Sandra Dérozier
email: sandra.derozier@inrae.fr

Pierre Nicolas

Ulirke Mäder

Cyprien Guérin
email: cyprien.guerin@inrae.fr.

Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations Sandra Dérozier 1,2,* , Pierre Nicolas 1 , Ulrike Mäder 3 , and Cyprien Guérin 1,* December 22, 2020

Introduction

Analysis of quantitative genome-wide data, and in particular transcriptome data [Hör et al., 2018], allows to constantly improve structural and functional annotation of bacterial genomes.

Albeit collected along the whole genome, transcriptome data are usually aggregated at gene level based on a predefined genome annotation in order to analyze differential expression. This aggregation masks local variations in signal intensity that are useful to understand technical biases or non-canonical biological mechanisms such as inefficient or early transcription termination [Bidnenko et al., 2017]. More generally, examination of non-aggregated condition-dependent or strain-dependent transcription profiles along genomes is essential for delineation of transcription units and characterization of new genes such as antisense and other non-coding RNAs. Illustrative of this interest in profiles, websites (http://genome.jouy.inra.fr/seb and http://genome.jouy.inra.fr/aeb) were released with transcriptome-based reannotation studies of Bacillus subtilis [START_REF] Nicolas | Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis[END_REF] and Staphylococcus aureus [Mäder et al., 2016] to allow the community to further exploit the large-scale datasets generated for these studies. However, these widely used genome browsers are based on static images pregenerated using MuGeN [Hoebeke et al., 2003] and thus only display a subset of transcription profiles, selected as representative of the diversity of the full dataset.

Since MuGeN, new genome browsers have been developed as independent applications such as IGV [James et al., 2011] and JBrowse [Buels et al., 2016], or as parts of broader projects, such as in Panoptes [Vauterin et al., 2017]. Some allow visualization of multiple profiles stacked on one track but do not combine interactive profile selection and colouring with the easy access of an online tool, as useful to efficiently browse large sets of profiles. Here we present Genoscapist that answers this specific need. We designed Genoscapist as an independent application instead of a plugin to another -more generictool to have maximum freedom for the development and integration of graphical representation and browsing. As a proof of concept and to meet the demand of the respective scientific communities for customizable views of condition-dependent transcription profiles along genomes, we re-implemented the aforementioned websites dedicated to B. subtilis and S. aureus.

Implementation

Genoscapist runs on an Apache webserver. It is written in HTML5/Javascript (client-side) and Python with Flask web framework (server-side). Using AJAX reduces response time by minimizing data transfers, by sending simultaneous server requests, and by taking advantage of the processing capability of the clients. Graphical rendering follows the Scalable Vector Graphics (SVG) Web standard.

Genome annotations and quantitative profiles are stored in a PostgreSQL database whose relational scheme is derived from FLAGdb++ [Dérozier et al., 2011]. The database is typically created from a genome record (e.g. GenBank format) enriched by experimental data provided as tabulated (csv) files. Data integration is based on genomic coordinates on which indexes are built to optimize retrieval speed.

The source code, placed under the GNU General Public License, is available on a GitHub repository (https://github.com/sderozier/genoscapist).

Tool

As illustrated by the two websites http://genoscapist.migale.inrae.fr/aeb and http://genoscapist.migale.inrae.fr/seb, the main entry view consists of a list of transcribed loci (coding sequences, non-coding RNAs, 5', 3' and intra-operonic untranslated regions) hereafter collectively referred as segments, with basic information such as genomic coordinates and membership to co-expression clusters. Filters can be applied to shorten this list. Clicks on loci lead to segment-centric views from which genomic views can be accessed.

Segment-centric views present various information such as i) the genomic view centered on the segment, ii) links to entry views filtered by the co-expression clusters of the segment, iii) tables of conditions of highest and lowest expression for this segment, ii) links to other segments with highly (positively and negatively) correlated expression levels, iv) a figure of the expression level aggregated at segment-level across all profiles and v) functional annotation and direct link to an organism-oriented wiki [START_REF] Zhu | SubtiWiki in 2018: from genes and proteins to functional network annotation of the model organism Bacillus subtilis[END_REF], Fuchs et al., 2018].

Genomic views present a default selection of expression profiles (Figure 1A) which can be customized by clicking on a profile line to change its colour (Figure 1B) or by using the "profile selection" menu (Figure 1C) to select profiles and change colours. Figures 1D and1E show examples of customized views which can be centered on any selected segment and can also be applied when scanning the entire genome using the left/right arrows and forward/backward zooming (Figure 1A). Clicking on a segment activates the corresponding segment-centric view. Clicking elsewhere places a vertical mark and gives the exact position.

Taken together, segment-centric and genomic views provide an integrated framework to interactively explore datasets by navigating along relationships in the expression space and along the genome sequence.

The interface also allows to i) change the normalization method of the expression profiles, ii) add complementary expression data from a rho-null mutant impaired in factor-dependent transcription ter-mination, iii) display all segment names, iv) export the genomic view as a SVG file, v) display a legend and vi) obtain a link to save and/or share a customized view.

Conclusions

Genoscapist allows interactive visualization of hundreds of quantitative profiles along a reference genome together with various annotations. Images saved in SVG format can directly be processed by vector graphics editors to assemble high-quality figures for scientific communication. Relevance is demonstrated by deployment of websites dedicated to condition-dependent transcriptomes of two important bacteria. Genoscapist can handle multi-chromosome reference genomes and the genomic view can readily display any type of quantitative profiles along genomes, such as those obtained for protein-DNA and protein-RNA interactions by immunoprecipitation.

Figure 1 :

 1 Figure 1: The genomic view of Genoscapist. Panels A to D illustrate interactive customization of the graphical representation of transcription profiles in a region of the B. subtilis genome.

