Assessing and remediating altered reinforcement learning in depression
| dc.contributor.author | Brown, Vanessa | en |
| dc.contributor.committeechair | Chiu, Pearl H. | en |
| dc.contributor.committeemember | Casas, Brooks | en |
| dc.contributor.committeemember | Ollendick, Thomas H. | en |
| dc.contributor.committeemember | Richey, John A. | en |
| dc.contributor.department | Psychology | en |
| dc.date.accessioned | 2019-12-29T07:00:26Z | en |
| dc.date.available | 2019-12-29T07:00:26Z | en |
| dc.date.issued | 2018-07-06 | en |
| dc.description.abstract | Major depressive disorder is a common, impairing disease, but current treatments are only moderately effective. Understanding how processes such as reward and punishment learning are disrupted in depression and how these disruptions are remediated through treatment is vital to improving outcomes for people with this disorder. In the present set of studies, computational reinforcement learning models and neuroimaging were used to understand how symptom clusters of depression (anhedonia and negative affect) were related to neural and behavioral measures of learning (Study 1, in Paper 1), how these alterations changed with improvement in symptoms after cognitive behavioral therapy (Study 2, in Paper 1), and how learning parameters could be directly altered in a learning retraining paradigm (Study 3, in Paper 2). Results showed that anhedonia and negative affect were uniquely related to changes in learning and that improvement in these symptoms correlated with changes in learning parameters; these parameters could also be changed through targeted queries based on reinforcement learning theory. These findings add important information to how learning is disrupted in depression and how current and novel treatments can remediate learning and improve symptoms. | en |
| dc.description.abstractgeneral | Major depression is very common and current treatments are sometimes helpful and sometimes not. In order to create more effective treatments, we need to better understand what exactly goes wrong when people are depressed. The present set of studies uses computational modeling and imaging of brain function to gain a clearer understanding of how people with depression learn from rewarding and punishing events differently, how these differences in learning improve with symptom improvement after receiving treatment for depression, and how learning differences can be directly targeted by teaching people to learn differently. I found that a reduced ability to experience pleasure, or anhedonia, in depression was related to differences learning from good outcomes while low mood was related to perceiving bad outcomes as worse. Both of these differences improved with successful treatment, and asking people questions related to learning also changed the way people learned in a way that may be useful for improving treatments. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.other | vt_gsexam:14404 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/96227 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Depression | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | fMRI | en |
| dc.subject | Cognitive Behavioral Therapy | en |
| dc.subject | Computational Modeling | en |
| dc.subject | Computational Psychiatry | en |
| dc.title | Assessing and remediating altered reinforcement learning in depression | en |
| dc.type | Dissertation | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Psychology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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