Spatial inhibition of return promotes changes in Response-related mu and beta oscillatory patterns

SPATIAL INHIBITION OF RETURN PROMOTES CHANGES IN RESPONSE-RELATED MU AND BETA OSCILLATORY PATTERNS Authors: E. Amenedo, E., Gutiérrez-Domínguez, F-J., Darriba, Á and Pazo-Álvarez, P This is the peer reviewed version of the following article: E. Amenedo, E., Gutiérrez-Domínguez, F-J., Darriba, Á and Pazo-Álvarez, P (2015). Spatial Inhibition of Return promotes changes in response-related mu and beta oscillatory patterns. Neuroscience, 310, 616-628 doi: 10.1016/j.neuroscience.2015.09.072 This article may be used for non-commercial purposes in accordance with Elsevier and Pergamon terms and conditions for use of self-archived versions. Post-print (final draft post-refereeing) 2 Spatial inhibition of return promotes changes in Response-related mu and beta oscillatory patterns Authors: E. Amenedo*, E., Gutiérrez-Domínguez, F-J., Darriba, Á and Pazo-Álvarez, P Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Santiago de Compostela, Spain *Corresponding author. Address: Department of Clinical Psychology and Psychobiology, Faculty of Psychology, Rúa Xosé María Suárez Núñez, S/N, 15782 Campus Vida, University of Santiago de Compostela, Spain. Post-print (final draft post-refereeing) 3 Abstract The possible role that response processes play in Inhibition of Return (IOR), traditionally associated with reduced or inhibited attentional processing of spatially cued target stimuli presented at cue-target intervals longer than 300 ms, is still under debate. Previous psychophysiological studies on response-related Electroencephalographic (EEG) activity and IOR have found divergent results. Considering that the ability to optimize our behavior not only resides in our capacity to inhibit the focus of attention from irrelevant information but also to inhibit or reduce motor activation associated with responses to that information, it is conceivable that response processes are also affected by IOR. In the present study, time–frequency (T–F) analyses were performed on EEG oscillatory activity between 2 and 40 Hz to check whether spatial IOR affects response preparation and execution during a visuospatial attention task. To avoid possible spatial stimulus–response compatibility effects and their interaction with the IOR effects, the stimuli were presented along the vertical meridian of the visual field. The results differed between lower and upper visual fields. In the lower visual field spatial IOR was related to a synchronization in the pre-movement mu band at bilateral precentral and central electrodes, and in the post-movement beta band at contralateral precentral and central electrodes, which may be associated with an attention-driven reduction of somatomotor processing prior to the execution of responses to relevant stimuli presented at previously cued locations followed by a post-movement deactivation of motor areas. In the upper visual field, spatial IOR was associated with a decrease in desynchronization around response execution in the beta band at contralateral postcentral electrodes that might indicate a late (last moment) reduction of motor activation when responding to spatially cued targets. The present results suggest that different response processes are affected by spatial IOR depending on the visual field where the target is presented. 2015 IBRO. Published by Key words: EEG oscillatory changes, spatial IOR, response preparation and execution, mu band, beta band, vertical asymmetries Post-print (final draft post-refereeing) 4 Introduction The limited capacity of human brain makes it impossible to incorporate all available information. Visuospatial attention studies have shown that attention-shifting processes provide adaptive benefits by allowing the brain to select the information relevant in each moment as a basis for other processes, such as memory, learning and decision-making, which are essential for adaptation and correct functioning in everyday life. In this context, Posner and Cohen (1984) found that when responding to targets previously signaled by a peripheral cue two possible effects are observed on reaction time (RT) depending on the time interval between the cue and the target presentation. When that time is shorter than 250–300 ms, RTs are faster (facilitation effect). At longer time intervals, however, RTs are slower. The authors explained this increase in RT as a mechanism that helps in selecting relevant information units by inhibiting attention from focusing on previously explored locations when there is enough time to process them. Posner et al. (1985) retrospectively named this mechanism Inhibition of Return (IOR). Since its discovery, IOR has been observed in a wide variety of experimental situations within the visual, auditory, and tactile modalities (e.g., Spence et al., 2000). IOR has also been observed across a variety of tasks, including detection, localization, and discrimination tasks, and even in natural scenes (see Klein, 2000, for a review). However, at present no consensus has yet been reached regarding either the mechanisms of IOR or their functional significance. The existing difficulty in characterizing the functional significance of IOR and its neural locus, led several research groups to examine the underlying electrophysiological mechanisms of behavioral IOR effects by means of event-related potential (ERP) analyses. Specifically, these ERP studies have mainly focused on stimulus-related components, and have shown that spatial IOR is frequently, but not always, associated with amplitude modulations in P1 and N1 targetlocked visual components. Such modulations have been generally interpreted as neural correlates of the effects of IOR on the perceptual–attentional processing of spatially cued target stimuli. Moreover, IOR effects on target-locked ERPs have resulted in other amplitude modulations in the ERP waveforms within latency intervals that do not coincide specifically with the peak of any component, and consisting in amplitude shifts whose functional interpretation is still under debate (see Gutiérrez-Domínguez et al., 2014). However, the above-described IOR effects on target locked ERPs have not always been associated with behavioral IOR effects (i.e. slower RTs to cued targets; see for example Hopfinger and Mangun, 1998; McDonald et al., 1999; Doallo et al., 2004), suggesting that, possibly, response processing is also influenced by it (Kingstone and Pratt, 1999; Pasto¨ tter et al., 2008). Responselevel explanations of IOR have received support from behavioral evidence showing that it might be associated with a more conservative response criterion on cued trials (Ivanoff and Klein, 2001), and that IOR can affect oculomotor programing (Ro et al., 2000). To explore more directly the response-related processes affected by IOR, Prime and Ward (2004, 2006) measured the effects of IOR on the lateralized readiness potential (LRP), and examined the possibility that response related effects of IOR may arise at either decisional or motor stages of response processing. To that end, they examined the target-locked LRP (T-LRP) and the response-locked LRP (R-LRP) components starting from the premise that if IOR arises from inhibition of motor processes, then the interval between the onset of the R-LRP and the response should be longer under IOR, while if IOR arises from decisional but not motor processes, then only the interval between the target presentation and the response would be affected by IOR (affecting the latency of the T-LRP). Post-print (final draft post-refereeing) 5 They found IOR effects on T-LRP latency but not on R-LRP latency, concluding that IOR may not be related to response preparation timing but only to pre-motor selection processes. More recently, Amenedo et al. (2014) examined the amplitude changes in R-LRP, and found that IOR was related to a significant amplitude reduction of this component when responding to previously cued targets, suggesting that response preparation could be affected when responding to targets presented at previously cued locations. Event-related changes in Electroencephalographic (EEG) activity may be studied with different approaches. One of the most frequently employed has been the ERP technique, which is based on the measurement of amplitude changes in the ongoing EEG activity time locked to stimulus presentation or to response execution. An alternative and complementary approach is the measurement of event-related changes in frequency oscillations that occur in the ongoing EEG activity in association with stimulus presentation or response production. One of the most extended methods for the analysis of EEG oscillations is the so-called time–frequency (T–F) analysis that allows examination of the spatio-temporal changes in spectral power within different frequencies relative to a baseline period and related to stimulus or response processing (see Roach and Mathalon, 2008 for a comprehensive review on ERP and T–F methodologies). In the context of movement execution, EEG activity within sensorimotor areas of the