Neuropsychology 2015, Vol. 29, No. 5, 767–775

© 2014 American Psychological Association 0894-4105/15/$12.00 http://dx.doi.org/10.1037/neu0000168

The Premotor Role of the Prefrontal Cortex in Response Consistency Rinaldo Livio Perri

Marika Berchicci

University of Rome “Foro Italico” and University of Rome “La Sapienza”

University of Rome “Foro Italico”

Giuliana Lucci, Donatella Spinelli, and Francesco Di Russo

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University of Rome “Foro Italico” and IRCCS Santa Lucia Foundation, Rome, Italy Objective: The aim of the present study was to investigate the cortical correlates of the intraindividual coefficient of variation (ICV) in a go/no-go task, focusing on the prefrontal cortex (PFC) contribution and evaluating both pre- and poststimulus brain activity. Method: We recorded event-related potentials (ERPs) in 40 subjects, arranged a posteriori in 2 groups on the basis of their ICV values. By this method, we formed the consistent (low ICV; n ⫽ 20) and inconsistent (high ICV; n ⫽ 20) group: the age, speed, and accuracy performance of the 2 groups were matched. Results: The prestimulus anticipatory PFC activity, as reflected by the prefrontal negativity (pN) wave, and the poststimulus P3 component were larger in the consistent than in the inconsistent group. In contrast, no differences were observed between groups in the brain activities associated to motor preparation and early sensory processing. Conclusions: Data are interpreted as an enhanced top-down control in consistent performers, likely characterized by a greater sustained attention on the task. Keywords: EEG, event related potentials (ERPs), intraindividual coefficient of variation (ICV), Bereitschaftspotential (BP), prefrontal negativity (pN)

Chee, 2006), attention deficit hyperactivity disorder (Barkley, Grodzinsky, & DuPaul, 1992; Leth-Steensen, Elbaz, & Douglas, 2000), and schizophrenia (Vinogradov, Poole, WillisShore, Ober, & Shenaut, 1998) the RTs variability represents a marker of the disease. The performance in response tasks is usually quantified using mean or median values of the response times (RTs); however, these statistical indices represent an oversimplification and might lead to erroneous inferences when the intraindividual variability is large (Nesselroade, 2002). In healthy subjects, many factors influence ICV, that is, there is an age-related effect of the intraindividual RTs variability, showing an U-shaped function across the life span (Berchicci, Lucci, Pesce, Spinelli, & Di Russo, 2012; Williams et al., 2005): response variability decreases from childhood to adolescence, stabilizes in adulthood and increases in old age (Hultsch et al., 2002; Rabbitt et al., 2001). At brain level, there is a inverse relationship between the amount of behavioral response variability and brain activity variability during optimal performance; in other words, more stable the brain activity is, less variable the behavioral response. On the other hand, EEG studies observed a larger signal variability in adults than in children, associated with faster and more consistent RTs and higher accuracy (McIntosh et al., 2008); similarly, the BOLD-variability was larger in younger adults having faster and more consistent RTs than older adults in various different cognitive tasks (Garrett, Kovacevic, McIntosh, & Grady, 2011). In brief, it seems that the more variable cerebral activity of healthy adults was related to more flexible brain mechanisms that facilitate performance (i.e., more con-

The study of Jenkins and Carlson (1903) on nerve impulse in mollusks first reported a measure of variability, and in the following year Yerkes (1904) published an article highlighting the importance of variability in behavioral measures. However, the relationship between behavioral performance and brain state was introduced for the first time by Head (1926, p. 145) more than 20 years later, reporting that “an inconsistent response is one of the most striking consequences of lesions to the cerebral cortex.” Understanding the neurocorrelates underlying the behavioral variability now represents the goal of many neuroscience studies (for a review see MacDonald et al., 2006), especially if we consider that in several pathological conditions, such as mild dementia (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000), brain injuries (Segalowitz, Dywan, & Unsal, 1997), sleep deprivation (Chuah, Venkatraman, Dinges, &

This article was published Online First December 22, 2014. Rinaldo Livio Perri, Department of Human Movement, Social and Health Sciences, University of Rome “Foro Italico” and Department of Psychology, University of Rome “La Sapienza”; Marika Berchicci, Department of Human Movement, Social and Health Sciences, University of Rome “Foro Italico”; Giuliana Lucci, Donatella Spinelli, and Francesco Di Russo, Department of Human Movement, Social and Health Sciences, University of Rome “Foro Italico” and Neuropsychological Unit, IRCCS Santa Lucia Foundation, Rome, Italy. Correspondence concerning this article should be addressed to Rinaldo Livio Perri. University of Rome “Foro Italico,” Largo Lauro De Bosis, 15, 00194 Rome, Italy. E-mail: [email protected] 767

