VTechWorks Repository :: Browsing by Author "Chiu, Pearl H."

Browsing by Author "Chiu, Pearl H."

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Altered Neural and Behavioral Associability-Based Learning in Posttraumatic Stress Disorder
Brown, 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 depression
Brown, 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.
Associability-modulated loss learning is increased in posttraumatic stress disorder
Brown, Vanessa M.; Zhu, Lusha; Wang, John M.; Frueh, B. Christopher; Casas, Brooks; Chiu, Pearl H. (eLife Sciences, 2018-01-09)
Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat- deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention- based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets.
Association between Reward Sensitivity and Smoking Status in Major Depressive Disorder
Feng, Shengchuang (Virginia Tech, 2017-05-10)
Chronic nicotine use has been linked to increased sensitivity to nondrug rewards as well as improvement in mood among individuals with depression, and these effects have been hypothesized to be mediated through alternations in striatal dopamine activity. Similarly, chronic nicotine use is hypothesized to influence the mechanisms by which healthy and depressed individuals learn about rewards in their environment. However, the specific behavioral and neural mechanisms by which nicotine influences the learning process is poorly understood. Here, we use a probabilistic learning task, functional magnetic resonance imaging and neurocomputational analyses, to show that chronic smoking is associated with higher reward sensitivity, along with lower learning rate and striatal prediction error signal. Further, we show that these effects do not differ between individuals with and without major depressive disorder (MDD). In addition, a negative correlation between reward sensitivity and striatal prediction error signal was found among smokers, consistent with the suggestion that enhanced tonic dopamine associated with increased reward sensitivity leads to an attenuation of phasic dopamine activity necessary for updating of reward value during learning.
The Behavioral and Neural Mechanisms of Social and Non-social Risky Decision-Making
Lauharatanahirun, Nina (Virginia Tech, 2013-05-06)
Decisions made under risk have been primarily studied within economic contexts (Platt & Huettel, 2008). This has led to the development of sound methods and models for studying risky choice behavior (Rangel, Camerer & Montague, 2008). In particular, these models are helpful for estimating how much risk an individual is willing to tolerate. However, there may be a limit in the extent to which we can generalize these estimations, in that economic models do not take into account the underlying social preferences that often guide decision makers (Fehr & Camerer, 2007; Fehr & Schmidt, 2004). This suggests that an individual's propensity for risk may be different depending on social or non-social information present within the environment (Bohnet, Greig, Herrmann & Zeckhauser, 2008). The present study aimed to: (i) assess how risk preferences may differ across social and non-social contexts; (ii) identify common and distinct neural correlates of social and non-social risk; and (iii) determine neural characteristics associated with individual sensitivities to social and non-social risk. Subjects (N=30) played an adaptation of the Trust Game while their blood-oxygen-level-dependent response was monitored using functional magnetic resonance imaging. Differences in risk preferences across social and non-social conditions as well as neuroimaging correlates of social and non-social risk will be discussed.
Behavioral and Neural Substrates of Decision-Making Under Perceptual and Reward Uncertainty: The Role of Task Structure
Ghane-Ezabadi, Merage (Virginia Tech, 2022-01-18)
Real world decision-making requires simultaneously determining what we are observing in our environment (perceptual decision-making; PDM) and what the stimuli and actions are worth (reward-based decision-making; RDM). There is evidence of a bi-directional relationship between reward and perceptual information in guiding choice, with some studies suggesting that individuals optimally combine the two. Uncertainty in both reward expectations and perception have been shown to alter choice behavior, however few studies have manipulated both variables simultaneously. Given the distinct theoretical and computational foundations of PDM and RDM, it has also been assumed that the underlying behavioral and neural substrates of perceptual and reward-based choice are separable. However, there is evidence that task structure and subjective value/uncertainty more generally contribute to activity in large-scale networks of the brain, rather than domain specific features (perceptual salience/reward). Variability in task structures and methods of manipulating and modeling sensory and reward uncertainty, make it hard to draw definitive conclusions across these perspectives with currently available data. The current study used behavioral and fMRI techniques to investigate the neurobehavioral substrates of decision-making under simultaneous perceptual and reward uncertainty in a sample of healthy adult volunteers. The primary objectives of this project were to test: a) how simultaneous manipulations in sensory and reward uncertainty influence choice, b) whether task structure alters the influence of sensory and reward information on choice behavior, and c) whether activity in underlying neural substrates reflect domain-specific or domain-general processes. Results showed that choices were best predicted by a combined model of perceptual salience and reward, with an overall bias towards perceptual salience information. Choice percentage was not impacted by task structure, however choices were better predicted by individual features (salience and reward) when they were manipulated stably, than dynamically. Activity in the brain showed greater overlap between dynamic task conditions when compared to both salience and reward conditions. There was also greater overlap between stable task conditions when compared to reward but not salience conditions. Preliminary evidence suggests that activity in decision-relevant regions of the brain varied by uncertainty and value rather than salience and reward per se.