human brain has long been known to exhibit oscillatory behavior, which makes the T–F approach suitable for studying possible effects of IOR on response processes. Of particular interest have been oscillations within two specific frequency bands, the mu (8–14 Hz) and beta (15–30 Hz) bands, as they have been shown to be modulated during and following the preparation and performance of voluntary movements (Salmelin et al., 1995; Pfurtscheller et al., 1996a,b; Leocani et al., 1997; Cassim et al., 2001; Jurkiewicz et al., 2006; Parkes et al., 2006), passive movements (Cassim et al., 2001), imagined movement (Pfurtscheller et al., 2005, 2006), and even tactile stimulation (Neuper and Pfurtscheller, 2001; Cheyne et al., 2003; Gaetz and Cheyne, 2006). Modulation of the mu and beta band oscillations that accompany voluntary movements has been described and takes one of two forms. Beginning as early as 2 s prior to movement initiation (Pfurtscheller and Berghold, 1989; Leocani et al., 1997), a reduction in power in both the mu and beta frequency bands, known as event-related desynchronization (ERD), has been observed over sensorimotor areas with a contralateral predominance in the case of the beta band (see Pfurtscheller and Lopes da Silva, 1999 for review) and more bilateral for the mu band (Salmelin et al., 1995) although contralateral predominance has also been shown in this frequency band (see Pfurtscheller et al., 1996a, for review). Desynchronization of these oscillations, the result of asynchronous activity within these cortical networks, has been related to neural activation (Pfurtscheller and Berghold, 1989). Following movement termination, while mu power returns slowly to baseline (Salmelin and Hari, 1994; Salmelin et al., 1995; Leocani et al., 1997), beta power consistently returns to and exceeds premovement levels (Pfurtscheller et al., 1996a; Jurkiewicz et al., 2006). This event, known as event-related synchronization (ERS) begins within several hundred milliseconds of movement termination and persists for several hundred more. Although it is generally believed that ERS reflects a neural deactivation, the socalled ‘idling’ hypothesis (Pfurtscheller et al., 1996a; Cassim et al., 2001), this specific beta band ERS that follows movement termination is known as postmovement beta rebound (PMBR) and has been suggested to represent an inhibition of motor cortex (Salmelin et al., 1995; Jurkiewicz et al., 2006) or a sensorimotor reafference (Cassim et al., 2001) after movement execution. Post-print (final draft post-refereeing) 6 Exploring spatial IOR effects on response-related EEG oscillatory activity by means of T–F analyses, Pastotter et al. (2008) examined changes in ERS–ERD patterns (temporal spectral evolution analysis, Hari and Salmelin, 1997) restricted to the pre-movement 15–25 Hz beta band in two IOR designs: a target–target design, and a cue-target design. They found that in the target– target design behavioral IOR was associated with an increase in contralateral beta ERS while in the cuetarget design IOR was related to a decrease in beta ERD. They concluded that IOR arises from the inhibition of motor processes affecting different mechanisms depending on the design employed. Specifically, the authors concluded that with the target–target design some kind of passive inhibition (idling state) would occur, while with the cue-target design an active inhibition would be associated with IOR. Taking into account the previous findings, and considering that the ability to optimize our behavior not only resides in our capacity to inhibit the focus of attention from returning to previously explored locations but also to inhibit or reduce motor activation associated with responses to stimuli appearing at those locations, it is conceivable that response processes are also affected by IOR. In this context, the present study aimed at exploring IOR effects on T–F changes in the EEG activity in those oscillatory patterns that have been found to systematically react to movement processing and execution (Salmelin et al., 1995; Pfurtscheller et al., 1996a,b). To that end, we examined the oscillatory pattern of EEG activity under spatial IOR during the execution of a cueback visuospatial attention task (see Gutierrez-Domínguez et al., 2014). Due to the well established effects of stimulus–response spatial compatibility on response processing (see Hommel, 2011 for a review), and to the possible interactions between these effects and IOR effects (see Ivanoff et al., 2002 for a review) the stimuli were presented along