Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms.

BACKGROUND: Digital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety. METHODS: We conducted an 8-week longitudinal usability test of the Small Steps SMS program, recruiting 20 participants who met criteria for major depressive disorder and/or generalized anxiety disorder. Participants used the automated intervention for 8 weeks and completed symptom severity and usability self-report surveys after 4 and 8 weeks of intervention use. Participants also completed individual interviews to provide feedback on the intervention. RESULTS: Participants responded to automated messages on 70 % of study days and with 85 % of participants sending responses to messages in the 8th week of use. Usability surpassed established cutoffs for software that is considered acceptable. Depression symptom severity decreased significantly over the usability test, but reductions in anxiety symptoms were not significant. Participants noted key areas for improvement including addressing message volume, aligning message scheduling to individuals' availability, and increasing the customizability of content. LIMITATIONS: This study does not contain a control group. CONCLUSIONS: An 8-week automated interactive text messaging intervention, Small Steps SMS, demonstrates promise with regard to being a feasible, usable, and engaging method to deliver daily mental health support to individuals with symptoms of anxiety and depression.

Mental Health Self-Tracking Preferences of Young Adults With Depression and Anxiety Not Engaged in Treatment: Qualitative Analysis.

BACKGROUND: Despite the high prevalence of anxiety and depression among young adults, many do not seek formal treatment. Some may turn to digital mental health tools for support instead, including to self-track moods, behaviors, and other variables related to mental health. Researchers have sought to understand processes and motivations involved in self-tracking, but few have considered the specific needs and preferences of young adults who are not engaged in treatment and who seek to use self-tracking to support mental health. OBJECTIVE: This study seeks to assess the types of experiences young adults not engaged in treatment have had with digital self-tracking for mood and other mental health data and to assess how young adults not seeking treatment want to engage in self-tracking to support their mental health. METHODS: We conducted 2 online asynchronous discussion groups with 50 young adults aged 18 years to 25 years who were not engaged in treatment. Participants were recruited after indicating moderate to severe symptoms of depression or anxiety on screening surveys hosted on the website of Mental Health America. Participants who enrolled in the study responded anonymously to discussion prompts on a message board, as well as to each other's responses, and 3 coders performed a thematic analysis of their responses. RESULTS: Participants had mixed experiences with self-tracking in the past, including disliking when tracking highlighted unwanted behaviors and discontinuing tracking for a variety of reasons. They had more positive past experiences tracking behaviors and tasks they wanted to increase, using open-ended journaling, and with gamified elements to increase motivation. Participants highlighted several design considerations they wanted self-tracking tools to address, including building self-understanding; organization, reminders, and structure; and simplifying the self-tracking experience. Participants wanted self-tracking to help them identify their feelings and how their feelings related to other variables like sleep, exercise, and events in their lives. Participants also highlighted self-tracking as useful for motivating and supporting basic activities and tasks of daily living during periods of feeling overwhelmed or low mood and providing a sense of accomplishment and stability. Although self-tracking can be burdensome, participants were interested and provided suggestions for simplifying the process. CONCLUSIONS: These young adults not engaged in treatment reported interest in using self-tracking to build self-understanding as a goal in and of itself or as a first step in contemplating and preparing for behavior change or treatment-seeking. Alexithymia, amotivation, and feeling overwhelmed may serve both as barriers to self-tracking and opportunities for self-tracking to help.

From formative design to service-ready therapeutic: A pragmatic approach to designing digital mental health interventions across domains.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype.

Background: Young adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). Methods: The aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18-25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. Results: Forty-eight individuals ages 18-25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. Conclusions: Young adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms.

BACKGROUND: Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristic of social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement learning in individuals who are anxious. OBJECTIVE: This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 types of social reinforcement learning relevant to social anxiety: learning about other people and learning about one's own social performance. METHODS: Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people in both laboratory sessions and a task that assessed social reinforcement learning about one's own social performance in the second session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed effects models. RESULTS: After the CBM-I intervention, participants updated their beliefs about others more slowly (P=.04; Cohen d=-0.29) but used what they learned to make more accurate decisions (P=.005; Cohen d=0.20), choosing rewarding faces more frequently. These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their expectancies about others more from rewarding (P=.003; Cohen d=0.43) and less from punishing outcomes (P=.001; Cohen d=-0.47), and they became more accurate at learning to avoid punishing faces (P=.001; Cohen d=0.20). CONCLUSIONS: Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious, although replication will be important.

