Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder

Bo Cao, Qinghua Luo, Yixiao Fu, Lian Du, Tian Qiu, Xiangying Yang, Xiaolu Chen, Qibin Chen, Jair C Soares, Raymond Y Cho, Xiang Yang Zhang, Haitang Qiu, Bo Cao, Qinghua Luo, Yixiao Fu, Lian Du, Tian Qiu, Xiangying Yang, Xiaolu Chen, Qibin Chen, Jair C Soares, Raymond Y Cho, Xiang Yang Zhang, Haitang Qiu

Abstract

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a granule cell layer (GCL), a molecular layer (ML), and the subiculum. It is unclear which subfields are affected by ECT and whether we predict the future treatment response to ECT by using volumetric information of hippocampal subfields at baseline? In this study, 24 patients with severe MDD received the ECT and their structural brain images were acquired with magnetic resonance imaging before and after ECT. A state-of-the-art hippocampal segmentation algorithm from Freesurfer 6.0 was used. We found that ECT induced volume increases in CA subfields, GCL, ML and subiculum. We applied a machine learning algorithm to the hippocampal subfield volumes at baseline and were able to predict the change in depressive symptoms (r = 0.81; within remitters, r = 0.93). Receiver operating characteristic analysis also showed robust prediction of remission with an area under the curve of 0.90. Our findings provide evidence for particular hippocampal subfields having specific roles in the response to ECT. We also provide an analytic approach for generating predictions about clinical outcomes for ECT in MDD.

Conflict of interest statement

Bo Cao, Qinghua Luo, Yixiao Fu, Lian Du, Tian Qiu, Xiangying Yang, Xiaolu Chen, Qibin Chen, Raymond Cho, Xiang Yang Zhang, and Haitang Qiu reported no biomedical financial interests or potential conflicts of interest. Jair C. Soares has received grants/research support from Forrest, BMS, J&J, Merck, Stanley Medical Research Institute, NIH and has been a speaker for Pfizer and Abbott.

Figures

Figure 1
Figure 1
Hippocampal subfield segmentation sample of a patient with major depression disorder. CA, cornu ammonis.
Figure 2
Figure 2
Baseline hippocampal subfield volumes of healthy controls, remitters and non-remitters of ECT. MDD, major depressive disorder; CA, cornu ammonis; GCL, granule cell layer; ML, molecular layer; Presub, presubiculum; Sub, subiculum and Tail, hippocampal tail. L, left; R, right.
Figure 3
Figure 3
The volume increase of hippocampal subfields in remitters and non-remitters of ECT. The asterisks show significant increases in left GCL and right CA3, CA4, GCL, ML and Sub in remitters of ECT with the Bonferroni correction. MDD, major depressive disorder; CA, cornu ammonis; GCL, granule cell layer; ML, molecular layer; Presub, presubiculum; Sub, subiculum and Tail, hippocampal tail. L, left; R, right.
Figure 4
Figure 4
Predicting treatment response to ECT with the volumes of hippocampal subfields and a machine learning algorithm, SVR. (A) The prediction of the HAM-D changes that indicates the response to ECT was highly accurate at the individual level (r = 0.81). (B) The ROC curve of predicting remitters based on the predicted HAM-D changes. The area under the curve is 0.90.

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