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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC Nov 6, 2009.
Published in final edited form as:
PMCID: PMC2692025
NIHMSID: NIHMS115536
Fronto-parietal cortex controls spatial attention through modulation of anticipatory alpha rhythms
Paolo Capotosto,1 Claudio Babiloni,2,3 Gian Luca Romani,1 and Maurizio Corbetta1,4
1 Dip. di Scienze Cliniche e Bioimmagini and ITAB, Istituto di Tecnologie Avanzate Biomediche Università “G. D’Annunzio”, Chieti, Italy
2 Dip. Scienze Biomediche, Univ. di Foggia, Foggia-Italy
3 Casa di Cura San Raffaele Cassino, Italy
4 Department of Neurology, Radiology, Anatomy & Neurobiology, Washington University School of Medicine, St.Louis, USA
Corresponding author: Claudio Babiloni PhD, Department of Biomedical Sciences, University of Foggia, Italy, e-mail: claudio.babiloni/at/uniroma1.it. MD, Department of Neurology, Washington University, St. Louis, e-mail: mau/at/npg.wustl.edu
A dorsal fronto-parietal network, including regions in intra-parietal sulcus (IPS) and frontal eye field (FEF), has been hypothesized to control the allocation of spatial attention to environmental stimuli. One putative mechanism of control is the de-synchronization of electroencephalography (EEG) alpha rhythms (~8–12 Hz) in visual cortex in anticipation of a visual target. We show that brief interference by repetitive trans-cranial magnetic stimulation (rTMS) with preparatory activity in right IPS or right FEF while subjects attend to a spatial location impairs identification of target visual stimuli ~2 seconds later. This behavioral effect is associated with the disruption of anticipatory (pre-stimulus) alpha desynchronization and its spatially selective topography in parieto-occipital cortex. Finally, the disruption of anticipatory alpha rhythms in occipital cortex after right IPS- or right FEF-rTMS correlates with deficits of visual identification. These results support the causal role of the dorsal fronto-parietal network in the control of visuo-spatial attention, and suggest that this is partly exerted through the synchronization of occipital visual neurons.
Keywords: Visuospatial attention, frontal-parietal network, visual cortex, rTMS, EEG, Alpha rhythms
Observers develop spatial expectations about visual scenes that guide and enhance perception (Eriksen et al., 1972; Posner, 1980). Neuroimaging studies have suggested that spatial attention biases perception through an interaction between ‘control’ regions in dorsal fronto-parietal cortex which generate and maintain expectations and occipital visual regions involved in sensory analysis (Kastner and Ungerleider, 2000; Corbetta and Shulman, 2002; Serences and Yantis, 2006). Recent studies have directly shown this interaction using electrical micro-stimulation (Moore et al., 2003, 2004), TMS (Ruff et al., 2006; Ruff et al., 2007), or analyses of directional mutual information on blood oxygenation level dependent (BOLD) signal time series (Bressler et al., 2008).
The neuronal mechanism through which higher order-control areas take control of visual neurons is unknown. Spatial attention may control visual cortex by synchronization of inputs or modulation of the temporal coherence of ongoing oscillatory activity (Fries, 2005; Engel et al., 2001). A putative marker of the physiological interaction between fronto-parietal regions and occipital visual areas is the modulation of the posterior alpha rhythms as recorded with electroencephalography (EEG). These rhythms show high power over parieto-occipital area in the absence of visual stimulation (Steriade and Llinas, 1988) which is then reduced in anticipation of visual targets (Klimesch et al., 1998). When subjects expects a target at a specific location the topography of alpha rhythms becomes spatially selective (Worden et al., 2000; Yamagishi et al., 2003; Sauseng et al., 2005; Thut et al., 2006), and predicts trial-by-trial the locus of attention and visual performance (Thut et al., 2006).
Here, we test the hypothesis that fronto-parietal regions control spatial attention in visual cortex through the anticipatory de-synchronization of ongoing alpha rhythms. We predict that disruption by rTMS of neural activity in fronto-parietal regions during the allocation of spatial attention will interfere both with the subsequent perception of visual stimuli and the desynchronization of alpha rhythms in occipital cortex, monitored by simultaneous recordings of EEG activity. The effect of interference in dorsal fronto-parietal regions, IPS and FEF, core regions of the ‘dorsal attention network’ (Kastner and Ungerleider, 2000; Corbetta and Shulman, 2002), is contrasted with the effects on a pre-central (PrCe) region part of a ‘ventral attention network’(Corbetta and Shulman, 2002; Corbetta et al., 2008). This is a right hemisphere dominant network involved in reorienting attention to relevant sensory stimuli, but not in directing attention based on endogenous expectations. Previous fMRI studies have reported robust attention-related activity in right FEF, following the presentation of a spatial cue and before the presentation of a target stimulus (delay activity), but not in right PrCe (Corbetta et al., 2002; Kincade et al 2005; Corbetta et al. 2005). Right PrCe is therefore an excellent control region for FEF given its anatomical proximity but different physiological profile. Given the ventral network is right hemisphere dominant, as well as for safety reasons related to the total dose of rTMS, we limited the stimulation to the right hemisphere.
Subjects
16 right-handed (Edinburgh Inventory) healthy adult volunteers (age range: 20–30 yrs old; 3 females) with no previous psychiatric or neurological history participated in the main experiment. Their vision was normal or corrected-to-normal. All experiments were conducted with the understanding and written consent of each participant according to the Code of Ethics of the World Medical Association, and the standards established by the University of Chieti Institutional Review Board and Ethics Committee. A second behavior-only control experiment involved 9 right-handed healthy volunteers (age range: 24–33 yrs old; 5 females), and checked for visual field differences in stimulus identification (see below). Finally, we enrolled an additional 8 right-handed healthy adult volunteers (age range: 22–31 yrs old; 4 females) in a separate rTMS experiment designed to control for the effects of cue encoding/attention shifts (see below).
Experimental task
All studies were conducted at the Institute of Technology and Advanced Bioimaging (ITAB) by the first author (P.C.). The subjects were seated in a comfortable reclining armchair and kept their arms resting on the keyboard of a computer. The computer monitor was placed in front of them at a distance of about 80 centimeters. The experimental paradigm is shown in Figure 1a. Subjects maintained fixation on a small white cross (subtending 0.7° of visual angle), displayed on a black background at the center of the screen. Every 5.9 ± 1 seconds (sec) a cue stimulus (a white small filled rectangle subtending about 0.2° visual angle and overlapping the left or right horizontal segment of the fixation cross) was presented for 200 ms duration, cueing randomly (50%) either a left or right visual field location. After 2 sec (stimulus onset asynchrony, SOA), a target stimulus was briefly presented for 70 msec at one of two target locations positioned in the left or right visual field along the horizontal meridian at 0.7° degrees of visual angle from the fixation point. The target stimulus was either the letter L (50 %) or the letter T (50%) shown either in the canonical upright orientation (50% of trials) or rotated 180 degrees along the vertical axis (the other 50%). Both letters had a diameter of 0.7° visual angle. The target stimulus appeared on 80% of the trials at the location indicated by the cue (valid trials), and on 20% of the trials at the uncued location (invalid trials) (Posner, 1980). Immediately after the target stimulus, a mask stimulus (130 ms duration) formed by all the possible line segments forming the letter stimuli L or T was presented to interrupt the visual processing of the target shape. The subject’s task was to maintain fixation throughout the trial, pay attention covertly to the location indicated by the cue, and discriminate the shape of the target by pressing a left keyboard button (key A) when they saw the letter T (upright or rotated), and a right keyboard button (key L) when they saw the letter L (upright or rotated). The assignment of ‘target’ (T or L) to the specific key for response (A or L) was randomized across subjects. Moreover, subjects were asked to maintain the hands on the keyboard and the sight fixed on the screen so that they could not really see the keyboard. The spatial cue indicated the position of the stimulus, but did not provide any information about the response. This is important to insure that preparatory processes are indeed visuo-spatial and not motor in origin (Broadbent, 1971). Reaction times and the accuracy of the response were recorded for behavioral analyses.