Examining social reinforcement learning in social anxiety.

BACKGROUND AND OBJECTIVES: Reinforcement learning biases have been empirically linked to anhedonia in depression and theoretically linked to social anhedonia in social anxiety disorder, but little work has directly assessed how socially anxious individuals learn from social reward and punishment. METHODS: N = 157 individuals high and low in social anxiety symptoms completed a social probabilistic selection task that involved selecting between pairs of neutral faces with varying probabilities of changing to a happy or angry face. Computational modeling was performed to estimate learning rates. Accuracy in choosing the more rewarding face was also analyzed. RESULTS: No significant group differences were found for learning rates. Contrary to hypotheses, participants high in social anxiety showed impaired punishment learning accuracy; they were more accurate at choosing the most rewarding face than they were at avoiding the most punishing face, and their punishment learning accuracy was lower than that of participants low in social anxiety. Secondary analyses found that high (vs. low) social anxiety participants were less accurate at selecting the more rewarding face on more (vs. less) punishing face pairs. LIMITATIONS: Stimuli were static, White, facial images, which lack important social cues (e.g., movement, sound) and diversity, and participants were largely non-Hispanic, White undergraduates, whose social reinforcement learning may differ from individuals at different developmental stages and those holding more marginalized identities. CONCLUSIONS: Socially anxious individuals may be less accurate at learning to avoid social punishment, which may maintain negative beliefs through an interpersonal stress generation process.

Effects of Mass Shootings on Mental Illness Stigma in the United States.

Although the vast majority of people with mental illness (PWMI) are not violent, Americans tend to think they are more dangerous than the general population. Because negative media portrayals may contribute to stigma, we used time-series analyses to examine changes in the public's perceived dangerousness of PWMI around six mass shootings whose perpetrators were reported to have a mental illness. From 2011 to 2019, 38,094 U.S. participants completed an online study assessing implicit and explicit perceived dangerousness of PWMI. There were large, upward spikes in perceived dangerousness the week of the Sandy Hook mass shooting that were relatively short-lived. However, there was not a consistent pattern of effects for other events analyzed, and any other spikes observed were smaller. Effects tended to be larger for explicit versus implicit perceived dangerousness. Sandy Hook seemed to temporarily worsen perceived dangerousness of PWMI, but this pattern was not observed for other mass shootings.

Stress and reward: A multimodal assessment of childhood sexual abuse.

Background: Childhood adversity has been found to impact stress and brain reward systems but it is unclear whether interactions between these systems might explain resilient vs. non-resilient trajectories following childhood sexual abuse (CSA). To address this gap, we adopted a multimodal approach in which cortisol reactivity to an acute stressor was assessed in conjunction with behavioral and neural measures of reward responsiveness in females with major depressive disorder (MDD) or no psychiatric disorders (i.e., resilient) who experienced CSA compared to females with and without MDD who did not experience abuse. Methods: Latent Class Mixed Modelling (LCMM) identified classes of adults (n = 62; MAge = 26.48, SD = 5.68) characterized by distinct cortisol trajectories in response to a combined social evaluative cold pressor task. Classes were examined for their history of CSA and resilience as well as behavioral and neural measures of reward responsiveness using 128-channel electroencephalography (event-related potentials and source localization analysis). Results: LCMM analysis identified two distinct classes of individuals with increased (Responders) or blunted (Non-Responders) cortisol reactivity to an acute stressor. Unlike Responders, Non-Responders did not modulate reward responses throughout the stress manipulation. No differences emerged between Responders and Non-Responders in terms of CSA or resilience. However, exploratory results showed that blunted cortisol response and non-modulation of reward responses emerged for those who experienced CSA at a younger age. Conclusions: Co-occurring blunted stress and reward reactivity emerged irrespective of adults' experience of CSA or resilience. However, preliminary findings showed that CSA ending during peripubertal development was associated with blunted cortisol and reward responsiveness. Future research needs to replicate findings in larger samples and could investigate if increasing reward responsiveness during critical times of neurodevelopment could normalize stress reactivity to future stressors and thus promote resilience.

Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder.

Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.

Lessons Learned: Providing Supportive Accountability in an Online Anxiety Intervention.