Behavioral Training of Reward Learning Increases Reinforcement Learning Parameters and Decreases Depression Symptoms Across Repeated Sessions
Goyal, Shivani (Virginia Tech, 2023-12)
Background: Disrupted reward learning has been suggested to contribute to the etiology and maintenance of depression. If deficits in reward learning are core to depression, we would expect that improving reward learning would decrease depression symptoms across time. Whereas previous studies have shown that changing reward learning can be done in a single study session, effecting clinically meaningful change in learning requires this change to endure beyond task completion and transfer to real world environments. With a longitudinal design, we investigate the potential for repeated sessions of behavioral training to create change in reward learning and decrease depression symptoms across time. Methods: 929 online participants (497 depression-present; 432 depression-absent) recruited from Amazon’s Mechanical Turk platform completed a behavioral training paradigm and clinical selfreport measures for up to eight total study visits. Participants were randomly assigned to one of 12 arms of the behavioral training paradigm, in which they completed a probabilistic reward learning task interspersed with queries about a feature of the task environment (11 learning arms) or a control query (1 control arm). Learning queries trained participants on one of four computational-based learning targets known to affect reinforcement learning (probability, average or extreme outcome values, and value comparison processes). A reinforcement learning model previously shown to distinguish depression related differences in learning was fit to behavioral responses using hierarchical Bayesian estimation to provide estimates of reward sensitivity and learning rate for each participant on each visit. Reward sensitivity captured participants’ value dissociation between high versus low outcome values, while learning rate informed how much participants learned from previously experienced outcomes. Mixed linear models assessed relationships between model-agnostic task performance, computational model-derived reinforcement learning parameters, depression symptoms, and study progression. Results: Across time, learning queries increased individuals’ reward sensitivities in depression-absent participants (β = 0.036, p =< 0.001, 95% CI (0.022, 0.049)). In contrast, control queries did not change reward sensitivities in depression-absent participants across time ((β = 0.016, p = 0.303, 95% CI (-0.015, 0.048)). Learning rates were not affected across time for participants receiving learning queries (β = 0.001, p = 0.418, 95% CI (-0.002, 0.004)) or control queries (β = 0.002, p = 0.558, 95% CI (-0.005, 0.009). Of the learning queries, those targeting value comparison processes improved depression symptoms (β = -0.509, p = 0.015, 95% CI (-0.912, - 0.106)) and increased reward sensitivities across time (β = 0.052, p =< 0.001, 95% CI (0.030, 0.075)) in depression-present participants. Increased reward sensitivities related to decreased depression symptoms across time in these participants (β = -2.905, p = 0.002, 95% CI (-4.75, - 1.114)). Conclusions: Multiple sessions of targeted behavioral training improved reward learning for participants with a range of depression symptoms. Improved behavioral reward learning was associated with improved clinical symptoms with time, possibly because learning transferred to real world scenarios. These results support disrupted reward learning as a mechanism contributing to the etiology and maintenance of depression and suggest the potential of repeated behavioral training to target deficits in reward learning.
Cocaine Use Modulates Neural Prediction Error During Aversive Learning
Wang, John Mujia (Virginia Tech, 2015-05-07)
Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (Î´+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence.
Cognitive and Affective Pathways to Nonsuicidal Self-Injury Among Youth in the Adolescent Brain Cognitive Development (ABCD) Study
Antezana, Ligia Danitsa (Virginia Tech, 2022-07-07)
Nonsuicidal self-injury (NSSI) is the deliberate destruction of one's own body tissue (e.g., cutting, skin picking, biting, hitting) without conscious suicidal intent. Cognitive and affective difficulties may contribute to the development and maintenance of NSSI, such that emotion regulation may mediate the link between cognitive control difficulties and NSSI in youth. This study examined developmental links between cognitive control and emotion regulation on several facets of self-injurious thoughts and behaviors in a large sample of youth, collected via the ABCD Study (N=6447). Although a mediation of emotion regulation on cognitive control and self-injurious thoughts and behaviors was not supported, important direct effects were found between neural correlates of inhibition (at ages 9-10 years) on NSSI at 11-12 years, and behavioral measures of cognitive flexibility (at 10-11 years) and inhibition (at 9-10 years) on suicidality at 11-12 years. Further, links between poorer cognitive control and poorer emotion regulation were found. An exploratory aim of this study was examining the potential moderating role of autistic traits on significant associations. Although greater autistic traits significantly predicted presence of self-injurious thoughts and behaviors, this study did not find a moderation of autistic traits. These results provide developmental risk markers for NSSI and suicidality in youth.