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sistent and accurate responses) with respect to the less variable activity of immature (i.e., children) or less efficient (i.e., elderly) brain. When high reaction time (RT) variability does occur in adulthood, it is often associated with changes in cerebral anatomy and physiology or in cognitive functioning. More specifically, high behavioral variability has been connected to microscopic white matter lesions in the frontal cortex (Bunce et al., 2007; Lövdén et al., 2013; Walhovd & Fjell, 2007) and hypofunctioning of the dopaminergic system (Cabeza et al., 2004; Li et al., 2001; Winterer et al., 2004). While, at cognitive level, RTs variability was inversely related to inhibitory success (Bellgrove et al., 2004) and it was explained as a deficit of sustained attention: reduced top-down control over attention, difficulties in temporal information processing, or executive decline (Johnson et al., 2007; Swick et al., 2013; Tamm et al., 2012). Many studies on behavioral variability considered the high RTs variability as an effect of the reduction of the prefrontal cortex (PFC) efficiency in the top-down control. This interpretation is confirmed by frontal patients that show an abnormally large RTs variability and poor accuracy in go/no-go tasks (Arnot, 1952; Picton et al., 2007; Stuss, 1991; Stuss et al., 1999, 2003;). Neuroimaging literature on RTs variability in healthy subjects reports contrasting results on PFC activity. Two studies employing a go/no-go task reported a greater dorsolateral PFC (DLPFC) activation associated with high intraindividual variability, which in turn was associated with poor inhibitory performance (Bellgrove et al., 2004; Simmonds et al., 2007). In brief, the augmented PFC activation was interpreted as a greater requirement of top-down executive control in subjects with greater RTs dispersion. By contrast, a study on the lapses of attention in a global/local selective-attention task reported that the RTs slowdown (leading to enhanced variability) was associated with reduced prestimulus activity of the right DLPFC and reduced poststimulus activity in the sensory areas (Weissman, Roberts, Visscher, & Woldorff, 2006). According to these results, the lapses of attention (behaviorally marked by longer RTs) produce a failure of the attentional enhancement of stimulus-triggered activity at sensory level. All these findings, although contrasting, point to the engagement of the frontal areas in discriminative tasks and, at the same time, they show the difficulty to isolate the functional role of the PFC in the response variability, which is not simply related to the averaged RTs (Bellgrove et al., 2004; Kaiser et al., 2008; Simmonds et al., 2007; Weissman et al., 2006). In the electrophysiological literature, several event-related potential (ERP) components were associated with behavioral variability. For example, some studies revealed an inverse relation between the P3 amplitude and the behavioral variability (Saville et al., 2011). Others (Segalowitz et al., 1997), comparing a healthy control with a brain injured group, showed a significant relation between some electrophysiological components and the behavioral variability only in the patients group. In the patients group, the lower variability was associated with an enhanced amplitude of both P3 and late contingent negative variation (CNV) recorded from central-posterior areas, although no mention was made on the PFC activity. Another study (McIntosh et al., 2008), considering the whole sample of both children and adults, showed a negative correlation between the RTs variability and the brain signal variability in the pre- and poststimulus 200-ms interval. Overall, few EEG data are available examining RTs variability in healthy adults, with no direct study of the PFC anticipatory activity.

