The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials

Decoding the Narcissistic Brain

There is a substantial knowledge gap in the narcissism literature: Less than 1% of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spontaneous neural oscillations. We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. Consistent with the agency-communion model of narcissism, agentic and communal forms of grandiose narcissism were reflected in distinct, non-overlapping patterns of spontaneous neural oscillations. Furthermore, consistent with a narcissistic admiration and rivalry concept model of narcissism, we observed largely non-overlapping patterns of spontaneous neural oscillations for admirative and rivalrous forms of narcissism. Vulnerable narcissism was negatively associated with power across fast and slow wave frequency bands. Taken together, the results suggest that the diverse forms of narcissism can be reliably predicted from spontaneous neural oscillations. The findings contribute to the burgeoning field of personality neuroscience.

Limited Evidence for Probabilistic Cueing Effects on Grating-Evoked Event-Related Potentials and Orientation Decoding Performance

We can rapidly learn recurring patterns that occur within our sensory environments. This knowledge allows us to form expectations about future sensory events. Several influential predictive coding models posit that, when a stimulus matches our expectations, the activity of feature-selective neurons in the visual cortex will be suppressed relative to when that stimulus is unexpected. However, after accounting for known critical confounds, there is currently scant evidence for these hypothesized effects from studies recording electrophysiological neural activity. To provide a strong test for expectation effects on stimulus-evoked responses in the visual cortex, we performed a probabilistic cueing experiment while recording electroencephalographic (EEG) data. Participants (n = 48) learned associations between visual cues and subsequently presented gratings. A given cue predicted the appearance of a certain grating orientation with 10%, 25%, 50%, 75%, or 90% validity. We did not observe any stimulus expectancy effects on grating-evoked event-related potentials. Multivariate classifiers trained to discriminate between grating orientations performed better when classifying 10% compared to 90% probability gratings. However, classification performance did not substantively differ across any other stimulus expectancy conditions. Our findings provide very limited evidence for modulations of prediction error signaling by probabilistic expectations as specified in contemporary predictive coding models.

Can personality traits be predicted from resting-state EEG oscillations? A replication study

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 min of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.

The late positive event-related potential component is time locked to the decision in recognition memory tasks

Two event-related potential (ERP) components are commonly observed in recognition memory tasks: the Frontal Negativity (FN400) and the Late Positive Component (LPC). These components are widely interpreted as neural correlates of familiarity and recollection, respectively. However, the interpretation of LPC effects is complicated by inconsistent results regarding the timing of ERP amplitude differences. There are also mixed findings regarding how LPC amplitudes covary with decision confidence. Critically, LPC effects have almost always been measured using fixed time windows relative to memory probe stimulus onset, yet it has not been determined whether LPC effects are time locked to the stimulus or the recognition memory decision. To investigate this, we analysed a large (n = 132) existing dataset recorded during recognition memory tasks with old/new decisions followed by post-decisional confidence ratings. We used ERP deconvolution to disentangle contributions to LPC effects (defined as differences between hits and correct rejections) that were time locked to either the stimulus or the vocal old/new response. We identified a left-lateralised parietal LPC effect that was time locked to the vocal response rather than probe stimulus onset. We also isolated a response-locked, midline parietal ERP correlate of confidence that influenced measures of LPC amplitudes at left parietal electrodes. Our findings demonstrate that, contrary to widespread assumptions, the LPC effect is time locked to the recognition memory decision and is best measured using response-locked ERPs. By extension, differences in response time distributions across conditions of interest may lead to substantial measurement biases when analysing stimulus-locked ERPs. Our findings highlight important confounding factors that further complicate the interpretation of existing stimulus-locked LPC effects as neural correlates of recollection. We recommend that future studies adopt our analytic approach to better isolate LPC effects and their sensitivity to manipulations in recognition memory tasks.