Figure 1
Figure 1
Task and rTMS localization
Procedures for rTMS and identification of target scalp regions
To interfere with neural activity during the allocation of spatial attention, we employed repetitive transcranial magnetic stimulation (rTMS). The stimulation was delivered through a focal, figure eight coil (outer diameter of each wing 7 cm), connected with a standard Mag-Stim Rapid 2 stimulator (maximum output 2.2 Tesla). Individual resting excitability threshold for right motor cortex stimulation was preliminarily determined by following standardized procedure (Rossini et al., 1994; Rossi et al., 2001). The rTMS train was delivered at the onset of the cue stimulus based on the following parameters: 150 ms duration, 20-Hz frequency, and intensity set at 100% of the individual motor threshold. These parameters are consistent with published safety guidelines for TMS stimulation (Anderson et al., 2006; Machii et al., 2006; Wassermann, 1998). While previous studies have successfully utilized rTMS for studying the role of prefrontal and parietal cortices in target detection and reorienting of attention to sensory stimuli (Pascual-Leone et al., 1994; Hilgetag et al., 2001; Rushworth et al., 2001; Grosbras et al., 2002, 2003; Chambers et al., 2004; Thut et al., 2005; Taylor et al., 2005), this experiment concentrates on the anticipatory delay activity between cue and target stimuli.
The experimental design included four conditions of rTMS, pseudo-randomized across subjects. In the “Sham” condition, a pseudo rTMS was delivered at scalp vertex; it was ineffective due to the reversed position of the coil with respect to the scalp surface (i.e. the magnetic flux was dispersed to air). In the FEF, IPS, and PrCe conditions, rTMS interfered with activity at the predetermined scalp sites since we placed the anterior end of the junction of the two coil wings. A mechanical arm maintained the handle of the coil angled at about 45° away from the midline.
The location of right FEF, IPS, and PrCe locations was automatically identified on the subject’s scalp using the SofTaxic navigator system (E.M.S. Italy, www.emsmedical.net), which uses a set of digitized skull landmarks (nasion, inion, and two pre-auricular points), and about 30 scalp points entered with a Fastrak Polhemus digitizer system (Polhemus), and an averaged stereotaxic MRI atlas brain in Talairach space (Talairach and Tournoux, 1988). The average Talairach coordinates in the SofTaxic navigator system were transformed through a linear transformation to each individual subject’s scalp. This strategy has been successful in previous rTMS studies of posterior parietal cortex and visuo-spatial attention (Babiloni et al. 2006).
The coordinates of the different cortical regions were based on a recent meta-analysis of task-evoked studies of spatial attention (He et al., 2007) and were as follows: right FEF: 32, −9, 48 (x, y, z); right IPS: 23, −65, 48; right PrCe: 38, −3, 50 (Figure 1b). The chosen coordinates correspond respectively to the epicenters of the two core regions of the dorsal attention fronto-parietal network (IPS, FEF), and one control region in the ventral attention network (PrCe) just lateral and anterior to FEF (vector distance=8.7 mm)(Figure 1C). The center of mass of these regions, although close, are reliably different across subjects as they were derived from a meta-analysis of several converging fMRI studies (N=4 studies including >80 subjects, as described in He et al., Neuron, 2007).
Right PrCe is an ideal control region for several reasons. It is part of the ventral attention network that responds to the presentation of targets especially when relevant and unattended. It has been proposed that the ventral attention network contributes a signal for re-orienting attention to important stimuli in the environment or ‘stimulus-driven re-orienting’ (Corbetta and Shulman, 2002; Corbetta et al., 2008). Therefore, similarly to FEF, right PrCe is sensitive to visual stimuli and attention shifts; but, unlike FEF, it does not respond when these shifts are driven by endogenous expectancies. Its location less than 1 cm away from FEF (see Figure 1B1C) makes it ideal to study the effect of rTMS on occipital alpha rhythms given both regions are approximately at the same distance from the parieto-occipital cortex where the alpha rhythms are more prominent.
Electroencephalography recordings
To assess the physiological impact of rTMS on anticipatory neural activity, we recorded simultaneously EEG activity from the scalp. Specifically, we measured the effect of magnetic stimulation at different cortical loci on the desynchronization of alpha rhythms in parieto-occipital cortex, a reliable physiological index of anticipatory spatial attention modulation (Worden et al., 2000; Yamagishi et al., 2003; Sauseng et al., 2005; Thut et al., 2006).
EEG data were recorded (BrianAmp; bandpass, 0.05–100Hz, sampling rate, 256 Hz) from 27 EEG electrodes placed according to an augmented 10–20 system, and mounted on an elastic cap resistant to magnetic pulses. Electrode impedance was below 5 KΩ. The artifact of magnetic stimulation on the EEG activity lasted about 10 ms (Supplementary figure 4a). Two electro-oculographic channels were used to monitor eye movement and blinking. The acquisition time for all data was set from −2 to +2s after cue stimulus. About 120 EEG trials were collected for each rTMS stimulation site and for each subject. The EEG single trials contaminated by eye movement, blinking, or involuntary motor acts (e.g. mouth, head, trunk or arm movements) were rejected offline. To remove the effects of the electric reference, EEG single trials were re-referenced by the common average reference. The common average procedure includes the averaging of amplitude values at all electrodes and the subtraction of the mean value from the amplitude values at each single electrode. The mean number of trials per condition that were artifact-free data was 92 (± 11).