Technology-delivered interventions have the potential to help address the treatment gap in mental health care but are plagued by high attrition. Adding coaching, or minimal contact with a nonspecialist provider, may encourage engagement and decrease dropout, while remaining scalable. Coaching has been studied in interventions for various mental health conditions but has not yet been tested with anxious samples. This study describes the development of and reactions to a low-intensity coaching protocol administered to N = 282 anxious adults identified as high risk to drop out of a web-based cognitive bias modification for interpretation intervention. Undergraduate research assistants were trained as coaches and communicated with participants via phone calls and synchronous text messaging. About half of the sample never responded to coaches' attempts to schedule an initial phone call or did not answer the call, though about 30% completed the full intervention with their coach. Some anxious adults may choose technology-delivered interventions specifically for their lack of human contact and may fear talking to strangers on the phone; future recommendations include taking a more intensive user-centered design approach to creating and implementing a coaching protocol, allowing coaching support to be optional, and providing users with more information about how and why the intervention works.

Building an emotion regulation recommender algorithm for socially anxious individuals using contextual bandits.

OBJECTIVES: Poor emotion regulation (ER) has been implicated in many mental illnesses, including social anxiety disorder. To work towards a scalable, low-cost intervention for improving ER, we developed a novel contextual recommender algorithm for ER strategies. DESIGN: N = 114 socially anxious participants were prompted via a mobile app up to six times daily for five weeks to report their emotional state, use of 19 different ER strategies (or no strategy), physical location, and social context. Information from passive sensors was also collected. METHODS: Given the large number of ER strategies, we used two different approaches for variable reduction: (1) grouping ER strategies into categories based on a prior meta-analysis, and (2) considering only the ten most frequently used strategies. For each approach, an algorithm that recommends strategies based on one's current context was compared with an algorithm that recommends ER strategies randomly, an algorithm that always recommends cognitive reappraisal, and the person's observed ER strategy use. Contextual bandits were used to predict the effectiveness of the strategies recommended by each policy. RESULTS: When strategies were grouped into categories, the contextual algorithm was not the best performing policy. However, when the top ten strategies were considered individually, the contextual algorithm outperformed all other policies. CONCLUSIONS: Grouping strategies into categories may obscure differences in their contextual effectiveness. Further, using strategies tailored to context is more effective than using cognitive reappraisal indiscriminately across all contexts. Future directions include deploying the contextual recommender algorithm as part of a just-in-time intervention to assess real-world efficacy. PRACTITIONER POINTS: Emotion regulation strategies vary in their effectiveness across different contexts. An algorithm that recommends emotion regulation strategies based on a person's current context may one day be used as an adjunct to treatment to help dysregulated individuals optimize their in-the-moment emotion regulation. Recommending flexible use of emotion regulation strategies across different contexts may be more effective than recommending cognitive reappraisal indiscriminately across all contexts.

Which variations of a brief cognitive bias modification session for interpretations lead to the strongest effects?

BACKGROUND: Brief computerized programs that train less threatening interpretations (termed Cognitive Bias Modification for Interpretations, or CBM-I) can shift interpretation biases and subsequent anxiety symptoms. However, results have been inconsistent, particularly for studies conducted over the Internet. METHODS: The current exploratory study tests 13 variations of a single brief session of CBM-I, a non-CBM-I cognitive flexibility condition, a neutral condition, and a no task control condition in an analogue sample with moderate to severe anxiety. RESULTS: Results suggest that all conditions, except the neutral scenarios condition and the alternative way to improve cognitive flexibility, led to changes in interpretations (when compared to the no task control condition). Only conditions geared toward increasing imagery during CBM-I and targeting flexibility related to emotional material differed from the no task control condition on other post-training measures. CONCLUSIONS: Presenting valenced interpretations of ambiguous information during brief CBM-I, regardless of the format, can lead to changes in interpretation bias. However, most conditions did not differ from the no task control condition on other post-training assessments (and differences that did occur may be due to chance). Future trials should consider further testing of CBM-I that targets flexibility related to emotional material, and should include an increased number of sessions and trials.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder.

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

Emotion Malleability Beliefs and Emotion Experience and Regulation in the Daily Lives of People with High Trait Social Anxiety.

BACKGROUND: The extent to which a person believes they can change or control their own emotions is associated with trait-level symptoms of mood and anxiety-related psychopathology. Method: The present study examined how this belief relates to momentary and daily self-reports of affect, emotion regulation tendencies, and perceived effectiveness of emotion regulation attempts throughout a five-week experience sampling study conducted in N = 113 high socially anxious people (https://osf.io/eprwt/). RESULTS: Results suggest that people with relatively stronger beliefs that their emotions are malleable experienced more momentary and daily positive affect (relative to negative affect), even after controlling for social anxiety symptom severity (although only daily positive affect, and not momentary positive affect, remained significant after correcting for false discovery rate). However, emotion malleability beliefs were not uniquely associated with other emotion regulation-related outcomes in daily life, despite theory suggesting malleability beliefs influence motivation to engage in emotion regulation. CONCLUSION: The paucity of significant associations observed between trait malleability beliefs and momentary and daily self-reports of emotion regulation (despite consistent findings of such relationships at trait levels) calls for additional research to better understand the complex dynamics of emotion beliefs in daily life.

Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD. METHODS: Using a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDD participants receiving placebo (MDDPlacebo), and 28 healthy control subjects receiving placebo (HCPlacebo) recruited through two independent studies. An importance-guided machine learning technique for model selection was used on whole-brain functional magnetic resonance imaging data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo). RESULTS: Highly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (area under the curve = 0.87) and MDDPlacebo from MDDAmisulpride (area under the curve = 0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only. CONCLUSIONS: Results indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.

Assessment of Striatal Dopamine Transporter Binding in Individuals With Major Depressive Disorder: In Vivo Positron Emission Tomography and Postmortem Evidence.

Importance: Major depressive disorder (MDD) might involve dopamine (DA) reductions. The DA transporter (DAT) regulates DA clearance and neurotransmission and is sensitive to DA levels, with preclinical studies (including those involving inescapable stressors) showing that DAT density decreases when DA signaling is reduced. Despite preclinical data, evidence of reduced DAT in MDD is inconclusive. Objective: Using a highly selective DAT positron emission tomography (PET) tracer ([11C] altropane), DAT availability was probed in individuals with MDD who were not taking medication. Levels of DAT expression were also evaluated in postmortem tissues from donors with MDD who died by suicide. Design, Setting, and Participants: This cross-sectional PET study was conducted at McLean Hospital (Belmont, Massachusetts) and Massachusetts General Hospital (Boston) and enrolled consecutive individuals with MDD who were not taking medication and demographically matched healthy controls between January 2012 and March 2014. Brain tissues were obtained from the Douglas-Bell Canada Brain Bank. For the PET component, 25 individuals with current MDD who were not taking medication and 23 healthy controls recruited from McLean Hospital were included (all provided usable data). For the postmortem component, 15 individuals with depression and 14 healthy controls were considered. Intervention: PET scan. Main Outcomes and Measures: Striatal and midbrain DAT binding potential was assessed. For the postmortem component, tyrosine hydroxylase and DAT levels were evaluated using Western blots. Results: Compared with 23 healthy controls (13 women [56.5%]; mean [SD] age, 26.49 [7.26] years), 25 individuals with MDD (19 women [76.0%]; mean [SD] age, 26.52 [5.92] years) showed significantly lower in vivo DAT availability in the bilateral putamen and ventral tegmental area (Cohen d range, -0.62 to -0.71), and both reductions were exacerbated with increasing numbers of depressive episodes. Unlike healthy controls, the MDD group failed to show an age-associated reduction in striatal DAT availability, with young individuals with MDD being indistinguishable from older healthy controls. Moreover, DAT availability in the ventral tegmental area was lowest in individuals with MDD who reported feeling trapped in stressful circumstances. Lower DAT levels (and tyrosine hydroxylase) in the putamen of MDD compared with healthy controls were replicated in postmortem analyses (Cohen d range, -0.92 to -1.15). Conclusions and Relevance: Major depressive disorder, particularly with recurring episodes, is characterized by decreased striatal DAT expression, which might reflect a compensatory downregulation due to low DA signaling within mesolimbic pathways.

Social anxiety and dynamic social reinforcement learning in a volatile environment.

Adaptive social behavior requires learning probabilities of social reward and punishment, and updating these probabilities when they change. Given prior research on aberrant reinforcement learning in affective disorders, this study examines how social anxiety affects probabilistic social reinforcement learning and dynamic updating of learned probabilities in a volatile environment. N=222 online participants completed questionnaires and a computerized ball-catching game with changing probabilities of reward and punishment. Dynamic learning rates were estimated to assess the relative importance ascribed to new information in response to volatility. Mixed-effects regression was used to analyze throw patterns as a function of social anxiety symptoms. Higher social anxiety predicted fewer throws to the previously punishing avatar and different learning rates after certain role changes, suggesting that social anxiety may be characterized by difficulty updating learned social probabilities. Socially anxious individuals may miss the chance to learn that a once-punishing situation no longer poses a threat.