Disentangling periodic and aperiodic resting EEG correlates of personality

Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity-Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

Subjectively preferred octave size is resolved at the late stages of cerebral auditory processing

Human listeners prefer octave intervals slightly above the exact 2:1 frequency ratio. To study the neural underpinnings of this subjective preference, called the octave enlargement phenomenon, we compared neural responses between exact, slightly enlarged, oversized, and compressed octaves (or their multiples). The first experiment (n = 20) focused on the N1 and P2 event-related potentials (ERPs) elicited in EEG 50-250 ms after the second tone onset during passive listening of one-octave intervals. In the second experiment (n = 20) applying four-octave intervals, musician participants actively rated the different octave types as 'low', 'good' and 'high'. The preferred slightly enlarged octave was individually determined prior to the second experiment. In both experiments, N1-P2 peak-to-peak amplitudes attenuated for the exact and slightly enlarged octave intervals compared with compressed and oversized intervals, suggesting overlapping neural representations of tones an octave (or its multiples) apart. While there were no differences between the N1-P2 amplitudes to the exact and preferred enlarged octaves, ERP amplitudes differed after 500 ms from onset of the second tone of the pair. In the multivariate pattern analysis (MVPA) of the second experiment, the different octave types were distinguishable (spatial classification across electroencephalography [EEG] channels) 200 ms after second tone onset. Temporal classification within channels suggested two separate discrimination processes peaking around 300 and 700 ms. These findings appear to be related to active listening, as no multivariate results were found in the first, passive listening experiment. The present results suggest that the subjectively preferred octave size is resolved at the late stages of auditory processing.

Visual Representations: Insights from Neural Decoding

Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Introduction

Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals.

Methods

This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A.

Results

The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk.

Discussion

The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

The causal mechanisms underlying analogical reasoning performance improvement by executive attention intervention

Analogical reasoning is important for human. We have found that a short executive attention intervention improved analogical reasoning performance in healthy young adults. Nevertheless, previous electrophysiological evidence was limited for comprehensively characterizing the neural mechanisms underlying the improvement. And although we hypothesized that the intervention improved active inhibitory control and attention shift first and then relation integration, it is still unclear whether there are two sequential cognitive neural activities were indeed changed during analogical reasoning. In the present study, we combined hypothesis with multivariate pattern analysis (MVPA) to explore the effects of the intervention on electrophysiology. Results showed that in the resting state after the intervention, alpha and high gamma power and the functional connectivity between the anterior and middle in the alpha band could discriminate the experimental group from the active control group, respectively. These indicated that the intervention influenced the activity of multiple bands and the interaction of frontal and parietal regions. In the analogical reasoning, alpha, theta, and gamma activities could also fulfill such discrimination, and furthermore, they were sequential (alpha first, theta, and gamma later). These results directly supported our previous hypothesis. The present study deepens our understanding about how executive attention contributes to higher-order cognition.

RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials

OBJECTIVE

Electroencephalography (EEG) is often used to examine neural activity time-locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG is influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning reduces artifacts, but often requires time-consuming manual decisions. Most automated methods filter frequencies <1 Hz out of the data, so are not recommended for ERPs (which contain frequencies <1 Hz). Our aim was to test the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline for use on ERP data.

METHODS

The cleaning performance of multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines across both artifact cleaning metrics and the amount of variance in ERPs explained by different conditions in a Go-Nogo task. Results RELAX with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis applied to artifacts identified with ICLabel (wICA_ICLabel) cleaned data most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect effects better for some ERPs.

CONCLUSIONS

RELAX shows high artifact cleaning performance even when data is high-pass filtered at 0.25 Hz (applicable to ERP analyses).

SIGNIFICANCE

RELAX is easy to implement via EEGLAB in MATLAB and freely available on GitHub. Given its performance and objectivity we recommend RELAX to improve artifact cleaning and consistency across ERP research.