Computation of the event-related desynchronization/synchronization
For the EEG spectral analysis, the frequency bands of interest were low alpha and high alpha. These frequencies were determined in according to a standard procedure based o the peak of individual alpha frequency (IAF; Klimesch et al., 1998). With respect to the IAF, these frequency bands were defined as follows: (i) low alpha, IAF −2 Hz to IAF, and (ii) high alpha, IAF to IAF + 2 Hz. This power spectrum analysis was based on an FFT approach using the Welch technique and Hanning windowing function. The length of the EEG periods used as an input for FFT was of 1 second. The event-related desynchronization/synchronization (ERD/ERS) of alpha EEG oscillations was obtained using:
equation M1
where E indicates the power density at the “event” (lasting 1s) and R the power density at the “rest” (lasting 1s). The ERD/ERS was computed for the IAF-based low and high alpha (Supplementary figure 4b). The “rest” of ERD/ERS computation was defined as a period from −1.5 to −0.5s before the cue stimulus. The “event” of ERD/ERS computation was defined as a period from 0.5 to 1.5s after the cue stimulus. Topographic maps of the alpha ERD/ERS were calculated in the period following the cue stimulus and rTMS stimulation (from 0.5 to 1.5s post-cue onset). The maps were represented on a 3D template cortical model by a spline interpolating function. This model is based on the magnetic resonance data of 152 subjects digitized at Brain Imaging Center of the Montreal Neurological Institute (SPM96).
Statistical analysis
Statistical comparisons were performed by ANOVAs for repeated measures. We used a Mauchley’s test to evaluate the sphericity assumption of the ANOVA, a Green-house-Geisser procedure for the correction of the degrees of freedom based, and Duncan tests for post-hoc comparisons (alpha, p<0.05). For the behavioral analyses on the effects of rTMS we used reaction times (RTs) or percentage of correct responses (Hits) with Condition (Sham, right PrCe, right FEF, right IPS), Side (target stimulus on the right or left side of the screen), and cue Validity (valid or invalid trials) as within-subject factors.
To verify the normal hemispheric lateralization of anticipatory alpha rhythms in parieto-occipital electrodes, we ran an Anova for each Condition (Sham, right PrCe, right FEF, right IPS), using the alpha ERD/ERS, separately for low and high alpha sub-bands, respectively, as dependent variable and Hemisphere (contra or ipsi to cue stimulus), and Electrode of interest (parietal or occipital) as within-subject factors,
To test the influence of rTMS on anticipatory EEG alpha rhythms, we used alpha ERD/ERS, separately for low and high alpha sub-bands, respectively, as dependent variable and Condition (Sham, right PrCe, right FEF, right IPS), Hemisphere (right or left hemisphere), and Electrode of interest (parietal or occipital) as within-subject factors.
To test for significant relationships between electrophysiological measures and visual performance, we computed a correlation analysis (Pearson test, p<0.05) between alpha ERD/ERS (at electrodes P3, P4, O1, O2) during the cue period, and RTs to target stimuli. For this analysis, alpha ERD/ERS values obtained for the three conditions of active rTMS stimulation (right FEF. IPS, PrCe) were normalized with respect to those obtained in the Sham condition according to the formula: rTMS site − Sham/rTMS site + Sham. This normalization insured that we correlated with behavior the specific changes induced by rTMS over different cortical sites above or below the physiological modulation observed in Sham. The two alpha sub-bands were considered separately (i.e. low- and high-frequency). We used the same formula for the normalization of RTs.
A significant correlation across subjects suggest that the modulation of alpha ERD/ERS induced by rTMS co-varies with behavioral performance differently in different individuals. A more stringent relationship is to show a correlation within each subject on a trial-by-trial basis. Trial-by-trial analyses are limited in EEG by low signal-to-noise, but we circumvented this problem through the following procedure. Trials for canonical and rotated targets, which yield on average faster and slower RT, respectively, were separately ranked in faster or slower RTs than the median value. ERD/ESR % change values for each group of trials (faster, slower) and for each condition (canonical, rotated) were averaged to generate four mean alpha ERD/ERS values, one for each category, i.e. canonical-fast, canonical-slow, rotated-fast, rotated-slow. For each stimulation site (FEF, IPS, PrCe, Sham), we ran a within-subject ANOVA on the ERD/ERS % change during the period preceding the target stimuli with Stimulus (canonical, rotated), RT (fast, slow) and electrodes (P3, O1, P4, O2) as within-factors.
Several control analyses were also performed. One ANOVA compared the size of the early sensory evoked components (P1/N1) across conditions for valid trials. Unfortunately, we did not have enough artifact-free trials to perform a proper random-effect analysis of the target-related P1/N1 component comparing valid and invalid trials. Invalid trials were only a small proportion (20%) of the total number of trials and invalid targets were detected with lower accuracy. Another ANOVA compared power changes in the period preceding the cue used to calculate a baseline to verify that rTMS at different cortical sites did not affect the baseline alpha power in both low and high-frequency sub-bands. The ANOVA factors were Condition (Sham, PrCe, FEF and IPS), hemisphere (left, right), and electrodes (P3, O1, P4, O2).
Control experiment
In a separate rTMS experiment the stimulation was delivered as Sham or on right IPS at two different times: simultaneously to the onset of the cue stimulus (R-IPS t(0)), or 350 msec after the cue (R-IPS t(350)). The parameters of rTMS and the task were identical to the main experiment. ANOVAs were run with reaction times (RTs) or percentage of correct responses (Hits) as dependent variable, and Condition (Sham, R-IPS t(0), and R-IPS t(350)), and cue Validity (valid or invalid trials) as within-subject factors.
Behavior
We assessed the effects of rTMS delivered at the onset of a spatial cue directing attention to a peripheral location on the accuracy and reaction time (RTs) of target identification ~1.5 seconds later (Figure 2). The effects of rTMS were different at different cortical sites both in terms of RTs (F (3,45)=11.19; p<0.0001) and accuracy of target identification (F (3,45)=10.88; p<0.0001). Magnetic stimulation on right FEF (544 ms ± 31) and right IPS (560 ms ± 32) significantly slowed down target responses as compared to Sham (524 ms ± 29; p<0.001 vs. FEF; p<0.0001 vs. IPS) or right PrCe stimulation (502 ms ± 31; p<0.001 vs. FEF; p<0.0001 vs. IPS). There was no significant difference between right PrCe and Sham stimulation (Figure 2a). Correct responses also occurred less frequently after rTMS on right FEF (86.3 % ±1.7) and right IPS (85.9 % ± 1.9) than after Sham (91.1 % ± 1.1; p<0.0001 vs. FEF; p<0.0001 vs. IPS) or right PrCe stimulation (88.7 % ± 1.9; p<0.01 vs. FEF; p<0.01 vs. IPS)(Figure 2b).
Figure 2
Figure 2
Behavioral effects of rTMS at different cortical sites
rTMS did not disrupt the observers’ ability to direct spatial attention to the target location. In fact there was an overall significant main effect of target validity (RTs: valid, 503 ms ± 30; invalid, 552 ms ± 32; F (1,15)=14.84 p<0.002; accuracy: valid, 90.4% correct ± 1.5; invalid, 85.6% correct ± 2 F (1,15)=16.74 p<0.001). However, rTMS of right FEF and right IPS more strongly impaired target detection at unattended (invalidly cued) locations (Accuracy: Validity × rTMS Condition, F(3,45)=2.74 p=0.054) (Supplementary Figure 1).
The effect of rTMS at different cortical sites was not differential for left (contralateral) or right (ipsilateral) visual field targets. However, targets presented in the right visual field were identified overall more accurately and more rapidly than targets presented in the left visual field (left VF: 507 msec ± 30.5; right VF: 499 msec ± 30 F (1,15)=18.64 p<0.0005; accuracy: left VF: 90% correct ± 1.6; right VF: 91% correct ± 2 F (1,15)=4.97 p<0.05)(Supplementary Figure 2; see Supplementary Figure 3 for a complete picture of behavioral results divided by site of stimulation, target visual field, and cue validity).
