Response inhibition in borderline personality disorder: Neural and behavioral correlates

Accepted Manuscript Title: Response inhibition in borderline personality disorder: neural and behavioral correlates Authors: Jacobo Albert, Sara L´opez-Mart´ın, Roc´ıo Arza, Nerea Palomares, Sandra Hoyos, Luis Carreti´e, Marina D´ıaz-Mars´a, Jos´e Luis Carrasco PII: DOI: Reference:

S0301-0511(18)30589-1 https://doi.org/10.1016/j.biopsycho.2019.02.003 BIOPSY 7662

To appear in: Received date: Revised date: Accepted date:

10 August 2018 18 November 2018 9 February 2019

Please cite this article as: Albert J, L´opez-Mart´ın S, Arza R, Palomares N, Hoyos S, Carreti´e L, D´ıaz-Mars´a M, Luis Carrasco J, Response inhibition in borderline personality disorder: neural and behavioral correlates, Biological Psychology (2019), https://doi.org/10.1016/j.biopsycho.2019.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Response inhibition in borderline personality disorder: neural and behavioral correlates Jacobo Albert*1,2, Sara López-Martín3,4, Rocío Arza5, Nerea Palomares5, Sandra Hoyos6, Luis Carretié1, Marina Díaz-Marsá5, José Luis Carrasco5

Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain

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Instituto Pluridisciplinar, Universidad Complutense de Madrid, Madrid, Spain

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Centro Neuromottiva, Madrid, Spain

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Universidad Rey Juan Carlos, Madrid, Spain

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Hospital Clínico San Carlos, Cibersam, Madrid, Spain

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Universidad Católica del Uruguay, Montevideo, Uruguay

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*Corresponding author. J. Albert. Departamento de Psicología Biológica y de la Salud.

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e-mail address: [email protected]

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Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spain.

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Response inhibition was examined in BPD combining ERP and source localization A modified go/nogo task that controls for attentional capture was used BPD patients recruit different regions for inhibiting responses compared to controls Recruitment of these alternative regions may be associated with their reduced performance

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Highlights

Abstract: Although response inhibition is thought to be important in borderline personality disorder (BPD), little is known about its neurophysiological basis. This study aimed to provide insight into this issue by capitalizaing on the the high temporal

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resolution of electroencephalography and information provided by source localization methods. To this end, twenty unmedicated patients with BPD and 20 healthy control subjects performed a modified go/no-go task designed to better isolate the brain activity specifically associated with response inhibition. Event-related potentials (ERP) were measured and further analyzed at the scalp and source levels. Patients with BPD made more commission errors (failed inhibitions) than control subjects. Scalp ERP data showed that both groups displayed greater frontocentral P3 amplitude for no-go (response

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inhbition) than for go trials (response execution). However, source reconstruction data

revealed that patients with BPD activated posterior parietal regions (precuneus) to inhibit

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their responses, whereas controls activated prefrontal regions (presupplementary motor

area, preSMA). This dissociation was supported by a significant Region (precuneus, preSMA) x Trial Type (no-go, go) x Group (BPD, control) interaction. These findings extend our understanding of the neurophysiological basis of abnormal response inhibition

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in BPD, suggesting that patients with BPD recruit different brain regions for inhibiting

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prepotent responses compared to controls. Future research in larger, medication-naïve

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samples of patients with BPD is required to confirm and extend these findings.

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1. Introduction

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Keywords: BPD, preSMA, precuneus, P3, response inhibition.

Borderline personality disorder (BPD) is estimated to occur in 0.5-6% of the general

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population, and is the most common personality disorder in both outpatients and inpatient clinical settings (Grant et al., 2008; Lenzenweger, Lane, Loranger, & Kessler, 2007). Although BPD is a heterogeneous diagnostic category (Leichsenring, Leibing, Kruse,

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New, & Leweke, 2011), impulsiveness is invoked as one the key clinical problems in patients with this disorder. Indeed, impulsive and reckless behavior, such as impulsiveaggression, suicide efforts, substance abuse, binge eating, unsafe sex or self-harm, is considered one of the diagnostic symptom criteria for BPD (American Psychiatry Association, 2013). 2

Impulsivity is a multidimensional construct that includes at least behavioral, cognitive and affective dimensions (Bari & Robbins, 2013; Reynolds, Ortengren, Richards, & de Wit, 2006; Stahl et al., 2014). Within the cognitive domain, an impulsive behavior could be the consequence of a failure in response inhibition (i.e., in the ability to suppress

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thoughts and actions that are inappropriate or no longer required: (Bari & Robbins, 2013). Response inhibition can be assessed using well-known paradigms such as the stop-signal

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and the go/no-go tasks (Chambers, Garavan, & Bellgrove, 2009; Verbruggen & Logan,

2008). These tasks involve the execution and suppression of motor responses, triggered by go and no-go/stop stimuli, respectively. Many more go than stop/no-go stimuli are

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generally presented to set up a prepotent response tendency, thereby increasing the

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mobilization of inhibitory resources to suppress the response to no-go/stop stimuli.

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Over the past 15 years, considerable progress has been made toward specifying the neural

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mechanisms underlying response inhibition in the human brain (Chambers et al., 2009; Huster, Enriquez-Geppert, Lavallee, Falkenstein, & Herrmann, 2013). This knowledge

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has mainly been derived from experiments using stop-signal and go/no-go tasks. Results from fMRI studies have shown that response inhibition is mainly associated with activity

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in several regions of the prefrontal cortex and basal ganglia (Aron, Robbins, & Poldrack, 2014; Horn, Dolan, Elliott, Deakin, & Woodruff, 2003; Li, Huang, Constable, & Sinha,

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2006; Li, Yan, Sinha, & Lee, 2008; Sharp et al., 2010). Specifically, the inferior frontal gyrus (IFG), pre-supplementary motor area (pre-SMA) and subthalamic nucleus (STN) are thought to be the core regions of the inhibition-related network, at least in its global form (Aron, 2011). Results from event-related potentials (ERPs) studies have further revealed the timing of the neural mechanisms supporting response inhibition, showing

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that neural processes related to motor inhibition last just a few hundred of milliseconds. Specifically, two frontocentral ERP components have been traditionally associated with response inhibition: N2 (200-400 ms) and P3 (300-600 ms)(Bokura, Yamaguchi, & Kobayashi, 2001; Falkenstein, Hoormann, & Hohnsbein, 1999; Huster et al., 2013; Kiefer, Marzinzik, Weisbrod, Scherg, & Spitzer, 1998). However, more recent evidence

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suggests that no-go/stop N2 may actually reflect a process that occurs prior to the

inhibition of the motor response, such as perceptual mismatch, novelty detection,

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premotor inhibition or conflict processing (Donkers & Van Boxtel, 2004; Gajewski &

Falkenstein, 2013; Smith, Johnstone, & Barry, 2008). By contrast, no-go/stop P3 has emerged as the strongest candidate for reflecting the response inhibition process (Albert,

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López-Martín, Hinojosa, & Carretié, 2013; Gajewski & Falkenstein, 2013; Sánchez-

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Carmona, Albert, & Hinojosa, 2016; Smith et al., 2008; Wessel, 2017; Wessel & Aron,

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2015). Interestingly, source localization analyses have identified the pre-SMA as one of

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the most likely generators of no-go P3 (Albert et al., 2013).

Although deficits in response inhibition have been implicated as a potentially important

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behavioral correlate of clinically impulsive disorders, there are surprisingly few studies investigating their behavioral and neural correlates in BPD. Evidence from behavioral

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studies is inconclusive. Whereas several investigations have found that BPD patients performed poorly on response inhibition tasks relative to controls (Leyton et al., 2001;

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Mortensen, Rasmussen, & Håberg, 2010; Rentrop et al., 2008; van Dijk et al., 2014), others reported similar performance between groups (Barker et al., 2015; Dinn et al., 2004; Hagenhoff et al., 2013; G. A Jacob et al., 2010; Lampe et al., 2007; LeGris, Links, van Reekum, Tannock, & Toplak, 2012). It should be noted that the absence of behavioral differences between BPD and control groups does not discard the possibility that patients

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activated the same inhibition-related regions than controls but to a larger extent (which may indicate that response inhibition is less efficient or more effortful for individuals with BPD) or even that patients used a different brain network to successfully withhold their responses (see e.g.,(Glass et al., 2011; López-Martín, Albert, Fernández-Jaén, & Carretié, 2015; Völlm et al., 2004). On the other hand, the finding of higher rates of commission

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errors (failed inhibitions) in patients with BPD neither necessarily reflects a failure in the inhibition process. A commission error during go/no-go tasks may also be the

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consequence of dysfunctions in previous processing stages, including stimulus perceptive and attentional processing (Albert et al., 2013; Verbruggen, McLaren, & Chambers, 2014). Functional brain techniques are therefore needed to complement these behavioral

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results.

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To date, however, results from the few studies that have examined the neural correlates

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of response inhibition in BPD have been mixed. Whereas some studies have found altered

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patterns of brain activity during emotionally neutral response inhibition tasks in BPD patients (Leyton et al., 2001; Ruchsow et al., 2008; Völlm et al., 2004), others have failed

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to observe activation differences between patients and controls (van Eijk et al., 2015). Indeed, some authors have proposed that response inhibition would be impaired in BPD

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under stress or negative emotions, but not under emotionally neutral conditions (Sebastian, Jacob, Lieb, & Tüscher, 2013; van Eijk et al., 2015). In this sense, a previous

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fMRI study showed aberrant inhibition-related activation during an emotional but not during a neutral go/no-go task in a sample of women with BPD (Gitta A Jacob et al., 2013). Therefore, it remains elusive whether BPD is related to neural abnormalities during non-emotional response inhibition tasks.

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The present study aimed to examine the neurophysiological basis underlying emotionally neutral response inhibition in BPD by capitalizing on the high temporal resolution of event-related potentials (ERP) and information provided by source localization methods. To our knowledge, there are no previous studies examining the electrophysiological correlates of response inhibition in BPD both at the scalp and the voxel levels. Analyses

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were focused on N2 and P3, the two ERP most typically associated with response inhibition (Bokura et al., 2001; Falkenstein et al., 1999; Huster et al., 2013; Kiefer et al.,

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1998). Notably, we used a modified version of the go/no-go task that allowed us to better isolate the neural correlates associated with the response inhibition process by controlling for the attentional capture of no-go stimuli (Albert et al., 2013; Sharp et al., 2010).

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Additionally, neural measures of response inhibition were correlated with self-reported

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impulsivity across participants to assess, from a dimensional perspective, possible

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relationships between neural signatures of response inhibition and impulsive behaviors.

2.1. Participants

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2. Methods

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The BPD group was composed of 20 in- and out-patients recruited from the specialized BPD unit of the San Carlos University Hospital, Madrid. They all had a formal diagnosis

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of BPD by a multidisciplinary team, including psychiatrists and psychologists, according to DSM-IV-TR criteria (American Psychiatry Association, 2000). Axis I and II disorders

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were assessed using the Mini-International Neuropsychiatric Interview (MINI 5.0; Sheehan et al., 1998) and the Spanish versions of the Structured Clinical Interviews for DSM-IV (First et al., 2003; Spitzer et al., 1996). Exclusion criteria included history of neurological disease or brain trauma, presence of substance abuse or dependence in the previous year, and previous bipolar or psychotic diagnosis. Patients with current

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comorbid Axis I disorder, except for dysthymia (n=1), panic disorder (n=2) and bulimia nervosa (n=1), were also excluded from the study. Past Axis I diagnoses included major depression (n= 8), dysthymia (n=16), panic disorder (n=2), posttraumatic stress disorder (n=2), eating disorders (n= 3), substance-related disorders (n=5), generalized anxiety disorder (n= 7), impulse control disorders (3), dissociative disorder (2), and attention-

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deficit hyperactivity disorder (n=4). Moreover, patients met criteria for the following

Axis II comorbid disorders: avoidant personality disorder (n=5), dependent personality

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disorder (n=3), paranoid personality disorder (n=6), obsessive-compulsive personality disorder (n=2), narcissistic personality disorder (n=2), histrionic personality disorder

(n=2), antisocial personality disorder (n=1), and schizoid personality disorder (n=1). All

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patients had at least a score of 4 on the Clinical Global Impression of Severity (CGI-S;

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(Guy, 1976). They were medication-naïve (n=4) or medication-free for at least 2 weeks

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prior to experiment (n=16). Non-medication-naïve patients had been previously treated

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with mood stabilizers (antiepileptics; n=10), antidepressants (n=9), second generation

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antipsychotics (n=7) and benzodiazepines (n=5).

Healthy control participants were 20 adults recruited from the local community. None of

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them had a history of neurological or psychiatry disorders or was taking medication. Absence of BPD and other psychiatry disorders was confirmed using the SCID-II (First

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et al., 2003) and the MINI (Sheehan et al., 1998). The BPD and control groups were matched on gender, age and education level (Table 1). By contrast, patients reported higher levels of BPD symptoms and trait-impulsivity as assessed with the Borderline Symptom List (BSL-23; (Bohus et al., 2009; Soler et al., 2013) and the Barratt Impulsiveness scale (BIS-11: (Oquendo et al., 2001; Patton & Stanford, 1995),

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respectively. The study was approved by the Ethical Research Committee of the Hospital Universitario San Carlos, Madrid. All subjects gave their written informed consent.

*** Table 1

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2.2. Experimental task

Participants were placed in an electrically shielded, sound-attenuated and video-

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monitored room while they performing a previously described go/no-go task from our

group (Albert et al., 2013). Briefly, participants were instructed to press a button with the thumb of their right hand, as fast and accurate as possible, whenever the letters “N”

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(frequent-go) or “M” (go) were presented, and to withhold pressing when the letter

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presented was “W” (no-go). The task consisted of a single block of 300 trials. Each trial

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began with the presentation of the letter (400 ms), followed by a black screen (700 or 900

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ms), so that the resulting onset asynchrony (SOA) was 1100 or 1300 ms (Figure S1). The

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letters “M” (go) and “W” (no-go) were presented with the same probability of occurrence (20%) in order to equalize both types of trials with respect to perceptual characteristics

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and novelty/oddball processing. This functional comparison (no-go vs. go) allowed us to better isolate the brain activity specifically associated with the suppression of a prepotent

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response. The letter “N” (frequent-go) was presented in the rest of trials (60%) to increase the participants’ tendency to respond, thereby increasing the mobilization of inhibitory

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resources needed to successfully withhold the motor response to no-go stimuli. This trial type was only included in the behavioral analysis. The task was programmed using Inquisit Millisecond software (Millisecond Software, Seattle, WA) and presented through a RGB projector on a backprojection screen. Further details regarding the task and stimuli are shown in Supplementary Material.

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2.3. EEG recording and preprocessing Electroencephalographic (EEG) activity was continuously recorded from 59 electrodes distributed over the whole scalp, and mounted in an elastic cap (Electro-Cap International, Eaton, OH).

