Relation of depression symptoms to sustained reward and loss sensitivity

College students with depression symptom are more sensitive to task difficulty in reinforcement learning

BACKGROUND

Depression is usually characterized by impairments in reward function, and shows altered motivation to reward in reinforcement learning. This study further explored whether task difficulty affects reinforcement learning in college students with and without depression symptom.

METHODS

The depression symptom group (20) and the no depression symptom group (26) completed a probabilistic reward learning task with low, medium, and high difficulty levels, in which task the response bias to reward and the discriminability of reward were analyzed. Additionally, electrophysiological responses to reward and loss feedback were recorded and analyzed while they performed a simple gambling task.

RESULTS

The depression symptom group showed more response bias to reward than the no depression symptom group when the task was easy and then exhibited more quickly decrease in response bias to reward as task difficulty increased. The no depression symptom group showed a decrease in response bias only in the high-difficulty condition. Further regression analyses showed that, the Feedback-related negativity (FRN) and theta oscillation could predict response bias change in the low-difficulty condition, the FRN and oscillations of theta and delta could predict response bias change in the medium and high-difficulty conditions.

LIMITATIONS

The electrophysiological responses to loss and reward were not recorded in the same task as the reinforcement learning behaviors.

CONCLUSIONS

College students with depression symptom are more sensitive to task difficulty during reinforcement learning. The FRN, and oscillations of theta and delta could predict reward leaning behavior.

Neural mechanisms of social comparison in subthreshold depression

Social comparison is a common phenomenon in our daily life, through which people get to know themselves, and plays an important role in depression. In this study, event-related potential (ERP) was used to explore the temporal course of social comparison processing in the subthreshold depression group. Electrophysiological recordings were acquired from 30 subthreshold depressed individuals and 31 healthy individuals while they conducted the adapted dot estimation task. The ERP results revealed that there was a significant difference of feedback-related negativity (FRN) in the process of social comparison. Especially only in the subthreshold depression, the FRN amplitudes of worse off than some, better off than many comparisons were larger than those of upward comparisons and downward comparisons. Our results suggested that the abnormal reward sensitivity for worse off than some, better off than many comparisons might be prodromal symptoms in the subthreshold depression.

Machine learning reveals differential effects of depression and anxiety on reward and punishment processing

Recent studies suggest that depression and anxiety are associated with unique aspects of EEG responses to reward and punishment, respectively; also, abnormal responses to punishment in depressed individuals are related to anxiety, the symptoms of which are comorbid with depression. In a non-clinical sample, we aimed to investigate the relationships between reward processing and anxiety, between punishment processing and anxiety, between reward processing and depression, and between punishment processing and depression. Towards this aim, we separated feedback-related brain activity into delta and theta bands to isolate activity that indexes functionally distinct processes. Based on the delta/theta frequency and feedback valence, we then used machine learning (ML) to classify individuals with high severity of depressive symptoms and individuals with high severity of anxiety symptoms versus controls. The significant difference between the depression and control groups was driven mainly by delta activity; there were no differences between reward- and punishment-theta activities. The high severity of anxiety symptoms was marginally more strongly associated with the punishment- than the reward-theta feedback processing. The findings provide new insights into the differences in the impacts of anxiety and depression on reward and punishment processing; our study shows the utility of ML in testing brain-behavior hypotheses and emphasizes the joint effect of theta-RewP/FRN and delta frequency on feedback-related brain activity.