The visual field lateralization may relate to the well-known superiority of the right visual field (left hemisphere) for alphabetical material (Rizzolatti et al., 1971). To insure that this effect did not depend on magnetic stimulation, we ran a novel group of healthy volunteers (N=9) without rTMS. Once again right visual field targets were detected faster and more accurately than left visual field targets (RTs: left VF: 663 msec ± 37.4; right VF: 612 msec ± 40 F (1,8)=30.52 p<0.001; accuracy: left VF: 86% correct ± 2.75; right VF: 93% correct ± 1.8 F (1,8)=5.49 p<0.05).
Finally, to verify that the behavioral deficits induced by rTMS in prefrontal and posterior parietal cortex did not reflect a cumulative effect building up over many trials, but actually reflected interference with preparatory processes on a trial-by-trial basis, we checked whether the size of the deficit differed in the first, second, third, and fourth quartile of each block of trials, and found no difference. Although this null result does not rule out that a cumulative effect occurred, it is more consistent with the notion that rTMS mainly interfered on the trial in which it was applied.
Overall these findings indicate that interference with preparatory processes in FEF and IPS during anticipatory visuo-spatial attention significantly altered visual perception of targets presented two seconds later in both visual fields.
EEG
The EEG signals chosen for the analysis of alpha rhythms (+0.5 s to +1.5 s after cue onset) were free of rTMS artifacts. Supplementary figure 4a shows EEG data at parietal and occipital electrodes of interest (P3, P4, O1, O2) from a single subject in the four conditions (Sham, right FEF, right IPS, right PrCe). The rTMS artifact practically lasted the stimulation period (150 msec) plus about 10 msec. Supplementary figure 4b shows EEG power spectra (3–40 Hz, 1 Hz resolution) for the ‘baseline’ (−1.5 s to −0.5 s before the cue stimulus onset) and the “cue event” period (+0.5 s to 1.5 s). The alpha frequency peak is clearly recognizable at all electrodes of interest, and the profile of the EEG spectra looks regular.
The main question of the study was whether anticipatory alpha rhythms in occipital visual cortex were affected by interference with neural activity in control regions, IPS and FEF, during a delay in which subjects covertly attended to a target location. Figure 3a illustrates the topography of parieto-occipital alpha ERD/ERS in the four conditions (Sham, right FEF, right IPS, right PrCe), during the anticipation of the target. During Sham, we observed a robust bilateral ERD (desynchronization) at both low- and high-frequency alpha sub-bands in parietal-occipital cortex. A weak anticipatory alpha ERD was also observed after rTMS on right FEF and right PrCe. In contrast, right IPS-rTMS abolished the normal desynchronization which was substituted by a paradoxical synchronization, or bilateral increase of alpha power (ERS). This qualitative impression was confirmed by statistical analysis. For the low-frequency alpha ERD/ERS (Fig. 3b), an ANOVA showed a significant main effect of site of stimulation (F (3,45)=5.96; p<0.002). This was accounted for by a greater anticipatory alpha power (ERS) for right IPS than Sham (p<0.001), right PrCe (p<0.02), or right FEF stimulation (p<0.02) regardless of electrodes of interest (occipital, parietal) or hemisphere (left, right). The same effect was observed for the high-frequency alpha ERD/ERS (F (3,45)=6.10; p<0.002) with greater anticipatory alpha ERS for right IPS than Sham (p<0.001), right PrCe (p<0.03), or right FEF (p<0.03) stimulation (Figure 3c). In summary, interference with right IPS preparatory activity during spatial attention abolished the normal anticipatory desynchronization of alpha rhythms in parieto-occipital cortex.
Figure 3
Figure 3
Topography of alpha power as function of rTMS conditions
Next, we examined whether IPS- or FEF-rTMS changed the spatially selective topography of alpha desynchronization in parieto-occipital cortex (Worden et al., 2000; Yamagishi et al., 2005; Sauseng et al., 2005; Thut et al., 2006). As predicted by previous studies, anticipatory alpha ERD in the high-frequency sub-band was stronger over the hemisphere contralateral to the side of attention during Sham (F (1,15)=13.60; p<0.003). Interestingly, a significant contralateral topography was still present after right PrCe stimulation (F (1,15)=6.37; p<0.03), but was completely disrupted by stimulation of both right FEF and right IPS (Fig. 4).
Figure 4
Figure 4
Contralateral spatial selectivity of alpha power by rTMS condition
If the physiological disruption of anticipatory alpha rhythms in occipital cortex after disruption of attention-delay activity in right FEF and right IPS is functionally significant, then we would expect a positive relationship between changes in alpha ERD/ERS and visual performance. A Pearson correlation analysis was run between parieto-occipital alpha ERD/ERS (at electrodes P3, P4, O1, O2) during the cue period, and RTs to target stimuli separately for each stimulation site. Only in the case of right IPS stimulation we found a positive correlation between low-frequency alpha ERD/ERS at the P3 electrode (left parietal region contralateral to the rTMS stimulation) and RTs (r = 0.61 p< 0.01) (Figure 5a). A second positive correlation was found between high-frequency alpha ERD/ERS at the O1 electrode (left occipital region contralateral to the rTMS stimulation) and RTs (r=0.58 p< 0.02) (Figure 5b). These findings suggest that subjects with higher paradoxical synchronization of occipito-parietal cortex after right IPS stimulation tend to identify the target letters more slowly. This relationship is predictive in the sense that the ERD/ERS changes produced by rTMS precede in time the identification of the target.
Figure 5
Figure 5
Across-subject correlation between alpha ERD/ERS and RTs
To more stringently correlate disruption of anticipatory alpha rhythms with target discrimination, we carried out, separately for each stimulation site (Sham, IPS, FEF, PrCe), a within-subject ANOVA with Stimulus (canonical, rotated), RT (fast, slow), and Electrode (P3, O1, P4, O2) as factors (see methods). Only in right FEF we found a significant interaction of Stimulus × RT × Electrode (F(3,45)=2.99; p<0.04) in the high-frequency alpha ERD/ERS (Fig. 6a,b). For targets that were more difficult to discriminate (rotated L or T), there was a stronger paradoxical synchronization after right FEF-rTMS on right parietal (P4) and right occipital (O2) electrodes when subjects were slower to respond; in contrast, a normal desynchronization occurred when subjects responded more quickly to the same stimuli (figure 6b). In contrast, for canonical letters associated with faster responses and likely requiring less attentional scrutiny, a normal desynchronization was observed in parieto-occipital cortex (figure 6a). A predictive relationship between disruption of anticipatory alpha rhythms and behavioral performance after right FEF-rTMS was also confirmed across subjects, with a positive correlation between parieto-occipital high-frequency alpha ERD/ERS (at electrodes P4, O2) and slow RTs for rotated targets. As shown in figure 5c, across-subjects higher alpha power at the P4 electrode (right parietal) after right FEF stimulation was positively correlated with slower (r = 0.49, p= 0.05; red dots, Fig. 6c), but not faster RTs (black dots, Fig. 6c).