Impaired reward prediction error encoding and striatal-midbrain connectivity in depression.

Anhedonia (hyposensitivity to rewards) and negative bias (hypersensitivity to punishments) are core features of major depressive disorder (MDD), which could stem from abnormal reinforcement learning. Emerging evidence highlights blunted reward learning and reward prediction error (RPE) signaling in the striatum in MDD, although inconsistencies exist. Preclinical studies have clarified that ventral tegmental area (VTA) neurons encode RPE and habenular neurons encode punishment prediction error (PPE), which are then transmitted to the striatum and cortex to guide goal-directed behavior. However, few studies have probed striatal activation, and functional connectivity between VTA-striatum and VTA-habenula during reward and punishment learning respectively, in unmedicated MDD. To fill this gap, we acquired fMRI data from 25 unmedicated MDD and 26 healthy individuals during a monetary instrumental learning task and utilized a computational modeling approach to characterize underlying neural correlates of RPE and PPE. Relative to controls, MDD individuals showed impaired reward learning, blunted RPE signal in the striatum and overall reduced VTA-striatal connectivity to feedback. Critically, striatal RPE signal was increasingly blunted with more major depressive episodes (MDEs). No group differences emerged in PPE signals in the habenula and VTA or in connectivity between these regions. However, PPE signals in the habenula correlated positively with number of MDEs. These results highlight impaired reward learning, disrupted RPE signaling in the striatum (particularly among individuals with more lifetime MDEs) as well as reduced VTA-striatal connectivity in MDD. Collectively, these findings highlight reward-related learning deficits in MDD and their underlying pathophysiology.

Electroencephalography Source Functional Connectivity Reveals Abnormal High-Frequency Communication Among Large-Scale Functional Networks in Depression.

BACKGROUND: Functional magnetic resonance imaging studies of resting-state functional connectivity have shown that major depressive disorder (MDD) is characterized by increased connectivity within the default mode network (DMN) and between the DMN and the frontoparietal network (FPN). However, much remains unknown about abnormalities in higher frequency (>1 Hz) synchronization. Findings of abnormal synchronization in specific frequencies would contribute to a better understanding of the potential neurophysiological origins of disrupted functional connectivity in MDD. METHODS: We used the high temporal resolution of electroencephalography to compare the spectral properties of resting-state functional connectivity in individuals with MDD (n = 65) with healthy control subjects (n = 79) and examined the extent to which connectivity disturbances were evident in a third sample of individuals in remission from depression (n = 30). Exact low resolution electromagnetic tomography was used to compute intracortical activity from regions within the DMN and FPN, and functional connectivity was computed using lagged phase synchronization. RESULTS: Compared to control subjects, the MDD group showed greater within-DMN beta 2 band (18.5-21 Hz) connectivity and greater beta 1 band (12.5-18 Hz) connectivity between the DMN and FPN. This hyperconnectivity was not observed in the remitted MDD group. However, greater beta 1 band DMN-FPN connectivity was associated with more frequent depressive episodes since first depression onset, even after controlling for current symptom severity. CONCLUSIONS: These findings extend our understanding of the neurophysiological basis of abnormal resting-state functional connectivity in MDD and indicate that elevations in high-frequency DMN-FPN connectivity may be a neural marker linked to a more recurrent illness course.

Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation.

Prior studies have shown that dopamine (DA) functioning in frontostriatal circuits supports reinforcement learning (RL), as phasic DA activity in ventral striatum signals unexpected reward and may drive coordinated activity of striatal and orbitofrontal regions that support updating of action plans. However, the nature of DA functioning in RL is complex, in particular regarding the role of DA clearance in RL behavior. Here, in a multi-modal neuroimaging study with healthy adults, we took an individual differences approach to the examination of RL behavior and DA clearance mechanisms in frontostriatal learning networks. We predicted that better RL would be associated with decreased striatal DA transporter (DAT) availability and increased intrinsic functional connectivity among DA-rich frontostriatal regions. In support of these predictions, individual differences in RL behavior were related to DAT binding potential in ventral striatum and resting-state functional connectivity between ventral striatum and orbitofrontal cortex. Critically, DAT binding potential had an indirect effect on reinforcement learning behavior through frontostriatal connectivity, suggesting potential causal relationships across levels of neurocognitive functioning. These data suggest that individual differences in DA clearance and frontostriatal coordination may serve as markers for RL, and suggest directions for research on psychopathologies characterized by altered RL.