Andreas Pedroni

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. Hum Brain Mapp, 2011.

Virtual Milgram: Empathic Concern or Personal Distress? Evidence from Functional MRI and Dispositional Measures

One motive for behaving as the agent of anothers aggression appears to be anchored in as yet unelucidated mechanisms of obedience to authority. In a recent partial replication of Milgrams 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.

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.

Open source projects in programming courses

One of the main shortcomings of programming courses is the lack of practice with real-world systs. As a result, students feel unprepared for industry jobs. In parallel, open source software is accepting contributions even from inexperienced programmers and achieves software that competes both in quality and functionality with industrial systs. This article describes: first, a setting in which students were required to contribute to existing open source software; second, the evaluation of this experience using a motivation measuring technique; and third, an analysis of the efficiency and commitment of students over the time. The study shows that students are at first afraid of failing the assignment, but end up having the impression of a greater achievent. It ses also that students are inclined to keep working on the project to which they contributed after the end of the course.

Individual preferences modulate incentive values: Evidence from functional MRI

BackgroundIn 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.MethodsA 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.ResultsParametrical 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.ConclusionOur 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.

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.

DAT1 Polymorphism Determines L-DOPA Effects on Learning about Others’ Prosociality

Despite that a wealth of evidence links striatal dopamine to individualś 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.

Risk preference shares the psychometric structure of major psychological traits

On the basis of 39 risk-taking measures, this study finds evidence for a general and stable factor of risk preference. 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.

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.

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.