The Development and Validation of a Neural Model of Affective States
McCurry, Katherine Lorraine (Virginia Tech, 2015-09-23)
Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback.
Functional Connectivity of Reward Networks: Characterizing Mechanistic Underpinnings Involved in Positive Affect Deficits within Social Anxiety Disorder
Carlton, Corinne N. (Virginia Tech, 2020)
Social Anxiety Disorder (SAD) is characterized by excessive concern or fear of negative evaluation in one or more social situations and ranks as one of the most common psychiatric disorders. SAD has also been characterized by significant deficits in social motivation and a lack of reactivity to pleasurable stimuli (i.e., positive affect; [PA]), particularly within social contexts. Recent neuroimaging work has shifted towards examining positively-valenced motivational systems in SAD focused on reward responses to social and nonsocial stimuli. These studies have revealed aberrant reward processing during social reward tasks in individuals with SAD. However, not all individuals with SAD exhibit reward circuitry dysfunction. Therefore, the current study aimed to examine if functional patterns of connectivity in the brain underlie heterogeneity in PA differences in individuals with SAD. Results revealed several functional connectivity strength differences between SAD and control groups within reward regions. Additionally, associations between regions of interest (ROIs)-couplings (i.e., OFC and insula, OFC and subgenual cingulate, insula and cingulate, and cingulate and subgenual cingulate) and diminished PA were present in individuals with SAD, but not controls. Lastly, results demonstrated that individuals with SAD had higher variability in their reward connectivity strength presentations and reports of PA as compared to controls. These results hold significance for the development of interventions for SAD that focus on the enhancement of PA to bolster social reward responsivity.
Gut-brain interactions in food reward
Burns, Amber Lynn (Virginia Tech, 2024-01-11)
Food choice and preference have been linked to post-ingestive consequences of food consumption. Many ultra-processed foods deliver calories rapidly and are highly rewarding. In literature surrounding substances of abuse, the speed at which a drug reaches the brain affects its abuse potential; this is known as the "rate hypothesis." Here, we test whether the rate hypothesis of addiction may apply to food, specifically whether caloric availability, or the speed at which carbohydrate becomes available for use, contributes to food reward and preference. To do this, we use beverages with novel flavors (conditioned stimulus (CS)) mixed with either a slow metabolizing carbohydrate (maltodextrin and inulin; CS+Slow), a fast-metabolizing carbohydrate (sucrose; CS+Fast), or no carbohydrate (sucralose; CS-). Participants are given each of these drinks 6 times to consume (conditioning period). 2 of these consumption periods occur during in-lab sessions. In one session, blood glucose is measured over one hour post-consumption. In another, we perform indirect calorimetry to assess post-consumption changes in substrate oxidation rates. At the post-testing session, changes in self-reported liking, wanting, and ad libitum intake of each beverage are recorded. Brain response to each flavor cue (without calories) is measured using fMRI at the post-test. We hypothesize the flavor paired with the CS+Fast will be the most liked, wanted, and consumed. We expect greater BOLD (blood oxygenated level dependent) activation to the CS+Fast relative to the CS+Slow and CS- in the nucleus accumbens and hypothalamus. This is an ongoing study and, here, we present our preliminary analysis of the data.