Figure 6
Figure 6
Within-subject relationship between alpha ERD/ERS and RTs
Overall these results suggest a strong link between disruption of right FEF preparatory activity, interference with parieto-occipital anticipatory alpha rhythms, and target discrimination. While disruption of IPS preparatory activity may have a more general effect on alpha desynchronization and target detection, preparatory activity in FEF may play an especially important role when visual selection/discrimination is more demanding.
A potential alternative interpretation of our results is that rTMS affects visual perception not by disrupting preparatory processes during the delay, but target evoked activity. We performed a control analysis on the earlier components of parieto-occipital potentials evoked by the target stimulus (P1, N1). The amplitude and latency of the P1-N1 complex were not affected by rTMS during the cue period (p>0.6, Sup. Fig. 5) at any of the sites. Hence visual discrimination impairment was not related to impaired target processing, but disrupted selection.
A second control analysis ruled out that changes in alpha power during the attention delay were not due to changes of the baseline before cue onset (see methods). This result was confirmed for both low and high-frequency alpha sub-bands (p>0.5 in both cases).
Control experiment
Given the rTMS train was presented coincident with the onset of the cue, an alternative explanation of the behavioral deficits induced by right IPS (and FEF) stimulation was that rTMS disrupted the sensory processing of the cue, the interpretation of its directional information, or the initial shift of attention. Although in the main experiment all subjects reported seeing clearly the cue stimulus, and all modulations on alpha rhythms (and related behavioral correlation) were measured from 0.5 to 1.5 seconds after the onset of the cue, these alternative interpretations, proposed by one of the reviewers, could not be ruled out with the present data set.
In a control experiment on 8 new right-handed healthy volunteers rTMS was delivered as either Sham or on right IPS at two different times: simultaneously to the onset of the cue stimulus (R-IPS t(0)), or 350 msec after the cue (R-IPS t(350)). If the behavioral deficits reported in the main experiment reflect ongoing preparatory processes during the delay, then similar effects should be measured at both intervals. Alternatively, if the behavioral deficits underlie transient processes occurring at the onset of the cue (e.g. cue encoding or shift of attention) then weaker effects should be obtained when the rTMS train is delivered later in the delay.
Irrespective of timing, we replicated the deficits produced on response speed (RTs: F(2,14)=4.92; p<0.025)) and identification accuracy (%correct: F(2,14)=7.03; p<0.01) by right IPS-rTMS (Sup. Fig. 6). When the stimulation was delivered simultaneously to the onset of the cue (RIPS t(0): 596 ms ± 44) or later in the delay (R-IPS t(350): 603 ms ± 40), target RTs were significantly slower as compared to Sham (560 ms ± 38; p<0.03 vs. R-IPS t(0); p<0.03 vs. R-IPS t(350)). There was no significant difference between R-IPS t(0) and R-IPS t(350) (Figure 7a). Correct responses were also less frequent after R-IPS t(0) (79.4 % ±7.7) and R-IPS t(350) (81.0.x % ± 6.1) stimulation than after Sham (86.8% ± 4.7; p<0.01 vs. R-IPS t(0); p<0.02 vs. R-IPS t(350)) (Figure 7b). Similarly to the main experiment, there was an overall significant effect of target validity (RTs: valid, 554 ms ± 42; invalid, 619 ms ± 40; F (1,7)=6.91 p<0.04; accuracy: valid, 88.2% correct ± 5.3; invalid, 76.6% correct ± 7.0 F (1,7)=7.43 p<0.03). The results of this control experiment closely replicate the main experiments and demonstrate that the behavioral deficits are not due to impaired sensory encoding of the cue, abnormal directional encoding, or early shifts of attention. Rather they support our interpretation of a deficit of spatial maintenance and selection.
Figure 7
Figure 7
Behavioral effects of rTMS as function of time of stimulation during delay
Visual deficits after disruption of anticipatory signals in right IPS and FEF
The behavioral deficits, we report, are consistent with the role of the dorsal fronto-parietal network in maintaining visual expectations during a delay (Kastner and Ungerleider, 2000; Corbetta and Shulman, 2002; Coull et al., 2003; Serences and Yantis, 2006). It is also consistent with prior studies showing that brief rTMS pulses can cause delayed behavioral effects, in the order of a few seconds as in our experiment (Klimesch et al., 2003).
While previous TMS studies have reported behavioral deficits in stimulus processing and reorienting of attention after interference with prefrontal and posterior parietal regions (Pascual-Leone et al., 1994; Hilgetag et al., 2001; Rushworth et al., 2001; Grosbras et al., 2002, 2003; Chambers et al., 2004; Thut et al., 2005;Taylor et al., 2005), this is the first study to report significant behavioral deficits during an anticipatory spatial delay. Another recent study applied rTMS to FEF during spatial cueing, but failed to find a significant disruption on target processing (Taylor et al., 2005).
The impairment of target discrimination was bilateral, i.e. involved both visual fields. This suggests that anticipatory activity in FEF and IPS in this task did not reflect only spatial selection (trial-by-trial cue location), but also, potentially, temporal (the onset of the target 2 seconds later) and feature expectancies (the shape and orientation of the target) that were also important for the correct completion of the task. Time- and feature-based selection recruit the fronto-parietal network bilaterally (Sylverster et al., 2008; Kanwisher and Driver, 1992; Coull, 2004), albeit with some functional specialization (Giesbrecht, et al, 2003; Coull, 2004; Slagter et al., 2007). Alternatively, neuroimaging studies of anticipatory endogenous spatial attention, as in this experiment, have clearly shown that preparatory activity in prefrontal and posterior parietal cortex is bilateral (Kastner et al., 2000; Corbetta and Shulman, 2002; Serences and Yantis, 2006), and that regions containing topographic specific signals are relatively small, like ‘small islands in a sea’ of bilateral responses (Jack et al., 2007). Given the size of higher-order parietal and frontal regions is within the spatial radius of rTMS inactivation (<1 cm2), it is really not surprising that both spatial and non-spatial selective sub-regions were affected.
Nonetheless, some behavioral effects were spatially selective consistent with the trial-to-trial cueing of location. Behavioral deficits were stronger when targets occurred at unexpected locations, presumably reflecting the importance of IPS and FEF in re-directing spatial attention as previously shown (Pascual-Leone et al., 1994; Hilgetag et al., 2001; Rushworth et al., 2001; Grosbras et al., 2002, 2003; Chambers et al., 2004). And, as later discussed, some physiological effects were also spatially selective (see below).
Finally, when compared vis-à-vis with the right precentral region (PrCe), our behavioral and physiological effects support a division of labor and functional segregation between a dorsal attention network, including right FEF and IPS, specialized in directing endogenous spatial attention, and a ventral attention network, including right PrCe, not recruited by endogenous shifts but recruited, as shown in other studies, by target processing and stimulus-driven reorienting (Corbetta and Shulman, 2002; Corbetta et al., 2008). This separation, originally proposed on the basis of fMRI activation studies, has been recently strengthened by fMRI connectivity studies in healthy subjects (Fox et al., 2006), fMRI studies of stroke patients with spatial neglect (Corbetta et al., 2005; He et al., 2007), and now inactivation studies with rTMS (this study).