The electrode positions used in the present study are represented

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schematically in Figure S1. All electrodes were referenced to the nose-tip and grounded with an additional electrode placed on the forehead. Eye movements and blink artifacts

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were monitored by means of two bipolarly recorded electrooculograms (vertical EOG and horizontal EOG). Recordings were continuously digitized at a sample rate of 420 Hz and filtered online with a frequency band-pass of 0.3–100 Hz using Contact Contact Precision

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Instruments amplifiers (http://psychlab.com).

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Data were processed off-line in Matlab using Fieldtrip (Oostenveld et al., 2011) and

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custom software (http://www.uam.es/carretie/soft/index.htm). Recordings were band-

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pass filtered off-line between 0.3 to 40 Hz. Ocular artefacts were removed using Independent Component Analysis (ICA). The continuous recording was divided into

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1000-ms epochs for each trial, beginning 200 ms before stimulus onset. Go trials in which participants did not respond (omission errors) and no-go trials in which they responded

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(commission errors) were not included in EEG analyses (the mean percentages of rejected trials due to behavioral errors for each condition and group are summarized in Table S2).

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Following the ICA-based correction, a careful visual inspection of the ERP data was conducted. If any further artifact was present, the corresponding trial was discarded. To ensure an appropriate signal-to-noise ratio, a minimum of 25 artifact-free trials per condition was set as a criterion before a subject was included in grand averages. ERPs

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were grand-averaged as a function of the two conditions (go and no-go) for patients and controls separately.

2.4. Data analysis Statistical analyses were performed using SPSS 20 (www.spss.com). In all statistical

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contrasts involving ANOVAs, the Greenhouse–Geisser (GG) epsilon correction was applied to adjust the degrees of freedom of the F ratios. Significant main effects and

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interactions (2-tailed, p<0.05) were further investigated using post-hoc t tests with

Bonferroni correction for multiple comparisons. Effect sizes were measured using partial

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eta-square -η2p- (F values) and Cohen’s d (t values).

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Behavioral analysis. Mean percentage errors (omissions -no responses in frequent-go and

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go trials-, and commission -button presses in no-go trials-) and mean reaction times (RTs)

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of correct responses (responses in frequent-go and go trials) were analyzed. For errors, an

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ANOVA was carried out using Group (BPD and control) as between-subjects factor and Trial type (frequent-go, go and no-go) as within-subjects factor. For RTs, independent

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samples t tests were conducted to determine whether there were any significant differences between groups in the mean RTs of correct responses in go and frequent-go

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trials. Mean RTs for commission errors (i.e., responses to no-go cues) were not analyzed

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because the low number of these trials in some subjects.

Scalp ERP analysis. It focused on the two components most typically associated with response inhibition: frontocentral N2 and frontocentral P3. Firstly, a frontocentral scalp region of interest (scalp-ROI) comprising 13 electrodes was selected for both components (Figure S2). This scalp-ROI was determined on the basis of visual inspection of grand

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averages and previous findings on response inhibition tasks (Albert, López-Martín, & Carretié, 2010; Albert et al., 2013; Bokura et al., 2001; Huster et al., 2013). Secondly, mean peak latencies and amplitudes were computed for each participant and each condition. Peak latency analyses (measured relative to stimulus onset) were conducted prior to amplitude analyses to guide the selection of amplitude windows for each

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component. The N2 amplitude was obtained by measuring the mean amplitude (µV) contained within a 50-ms time windows centered at the frontocentral N2 lantency peak,

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whereas the P3 amplitude was measured using a 200-ms time window centered at the

frontocentral P3 latency peak. Amplitude measurements were made relative to the mean voltage of the 200-ms prestimulus interval. Both mean peak latencies and amplitude

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voltage values for each component were analyzed with ANOVAs with Group (BPD and

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control) as between-subjects factor and Trial type (no-go and go) as within-subjects

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factor.

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Source localization analysis. To estimate the cortical regions underlying the experimental effects observed at the scalp level, exact low-resolution brain electromagnetic

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tomography (eLORETA) was applied (Pascual-Marqui, 2007; Pascual-Marqui et al., 2011). Specifically, three-dimensional current-density estimates for relevant components

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were computed for each subject and condition. Then, two different but complimentary analyses were performed. First, the voxel-based whole-cortex eLORETA images (6239

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voxels with a spatial resolution of 5 mm) were compared between no-go and go conditions within each group using the non-parametric mapping (SnPM) approach. Of note, SnPM inherently avoids multiple comparison-derived problems and does not require any assumption of Gaussianity. This within-group analysis was carried out to identify the regions specifically involved in response inhibition in each group. Second, a

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ROI approach was performed to assess the experimental effects through a full parametric factorial design (Albert, López‐Martín, Tapia, Montoya, & Carretié, 2012; Chiu, Holmes, & Pizzagalli, 2008). Spherical ROIs (radius=7 mm) were functionally defined using peak voxel coordinates emerging from previous whole-cortex SnPM analyses (no-go>go contrasts). Current-densities within these ROIs were computed for each participant and

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factor, and Trial type and Region as within-subjects factors.

