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J Psychiatry Neurosci. 2014 Mar;39(2):78-86.

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

Author information

1
The Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, and Department of Radiology, The Second People's Hospital of Yibin, Yibin, China.
2
The Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
3
The Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, and the department of Psychiatry, State Key Lab of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
4
The Department of Psychiatry, State Key Lab of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
5
The Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK.

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 first-episode, medication-naive patients.

CONCLUSION:

Our investigation demonstrates that multiple cortical features are affected in medication-naive 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.

PMID:
24083459
PMCID:
PMC3937284
[Indexed for MEDLINE]
Free PMC Article
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