# Oscillatory Brain Activity in the Canonical Alpha-Band Conceals Distinct Mechanisms in Attention

Cruz Gabriela, Melcón María, Sutandi Leonardo, Palva J Matias, Palva Satu, Thut Gregor

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Cruz Gabriela, Melcón María, Sutandi Leonardo, Palva J Matias, Palva Satu, et al.. Oscillatory Brain Activity in the Canonical Alpha-Band Conceals Distinct Mechanisms in Attention. Journal of Neuroscience, 2025, 45 (1), 10.1523/JNEUROSCI.0918-24.2024. hal-05350519

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Behavioral/Cognitive

# Oscillatory Brain Activity in the Canonical Alpha-Band Conceals Distinct Mechanisms in Attention

Gabriela Cruz,¹ María Melcón,¹ Leonardo Sutandi,¹ J. Matias Palva,¹,²,³ Satu Palva,¹,²* and Gregor Thut¹,⁴*

¹School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QB, United Kingdom, ²Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki 00014, Finland, ³Department of Neuroscience and Biomedical engineering, Aalto University, Helsinki 02150, Finland, and ⁴Centre de Recherche Cerveau et Cognition (Cerco), CNRS UMR5549 and Université de Toulouse, Toulouse 31059, France

Brain oscillations in the alpha-band (8–14 Hz) have been linked to specific processes in attention and perception. In particular, decreases in posterior alpha-amplitude are thought to reflect activation of perceptually relevant brain areas for target engagement, while alpha-amplitude increases have been associated with inhibition for distractor suppression. Traditionally, these alpha-changes have been viewed as two facets of the same process. However, recent evidence calls for revisiting this interpretation. Here, we recorded MEG/EEG in 32 participants (19 females) during covert visuospatial attention shifts (spatial cues) and two control conditions (neutral cue, no-attention cue), while tracking fixational eye movements. In disagreement with a single, perceptually relevant alpha-process, we found the typical alpha-modulations contra- and ipsilateral to the attention focus to be triple dissociated in their timing, topography, and spectral features: Ipsilateral alpha-increases occurred early, over occipital sensors, at a high alpha-frequency (10–14 Hz) and were expressed during spatial attention (alpha spatial cue > neutral cue). In contrast, contralateral alpha-decreases occurred later, over parietal sensors, at a lower alpha-frequency (7–10 Hz) and were associated with attention deployment in general (alpha spatial and neutral cue < no-attention cue). Additionally, the lateralized early alpha-increases but not alpha-decreases during spatial attention coincided in time with directionally biased microsaccades. Overall, this suggests that the attention-related early alpha-increases and late alpha-decreases reflect distinct, likely reflexive versus endogenously controlled attention mechanisms. We conclude that there is more than one perceptually relevant posterior alpha-oscillation, which need to be dissociated for a detailed account of their roles in perception and attention.

*Key words:* alpha-oscillations; attention; EEG/MEG; microsaccades; temporal expectation; visuospatial orienting

## Significance Statement

This study provides novel insights into perceptually relevant brain oscillations in the “canonical” alpha-band and the neural correlates of covert and overt attention processes. This by simultaneously recording MEG and EEG and isolating by design visuospatial and temporal expectations, respectively, while tracking fixational eye movements concurrently to the nominally covert attention shifts. The data reveal the presence of two, spatiotemporally and spectrally dissociated patterns of posterior alpha-changes that are distinctively associated with deployment of spatial and temporal anticipation and eye movement activity. This refines our understanding of the role of brain oscillations in perception and attention, the neural underpinnings of attention deployment in space and time, and provides methodological pointers for the study of perceptually relevant oscillatory activity through MEG/EEG.

Received May 14, 2024; revised Aug. 28, 2024; accepted Sept. 11, 2024.

Author contributions: G.C., J.M.P., S.P., and G.T. designed research; G.C., M.M., and L.S. performed research; J.M.P. and S.P. contributed unpublished reagents/analytic tools; G.C. and L.S. analyzed data; G.C. and G.T. wrote the paper.

This work was supported by the Medical Research Council to G.T., M.P., and S.P. (MR/V003623/1). We thank Alessio Fracasso for advice and help with the analysis of eye movements.

*S.P. and G.T. are the joint last authors

The authors declare no competing financial interests.

Correspondence should be addressed to Gabriela Cruz at cruz.gabriela@gmail.com or Gregor Thut at gregor.thut@glasgow.ac.uk.

