| Autor: |
Wade BSC; Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA.; Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA., Sui J; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA.; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China., Hellemann G; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA., Leaver AM; Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA., Espinoza RT; Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA., Woods RP; Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA., Abbott CC; Department of Psychiatry, University of New Mexico, Albuquerque, USA., Joshi SH; Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA., Narr KL; Department of Neurology, UCLA, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, USA. narr@ucla.edu.; Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA. narr@ucla.edu. |
| Abstrakt: |
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. |
