Browsing by Author "McCurry, Katherine L."

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    Opponent Effects of Hyperarousal and Re-experiencing on Affective Habituation in Posttraumatic Stress Disorder
    McCurry, Katherine L.; Frueh, B. Christopher; Chiu, Pearl H.; Casas, Brooks (2020-02)
    BACKGROUND: Aberrant emotion processing is a hallmark of posttraumatic stress disorder (PTSD), with neurobiological models suggesting both heightened neural reactivity and diminished habituation to aversive stimuli. However, empirical work suggests that these response patterns may be specific to subsets of those with PTSD. This study investigates the unique contributions of PTSD symptom clusters (re-experiencing, avoidance and numbing, and hyperarousal) to neural reactivity and habituation to negative stimuli in combat-exposed veterans. METHODS: Ninety-five combat-exposed veterans (46 with PTSD) and 53 community volunteers underwent functional magnetic resonance imaging while viewing emotional images. This study examined the relationship between symptom cluster severity and hemodynamic responses to negative compared with neutral images (NEG>NEU). RESULTS: Veterans exhibited comparable mean and habituation-related responses for NEG>NEU, relative to civilians. However, among veterans, habituation, but not mean response, was differentially related to PTSD symptom severity. Hyperarousal symptoms were related to decreased habituation for NEG>NEU in a network of regions, including superior and inferior frontal gyri, ventromedial prefrontal cortex, superior and middle temporal gyri, and anterior insula. In contrast, re-experiencing symptoms were associated with increased habituation in a similar network. Furthermore, re-experiencing severity was positively related to amygdalar functional connectivity with the left inferior frontal gyrus and dorsal anterior cingulate cortex for NEG>NEU. CONCLUSIONS: These results indicate that hyperarousal symptoms in combat-related PTSD are associated with decreased neural habituation to aversive stimuli. These impairments are partially mitigated in the presence of re-experiencing symptoms, such that during exposure to negative stimuli, re-experiencing symptoms are positively associated with amygdalar connectivity to prefrontal regions implicated in affective suppression.
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    Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy
    Brown, Vanessa M.; Zhu, Lusha; Solway, Alec; Wang, John M.; McCurry, Katherine L.; Casas, Brooks; Chiu, Pearl H. (American Medical Association, 2021-07-28)
    IMPORTANCE Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets. OBJECTIVE To determine associations among computational model–derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. DESIGN, SETTING, AND PARTICIPANTS In this mixed cross-sectional–cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021. MAIN OUTCOMES AND MEASURES Computational model–based analyses of participants’ choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT). RESULTS Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model–based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = −0.14; 95%credible interval [CrI], −0.12 to −0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t₉₇ = −2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = −0.11; 95% CrI, −0.20 to −0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = −0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77). CONCLUSIONS AND RELEVANCE In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.
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    Reinforcement learning processes as forecasters of depression remission
    Bansal, Vansh; McCurry, Katherine L.; Lisinski, Jonathan; Kim, Dong-Youl; Goyal, Shivani; Wang, John M.; Lee, Jacob; Brown, Vanessa M.; LaConte, Stephen M.; Casas, Brooks; Chiu, Pearl H. (Elsevier, 2024-09-11)
    Background: Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. Methods: We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. Results: Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. Limitations: Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). Conclusions: Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.
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    Valuation in major depression is intact and stable in a non-learning environment
    Chung, Dongil; Kadlec, Kelly; Aimone, Jason A.; McCurry, Katherine L.; Casas, Brooks; Chiu, Pearl H. (Nature, 2017-03-10)
    The clinical diagnosis and symptoms of major depressive disorder (MDD) have been closely associated with impairments in reward processing. In particular, various studies have shown blunted neural and behavioral responses to the experience of reward in depression. However, little is known about whether depression affects individuals’ valuation of potential rewards during decision-making, independent from reward experience. To address this question, we used a gambling task and a model-based analytic approach to measure two types of individual sensitivity to reward values in participants with MDD: ‘risk preference,’ indicating how objective values are subjectively perceived, and ‘inverse temperature,’ determining the degree to which subjective value differences between options influence participants’ choices. On both of these measures of value sensitivity, participants with MDD were comparable to nonpsychiatric controls. In addition, both risk preference and inverse temperature were stable over four laboratory visits and comparable between the groups at each visit. Neither valuation measure varied with severity of clinical symptoms in MDD. These data suggest intact and stable value processing in MDD during risky decision-making.