36
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge.

          Methods

          Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D 2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate.

          Results

          MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D 2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate.

          Conclusion

          Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior.

          Related collections

          Most cited references 64

          • Record: found
          • Abstract: found
          • Article: not found

          A rating scale for mania: reliability, validity and sensitivity.

          An eleven item clinician-administered Mania Rating Scale (MRS) is introduced, and its reliability, validity and sensitivity are examined. There was a high correlation between the scores of two independent clinicians on both the total score (0.93) and the individual item scores (0.66 to 0.92). The MRS score correlated highly with an independent global rating, and with scores of two other mania rating scales administered concurrently. The score also correlated with the number of days of subsequent stay in hospital. It was able to differentiate statistically patients before and after two weeks of treatment and to distinguish levels of severity based on the global rating.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A framework for mesencephalic dopamine systems based on predictive Hebbian learning.

            We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              New approaches to antidepressant drug discovery: beyond monoamines.

              All available antidepressant medications are based on serendipitous discoveries of the clinical efficacy of two classes of antidepressants more than 50 years ago. These tricyclic and monoamine oxidase inhibitor antidepressants were subsequently found to promote serotonin or noradrenaline function in the brain. Newer agents are more specific but have the same core mechanisms of action in promoting these monoamine neurotransmitters. This is unfortunate, because only approximately 50% of individuals with depression show full remission in response to these mechanisms. This review summarizes the obstacles that have hindered the development of non-monoamine-based antidepressants, and provides a progress report on some of the most promising current strategies.
                Bookmark

                Author and article information

                Journal
                Biol Mood Anxiety Disord
                Biol Mood Anxiety Disord
                Biology of Mood & Anxiety Disorders
                BioMed Central
                2045-5380
                2013
                19 June 2013
                : 3
                : 12
                Affiliations
                [1 ]Gatsby Computational Neuroscience Unit, UCL, London, UK
                [2 ]Wellcome Trust Centre for Neuroimaging, UCL, London, UK
                [3 ]Translational Neuromodeling Unit, University of Zurich and ETH Zurich, Switzerland
                [4 ]Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, Zurich, Switzerland
                [5 ]Department of Psychiatry, Harvard Medical School, MA, USA
                [6 ]Department of Psychology and Neuroscience, Duke University, NC, USA
                Article
                2045-5380-3-12
                10.1186/2045-5380-3-12
                3701611
                23782813
                Copyright ©2013 Huys et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research

                Comments

                Comment on this article