Biology subjects

Cohen, M. X.

Publications and source records attributed to Cohen, M. X..

At least 19 recordsLinked to original sources

bioRxiv2024

Stopping Speed as State, Not Trait: Exploring Within-Animal Varying Stopping Speeds in a Multi-Session Stop-Signal Task

Being able to reactively stop ongoing movements is important for safe navigation through the environment. Reactive stopping is typically studied using the stop-signal task, where participants are occasionally instructed to stop initiated movements. The speed of stopping, also referred to as the stop-signal reaction time (SSRT), is not observable because successful stopping lacks a response, but can be estimated. Researchers most often acquire one session of data per participant to estimate the speed of stopping, but sometimes more sessions of data are acquired to maximize the signal-to-noise ratio, for example when the task is combined with neural recordings such as electrophysiology. However, it is unknown whether the estimated stopping speed is a fixed trait or a state that can vary under identical experimental conditions. In this study, we investigate whether a separately estimated SSRT for each acquired session is statistically meaningful compared to estimating an across-session SSRT, by collecting many sessions in which male rats performed a stop-signal task. Results revealed that within-animal stopping speeds meaningfully changed from session to session and were not following a trend over time (e.g., due to task learning). Single-session SSRT estimates with lower reliabilities were associated with higher go trial response time variabilities, lower skewness levels of the go trial response time distribution, and lower stop accuracies. We also explored which factors explained changing SSRTs, and showed that motivation, shared motor dynamics, and attention could play a role. In conclusion, we encourage researchers to treat SSRTs as state-like variables when collecting multi-session stop-signal task data, as our results have convincingly shown that stopping speeds are far from trait-like under identical experimental conditions. This session-by-session approach will help future research in which neural signatures of reactive stopping need to be extracted in a time-precise manner, because time-locking stop-related neural activity to session-specific SSRTs is expected to capture the signature more precisely as opposed to an across-session SSRT.

animal behavior and cognition↗

bioRxiv2023

Spatiotemporal resonance in mouse primary visual cortex

Human primary visual cortex (V1) responds more strongly, or resonates, when exposed to [~]10, [~]15-20, [~]40-50 Hz rhythmic flickering light. Full-field flicker also evokes perception of hallucinatory geometric patterns, which mathematical models explain as standing-wave formations emerging from periodic forcing at resonant frequencies of the simulated neural network. However, empirical evidence for such flicker-induced standing waves in the visual cortex was missing. We recorded cortical responses to flicker in awake mice using high spatial resolution widefield imaging in combination with high temporal resolution glutamate-sensing fluorescent reporter (iGluSnFR). The temporal frequency tuning curves in the mouse V1 were similar to those observed in humans, showing a banded structure with multiple resonance peaks (8 Hz, 15 Hz, 33 Hz). Spatially, all flicker frequencies evoked responses in V1 corresponding to retinotopic stimulus location, but some evoked additional peaks. These flicker-induced cortical patterns displayed standing wave characteristics and matched linear wave equation solutions in an area restricted to the visual cortex. Taken together, the interaction of periodic traveling waves with cortical area boundaries leads to spatiotemporal activity patterns that may affect perception.

neuroscience↗

bioRxiv2023

Using the SSVEP to measure the SNARC-spatial attention effects in a parity judgment task

