title: Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans.
abstract: BACKGROUND
  Previous studies have found numerous brain changes in patients with major depressive disorder (MDD), but no neurological biomarker has been developed to diagnose depression or to predict responses to antidepressants. In the present study, we used multivariate pattern analysis (MVPA) to classify MDD patients with different therapeutic responses and healthy controls and to explore the diagnostic and prognostic value of structural neuroimaging data of MDD.
  
  
  RESULTS
  Eighteen patients with treatment-resistant depression (TRD), 17 patients with treatment-sensitive depression (TSD) and 17 matched healthy controls were scanned using structural MRI. Voxel-based morphometry, together with a modified MVPA technique which combined searchlight algorithm and principal component analysis (PCA), was used to classify the subjects with TRD, those with TSD and healthy controls. The results revealed that both gray matter (GM) and white matter (WM) of frontal, temporal, parietal and occipital brain regions as well as cerebellum structures had a high classification power in patients with MDD. The accuracy of the GM and WM that correctly discriminated TRD patients from TSD patients was both 82.9%. Meanwhile, the accuracy of the GM that correctly discriminated TRD or TSD patients from healthy controls were 85.7% and 82.4%, respectively; and the WM that correctly discriminated TRD or TSD patients from healthy controls were 85.7% and 91.2%, respectively.
  
  
  CONCLUSIONS
  These results suggest that structural MRI with MVPA might be a useful and reliable method to study the neuroanatomical changes to differentiate patients with MDD from healthy controls and patients with TRD from those with TSD. This method might also be useful to study potential brain regions associated with treatment response in patients with MDD.
authors: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
authors: Feng
authors: F
authors: Liu
authors: Wenbin
authors: W
authors: Guo
authors: Dengmiao
authors: D
authors: Yu
authors: Qing
authors: Q
authors: Gao
authors: Keming
authors: K
authors: Gao
authors: Zhimin
authors: Z
authors: Xue
authors: Handan
authors: H
authors: Du
authors: Jianwei
authors: J
authors: Zhang
authors: Changlian
authors: C
authors: Tan
authors: Zhening
authors: Z
authors: Liu
authors: Jingping
authors: J
authors: Zhao
authors: Huafu
authors: H
authors: Chen
mesh_terms: D000328:Adult; D000465:Algorithms; D000928:Antidepressive Agents / Q000627:therapeutic use*; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D003865:Major Depressive Disorder / Q000473:pathology* / Q000628:therapy*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D059906:Neuroimaging / Q000379:methods; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D011379:Prognosis; D015203:Reproducibility of Results; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents