Electrophysiological evidence of sustained spatial attention effects over anterior cortex: Possible contribution of the anterior Insula.

Marika Berchicci1*, Antonia Francisca Ten Brink2, Federico Quinzi3, Rinaldo Livio Perri4, Donatella Spinelli1 & Francesco Di Russo1

1Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome – Italy

2Center of Excellence for Rehabilitation Medicine, Brain Center Rudolf Magnus, University Medical Center Utrecht, and De Hoogstraat Rehabilitation, Utrecht – The Netherlands

3IRCCS Santa Lucia Foundation, Rome – Italy

4University of Rome “Niccolò Cusano”

*Corresponding author: Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 15 Piazza Lauro de Bosis, 00135, Rome, Italy.

Tel. +39 06 36733383.

Email: marika.berchicci@uniroma4.it

Running head: EEG evidence of spatial attention over anterior cortex.

Abstract

Spatial attention can improve performance in terms of speed and accuracy; this advantage may be mediated by brain processing at both post-stimulus (reactive) and pre-stimulus (proactive) stages. Here, we studied how visuo-spatial attention affects both proactive and reactive brain functions using event-related-potentials (ERPs). At reactive stage, attention effects on parietal-occipital components is well documented; few data are available on anterior components. Seventeen participants performed simple and discriminative response tasks, while voluntarily and steadily attending either left or right visual hemifield throughout one block. Response speed was faster for the attended side. At ERP level, attending to one hemifield did not produce lateralization of proactive components, i.e. the BP and the pN. As for post-stimulus components, we confirmed the well-known amplitude effects on the P1, N1 and P3. More interesting are results for the prefrontal components not previously investigated in tasks modulating spatial attention. Previous studies suggest that these components reflect perceptual and sensory-motor awareness (pN1 and pP1 components), and stimulus-response mapping (pP2 component) associated to anterior Insular activity. Spatial attention enhanced the pN1 and the pP1 amplitude but had no effect on the pP2. Overall, results extend knowledge on spatial attention, showing that sustained spatial attention affects the activity of anterior areas, such as the anterior insula in addition to the known influence on occipital-parietal areas. Top-down spatial attention is likely mediated by increased sensory and sensory-motor awareness for attended events; this effect is evident in reactive, not proactive, brain activity.

Key words: event-related potentials, prefrontal cortex, sustained spatial attention, anterior insula.

1. Introduction

Human sensory systems can process a limited amount of simultaneous information and selective attention is the supramodal cognitive mechanism that allows to allocate cognitive resources according to internal and external demands. Visual attention can be directed towards spatial locations, objects and features, either in overt or covert way (e.g., Bartolomeo et al., 2007; Szczepanski & Knight, 2014). Spatial selective attention plays a critical role in perception and action by filtering irrelevant information. As summarized by Eimer (1993), visual-spatial attention (also defined as “special” by Hillyard & Annlo-Vento, 1998) is the most studied form of selective attention since Posner showed facilitation effects (reflected by faster speed and increased accuracy) when spatial attention is explicitly oriented towards the stimulus location (Posner, 1980; Posner, et al., 1980). Neuroimaging and electrophysiological studies showed that visuo-spatial attention increases neural activity within occipital and posterior parietal areas in a retinotopic organized way (e.g., Martinez et al., 2006, Di Russo et al., 2012; Di Russo & Pitzalis 2014). In addition, the frontal cortex seems crucial for attentional selection, which involves gating the information flow from perception up to consciousness processing (Giessing et al., 2004; Niebur et al., 2002; Kastner & Ungerleider, 2000). These neural activities have often been referred to as the frontal-parietal attention network (Corbetta & Shulman, 2002).

Although fMRI studies found attention-related activities in distributed brain networks including frontal areas, event-related potential (ERP) studies found the effects of visuo-spatial attention only in parietal-occipital areas (for a review see Di Russo & Pitzalis, 2014). ERP studies, usually focusing on post-stimulus brain activity, showed that visuo-spatial attention enhances activities originating in dorsal and ventral extrastriate pathways, indexed by the P1 and N1 components. Furthermore, visuo-spatial attention does not affect the afferent activities originating in the primary visual area (V1), indexed by the C1 component (Baumgartner et al., 2017; Di Russo, 2017; Gómez-González et al., 1994; Pitts & Hillyard, 2017; but see Slotnick, 2017 for opposite results). Regarding attention effects on frontal areas, ERP evidence is scarce. So far, only one ERP study suggested the presence of a possible early dorsolateral frontal cortex activity in the context of an attention cuing paradigm (Foxe & Simpson, 2002).

Starting from the pioneering studies of our group (Berchicci et al., 2012), some prefrontal ERP components have been identified in the context of passive and visuomotor tasks: they are the prefrontal N1, P1 and P2 (hereafter called pN1, pP1 and pP2). Nowadays, these components are intended as a complex of ERPs reflecting consecutive stages of stimulus processing within the anterior insulae. Independent investigations coherently described the Insular components as the electrophysiological index of previous neuroimaging data showing the anterior insula contribution in perceptual decision-making (for review see Sterzer & Kleinschmidt, 2010; Menon & Uddin, 2010; Droutman et al., 2015; Lamichhane et al., 2016), action selection (Feinstein et al., 2006), and perceptual awareness (Craig, 2009). Indeed, the insular source of the prefrontal ERPs emerged from articulated research combining EEG and fMRI measures in perceptual paradigms, without a priori hypothesis on its origin (Di Russo et al., 2016; Sulpizio et al., 2017; Ragazzoni et al., 2019), which were corroborated by source analysis of three independent neuroelectric imaging algorithms (Perri et al., 2018a,b, 2019). Further, these and other studies were also determinant to exclude the functional similarity between prefrontal ERPs and the typical ERPs coming from visual areas in the same time windows. In fact, as similar in latency but opposite in polarity, one could argue that the pN1 and the pP1 represent the other side (or the subcomponents) of the dipolar activity of the posterior P1 and N1. However, this was not the case, as documented by several investigations revealing a number of anatomo-functional dissociations of prefrontal and posterior ERPs, as follows: i) the neural substrates of the concomitant prefrontal and occipital ERPs are different, ii) the prefrontal ERPs are multimodal and not specific for vision, iii) the anterior complex of the pN1-pP1-pP2 ERPs and the complex of the classic P1-N1-P2 are differently affected by perceptual and cognitive factors, iv) activity of the purely endogenous pP2 emerges even in absence of percepts, and, v) visual ERPs emerge in patients with selective deficit of visual awareness, but prefrontal ERPs do not (for comprehensive reviews on these points see Di Russo et al., 2017; Perri & Di Russo, 2017; Ragazzoni et al., 2019).

With respect to the prefrontal label of these components, it should be noted that it arises from the needs to characterize them better than other anterior components (mostly the so called anterior N1 and P2), also considering them as a sequence of processes from the same brain structure. In particular, as regard the pN1, the suspect that the classic N1 arises from multiple brain systems came from Neville and Lawson (1987) and was confirmed by Heinze et al. (1990). Signal decomposition procedures showed up to four subcomponents of the N1 (Makeig et al., 1999). The labelling and definition of the N1 subcomponents has been inconsistent (e.g., posterior and anterior N1, frontal N1, N1a, N1b, N1aL and N1aR) and, when localized using fMRI and EEG measures (e.g. Martinez et al., 2001), the N1 subcomponents were identified within posterior parietal areas. We decided to label as prefrontal N1 the anterior negative wave peaking at 100-120 ms over the AFz electrode (and spreading further anteriorly) as it was identified as the first step of perceptual processing from the anterior insula, segregated from the visual processing in the time range of the N1 (also note that the pN1 is concomitant to the posterior P1, that is earlier than the N1). As regard the prefrontal P2, Darriba and Waszak (2018) recently observed that it can be assimilated to other ERPs labeled differently across years, but all reflecting stimulus processing in the anterior areas of the brain (Kenemans et al., 1993; Potts et al., 1996; Makeig et al., 1999; Barceló et al., 2000; Gajewski and Falkenstein, 2013). However, none of them identified this component as the later and multimodal decisional process of the anterior insula. Summarizing, studies in this field associated each of the three prefrontal ERPs with specific and known stages of processing of the anterior insula: the pN1 with the perceptual awareness, the pP1 with the conscious experience of the sensory-motor coupling, and the pP2 with the decisional process of categorization and evidence accumulation (Berchicci et al., 2016; Bianco et al., 2017b; Di Russo et al., 2016, 2017; Gonçalves et al., 2018; Perri et al., 2014, 2015a,b, 2016, 2017,2018a.b; Perri & Di Russo, 2017; Ragazzoni et al., 2019;Sulpizio et al., 2017). So far, these prefrontal components were measured using centrally displayed stimuli, without manipulating spatial attention; thus, question is open on whether spatial attention may influence them. Since the anterior insula is part of the attention salience network responsible for cognitive and behavioral control (for reviews see Sterzer & Kleinschmidt, 2010; Menon & Uddin, 2010; Droutman et al., 2015), we hypothesize that manipulating visuo-spatial attention could affect prefrontal ERPs, increasing their amplitude. In the present paradigm, a cue directed attention constantly toward one side, and subjects were instructed to attend to a lateralized specific location throughout the entire experiment block. Thus, we investigated the effect of top-down spatial attention especially focusing on prefrontal components starting from 150 ms after stimulus.

As a secondary aim, we also evaluated the effect of spatial attention on proactive (pre-stimulus) components, reflecting motor and cognitive preparation. A typical observation in any task requiring motor responses and voluntary movements is the presence of a slow negative ERP activity indicating brain preparation (the Bereitschaftspotential or BP; Kornhuber & Deecke, 1965) in the cingulate motor area (CMA) and the supplementary motor area (SMA) (e.g., Shibasaki & Hallett, 2006; Di Russo et al., 2005, 2016; Sulpizio et al., 2017). Before the onset of stimuli requiring discriminative responses and involving a wide range of cognitive processes (see Criaud & Boulinguez, 2013 for a review), another slow negative wave has been described over prefrontal areas (the pN component) (Berchicci et al., 2012; Di Russo et al., 2017; Bianco et al., 2017a). EEG/fMRI combination studies (Di Russo et al., 2016; Ragazzoni et al., 2019; Sulpizio et al., 2017) localized the source of the pN component in the inferior frontal gyrus (iFg) and we proposed that this component may reflect proactive cognitive control of the action, such as top-down attention and inhibition postulated in the literature (Aron, 2011; Braver et al., 2009). Indeed, cognitive control could be exerted by engaging in either proactive or reactive strategies of control: the former relies on anticipating critical events and processing, whereas the latter responds to events or action (see, Braver, 2012). We proposed that the pN would reflect a cognitive-readiness activity of the frontal cortex, and the BP a motor-readiness activity of the premotor cortex, both acting in synchrony as a sort of brake/accelerator balance (Di Russo et al., 2016). As the pN represents endogenous cognitive control, we hypothesized that lateralized spatial attention would also enhance the need of cognitive brain preparation compared to previous studies with central stimuli not requiring spatial attention shifts (Berchicci et al., 2012; Di Russo et al., 2017; Bianco et al., 2017a).

2. Materials and methods

2.1. Participants

Seventeen healthy young participants volunteered to participate in the study (mean age 22.3 years, SD 3.8; 4 males). All participants had a normal or corrected-to-normal visual acuity. All participants gave written informed consent to participate in the study. The experiment was performed in accordance with the Declaration of Helsinki ad approved by the Santa Lucia Foundation ethic committee.