the vertical axis of the visual field. Moreover, due to the frequently reported existence of visual asymmetries when the stimuli are presented along the vertical meridian (Rezec and Dobkins, 2004; Amenedo et al., 2007; Karim and Kojima, 2010; Thomas and Elias, 2011), and to previous results showing the existence of such asymmetries on the effects of spatial IOR on stimulus-locked and on responselocked ERPs (Amenedo et al., 2014; Gutiérrez-Domínguez et al., 2014) the data from each visual field were analyzed separately. With this design, we aimed at exploring IOR effects on EEG activity related to response processing by analyzing the spatial and temporal changes in frequency oscillations between 2 and 40 Hz related to response preparation and execution. Experimental procedures Participants Seventeen young adults (10 females, 24.85±5.99 years, range 19–37) participated in the study. All participants were healthy well functioning without a history of neurological or psychiatric disorders, had normal or corrected-to-normal visual acuity, reported normal color vision, and were right handed (Oldfield, 1971). Informed consent was obtained from all participants, and they received a monetary compensation for their participation. Stimuli and experimental design During the task (see Fig. 1), one central and two peripheral (external edge 4.5º of visual angle from the center of the screen) light gray boxes (RGB 200,200,200, 1.5º x 1.5º of visual angle) were always present in the vertical meridian of a computer screen (100-Hz refresh rate). A central fixation cross (RGB 150,150,150, 0.1ºx 0.1ºof visual angle) was also present and participants Post-print (final draft post-refereeing) 7 were instructed to maintain their gaze on it during the task performance. Each trial began with a 1500-ms blank screen (RGB 50,50,50, average luminance 2.4 cd/m2) that defined the background screen. After this, a blue (RGB 0,0,255, average luminance 8.3 cd/m2) or red (RGB 175,0,0, average luminance 8.2 cd/m2) patch was presented during 100 ms filling one of the two peripheral boxes (0.5 probability). This patch served as a cue for location or color dimension, and it was uninformative related to both location and color dimensions of the target. After a new blank screen of 500-ms duration, a cue-back consisting of a green patch (RGB 0,95,0, average luminance 8.4 cd/m2) filling the central box was presented for 100 ms. After another blank interval of 1300 ms, a target was presented until response or a maximum of 1500 ms. Target stimuli consisted of a blue or red patch (0.5 probability), identical to the cue, filling the lower or upper box (0.5 probability). Target shared color or location with the cue in 50% of trials. The duration of the interval between the cue onset and the target onset (see Fig. 1) defined a cue-target onset asynchrony (CTOA) of 2000 ms. Four types of trials resulted from cue and target color and location combinations: trials with both location and color cued (cue and target appearing at the same location and with the same color), trials with only location cued (cue and target appearing at the same location but with different color), trials with only color cued (cue and target sharing color but appearing at different locations), and trials with neither location nor color cued (cue and target appearing at different locations and with different color). Participants were sitting in an armchair placed at 112 cm distance from the computer screen; they were asked to respond to target color (red or blue) irrespective of its location by pressing a button (Response Box RB-834 model, Cedrus Corporation) with their right hand to one color and another button with their left hand to the other color while maintaining central fixation. Assignment of response hand to each color was counterbalanced across participants. Both speed and accuracy were stressed in the instructions (please see Gutiérrez-Domínguez et al., 2014, for a more complete description and justification of this discrimination task). To prevent the increased variability observed in scalp distribution of oscillatory brain activity associated with nondominant hand executions (Stanca´ k and Pfurtscheller, 1996; Gaetz et al., 2010), only trials with responses executed with the dominant hand (right hand in our participants) were included in the statistical analyses. The task was divided into 25 blocks of 64 trials, mixing different trial conditions in each block randomly. There was a 3-s rest between each block. After that, participants could continue with the task