Mental representation of numbers in the brain has been described as spatially organized on what is known as the "mental number line," with small numbers to the left and large numbers to the right. This representation leads to the "SNARC effect" (Spatial-Numerical Association of Response Codes), which refers to (1) improved behavioral performance for "congruent" conditions (e.g., left-handed response to small numbers) as compared to "incongruent" conditions (e.g., right-handed response to small numbers) and (2) perceiving numbers induces an automatic shift in covert spatial attention. Nonetheless, there is scarce physiological evidence for (or against) the prediction that number magnitude induces an automatic shift of spatial attention. Here we recorded EEG during the bimanual SNARC-parity judgment task (classifying numbers as odd or even) in attempt to find electrophysiological evidence for number-magnitude-induced automatic shifts of spatial attention. Attention was measured using the SSVEP (steady-state visual evoked potential) from four task-irrelevant flickering frequencies (two to the left: 15 and 20 Hz and two to the right: 24 and 17.14 Hz) in combination with an optimal spatial filtering method. EEG analysis was performed on the flicker frequencies closest to the fixation point: 20 and 24 Hz for the left and right visual hemifields, respectively. We replicated the expected behavioral patterns predicted by the SNARC effect, such that participants performed better for congruent conditions than on incongruent conditions. We observed significant changes in the SSVEP amplitude with respect to the baseline for the left (20 Hz) and right (24 Hz) flicker for both the congruent and incongruent conditions. Statistically significant differences between the congruent and incongruent conditions were larger for the congruent conditions for the flicker stimuli on the left. Together, these findings support the hypothesis that when numbers are part of the task, but their magnitude is not relevant, the SNARC-spatial attention effect is elicited as a cognitive effect resulting from the mental representation of numbers and its relationship with the space representation, more than merely a motor effect.

physiology↗

bioRxiv2022

Two modes of mid-frontal theta suggest a role in conflict and error processing

Mid-frontal theta increases during scenarios when conflicts are successfully resolved. Often considered as a generic signal of cognitive control, its temporal nature has hardly been investigated. Using advanced spatiotemporal techniques, we uncover that mid-frontal theta occurs as a transient oscillation or "event" at single trials with their timing reflecting computationally distinct modes. Single-trial analyses of electrophysiological data from participants performing the Flanker (N = 28) and Simon task (N = 18) were used to probe the relationship between theta and metrics of response conflict. We specifically investigated "partial errors", in which a small burst of muscle activity in the incorrect response effector occurred, quickly followed by a correction. We found that transient theta events in single trials could be categorized into two distinct theta modes based on their relative timing to different task events. Theta events from the first mode occurred briefly after the task stimulus and might reflect conflict-related processing of the stimulus. In contrast, theta events from the second mode were more likely to occur around the time partial errors were committed, suggesting they were elicited by a potential upcoming error. Importantly, in trials in which a full error was committed, this "error-related theta" occurred too late with respect to the onset of the erroneous muscle response, supporting a role of theta also in error correction. We conclude that different modes of transient mid-frontal theta can be adopted in single trials not only to process stimulus conflict, but also to correct erroneous responses.

neuroscience↗

bioRxiv2021

Prefrontal, striatal, and VTA subnetwork dynamics during novelty and exploration

Multiple distinct brain areas have been implicated in memory including the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA). Information-exchange across these widespread networks requires flexible coordination at a fine time-scale. In the present study, we collected high-density recordings from the PFC, STR, and VTA of male rats during baseline, encoding, consolidation, and retrieval stages of memory formation. Novel sub-regional clustering analyses identified patterns of spatially restricted, temporally coherent, and frequency specific signals that were reproducible across days and were modulated by behavioral states. Clustering identified miniscule patches of neural tissue. Generalized eigen decomposition (GED) reduced each cluster to a single time series. Amplitude envelope correlation of the cluster time series was used to assess functional connectivity between clusters. Dense intra- and inter regional functional connectivity characterized the baseline period, with delta oscillations playing an outsized role. There was a dramatic pruning of network connectivity during encoding. Connectivity rebounded during consolidation, but connections in the theta band became stronger, and those in the delta band were weaker. Finally, during retrieval, connections were not as severely reduced as they had been during encoding, and specifically theta and higher-frequency connections were stronger. Underlying these connectivity changes, the anatomical extent of clusters observed in the gamma band in the PFC and in both the gamma and delta bands in the VTA changed markedly across behavioral conditions. These results demonstrate the brains ability to reorganize functionally at both the intra- and inter-regional levels during different stages of memory processing. SIGNIFICANCE STATEMENTThe brain is often thought of as a mosaic of areas each with static functions that activate or deactivate with task demands. Here, we used large-scale recordings (196 simultaneous electrodes) and developed a multivariate analysis approach to analyze data from all our recording locations simultaneously. This analysis revealed that the brain dramatically reorganized itself at both local and long-distance spatial scales during different stages of memory processing. These results demonstrate an extreme degree of flexibility in functional anatomy. Rather than thinking about the brain as a set of static mosaic tiles, it may better be characterized as a quickly moldable piece of clay where each parts function changes as the whole is reshaped from moment to moment.