Position representations of moving objects align with real-time position in the early visual response

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX)

Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

Self-interest is intuitive during opportunity (in)equity: Evidence from multivariate pattern analysis of electroencephalography data

Fairness is a remarkable preference for human society, involving both outcome and opportunity equity. Most previous studies have explored whether fairness itself or self-interest is intuitive during outcome (in)equity. However, intuition during outcome (in)equity can be affected by both fairness level and actual payoff. Since opportunity (in)equity is only affected by the fairness level, we explored only intuition during fairness by measuring event-related potential responses to opportunity (in)equity. Participants played a social non-competitive two-person choice game with advantage opportunity inequity (AI), opportunity equity (OE), and disadvantage opportunity inequity (DI). The behavioral results suggested an opportunity inequity bias, with greater feelings of fairness and pleasantness during OE than during AI and DI. However, multivariate pattern analysis of the event-related potential (ERP) data suggested that AI, OE, and DI can be significantly distinguished from each other in relatively early windows overlapping with early positive negativity (EPN), and AI and DI can be significantly further distinguished during a relatively late window overlapping with late positive potential (LPP). Moreover, the conventional ERP analysis found that EPN amplitudes were more negative for AI than for OE and DI, as well as for OE than for DI, suggesting a pleasure bias for increased self-interest. LPP amplitudes were greater for DI than for AI and OE, suggesting enhanced sensitivity to DI. These results suggest that self-interest is intuitive during opportunity (in)equity.

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal (IT) cortex, an area known for its relevance in visual form processing, has access to motion information during recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons responded stronger to degraded shapes than clear ones. Furthermore, neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion patterns could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multifeature recognition tasks to understand the role of area IT in naturalistic visual object recognition.

Brainwave Phase Stability: Predictive Modeling of Irrational Decision

A predictive model applicable in both neurophysiological and decision-making studies is proposed, bridging the gap between psychological/behavioral and neurophysiological studies. Supposing the electromagnetic waves (brainwaves) are carriers of decision-making, and electromagnetic waves with the same frequency, individual amplitude and constant phase triggered by conditions interfere with each other and the resultant intensity determines the probability of the decision. Accordingly, brainwave-interference decision-making model is built mathematically and empirically test with neurophysiological and behavioral data. Event-related potential data confirmed the stability of the phase differences in a given decision context. Behavioral data analysis shows that phase stability exists across categorization-decision, two-stage gambling, and prisoner’s dilemma decisions. Irrational decisions occurring in those experiments are actually rational as their phases could be quantitatively derived from the phases of the riskiest and safest choices. Model fitting result reveals that the root-mean-square deviations between the fitted and actual phases of irrational decisions are less than 10°, and the mean absolute percentage errors of the fitted probabilities are less than 0.06. The proposed model is similar in mathematical form compared with the quantum modeling approach, but endowed with physiological/psychological connection and predictive ability, and promising in the integration of neurophysiological and behavioral research to explore the origin of the decision.

Interpersonal coordination enhances brain-to-brain synchronization and influences responsibility attribution and reward allocation in social cooperation

Fair distribution of resources matters to both individual interests and group harmony during social cooperation. Different allocation rules, including equity- and equality-based rules, have been widely discussed in reward allocation research; however, it remains unclear whether and how individuals' cooperative manner, such as interpersonal coordination, influence their subsequent responsibility attribution and reward allocation. Here, 46 dyads conducted a time estimation task-either synergistically (the coordination group) or solely (the control group)-while their brain activities were measured using a functional near-infrared spectroscopy hyperscanning approach. Dyads in the coordination group showed higher behavioral synchrony and higher interpersonal brain synchronization (IBS) in the dorsal lateral prefrontal cortex (DLPFC) during the time estimation task than those in the control group. They also showed a more egalitarian tendency of responsibility attribution for the task outcome. More importantly, dyads in the coordination group who had higher IBS in the dorsal medial prefrontal cortex (DMPFC) were more inclined to make egalitarian reward allocations, and this effect was mediated by responsibility attribution. Our findings elucidate the influence of interpersonal coordination on reward allocation and the critical role of the prefrontal cortex in these processes.

MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data

BACKGROUND AND OBJECTIVE

The study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data.

METHODS

The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach.

RESULTS

A sample electroencephalography dataset was compiled to test all the MVPAlab main functionalities. Significant clusters (p<0.01) were found for the proposed decoding analyses and different configurations, proving the software capability for discriminating between different experimental conditions.