Control of visual attention and alpha de-synchronization of occipital rhythms
The most important result of this study is the establishment of a three-way link between anticipatory activity in prefrontal and parietal cortex during spatial attention, behavioral deficits following rTMS interference, and secondary disruption of anticipatory alpha rhythms in occipital cortex. This three-way relationship is supported by three main findings. First, rTMS on right IPS disrupted the desynchronization of anticipatory (pre-stimulus) alpha rhythms in parieto-occipital cortex. Second, rTMS on both right IPS and right FEF disrupted the spatially selective topography of alpha power in occipital cortex (Worden et al., 2000; Yamagishi et al., 2003; Sauseng et al., 2005; Thut et al., 2006). Third, the degree of paradoxical alpha synchronization caused by right FEF- and right IPS-rTMS correlated, across- and within-subjects, with behavioral deficits in target discrimination.
The parieto-occipital alpha is the strongest cortical EEG rhythm and is profoundly modulated by attention. Although the generators are not known, alpha power is most consistently localized to the parieto-occipital cortex (Vanni et al., 1997) as in our study (Figure 3). This has been recently confirmed by MEG studies (Donner et al., 2007; Siegel et al., 2007). Spontaneous oscillations of alpha power have been also recently related to the excitability of occipital cortex to visual stimuli (Romei et al., 2007).
When attention is directed to a spatial location, alpha EEG rhythms in parieto-occipital cortex desynchronize, and lateralize with a stronger modulation contralateral to the locus of attention. The gradient of occipital alpha desynchronization correlates with focusing of spatial attention, and suppression of stimuli at ignored locations (Worden et al., 2000; Thut et al., 2006; Sauseng et al., 2005). It also predicts trial-by-trial the locus of attention and speed of visual perception (Thut et al., 2006). Interestingly, this inter-hemispheric EEG alpha power gradient is highly reminiscent of anticipatory BOLD signal changes recorded in occipital cortex with fMRI during the allocation of spatial attention, which also show a functionally significant gradient of anticipatory activity (Sylvester et al., 2007; Sestieri et al., 2007; Sylvester et al., 2008).
Here, we show for the first time that interference with right FEF and IPS anticipatory activity during a spatial delay disrupts the normal alpha desynchronization, and its spatially selective topography in parieto-occipital cortex. Moreover, the paradoxically induced alpha synchronization was functionally significant across- and within-subjects as it correlated with response times to target stimuli. Subjects or trials in which the synchronization was stronger resulted in slower RTs.
These novel results suggest that behavioral deficits caused by rTMS interference of anticipatory activity in FEF and IPS is, at least partly, mediated by disruption of anticipatory alpha rhythms, which previous studies have related to spatial selection in the occipital lobe. We also show that the behavioral impairment reflects a problem of selection, rather than a deficit of perception given the lack of any modulation of sensory evoked potentials (P1, N1). The control experiment shows that the behavioral deficits do not reflect problems with sensory analysis of the cue, encoding of the direction, or early shifts of attention. Of note, using shorter cue-target intervals two previous studies reported modulation of sensory evoked activity after right FEF-rTMS (Fuggetta et al., 2003; Taylor et al., 2007). Both studies reported significant changes in visually evoked activity consistent with top-down interference from parietal and prefrontal cortex on target processing in visual cortex.
The disruptive effects of frontal or parietal rTMS on ongoing EEG occipital rhythms can not be explained by the induction of local oscillatory activity given the distance between the stimulation and recording sites, and the duration of the effects prolonging nearly 1.5 seconds after the end of the rTMS train. The most likely explanation is that rTMS disrupted neural signals generated during spatial attention in fronto-parietal cortex, which in turn interfered with ongoing oscillations in the occipital lobe. Within the limited spatial resolution of EEG recordings, our results directly show such top-down interaction between control and sensory regions. Top-down effects of control regions onto sensory areas are often postulated, but only recently there has been direct support for this idea. Several studies have shown that electric or magnetic stimulation of FEF (or IPS) induce neural changes in visual cortex (Moore et al., 2003; Ekstrom et al., 2008; Ruff et al., 2006, 2007),. One recent fMRI study reports in physiological conditions, i.e. not during stimulation, that BOLD signal time series in IPS and FEF predict signal time series in occipital cortex, consistent with top-down influences from higher-order to visual regions during anticipatory spatial attention (Bressler et al 2008).
Our results provide a potential neural mechanism through which such top-down interaction may occur. Attention signals from fronto-parietal regions may take control of visual neurons though synchronization. At the local level, i.e. either individual areas or groups of neurons, it is known that attention has a powerful effect on synchronization of neural activity, especially at higher frequencies. Several studies have reported spike-triggered increases in gamma coherence during object selection and spatial attention (Fries et al., 2001; Bichot et al., 2005; Womelsdorf et al., 2006). Gamma coherence modulations can also occur prior to visual stimulation and be predictive of performance (Womelsdorf et al., 2006). Recent evidence indicates that attention-induced modulation of gamma coherence can be observed across multiple visual areas (V1, V2, V4), and that they occur predominantly in the superficial layers of cortex. Interestingly, parallel changes occur at lower frequencies (alpha and beta bands) in deeper layers of cortex (Robert Desimone, personal communication). These low frequency changes may correspond to the modulations recorded with EEG or MEG. Our findings show that anticipatory alpha rhythms in parieto-occipital cortex, one of the local mechanisms of attention-related neural synchronization, are controlled by top-down signals from IPS and FEF, and that their disruption leads to a sub-optimal state of neural synchronization that delays target processing.
Conclusions
To our knowledge, this is the first experiment that directly links the control of spatial attention by frontal and parietal cortex with anticipatory alpha rhythms in occipital cortex. This study underscores the importance of oscillatory neural activity in linking widely separate neuronal populations (Engel et al., 2001; Fries, 2005) during cognitive functions like spatial attention that require a dynamic interaction between cognitive systems for control and areas specialized in sensory analysis.
Supp1: Supplementary Figure 1. Behavioral effects of rTMS as function of cue validity
The accuracy of visual discrimination was significantly more impaired after rTMS in right IPS and right FEF on invalid than valid trials. ANOVA Target validity (valid and invalid trials) and Conditions (Sham, Right PrCe, Right FEF, Right IPS). Duncan post-hoc test: one (p<0.01) asterisk.
Supp2: Supplementary Figure 2. Behavioral effects of rTMS as function of target visual field
Left visual field discrimination was slower and less accurate than right visual field discrimination irrespective of rTMS site of stimulation. This results was confirmed in a separate group of subjects without rTMS (see results) and relates to the alpha-numeric nature (letters) of the stimuli that have a right visual field superiority (a): Group means (± standard error, SE) of the reaction time (ms) for the four Conditions (Sham, Right PrCe, Right FEF, Right IPS) divided by visual field (right or left). (b): Group means (± standard error, SE) of the accuracy (%) for the four Conditions (Sham, Right PrCe, Right FEF, Right IPS) divided by visual field (right or left).
