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Original Investigation |

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data

Ronny Redlich, PhD1; Nils Opel, MD1; Dominik Grotegerd, MSc1; Katharina Dohm, MSc1; Dario Zaremba, MSc1; Christian Bürger, MSc1; Sandra Münker1; Lisa Mühlmann, MD1; Patricia Wahl, MSc1; Walter Heindel, MD2; Volker Arolt, MD, PhD1; Judith Alferink, MD1,3; Peter Zwanzger, MD, PhD1,4; Maxim Zavorotnyy, MD5; Harald Kugel, PhD2; Udo Dannlowski, MD, PhD1,5
[+] Author Affiliations
1Department of Psychiatry, University of Muenster, Muenster, Germany
2Department of Clinical Radiology, University of Muenster, Muenster, Germany
3Cells-in-Motion Cluster of Excellence, University of Muenster, Muenster, Germany
4Department of Psychiatry, Inn-Salzach Hospital, Wasserburg am Inn, Germany
5Department of Psychiatry, University of Marburg, Marburg, Germany
JAMA Psychiatry. 2016;73(6):557-564. doi:10.1001/jamapsychiatry.2016.0316.
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Importance  Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified.

Objective  To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response.

Design, Setting, and Participants  In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21).

Main Outcomes and Measures  Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes.

Results  One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = −18; Z score, 4.00; P < .001; peak voxel r = 0.73). Furthermore, the analysis of treatment effects revealed a increase in hippocampal volume in the ECT sample (MNI coordinates x = −28, y = −9, z = −18; Z score, 7.81; P < .001) that was missing in the medication-only sample.

Conclusions and Relevance  A relatively small degree of structural impairment in the subgenual cingulate cortex before therapy seems to be associated with successful treatment with ECT. In the future, neuroimaging techniques could prove to be promising tools for predicting the individual therapeutic effectiveness of ECT.

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Figure 1.
Results of Support Vector Regression

For the sample of 23 patients undergoing electroconvulsive therapy, a positive association was found between predicted and true individual percentage of change in the Hamilton Depression Rating Scale (HDRS) score. Diagonal line indicates the association between predicted and true individual percentage of change in the HDRS score.

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Figure 2.
Association of Pretreatment Subgenual Anterior Cingulate Gray Matter Volume (GMV) and Symptom Relief

A, Sagittal view depicts the positive association between the percentage of change in the Hamilton Depression Rating Scale (HDRS) score and subgenual anterior cingulate volume before electroconvulsive therapy (ECT) (peak voxel r = 0.73). Statistics were corrected for the entire brain volume (P < .001; k = 403), yielding only 1 significant cluster mapping to this area. B, Scatterplot depicts the positive correlation of the subgenual cingulate gyrus GMVs and the percentage of symptom improvement as measured by HDRS scores (ECT response). Contour lines indicate individual 95% CIs; central diagonal line indicates the association between subgenual anterior cingulate volume before ECT and percentage of change in the HDRS score.

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Figure 3.
Longitudinal Effects of Electroconvulsive Therapy (ECT) on Whole-Brain Gray Matter Volume (GMV)

The sagittal and coronal sections (coordinates according to Montreal Neurological Institute space) feature the GMV increases in the ECT group mapping predominantly to the hippocampal formation. A corrected false-positive detection rate of P < .05 using a voxel threshold of P < .001 with an empirically determined cluster extent threshold (k), determined by Monte Carlo simulations (yielding k = 403 voxels), was used.

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