CONCLUSIONS

This toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

The influence of error detection and error significance on neural and behavioral correlates of error processing in a complex choice task

Error detection and error significance form essential mechanisms that influence error processing and action adaptation. Error detection often is assessed by an immediate self-evaluation of accuracy. Our study used cognitive neuroscience methods to elucidate whether self-evaluation itself influences error processing by increasing error significance in the context of a complex response selection process. In a novel eight-alternative response task, our participants responded to eight symbol stimuli with eight different response keys and a specific stimulus-response assignment. In the first part of the experiment, the participants merely performed the task. In the second part, they also evaluated their response accuracy on each trial. We replicated variations in early and later stages of error processing and action adaptation as a function of error detection. The additional self-evaluation enhanced error processing on later stages, probably reflecting error evidence accumulation, whereas earlier error monitoring processes were not amplified. Implementing multivariate pattern analysis revealed that self-evaluation influenced brain activity patterns preceding and following the response onset, independent of response accuracy. The classifier successfully differentiated between responses from the self- and the no-self-evaluation condition several hundred milliseconds before response onset. Subsequent exploratory analyses indicated that both self-evaluation and the time on task contributed to these differences in brain activity patterns. This suggests that in addition to its effect on error processing, self-evaluation in a complex choice task seems to have an influence on early and general processing mechanisms (e.g., the quality of attention and stimulus encoding), which is amplified by the time on task.

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) to estimate a regression model predicting ratings of tastiness and healthiness for each participant, based on their neural activity occurring in the first second of food cue processing. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness. In Experiment 2, 89 healthy participants completed a similar rating task, followed by an additional experimental phase, in which they indicated their desire to consume snack foods with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

Early EEG responses to pre-electoral survey items reflect political attitudes and predict voting behavior

Self-reports are conventionally used to measure political preferences, yet individuals may be unable or unwilling to report their political attitudes. Here, in 69 participants we compared implicit and explicit methods of political attitude assessment and focused our investigation on populist attitudes. Ahead of the 2019 European Parliament election, we recorded electroencephalography (EEG) from future voters while they completed a survey that measured levels of agreement on different political issues. An Implicit Association Test (IAT) was administered at the end of the recording session. Neural signals differed as a function of future vote for a populist or mainstream party and of whether survey items expressed populist or non-populist views. The combination of EEG responses and self-reported preferences predicted electoral choice better than traditional socio-demographic and ideological variables, while IAT scores were not a significant predictor. These findings suggest that measurements of brain activity can refine the assessment of socio-political attitudes, even when those attitudes are not based on traditional ideological divides.

Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Charitable donations are an altruistic behavior whereby individuals donate money or other resources to benefit others while the recipient is normally absent from the context. Several psychological factors have been shown to influence charitable donations, including a cost-benefit analysis, the motivation to engage in altruistic behavior, and the perceived psychological benefits of donation. Recent work has identified the ventral medial prefrontal cortex (MPFC) for assigning value to options in social decision making tasks, with other regions involved in empathy and emotion contributing input to the value computation (e.g. Hare et al., 2010; Hutcherson et al., 2015; Tusche et al., 2016). Most impressively, multivariate pattern analysis (MVPA) has been applied to fMRI data to predict donation behavior on a trial-by-trial basis from ventral MPFC activity (Hare et al., 2010) while identifying the contribution of emotional processing in other regions to the value computation (e.g. Tusche et al., 2016). MVPA of EEG data may be able to provide further insight into the timing and scalp topography of neural activity related to both value computation and emotional effects on donation behavior. We examined the effect of incidental emotional states and the perceived urgency of the charitable cause on donation behavior using support vector regression on EEG data to predict donation amount on a trial by trial basis. We used positive, negative, and neutral pictures to induce incidental emotional states in participants before they made donation decisions concerning two types of charities. One category of charity was oriented toward saving people from current suffering, and the other was to prevent future suffering. Behaviorally, subjects donated more money in a negative emotional state relative to other emotional states, and more money to alleviate current over future suffering. The data-driven multivariate pattern analysis revealed that the electrophysiological activity elicited by both emotion-priming pictures and charity cues could predict the variation in donation magnitude on a trial-by-trial basis.

