Browsing by Author "Brown, Vanessa"
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- Altered Neural and Behavioral Associability-Based Learning in Posttraumatic Stress DisorderBrown, Vanessa (Virginia Tech, 2015-02-26)Posttraumatic stress disorder (PTSD) is accompanied by marked alterations in cognition and behavior, particularly when negative, high-value information is present (Aupperle, Melrose, Stein, & Paulus, 2012; Hayes, Vanelzakker, & Shin, 2012) . However, the underlying processes are unclear; such alterations could result from differences in how this high value information is updated or in its effects on processing future information. To untangle the effects of different aspects of behavior, we used a computational psychiatry approach to disambiguate the roles of increased learning from previously surprising outcomes (i.e. associability; Li, Schiller, Schoenbaum, Phelps, & Daw, 2011) and from large value differences (i.e. prediction error; Montague, 1996; Schultz, Dayan, & Montague, 1997) in PTSD. Combat-deployed military veterans with varying levels of PTSD symptoms completed a learning task while undergoing fMRI; behavioral choices and neural activation were modeled using reinforcement learning. We found that associability-based loss learning at a neural and behavioral level increased with PTSD severity, particularly with hyperarousal symptoms, and that the interaction of PTSD severity and neural markers of associability based learning predicted behavior. In contrast, PTSD severity did not modulate prediction error neural signal or behavioral learning rate. These results suggest that increased associability-based learning underlies neurobehavioral alterations in PTSD.
- Assessing and remediating altered reinforcement learning in depressionBrown, Vanessa (Virginia Tech, 2018-07-06)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.