Details

Autor(en) / Beteiligte

• Cao, Longlong
• Guo, Shuixia
• Xue, Zhimin
• Hu, Yong
• Liu, Haihong
• Mwansisya, Tumbwene E.
• Pu, Weidan
• Yang, Bo
• Liu, Chang
• Feng, Jianfeng
• Chen, Eric Y. H.
• Liu, Zhening

Titel

Aberrant functional connectivity for diagnosis of major depressive disorder: A discriminant analysis

Ist Teil von

• Psychiatry and clinical neurosciences, 2014-02, Vol.68 (2), p.110-119

Ort / Verlag

Australia: Wiley Subscription Services, Inc

Erscheinungsjahr

2014

Link zum Volltext

Quelle

PBSC : Psychology and Behavioral Sciences Collection - Journals

Beschreibungen/Notizen

• Aim Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer‐assisted diagnosis. Methods Based on resting‐state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well‐matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated. Results After subset selection of high‐dimension features, the support vector machine classifier reachedup to approximately 84% with leave‐one‐out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule‐posterior cingulated gyrus module and middle temporal gyrus‐inferior temporal gyrus module. Conclusion The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD.