Altered neural processing of reward and punishment in adolescents with Major Depressive Disorder

2018 ◽  
Vol 232 ◽  
pp. 23-33 ◽  
Author(s):  
I. Landes ◽  
S. Bakos ◽  
G. Kohls ◽  
J. Bartling ◽  
G. Schulte-Körne ◽  
...  
2020 ◽  
Vol 129 (8) ◽  
pp. 810-823
Author(s):  
Dahlia Mukherjee ◽  
Alexandre L. S. Filipowicz ◽  
Khoi Vo ◽  
Theodore D. Satterthwaite ◽  
Joseph W. Kable

2018 ◽  
Vol 49 (10) ◽  
pp. 1629-1638 ◽  
Author(s):  
Timothy A. Allen ◽  
Raymond W. Lam ◽  
Roumen Milev ◽  
Sakina J. Rizvi ◽  
Benicio N. Frey ◽  
...  

AbstractBackgroundIn an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD.MethodsParticipants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later.ResultsIndividuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment.ConclusionsFindings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.


2012 ◽  
Vol 169 (2) ◽  
pp. 152-159 ◽  
Author(s):  
Oliver J. Robinson ◽  
Roshan Cools ◽  
Christina O. Carlisi ◽  
Barbara J. Sahakian ◽  
Wayne C. Drevets

2020 ◽  
Author(s):  
Dahlia Mukherjee ◽  
Alexandre Leo Stephen Filipowicz ◽  
Khoi D. Vo ◽  
Theodore Sattherwaite ◽  
Joe Kable

Depression has been associated with impaired reward and punishment processing, but the specific nature of these deficits is less understood and still widely debated. We analyzed reinforcement-based decision-making in individuals diagnosed with major depressive disorder (MDD) to identify the specific decision mechanisms contributing to poorer performance. Individuals with MDD (n = 64) and matched healthy controls (n = 64) performed a probabilistic reversal learning task in which they used feedback to identify which of two stimuli had the highest probability of reward (reward condition) or lowest probability of punishment (punishment condition). Learning differences were characterized using a hierarchical Bayesian reinforcement learning model. While both groups showed reinforcement learning-like behavior, depressed individuals made fewer optimal choices and adjusted more slowly to reversals in both the reward and punishment conditions. Our computational modeling analysis found that depressed individuals showed lower learning rates and, to a lesser extent, lower value sensitivity in both the reward and punishment conditions. Learning rates also predicted depression more accurately than simple performance metrics. These results demonstrate that depression is characterized by a hyposensitivity to positive outcomes, which influences the rate at which depressed individuals learn from feedback, but not a hypersensitivity to negative outcomes as has previously been suggested. Additionally, we demonstrate that computational modeling provides a more precise characterization of the dynamics contributing to these learning deficits, and offers stronger insights into the mechanistic processes affected by depression.


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