, temporal and parietal regions.

Limitations: The sample size was relatively small and our results were based on firstepisode, medication-naive patients.

Conclusion: Our investigation demonstrates that multiple cortical features are affected in medicationnaive patients with first-episode MDD. These findings extend the current understanding of the neuropathological underpinnings of MDD and provide preliminary support for the use of neuroanatomical scans in the early detection of MDD.


Submitted Feb. 22, 2013; Revised May 25, 2013; Accepted June 7, 2013.

Acknowledgments: This study was supported by the National Natural Science Foundation (Grant Nos. 81030027, 81171488, 81227002 and 81220108013), National Key Technologies R&D Program of China (Program No. 2012BAI01B03) and Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of China. We acknowledge the support of the CMB Distinguished Professorship Award (No. F510000/ G16916411) for Q. Gong, who is an Adjunct Professor in the Department of Radiology at the University of Illinois Hospital & Health Sciences System.

Competing interests: None declared.

Contributors: L. Qiu, X. Huang, A Mechelli and Q. Gong designed the study and wrote the article. L. Qiu, X. Huang, X. Yang, X. Wang and L. Wang acquired the data, which L. Qiu, X. Huang, J. Zhang, Y.Wang, X. Yang, W. Kuang, J. Li and S. Lui analyzed. All authors reviewed the article and approved its publication.

DOI: 10.1503/jpn.130034

Correspondence to: Q. Gong, Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; qiyonggong@hmrrc.org.cn