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Neuropsychopharmacology. 2018 Apr;43(5):1078-1087. doi: 10.1038/npp.2017.165. Epub 2017 Jul 31.

SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets.

Jiang R1,2, Abbott CC3, Jiang T1,2,4, Du Y5,6, Espinoza R7, Narr KL7,8, Wade B8, Yu Q5, Song M1, Lin D5, Chen J5, Jones T3, Argyelan M9,10,11, Petrides G9,10,11, Sui J1,2,4,5, Calhoun VD3,5,12.

Author information

1
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
2
University of Chinese Academy of Sciences, Beijing, China.
3
Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA.
4
Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
5
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA.
6
School of Computer and Information Technology, Shanxi University, Taiyuan, China.
7
Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA.
8
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California at Los Angeles, Los Angeles, CA, USA.
9
Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA.
10
Division of Psychiatry Research, Zucker Hillside Hospital, Northwell System, Glen Oaks, NY, USA.
11
Departments of Psychiatry and Molecular Medicine, Hofstra Northwell School of Medicine, Hempstead, NY, USA.
12
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA.

Abstract

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.

PMID:
28758644
PMCID:
PMC5854791
[Available on 2019-04-01]
DOI:
10.1038/npp.2017.165
[Indexed for MEDLINE]

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