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condition, and subsequently submitted to an ANOVA with Group as the between-subjects

3. Results

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3.1. Behavioral results

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The ANOVA on percentage errors revealed significant main effects of Group

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(F(1,38)=5.7, p=0.02, ƞ2p=0.1) and Trial Type (F(2,76)=176.8, p<0.001, ƞ2p=0.8). The

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main effect of Group showed that patients committed more overall errors than controls. The main effect of Trial type showed higher errors for no-go than for go and frequent-go

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trials. The interaction of Group and Trial type was also significant (Figure 1; F(2,76)=4.3, p=0.04, ƞ2p=0.1). Whereas patients made more errors than control subjects in no-go trials

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(p=0.03, Cohen's d=0.7), no differences between groups were found in go and frequent-

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go trials (p=0.2 and p=0.4, respectively). For mean RTs of correct responses to go and frequent-go stimuli, the independent-samples t tests did not reveal significant differences between groups (t(38)=0.6, p=0.5 and t(38)=1.2, p=0.2, respectively). Means and

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standard deviations of both behavioral and neural dependent measures are shown in Supplementary Material (Tables S1 and S2).

*** Figure 1

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3.2. Scalp ERP results A selection of grand averages once the baseline value (prestimulus recording) had been substracted from each ERP can be seen in Figure 2. These grand averages correspond to frontocentral scalp sites, where the relevant components are clearly visible. Supplementary Table 1 shows the means and standard deviations of the frontocentral N2

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and frontocentral P3 amplitudes and latencies for each group and condition.

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*** Figure 2

3.2.1. Frontocentral N2. The ANOVA on peak latencies did not reveal any significant

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effect of Group (F(1,38)=0.7, p=0.4), Trial type (F(1,38)=1.8, p=0.2), or their interaction

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(F(1,38)=1.6, p=0.2). Thus, for each participant, the voltage amplitude of the

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frontocentral N2 was computed in a time window of 50 ms around the mean peak latency

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across groups and trial types. The ANOVA on frontocentral N2 amplitudes neither

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showed significant main effect effects (Group: F(1,38)=0, p=0.9; Trial type:F(1,38)=0.8,

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p=0.4), nor a significant Group by Trial type interaction (F(1,38)=0.1, p=0.8).

3.2.2. Frontocentral P3. The visual inspection of grand averages suggested latency shifts

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for the frontocentral P3 depending on Group and Trial type (Figure 2). This impression was confirmed by the ANOVA on peak latencies: the interaction of Group and Trial type

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was significant (F(1,38)=7.7, p=0.01, ƞ2p=0.2). Thus, for each participant, the voltage amplitude of the frontocentral P3 was computed in a time window of 200 ms around the mean peak latency for each group and trial type.

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The ANOVA on these amplitude values revealed significant main effects of Group (F(1,38)=6.8, p=0.01, ƞ2p=0.1) and Trial type (F(1,38)=116.7, p<0.001, ƞ2p=0.7). With respect to Group effect, larger frontocentral P3 amplitude was found in the control than in the patient group across trial types. With respect to Trial type effect, post hoc t tests showed that frontocentral P3 amplitude was larger for no-go than for go trials across all

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participants. The interaction of Group and Trial type was not significant (F(1,38)=1.1,

p=0.3): both groups displayed greater frontocentral P3 amplitude for no-go than for go

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trials (p<0.001). It should be noted, however, that a larger effect size was found for no-

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go versus go in the control (Cohen's d=2.1) than in the BPD group (Cohen's d=1.5).

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3.3. Source localization results

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In order to localize and quantify the cortical activity underlying response inhibition in

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each group, the voxel-based whole-cortex eLORETA-images were compared between no-go and go using SnPM. In the control group, greater P3-associated activation in the

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superior medial frontal cortex was found for no-go than for go trials (t=3.49, p=0.04; Figure 3). Specifically, this increased activation was primarily observed in the left pre-

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SMA (BAs 6/8; peak MNI coordinates: X= -15, Y= 25, Z= 60). By contrast, in the BPD

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group, greater P3 associated activation in the no-go versus go comparison was found in superior medial parietal cortex (t=2.85, p=0.001; Figure 3). Concretely, this increased activation was observed in the precuneus (BA 7; peak MNI coordinates: X= 5, Y= -75,

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Z= 50).

***Figure 3

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To assess for differences in the recruitment of precuneus and preSMA in response inhibition between BPD and controls, we subjected the average current density in the P3 time window in these ROIs to a Region (preSMA, precuneus) x Trial Type (go, no-go) x Group (BPD, control) repeated measures ANOVA. All current density values and standard deviations are reported in Supplementary Table 1. The three-way interaction was

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significant (F(1,38)= 14.5, p<0.001, ƞ2p=0.28). The nature of this three-way interaction

can be seen in Figure 4. Patients with BPD showed greater activation in the precuneus for

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no-go than for go trials (p<0.001, Cohen's d=1.9), whereas no activation differences between trials was found in the preSMA (p=0.2). By contrast, healthy controls showed

greater activation in the preSMA for no-go than for go trials (p=0.009, Cohen's d=0.72),

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whereas no activation differences between trials was found in the precuneus (p=0.8).