Tracking dynamic adjustments to decision making and performance monitoring processes in conflict tasks

How we exert control over our decision-making has been investigated using conflict tasks, which involve stimuli containing elements that are either congruent or incongruent. In these tasks, participants adapt their decision-making strategies following exposure to incongruent stimuli. According to conflict monitoring accounts, conflicting stimulus features are detected in medial frontal cortex, and the extent of experienced conflict scales with response time (RT) and frontal theta-band activity in the Electroencephalogram (EEG). However, the consequent adjustments to decision processes following response conflict are not well-specified. To characterise these adjustments and their neural implementation we recorded EEG during a modified Flanker task. We traced the time-courses of performance monitoring processes (frontal theta) and multiple processes related to perceptual decision-making. In each trial participants judged which of two overlaid gratings forming a plaid stimulus (termed the S1 target) was of higher contrast. The stimulus was divided into two sections, which each contained higher contrast gratings in either congruent or incongruent directions. Shortly after responding to the S1 target, an additional S2 target was presented, which was always congruent. Our EEG results suggest enhanced sensory evidence representations in visual cortex and reduced evidence accumulation rates for S2 targets following incongruent S1 stimuli. Results of a follow-up behavioural experiment indicated that the accumulation of sensory evidence from the incongruent (i.e. distracting) stimulus element was adjusted following response conflict. Frontal theta amplitudes positively correlated with RT following S1 targets (in line with conflict monitoring accounts). Following S2 targets there was no such correlation, and theta amplitude profiles instead resembled decision evidence accumulation trajectories. Our findings provide novel insights into how cognitive control is implemented following exposure to conflicting information, which is critical for extending conflict monitoring accounts.

Predicting participants' attitudes from patterns of event-related potentials during the reading of morally relevant statements - An MVPA investigation

Morality and language are hardly separable, given that morality-related aspects such as knowledge, emotions, or experiences are connected with language on different levels. One question that arises is: How rapidly do neural processes set in when processing statements that reflect moral value containing information? In the current study, participants read sentences about morally relevant statements (e.g., 'Wars are acceptable') and expressed their (dis)agreement with the statements while their electroencephalogram (EEG) was recorded. Multivariate pattern classification (MVPA) was used during language processing to predict the individual's response. Our results show that (1) the response ('yes' vs. 'no') could be predicted from 180 ms following the decision-relevant word (here acceptable), and (2) the attitude (pro vs. contra the topic) could be predicted from 170 ms following the topic word (here wars). We suggest that the successful MVPA classification is due to different brain activity patterns evoked by differences in activated mental representations (e.g. valence, arousal, etc.) depending on whether the attitude towards the topic is positive or negative and whether it is in accordance with the presented decisive word or not.

Orientation-dependency of perceptual surround suppression and orientation decoding of centre-surround stimuli are preserved with healthy ageing

A key visual neuronal property that is mirrored in human behaviour is centre-surround contrast suppression, which is orientation-dependent. When a target is embedded in a high-contrast surround, the centre appears reduced in contrast, the magnitude of which depends on the relative orientation between centre and surround. Previous reports demonstrate changes in perceptual surround suppression with ageing; however, whether the orientation-dependency of surround suppression is impacted by ageing has not been explored. Here, we tested 18 younger (aged 19-33) and 18 older (aged 60-77) adults. Perceptual surround suppression was stronger for parallel than orthogonal stimuli; however contrary to previous work, here we found no difference in perceptual suppression strength between age-groups. In the same participants, we measured event-related potentials (ERPs) and conducted multivariate pattern analysis to confirm that parallel and orthogonal centre-surround stimuli elicit distinguishable brain activity, predominantly over occipital areas. Despite a delay in the first prominent ERP component (P1) in response to each pattern, older adults showed similar decoding of orientation information (i.e. distinguish between parallel and orthogonal centre-surround stimuli from 70 ms post-stimulus onset) as younger adults. This suggests that sufficient information to distinguish orientation in centre-surround stimuli becomes available to the older human brain as early as in younger adults.

