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

• Published in: Journal of psychiatry & neuroscience 2014-03, Vol.39 (2), p.78-86
• Main Authors: Qiu, Lihua, MD, Huang, Xiaoqi, MD, PhD, Zhang, Junran, PhD, Wang, Yuqing, PhD, Yang, Xun, MD, Lui, Su, MD, PhD, Gong, Qiyong, MD, PhD, Kuang, Weihong, MD, Li, Jing, MD, Wang, Xiuli, MD, Wang, Lijuan, MD, Mechelli, Andrea, PhD
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Adult and adolescent clinical studies; Autism; Biological and medical sciences; Cerebral Cortex - pathology; Classification; Depression; Depressive Disorder, Major - pathology; Diagnosis; Female; Functional Laterality; Fundamental and applied biological sciences. Psychology; Humans; Identification and classification; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Major depressive disorder; Male; Medical Education; Medical imaging; Medical sciences; Mental disorders; Mood disorders; Organ Size; Psychiatry; Psychoanalysis; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychopathology. Psychiatry; Research Papers; Studies; Support Vector Machine
• ISSN: 1180-4882; 1488-2434
• DOI: 10.1503/jpn.130034

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.