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*** Figure 4

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3.4. Relationship between self-report and neural data Pearson´s correlations between questionnaire scores and brain activity were performed.

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The BIS-11 total score (trait impulsivity) negatively correlated with no-go-P3 amplitude and no-go-preSMA activation across the whole sample (r= -0.39, p=0.01 and r= -0.37,

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p=0.02, respectively; Figure S3). By contrast, no-go-precuneus activation was not related

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to BIS-11 total score (r=0.11, p=0.5; Figure S3).

4. Discussion The present study is, to our knowledge, the first to examine the neurophysiological basis underlying response inhibition in BPD combining the fine temporal resolution of ERP with the spatial information provided by source localization methods. Notably, we employed a modified version of the go/no-go task that allowed us to better isolate the 15

brain activity associated with the response inhibition process by comparing two conditions that differed for this process but were equated for visual complexity and novelty/oddball processing (Albert et al., 2013). Scalp level analysis revealed that both patients and controls exhibited larger frontocentral P3 amplitude for no-go than for go trials, although the magnitude of the effect size was greater in the control than in the BPD

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group. Source localization during P3 time range revealed that patients with BPD activated posterior parietal regions (precuneus) to succesfully inhibit their prepotent responses,

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whereas controls activated prefrontal regions (preSMA). Remarkably, this dissociation

was supported by a significant Region (precuneus, preSMA) x Trial Type (no-go, go) x Group (BPD, control) interaction. These findings suggest that patients with BPD recruit

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different brain regions for inhibiting the responses compared to control. Furthermore,

subjects.

Although

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inhibitions) than control

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behavioral analysis showed that patients with BPD made more commission errors (failed a causal

relationship

between

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electrophysiological activity and behavior cannot be established from present

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(descriptive) study, it could be hypothesized that the decreased inhibition-related performance observed in patients with BPD may be associated with the activation of these

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alternative brain regions (parietal rather than prefrontal areas). Further research using approaches that allow causal inferences between brain activity and performance (such as

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neurofeedback based on real-time fMRI and EEG) is therefore needed to confirm this

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assumption. Below we discuss these findings in more detail.

Consistent with fMRI investigations (Li et al., 2006; Sharp et al., 2010) and our previous source localization ERP study (Albert et al., 2013), healthy control participants activated prefrontal regions (specifically, the preSMA) for successfully inhibiting their motor responses (this activity emerged from the no-go vs. go contrast during the P3 time range).

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Note that this activation was primarily driven by inhibition-related processing, as the visual complexity and novelty/oddball processing were equivalent between our no-go and go conditions. Although solutions provided by EEG-based source-localization methods should be interpreted with caution due to their potential error margins, LORETA solutions have shown good correspondence with those provided by fMRI in the same

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tasks (Pizzagalli et al., 2004), including the go/no-go paradigm (Albert et al., 2012; Chiu

et al., 2008). Indeed, present results join a growing body of evidence from lesion studies

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of human subjects, as well as from descriptive and causal neuroimaging investigations, suggesting that the preSMA is a critical site for motor inhibition (Albert et al., 2013; Cai, George, Verbruggen, Chambers, & Aron, 2012; Chen, Muggleton, Tzeng, Hung, & Juan,

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2009; Li et al., 2006; Picton et al., 2006). Indeed, the preSMA is thought to be a key node

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of the so-called hyperdirect pathway, in which activation of the prefrontal regions

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(Aron, 2011; Chambers et al., 2009).

A

(IFG/preSMA) leads to a suppression of a motor response via a direct projection to STN

By contrast, BPD patients activated posterior regions (precuneus) for successfully

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inhibiting their prepotent responses. Similarly, this activation was observed during the P3 time range and emerged from the no-go versus go comparison. The precuneus is not a

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region considered to be integral to the inhibition-related brain network (Chambers et al., 2009; Verbruggen & Logan, 2008). Indeed, precuneus activation was similar between no-

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go and go conditions in our healthy comparison subjects. Therefore, precuneus engagement by patients with BPD during successful response inhibition may represent a compensatory mechanism to overcome a prefrontal neural dysfunction. This interpretation is consistent with the fact that, in comparison to healthy comparison subjects, patients did not exhibit greater prefrontal (preSMA) activation during no-go

17

compared to go condition. Therefore, present results suggest that BPD is related to an abnormal recruitment of brain regions during response inhibition. It has been proposed that the precuneus is a core node of the default mode network (DMN; (Fransson & Marrelec, 2008), which is typically more activated and showed stronger functional connectivity during rest than during goal-directed tasks. Persistence of DMN activity

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during task has been associated with lees accurate performance in several paradigms,

including those measuring response inhibition (Li, Yan, Bergquist, & Sinha, 2007).

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Interestingly, the DMN (including the precuneus) has been found to be altered in BPD,

both in structure and in function (Wolf et al., 2011; Yang, Hu, Zeng, Tan, & Cheng, 2016). Thus, the aberrant precuneus activity observed here might be also associated with

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disruptions in the DMN in BPD. In any case, further research is needed to assess this

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assumption.