In the present study, we investigated the neural correlates of the intraindividual coefficient of variation (ICV) by means of highdensity EEG and ERP analysis in a go/no-go task. We evaluated the pre- and poststimulus activity in two healthy groups, formed a posteriori on the basis of the high versus low ICV values. Our hypothesis was that within a cohort of healthy adults the behavioral consistency might be associated with specific cortical patterns in the prestimulus and/or poststimulus processing stages. To this aim, we investigated the brain activity within a large time interval including both pre- and poststimulus phase. More specifically, we studied two brain activities preceding the stimulus onset, that is, the Bereitschaftspotential (BP) and the prefrontal negativity (pN) components, and three poststimulus components that is, the P1, N1, and P3 components. The BP is a well-known negative slow wave associated to motor preparation originating from the supplementary motor area (SMA) and showing its maximum over the central locations of the EEG (e.g., Shibasaki & Hallett, 2006). The BP amplitude was positively correlated with the response speed in a go/no-go task (Perri et al., 2014); however, its activity was never studied in relation to the variability of the response. The pN component, which has been recently described in go/no-go tasks (Berchicci et al., 2012), is a negative slow wave concomitant to the BP, but with earlier onset and bilaterally distributed on prefrontal scalp regions. The neural source of the pN component was localized in the inferior frontal gyrus (iFg; Di Russo et al., 2013a) and it was associated with cognitive preparation during execution of discriminative motor tasks. In particular, it was proposed that more attentional resources are needed, larger is the pN (Berchicci et al., 2014, 2012; Di Russo et al., 2013b). The pN might also be modulated by different aspects, such as ageing (Berchicci et al., 2012) and physical activity (Berchicci et al., 2013) Regarding the poststimulus components, because Weissman, Roberts, Visscher, and Woldorff (2006) reported an association between the ICV and sensory activities, we expected to observe differences between groups with high and low ICV in the visual P1 and N1 components. Both these waves originate from the extrastriate areas (Di Russo et al., 2003; Hillyard et al., 1998) and are affected by visual-spatial attention (Clark & Hillyard, 1996; Hillyard et al., 1998; Wijers et al., 1997). However, the P1 enhancement was more specifically related to the facilitation at the early sensory processing level for items presented at attended location (Di Russo et al., 2003), and the N1 component was associated with the discrimination processes within the focus of attention (Luck et al., 1990; Vogel & Luck, 2000). Finally, we analyzed the P3 component that previous studies have related to the response variability (Ramchurn et al., 2014; Saville et al., 2011; Segalowitz et al., 1997). As the P3 amplitude was shown to index the allocation of attentional resources to the task (Kramer & Strayer, 1988; Polich, 1987; Polich & Kok, 1995), we also expected to observe a modulation at this level as a function of the response consistency.

Method Subjects and General Design Forty subjects (24 males; mean age ⫽ 42.4, SD ⫽ 14) participated in the study. Nine of them were part of a middle-aged group in a previous study using the same paradigm of the present one (Berchicci et al., 2013), and the others were recruited from em-

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ployers and students of the University of Rome “Foro Italico.” The students received an extra credit on the psychology exam for their participation in the experiment. The participants had normal or corrected-to-normal vision and no history of neurological or psychiatric disorders; all of the subjects were right-handed (Edinburgh handedness inventory; Oldfield, 1971). After procedures were explained, all of the participants provided written informed consent, approved by the local ethical Committee. The RTs collected during the present visuomotor task (see below) allowed evaluating the individual ICV as follows: ICV ⫽ standard deviation of RT/mean of RT. Based on the median ICV value of the whole sample (0.153), two groups were formed, that is, the consistent (with ICV below the median) and inconsistent performers (with ICV higher than the median): Each group was composed by 20 subjects. The demographic and behavioral data of the two groups are shown in Table 1, together with the value of statistical comparisons (performed by t test); the false alarms (FAs) were calculated as the percentage of the responses to no-go stimuli; the median values were reported for RTs. Inspection of the Table 1 shows that although the two groups had different ICV (0.14 and 0.25 for consistent and inconsistent performers, respectively), they were comparable for RTs and FAs. See Figure 1 for the distribution of the behavioral data in the two groups.

Procedure and Task Subjects were tested in a sound attenuated, dimly lit room; they were comfortably seated in front of a computer monitor at a distance of 114 cm, and a board was fixed on the armchair allowing them to freely push the button panel positioned on it. Four visual stimuli (i.e., four squared configurations made by vertical and horizontal bars) were randomly presented for 260 ms with equal probability (p ⫽ .25). Two stimuli were defined as targets (go stimuli, p ⫽ .5), the other two were defined as nontargets (no-go stimuli, p ⫽ .5). The stimulus onset asynchrony varied from 1 s to 2 s to avoid time prediction effects on the RTs. The entire experiment consisted of eight blocks, each of which contained 100 trials and lasted 2.5 min with a rest period in between: The total duration was about 30 min, depending on the subjective rest time. A total of 800 trials were delivered in the experiment: 400 for go and 400 for no-go condition. Participants were asked to be very accurate in the stimuli discrimination and to press a button as fast as possible with the right hand index when go stimuli appeared and withhold the response when no-go stimuli appeared on the monitor. As previous de-

Table 1 Comparison of Demographic and Behavioral Data of the Two Groups

N (male) Age (SD) ICV (SD) RT (SD) FA (SD) ⴱⴱ

p ⬍ .01.

