TY - JOUR
T1 - SMRI biomarkers predict electroconvulsive treatment outcomes
T2 - Accuracy with Independent Data Sets
AU - Jiang, Rongtao
AU - Abbott, Christopher C.
AU - Jiang, Tianzi
AU - Du, Yuhui
AU - Espinoza, Randall
AU - Narr, Katherine L.
AU - Wade, Benjamin
AU - Yu, Qingbao
AU - Song, Ming
AU - Lin, Dongdong
AU - Chen, Jiayu
AU - Jones, Thomas
AU - Argyelan, Miklos
AU - Petrides, Georgios
AU - Sui, Jing
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was supported in part by the National High Tech Program (863, number 2015AA020513) and China National Natural Science Foundation (number 81471367), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDB02060005), Natural Science Foundation of Shanxi Province (grant number 2016021077), and National Institute of Health (1R01EB005846, 1R01MH094524, and P20GM103472).
Funding Information:
1Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 2University of Chinese Academy of Sciences, Beijing, China; 3Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA; 4Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China; 5The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA; 6School of Computer and Information Technology, Shanxi University, Taiyuan, China; 7Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA; 8Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California at Los Angeles, Los Angeles, CA, USA; 9Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA; 10Division of Psychiatry Research, Zucker Hillside Hospital, Northwell System, Glen Oaks, NY, USA; 11Departments of Psychiatry and Molecular Medicine, Hofstra Northwell School of Medicine, Hempstead, NY, USA; 12Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
Publisher Copyright:
© 2018 American College of Neuropsychopharmacology.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.
AB - Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.
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U2 - 10.1038/npp.2017.165
DO - 10.1038/npp.2017.165
M3 - Article
C2 - 28758644
AN - SCOPUS:85044375920
SN - 0893-133X
VL - 43
SP - 1078
EP - 1087
JO - Neuropsychopharmacology
JF - Neuropsychopharmacology
IS - 5
ER -