2.2. Procedure and tasks

Data were collected in a sound attenuated, dimly lit room. Participants sat at 114 cm distance from the computer monitor and had to place their right index finger on a button panel that was placed on a board on their armchair. Stimulus presentation and behavioral data acquisition were performed by PresentationTM software.

The fixation point (yellow circle, diameter 0.15° of visual angle) was presented on a black background at the center of the screen and set at the participant’s eye level. In addition, a visual cue (yellow arrow pointing toward left or right) was continuously presented, superimposed to the fixation point. The direction of the cue alternated between blocks. Prior to each block, participants were instructed to continuously pay attention towards the cued side for the entire block, keeping their gaze on the fixation point (i.e., covert attention). Trials consisted of the presentation of a visual stimulus for 100 ms within a placeholder box (yellow square, 4x4°) in either the left or right hemifield (3° from the fixation point), with equal probability (p=.5). This stimulus duration was chosen to avoid saccades. The stimuli were considered as attended or unattended if their position was respectively congruent or incongruent with the cued-side.

The stimuli consisted of four squared configurations made by vertical and horizontal bars, that appeared with equal probability (p=.25). The order of presentation was randomized within blocks. The inter-stimulus interval (ISI) varied from 1.5 to 2.5 s to reduce anticipatory responses and to avoid ERP overlap between subsequent trials. Each block lasted approximately 3 min. After each block, participants were granted a pause. Participants performed the simple response task (SRT) in the first session and a discriminative response tasks (DRT) in the second session (please see Figure 1 for tasks representation).

The SRT consisted of 12 blocks, each containing 80 stimuli. The recording session lasted approximately 45 min (960 trials in total). Paying spatial attention to one hemifield a time, participants were instructed to respond as fast as possible to any stimulus (avoiding anticipation errors and omissions) in both hemifields (any stimulus location) by pressing the button with their right index finger. Trials with response anticipations (i.e., responses within 100 ms from stimulus onset; 1.95%) and very slow responses (i.e., more than 1000 ms from stimulus onset; 0.17%) were excluded from further analyses in both SRT and DRT.

The stimuli presented in the DRT were identical to those used in the SRT, but two of them were defined as targets (p=.5) and two as non-targets (p=.5). The DRT comprised 20 blocks (each containing 80 stimuli), allowing a total of 1600 trials (800 targets and 800 non-targets, about 70 min recording session). Participants were instructed to pay attention toward the cued hemifield, to be very accurate (avoiding anticipation and commission errors) and press the button as fast as possible with their right index finger when targets were displayed in the cued or uncued hemifield and withhold their response when non-targets appeared. Participants received a short task training to familiarize with stimulus categories. SRT and DRT were administered in separate days (within a week) in a counterbalanced order.

-Please, include Figure 1 approximately here-

2.3. Analysis of the behavioral data

The mean response time (RT) for correct responses was calculated for the attended and the unattended condition. For each condition (i.e., attended vs. unattended), accuracy was measured by the percentage of omission errors (i.e., missing responses to target stimuli in SRT; commission errors to target stimuli in DRT) and false alarms (i.e., responses to non-target stimuli in the DRT). The individual coefficient of variation (ICV) was calculated as standard deviation/mean of the individual RT. Behavioral variables were separately submitted to a 2 (Attention: Attended, Unattended) x 2 (Stimulus position: Left, Right) repeated measures analysis of variance (RMs-ANOVA) for SRT and DRT. The alpha level was set at 0.05 and the partial eta squared (pη2) was computed to evaluate the effect size.

2.4. Electrophysiological (EEG) recording and analysis

Continuous EEG was recorded using three BrainAmpTM amplifiers, two of them connected to 64-active sensors ActiCap. Data were recorded and processed using the BrainVision Recorder 1.2 and the Analyzer 2.1 software (all by BrainProducts GmbH., Munich, Germany). Electrodes were mounted according to the 10-10 International System and were initially referenced to the left mastoid and then re-referenced to average reference. The EEG was amplified, digitized at 250 Hz, filtered (bandpass of 0.01-80 Hz) and stored for off-line analyses. Eye movements were monitored by electro-oculogram (EOG) recorded by the third BrainAmp amplifier (ExG type) in bipolar modality. Horizontal EOG was detected with electrodes placed at the left and right outer canthi of the eyes. Vertical EOG were recorded with an electrode pair below and above the left eye to detect blinks and vertical eye movements. Electrode impedances were kept below 5KΩ.

Prior to signal averaging, raw EEG data were visually inspected to identify, and discard epochs contaminated with artifacts. The first trial of each block was discarded from further analyses. Blink and vertical eye movement artifacts were corrected by using independent component analysis (ICA; Jung et al., 2000); trials with horizontal eye movements were discarded by applying the artifact rejection. Indeed, trials with amplitudes exceeding the threshold of ±70µV were semi-automatically identified and excluded from the averaging (about 9% of trials). Nonetheless, we must recognize the EOG monitoring limits in detecting small eye movements; the use of infrared and eye tracking techniques would be necessary to reach optimal accuracy.

To further reduce high-frequency noise, the time-locked EEG grand-averages were band-pass filtered using an IIR filter (0.01-30 Hz; 24 dB/oct). Trials with RTs outside the 100-1000 ms time window, omissions and false alarms were discarded from further analyses.