Acknowledgements

The authors thank INRAE Migale (doi: 10.15454/1.5572390655343293E12) for hosting the websites, and Thomas Lacroix, Franck Samson, and the CATI SYSMICS group for sharing expertise.

Funding

PN and CG received support from ANR CoNoCo (ANR-18-CE12-0025).

//genoscapist.migale.inrae.fr


Delevoye-Turrell Grisetto

Fanny Grisetto

Yvonne N Delevoye-Turrell

Clémence Roger
email: clemence.roger@univ-lille.fr

PhD Clémence Roger

Efficient but less active monitoring system in individuals with high aggressive predispositions

Keywords: aggressiveness, cognitive control, EEG, ERN/Ne, CRN/Nc, performance monitoring

. EMG recordings were used to reveal three response types by uncovering small incorrect muscular activations in ~15% of correct trials (i.e., partialerrors) that must be distinguished from full-error and pure-correct responses. For these three response types, EEG recordings were used to extract fronto-central negativities indicative of performance monitoring, the error and correct (-related) negativities (ERN/Ne and CRN/Nc).

Behavioral results indicated that the high aggressiveness group had a larger congruency effect compared to the low aggressiveness group, but there were no differences in accuracy. EEG results revealed a global reduction in performance-related negativity amplitudes in all the response types in the high aggressiveness group compared to the low aggressiveness group.

Interestingly, the distinction between the ERN/Ne and the CRN/Nc components was preserved both in high and low aggressiveness groups. In sum, high aggressive traits do not affect the capacity to self-evaluate erroneous from correct actions but are associated with a

Introduction

Aggressiveness is defined as an individual's predispositions to respond aggressively, to experience negative emotions and to hold hostile thoughts [START_REF] Buss | The Aggression Questionnaire[END_REF]. Previous studies in pathological and healthy individuals have demonstrated that aggressive tendencies are linked to poor executive capacities. In maladapted populations, for example, violent offenders, [START_REF] Hancock | Role of executive dysfunction in predicting frequency and severity of violence[END_REF] showed that deficits in executive functioning predicted the frequency and severity of intentional acts in physical aggression. Even in a normal population, higher aggressive traits have been associated with low inhibitory control capacities [START_REF] Pawliczek | Inhibitory control and trait aggression: Neural and behavioral insights using the emotional stop signal task[END_REF][START_REF] Zajenkowski | Intelligence and aggression: The role of cognitive control and test related stress[END_REF]. Conversely, higher cognitive control abilities predicted less aggressive behaviors in response to provocation [START_REF] Wilkowski | How does cognitive control reduce anger and aggression? The role of conflict monitoring and forgiveness processes[END_REF]. These evidences indicate that executive functioning and in particular cognitive control, may be valuable research targets to gain a better understanding of aggressiveness.

Cognitive control is a set of executive functions that are orchestrated to adjust behaviors, according to internal goals and environmental constraints [START_REF] Ridderinkhof | Neurocognitive mechanisms of action control: resisting the call of the Sirens[END_REF]. Among cognitive control mechanisms, some of them are mobilized proactively and others reactively, according to when a risk of making an error is detected [START_REF] Braver | Explaining the many variaties of working memory variation: dual mechanisms of cognitive control[END_REF][START_REF] Braver | The variable nature of cognitive control: a dual mechanisms framework[END_REF]. Indeed, the risk of making an error must be monitored efficiently in order to involve the appropriate proactive or reactive adjustment mechanism [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task[END_REF]. Monitoring capacities are often investigated using electroencephalography (EEG) in participants performing choice reaction time tasks in which the stimulus-response congruency is manipulated, such as the Flanker or the Simon tasks. [START_REF] Falkenstein | Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks[END_REF] and [START_REF] Gehring | A Neural System for Error Detection and Compensation[END_REF] reported a negative fronto-central activity emerging at the time of the response and peaking around 100 ms after the response, but only when participants were making errors. This event-related potential (ERP) has been called the error (-related) negativity (ERN/Ne) and is predominantly considered to reflect the involvement of the error detection mechanisms (e.g., [START_REF] Hajcak | On the ERN and the significance of errors[END_REF][START_REF] Maier | Error monitoring is related to processing internal affective states[END_REF]. Indeed, when the situation emphasizes accuracy over speed (e.g., financial penalties for errors), it is important not to make errors and the ERN/Ne is increased, whereas the ERN/Ne is reduced in situations for which the errors are not meaningful because the instructions emphasize speed over accuracy (e.g., [START_REF] Gehring | A Neural System for Error Detection and Compensation[END_REF][START_REF] Hajcak | On the ERN and the significance of errors[END_REF]. Another ERP component linked to the performance monitoring system was also reported by [START_REF] Falkenstein | Event-related potential correlates of errors in reaction tasks[END_REF] and [START_REF] Falkenstein | Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks[END_REF]. This parameter referred as the error positivity (Pe) is a later centro-parietal component that peaks between 200 and 400 ms after the response. It is only observable during trials for which participants consciously detected their errors [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task[END_REF]; its magnitude varies according to the degree of error consciousness [START_REF] Leuthold | ERP correlates of error processing in spatial S-R compatibility tasks[END_REF].

Therefore, whereas the ERN/Ne indicates automatic internal performance feedback, the Pe is considered to reflect the conscious detection and evaluation of errors [START_REF] Falkenstein | ERP components on reaction errors and their functional significance: a tutorial[END_REF].

