TY - JOUR
T1 - Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy
T2 - A multisite study
AU - Wade, Benjamin S.C.
AU - Sui, Jing
AU - Hellemann, Gerhard
AU - Leaver, Amber M.
AU - Espinoza, Randall T.
AU - Woods, Roger P.
AU - Abbott, Christopher C.
AU - Joshi, Shantanu H.
AU - Narr, Katherine L.
N1 - Funding Information:
This work is supported in part by the National Institute of Mental Health (MH092301, MH110008 and MH102743 to UCLA investigators) and the National High Tech Program (863, No. 2015AA020513 to SJ).
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches.
AB - Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches.
UR - http://www.scopus.com/inward/record.url?scp=85038075398&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038075398&partnerID=8YFLogxK
U2 - 10.1038/s41398-017-0020-7
DO - 10.1038/s41398-017-0020-7
M3 - Article
C2 - 29217832
AN - SCOPUS:85038075398
VL - 7
JO - Translational Psychiatry
JF - Translational Psychiatry
SN - 2158-3188
IS - 12
M1 - 1270
ER -