Supp3: Supplementary Figure 3. Behavioral effects of rTMS as function of cue validity and target visual field
This figure shows the whole complement of results for all rTMS conditions, already shown divided by cue validity and target visual field respectively in Supplementary figure 1 and 2.
Supp4: Supplementary Figure 4. rTMS and EEG artifacts/analysis
(a): Example of event-related potential (ERP) computed at electrodes of the bilateral dorsal frontal-parietal circuit (P3, P4, O1, O2) preceding and following the cue stimulus at the four experimental conditions (Sham, right PrCe, right FEF, right IPS). An artifact is present in the Sham condition due to coil vibration on the EEG electrode. Note artifact of rTMS lasting 150 msec of stimulation plus an additional ~10 msec of artifact. (b): EEG power density spectra (grand average) at electrodes of interest (P3, P4, O1, O2) during baseline period (from −1.5 to −0.5s before the cue stimulus), and cue period (from 0.5 to 1.5s after the cue stimulus). Note peak in the alpha range as well as relative suppression of power at low frequencies, and increase power at high frequencies in going from the inter-trial interval to the cue period.
Supp5: Supplementary Figure 5
Group means (± standard error, SE) of the amplitude of the P1-N1 complex for the valid trials, for each experimental condition. Note no difference in early visual evoked components as function of rTMS. This result suggest that the behavioral effects after right IPS and right FEF stimulation cannot be due to abnormal sensory processing of the target stimulus.
Acknowledgments
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013), Grant Agreement ‘BrainSynch’ n° HEALTH-F2-2008-200728”. M.C was also supported by the National Institute of Neurological Disorders and Stroke Grants R01 NS48013, National Institute of Mental Health Grant R01 MH71920-06. We thank Vittorio Pizzella for support, and Chris Lewis for editing.
  • Anderson B, et al. Tolerability and safety of high daily doses of repetitive transcranial magnetic stimulation in healthy young men. J Ect. 2006;22:49–53. [PubMed]
  • Babiloni C, Vecchio F, Miriello M, Romani GL, Rossini PM. Visuo-spatial consciousness and parieto-occipital areas: a high-resolution EEG study. Cereb Cortex. 2006 Jan;16(1):37–46. Epub 2005 Mar 30. [PubMed]
  • Bichot NP, Rossi AF, Desimone R. Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4. Science. 2005;308:529–534. [PubMed]
  • Bressler SL, Tang W, Sylvester CM, Shulman GL, Corbetta M. Top-down control of human visual cortex by frontal and parietal cortex in anticipatory visual spatial attention. J Neurosci. 2008 Oct 1;28(40):10056–61. [PMC free article] [PubMed]
  • Broadbent D. The psychological demands of work. Proc R Soc Med. 1971;64(7):703–7. No abstract available. [PMC free article] [PubMed]
  • Broadbent D. Task combination and selective intake of information. Acta Psychologica. 1982;50:253–290. [PubMed]
  • Chambers CD, Payne JM, Stokes MG, Mattingley JB. Fast and slow parietal pathways mediate spatial attention. Nat Neurosci. 2004;7:217–8. [PubMed]
  • Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3:201–15. [PubMed]
  • Corbetta M, Patel G, Shulman GL. The reorienting system of the human brain: from environment to theory of mind. Neuron. 2008;8;58(3):306–24. Review. [PMC free article] [PubMed]
  • Corbetta M, Kincade MJ, Lewis C, Snyder AZ, Sapir A. Neural basis and recovery of spatial attention deficits in spatial neglect. Nat Neurosci. 2005;8:1603–10. [PubMed]
  • Coull JT. fMRI studies of temporal attention: allocating attention within, or towards, time. Brain Res Cogn Brain Res. 2004;21:216–26. [PubMed]
  • Coull JT, Walsh V, Frith CD, Nobre AC. Distinct neural substrates for visual search amongst spatial versus temporal distractors. Brain Res Cogn Brain Res. 2003 Jul;17(2):368–79. [PubMed]
  • Donner TH, et al. Population activity in the human dorsal pathway predicts the accuracy of visual motion detection. J Neurophysiol. 2007;98:345–59. [PubMed]
  • Driver J, Frith C. Shifting baselines in attention research. Nat Rev Neurosci. 2000;1:147–8. [PubMed]
  • Ekstrom LB, Roelfsema PR, Arsenault JT, Bonmassar G, Vanduffel W. Bottom-up dependent gating of frontal signals in early visual cortex. Science. 2008;18;321(5887):414–7. [PMC free article] [PubMed]
  • Engel AK, Fries P, Singer W. Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews Neuroscience. 2001;2:704–716. [PubMed]
  • Eriksen CW, Hoffman JE. Temporal and spatial characteristics of selective encoding from visual displays. Perception and Psychophysics. 1972;12:201–204.
  • Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A. 2006;103:10046–51. [PubMed]
  • Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences. 2005;9:474–480. [PubMed]
  • Fries P, Reynolds JH, Rorie AE, Desimone R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science. 2001;291:1560–3. [PubMed]
  • Fuggetta G, Pavone EF, Walsh V, Kiss M, Eimer M. Cortico-cortical interactions in spatial attention: A combined ERP/TMS study. J Neurophysiol. 2006;95:3277–80. [PMC free article] [PubMed]
  • Giesbrecht B, Woldorff MG, Song AW, Mangun GR. Neural mechanisms of top-down control during spatial and feature attention. Neuroimage. 2003;19(4):1361–8. [PubMed]
  • Grosbras MH, Paus T. Transcranial magnetic stimulation of the human frontal eye field: effects on visual perception and attention. J Cogn Neurosci. 2002;14:1109–20. [PubMed]
  • Grosbras MH, Paus T. Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness. Eur J Neurosci. 2003;18:3121–6. [PubMed]
  • He BJ, et al. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron. 2007;53:905–18. [PubMed]
  • He BJ, et al. Breakdown of intrinsic brain synchrony in spatial neglect: a novel mechanism to explain brain-behavior relationships after stroke. Neuron. 2007;53:905–918. [PubMed]
  • Hilgetag CC, Theoret H, Pascual-Leone A. Enhanced visual spatial attention ipsilateral to rTMS-induced ‘virtual lesions’ of human parietal cortex. Nat Neurosci. 2001;4:953–7. [PubMed]
  • Hopfinger JB, Buonocore MH, Mangun GR. The neural mechanisms of top-down attentional control. Nature Neuroscience. 2000;3:284–291. [PubMed]
  • Jack AI, et al. Changing human visual field organization from early visual to extra-occipital cortex. PLoS ONE. 2007;2:e452. [PMC free article] [PubMed]
  • Kanwisher N, Driver J. Objects, attributes, and visual attention: which, what, and where. Current Directions in Psychological Science. 1992;1:1–5.