The error-specificity of the ERN/Ne has been challenged by the findings of [START_REF] Vidal | Is the 'error negativity' specific to errors?[END_REF] who observed an ERN/Ne-like in non-error trials thanks to the use of electromyography (EMG) and a methodology to increase the spatial resolution of EEG recordings. More specifically, the EMG was recorded to reveal partialerrors that are engaged erroneous actions that are successfully detected, inhibited and corrected [START_REF] Eriksen | An electromyographic examination of response competition[END_REF][START_REF] Hasbroucq | Effect of the irrelevant location of the response signal on choice reaction time: an electromyographic study in humans[END_REF]. Furthermore, in their work, the low spatial resolution of EEG was improved using the Laplacian transform technique [START_REF] Babiloni | Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models[END_REF][START_REF] Burle | Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view[END_REF]. [START_REF] Vidal | Is the 'error negativity' specific to errors?[END_REF] reported the classical ERN/Ne in errors, but also described an ERN/Ne-like following both partial-errors and correct responses, which differed by their magnitudes. Indeed, the ERN/Ne-like observed in correct responses, also called the correct-related negativity (CRN/Nc), is largely smaller in amplitude than the ERN/Ne observed after a partial-error, which is in turn smaller than the ERN/Ne in full-error responses. Despite these differences in amplitude, the three ERPs have similar temporal dynamics, topographies and are generated by the same cerebral regions: the supplementary motor area and/or the dorsal anterior cingulate cortex [START_REF] Bonini | Action Monitoring and Medial Frontal Cortex: Leading Role of Supplementary Motor Area[END_REF][START_REF] Fu | Single-Neuron Correlates of Error Monitoring and Post-Error Adjustments in Human Medial Frontal Cortex[END_REF][START_REF] Roger | Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct-and error-negativities[END_REF]. Therefore, these components are thought to reflect an identical process varying in degrees according to performance [START_REF] Weinberg | Error-related brain activity in the age of RDoC: A review of the literature[END_REF]. Even though the debates remain around the question of the functional significance of these brain activities, the observed differences in magnitudes between full-error ERN/Ne, partial-error ERN/Ne and CRN/Nc negativities confirm the capacity of the brain to monitor behavioral motor performances. Additionally, the combined analysis of the performance-related negativities (i.e., ERN/Ne and CRN/Nc) enables the precise distinction between the ability to self-evaluate one's own performance from the strength of the monitoring processes that is mobilized during the task. On the one hand, a reduced ERN/Ne in error trials along with an increased CRN/Nc in correct trials indicates a loss in the ability to self-evaluate the ongoing performance with less differentiation between erroneous and correct actions. This is the case for example in schizophrenic patients, in de novo patients with Parkinson disease and in patients with frontal lesions [START_REF] Mathalon | Responsemonitoring dysfunction in schizophrenia: an event-related brain potential study[END_REF][START_REF] Turken | The effect of orbitofrontal lesions on the error-related negativity[END_REF][START_REF] Willemssen | Response Monitoring in De Novo Patients with Parkinson's Disease[END_REF]. On the other hand, a reduced ERN/Ne combined to a reduced CRN/Nc indicates a global reduction of the importance given to the evaluation of the motor performance. Such a pattern suggests that the monitoring system is less activated throughout the task, but that the ability to self-evaluate an ongoing performance is preserved. Consequently, investigating the CRN/Nc component appears to be relevant since it enables to decide between several interpretations that could be drawn from the reduction in ERN/Ne. Using the same methodology as [START_REF] Vidal | Is the 'error negativity' specific to errors?[END_REF], the current study combined the analysis of the negativities of all response types to highlight the monitoring processes in high and low aggressive individuals.

Numerous factors differentially modulate the magnitude of the ERN/Ne and the Pe (for a review, see [START_REF] Overbeek | Dissociable Components of Error Processing[END_REF]. In particular, a reduction in ERN/Ne is often associated with psychiatric disorders such as borderline personality disorders [START_REF] De Bruijn | Neural correlates of impulsive responding in borderline personality disorder: ERP evidence for reduced action monitoring[END_REF] and schizophrenia [START_REF] Charles | Conscious and unconscious performance monitoring: Evidence from patients with schizophrenia[END_REF][START_REF] Mathalon | Responsemonitoring dysfunction in schizophrenia: an event-related brain potential study[END_REF]. These studies suggest that a reduced ERN/Ne is a marker of psychopathology [START_REF] Olvet | The error-related negativity (ERN) and psychopathology: Toward an endophenotype[END_REF]. However, reduced ERN/Ne are also observed in externalizing populations in a broader sense such as conduct disorders and substance dependences [START_REF] Hall | Externalizing Psychopathology and the Error-Related Negativity[END_REF][START_REF] Pasion | ERN as a transdiagnostic marker of the internalizing-externalizing spectrum: A dissociable meta-analytic effect[END_REF] and in juvenile offenders (Vilà-Balló, Hdez-Lafuente, Rostan, Cunillera, & Rodriguez-Fornells, 2014). Aggressive behaviors, a common factor between psychiatric populations, externalizing behaviors and offenders (e.g., [START_REF] Mancke | Aggression in borderline personality disorder: A multidimensional model[END_REF][START_REF] Zhou | Prevalence of aggression in hospitalized patients with schizophrenia in China: A metaanalysis: Aggression and schizophrenia[END_REF], thus seem to be associated with a reduction in ERN/Ne.

Fewer studies investigated the effect of aggressiveness on the Pe amplitudes. Moreover, their results are less consistent. Comparing offenders and controls, [START_REF] Brazil | Early and Late Components of Error Monitoring in Violent Offenders with Psychopathy[END_REF] showed reduced Pe amplitudes in the offenders whereas [START_REF] Vilà-Balló | Neurophysiological correlates of error monitoring and inhibitory processing in juvenile violent offenders[END_REF] did not find any difference between the two groups. This inconsistency might be explained by the presence of psychopathic traits in the population of the [START_REF] Brazil | Early and Late Components of Error Monitoring in Violent Offenders with Psychopathy[END_REF]) study compared to Vilà-Balló et al. (2014). Indeed, [START_REF] Steele | Error-related processing in adult males with elevated psychopathic traits[END_REF] found larger Pe amplitudes in offenders scoring high in psychopathic traits compared to those scoring low.

Psychopathic traits rather than aggressiveness itself may affect Pe amplitudes in these aggressive populations.

In the current study, the main goal was to investigate the effect of aggressiveness on performance monitoring. Considering the inconsistency of the findings relative to the Pe component, we did not set a hypothesis of the effect of aggressiveness on its amplitude. However, based upon previous studies (e.g. [START_REF] Charles | Conscious and unconscious performance monitoring: Evidence from patients with schizophrenia[END_REF][START_REF] Hall | Externalizing Psychopathology and the Error-Related Negativity[END_REF][START_REF] Vilà-Balló | Neurophysiological correlates of error monitoring and inhibitory processing in juvenile violent offenders[END_REF], we expected that the reduction in ERN/Ne in full-errors would be revealed in individuals showing high aggressive traits compared to those with low aggressive traits.

Moreover, because our participants were well-adapted non-clinical adults, we hypothesized that the monitoring system in our sample would remain as efficient in distinguishing between erroneous and correct actions unlike what it is found in pathological populations [START_REF] Mathalon | Responsemonitoring dysfunction in schizophrenia: an event-related brain potential study[END_REF][START_REF] Turken | The effect of orbitofrontal lesions on the error-related negativity[END_REF][START_REF] Willemssen | Response Monitoring in De Novo Patients with Parkinson's Disease[END_REF]. However, if the reduction in the ERN/Ne in erroneous trials is confirmed, then we should observe a similar reduction in the other ERPs to preserve the distinction between each ERN/Ne in the three response types.

Consequently, in addition of the reduction in the ERN/Ne in full-error trials, we should observe a reduction both in the ERN/Ne in partial-error trials, and in the CRN/Nc in purecorrect responses.

Material and methods

Participants

Thirty-two right-handed volunteers recruited at the University of Lille participated in the study (18 women, mean age = 22.40 years, range from 19 to 28). Handedness was assessed with the Edinburgh Handedness Inventory [START_REF] Oldfield | The assessment and analysis of handedness: The Edinburgh inventory[END_REF]. Exclusion criteria included motor and/or sensory disorders and a current medical treatment. They all gave written informed consent for taking part in this study. The ethics committee of the University of Lille (2015-9-S35) approved the experiment.

Procedure and task

Experimental task

The participant sat in a closed room facing a computer screen. She/he performed a modified version of the Simon task [START_REF] Simon | The effects of an irrelevant directional cue on human information processing[END_REF]. Visual stimuli were created in the shape of a circle and of a square. Each participant was invited to respond as quickly and accurately as possible as a function of the shape of the stimulus. For example, holding a response button in each hand, the participant was required to press with the right hand if the stimulus was a circle and with the left hand if it was a square. Shape-to-response mapping rules were counterbalanced across participants. Importantly, the shapes were displayed on the right or on the left part of the screen. Although this dimension of the stimuli was salient, it was irrelevant for the task. Hence, 50% of the trials were labeled as "congruent" since the expected response was ipsilateral to the position of the stimulus (e.g. when a circle requiring a left response was presented on the left side of the display). Inversely, 50% of the trials were labeled as "incongruent" since the expected response was contralateral to the position of the stimulus (e.g. when a circle requiring a left response was presented on the right side of the display).

Each trial begun with the presentation of a fixation cross at the center of the screen during 300 ms. The stimulus appeared and remained displayed until a response was given or after a 1000 ms time lapse. Then, a black screen was presented during 1000 ms before the start of the next trial.

The experiment begun with a training block of 20 trials. During this training, visual feedback appeared for 500 ms after each response providing information about the accuracy of the current trial ("Bonne réponse" for a correct response, "Mauvaise réponse" for an error, "Essayez d'aller plus vite" for responses longer than 1000 ms). Then, the participant performed 10 blocks of 129 trials. A pause of 15 seconds was implemented between each block. The experiment lasted about 30 min.

Aggressiveness indices

Participants responded to the BPAQ Aggression Questionnaire [START_REF] Buss | The Aggression Questionnaire[END_REF] french version: Pham, Ducro, & Saloppé, 2011) after the end of the Simon task to avoid potential influences of the questionnaire on behaviors.

The BPAQ reveals traits in aggression and contains four subscales. The subscales Physical Aggression and Verbal Aggression) evaluate the external forms of aggression (i.e., the tendency to act with the focus to hurt someone). The subscale Anger evaluates the affective aspect of aggression and is defined as the physiological arousal associated with the preparation for aggression. This subscale assesses the individual differences in the frequency of experiencing the urge to act and the behavioral reactivity towards angry feelings [START_REF] Poland | Cool and hot executive function as predictors of aggression in early childhood: Differentiating between the function and form of aggression[END_REF]. Finally, the subscale Hostility relates to a more cognitive aspect of aggression and is defined as the tendency to evaluate negatively other people, which is often accompanied by a desire to harm others [START_REF] Poland | Cool and hot executive function as predictors of aggression in early childhood: Differentiating between the function and form of aggression[END_REF]

Data Acquisition and Preprocessing

All electrophysiological data were recorded simultaneously using Ag/AgCl electrodes with the BioSemi© system (BioSemi ActiveTwo electrodes, Amsterdam). EEG signals were

Electrophysiological data pre-processing

All the electrophysiological data pre-processing steps were done using BrainVision Analyzer 2.1© software (Brain Products, Munich, Germany).

The EMG data were filtered with a 10 Hz high-pass filter. Onsets of EMG activities were manually marked after visual inspection as it remained more precise than automatic algorithms [START_REF] Staude | Onset Detection in Surface Electromyographic Signals: A Systematic Comparison of Methods[END_REF]. Experimenters were not aware of the nature of the trial being inspected. Based on the manual markers, all trials were classified as (1) pure-correct trials (i.e., trials with only one muscular burst on the correct side), (2) full-error trials (i.e., trials with only one muscular burst on the incorrect side), and (3) partial-error trials (i.e., trials containing two EMG activations, one on the incorrect side preceding the correct response).

The raw EEG data were filtered with a 0.16 Hz high-pass filter only and were referenced offline to the left mastoid. The EOGs were used to perform the ocular corrections on the EEG signals following the statistical method by [START_REF] Gratton | A new method for off-line removal of ocular artifact[END_REF]. All other artifacts were manually rejected after visual inspection of individual traces. To improve the spatial and the temporal resolutions of the EEG signals, the Laplacian transform was applied to the monopolar data [START_REF] Babiloni | Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models[END_REF][START_REF] Burle | Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view[END_REF]. To perform this operation, signals were interpolated with the spherical spline interpolation procedure using 3 as the degree of spline and 15 degrees, for the Legendre polynomial [START_REF] Perrin | Scalp Current Density Mapping: Value and Estimation from Potential Data[END_REF][START_REF] Perrin | Spherical splines for scalp potential and current density mapping[END_REF]. Then, the second derivatives in two dimensions of space were computed. Thus, electrical brain activities are expressed in μV/cm².

EEG data processing

Information related to EMG onsets was superimposed upon the EEG signals. EEG data were segmented with respect to the EMG onsets of pure-correct, full-error, and partialerror EMG bursts. EEG segments ranging from 500 ms before and 500 ms after EMG onsets were baseline corrected (100 ms pre-EMG window). Time courses were averaged as a function of the response type.

Previous studies showed that the ERN/Ne is maximal at the FCz electrode (e.g. [START_REF] Bates | Error-related negativity and correct response negativity in schizophrenia[END_REF][START_REF] Ladouceur | Error-related brain activity in pediatric anxiety disorders remains elevated following individual therapy: a randomized clinical trial[END_REF][START_REF] Taylor | The error-related negativity (ERN) is an electrophysiological marker of motor impulsiveness on the Barratt Impulsiveness Scale (BIS-11) during adolescence[END_REF][START_REF] Weinberg | Errorrelated negativity (ERN) and sustained threat: Conceptual framework and empirical evaluation in an adolescent sample: ERN and sustained threat[END_REF]. Therefore, the magnitudes of the central negativities were measured at the FCz electrode. A peak-to-peak method (i.e., baseline-free method, [START_REF] Falkenstein | ERP components on reaction errors and their functional significance: a tutorial[END_REF][START_REF] Meckler | On-line action monitoring of response execution: An electrophysiological study[END_REF][START_REF] Olvet | Lack of association between the 5-HTTLPR and the error-related negativity (ERN)[END_REF] was applied in the time window between 50 ms and 250 ms after EMG onsets. However, as a peak-to-peak method to measure Pe amplitudes would have been contaminated by the variability of the ERN/Ne amplitudes, the mean positivity in a window frame between 200 ms and 400 ms after EMG onsets was used as an index of Pe amplitudes.

Experimental groups and statistical analyses

The aim of this study was to evaluate the relation between cognitive control capacities and aggressiveness. In the present study, ERN/Ne amplitudes in all response types were used as an indicator of the performance monitoring capacities. Partial-error rates were used as an indicator of the efficiency in reactive control. Finally, the post-error slowing and the Gratton effects [START_REF] Gratton | Optimizing the use of information: Strategic control of activation of responses[END_REF][START_REF] Rabbitt | Errors and error correction in choice-response tasks[END_REF] were used as indicators of the efficiency in proactive control.

The BPAQ median score was used to categorize participants as possessing high/low aggressiveness trait personalities. In our sample, the BPAQ scores ranged from 44 to 101, with 67 as the median score. The median-split method categorized 15 participants as low aggressive (i.e. they scored strictly less than 67 in the BPAQ) and 14 participants as highly aggressive (i.e. they scored strictly more than 67 in the BPAQ). Three participants were excluded from ANOVA analyses because their BPAQ scores were equal to the median score.

ANOVAs were performed using the R aov function available in the stats package (R Core Team, 2018). The behavioral performances (i.e., reaction times -RTs, accuracy, reactive and proactive control indices) were submitted to a one-level ANOVA with Aggressiveness as between-group factor. Performance monitoring indices were submitted to a two-level ANOVA with Aggressiveness (2) as between-group factor and Response-Type (3) as within-group factor. Post hoc Scheffé were applied when required and the effect sizes were calculated as eta-squared and partial eta-squared (η² and ηp², respectively) using the R etaSquared function available in the lsr package [START_REF] Navarro | Learning statistics with R: a tutorial for psychology students and other beginners[END_REF]. The alpha level was set to

.05 for all analyses.

Results

The following results present the findings obtained during the Simon task in the total sample of 32 participants. We then report the results obtained in the sub-groups after categorizing participants with high and low aggressiveness traits using the total BPAQ score.

Global analyses 1. Accuracy and reaction times

Among the exploitable EMG recordings, a total of 72.6 %, 23.2 % and 4.1 % of trials were classified as pure-correct, partial-error and full-error, respectively. As classically found, incongruent RTs (478 ms) were longer than congruent RTs (453 ms), t( 31

Performance monitoring

The ANOVA revealed the classical main effect of Response Type on ERN/Ne amplitudes, F(2,93) = 23.52, p < .001, η ² = .34. The post hoc Scheffé test confirmed that ERN/Ne was larger in full-error trials than both in partial-error trials (p < .001) and in purecorrect trials (p < .001). ERN/Ne amplitudes were smaller in pure-correct trials than in partialerror trials (p < .001).