The Impact of Threat on Behavioral and Neural Markers of Learning in Anxiety
Valdespino, Andrew (Virginia Tech, 2019-08-28)
Anxiety is characterized by apprehensive expectation regarding the forecasted outcomes of choice. Decision science and in particular reinforcement learning models provide a quantitative framework to explain how the likelihood and value of such outcomes are estimated, thus allowing the measurement of parameters of decision-making that may differ between high- and low- anxiety groups. However, the role of anxiety in choice allocation is not sufficiently understood, particularly regarding the influence of transient threat on current decisions. The presence of threat appears to alter choice behavior and may differentially influence quantitatively derived parameters of learning among anxious individuals. Regarding the neurobiology of reinforcement learning, the dorsolateral prefrontal cortex (dlPFC) has been suggested to play a role in temporally integrating experienced outcomes, as well as in coordinating an overall choice action plan, both of which can be described computationally by learning rate and exploration, respectively. Accordingly, it was hypothesized that high trait anxiety would be associated with a lower reward learning rate, a higher loss learning rate, and diminished exploration of available options, and furthermore that threat would increase the magnitude of these parameters in the high anxiety group. We also hypothesized that the magnitude of neural activation (measured by functional near-infrared spectroscopy; FNIRS) across dissociable regions of the left and right dlPFC would be associated with model parameters, and that threat would further increase the magnitude of activation to model parameters. Finally, it was hypothesized that reward and loss outcomes could be differentiated based on FNIRS channel activation, and that a distinct set of channels would differentiate outcomes in high relative to low anxiety groups. To test these hypotheses, a temporal difference learning model was applied to a decision-making (bandit) task to establish differences in learning parameter magnitudes among individuals high (N=26) and low (N=20) in trait anxiety, as well as the impact of threat on learning parameters. Results indicated a positive association between anxiety and both the reward and loss learning rate parameters. However, threat was not found to impact model parameters. Imaging results indicated a positive association between exploration and the left dlPFC. Reward and loss outcomes were successfully differentiated in the high, but not low anxiety group. Results add to a growing literature suggesting anxiety is characterized by differential sensitivity to both losses and rewards in reinforcement learning contexts, and further suggests that the dlPFC plays a role in modulating exploration-based choice strategies.
In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use
Wang, John M.; Zhu, Lusha; Brown, Vanessa M.; De La Garza, Richard II; Newton, Thomas F.; Casas, Brooks; Chiu, Pearl H. (Elsevier, 2018-08-03)
Background: In substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state–dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility. Methods: In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects’ learning effects were assessed with computational model–derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α−), neural prediction error signals (δ+, δ−), cocaine use, and desire to use were assessed. Results: During cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine. Conclusions: Neurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction.
Neural Correlates of Temporal Context Processing
Wang, Fang (Virginia Tech, 2016-12-20)
Temporal context memory is a type of episodic memory that refers to memory for the timing of events. Temporal context includes environmental cues that provide information about the time point at which an event happened. The purpose of the present studies is to investigate the brain mechanisms underlying temporal context processing by using both fMRI and ERP techniques. The fMRI study investigated whether hippocampal representations in CA1 and DG/CA3 subfields were sensitive to the flow of physical time, and if so, whether the number of events that occur during a time period influences the temporal representation of a target event. Results showed that both CA1 and DG/CA3 were sensitive to the flow of physical time, which was indicated by higher representational similarity between two pictures that occurred closer in time than those that occurred more distant in time. However, the variety of preceding events did not influence temporal representation, which was demonstrated by the lack of a significant representational similarity difference between two pictures that were interleaved with variable events as opposed to similar events. The ERP study compared the ERP correlates of temporal to spatial context. Results showed that temporal and spatial contexts had overlapping ERP effects except that the ERP effects of temporal context were more frontally distributed than spatial context. Both the fMRI and ERP studies indicate that temporal context is associated with similar neural correlates to other types of context in episodic memory.
Neuroeconomic Predictors of Adolescent Risky Decision-Making
Lauharatanahirun, Nina (Virginia Tech, 2017-12-07)
Adolescence is a critical developmental period characterized by neurobiological changes and exposure to novel experiences. According to the Center for Disease Control, approximately 70% of adolescent deaths in the United States are due to risky behaviors such as reckless driving and risky sexual behavior (Kann et al., 2016). In order to better understand what drives adolescent risk-taking, the current studies utilized an interdisciplinary approach, which combined behavioral economic models and functional magnetic resonance imaging (fMRI) to understand neurobehavioral mechanisms of risky choice. The focus of the current studies is to investigate the extent to which neurobehavioral mechanisms of risky choice change across adolescence, and to identify individual differences that explain real-world risky behavior. In Study 1, we show that behavioral sensitivity to risk and neural correlates of risk processing change across a critical period of adolescence. Importantly, our results indicate that individual differences in neural, not behavioral risk sensitivity are predictive of future engagement in health risk behaviors. In Study 2, we examined the relation between inter-individual differences in adolescent expectations of valued rewards and self-reported risky behavior using an adapted behavioral economic model. Implications and future directions for adolescent risky decision-making are discussed.