Feedback negativity and feedback-related P3 in individuals at risk for depression: Comparing surface potentials and current source densities

Blunted responses to reward feedback have been linked to major depressive disorder (MDD) and depression risk. Using a monetary incentive delay task (win, loss, break-even), we investigated the impact of family risk for depression and lifetime history of MDD and anxiety disorder with 72-channel electroencephalograms (EEG) recorded from 29 high-risk and 32 low-risk individuals (15-58 years, 30 male). Linked-mastoid surface potentials (ERPs) and their corresponding reference-free current source densities (CSDs) were quantified by temporal principal components analysis (PCA). Each PCA solution revealed a midfrontal feedback negativity (FN; peak around 310 ms) and a posterior feedback-P3 (fb-P3; 380 ms) as two distinct reward processing stages. Unbiased permutation tests and multilevel modeling of component scores revealed greater FN to loss than win and neutral for all stratification groups, confirming FN sensitivity to valence. Likewise, all groups had greater fb-P3 to win and loss than neutral, confirming that fb-P3 indexes motivational salience and allocation of attention. By contrast, group effects were subtle, dependent on data transformation (ERP, CSD), and did not confirm reduced FN or fb-P3 for at-risk individuals. Instead, CSD-based fb-P3 was overall reduced in individuals with than without MDD history, whereas ERP-based fb-P3 was greater for high-risk individuals than for low-risk individuals for monetary, but not neutral outcomes. While the present findings do not support blunted reward processing in depression and depression risk, our side-by-side comparison underscores how the EEG reference choice affects the characterization of subtle group differences, strongly advocating the use of reference-free techniques.

Depression is associated with reduced outcome sensitivity in a dual valence, magnitude learning task

BACKGROUND

Learning from rewarded and punished choices is perturbed in depressed patients, suggesting that abnormal reinforcement learning may be a cognitive mechanism of the illness. However, previous studies have disagreed about whether this behavior is produced by alterations in the rate of learning or sensitivity to experienced outcomes. This previous work has generally assessed learning in response to binary outcomes of one valence, rather than to both rewarding and punishing continuous outcomes.

METHODS

A novel drifting reward and punishment magnitude reinforcement-learning task was administered to patients with current (n = 40) and remitted depression (n = 39), and healthy volunteers (n = 40) to capture potential differences in learning behavior. Standard questionnaires were administered to measure self-reported depressive symptom severity, trait and state anxiety and level of anhedonic symptoms.

RESULTS

Our findings demonstrate that patients with current depression adjust their learning behaviors to a lesser degree in response to trial-by-trial variations in reward and loss magnitudes than the other groups. Computational modeling revealed that this behavioral signature of current depressive state is better accounted for by reduced reward and punishment sensitivity (all p < 0.031), rather than a change in learning rate (p = 0.708). However, between-group differences were not related to self-reported symptom severity or comorbid anxiety disorders in the current depression group.

CONCLUSION

These findings suggest that current depression is associated with reduced outcome sensitivity rather than altered learning rate. Previous findings reported in this domain mainly from binary learning tasks seem to generalize to learning from continuous outcomes.

Towards thoughtful planning of ERP studies: How participants, trials, and effect magnitude interact to influence statistical power across seven ERP components

In the field of EEG, researchers generally rely on rules of thumb, rather than a priori statistical calculations, when planning the number of trials to include in an ERP study. To aid in this practice, studies have tried to establish minimum numbers of trials required to reliably isolate ERPs. However, these guidelines do not necessarily apply across different study designs, as the reliability of an ERP waveform is not the same as the statistical power of a given experiment. Experiment parameters such as number of participants, trials, and effect magnitude interact to affect power in complex ways. Both under- and over-powered ERP studies represent a waste of time and resources that impedes the progress of the field. The current study fills this gap by subsampling real ERP data to estimate the relationship between experiment design parameters and statistical power. The simulations include seven commonly studied ERP components: the ERN, LRP, N170, MMN, P3, N2pc, and N400. In the first set of experiments, we determined the probability of obtaining a statistically significant ERP effect for each component. In the second and third set of experiments, we determined the probability of obtaining a statistically significant difference in ERP amplitude within and between groups for each component. Results indicate that the rules of thumb for ERP experiment design in the literature often lead to underpowered studies. Going forward, these results provide researchers with experiment design guidelines that are specific to the component under study, allowing for the design of sufficiently powered ERP studies.