neuroscience↗

bioRxiv2021

Power spectrum slope confounds estimation of instantaneous oscillatory frequency

Oscillatory neural dynamics are highly non-stationary and require methods capable of quantifying time-resolved changes in rhythmic activity in order to understand neural function. Recently, a method termed frequency sliding was introduced to estimate the instantaneous frequency of oscillatory activity, providing a means of tracking temporal changes in the dominant frequency within a sub-band of field potential recordings. Here, the ability of frequency sliding to recover ground-truth oscillatory frequency in simulated data is tested while the exponent (slope) of the 1/fx component of the signal power spectrum is systematically varied, mimicking real electrophysiological data. The results show that 1) in the presence of 1/f activity, frequency sliding systematically underestimates the true frequency of the signal, 2) the magnitude of underestimation is correlated with the steepness of the slope, suggesting that, if unaccounted for, slope changes could be misinterpreted as frequency changes, 3) the impact of slope on frequency estimates interacts with oscillation amplitude, indicating that changes in oscillation amplitude alone may also influence instantaneous frequency estimates in the presence of strong 1/f activity; and 4) analysis parameters such as filter bandwidth and location also mediate the influence of slope on estimated frequency, indicating that these settings should be considered when interpreting estimates obtained via frequency sliding. The origin of these biases resides in the output of the filtering step of frequency sliding, whose energy is biased towards lower frequencies precisely because of the 1/f structure of the data. We discuss several strategies to mitigate these biases and provide a proof-of-principle for a 1/f normalization strategy.

neuroscience↗

bioRxiv2021

Characterizing neural phase-space trajectories via Principal Louvain Clustering

BackgroundWith the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges. New MethodIn this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments. ResultsPLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals ongoing behavior. ConclusionsPLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

neuroscience↗

bioRxiv2020

Large- and multi-scale networks in the rodent brain during novelty exploration

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration. Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

neuroscience↗

bioRxiv2020

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Long-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation. Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

neuroscience↗

bioRxiv2020

Dynamics of neural microstates in the VTA-striatal-prefrontal loop during novelty exploration in the rat

EEG microstates refer to quasi-stable spatial patterns of scalp potentials, and their dynamics have been linked to cognitive and behavioral states. Neural activity at single and multiunit levels also exhibit spatiotemporal coordination, but this spatial scale is difficult to relate to EEG. Here, we translated EEG microstate analysis to triple-area local field potential (LFP) recordings from up to 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions. We performed simultaneous recordings from the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring, quasi-stable spatial activity patterns with an average period of stability of ~60-100 ms. The top four maps accounted for 60-80% of the total variance, compared to ~25% for shuffled data. Cross-correlation of the microstate time-series across brain regions revealed rhythmic patterns of microstate activations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, microstate features, and patterns of temporal correlations across microstates, were modulated by behavioural states such as movement and novel object exploration. These results support the existence of a functional mesoscopic organization across multiple brain areas, and open up the opportunity to investigate their relation to EEG microstates, of particular interest to the human research community. Significance StatementThe coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates, i.e. short-lived, recurring spatial patterns of neural activity. We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioural states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

neuroscience↗

bioRxiv2020

Synchronization between keyboard typing and neural oscillations

Rhythmic neural activity synchronizes with certain rhythmic behaviors, such as breathing, sniffing, saccades, and speech. The extent to which neural oscillations synchronize with higher-level and more complex behaviors is largely unknown. Here we investigated electrophysiological synchronization with keyboard typing, which is an omnipresent behavior daily engaged by an uncountably large number of people. Keyboard typing is rhythmic with frequency characteristics roughly the same as neural oscillatory dynamics associated with cognitive control, notably through midfrontal theta (4 -7 Hz) oscillations. We tested the hypothesis that synchronization occurs between typing and midfrontal theta, and breaks down when errors are committed. Thirty healthy participants typed words and sentences on a keyboard without visual feedback, while EEG was recorded. Typing rhythmicity was investigated by inter-keystroke interval analyses and by a kernel density estimation method. We used a multivariate spatial filtering technique to investigate frequency-specific synchronization between typing and neuronal oscillations. Our results demonstrate theta rhythmicity in typing (around 6.5 Hz) through the two different behavioral analyses. Synchronization between typing and neuronal oscillations occurred at frequencies ranging from 4 to 15 Hz, but to a larger extent for lower frequencies. However, peak synchronization frequency was idiosyncratic across subjects, therefore not specific to theta nor to midfrontal regions, and correlated somewhat with peak typing frequency. Errors and trials associated with stronger cognitive control were not associated with changes in synchronization at any frequency. As a whole, this study shows that brain-behavior synchronization does occur during keyboard typing but is not specific to midfrontal theta. Significance statementEvery day, millions of people type on keyboards. Keyboard typing is a rhythmic behavior, with inter-keystroke-intervals of around 135 ms (~7 Hz), which is roughly the same frequency as the brain rhythm implicated in cognitive control ("theta" band, ~6 Hz). Here we investigated the hypothesis that the EEG signature of cognitive control is synchronized with keyboard typing. By recording EEG during typing in 30 healthy subjects we showed that keyboard typing indeed follows theta rhythmicity, and that synchronization between typing and neural oscillations occurs. However, synchronization was not limited to theta but occurred at frequencies ranging from 4 to 15 Hz, and in several regions. Brain-behavior synchronization during typing thus seems more nuanced and complex than we originally hypothesized.

neuroscience↗

bioRxiv2020

Multiple midfrontal thetas revealed by sourceseparation of simultaneous MEG and EEG

Theta-band ([~]6 Hz) rhythmic activity within and over the medial prefrontal cortex ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in nine human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition (GED) yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level due to signal mixing. Significance statement"Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous due to "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal M/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal de-mixing to understand the neural dynamics underlying cognitive processes.

neuroscience↗

bioRxiv2020

Narrowband multivariate source separation for semi-blind discovery of experiment contrasts

BackgroundElectrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions. New MethodWe demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method -- comparison scanning generalized eigendecomposition, csGED -- harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED. ResultsTo validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band. Comparison with Existing Method(s)GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology. ConclusionsWe demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

neuroscience↗

bioRxiv2020

Mesoscopic-scale functional networks in the primate amygdala

The primate amygdala performs multiple functions that may be related to the anatomical heterogeneity of its nuclei. At the level of single neurons, each function is reflected in stimulus- and task-specific responses. Given that neurons with a particular response profile are not clustered in any of the nuclei, single units may be too fine-grained to shed light on the mesoscale organization of the amygdala. We have extracted from local field potentials recorded simultaneously from multiple locations within the primate amygdala (Macaca mulatta) spatially defined and statistically separable responses to visual, tactile, and auditory stimuli. A generalized eigendecomposition-based method of source separation isolated coactivity patterns, or components, that in neurophysiological terms correspond to putative subnetworks. Some component spatial patterns mapped onto the anatomical organization of the amygdala, while other components reflected integration across nuclei. These components differentiated between visual, tactile, and auditory stimuli suggesting the presence of functionally distinct parallel subnetworks.

neuroscience↗

bioRxiv2018

Midfrontal theta phase coordinates behaviorally relevant brain computations during response conflict