Pre-stimulus ERPs

To evaluate the pre-stimulus activity, ERPs for the SRT and DRT were separately segmented into 2000 ms epochs, starting 1100 ms before and ending 900 ms after the stimulus onset, with the first 200 ms (-1100/-900 ms, in which the signal was flat and stable) serving as baseline according to previous studies (e.g., Berchicci et al., 2016). Since stimulus category was unpredictable, target and non-target trials were averaged together in the DRT for the pre-stimulus analyses. To investigate whether the direction of spatial attention affects brain preparation to stimulus presentation, for each task (SRT and DRT), ERPs were divided into two categories: cue orienting attention toward the left and cue orienting attention toward the right. A preliminary statistical analysis was carried out to test for the presence of possible hemispheric asymmetries between left- and right-cued blocks. In detail, for each cue direction (Left cue; Right cue) and in both SRT and DRT, inter-hemispheric differential waves were computed (all the electrodes of the left side minus their homologues of the right side, excluding the medial electrodes). Afterwards, the differential waves for each condition (Left cue SRT, Right cue SRT, Left cue DRT and Right cue DRT) was submitted to a point-by-point t-test against zero to check for any significant difference from zero using Analyzer 2.1 software. This analysis produced non-significant results; therefore, we statistically analyzed the pre-stimulus ERP with a- priori measurement parameter at electrodes with larger activity as done in previous studies (e.g., Di Russo et al., 2017) at prefrontal (Fp1, Fp2, and Fpz) and medial central and parietal (Cz and Pz) electrodes reflecting the pN and the BP components, respectively. For statistical purposes, the 900 ms time window preceding the stimulus onset was divided in three sub-windows each lasting 300 ms: -900/-600 ms, -600/-300 ms, and -300/0 ms.

Post-stimulus ERPs

Post-stimulus ERPs for the SRT and DRT were segmented into 1200 ms epochs from -200 to 1000 ms after the stimulus onset (-200/0 ms baseline) and then averaged. To test the effect of spatial attention, four ERP categories were obtained: 1) Left-Attended, 2) Left-Unattended, 3) Right-Attended, 4) Right-Unattended. This analysis allowed 240 trials per condition for the SRT and 200 for the DRT (averaging together DRT Target and Non-Target trials). In addition, to test the discriminatory task effects, the DRT trials were further composed in four other categories of 200 trials each: 1) Left-Target, 2) Left-Non-target, 3) Right-Target, 4) Right-Non-Target, averaging together attended and unattended conditions. This approach was adopted to improve the signal-to-noise ratio in ERP data of the DRT, because only 100 trials per condition would be obtained if all the 16 possible conditions were separately averaged.

To avoid biasing ERP component measurement procedures (see Luck and Gaspelin, 2017), for each component, peak amplitude (µV) was semi-automatically calculated as the maximum value with respect to the baseline in a pre-defined (a-priori) temporal windows for each participant, chosen based on their polarity, morphology and topography, as previously described (Berchicci et al., 2012; Di Russo et al., 2007, 2012, 2016). The considered temporal windows for each component were the following: the C1: 60-100 ms; the P1 and the prefrontal N1 (pN1): 80-130 ms; the N1 and the prefrontal P1 (pP1): 140–200 ms; the P2: 150-250 ms; the N2: 200-300 ms; and the P3: 300-400 ms for the SRT and 500–600 ms for the DRT. The peak amplitude was then chosen as the individual maximum value in the above temporal windows for each component. Selection of electrodes used for analyses was based on the largest activity for a given component at group level (analyses at individual level produced comparable results) and based on extensive previous literature, as follows: the C1 at POz, the P1 and the N1 at PO7 for the right-presented stimuli and PO8 for the left-presented stimuli; the P2 at O1 for the right-presented stimuli and O2 for the left-presented stimuli; the pN1, the pP1 and the pP2 at AFz; the N2 at Cz; the P3 at Cz and Pz in the SRT and DRT.

The pP2 amplitude was calculated as the mean amplitude in the 300–400 ms time window and since it is masked by concomitant anterior activity of negative polarity, and it is described as a decision-oriented increasing activity for target stimuli, we could de-mask the pP2 activity by computing the Target minus Non-target differential waves (dpP2), as in other studies (e.g. Bianco et al., 2017b). This subtraction procedure was made separately either for left and right stimuli and for attended and unattended stimuli.

Topographical maps

To visualize the ERP topography, spherical spline maps were rendered using BrainVision Analyzer 2.1 tools and were visualized with both top-flat views 120° wide and frontal, posterior or lateral views. Both voltage and current source density (CSD) maps were used, because the latter reduces the negative impact of volume conduction and are reference-free.

2.5. Source Analysis

The intracranial sources of the attention effect were determined using two different approaches in order to improve the reliability of each approach in case of consistent results. Source analyses were performed on grand-averaged data; further, it must be noted that the software automatically replaces the original reference with the average reference. We first used the “exact low-resolution brain electromagnetic tomography” (eLORETA) software (freely available at www.uzh.ch/keyinst/loreta.htm) to compute the cortical three-dimensional distribution of current density. The eLORETA method is a discrete, three-dimensional distributed, linear, weighted minimum-norm inverse solution. The weights used in eLORETA endow the tomography with the property of exact localization to test point sources, yielding images of current density with exact localization, albeit with low spatial resolution (i.e., neighboring neuronal sources will be highly correlated). eLORETA has no localization bias, even in the presence of structured noise, and, in this sense, it is an improvement over LORETA (Pascual-Marqui et al., 1994) and the standardized version sLORETA (Pascual-Marqui, 2002).

As a second approach we used the spatiotemporal source analysis module of the BESA system (GmbH, Gräfelfing, Germany) to estimate the location, orientation and time-course of the early attention effect by calculating the scalp distribution obtained for a given model (forward solution). This distribution was then compared to that of the actual ERP. Interactive changes in the source location and orientation led to minimization of the residual variance between the model and the observed spatiotemporal ERP distribution. The three-dimensional coordinates of each dipole in the BESA model were determined with respect to the Talairach axes. The possibility of interacting dipoles was reduced by selecting solutions with relatively low dipole moments with the aid of an “energy” constraint (weighted 20% in the compound cost function, as opposed to 80% for the residual variance). The optimal set of parameters was found in an iterative manner by searching for a minimum in the compound cost function. Latency range for fitting was from 100-140 ms, because present previous studies identified the earliest attentional effect in this latency range (please see introduction). In every approach, the BESA assumed a realistic approximation of the head (based on the MRI of 24 subjects) and source were displayed on this MRI template.