Task performance, cognitive control as a function of aggressiveness

ANOVA revealed no main effects of Aggressiveness on error rates, on RTs in fullerror trials and on RTs in correct trials F(1,27) = 0.12, p = .737, η² < .01, F(1,27) = 0.55, p = .466, η² = .02 and F(1,27) = 0.34, p = .565, η ² = .01, respectively. These results are presented in Table 1. There was a main effect of Aggressiveness on the Simon effect, F(1,27) = 7.35, p = .012, η² = .21 (cf. Figure 1). The high aggressiveness group had a larger Simon Table 1.

Means and Standard Deviations for the Task Performance Indices in the High and the Low

Aggressiveness Groups.

Performance monitoring and aggressiveness

The EEG traces are represented in Figure 2

Discussion

Aggressiveness is often associated with poor executive functioning and reduced performance monitoring in pathological and maladapted populations (e.g., [START_REF] Hancock | Role of executive dysfunction in predicting frequency and severity of violence[END_REF][START_REF] Vilà-Balló | Neurophysiological correlates of error monitoring and inhibitory processing in juvenile violent offenders[END_REF][START_REF] Zajenkowski | Intelligence and aggression: The role of cognitive control and test related stress[END_REF]. The aim of the current study was to explore cognitive control capacities, and in particular performance monitoring, in a non-clinical population characterized with low and high aggressiveness personality traits.

At the behavioral level, the findings showed that the high aggressiveness group globally performed as well as the low aggressiveness group in terms of accuracy and RTs. However, individuals with high aggressive traits were characterized by a greater congruency effect compared to those with low aggressive traits. At the brain level, the current study confirmed the expected reduction in ERN/Ne amplitudes in full-error trials in the high aggressiveness group compared to the low aggressiveness group. Interestingly, the results extended this finding by also revealing a reduction in performance-related negativities after both partialerrors and pure-correct responses. Additionally, the global reduction that was observed in all performance-related negativities amplitudes in the high aggressiveness group did not interact with the response types. Overall, the present results showed a reduction in the global activation of the performance monitoring system, and not a decrease in its efficiency.

Aggressiveness effects on cognitive control at behavioral level

Aggressive behaviors are known to be related to disturbed cognitive control abilities.

Indeed, several studies investigating aggressiveness in tasks manipulating the stimulusresponse congruency have reported worse performances in pathological and populations with problematic behaviors than in healthy controls (e.g., [START_REF] Gastaldo | The Simon Effect in Schizophrenic Patients with Negative Symptoms[END_REF][START_REF] Hancock | Role of executive dysfunction in predicting frequency and severity of violence[END_REF]. In the current study, the performances of the participants in a choice RT task requiring cognitive control mechanisms were considered through the prism of their predispositions to act aggressively. We selected the BPAQ to assess the levels of aggressiveness through a 35-item questionnaire [START_REF] Buss | The Aggression Questionnaire[END_REF][START_REF] Pham | Psychometric assessment of the Buss and Perry Aggression Questionnaire (1992) in a prison population[END_REF]. A modified version of the Simon task manipulating the stimulus-response congruency was used to reveal cognitive control functioning. Results showed no differences in the global performance in the Simon task between groups of individuals categorized as having high and low traits in aggressiveness (i.e., RTs and error rates). Additionally, there were no differences neither in partial-error rates nor in behavioral adjustments indicating no effects of aggressive traits, both on reactive and proactive control mechanisms, respectively. However, the high aggressiveness group showed a larger congruency effect than the low aggressiveness group, revealing higher difficulty in inhibiting irrelevant information with higher aggressive tendencies. This result is consistent with a previous study showing longer reaction times in incongruent trials in schizophrenic patients compared to control participants [START_REF] Gastaldo | The Simon Effect in Schizophrenic Patients with Negative Symptoms[END_REF]. While the few differences at the behavioral level between the two groups in our study may seem disconcerting at first sight, they might not be so surprising. Firstly, our participants were young individuals, recruited at the University, without maladapted behaviors. Secondly, the experimental setup was made to not induce aggressive behaviors, like it has been done in some previous studies (e.g., [START_REF] Krämer | The Role of Executive Functions in the Control of Aggressive Behavior[END_REF][START_REF] Pawliczek | Inhibitory control and trait aggression: Neural and behavioral insights using the emotional stop signal task[END_REF]. Therefore, the traits in aggressiveness were not amplified, and consequently the impact of such a trait may be too weak to be observable within the global indices of behavioral performances (i.e., RTs and error rates). Even if the global performances were not sensitive enough to the trait of aggressiveness in our study, the brain responses revealed a different pattern.

Aggressiveness effects on cognitive control at the brain level

Electroencephalographic recordings were used to assess performance monitoring abilities through the analysis of a specific response-locked ERP that is the error(-related) negativity (ERN/Ne) and its equivalent in pure-correct trials (CRN/Nc). These EEG components are fronto-central negativities peaking rapidly after the response. The amplitude of the ERN/Ne is known to vary as a function of the need to self-evaluate behavioral performances. The more the error is meaningful, the larger is the ERN/Ne amplitude [START_REF] Gehring | A Neural System for Error Detection and Compensation[END_REF][START_REF] Hajcak | On the ERN and the significance of errors[END_REF]. In the current study, we choose to use the Laplacian transform to improve the temporal and spatial resolutions of the EEG [START_REF] Babiloni | Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models[END_REF][START_REF] Burle | Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view[END_REF] and especially to uncover the CRN/Nc, usually masked by a large parietal positivity [START_REF] Roger | Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct-and error-negativities[END_REF][START_REF] Vidal | Error negativity on correct trials: a reexamination of available data[END_REF][START_REF] Vidal | Is the 'error negativity' specific to errors?[END_REF]. We conducted a combined analysis of the negativities in full-errors, in partialerrors and in pure-correct trials. This joint analysis provides the means to specify how a variable can affect patterns of performance monitoring abilities (e.g. task instructions, disorders). In our opinion, only investigating the effect of a variable on the amplitude of the ERN/Ne can mislead interpretations. A reduced ERN/Ne cannot be a marker of error monitoring deficit if the CRN/Nc is also reduced: the monitoring system still distinguishes erroneous and correct actions. This pattern of results should be interpreted as a global decrease in monitoring engagement (i.e., reduction in the value placed on the performance).