Neural oscillations are thought to provide a cyclic time frame for orchestrating brain computations. Following this assumption, midfrontal theta oscillations have recently been proposed to temporally organize brain computations during conflict processing. Using a multivariate analysis approach, we show that brain-behavior relationships during conflict tasks are modulated according to the phase of ongoing endogenous midfrontal theta oscillations recorded by scalp EEG. We found reproducible results in two independent datasets, using two different conflict tasks: brain-behavior relationships (correlation between reaction time and theta power) were theta phase-dependent in a subject-specific manner, and these \"behaviorally optimal\" theta phases were also associated with fronto-parietal cross-frequency dynamics emerging as theta phase-locked beta power bursts. These effects were present regardless of the strength of conflict. Thus, these results provide empirical evidence that midfrontal theta oscillations are involved in cyclically orchestrating brain computations likely related to response execution during the tasks rather than purely related to conflict processing. More generally, this study supports the hypothesis that phase-based computation is an important mechanism giving rise to cognitive processing.

neuroscience↗

bioRxiv2018

Sustaining attention for a prolonged period of time increases temporal variability in cortical responses

Our ability to stay focused is limited: prolonged performance of a task typically results in mental fatigue and decrements in performance over time. This so-called vigilance decrement has been attributed to depletion of attentional resources, though other factors such as reductions in motivation likely also play a role. In this study, we examined three EEG markers of attentional control, to elucidate which stage of attentional processing is most affected by time-on-task and motivation. To elicit the vigilance decrement, participants performed a sustained attention task for 80 minutes without breaks. After 60 minutes, participants were motivated by an unexpected monetary incentive to increase performance in the final 20 minutes. We found that task performance and self-reported motivation declined rapidly, reaching a stable levels well before the motivation manipulation was introduced. Thereafter, motivation increased back up to the initial level, and remained there for the final 20 minutes. While task performance also increased, it did not return to the initial level, and fell to the lowest level overall during the final 10 minutes. This pattern of performance changes was mirrored by the trial-to-trial consistency of the phase of theta (3-7 Hz) oscillations, an index of the variability in timing of the neural response to the stimulus. As task performance decreased, temporal variability increased, suggesting that attentional stability is crucial for sustained attention performance. The effects of attention on our two other EEG measures--early P1/N1 event-related potentials and pre-stimulus alpha (9-14 Hz) power--did not change with time-on-task or motivation. In sum, these findings show that the vigilance decrement is accompanied by a decline in only some facets of attentional control, which cannot be fully brought back online by increases in motivation. The vigilance decrement might thus not occur due to a single cause, but is likely multifactorial in origin.

neuroscience↗

bioRxiv2018

How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning

The extent to which subjective awareness influences reward processing, and thereby affects future decisions is currently largely unknown. In the present report, we investigated this question in a reinforcement-learning framework, combining perceptual masking, computational modeling and electroencephalographic recordings (human male and female participants). Our results indicate that degrading the visibility of the reward decreased -without completely obliterating- the ability of participants to learn from outcomes, but concurrently increased their tendency to repeat previous choices. We dissociated electrophysiological signatures evoked by the reward-based learning processes from those elicited by the reward-independent repetition of previous choices and showed that these neural activities were significantly modulated by reward visibility. Overall, this report sheds new light on the neural computations underlying reward-based learning and decision-making and highlights that awareness is beneficial for the trial-by-trial adjustment of decision-making strategies.\n\nSignificance statementThe notion of reward is strongly associated with subjective evaluation, related to conscious processes such as \"pleasure\", \"liking\" and \"wanting\". Here we show that degrading reward visibility in a reinforcement learning task decreases -without completely obliterating- the ability of participants to learn from outcomes, but concurrently increases subjects tendency to repeat previous choices. Electrophysiological recordings, in combination with computational modelling, show that neural activities were significantly modulated by reward visibility. Overall, we dissociate different neural computations underlying reward-based learning and decision-making, which highlights a beneficial role of reward awareness in adjusting decision-making strategies.

neuroscience↗

bioRxiv2018

A better way to define and describe Morlet wavelets for time-frequency analysis

Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the \"number of cycles,\" but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.

neuroscience↗