  • Kastner S, Ungerleider LG. Mechanisms of visual attention in the human cortex. Annu Rev Neurosci. 2000;23:315–41. [PubMed]
  • Kastner S, De Weerd P, Desimone R, Ungerleider LG. Mechanisms of directed attention in the human exstrastriate cortex as revealed by functional MRI. Science. 1998;282:108–11. [PubMed]
  • Klimesch W, Doppelmayr M, Russegger H, Pachinger T, Schwaiger J. Induced alpha band power changes in the human EEG and attention. Neurosci Lett. 1998;244:73–6. [PubMed]
  • Laufs H, et al. Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proc Natl Acad Sci U S A. 2003;100:11053–8. [PubMed]
  • Machii K, Cohen D, Ramos-Estebanez C, Pascual-Leone A. Safety of rTMS to non-motor cortical areas in healthy participants and patients. Clin Neurophysiol. 2006;117:455–71. [PubMed]
  • Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M. Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A. 2007;104:13170–5. [PubMed]
  • Moore T, Armstrong KM. Selective gating of visual signals by microstimulation of frontal cortex. Nature. 2003;421:370–3. [PubMed]
  • Moore T, Fallah M. Microstimulation of the frontal eye field and its effects on covert spatial attention. J Neurophysiol. 2004;91:152–62. [PubMed]
  • Pascual-Leone A, et al. Induction of visual extinction by rapid-rate transcranial magnetic stimulation of parietal lobe. Neurology. 1994;44:494–8. [PubMed]
  • Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110:1842–57. [PubMed]
  • Posner MI. Orienting of attention. Quarterly Journal of Experimental Psychology. 1980;32:3–25. [PubMed]
  • Ress D, Backus BT, Heeger DJ. Activity in primary visual cortex predicts performance in a visual detection task. Nat Neurosci. 2000;3:940–5. [PubMed]
  • Rizzolatti G, Umilta’ C, Berlucchi G. Opposite superiorities of the right and left cerebral hemispheres in discriminative reaction time to physiognomical and alphabetical material. Brain. 1971;94:431–442. [PubMed]
  • Romei V, et al. Spontaneous Fluctuations in Posterior {alpha}-Band EEG Activity Reflect Variability in Excitability of Human Visual Areas. Cereb Cortex. 2007 [PMC free article] [PubMed]
  • Rossi S, et al. Prefrontal [correction of Prefontal] cortex in long-term memory: an “interference” approach using magnetic stimulation. Nat Neurosci. 2001;4:948–52. [PubMed]
  • Rossini PM, et al. Non invasive electrical and magnetic stimulation of the brain, spinal cord and roots: Basic principles and procedures for routine clinical application. Electroencephalography and Clinical Neurophysiology. 1994;91:79–92. [PubMed]
  • Ruff CC, et al. Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex. Curr Biol. 2006;16:1479–88. [PubMed]
  • Ruff CC, et al. Distinct Causal Influences of Parietal Versus Frontal Areas on Human Visual Cortex: Evidence from Concurrent TMS fMRI. Cereb Cortex. 2007 [PMC free article] [PubMed]
  • Rushworth MF, Ellison A, Walsh V. Complementary localization and lateralization of orienting and motor attention. Nat Neurosci. 2001;4:656–61. [PubMed]
  • Sapir A, d’Avossa G, McAvoy M, Shulman GL, Corbetta M. Brain signals for spatial attention predict performance in a motion discrimination task. Proc Natl Acad Sci U S A. 2005;102:17810–5. [PubMed]
  • Sauseng P, et al. A shift of visual spatial attention is selectively associated with human EEG alpha activity. Eur J Neurosci. 2005;22:2917–26. [PubMed]
  • Serences JT, Yantis S. Selective visual attention and perceptual coherence. Trends Cogn Sci. 2006;10:38–45. [PubMed]
  • Serences JT, Yantis S, Culberson A, Awh E. Preparatory activity in visual cortex indexes distractor suppression during covert spatial orienting. J Neurophysiol. 2004;92:3538–45. [PubMed]
  • Sestieri C, et al. Independence of anticipatory signals for spatial attention from number of nontarget stimuli in the visual fiel. J Neurophysiol. 2008;100:829–38. [PubMed]
  • Siegel M, Donner TH, Oostenveld R, Fries P, Engel AK. High-frequency activity in human visual cortex is modulated by visual motion strength. Cereb Cortex. 2007;17:732–41. [PubMed]
  • Silver MA, Ress D, Heeger DJ. Neural correlates of sustained spatial attention in human early visual cortex. J Neurophysiol. 2007;97:229–37. [PMC free article] [PubMed]
  • Slagter HA, Giesbrecht B, Kok A, Weissman DH, Kenemans JL, Woldorff MG, Mangun GR. fMRI evidence for both generalized and specialized components of attentional control. Brain Res. 2007;26(1177):90–102. [PMC free article] [PubMed]
  • Steriade M, Llinas RR. The functional states of the thalamus and the associated neuronal interplay. Physiol Rev. 1988;68:649–742. [PubMed]
  • Sylvester CM, Shulman GL, Jack AI, Corbetta M. Asymmetry of anticipatory activity in visual cortex predicts the locus of attention and perception. Journal of Neuroscience. 2007;27:14424–33. [PubMed]
  • Sylvester CM, Jack AI, Corbetta M, Shulman GL. Anticipatory suppression of nonattended locations in visual cortex marks target location and predicts perception. Journal of Neuroscience. 2008;25;28(26):6549–56. [PMC free article] [PubMed]
  • Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers, Inc.; New York: 1988.
  • Taylor PC, Nobre AC, Rushworth MF. FEF TMS affects visual cortical activity. Cereb Cortex. 2007;17:391–9. [PubMed]
  • Thut G, Nietzel A, Pascual-Leone A. Dorsal posterior parietal rTMS affects voluntary orienting of visuospatial attention. Cereb Cortex. 2005;15:628–38. [PubMed]
  • Thut G, Nietzel A, Brandt SA, Pascual-Leone A. Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J Neurosci. 2006;26:9494–502. [PubMed]
  • Vanni S, Revonsuo A, Hari R. Modulation of the parieto-occipital alpha rhythm during object detection. J Neurosci. 1997;15;17(18):7141–7. [PubMed]
  • Wassermann EM. Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial Magnetic Stimulation, June 5–7, 1996. Electroencephalogr Clin Neurophysiol. 1998;108:1–16. [PubMed]
  • Womelsdorf T, Fries P, Mitra PP, Desimone R. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature. 2006;439:733–6. [PubMed]
  • Worden MS, Foxe JJ, Wang N, Simpson GV. Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J Neurosci. 2000;20:RC63. [PubMed]
  • Yamagishi N, et al. Attentional modulation of oscillatory activity in human visual cortex. Neuroimage. 2003;20:98–113. [PubMed]
  • Yamagishi N, Goda N, Callan DE, Anderson SJ, Kawato M. Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex. Brain Res Cogn Brain Res. 2005;25:799–809. [PubMed]
  • Xiong H, Zhang T, Moon YS. A translation- and scale-invariant adaptive wavelet transform. IEEE Trans Image Process. 2000;9(12):2100–8. [PubMed]