On the contrary, if a reduced ERN/Ne goes along with a large CRN/Nc, this pattern should be interpreted as a specific difficulty in the evaluation of one's own performance: the monitoring system becomes less discriminant.

In the current study, the performance-related negativities peaks were observed within a time window of 100 to 200 ms after the onset of the muscular responses recorded using EMG.

Its amplitude was the highest in full-error, intermediate in partial-error and the smallest in pure-correct responses. This pattern of results replicated previously reported findings (e.g., [START_REF] Meckler | Executive control and response expectancy: A Laplacian ERP study: Executive control and response expectancy[END_REF][START_REF] Roger | Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct-and error-negativities[END_REF][START_REF] Roger | Changing your mind before it is too late: The electrophysiological correlates of online error correction during response selection[END_REF][START_REF] Vidal | Error negativity on correct trials: a reexamination of available data[END_REF][START_REF] Vidal | Is the 'error negativity' specific to errors?[END_REF]. Considering the aggressiveness effect, the ERN/Ne in full-errors was reported to be decreased in individuals showing high traits in aggressiveness compared to those with low aggressive traits. The current study replicated the findings by [START_REF] Vilà-Balló | Neurophysiological correlates of error monitoring and inhibitory processing in juvenile violent offenders[END_REF] in an adapted population and without manipulating the aggressive states of the participants.

Interestingly, thanks to the Laplacian transform and the use of EMG, the current study extended these findings by revealing a reduction in the ERN/Ne in partial-errors and in CRN/Nc in pure-correct trials in the high aggressiveness group compared to the low aggressiveness group. Since all the negativities were affected in the same way by aggressiveness, the current findings suggest that aggressiveness does not affect the ability to self-evaluate actions, but instead reduces the importance attached to one's own performance.

This global reduction in negativities amplitudes is consistent with several previous studies.

Indeed, aggressiveness has already been linked to reduced prefrontal activities in an emotionmanipulated context [START_REF] Pawliczek | Inhibitory control and trait aggression: Neural and behavioral insights using the emotional stop signal task[END_REF] and after exposure to violent video games [START_REF] Hummer | Decreased prefrontal activity during a cognitive inhibition task following violent video game play: A multi-week randomized trial[END_REF]. Nevertheless, the study showed that high aggressive predispositions are characterized by a less active, but still efficient monitoring system even in a neutral situation. It seems that high aggressive individuals care less about their performance than low aggressive individuals do, but are still able to clearly evaluate it.

Further studies should consider the functioning of the monitoring system in high aggressive individuals in motivational situations to understand whether the global reduction observed here, in a neutral environment, is due to a genuine inability to mobilize the monitoring system.

Aggressive behaviors are characteristic of psychiatric populations (e.g., [START_REF] Mancke | Aggression in borderline personality disorder: A multidimensional model[END_REF][START_REF] Zhou | Prevalence of aggression in hospitalized patients with schizophrenia in China: A metaanalysis: Aggression and schizophrenia[END_REF] and these disorders are also associated with a reduction in the ERN/Ne amplitude (e.g., [START_REF] Charles | Conscious and unconscious performance monitoring: Evidence from patients with schizophrenia[END_REF][START_REF] De Bruijn | Neural correlates of impulsive responding in borderline personality disorder: ERP evidence for reduced action monitoring[END_REF]. [START_REF] Olvet | The error-related negativity (ERN) and psychopathology: Toward an endophenotype[END_REF] even proposed that the ERN/Ne should be considered as an endophenotype of psychopathology. In the current study, we did not carry out precise psychiatric screenings for ethical reasons and, thus, it might be possible that in our sample, especially in the high aggressiveness group, some may meet criteria of psychiatric disorders. Moreover, aggressive predispositions are risk factors for psychiatric disorders (e.g., [START_REF] Mula | Self-reported aggressiveness during treatment with levetiracetam correlates with depression[END_REF]. Considering the high aggressiveness group as at risk to develop psychiatric disorders, the current results suggest that the global reduction in performance monitoring activities reflects a neurophysiological marker for psychiatric vulnerability. However, this decrease in involvement of the monitoring resources itself is not sufficient to suggest the existence of a psychopathological state. In contrast, a difficulty in distinguishing between erroneous and correct actions may be a more accurate marker of maladapted behaviors. Accordingly to this hypothesis, [START_REF] Hall | Externalizing Psychopathology and the Error-Related Negativity[END_REF] and [START_REF] Mathalon | Responsemonitoring dysfunction in schizophrenia: an event-related brain potential study[END_REF] both showed weaker differences between ERN/Ne and CRN/Nc amplitudes in externalizing disorders and in schizophrenia, respectively, than in healthy individuals. The interpretation of the reduction in ERN/Ne as a marker of psychopathology [START_REF] Olvet | The error-related negativity (ERN) and psychopathology: Toward an endophenotype[END_REF] should also take into account the modulation of the CRN/Nc to confirm the presence of disturbed self-evaluation capacities and to specify the true nature of abnormal cognitive control functioning of the monitoring system.