Consistent

Inconsistent

t value

20 (13) 45.5 (12.4) 0.14 (0.007) 446.6 (56.7) 5.23 (3.09)

20 (11) 39.3 (15.2) 0.25 (0.17) 454.8 (49.2) 8.09 (5.8)

1.41 ⴚ2.77ⴱⴱ ⫺0.48 ⫺1.92

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scribed, three behavioral indices were calculated: the median RT, the FA percentage, and the ICV.

Electrophysiological Recording and Data Analysis The EEG signal was recorded using BrainVisionTM system (BrainProducts GmbH, Munich, Germany) with 64 electrodes mounted according to the 10 –10 International System. All electrodes were referenced to the left mastoid. Horizontal and vertical electrooculogram (EOG) were also recorded using electrodes at the right external canthi and below the left eye, respectively. Electrode impedance was kept below 5K⍀. The EEG was digitized at 250 Hz, amplified (band-pass of 0.01 Hz– 80 Hz including a 50 Hz notch filter) and stored for offline averaging. Artifact rejection was performed prior to signal averaging to discard epochs contaminated by blinks, eye movements or other signals exceeding the amplitude threshold of ⫾ 100 ␮V: by applying this procedure, a mean of 19.75% and 18.25% of trials were rejected in the consistent and inconsistent group, respectively. In order to investigate both the pre- and the poststimulus activities, the artifact-free signals were separately segmented into go and no-go trials, averaged in 2,000 ms epochs (from 1,100 ms before to 900 ms after the stimulus onset) and adjusted through ocular correction (Gratton et al., 1983). The baseline was defined as the mean voltage during the initial 200 ms of the averaged epochs. To further reduce high frequency noise, the averaged signals were low pass filtered (i.e., Butterworth) at 25 Hz (slope 24 dB/octave). First, in order to select the locations and the time windows where the differences were consistently significant, statistical differences in the prestimulus mean amplitude were computed with a sample-by-sample t test in all electrodes. Because this preliminary analysis revealed that amplitude of the prefrontal derivations (i.e., Fp1, Fp2) was significantly different between groups from ⫺600 m to 0 ms, we submitted the Fp1 and Fp2 mean amplitude in that time window to a 2 ⫻ 2 ⫻ 2 ANOVA with Group (consistent vs. inconsistent) as between factor, Site (Fp1 vs. Fp2) and Condition (go vs. no-go) as within factors. Based on the visual inspection of the waveforms and according to previous studies (e.g., Di Russo et al., 2006; Berchicci et al., 2012; Perri et al., 2014; Polich & Kok, 1995; Van Boxtel et al., 2001), the typical poststimulus ERPs were measured on the maximal peak electrodes as follows: the P1 component on PO8, the N1 on PO7, the N2 on Cz, the P3 on Pz, and Cz in the go and no-go condition, respectively. The peak amplitude and latency of the poststimulus ERPs on the above reported sites were submitted to separate 2 ⫻ 2 ANOVAs with group as between factor and condition as within factor. Post hoc comparisons were conducted using Bonferroni test. The correlation coefficients (Pearson’s r) were performed across the behavioral data (RTs, FAs, and ICVs). The overall alpha level was fixed at 0.05.

Results Figure 1 shows the behavioral data for the groups of consistent and inconsistent performers. The Pearson’s test on the behavioral indices did not reveal any significant effect: The ICV was not correlated with RTs or with percentage of FAs.

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Figure 1. Box plots of the distribution of behavioral data for the two ICV-groups. The box includes observations from the 25th to the 75th percentile; the square within the box represents the median value. Lines and circle outside the box represent the nonoutlier range and the outlier values, respectively.

Figure 2 shows the grand-average stimulus-locked waveforms of the consistent and inconsistent performers for both go and no-go conditions. In order to show the modulation of the main taskrelated activities, the Fp2, Cz, and PO8 sites were reported as these electrodes are especially representative for the pN, the BP, and the visual components (P1, N1), respectively.