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The current findings are consistent with other neuroimaging and electrophysiological

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studies reporting disturbances in the neural mechanisms subserving inhibitory control in BPD (Leyton et al., 2001; Ruchsow et al., 2008; Silbersweig et al., 2010; Soloff et al.,

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2003; Völlm et al., 2004). For example, Leyton and colleagues (Leyton et al., 2001) found that, compared to healthy controls, patients with BPD had an altered activation pattern

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during a traditional go/no-go task characterized by decreases and increases in the metabolism of several brain regions including those within the fronto-striatal circuitry.

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Support for a deficit in inhibitory control in BPD also comes from a scalp ERP study that used a hybrid flanker-go/no-go paradigm (Ruchsow et al., 2008). These authors found that, compared to controls, patients showed smaller amplitudes of P3 during the processing of no-go but not go cues. However, although these previous studies revealed abnormal patterns of activation elicited by inhibitory control tasks in BPD, such neural

18

disturbances might have been due to deficits in other cognitive processes beyond response inhibition because traditional or complex response inhibition tasks were used (Albert et al., 2013; Criaud & Boulinguez, 2013; Sánchez-Carmona et al., 2016). In traditional go/no-go or stop-signal tasks, infrequent inhibition trials (no-go/stop) conflate processing associated with attentional capture/novelty and response inhibition. Thus, activation

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differences between BPD patients and controls during no-go/stop trials could have been

caused by deficits in the attentional processing of the unexpected no-go/stop stimulus

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(Sharp et al., 2010). Indeed, attention and novelty deficits have been previously associated

with BPD (Meares, Melkonian, Gordon, & Williams, 2005). In complex go-no-go/stopsignal tasks with demands of working memory or response interference (e.g., flanker-

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go/no-go paradigms), response inhibition related activity is difficult to disentangle from,

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and may be confounded with, activity associated with interference inhibition and working

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memory. The present findings would confirm and extend these previous results by

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showing that the neural correlates specifically associated with response inhibition (i.e.,

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the cancellation of a planned or prepotent response) are disturbed in BPD.

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It should be noted that a recent study using fMRI failed to find activation differences between BPD patients and controls while performing traditional emotionally neutral

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response inhibition paradigms, including a go/no-go and a stop-signal task (van Eijk et al., 2015). Differences in the experimental paradigm (traditional vs. controlled version of

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the go/no-go task) and/or brain recording techniques (fMRI vs. EEG), might be contributing to the discrepancies between our results and those reported by van Eijk and colleagues (van Eijk et al., 2015). Likewise, it is important to note that BPD seems to represent a heterogeneous diagnostic disorder, which can include different phenotypic and etiological subgroups of patients (Leichsenring et al., 2011). Interestingly, a previous

19

neuropsychological study found that response inhibition was impaired in some (but not all) patients with BPD and their unaffected first-degree relatives (Ruocco, Laporte, Russell, Guttman, & Paris, 2012), highlighting therefore the heterogeneity of the disorder and suggesting that response inhibition may represent a promising endophenotype for BPD. Future studies employing sizeable samples will be necessary to confirm present

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findings and to explore the neural underpinnings of response inhibition in these potential

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subgroups of BPD probands and their biological relatives.

Dimensional analyses showed that the amplitude of frontocentral no-go P3, as well as the magnitude of inhibition-related preSMA activation, correlated negatively with

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impulsivity, as measured by BIS-11. These data, in line with a previous ERP study

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(Ruchsow et al, 2008), would suggest that response inhibition plays a role in the genesis

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of impulsive behaviors in the population. However, because behavioral inhibition is a

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multidimensional construct that includes a variety of subcomponents (Bari & Robbins,

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2013; Reynolds et al., 2006; Stahl et al., 2014), a single deficit in response inhibition is unlikely to account for the entire impulsivity-related behaviors shown by BPD patients.

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Further research examining different subcomponents of behavioral inhibition, including deferred gratification/reward-related impulsivity (Bari & Robbins, 2013), will be

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important to further progress in identifying the neural abnormalities associated with impulsivity in the population and in BPD. Indeed, reward-related impulsivity (choice

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impulsivity), as measured by delay, effort or probability discounting tasks, has been strongly implicated in BPD (Barker et al., 2015). On the other hand, given previous findings of weak correspondence between task-based and self-report measures of impulsivity (Cyders & Coskunpinar, 2011), the current results of a significant relationship between electrophysiological correlates of response inhibition and self-reported

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impulsivity highlight the usefulness of neural measures for studying dimensional aspects of impulsivity.

