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Feng Liu; Wenbin Guo; Dengmiao Yu; Qing Gao; Keming Gao; Zhimin Xue; Handan Du; Jianwei Zhang; Changlian Tan; Zhening Liu; Jingping Zhao; Huafu Chen (2016). Classification of Different Therapeutic Responses of Major Depressive Disorder with Multivariate Pattern Analysis Method Based on Structural MR Scans [Dataset]. http://doi.org/10.1371/journal.pone.0040968
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Classification of Different Therapeutic Responses of Major Depressive Disorder with Multivariate Pattern Analysis Method Based on Structural MR Scans

34 scholarly articles cite this dataset (View in Google Scholar)
tiff, docAvailable download formats
Dataset updated
Jan 19, 2016
Dataset provided by
PLOShttp://plos.org/
Authors
Feng Liu; Wenbin Guo; Dengmiao Yu; Qing Gao; Keming Gao; Zhimin Xue; Handan Du; Jianwei Zhang; Changlian Tan; Zhening Liu; Jingping Zhao; Huafu Chen
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

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. Methodology/Principal Findings 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/Significance 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.

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