Prestimulus Brain Activity Before the stimulus onset, two main slow negative potentials, very similar in the go and no-go conditions, were detectable: the pN, starting at about 800 ms before stimulus onset, and the BP, starting to slope at about ⫺700 ms. The ANOVA on the pN

Figure 2. Grand-averaged waveforms of consistent and inconsistent performers in three relevant sites (Fp2, Cz, PO8); the Time 0 corresponds to the stimulus onset. The groups and task conditions are superimposed with different colors (see legend in the figure). pN: prefrontal negativity; BP: Bereitschaftspotential. See the online article for the color version of this figure.

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amplitude showed significant main effects of group, F(1, 38) ⫽ 4.22, p ⬍ .05, that is, the consistent performers showed a larger pN (M ⫽ ⫺1.73 ␮V, SD ⫽ 1.18) than the inconsistent (M ⫽ ⫺1.02 ␮V, SD ⫽ 1), and site, F(1, 38) ⫽ 5.01, p ⬍ .05: This latter effect revealed a larger pN activity on the right hemisphere in both groups, that is, the Fp2 amplitude (M ⫽ ⫺1.5 ␮V, SD ⫽ 1.23) was larger than the Fp1 (M ⫽ ⫺1.2 ␮V, SD ⫽ 1.15). Neither task condition nor interaction effects reached statistical significance. Even though Figure 2 inspection shows the presence of a small difference between groups in the BP, no statistical difference emerged from the ANOVA on the BP amplitude. Figure 3a shows the topographical distribution (front view) of the pN in the ⫺600/0 ms time window showing that the pN component was larger in the consistent than inconsistent performers, and bilaterally present in frontal areas, even if more pronounced on the right side. In Figure 3b the consistent minus inconsistent group differential waves are reported: The differential waveforms further confirm the greater pN amplitude of the consistent performers.

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and the N1 component was larger in the consistent than inconsistent performers, even if this trend did not reach statistical significance (see below). The N2 component, peaking at about 250 ms on the frontal-central site (Cz), showed larger amplitude in the consistent than inconsistent performers (again, this difference was not supported by statistical analysis), and in the no-go than go condition. Finally, the P3 component peaked at 480 ms on the central-parietal areas: In both task conditions the consistent performers showed a larger amplitude than the inconsistent. Analyses on the P1 and N1 components did not show significant differences between groups, whereas ANOVA on the N2 amplitude showed a significant main effect of condition, F(1, 38) ⫽ 27.35, p ⬍ .0001, indicating that in both groups the N2 component was larger for the no-go (M ⫽ ⫺5.8 ␮V, SD ⫽ 3.5) than the go condition (M ⫽ ⫺4.9 ␮V, SD ⫽ 3.6). Significant group differences emerged only with respect to the P3 amplitude: ANOVA showed a main effect of group, F(1, 38) ⫽ 4.32, p ⬍ .05, indicating that the activity of the consistent performers (M ⫽ 8.3 ␮V, SD ⫽ 3) was larger than inconsistent (M ⫽ 6.4 ␮V, SD ⫽ 2.7) in both go and no-go conditions. No differences were detected in the latency of any components.

Poststimulus Brain Activity As can be seen in Figure 2, the stimulus presentation evoked the P1 (at 110 ms) and the N1 (at 170 ms) components on the posterior sites (PO8). The P1 component was very similar in the two groups,

Figure 3. (a) Scalp topographies (front view) of the grand-averaged activities in the ⫺600/0 ms time window for consistent and inconsistent performers. (b) Consistent minus inconsistent group brain activity before stimulus onset. Differential waves are reported. See the online article for the color version of this figure.

Discussion The main finding of the present study is the observation that the group with more consistent RTs showed a larger pN component over the prefrontal areas: a sort of inverse relation between RTs variability and PFC activity before the stimulus onset. A recent ERP/fMRI coregistration study (Di Russo et al., 2013a) that adopted the same paradigm of the present one showed that the pN component is generated in the iFg, likely reflecting the top-down proactive control devoted to the task execution. Thus, we interpreted the present pN data as an evidence of an enhanced top-down control in consistent compared with inconsistent performers. With respect to the premotor BP component, no difference between consistent and inconsistent performers was found. It was shown that BP component is related to the response speed (Band et al., 2003; Sangals et al., 2002), and because the two ICV-groups were matched for speed (and accuracy), the absence of difference between them in the BP component could be due to the RTs matching. This latter hypothesis is in line with the findings of a recent study of our group (Perri et al., 2014) showing a positive correlation between the BP amplitude and RTs and further suggesting that the BP component reflects more the response speed than the consistency. The BP similarity between present groups, together with the absence of correlation between ICV and RTs, also support the view that response consistency is independent from the average response speed, in agreement with some previous works (Bellgrove et al., 2004; Kaiser et al., 2008; McIntosh et al., 2008; Segalowitz et al., 1997; Simmonds et al., 2007; Weissman et al., 2006). It should also be noted that the two groups of the present study were matched for FAs, and the ICV was not correlated with accuracy; thus, the conclusions regarding group differences in brain activity cannot be confounded by group differences in accuracy (or speed). At the opposite, fMRI studies that showed an association between increased PFC activity and higher RT variability reported a relationship between ICV and accuracy (i.e., the greater the variability, the greater the errors; Bellgrove et al., 2004;