Does cognitive control ability mediate the relationship between reward-related mechanisms, impulsivity, and maladaptive outcomes in adolescence and young adulthood?

Neurobiological models explain increased risk-taking behaviours in adolescence and young adulthood as arising from staggered development of subcortical reward networks and prefrontal control networks. In this study, we examined whether individual variability in impulsivity and reward-related mechanisms is associated with higher level of engagement in risky behaviours and vulnerability to maladaptive outcomes and whether this relationship is mediated by cognitive control ability. A community sample of adolescents, young adults, and adults (age = 15-35 years) completed self-report measures and behavioural tasks of cognitive control, impulsivity, and reward-related mechanisms, and self-reported level of maladaptive outcomes. Behavioural, event-related potential (ERP), and multivariate pattern analysis (MVPA) measures of proactive control were derived from a task-switching paradigm. Adolescents, but not young adults, reported higher levels of impulsivity, reward-seeking behaviours and maladaptive outcomes than adults. They also had lower cognitive control ability, as measured by both self-report and task-based measures. Consistent with models of risk-taking behaviour, self-reported level of cognitive control mediated the relationship between self-reported levels of impulsivity and psychological distress, but the effect was not moderated by age. In contrast, there was no mediation effect of behavioural or EEG-based measures of cognitive control. These findings suggest that individual variability in cognitive control is more crucial to the relationship between risk-taking/impulsivity and outcomes than age itself. They also highlight large differences in measurement between self-report and task-based measures of cognitive control and decision-making under reward conditions, which should be considered in any studies of cognitive control.

MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data

With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.

Decoding personality trait measures from resting EEG: An exploratory report

Can personality be predicted from oscillatory patterns produced by the brain at rest? To date, relatively few studies using electroencephalography (EEG) have yielded consistent relations between personality trait measures and spectral power. Thus, new exploratory research may help develop targeted hypotheses about how neural processes associated with EEG activity may relate to personality differences. We used multivariate pattern analysis to decode personality scores (i.e., Big Five traits) from resting EEG frequency power spectra. Up to 8 minutes of EEG data was recorded per participant prior to completing an unrelated task (N = 168, M_age = 23.51, 57% female) and, in a subset of participants, after task completion (N = 96, M_age = 23.22, 52% female). In each recording, participants alternated between open and closed eyes. Linear support vector regression with 10-fold cross validation was performed using the power from 62 scalp electrodes within 1 Hz frequency bins from 1 to 30 Hz. One Big Five trait, agreeableness, could be decoded from EEG power ranging from 8 to 19 Hz, and this was consistent across all four recording periods. Neuroticism was decodable using data within the 3-6 Hz range, albeit less consistently. Posterior alpha power negatively correlated with agreeableness, whereas parietal beta power positively correlated with agreeableness. We suggest methods to draw from our results and develop targeted future hypotheses, such as linking to individual alpha frequency and incorporating self-reported emotional states. Our open dataset can be harnessed to reproduce results or investigate new research questions concerning the biological basis of personality.

Preserved performance monitoring and error detection in left hemisphere stroke

Depending on the lesion site, a stroke typically affects various aspects of cognitive control. While executing a task, the performance monitoring system constantly compares an intended action plan with the executed action and thereby registers inaccurate actions in case of any mismatch. When errors occur, the performance monitoring system signals the need for more cognitive control, which is most efficient when the subject notices errors rather than processing them subconsciously. The current study aimed to investigate performance monitoring and error detection in a large sample of patients with left hemisphere (LH) stroke. In addition to clinical and neuropsychological tests, 24 LH stroke patients and 32 healthy age-matched controls performed a Go/Nogo task with simultaneous electroencephalography (EEG) measurements. This set-up enabled us to compare performance monitoring at the behavioral and the neural level. EEG data were analyzed using event-related potentials [ERPs; e.g., the error-related negativity (Ne/ERN) and error positivity (Pe)] and additionally more sensitive whole-brain multivariate pattern classification analyses (MVPA). We hypothesized that LH stroke patients would show behavioural deficits in error detection when compared to healthy controls, mirrored by differences in neural signals, in particular reflected in the Pe component. Interestingly, despite clinically relevant cognitive deficits (e.g., aphasia and apraxia) including executive dysfunction (trail making test), we did not observe any behavioral impairments related to performance monitoring and error processing in the current LH stroke patients. Patients also showed similar results for Ne/ERN and Pe components, compared to the control group, and a highly similar prediction of errors from multivariate signals. ERP abnormalities during stimulus processing (i.e., N2 and P3) demonstrated the specificity of these findings in the current LH stroke patients. In contrast to previous studies, by employing a relatively large patient sample, a well-controlled experimental paradigm with a standardized error signaling procedure, and advanced data analysis, we were able to show that performance monitoring (of simple actions) is a preserved cognitive control function in LH stroke patients that might constitute a useful resource in rehabilitative therapies for re-learning impeded functions.