https://doi.org/10.1523/JNEUROSCI.0918-24.2024

Copyright © 2024 the authors

## Introduction

Perception of external events relies on sensory processing, which in turn is regulated by endogenous attention (Carrasco, 2011). Many studies seeking to identify the neural basis of these perceptually relevant, endogenous processes in the human brain have focused on oscillatory activity in electro- and magnetoencephalography (EEG/MEG) and point to alpha-band (8–14 Hz) activity being functionally relevant: Ongoing alpha-oscillations over posterior sites are modulated in amplitude by deployment of endogenous attention, whereby one of the most robust patterns
of visuospatial attention deployment is a suppression of alpha-amplitude over areas tuned to the attended hemifield (Sauseng et al., 2005; Thut et al., 2006; Gould et al., 2011; Lobier et al., 2018; Sokoliuk et al., 2019) and/or an enhancement over areas covering ignored space (Worden et al., 2000; Kelly et al., 2006; Rihs et al., 2007, Händel et al., 2011; Capilla et al., 2014; Gallotto et al., 2020). While the above applies to anticipatory attention, this pattern also holds for attention shifts in working memory space (Sauseng et al., 2009; Poch et al., 2014, 2017a,b; Myers et al., 2015; Schneider et al., 2016; van Ede et al., 2017; Liu et al., 2022). In addition, prestimulus alpha-amplitude is inversely linked to excitability of sensory brain areas (Romei et al., 2008; Haegens et al., 2011; Samaha et al., 2017a; Iemi et al., 2022). Accordingly, for some time, we and others have interpreted the attention-driven comodulations of alpha-activity contra- and ipsilateral to the attention focus to reflect expressions of the same underlying processes: The up- or downregulation of excitability in sensory areas facilitating target selection or suppressing distractor information in attended versus unattended space, as a consequence of attentional engagement or disengagement, in line with inhibitory/gating models of alpha-oscillations (Klimesch et al., 2007; Jensen and Mazaheri, 2010).

However, recent EEG/MEG studies question this basic model of alpha-modulations in spatial attention (Noonan et al., 2016; Wöstmann et al., 2019; Antonov et al., 2020). This alongside EEG/MEG evidence that posterior brain oscillations in the canonical alpha-band conceal multiple perceptually relevant processes: There is EEG evidence of distinct alpha-sources in baseline activity that underlie changes in alpha-amplitude versus alpha-frequency which dissociate with time-on-task (Benwell et al., 2019), distinctively relate to specific visually evoked components (Trajkovic et al., 2024), and predict different aspects of perception (Benwell et al., 2017, 2022; Di Gregorio et al., 2022; Iemi et al., 2017; Samaha et al., 2017b, 2022). In further support of multiple, perceptually relevant alpha-rhythm, there is evidence for more than one functionally distinct source of alpha-modulations during spatial attention orienting (Capilla et al., 2014; Sokoliuk et al., 2019). Likewise, spatially, functionally, and/or spectrally distinct posterior alpha-sources were reported in the context of working memory tasks (Poch et al., 2017b; Rodriguez-Larios et al., 2022), traveling waves (Zhigalov and Jensen, 2023), sequential sampling (Jia et al., 2019), and animal models of attention (Bollimunta et al., 2011). Finally, recent studies on alpha-modulations in relation to fixational microsaccades report changes that mirror in topography those observed during covert spatial attention deployment (Liu et al., 2022, 2023), highlighting the need for considering microsaccades when studying the roles of alpha-oscillations in attention.

The present study revisits alpha-modulations by endogenous attention. To shed new light on the above controversies, we simultaneously recorded EEG/MEG activity, isolated spatial and temporal expectations based on three cue types (spatial, neutral, and a no-attention cue), and examined alpha-changes relative to eye movement activity. This for a comprehensive view on attention-driven alpha-changes. Our results provide evidence for a heterogeneity of attention-related alpha-oscillations within the canonical alpha-band mapping onto distinct attentional processes.

## Materials and Methods

### Participants

Thirty-two healthy participants (19 females, age: 24 ± 6 years old, 3 left-handed) took part in the experiment. All participants had normal or corrected-to-normal vision and were eligible for MEG and MRI

recordings. Exclusion criteria comprised any diagnosed neurologic or mental disorder, family history of epilepsy, ongoing medical treatment affecting CNS function or CNS-influencing drugs, and any contraindication to MRI or TMS (as participants also took part in a follow-up TMS study, not reported here). The study protocol was approved by the Ethical Committee of the University of Glasgow (ethical number 300210031), written informed consent was obtained from each participant prior to the experiment, and all research was carried out according to the Declaration of Helsinki.

### Procedure

Data was collected over three sessions spaced within the course of 3 weeks. Each session started with electro-oculogram (EOG), electrocardiogram (ECG), EEG, and MEG setup. On the first session, instructions and practice blocks were delivered in parallel with the EEG setup. Preparation was completed in ~1.5 h. In each session, participants performed 10 experimental blocks of the task (~12 min duration each) with breaks in between, while seated upright (MEG gantry at 60° angle) inside a magnetically shielded room (MSR). Stimuli were back projected onto a translucent screen 115 cm in front of the subjects using a PROPixx projector located outside the MSR. Eye movements were recorded concurrently with EEG/MEG data using an eye tracker. Sessions lasted 5 h each including washing hair and debriefing.

### Stimuli and task

Participants engaged in a covert visual-spatial attention task (programmed in PsychoPy; Peirce, 2007), requiring endogenous shifts of attention to the left or right visual field (prompted by a symbolic spatial cue) in preparation of a lateralized, to-be-detected visual target appearing more often at the attended position (80% cue validity). A neutral cue and no-attention cue were employed as control conditions. While the neutral cue prompted anticipatory attention based on temporal expectations without spatial information, the no-attention cue indicated that no target is to be expected and hence that attention does not need to be engaged.