Conclusions

This study used the BPAQ questionnaire in order to evaluate aggressive tendencies in a non-clinical and adapted population to compare cognitive control capacities between individuals with low and high aggressive personality traits. At the behavioral level, the high aggressiveness group was associated with a larger congruency effect compared to the low aggressiveness group. However, this difficulty in inhibiting irrelevant information was not reflected in performance: the high and low aggressiveness groups differed neither on error rates nor on reaction times. More particularly, the aim of the current research was to study the influence of these personality traits on the performance monitoring system, which plays a crucial role in cognitive control. Individuals with high aggressive traits showed a reduction in performance-related negativities amplitudes independent of the response type compared to those in the low aggressiveness group. This reduction reveals a decrease in the value placed on performance, but an intact capacity to self-evaluate one's own performance in high aggressiveness. Further studies should be conducted in order to disentangle the influence of aggressive personality traits from the influence of a psychiatric diagnosis on performance monitoring capacities.

  collected with 64 electrodes (10-20 system positions) mounted on an elastic cap. The vertical electro-oculogram (EOG) was recorded by means of two external electrodes placed below and above the left eye. The horizontal EOG was recorded by means of two external electrodes placed on the temples. The EOGs measurements were recorded to control for eye movement artefacts. The left and the right electromyographic activities (EMG) were recorded by means of two pairs of electrodes placed on the surface of the skin above the thumb-flexor pollicis brevis of each hand. These EMG measurements were recorded to detect partial-errors and the onset of all the muscular activities. The sampling rate was set to 1024 Hz. Electrophysiological data were collected during the experimental blocks of the Simon task only.

  ) = -12.41, p < .001, Cohen's d = 2.19. The RTs in full-error trials were shorter (416 ms) than the RTs measured when correct responses were observed (i.e., combined pure-correct and partial-error trials, 465 ms), t(31) = 10.65, p < .001, Cohen's d = 1.88. The post-error slowing was significant: RTs in correct trials following an error were longer (511 ms) than the RTs in correct trials following a correct response (463 ms), t(31) = -6.96, p < .001, Cohen's d = 1.23. The Gratton effect was significant: the congruency effect was smaller after incompatible trials (-17 ms) compared to the congruency effect observed after compatible trials (69 ms), F(1,124) = 26.88, p < .001, ηp² = .18. Overall, the classical effects of the Simon task were replicated.

  effect (31.13 ms) than the low aggressiveness group (21.74 ms). When considering the partial-error rates reflecting reactive control, results revealed no main effects of Aggressiveness, F(1,27) = 0.06, p = .816, η² < .01. When considering proactive control, the ANOVA revealed no main effects of Aggressiveness on neither the post-error slowing nor the Gratton effect, F(1,27) = 1.45, p = .239, η² = .05 and F(1,27) = 0.38, p = .545, η² = .01, respectively.Note. Correct trials refer to both pure-correct and partial-error trials. Low and high aggressiveness groups were created based upon the median BPAQ score. M = mean, SD = standard deviation, RT = reaction time, η² = etasquared.

Figure 1 .

 1 Figure 1. Mean Simon effect (ms) in the high and the low aggressiveness groups. Error bars

  as a function of aggressiveness groups and responses types. The analyses revealed a main effect of Aggressiveness on ERN/Ne amplitudes, F(1,81) = 10.53, p = .002, ηp² = .12. The ERN/Ne amplitudes were smaller in the high aggressiveness group (-0.56 µV/cm²) than in the low aggressiveness group (-0.77 µV/cm²). Reductions in ERN/Ne amplitudes in the high aggressiveness group were not modulated by Response Type, F(1,81) = 0.18, p = .833, ηp² < .01. Concerning the Pe observed in full-error trials, there were no differences in amplitudes between the high and the low aggressiveness groups, F(1, 27) = 1.00, p = .326, ηp² = .04.

Figure 2 .

 2 Figure 2. The Laplacian transformed performance-related negativities (both ERN/Ne and CRN/Nc) and Pe (FCz electrode) and the corresponding Laplacian topographies measured in the full-error (top panel), in the partial-error (middle panel) and in the pure-correct responses (bottom panel). Continuous lines and dotted lines represent the results obtained in the high and the low aggressiveness groups, respectively. Signals were locked to the EMG onset of the motor responses. The topographical maps (right column) show a top/horizontal view of the scalp (nose up) with the actual distribution of the Laplacian-transformed EEG data, separately for each response type, at the time of the negativity peak maximum.

  These internal consistency values correspond to those found by Pham et al. (2011) with Cronbach's α below .70 both for the Anger and for the Verbal Aggression subscales.

. Internal consistency was adequate in our sample for the total BPAQ scores, α = .86, 95% CI [.79, .93] as well as for the four subscores: Anger, α = .68, 95% CI [.51, .85], Hostility, α = .76, 95% CI [.63, .89], Physical Aggression, α = .86, 95% CI [.80, .93] and Verbal Aggression, α = .55, 95% CI [.30, .79].

Table 2

 2

	presents the ANOVA

Table 2 .

 2 ANOVA Results for Mean Performance-Related Negativities and Pe Amplitudes.

		Performance-related negativities		Pe
	Factors	F	p	ηp²	F	p	ηp²
	Aggressiveness	10.53	.002**	.12	1.00	.326	.04
	Response Type	23.21	<.001***	.36
	Aggressiveness x Response Type	0.18	.832	< .01

Grisetto, Delevoye-Turrell & Roger. 16 Note. Performance-related negativities refer to ERN/Ne in full-errors and in partial-errors responses and CRN/Nc in pure-correct responses. Bold text refers to significant effects. **: p < .01, ***: p < .001. ηp²: partial eta-squared.

Acknowledgements

This research was funded in part by the Maison Européenne des Sciences Humaines et Sociales (MESHS; project IMPULS-ACT) and was supported by a grant from the Ministry of Research (program "Investments for the Future") and managed by the National Research Agency under the reference ANR-11-EQPX-0023, as well as by European funds through the

program FEDER SCV-IrDIVE. The authors thank the engineers working on the IrDIVE platform for their assistance and Pierre Le Denmat for the proofreading of this paper. This research was supported by a doctoral grant from the University of Lille and the region Hautsde-France to F.G.

The authors declare that there is no conflict of interest regarding the publication of this article.