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox. After introducing MVPA-Light, example analyses of MEG and fMRI datasets, and benchmarking results on the classifiers and regression models are presented.

Role of spontaneous and sensory orexin network dynamics in rapid locomotion initiation

Appropriate motor control is critical for normal life, and requires hypothalamic hypocretin/orexin neurons (HONs). HONs are slowly regulated by nutrients, but also display rapid (subsecond) activity fluctuations in vivo. The necessity of these activity bursts for sensorimotor control and their roles in specific phases of movement are unknown. Here we show that temporally-restricted optosilencing of spontaneous or sensory-evoked HON bursts disrupts locomotion initiation, but does not affect ongoing locomotion. Conversely, HON optostimulation initiates locomotion with subsecond delays in a frequency-dependent manner. Using 2-photon volumetric imaging of activity of >300 HONs during sensory stimulation and self-initiated locomotion, we identify several locomotion-related HON subtypes, which distinctly predict the probability of imminent locomotion initiation, display distinct sensory responses, and are differentially modulated by food deprivation. By causally linking HON bursts to locomotion initiation, these findings reveal the sensorimotor importance of rapid spontaneous and evoked fluctuations in HON ensemble activity.

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox. After introducing MVPA-Light, example analyses of MEG and fMRI datasets, and benchmarking results on the classifiers and regression models are presented.

From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.

Neural Decoding of Bistable Sounds Reveals an Effect of Intention on Perceptual Organization

Auditory signals arrive at the ear as a mixture that the brain must decompose into distinct sources based to a large extent on acoustic properties of the sounds. An important question concerns whether listeners have voluntary control over how many sources they perceive. This has been studied using pure high (H) and low (L) tones presented in the repeating pattern HLH-HLH-, which can form a bistable percept heard either as an integrated whole (HLH-) or as segregated into high (H-H-) and low (-L-) sequences. Although instructing listeners to try to integrate or segregate sounds affects reports of what they hear, this could reflect a response bias rather than a perceptual effect. We had human listeners (15 males, 12 females) continuously report their perception of such sequences and recorded neural activity using MEG. During neutral listening, a classifier trained on patterns of neural activity distinguished between periods of integrated and segregated perception. In other conditions, participants tried to influence their perception by allocating attention either to the whole sequence or to a subset of the sounds. They reported hearing the desired percept for a greater proportion of time than when listening neutrally. Critically, neural activity supported these reports; stimulus-locked brain responses in auditory cortex were more likely to resemble the signature of segregation when participants tried to hear segregation than when attempting to perceive integration. These results indicate that listeners can influence how many sound sources they perceive, as reflected in neural responses that track both the input and its perceptual organization.

SIGNIFICANCE STATEMENT Can we consciously influence our perception of the external world? We address this question using sound sequences that can be heard either as coming from a single source or as two distinct auditory streams. Listeners reported spontaneous changes in their perception between these two interpretations while we recorded neural activity to identify signatures of such integration and segregation. They also indicated that they could, to some extent, choose between these alternatives. This claim was supported by corresponding changes in responses in auditory cortex. By linking neural and behavioral correlates of perception, we demonstrate that the number of objects that we perceive can depend not only on the physical attributes of our environment, but also on how we intend to experience it.