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Simmonds et al., 2007). For this reason, we suggest that contrasting findings of neuroimaging studies (such as decreased vs. increased PFC activity in consistent performers, as reported; e.g., by Weissman et al., 2006 and Simmonds et al., 2007) could be ascribed to the involvement of accuracy-related processes that engaged PFC regions similar to those subserving the variabilityrelated processes (i.e., Ivanoff et al., 2008). Moreover, one should remind that fMRI technique does not allow a fine temporal discrimination of the premotor processes, and the duration of the decision processes can also affects the amplitude of the BOLD responses, as suggested by Bogacz, Wagenmakers, Forstmann, and Nieuwenhuis (2010). Thus, it is also possible that the engagement of accuracy-related processes, together with the longer time needed to reach a response (resulting in enhanced variability) explains the greater PFC activity documented by some studies employing the go/no-go response task (Bellgrove et al., 2004; Simmonds et al., 2007). Summarizing, because there was a reduced effect of the confounding factors, that is, speed and accuracy performance of the two groups were matched, we can conclude that there is a relationship between the ICV and the amplitude of the pN component: The larger the pN, the higher the RTs consistency. The relationship between pN and RTs consistency likely reflects the efficiency of the sustained attentional mechanisms contributing to the top-down control in the present task. Support to this view may be found in some clinical studies on patients with frontal trauma showing an increased variability in RTs (Stuss et al., 2003), partly due to a reduced top-down attentional control (Fink et al., 1997). Moreover, larger pN were recorded on the right hemisphere with respect to the left in both ICV-groups of the present study; this result is consistent with the right-lateralization of the stimulus detection system described in healthy subjects by Corbetta and Shulman (2002), as well as with several studies showing that right frontal lesions correlated with deficits in a number of tasks requiring sustained attention (Glosser & Goodglass, 1990; Stuss et al., 1987; Wilkins et al., 1987). However, it should also be noted that it is difficult to determine the specific effect of the frontal lesions on the response variability, because they could account for a “generic” impairment of the executive control (Stuss et al., 2003), or could be related to specific task conditions: This is the case, for example, of the lexical decision tasks in which the major increment of RTs variability was reported by patients with left frontal damage (Milberg et al., 2003). A further problem in interpreting the relation between the activity of the PFC and the behavioral outcomes derives from the poor understanding we have about the exact role of these regions in the neural networks they are part of. For example, recent studies showed a strong relationship between riFg, pre-SMA, and M1 during response inhibition and action reprogramming (Mars et al., 2009; Neubert et al., 2010); however, the specific role of each region in the whole network is not yet clear (for a review see Neubert et al., 2013). Contrasting literature in this field also suggests the need to employ techniques, such as EEG, allowing to investigate the exact timing of the PFC engagement; indeed, as outlined by the present and previous studies of our group (Berchicci et al., 2013, 2014; 2012; Di Russo et al., 2013b; Perri et al., 2014), the two main EEG frontal activities taking place before the stimulus onset (i.e., pN and BP) are selectively associated to different aspects of the behavioral performance. The anticipatory

activities of the PFC regions (as reflected by the pN) seem to mainly affect the proactive inhibitory and top-down control on the task (Di Russo et al., 2013a; Weissman et al., 2006); by contrast, the pre-SMA activity (as reflected by the BP) selectively affect the response speed (Perri et al., 2014), possibly overcoming the tonic inhibition provided by the output nuclei of basal ganglia (Lo and Wang, 2006). Moreover, the frontal activities may have different functions after the stimulus onset; for instance it has been proposed that they are related to the reactive inhibition (Bokura et al., 2001; van Boxtel et al., 2001) and the action monitoring processes (Luu et al., 2000; van Veen & Carter, 2002). Based on the above reported observations, two main hypotheses emerge: (a) the surface recorded frontal activities could affect the behavioral performance in a different way (e.g., with respect to variability, speed or accuracy), depending on their neural source (e.g., iFg or pre-SMA) and timing (e.g., before or after the stimulus); and (b) the relation between attentional factors and behavioral performance could be mainly mediated by the PFC (i.e., pN) rather than the pre-SMA (i.e., BP). Based on findings of Weissman et al. (2006) we expected to find an association between response variability and sensory cortices processing. We hypothesized that P1 and N1 components could be sensitive markers of this relationship; however, the ICV difference was not associated to differences in the P1 or N1 components. It is possible that the absence of group difference in the visual components is due to the matched speed and accuracy performance. These findings suggest that response variability is mostly determined by sensory-independent processes, unlike the speed or accuracy of the response (Di Russo et al., 2006; Heitz & Schall, 2012; Perri et al., 2014; Rae et al., 2014; Zhang & Rowe, 2014). Regarding the N2 component, we did not observe differences between groups. Finally, as expected, we found differences in the P3 component showing a larger activity in the consistent than inconsistent group, confirming the results of previous studies (Ramchurn et al., 2014; Saville et al., 2011; Segalowitz et al., 1997). The P3 is a very complex wave representing the summation of many cognitive, motor, and response-related processes; however, because the P3 component reflects both the stimulus categorization (Mecklinger & Ullsperger, 1993) and the amount of attention resources allocated to the stimulus (Johnson, 1993; Kramer & Strayer, 1988; Polich, 1999), we suggest that these processing are enhanced in the consistent performers, likely reflecting a greater level of sustained attention, already present during the premotor phase and persisting across time until response execution.

Conclusion This study sheds light on the electrophysiological correlates of the RTs variability in a go/no-go task. We confirmed that the poststimulus cognitive P3 component was larger in the consistent than inconsistent group. The novel result of the present study is that the ICV is related to premotor PFC adjustments that precede the stimulus onset of about 600 ms, as reflected by the right lateralized pN component, larger in the consistent than inconsistent performers. In contrast, the ICV did not correlate with speed and accuracy scores, and it was not accounted by group difference in motor preparation (as resulted by the BP component), nor in the poststimulus sensory cortical processing, as reflected by the P1 and

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PREFRONTAL CORRELATES OF RESPONSE CONSISTENCY

N1 visual components. Briefly, we hypothesized that the greater the prestimulus PFC involvement, the more stable the behavior. The PFC role in RTs stability is consistent with some literature revealing that the high RTs variability predicts five years later maladaptive cognitive outcomes, much better than the RTs mean does (Bielak et al., 2010). It should also be noted that success in real-life tasks such as driving performance (Bunce et al., 2012), that implicates a PFC involvement, may depend more on response consistency than average performance. Present results stress the issue of the urgency to upgrade clinical assessments of the frontal functions that not always are able to capture the dispersion or inconsistency of RTs (Stuss et al., 2003). Future studies should investigate the relationship between the pN component and the executive functions, with the aim of developing tools of predictive markers detection for people at risk of decline of response consistency. Indeed, the knowledge about the EEG correlates of the PFC could also help clinicians to develop more tailored rehabilitation trainings. In other words, patients with high behavioral variability associated to impaired sustained attention (as probably reflected by smaller pN) might respond to alerting cues to maintain appropriate task control. Otherwise, patients with behavioral impairments at the level of reactive inhibition and action monitoring (as reflected by the poststimulus activities of the PFC) might benefit of a feedback- and response delay-based training. A limitation of the present study is the lack of specific neuropsychological measures to assess the frontal mediated attentional processes. Indeed, a deeper assessment including those measures could further clarify the relationship between the pN modulation and the cognitive processes subserving it. Finally, the present ICV-groups showed a slight FAs difference (5.23% and 8.09% for the consistent and inconsistent group, respectively): Even if not statistically significant, this data suggests to investigate the effect that the go trials after the FAs have on the ICV values. This investigation could clarify if the posterror slowing (e.g., Notebaert et al., 2009) might affect the individual ICV value on the whole task.

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Received July 17, 2014 Revision received September 21, 2014 Accepted October 30, 2014 䡲