as soon as they wanted by pressing a button of the response device. Due to previous results in our laboratory with the same task showing neither IOR effects of color cueing nor interactions between location and color cueing (see Gutierrez- Domínguez et al., 2014) we focus our analyses on the comparison between trials with neither location nor color cued (uncued condition thereafter) and trials with only location cued (locationcued condition thereafter). Recording and analysis Behavioral data Reaction times (RTs) and accuracy (hits and errors) were on-line recorded for all participants under all conditions in all experimental blocks. Only RT values associated with correct responses were included in the analyses. Responses were considered correct when RTs were within ±3 standard deviations of the mean RT for each condition. Mean correct RTs and error rates (%) were submitted to a repeated measures analysis of variance (ANOVA) with cueing (uncued vs. Post-print (final draft post-refereeing) 8 location-cued) as within-subject factor. Whenever appropriate, degrees of freedom were corrected by the conservative Greenhouse-Geisser estimate. An alpha level of .05 was used for all analyses. EEG recording Recordings were made in an electrical shielded and sound-attenuated room. Continuous EEG activity was recorded with a Brain Vision Recorder (Brain Products, Inc.) from 60 scalp Ag-AgCl electrodes according to the extended 10/20 International System (see Fig. 2). The cephalic electrodes were referred to the nose tip and grounded with an electrode placed at 10% of the nasion-inion distance above nasion. Vertical and horizontal electrooculogram (EOG) was recorded from above and below the participant’s left eye and from the outer canthi of both eyes, respectively. Electrode impedances were kept below 10 kΩ. Sampling rate was 500 Hz/channel. EEG signal was continuously amplified (10 K) and filtered online with a band pass of 0.01– 100 Hz. EEG analysis Data from all conditions were epoched into segments of 4500 ms (-3500 ms to 1000 ms relative to button-press) and merged together for each subject. Prior to the spectral analyses, artifacts were removed using EEGLAB (Delorme and Makeig, 2004), a freely available open source software toolbox (Swartz Center for Computational Neurosciences, La Jolla, CA; http://www.sccn. ucsd.edu/eeglab) running under Matlab (MathWorks, Inc, Natick, MA, USA) in the following way. First, epochs containing non-stereotyped artifacts (e.g. cable movement, swallowing) were manually removed whereas epochs containing repeatedly occurring, stereotyped artifacts (e.g., eye blinks, muscle artifact etc) were kept. Then, extended infomax independent component analysis(ICA, Bell and Sejnowski, 1995; Lee et al., 1999) was applied individually for each subject, using a weight change of <10-7 or 512 interactions as a stop criterion. Component activations were subsequently assessed and categorized as brain activity or non-brain artifact (e.g., muscle, electrode artifact or eye movement activity) by visual inspection based on their scalp topographies, time courses, ERP-image, and activation spectra. Rejected ICAs comprised three main artifact categories: (A) Ocular artifacts were defined as smoothly decreasing EEG spectrum typical of an eye artifact and a scalp map showing a strong far-frontal projection. (B) Muscular artifacts were spatially localized, showing high power at high frequencies (20–50 Hz). (C) Electrode artifacts were characterized for their topography (affecting only one electrode) and ERP images showing non time-locked artifacts with generally large amplitudes. After identification of components constituting artifacts, individual EEG data containing all conditions were reconstructed without those components. Finally, epochs corresponding to each experimental condition were extracted from ICA-pruned data for each subject. In order to make comparisons between conditions, the number of trials was adjusted for each subject. Trials on those conditions were randomly selected. This procedure resulted in around 30 trials per condition for each subject on average. T–F analysis ICA-corrected data from uncued conditions and locationcued conditions were submitted to trialby-trial T–F analysis using EEGLAB (Delorme and Makeig, 2004) running under Matlab (MathWorks, Inc., Natick, MA). Spectral changes in oscillatory activity were analyzed using a wavelet transform. T–F analysis was computed from- 3500 to 1000 ms relative to button press Post-print (final draft post-refereeing) 9 for every electrode, subject