2.6. Statistical analysis

Pre-stimulus ERPs

Mean voltage of the three time-windows preceding stimulus onset was exported and analyzed using RM-ANOVA. For the pN, a 2x2x3 RM-ANOVA was carried out to investigate effects of Task (SRT vs. DRT), Cue direction (Left vs. Right) and Site (Fp1, Fp2, Fpz). For the BP a 2 x 2 x 2 RM-ANOVA was used with Task, Cue direction and Site (Cz, Pz) as repeated factors.

Post-stimulus ERPs

Peak amplitudes of the C1, P1, N1, pN1 and pP1 components were separately submitted to a 2x2x2 within subjects RMs-ANOVA with Task (SRT, DRT), Attention (Attended, Unattended), and Stimulus position (Left, Right) as repeated factors.

Other components were separately considered for SRT and DRT for several reasons, such as different latency of the peak amplitude, presence of the component in one condition only (e.g., the P2 is present only in the SRT and the N2 in the DRT), differences between target and non-target, different topographies between components (see the P3).

For the SRT peak amplitude of the P2 was analyzed using a 2 (Attention: Attended, Unattended) by 2 (Stimulus position: Left, Right) RM-ANOVA. Peak amplitude of the P3 was submitted to a 2x2x2 RM-ANOVA, with Attention, Stimulus position, and Site (Cz, Pz) as repeated factors.

For the DRT, peak amplitude of the N2 was analyzed using a 2 (Attention: Attended, Unattended) x 2 (Stimulus position: Left, Right) x2 (Condition: Target, Non-target) RM-ANOVA. Peak amplitude of the P3 was submitted to a 2x2x2x2 RM-ANOVA, with Attention, Stimulus position, Condition, and Site (Cz, Pz) as repeated factors. The dpP2 was submitted to a 2x2 RM-ANOVA with Attention and Stimulus position as factors.

For all statistical analyses, only panned-comparison were admitted, and alpha level was set at 0.05 after Bonferroni corrections for repeated measures. The partial eta squared (pη2) was computed to evaluate the effect size.

3. Results

3.1. Behavioral data

In the SRT, a main effect was observed for Attention, with the mean RT in the attended condition (233 ms, SD=22) faster (by 4.7%) than the unattended condition (244 ms, SD=24; F1,16=12.35, p=0.006, pη2=0.43). No effects were observed for Side or Interactions (ps>0.4). The percentage of omissions (e.g., attended: 2%, SD=1.3; unattended: 2.2%, SD=1) and the ICV (e.g., attended: 0.22, SD=0.01; unattended: 0.21, SD=0.02) were not significantly different for Attention (ps>0.2), Side (ps>0.6), nor Interactions (ps>0.4).

In the DRT, a main effect was observed for Attention, with the mean RT faster (by 3.9%) in the attended (529 ms, SD=50) than the unattended condition (550 ms, SD=45; F1,16=12.61, p=0.003, pη2=0.44). No effects were observed for Side or Interactions (ps >0.5).

The percentage of omissions (e.g., attended: 3.5%, SD=4.9; unattended: 4.7%, SD=5.5), false alarms (attended: 7%, SD=5.8; unattended: 7%, SD=7.3) and the ICV (e.g., attended: 0.17, SD=0.002; unattended: 0.16, SD=0.001) were not significantly different for Attention (ps>0.07), Side (ps>0.4), nor Interactions (ps>0.5).

3.2. ERP Data

Pre-stimulus activity

To analyze the pre-stimulus activity, all conditions of each block type (attended left and attended right) were averaged. The left panel of Figure 2 shows waveforms obtained in the attended left blocks (regardless the future stimulus position and category) and the right panel shows waveforms obtained in the attended right. The SRT (red traces) and DRT (blue traces) are superimposed on medial frontopolar (Fpz), frontal (Fz), central (Cz) and parietal (Pz) derivations. In both the SRT and DRT, the prefrontal negativity (pN) was detectable over the prefrontal site (Fpz), without differences between tasks and cued side. The BP component was larger in the SRT than DRT over medial central (Cz) and frontal (Fz) sites, whereas there were no differences between SRT and DRT in the parietal BP (Pz) or between cued sides.

RM-ANOVA did not show any significant effect of Task, Cue direction and Site on the pN amplitude in the three time-windows. In contrast, we observed a significant Site effect (F1,16=13.4, p=0.016, pη2=0.44) on the -900/-600 ms time window for the BP, indicating larger amplitude at Pz (-0.41 µV) than Cz (-0.27 µV). In the -600/-300 ms time window, the Task by Site interaction was significant (F1,16=13.3, p=0.016, pη2=0.45). Post-hoc analyses showed larger BP in the SRT (-1.09. µV) than DRT (-0.50 µV) over Cz (p=0.002), and a larger BP over Pz (-0.8 µV) than Cz (-0.4 µV) in the DRT (p=0.001). In the last time window, before stimulus onset (-300/0 ms), the Task by Site interaction was significant (F1,16=9.8, p=0.048, pη2=0.38). Post-hoc analyses showed larger BP at Cz (-2.2 µV) than Pz (-2.0 µV) in the SRT (p=0.032), and larger BP amplitude at Pz (-1.8 µV) than Cz (-1 µV) in the DRT (p=0.02).

Overall, in the pre-stimulus phase, no effects of spatial attention were found, but only a slight BP anteriorization in the SRT than in the DRT.

Figure 3 shows topographical voltage maps of the pre-stimulus activity for both SRT (upper panel) and DRT (lower panel) in the three analyzed time-windows. In both tasks the pN component is clearly detectable over prefrontal sites; this activity tends to focus on the left prefrontal region in SRT (especially when attending to the right side), and to the right in DRT. On the other hand, the BP has a central-parietal distribution and slightly spread over the right side. However, laterality effects did not reach the statistical significance. Figure 4a shows CSD scalp topography in the last pre-stimulus interval, confirming the presence of three foci of activity (prefrontal, frontal and parietal) in both SRT and DRT, but no differences induced by the cue direction. Figure 4b shows eLORETA analysis in the same interval; in both tasks, it can be observed a network of activities focusing at medial-frontal cortex (SMA and CMA) and, more anteriorly and laterally, over the prefrontal cortex (iFg). Additional activity was present in bilateral extrastriate areas (not shown). In this analysis, left and right cues conditions were averaged to get better signals-to-noise ratio, since no differences emerged.

-Please, include Figures 2-4 approximately here-

Post-stimulus ERPs

Figure 5 shows the post-stimulus ERPs that were obtained when the stimuli were presented on the left or right hemifield for the two tasks for target trials, separately for attended and unattended condition (depending on cue direction). Figure 6 shows voltage scalp topography of the observed components in the DRT. The earliest component, the C1 peaked at POz at 85 ms with negative medial distributions. About 40 ms later, peaking at 120-130 ms, the P1 could be detected bilaterally at lateral parietal-occipital sites with larger amplitude contralateral to the stimulus presentation (PO8 for the left stimuli and PO7 for the right stimuli). The pN1 focused over medial prefrontal sites peaking at AFz at 140 ms. The N1 peaked at 170 ms over parietal-occipital sites with larger contralateral amplitudes. The pP1 peaked over AFz at 200 ms. To follow the time sequence of cortical peaks, we move backwards to the posterior sites, where the P2, which was only detectable in the SRT, peaked at 250 ms over contralateral parieto-occipital electrodes. The N2 (not labelled in the figures) peaked at 280 ms at Fz in the DRT and was not present in the SRT. The P3 peaked at 340 ms in the SRT, and at 540 ms in the DRT in the medial parietal scalp. The anterior pP2 was not visible because covered by concomitant activities of negative polarity. However, Figure 7 shows the differential pP2 (dpP2) peaking at 380 ms (Figure 7 a-b at AFz) with medial and radial prefrontal scalp distribution (Figure 7 c-d front-view maps), confirming that prefrontal positivity was larger for target than non-target stimuli. The dpP2 (related to sensory evidence accumulation according to Perri et al., 2014) was not affected by stimulus position (left or right side) and attention.

-Please, include Figures 5-7 approximately here-

Statistical data are below reported for the significant effects only. No effects were found for the C1. The P1 amplitude was significantly affected by Attention (F1,16=22.0, p=0.0008 pη2=0.58), being larger in the attended (2.7 µV) than in the unattended (2.3 µV) trials. The N1 was affected by Attention (F1,16=12.2, p=0.024, pη2=0.4), with a larger amplitude in the attended (-1.4 µV) than unattended (-1.0 µV) trials, and by Task (F1,16=16.0, p=0.008, pη2=0.80), with a larger amplitude in the DRT (-1.7 µV) than SRT (0.8 µV). The pN1 component was significantly influenced by Attention (F1,16=10.0, p=0.048, pη2=0.38), presenting larger amplitude in the attended (-2.7 µV) than unattended (-2.3 µV) trials. A significant Attention effect (F1,16=18.0, p=0.008, pη2=0.53) was observed for the pP1, being larger in attended (3.1 µV) than unattended (2.5 µV) trials.

For the SRT, no significant effects were observed for the P2, whereas it was observed an Attention effect (F1,16=4.9, p=0.042, pη2=0.27) on the P3, with larger amplitude in attended (7.3 µV) than unattended (6.9 µV) trials.

For the DRT, the effect of both attention and stimulus position was not significant for both the dpP2 and the N2. We observed an Attention effect (F1,16=13.2, p=0.032, pη2=0.45) on the P3, with larger amplitude in the attended (7.9 µV) than unattended (7.1 µV) trials.

Overall, lateralized spatial attention enhanced the amplitude of multiple post-stimulus components i.e, the P1, the N1, the pN1, the pP1 and the P3; in contrast, the C1, the P2 and the dpP2 were not affected by spatial attention (see Table 1).

-Please, include Table 1 approximately here-

To show possible cortical origin of the earliest spatial attention effect found in the study, i.e. on the P1 and the pN1 components, Figure 8a shows CSD topography of the attended minus unattended ERPs in the 100-140 ms interval. The figure shows that the attentional effect on these components is explained by enhanced activity of two independent bilateral current source pairs, one explaining the P1 effect in parietal-occipital scalp and another explaining the pN1 effect in lateral prefrontal scalp areas. In Figure 8b, eLORETA shows the possible cortical activity present in the same early interval and confirms the P1 attention effect in bilateral extrastriate occipital areas and the pN1 effect in bilateral anterior areas including the anterior insular cortex.

-Please, include Figures 8 approximately here-

Figure 9 shows the locations and orientations of two dipolar source pairs fitted in the 100-140 ms interval, critical for early attention effect (attended minus unattended ERP), for both SRT and DRT. To obtain lower signal-to-noise ratio, left- and right-side stimuli as well as Go and No-go stimuli were averaged. This analysis confirms previous CSD and eLORETA results explaining the P1 effect with a source pair in bilateral extrastriate Brodmann area (BA) 19 for both SRT and DRT (Talairach coordinates: SRT ±27, -81, 25; DRT: ±30, -78, 20). The pN1 effect was explained with a pair in area BA 13 very close to the anterior Insula (SRT ±43, 10, -4; DRT ±44, 12, 0). The residual variance in the 100-140 ms interval was 3.1% for SRT and 2.9% for DRT.