The results of this study must be considered in light of its limitations. First, although comparable to the sample sizes used in previous electrophysiological and neuroimaging

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studies in BPD (Gitta A Jacob et al., 2013; Ruchsow et al., 2008; Silbersweig et al., 2010;

Völlm et al., 2004), our sample size is modest. Thus, further research with larger samples

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is needed to substantiate present findings. Moreover, the size of our sample did not allow

us to explore whether inhibition-related activation patterns differ between males and females with the disorder, between patients with BPD alone and those comorbid with

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other disorders, or between BPD patients themselves. As noted before, BPD represents a

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heterogeneous diagnostic category comprised of individuals with a variety of symptoms

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and cognitive/affective dysfunctions (Leichsenring et al., 2011). Within this framework,

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it has been proposed that response inhibition might be compromised in some, but not all,

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patients with BPD (Ruocco et al., 2012). Future studies employing sizeable samples are therefore needed to explore the electrophysiological correlates of response inhibition in

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these subtypes of patients with BPD. Second, although all patients were free of medication for at least two weeks prior to study, we cannot exclude the potential medium-

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and long-term effects of pharmacological treatment of BPD on the behavioral and neural correlates of response inhibition. Thus, in future research it will be important to examine

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whether the atypical response inhibition-related activation pattern observed in our medication-free patients is also present in medication-naïve individuals with the disorder. Third, although the clinical characteristics of our sample of patients with BPD are similar to previous neuroimaging studies in terms of past Axis I diagnoses, current Axis II comorbidity and the minimum level of clinical severity to be included in the study (as

21

assessed by CGI-S), they had less comorbidity with current Axis I disorders. This has significant advantages (e.g., confounds due to Axis I comorbidity are minimized), but also some disadvantages (present findings could not be representative of the most severe forms/periods of the disorder). Finally, we did not include BPD adults with persistent and remitted symptoms, and therefore we could not assess whether the neural abnormalities

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associated with response inhibition observed here diminish or disappear with symptom

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remission.

5. Conclusion

Despite its limitations, this study provides novel data on the neurophysiological basis

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underlying response inhibition in BPD. By exploiting the high temporal resolution of

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EEG and the spatial information provided by source localization methods, and by using

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a more stringent go/no-go design to control for confounding factors, we found that

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response inhibition is subserved by distinct brain regions in patients with BPD than in

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healthy comparison subjects. Further research in larger, medication-naïve samples of

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patients with BPD should be carried out in order to confirm and extend these findings.

Funding: This work was supported by the Instituto de Salud Carlos III (grant numbers

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PI11-00725 and PI14/01449), and the MINECO (grant numbers PSI2014-54853-P and PSI2017-84922-R). The funders had no role in the study design, data collection, data

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analysis, data interpretation, or writing of the manuscript.

Declaration of interest All authors report no biomedical financial interests or potential conflicts of interest.

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Ethical Standards The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

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Wessel, J. R. (2017). Prepotent motor activity and inhibitory control demands in different variants of the go/no‐ go paradigm. Psychophysiology. Wessel, J. R., & Aron, A. R. (2015). It's not too late: The onset of the frontocentral P3 indexes successful response inhibition in the stop‐ signal paradigm. Psychophysiology, 52(4), 472-480. Wolf, R. C., Sambataro, F., Vasic, N., Schmid, M., Thomann, P. A., Bienentreu, S. D., & Wolf, N. D. (2011). Aberrant connectivity of resting-state networks in borderline personality disorder. Journal of psychiatry & neuroscience: JPN, 36(6), 402. Yang, X., Hu, L., Zeng, J., Tan, Y., & Cheng, B. (2016). Default mode network and frontolimbic gray matter abnormalities in patients with borderline personality disorder: A voxel-based meta-analysis. Scientific reports, 6.

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Figure 1. Performance (mean percentage errors) in the modified go/no-go task. Error bars

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represent ±1 SEM (*=significant differences between groups, p <0.05).

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Figure 2. Scalp-level analysis. Top: Grand averages at frontocentral scalp sites where the

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relevant ERP components were clearly visible. Bottom: Topographic voltage maps showing the difference between no-go and go conditions for each group separately during

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the P3 time range (350-550 ms); the color scale represents voltage scores (warm colors

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indicate positive amplitudes).

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Figure 3. Whole-cortex source localization analysis revealing increased P3-related

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activation during no-go relative to go trials for each group separately. Color bar represents

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voxel t values (red and yellow colours indicate increased activity for no-go versus go

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condition).

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Figure 4. Region of interest (ROI) source localization analysis showing dissociable

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recruitment of preSMA and precuneus during response inhibition between BPD patients

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and controls. Error bars represent ±1 SEM (*=significant differences between groups, p

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<0.05).

Table 1. Participant characteristics for patients with BPD and healthy controls. BPD (n=20)

Control (n=20)

Statistics

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Sex (female/male)

16/4

16/4

-

26.15±5.96

25.25±3.42

t(38)=0.58, p=0.56

2.3±0.86

2.4±0.94

t(38)=0.73, p=0.73

BSL-23 (total)

48.05±20.99

11.3±8.33

t(38)=7.28, p<0.001

BIS-11 (total)

72.45±14.4

41.15±16.09

t(38)=6.48, p<0.001

Age (years) Education level*

* The education level was quantified using a scale from 1 to 4 coding different levels from basic

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school to graduate university studies according to the Spanish educational system. Abbreviations: BPD, Borderline Personality Disorder; BSL-21, borderline symptom list; Barratt

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Impulsiveness Scale-11.

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