Noradrenaline tracks emotional modulation of attention in human amygdala
Bang, Dan; Luo, Yi; Barbosa, Leonardo S.; Batten, Seth R.; Hadj-Amar, Beniamino; Twomey, Thomas; Melville, Natalie; White, Jason P.; Torres, Alexis; Celaya, Xavier; Ramaiah, Priya; McClure, Samuel M.; Brewer, Gene A.; Bina, Robert W.; Lohrenz, Terry; Casas, Brooks; Chiu, Pearl H.; Vannucci, Marina; Kishida, Kenneth T.; Witcher, Mark R.; Montague, P. Read (Elsevier, 2023-11-20)
The noradrenaline (NA) system is one of the brain’s major neuromodulatory systems; it originates in a small midbrain nucleus, the locus coeruleus (LC), and projects widely throughout the brain. The LC-NA system is believed to regulate arousal and attention and is a pharmacological target in multiple clinical conditions. Yet our understanding of its role in health and disease has been impeded by a lack of direct recordings in humans. Here, we address this problem by showing that electrochemical estimates of sub-second NA dynamics can be obtained using clinical depth electrodes implanted for epilepsy monitoring. We made these recordings in the amygdala, an evolutionarily ancient structure that supports emotional processing, and receives dense LC-NA projections, while patients (n = 3) performed a visual affective oddball task. The task was designed to induce different cognitive states, with the oddball stimuli involving emotionally evocative images, which varied in terms of arousal (low versus high) and valence (negative versus positive). Consistent with theory, the NA estimates tracked the emotional modulation of attention, with a stronger oddball response in a high-arousal state. Parallel estimates of pupil dilation, a common behavioral proxy for LC-NA activity, supported a hypothesis that pupil-NA coupling changes with cognitive state, with the pupil and NA estimates being positively correlated for oddball stimuli in a high-arousal but not a lowarousal state. Our study provides proof of concept that neuromodulator monitoring is now possible using depth electrodes in standard clinical use.
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.
Recovery in Major Depressive Disorder: Neural and Clinical Perspectives
Strege, Marlene Vernette (Virginia Tech, 2021-06-24)
Major depressive disorder (MDD) is considered the current leading cause of disability worldwide (Friedrich, 2017), yet the recovery process in MDD, including neurobiological underpinnings, clinical features and optimal approaches to treatment remains ambiguous. Current definitions of recovery are disputed and involve measures considered subjective in nature, such as thresholds for questionnaires and clinical interviews of symptoms and their duration (De Zwart and Jeronimus, 2019; Fava et al., 2007; Keller, 2003, 2004). Symptom-based measures, although informative of clinical presentation, are not informative of neurobiological underpinnings that may persist even when symptoms are reduced. Indeed, even after treatment, persistent residual symptoms, impairments in quality of life, and vulnerabilities for future return to more severe psychopathology persist (Gotlib and Hammen, 2008; IsHak et al., 2011; Judd et al., 1998a; Kennedy et al., 2004; Kennedy and Foy, 2005; Kennedy and Paykel, 2004). Without assessment of neural mechanisms of recovery in MDD, efforts toward developing novel treatment approaches that are able to address neural processes of illness and to provide sustained remission are slowed. The following collection of studies provide neural and clinical insights into MDD recovery and relate findings to potential treatment approaches that are optimized to individual differences in symptoms and neural functioning and able to address neural vulnerabilities to provide sustained remission. In pursuit of individualized treatment selection in MDD, study one involved a meta-analysis of prior prognostic fMRI studies of response to cognitive behavioral therapy (CBT) or a selective serotonin reuptake inhibitor (SSRI) in MDD. Study one also reported on the application of resulting meta-analytic regions (subgenual and perigenual anterior cingulate cortex) in a confirmatory MDD sample. Although regions showed some predictive potential in the confirmatory sample, when predicting SSRI response, effects were inconsistent with prior studies, suggesting methodological confounds may hinder ready translation. In an assessment of the course of MDD, the second study documented depression symptoms and quality of life across 9-14 years after acute treatment (CBT or SSRI) and found that persistent residual depression symptoms and quality of life deficits were common. In light of the normality of chronic symptoms and impairment, the third study evaluated neural features of treatment (CBT) resistance in MDD within the context of neural mechanisms of change. The third study found a vermis-centered cerebellar cluster that was unresponsive to CBT, whereas prefrontal and parietal cortical regions were responsive, providing support of prior theories that CBT directly affects cognitive control and cortical regulatory processes in contrast to salience-driven subcortical functioning (Clark and Beck, 2010; DeRubeis et al., 2008; Frewen et al., 2008; Mayberg, 2003). In consideration of findings, clinical recommendations that pertain to treating residual symptoms and associated neural features toward asymptomatic remission are provided. Future research directions are also provided regarding neuroscience informed precision medicine, current therapy and medication practices, and the larger picture of MDD chronicity broadly.
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.

Browsing by Author "Chiu, Pearl H."

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