-Please, include Figures 9 approximately here-

4. Discussion

In the present study, participants were instructed to continuously attend to one side of a screen based on a stationary cue that was presented throughout an experimental block. Stimuli were displayed at the attended location in half of the trials, and at the unattended location in the other half of the trials. In the SRT, participants had to respond as fast as possible to any stimulus. In the DRT, half of the trials included non-targets and participants had to withhold their response to these stimuli, while responding as fast as possible to the target stimuli in the other half of the trials. Even though the cue was valid in only 50% of trials, results confirm beneficial effects of visuo-spatial attention i.e., RTs were shorter for targets presented at the attended compared to the unattended location. Further, parietal-occipital ERP components (the P1, N1 and P3) were larger for stimuli displayed at the attended than the unattended location. Effects of spatial attention would have been larger with higher cue validity (usually 80%); however, the instruction to attend one side throughout an experimental block was sufficient to appreciate the typical attention effects in terms of faster RTs and larger ERPs, including the P1 that is enhanced by spatial attention only (e.g., Luck et al., 2000; Di Russo & Pitzalis 2014). On the other hand, we found no effect of spatial attention on the C1 component (e.g., Baumgartner et al., 2017). Overall, these findings confirm previous studies showing that the first locus of spatial attention effect is extrastriate (i.e., the P1 component) and not striate visual area (e.g., Clark & Hillyard, 1996; Gómez Gonzalez et al., 1994; Di Russo et al., 2007).

The original contribution of the present study concerns the effect of spatial attention on anterior post-stimulus components. As expected, spatial attention effects emerged on the pN1 and the pP1 components peaking at 130 and 210 ms, respectively. We observed larger amplitudes in the attended compared to the unattended condition for both pN1 and pP1 peaks, which can reflect higher salience associated with attended stimuli. This interpretation is also supported by a recent study about the effects of induced ocular anisotropy (monovision) on ERPs (Zeri et al., 2017).

The pN1 and pP1 have been localized in the rostral part of the anterior Insula in several prior studies combining ERP and fMRI measures covering the whole brain (Di Russo et al., 2017; Ragazzoni et al., 2019; Sulpizio et al., 2017). Insular localization and independence of anterior components from posterior extrastriate activity has been confirmed in the current study using two separate and converging source analyses (eLORETA and BESA), as found in other ERP studies using sLORETA and BESA source localization methods (Perri et al., 2018a,b). In addition, dissociated effects on prefrontal (pN1 and pP1) and occipital (P1 and N1) components demonstrate the independency of these components, e.g. stimulus visibility reduced the occipital components, but increased the prefrontal components (Perri et al., 2018a,b; Zeri et al., 2017).

Increased amplitude of the pN1 and pP1 supports the evidence that anterior brain regions (in this case the anterior insula) are part of an attentional network engaged in early stages of perceptual processing (Foxe & Simpson, 2002; Menon & Uddin, 2010; Perri et al., 2017b,c). In ERP studies, effects of spatial attention in anterior regions have never been mentioned before; however, if we look closely at previous literature, similar anterior activities were already reported, although not associated with the anterior insula. For instance, in an ERP-fMRI study, Martinez et al. (2001) reported a broad medial-frontally distributed negativity that was significantly larger in the attended ERP. This component was called anterior N1 and its source was estimated in superior parietal cortex. However, it should be noted that it was not possible to localize anterior sources based on the fMRI scans used in that study, since its partial imaged brain volume was restricted to posterior brain regions. Similar methodological limits may have restricted the findings of Hillyard’s and Luck’s laboratories as well (Luck & Hillyard, 1995; Awh et al., 2000; Vogel & Luck, 2000; Di Russo et al., 2003, 2012). Overall, we think that the ERP component called “anterior N1” corresponds to the present pN1, whose sources have been localized by ERP and whole-brain fMRI recordings (Sulpizio et al., 2017; Ragazzoni et al., 2019). We think that the present name “prefrontal N1” is more appropriate and conveys more direct information about this component origin.

Thus, present data suggest that the anterior insular functions are affected by spatial attention and the pN1 component (or anterior N1; Martinez et al., 2001) is the earliest anterior ERP sign of spatial attention effect (peaking about 10 ms later than the posterior P1). The role of anterior areas in visual processing has been extensively described in terms of top-down control of the prefrontal cortex on extrastriate areas (Barceló et al., 2000; Knight et al., 1999; Büchel & Friston, 1997; Kastner et al., 1999). We extended this knowledge by describing the influence of spatial attention on the awareness of both the presence of the stimulus and the sensory–motor integration. In contrast, the late anterior component pP2 was not affected by spatial attention in the present study. This confirms previous results indicating this component as the correlate of stimulus categorization processes (Perri et al., 2018a,b), i.e., independent of spatial information, which is processed at an earlier visual stage (Rizzolatti & Craighero, 2010; Corbetta & Shulman, 2002).

A second result of the present study concerns the preparatory phase marked by the pN and the BP components. These proactive ERPs, i.e., pre-stimulus components reflecting motor and cognitive preparation, have never been considered in sustained spatial-attention tasks, while previous studies analyzed the CNV (e.g. Hopf & Mangun, 2000) and the LRP (e.g., Eimer, 1993) components, reporting effects of the cued side and the responding hand. In contrast to previous data, the present blocked sustained attention paradigm shows that both the pN and the BP amplitudes were unaffected by the cue direction and therefore by spatial attention. The difference between present results and CNV data is not surprising, since the CNV amplitude modulation by spatial attention is contingent to the cue onset. The lack of lateralized spatial attention effects in the present study is likely due to the absence of variation regarding the informational cue value (i.e., the cue steadily indicated the attended side throughout each experimental block; therefore, there was no variable contingency between cues and stimuli); in contrast, attention orientation had a trial-by-trial shift in most CNV studies. Further, in the present task, exogenous attention was evoked only by stimulus appearance, and the post-stimulus stages were clearly disentangled from pre-stimulus stage. In contrast, CNV studies do not entirely disentangle these two stages, and the observed spatial attention effects in the preparation phase (i.e., larger CNV amplitude for the attended versus unattended side) derive, at least in part, from exogenous attention captured by cue onset.

A last comment is about the observation that the pN had similar amplitude and onset in the DRT and SRT. In previous studies in which spatial attention was not modulated (based on centrally-displayed stimuli), the pN was detected in the DRT only (for a review see Di Russo et al., 2017) and its presence was explained in terms of a major engagement of top-down control in DRT. We noted that the pN in the SRT strongly increased in participants older than 40 years, being nearly as large as in the DRT in elderly; this suggested an increased need of executive control (Berchicci et al., 2012, 2014). The presence of the pN in young adults even in the SRT in the present spatial attention task is in line with our hypothesis and an explanation of this component in terms of proactive cognitive preparation, because covertly direct attention toward lateral stimuli is more cognitively demanding than doing the same SRT with a central stimulus. As for the BP component, its scalp topography was more posterior than reported in previous studies with central stimuli. This posterior shift may indicate the contribution of the dorsal visual pathway to control the allocation of spatial attention, possibly mediated by the inferior parietal sulcus (IPS; Capotosto et al., 2012), the temporal-parietal junction (Doricchi et al., 2010) and frontal-parietal and occipital networks (Gómez et al., 2007). A dissociation between motor and sensory anticipation has been offered by previous CNV studies (Flores et al., 2009; Gómez et al., 2004) following previous observations of on Stimulus preceding negativity, see Brunia, 1988), supporting the view that a sensory task-specific pattern of activation is present over the hemisphere contralateral to the cue presentation.

Finally, we need to mention some limitations of the study. Beside the general constraint of ERP measures and especially source analysis, results on proactive brain activity are limited to the specific blocked design used and cannot be generalized to different paradigms. Different experimental conditions may well produce lateralization also in the proactive phase.

In conclusion, present data indicate that voluntarily orienting visuo-spatial attention to one hemifield influences stimulus processing in multiple frontal, parietal and occipital areas. In particular, we found that spatial attention affected anterior components, possibly indicating that stimuli perceived at the attended location are associated with increased sensory and sensory-motor awareness. Overall, these findings reflect neural signatures of greater saliency of perceived attended events. Further, sustained attention toward one side is not associated with detectable lateralized activity in the proactive processing.

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Author notes

Compliance with Ethical Standards: All procedures performed in the study involved human participants and all procedures were reviewed and approved by the Santa Lucia Foundation Ethical Committee.

Funding: The work was supported by the University of Foro Italico (grant number RIC162015) to Francesco Di Russo.

Disclosure of potential conflict of interest: The authors declare that they have no conflict of interest.

Ethical approval: All participants gave their informed consent according to 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent: Informed consent was obtained from all individual participants included in the study.

Figures

Figure 1: Experimental paradigm. a) Schematic representation of the experimental paradigm in the attended left and right blocks. b). Schematic representation of the four stimuli used in the experiments. In the SRT, any stimulus was a target. In the DRT, two stimuli were defined as targets and two as non-targets.

Figure 2: ERP waveforms focussing on pre-stimulus activity. Grand-averaged ERPs from the SRT (red lines) and DRT (black lines) for attended left (left panel) and right (right panel) blocks at the most relevant sites (Fpz, Fz, Cz and Pz). All the other conditions were averaged. The main components are labelled within the figure.

Figure 3: Topographical voltage maps of the pre-stimulus activities. Scalp topography of the grand-averaged data from the SRT (upper row) and DRT (bottom row) when the cue pointed to the left side (left panel) and to the right side (right panel). Top-flat view maps are arranged in a chronological order (from 900 ms before to the stimulus onset) from left to right and report the mean activities in 300 ms epochs.

Figure 4: a) Topographical CSD mapping and b) eLoreta analysis. Pre-stimulus activities distribution in the last 300 ms before stimulus onset.

Figure 5: Post-stimulus ERP waveforms. Grand-averaged waveforms of the ERPs from the SRT (red lines) and DRT (black lines) for stimuli appearing in the left hemifield (left panel) and in the right hemifield (right panel) at the most relevant sites (AFz, CPz, POz, PO7 and PO8). Attended (solid lines) and unattended (dotted lines) trials are superimposed to facilitate comparison; the main components are labelled within the figure.

Figure 6: Topographical maps of the post-stimulus activities. Scalp topography of the grand-averaged DRT data from attended (upper row) and unattended (bottom row) conditions for stimuli appearing in the left hemifield (left panel) and in the right hemifield (right panel) using top-flat view maps arranged in a chronological order (from 60 to 600 ms after stimulus) from left to right. The intervals and the components are indicated below; ipsi: ipsilateral component; contra: contralateral component.

Figure 7: Target minus Non-target differential waves and topographical maps. Differential pP2 (dpP2) waveforms (a-b) and scalp topography (c-d) comparing left versus right stimuli (a, c) and attended versus unattended stimuli (b, d).

Figure 8: Mapping early attention effects in DRT. a) CSD distribution of the attention effect (attended minus unattended) in the P1 and pN1 range (100-140 ms), in both left and right hemifields. b) eLORETA analysis showing the brain area active in the same early interval, in both left and right hemifields.

Figure 9: Attention effect source analysis: Location and orientation of the two dipolar source pairs fitted in the 100-140 ms interval of the attention effect for both SRT and DRT. Sources are plotted on a 3D MRI template. Left and right hemifields are averaged.

Table 1. The table reports the more relevant significant effects for the various components (listed according to the timing of their peak) in the two tasks (SRT and DRT). Stars indicate significant differences with p<0.05.

Component

Temporal window

Effect of spatial attention

Effect of

condition

SRT

DRT

C1

60-100 ms

P1

80-130 ms

*

pN1

80-130 ms

*

N1

140-200 ms

*

pP1

140-200 ms

*

P2

150-250 ms

-

N2

-

200-300 ms

dpP2

-

300-400 ms

*

P3

300-400 ms

500-600 ms

*