and condition separately using a Gaussian-windowed sinusoidal moving Morlet wavelet with linearly increased cycles from 2 cycles for the lowest frequency (2 Hz) to 20 cycles for the highest frequency (40 Hz) analyzed (step size, 0.5 Hz). Changes in eventrelated spectral power response at different time points, relative to power in baseline (from -3000 to -2500 ms), were computed by the Event-Related Spectral Perturbation (ERSP) index (Delorme and Makeig, 2004). The ERSP can be viewed as a generalization of the ERD. The ERSP measures average dynamic changes in amplitude of the broad band EEG frequency spectrum as a function of time relative to an experimental event. That is, the ERSP measures the average time course of relative changes in the spontaneous EEG amplitude spectrum induced by a set of similar experimental events. These spectral changes typically involve more than one frequency or frequency band; so full-spectrum ERSP analysis yields more information on brain dynamics than the narrow-band ERD. The ERPS are color-coded T–F images of mean log spectral differences that have been normalized by subtracting the mean log power spectrum within a defined baseline period (in our case -3000 to -2500 ms) (see Makeig and Onton, 2012). Once the spectral power changes as specified above were obtained, the differences between conditions in the resulting changes in power (dB) were addressed using permutation analyses (2000 permutations, alpha level 0.05). The obtained power values across the frequency bands, time intervals, and electrodes where permutations showed significant differences were corrected for multiple comparisons with the false discovery rate (fdr). Moreover, the power values across the electrodes, the time intervals, and the frequency bands where these permutations with fdr correction showed significant differences between uncued and location-cued conditions were submitted to parametric analyses by means of separate repeated measures ANOVAs for each visual field with the within-subject factors location cueing (uncued vs. location-cued) and electrode (for lower visual field, LVF, analysis, mu band (9–11 Hz): F3, F1, Fz, F2, F4, F6, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C3, C1, Cz, C2, C4, C6, FT8, T8; beta band (28–32 Hz): F7, F5, F3, F1, FC3, FC1, FCz, C3, C1, Cz, and for upper visual field, UVF, analysis, beta band (26–28 Hz): CP5, CP1, P7, P5, P3, P1, Pz, PO7, PO3, POz, O1). The level of significance was established in 0.05. Results Behavioral data The repeated measures ANOVAs showed significantly slower RTs to targets appearing at previously cued locations (Table 1) in both the LVF (F(1,16)=4.14, p<.05, g2=.21) and the UVF (F(1,16)=13.6, p<.01, g2=.46), whereas there were no significant differences in error rates (Table 1) between uncued and location-cued conditions in both the LVF (F(1,16) =1.11, p=.3, g2=.07) and the UVF (F(1,16)=1.74, p=.2, g2=.1). EEG data. T–F results Fig. 2 shows the ERSP image corresponding to power changes (dB) across time at C3 electrode under the uncued condition in the LVF. In this image, and in the corresponding topographical maps, with descriptive purposes, it can be seen the typical oscillatory pattern showing mu and beta changes associated with the execution of a voluntary movement, and described in previous literature (see, for example, Pfurtscheller et al., 1996b; Jurkiewicz et al., 2006). This pattern consisted in contralateral mu and beta desynchronization prior to movement, and in contralateral beta synchronization after movement. Fig. 3 shows the ERSP images corresponding to power (dB) changes across time under location-cued and uncued conditions in both visual fields at the C3 electrode. The images show different patterns of event-related power changes in each visual Post-print (final draft post-refereeing) 16 neuronal filters in the visual cortex, even in simple visual tasks requiring an overt response (i.e., detection of lines of specific orientation), better performance has been repeatedly observed when attentional focus is directed to stimuli. This effect has been termed attentional resolution, and it is larger when the stimuli are presented in the LVF (Cavanagh et al., 1999). It has been suggested that this LVF advantage in attentional resolution may be partly due to the fact that this visual field is represented in the upper part of the visual cortex, which is anatomically adjacent to and projects more heavily to the occipital-parietal regions that are often linked to spatial attentional control (Gazzaniga and Ladavas, 