Dataset Information

"Engage" Therapy: Behavioral Activation and Improvement of Late-Life Major Depression.

ABSTRACT: Engage is a treatment for late-life depression developed to match the skills of community clinicians based on the theory that dysfunction in the Research Domain Criteria Project positive valence systems is a critical mechanism of late-life depression. Accordingly, it uses "reward exposure" (engagement in meaningful, rewarding activities) as its principal intervention. This study tests the hypothesis that change in behavioral activation, an index of positive valence systems function, during successive treatment periods with Engage and during follow-up predicts depression at the end of each period.Forty-eight nondemented, older adults with unipolar major depression were treated openly with 9 weekly sessions of Engage and assessed 36 weeks after entry. Depression severity was assessed with the 24-item Hamilton Depression Rating Scale (HAM-D) and behavioral activation with the Behavioral Activation for Depression Scale (BADS) at baseline, 6 weeks (mid-treatment), 9 weeks (end of treatment), and 36 weeks.A mixed-effects model examined whether change in BADS in successive periods occurring during Engage treatment and during follow-up predicts depression at the end of each period. Both BADS change (F1,52 = 18.63, p < 0.0001) and time (F2,52 = 7.68, p = 0.0012) predicted HAM-D scores at the end of each observation period. Every point of increase in BADS change reduced the HAM-D by 0.105 points. HAM-D at each point did not predict subsequent change in BADS (F1,52 = 2.17, p = 0.146).During Engage treatment and follow-up, change in behavioral activation is followed by improvement of depressive symptoms and signs.

SUBMITTER: Alexopoulos GS

PROVIDER: S-EPMC4818673 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

"Engage" Therapy: Behavioral Activation and Improvement of Late-Life Major Depression.

Alexopoulos George S GS Raue Patrick J PJ Gunning Faith F Kiosses Dimitris N DN Kanellopoulos Dora D Pollari Cristina C Banerjee Samprit S Arean Patricia A PA

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 20151217 4

<h4>Objective</h4>Engage is a treatment for late-life depression developed to match the skills of community clinicians based on the theory that dysfunction in the Research Domain Criteria Project positive valence systems is a critical mechanism of late-life depression. Accordingly, it uses "reward exposure" (engagement in meaningful, rewarding activities) as its principal intervention. This study tests the hypothesis that change in behavioral activation, an index of positive valence systems func ...[more]

PMID: 26905044

Similar Datasets

Project description:Pharmacological treatment of major depressive disorder (MDD) typically involves a lengthy trial and error process to identify an effective intervention. This lengthy period prolongs suffering and worsens all-cause mortality, including from suicide, and is typically longer in late-life depression (LLD). Our group has recently demonstrated that during an open-label venlafaxine (serotonin-norepinephrine reuptake inhibitor) trial, significant changes in functional resting state connectivity occurred following a single dose of treatment, which persisted until the end of the trial. In this work, we propose an analysis framework to translate these perturbations in functional networks into predictors of clinical remission. Participants with LLD (N?=?49) completed 12-weeks of treatment with venlafaxine and underwent functional magnetic resonance imaging (fMRI) at baseline and a day following a single dose of venlafaxine. Data was collected at rest as well as during an emotion reactivity task and an emotion regulation task. Remission was defined as a Montgomery-Asberg Depression Rating Scale (MADRS) ?10 for two weeks. We computed eigenvector centrality (whole brain connectivity) and activation during the emotion regulation and emotion reactivity tasks. We employed principal components analysis, Tikhonov-regularized logistic classification, and least angle regression feature selection to predict remission by the end of the 12-week trial. We utilized ten-fold cross-validation and Receiver Operator Curves (ROC) curve analysis. To determine task-region pairs that significantly contributed to the algorithm's ability to predict remission, we used permutation testing. Using the fMRI data at both baseline and after the first dose of treatment yielded a sensitivity of 72% and a specificity of 68% (AUC?=?0.77), a 15% increase in accuracy over baseline MADRS. In general, the accuracy at baseline was further improved by using the change in activation following a single dose. Activation of the frontal cortex, hippocampus, parahippocampus, caudate, thalamus, medial temporal cortex, middle cingulate, and visual cortex predicted treatment remission. Acute, dynamic trajectories of functional imaging metrics in response to a pharmacological intervention are a valuable tool for predicting treatment response in late-life depression and elucidating the mechanism of pharmacological therapies in the context of the brain's functional architecture.

Dataset Information

"Engage" Therapy: Behavioral Activation and Improvement of Late-Life Major Depression.

Publications

"Engage" Therapy: Behavioral Activation and Improvement of Late-Life Major Depression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets