Decomposing age effects in EEG alpha power.

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Brain-Behavior Associations for Risk Taking Depend on the Measures Used to Capture Individual Differences.

Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain-behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel-Berlin Risk Study (N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention.

Automagic: Standardized preprocessing of big EEG data.

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading.

We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset combining electroencephalography (EEG) and eye-tracking recordings from subjects reading natural sentences. ZuCo includes high-density EEG and eye-tracking data of 12 healthy adult native English speakers, each reading natural English text for 4-6 hours. The recordings span two normal reading tasks and one task-specific reading task, resulting in a dataset that encompasses EEG and eye-tracking data of 21,629 words in 1107 sentences and 154,173 fixations. We believe that this dataset represents a valuable resource for natural language processing (NLP). The EEG and eye-tracking signals lend themselves to train improved machine-learning models for various tasks, in particular for information extraction tasks such as entity and relation extraction and sentiment analysis. Moreover, this dataset is useful for advancing research into the human reading and language understanding process at the level of brain activity and eye-movement.

Risk preference shares the psychometric structure of major psychological traits.

To what extent is there a general factor of risk preference, R, akin to g, the general factor of intelligence? Can risk preference be regarded as a stable psychological trait? These conceptual issues persist because few attempts have been made to integrate multiple risk-taking measures, particularly measures from different and largely unrelated measurement traditions (self-reported propensity measures assessing stated preferences, incentivized behavioral measures eliciting revealed preferences, and frequency measures assessing actual risky activities). Adopting a comprehensive psychometric approach (1507 healthy adults completing 39 risk-taking measures, with a subsample of 109 participants completing a retest session after 6 months), we provide a substantive empirical foundation to address these issues, finding that correlations between propensity and behavioral measures were weak. Yet, a general factor of risk preference, R, emerged from stated preferences and generalized to specific and actual real-world risky activities (for example, smoking). Moreover, R proved to be highly reliable across time, indicative of a stable psychological trait. Our findings offer a first step toward a general mapping of the construct risk preference, which encompasses both general and domain-specific components, and have implications for the assessment of risk preference in the laboratory and in the wild.

A test of the diffusion model explanation for the worst performance rule using preregistration and blinding.

People with higher IQ scores also tend to perform better on elementary cognitive-perceptual tasks, such as deciding quickly whether an arrow points to the left or the right Jensen (2006). The worst performance rule (WPR) finesses this relation by stating that the association between IQ and elementary-task performance is most pronounced when this performance is summarized by people's slowest responses. Previous research has shown that the WPR can be accounted for in the Ratcliff diffusion model by assuming that the same ability parameter-drift rate-mediates performance in both elementary tasks and higher-level cognitive tasks. Here we aim to test four qualitative predictions concerning the WPR and its diffusion model explanation in terms of drift rate. In the first stage, the diffusion model was fit to data from 916 participants completing a perceptual two-choice task; crucially, the fitting happened after randomly shuffling the key variable, i.e., each participant's score on a working memory capacity test. In the second stage, after all modeling decisions were made, the key variable was unshuffled and the adequacy of the predictions was evaluated by means of confirmatory Bayesian hypothesis tests. By temporarily withholding the mapping of the key predictor, we retain flexibility for proper modeling of the data (e.g., outlier exclusion) while preventing biases from unduly influencing the results. Our results provide evidence against the WPR and suggest that it may be less robust and less ubiquitous than is commonly believed.

Temporal Characteristics of EEG Microstates Mediate Trial-by-Trial Risk Taking.

People seem to have difficulties when perceiving events whose outcome has no influence on the outcome of future events. This illusion that patterns exist where there are none may lead to adverse consequences, such as escalating losses in financial trading or gambling debt. Despite the enormous social consequences of these cognitive biases, however, their neural underpinnings are poorly understood. Attempts to investigate them have so far relied on evoked neural activity, whereas spontaneous brain activity has been treated as noise to be averaged out. Here, we focus on the spontaneous electroencephalographic (EEG) activity during inter-trial-intervals (ITI) in a sequential risky decision-making task. Using multilevel mediation analyses, our results show that the percentage of time covered by two EEG microstates (i.e., functional brain-states of coherent activity) mediate the influence of outcomes of prior decisions on subsequent risk taking on a trial-by-trial basis. The devised multilevel mediation analysis of the temporal characteristics of EEG microstates during ITI provides a new window into the neurobiology of decision making by bringing the spontaneous brain activity to the forefront of the analysis.

The risk elicitation puzzle.

Evidence shows that people's preference for risk changes considerably when measured using different methods, which led us to question whether the common practice of using a single behavioural elicitation method (EM) reflects a valid measure. The present study addresses this question by examining the across-methods consistency of observed risk preferences in 1,507 healthy participants using six EMs. Our analyses show that risk preferences are not consistent across methods when operationalized on an absolute scale, a rank scale or the level of model parameters of cumulative prospect theory. This is at least partly explained by the finding that participants do not consistently follow the same decision strategy across EMs. After controlling for methodological and human factors that may impede consistency, our results challenge the view that different EMs manage to stably capture risk preference. Instead, we interpret the results as suggesting that risk preferences may be constructed when they are elicited, and different cognitive processes can lead to varying preferences.

Dopaminergic stimulation increases selfish behavior in the absence of punishment threat.

RATIONALE: People often face decisions that pit self-interested behavior aimed at maximizing personal reward against normative behavior such as acting cooperatively, which benefits others. The threat of social sanctions for defying the fairness norm prevents people from behaving overly selfish. Thus, normative behavior is influenced by both seeking rewards and avoiding punishment. However, the neurochemical processes mediating the impact of these influences remain unknown. Several lines of evidence link the dopaminergic system to reward and punishment processing, respectively, but this evidence stems from studies in non-social contexts. OBJECTIVES: The present study investigates dopaminergic drug effects on individuals' reward seeking and punishment avoidance in social interaction. METHODS: Two-hundred one healthy male participants were randomly assigned to receive 300 mg of L-3,4-dihydroxyphenylalanine (L-DOPA) or a placebo before playing an economic bargaining game. This game involved two conditions, one in which unfair behavior could be punished and one in which unfair behavior could not be punished. RESULTS: In the absence of punishment threats, L-DOPA administration led to more selfish behavior, likely mediated through an increase in reward seeking. In contrast, L-DOPA administration had no significant effect on behavior when faced with punishment threats. CONCLUSIONS: The results of this study broaden the role of the dopaminergic system in reward seeking to human social interactions. We could show that even a single dose of a dopaminergic drug may bring selfish behavior to the fore, which in turn may shed new light on potential causal relationships between the dopaminergic system and norm abiding behaviors in certain clinical subpopulations.

Predicting risk-taking behavior from prefrontal resting-state activity and personality.

Risk-taking is subject to considerable individual differences. In the current study, we tested whether resting-state activity in the prefrontal cortex and trait sensitivity to reward and punishment can help predict risk-taking behavior. Prefrontal activity at rest was assessed in seventy healthy volunteers using electroencephalography, and compared to their choice behavior on an economic risk-taking task. The Behavioral Inhibition System/Behavioral Activation System scale was used to measure participants' trait sensitivity to reward and punishment. Our results confirmed both prefrontal resting-state activity and personality traits as sources of individual differences in risk-taking behavior. Right-left asymmetry in prefrontal activity and scores on the Behavioral Inhibition System scale, reflecting trait sensitivity to punishment, were correlated with the level of risk-taking on the task. We further discovered that scores on the Behavioral Inhibition System scale modulated the relationship between asymmetry in prefrontal resting-state activity and risk-taking. The results of this study demonstrate that heterogeneity in risk-taking behavior can be traced back to differences in the basic physiology of decision-makers' brains, and suggest that baseline prefrontal activity and personality traits might interplay in guiding risk-taking behavior.

DAT1 polymorphism determines L-DOPA effects on learning about others' prosociality.

Despite that a wealth of evidence links striatal dopamine to individuals reward learning performance in non-social environments, the neurochemical underpinnings of such learning during social interaction are unknown. Here, we show that the administration of 300 mg of the dopamine precursor L-DOPA to 200 healthy male subjects influences learning about a partners' prosocial preferences in a novel social interaction task, which is akin to a repeated trust game. We found learning to be modulated by a well-established genetic marker of striatal dopamine levels, the 40-bp variable number tandem repeats polymorphism of the dopamine transporter (DAT1 polymorphism). In particular, we found that L-DOPA improves learning in 10/10R genoype subjects, who are assumed to have lower endogenous striatal dopamine levels and impairs learning in 9/10R genotype subjects, who are assumed to have higher endogenous dopamine levels. These findings provide first evidence for a critical role of dopamine in learning whether an interaction partner has a prosocial or a selfish personality. The applied pharmacogenetic approach may open doors to new ways of studying psychiatric disorders such as psychosis, which is characterized by distorted perceptions of others' prosocial attitudes.

The problem of thresholding in small-world network analysis.

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.

The rewarding value of good motor performance in the context of monetary incentives.

Whether an agent receives positive task feedback or a monetary reward, neural activity in their striatum increases. In the latter case striatal activity reflects extrinsic reward processing, while in the former, striatal activity reflects the intrinsically rewarding effects of performing well. There can be a "hidden cost of reward", which is a detrimental effect of extrinsic on intrinsic reward value. This raises the question how these two types of reward interact. To address this, we applied a monetary incentive delay task: in all trials participants received feedback depending on their performance. In half of the trials they could additionally receive monetary reward if they performed well. This resulted in high performance trials, which were monetarily rewarded and high performance trials that were not. This made it possible to dissociate the neural correlates of performance feedback from the neural correlates of monetary reward that comes with high performance. Performance feedback alone elicits activation increases in the ventral striatum. This activation increases due to additional monetary reward. Neural response in the dorsal striatum on the other hand is only significantly increased by feedback when a monetary incentive is present. The quality of performance does not significantly influence dorsal striatum activity. In conclusion, our results indicate that the dorsal striatum is primarily sensitive to optional or actually received external rewards, whereas the ventral striatum may be coding intrinsic reward due to positive performance feedback. Thus the ventral striatum is suggested to be involved in the processing of intrinsically motivated behavior.

Functional brain network efficiency predicts intelligence.

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Differential magnitude coding of gains and omitted rewards in the ventral striatum.

Physiologic studies revealed that neurons in the dopaminergic midbrain of non-human primates encode reward prediction errors. It was furthermore shown that reward prediction errors are adaptively scaled with respect to the range of possible outcomes, enabling sensitive encoding for a large range of reward values. Congruently, neuroimaging studies in humans demonstrated that BOLD-responses in the ventral striatum encode reward prediction errors in similar fashion as dopaminergic midbrain neurons, suggesting that these BOLD-responses may be driven by dopaminergic midbrain activity. However, neuroimaging results are ambiguous with respect to the adaptive scaling of reward prediction errors, leading to the conjecture that under certain circumstances other than dopaminergic midbrain input may drive ventral striatal BOLD-responses. The goal of this study was to substantiate whether BOLD-responses in the ventral striatum rather respond to adaptively scaled reward prediction errors or absolute reward magnitude. In addition, we aimed to identify neuronal structures modulating activity in the ventral striatum. Sixteen healthy participants played a wheel of fortune game, where they could win three differently valued rewards while being scanned. BOLD-responses increased after gaining rewards; this gain was however independent of the absolute reward magnitude. In contrast BOLD-responses upon reward omission decreased with reward magnitude. A psychophysiological interaction analysis identified a cluster in the brainstem in proximity of the dorsal raphe nucleus, a cluster in the lateral orbitofrontal cortex, and a cluster in the rostral cingulate zone. These clusters changed their connectivity with the ventral striatum in relation to the absolute reward magnitude in reward omission trials.

Electroencephalographic topography measures of experienced utility.

Economic theory distinguishes two concepts of utility: decision utility, objectively quantifiable by choices, and experienced utility, referring to the satisfaction by an obtainment. To date, experienced utility is typically measured with subjective ratings. This study intended to quantify experienced utility by global levels of neuronal activity. Neuronal activity was measured by means of electroencephalographic (EEG) responses to gain and omission of graded monetary rewards at the level of the EEG topography in human subjects. A novel analysis approach allowed approximating psychophysiological value functions for the experienced utility of monetary rewards. In addition, we identified the time windows of the event-related potentials (ERP) and the respective intracortical sources, in which variations in neuronal activity were significantly related to the value or valence of outcomes. Results indicate that value functions of experienced utility and regret disproportionally increase with monetary value, and thus contradict the compressing value functions of decision utility. The temporal pattern of outcome evaluation suggests an initial (∼250 ms) coarse evaluation regarding the valence, concurrent with a finer-grained evaluation of the value of gained rewards, whereas the evaluation of the value of omitted rewards emerges later. We hypothesize that this temporal double dissociation is explained by reward prediction errors. Finally, a late, yet unreported, reward-sensitive ERP topography (∼500 ms) was identified. The sources of these topographical covariations are estimated in the ventromedial prefrontal cortex, the medial frontal gyrus, the anterior and posterior cingulate cortex and the hippocampus/amygdala. The results provide important new evidence regarding "how," "when," and "where" the brain evaluates outcomes with different hedonic impact.

Virtual milgram: empathic concern or personal distress? Evidence from functional MRI and dispositional measures.

One motive for behaving as the agent of another's aggression appears to be anchored in as yet unelucidated mechanisms of obedience to authority. In a recent partial replication of Milgram's obedience paradigm within an immersive virtual environment, participants administered pain to a female virtual human and observed her suffering. Whether the participants' response to the latter was more akin to other-oriented empathic concern for her well-being or to a self-oriented aversive state of personal distress in response to her distress is unclear. Using the stimuli from that study, this event-related fMRI-based study analysed brain activity during observation of the victim in pain versus not in pain. This contrast revealed activation in pre-defined brain areas known to be involved in affective processing but not in those commonly associated with affect sharing (e.g., ACC and insula). We then examined whether different dimensions of dispositional empathy predict activity within the same pre-defined brain regions: While personal distress and fantasy (i.e., tendency to transpose oneself into fictional situations and characters) predicted brain activity, empathic concern and perspective taking predicted no change in neuronal response associated with pain observation. These exploratory findings suggest that there is a distinct pattern of brain activity associated with observing the pain-related behaviour of the victim within the context of this social dilemma, that this observation evoked a self-oriented aversive state of personal distress, and that the objective "reality" of pain is of secondary importance for this response. These findings provide a starting point for experimentally more rigorous investigation of obedience.

Individual preferences modulate incentive values: Evidence from functional MRI.

BACKGROUND: In most studies on human reward processing, reward intensity has been manipulated on an objective scale (e.g., varying monetary value). Everyday experience, however, teaches us that objectively equivalent rewards may differ substantially in their subjective incentive values. One factor influencing incentive value in humans is branding. The current study explores the hypothesis that individual brand preferences modulate activity in reward areas similarly to objectively measurable differences in reward intensity. METHODS: A wheel-of-fortune game comprising an anticipation phase and a subsequent outcome evaluation phase was implemented. Inside a 3 Tesla MRI scanner, 19 participants played for chocolate bars of three different brands that differed in subjective attractiveness. RESULTS: Parametrical analysis of the obtained fMRI data demonstrated that the level of activity in anatomically distinct neural networks was linearly associated with the subjective preference hierarchy of the brands played for. During the anticipation phases, preference-dependent neural activity has been registered in premotor areas, insular cortex, orbitofrontal cortex, and in the midbrain. During the outcome phases, neural activity in the caudate nucleus, precuneus, lingual gyrus, cerebellum, and in the pallidum was influenced by individual preference. CONCLUSION: Our results suggest a graded effect of differently preferred brands onto the incentive value of objectively equivalent rewards. Regarding the anticipation phase, the results reflect an intensified state of wanting that facilitates action preparation when the participants play for their favorite brand. This mechanism may underlie approach behavior in real-life choice situations.