1987; Maunsell and Newsome, 1987Posner et al., 1987). Moreover, studies on visuospatial attention have found that spatial attention guided by exogenous orienting (peripheral cues) increases the apparent contrast of visual stimulus (Carrasco et al., 2004; Fuller et al., 2008), and this effect has been found to be greater in the LVF (Fuller et al., 2008). Taking the above into account, the Nd observed in our laboratory in the LVF was interpreted as an N2- like effect reflecting a re-focusing of spatial attention into target stimuli appearing in the visual field with higher attentional resolution, and at a location where discrimination processes had been previously inhibited (N1 amplitude reductions observed at posterior electrodes). The positive deflection observed in the UVF under spatial IOR, with a later latency and a more anterior scalp distribution than the Nd observed in the LVF, was related to the elicitation of an orienting response to select a target stimulus presented at a previously inhibited location in the visual field with less attentional resolution. In this sense, the spatial cueing of targets in the UVF could trigger an anterior P2-like effect that would be related to an extra evaluation of, and/or a conflict resolution in working memory, in trials with targets presented in previously inhibited locations (see Gutiérrez-Domínguez et al., 2014). Regarding response-related ERP activity, although previous research on lateralized readiness potential (LRP) changes under spatial IOR had concluded that spatial IOR does not affect motor processes (Prime and Ward, 2004, 2006), Amenedo et al. (2014) found significant decreases in the amplitude of response-locked LRP when preparing correct responses to cued targets that were interpreted as reflecting less motor activation during response preparation to those stimuli. Taking the above into account, the reduction in beta desynchronization observed in the UVF in the present study could be interpreted as indicating a reduction of motor activation, at the ‘last moment’, to targets appearing at previously cued locations in the visual field with less attentional resolution. The existence of poorer attentional resolution in the UVF, therefore could require more evaluation of these stimuli, and/or some conflict resolution in working memory, which would lead to an active inhibition just at the moment of pressing the button instead of reducing the premovement sensorimotor activation when preparing the responses. Conclusions The present results showed IOR effects on response related oscillatory EEG activity that differed between visual fields, which would suggest that different underlying processes are activated depending on the visual field where the target has to be discriminated and a response has to be executed. In the LVF, IOR was related to synchronization in the pre-movement bilateral mu band, and in the post-movement contralateral beta band. In the UVF, however, reduced desynchronization in contralateral beta band was observed around response execution. Post-print (final draft post-refereeing) 17 In the LVF, where there is more attentional resolution, IOR may be associated with an attentiondriven reduction of sensorimotor processing while preparing the execution of responses to relevant stimuli that are presented at previously cued locations whose processing has been reduced in visual areas. The deactivation of motor areas after response execution observed in this visual field could indicate an effective resolution of the response processing. However, in the UVF, the absence of oscillatory changes in the mu rhythm along with a decrease in beta desynchronization around response execution might indicate a late (last moment) inhibition of motor activation when responding to spatially cued targets that require more evaluation, and/or whose processing generates conflict resolution in working memory, because they appear in a visual field with less attentional resolution. Acknowledgments This study was supported by grants from the Spanish MICINN (PSI2010-21427), Spanish MINECO (PSI2014-53743-P), and Xunta de Galicia (10PXIB211220PR). References Amenedo E, Pazo-Álvarez P, Cadaveira F (2007) Vertical asymmetries in preattentive detection of changes in motiondirection. Int J Psychophysiol 64(2):184–189. Amenedo E, Gutiérrez-Domínguez FJ, Mateos-Ruger SM, Pazo- Álvarez P (2014) Stimulus-locked and response-locked ERP correlates of spatial inhibition of return (IOR) in old age. J Psychophysiol 28(3):105–123. 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