You Were Better Than Expected-An Experimental Study to Examine Expectation Change in a Non-clinical Sample

Background

Reduced sensitivity to rewards as well as the tendency to maintain dysfunctional expectations despite expectation-disconfirming evidence (cognitive immunization) are considered core features of various mental disorders. It is therefore important for clinical research to have paradigms that are suitable to study these phenomena. We developed a new experimental paradigm to study explicit expectation change after prior expectation induction and violation. Its validity is tested by applying the paradigm to healthy individuals.

Materials and Methods

In the main part of the study (experiment 1) we examined whether it is possible to change healthy individuals' (Sample size 56) task-specific and generalized performance expectations through expectation-disconfirming experiences. We used a high-difficulty performance task to induce initially negative expectations regarding participants' ability to successfully work on that unknown task. In the second part of the study, the difficulty of the test was lowered in one experimental condition, in order to disconfirm the negative expectations of the first part, while the other group continued with high test difficulty to confirm the negative expectations. We measured the participant's explicit performance expectations before and after completing the tests. In experiment 2 (Sample size 57), we investigated the impact of different test instructions on expectation change. Using the same paradigm as in experiment 1, we added an "immunization-inhibiting" manipulation for one group and an "immunization-enhancing" manipulation for the other group.

Results

In experiment 1, we were able to show that individuals changed their expectations according to variations of task difficulty. Adding instructions to manipulate cognitive immunization inhibited expectation change regardless of condition (experiment 2).

Conclusion

Our approach allowed us to examine the effects of implicitly acquired performance expectations on explicit, verbalized expectation change. The new experimental paradigm used in this study is suitable to induce performance expectations, and to examine expectation-change among healthy individuals (experiment 1). Instructions to enhance or inhibit cognitive immunization processes both inhibited expectation change (experiment 2). The results are discussed within the context of current models of expectation change, cognitive immunization, and reward sensitivity.

Utility of linear mixed effects models for event-related potential research with infants and children

Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children's difficulty in sitting through the multiple trials required in an ERP task. Thus, a large problem in developmental ERP research is high subject exclusion due to too few analyzable trials. Common analytic approaches (that involve averaging trials within subjects and excluding subjects with too few trials, as in ANOVA and linear regression) work around this problem, but do not mitigate it. Moreover, these practices can lead to inaccuracies in measuring neural signals. The greater the subject exclusion, the more problematic inaccuracies can be. We review recent developmental ERP studies to illustrate the prevalence of these issues. Critically, we demonstrate an alternative approach to ERP analysis-linear mixed effects (LME) modeling-which offers unique utility in developmental ERP research. We demonstrate with simulated and real ERP data from preschool children that commonly employed ANOVAs yield biased results that become more biased as subject exclusion increases. In contrast, LME models yield accurate, unbiased results even when subjects have low trial-counts, and are better able to detect real condition differences. We include tutorials and example code to facilitate LME analyses in future ERP research.

Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline

Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (M_age  = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.

Open Science in human electrophysiology

Open science practices are gaining momentum in psychophysiological research, but at the nascent stage of this special issue in the International Journal of Psychophysiology there was no systematic collection of resources to support the adoption of open science practices specific to studies of human electrophysiology (EEG). The purpose of this special issue was to gather and provide resources that identify the idiosyncratic considerations and implications of open science practices specifically for studies of human EEG and event-related potentials (ERPs). Papers also show the importance of promoting good scientific practices in the application of open science principles to EEG and ERPs. This introduction to the special issue provides a roadmap for identifying the resources necessary to begin and improve the application of open science principles to EEG and ERP research. We are optimistic that open science practices will help increase the robustness, rigor, and replicability of EEG research and ultimately become the norm in studies of EEG and ERPs.

Win, lose, or draw: Examining salience, reward memory, and depression with the reward positivity

The reward positivity (RewP) is a putative biomarker of depression. Careful control of stimulus properties and manipulation of both stimulus valence and salience could facilitate interpretation of the RewP. RewP interpretation could further be improved by investigating functional outcomes of a blunted RewP in depression, such as reduced memory for rewarding outcomes. This study sought to advance RewP interpretation first by advancing task design through use of neutral (i.e., draw) control trials and counterbalanced feedback stimuli. Second, we examined the RewP's association with memory and the impact of depression. Undergraduates completed self-report measures of depression and anhedonia prior to a modified doors task in which words were displayed in colored fonts that indicated win, loss, or draw feedback. Memory of the feedback associated with each word (i.e., source memory) was tested. Results showed that RewP response to wins was more positive than to losses, which was more positive than to draws. The RewP was not associated with depression or anhedonia. The low depression group showed a source memory advantage for win words, but the high depression group did not. Source memory showed small relations to the RewP, but these did not survive Bonferroni correction. Results suggest the RewP is sensitive to salience and highlight challenges in detecting an association between the RewP and depression in modified doors tasks. Findings indicate that depression is related to dysfunctional source memory for reward but not loss and that future research should probe the possible associations between the RewP and memory in depression.

The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a 0.10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.

The neural habituation to hedonic and eudaimonic rewards: Evidence from reward positivity

Rewards play an important role in people's well-being. However, the mechanisms underlying neural habituation to hedonic rewards (attainment of pleasure) and eudaimonic rewards (attainment of meaning and self-realization) and their implications for longitudinal changes in well-being remain unknown. By operationalizing hedonic rewards as "winning money for oneself" and eudaimonic rewards as "winning money for a charity", 78 participants (41 women, aged from 17 to 24 years) completed a revised monetary gambling task during event-related potential (ERP) recording. Subsequently, the participants' well-being was measured after one year. The results showed that the reward positivity (RewP) effect readily decreased as the hedonic rewards were repeated, whereas the RewP effect in response to eudaimonic rewards was relatively sustained over time. Moreover, the declining RewP effect for repeated eudaimonic rewards was marginally positively associated with longitudinal decreases in well-being. These findings demonstrate at the neural level that sensitivity to repeated hedonic rewards is more prone to decrease than sensitivity to repeated eudaimonic rewards, and sustained eudaimonic reward sensitivity in the short term has greater implications for changes in well-being in the long term.

A Functional Coupling of Brain and Behavior During Social Categorization of Faces

Considerable research has focused on how people derive information about others' social category memberships from their faces. Theoretical models posit that early extraction of task-relevant information from a face should determine the efficiency with which that face is categorized, but evidence supporting this idea has been elusive. Here, we used a novel trial-level data analytic approach to examine the relationship between two event-related potential components-the P2, indexing early attention to category-relevant information, and the P3, indexing stimulus evaluation-and the speed of overt categorization judgments. As predicted, a larger face-elicited P2 on a particular trial was associated with faster overt race or gender categorization of that face. Moreover, this association was mediated by P3 latency, indicating that extraction of more category-relevant information early in processing facilitated stimulus evaluation. These findings support continuous flow models of information processing and the long-theorized functional significance of face-elicited neurophysiological responses for social categorization.

Using multilevel models for the analysis of event-related potentials

Multilevel modeling (MLM) is becoming increasingly accessible and popular in the analysis of event-related potentials (ERPs). In this article, we review the benefits of MLM for analyzing psychophysiological data, which often contains repeated observations within participants, and introduce some of the decision-making points in the analytic process, including how to set up the data set, specify the model, conduct hypothesis tests, and visualize the model estimates. We highlight how the use of MLM can extend the types of theoretical questions that can be answered using ERPs, including investigations of how ERPs vary meaningfully across trials within a testing session. We also address reporting practices and provide tools to calculate effect sizes and simulate power curves. Ultimately, we hope this review contributes to emerging best practices for the use of MLM with psychophysiological data.