In addition, we continuously presented bilateral concentric moving gratings that overlapped with the possible target positions (in grating trials), no gratings (in no-grating trials), or unilateral concentric moving gratings (not included here), each with randomized one-third probability across trials. Moving gratings were shown to induce gamma-activity (Hoogenboom et al., 2006, 2010; van Pelt and Fries, 2013) during attention orienting, with the goal to study top-down over bottom-up interaction in a more large-scale network synchronization framework, not analyzed here.

Figure 1 depicts the trial structure and the visual stimuli presented during the task (note that the colors used for the cues were isoluminant relative to the gray background and are adapted in the figure for illustrative purposes to be accessible to color-blind readers). Each trial (Fig. 1A) began with a central fixation diamond (color, purple; size, 0.5 degrees of visual angle, dva) presented on a gray background (50% gray value). The fixation diamond was presented for a long period of variable duration (2–3 s) to ensure a baseline that is free of transients from grating onset in grating trials. Subjects were instructed to keep fixation at the diamond throughout the trial. After the baseline period, the central diamond changed color for the whole duration of the cue-target interval (1–1.5 s; Fig. 1A, attention window = cue-target interval). The color change indicated to the participants how/whether they needed to prepare for the upcoming visual target as follows (Fig. 1B): For spatial attention, either the left or right side of the diamond changed color from isoluminant purple to isoluminant green (spatial cue). This prompted the participants to shift their attention toward the side of the color change. Spatial cues pointed with 80% validity to the target position. For anticipatory attention with only the temporal but no spatial component (neutral cue), the full diamond changed color to isoluminant green, indicating that an upcoming target is to be expected but that it can appear at either side. In the no-attention condition, the full diamond changed color to isoluminant red (no-attention cue), indicating with 100% validity that no target will appear and hence that attention does not need to be engaged. In a small portion of trials, no target was presented (10% catch trials, spatial and neutral cue trials only). Participants were instructed to
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Experimental task. A, Stimuli, trial structure, and B, cues per attention condition. A, Each trial started with the presentation of a central diamond for 2–3 s. In half of the analyzed trials, inward moving gratings were presented bilaterally throughout the trial, while in the other half, no gratings were present. We used a long baseline window to make sure of having at least a 1 s baseline epoch that is clean of transients elicited by the onset of the gratings. A cue then prompted the participants as to whether and how they needed to deploy attention. Cues that prompted engagement of attention (B, spatial and neutral cues) were followed at 1–1.5 s postcue onset by the presentation of a brief, 0.1 s target. Spatial cues were 80% valid as to upcoming target position. When cues indicated no attention deployment is needed (B, no-attention cue), no target was presented. The target was a gap in the outer ring of the gratings (grating trials), or a gap in a flashed Landolt C (no-grating trials). The example trial in A illustrates a neutral cue followed by a left-sided target at position no 4. Colors used were isoluminant relative to the gray background and were purple (baseline diamond) changing to green (spatial and neutral cue) or red (no-attention cue). Cues stayed on the screen until a detection response was made or the detection window (of 1–1.5 sec) lapsed. Colors are adapted here for illustrative purposes to be accessible to color-blind readers.</figcaption></figure>

covertly shift attention or to disengage attention in accordance with the cue following cue onset, while keeping fixation, and to report target detection and then its location by means of two consecutive manual responses (Fig. 1A, detection window followed by precision window).

The target consisted of a gap presented either on the outer ring of one of the concentric gratings or on a flashed Landolt C. Both had the same dimensions (3 dva in diameter) and were presented with their center at 4 dva either to the left or right and at 4 dva below the center of the diamond (Fig. 1A, target frame). The gratings had a spatial frequency of 3 cycles/dva and drifted inward at a speed of 0.66 dva/s. Target gaps could appear for 0.1 s at one of eight positions on the rings spaced by 30° (right-sided gaps at 45, 75, 105, or 135°; left-sided gaps at 225, 255, 285, or 315°; Fig. 1A). Participants were instructed to press a button with their right index finger if they detected the gap (Fig. 1A, detection window). This was followed by a delayed alternative forced-choice (8-AFC) precision response (Fig. 1A, precision response window), which was prompted by a target discrimination question screen. The screen appeared right after a detection response was made or after 1–1.5 s (the maximum duration of the detection window) if no detection response was given. Upon this screen, participants were asked to select one of the eight possible target locations by button press, indicating where they saw the gap, or to guess target position if unsure. The mapping of the target position to the manual response was presented on the screen in each trial as part of the discrimination prompt (Fig. 1A, precision response window). The precision response was designed to measure accuracy/precision of perception without time constraints, which is why we only analyzed accuracy but not reaction time for this delayed response (see analyses below).

Detection performance was kept at ~0.8 hit rate (collapsed across all conditions). To this end, the target gap was pretitrated (see below) and performance was monitored at the end of each block, with the contrast of the target gap being adjusted if performance was below 0.6 hit rate or at ceiling in two consecutive blocks. The experimental design

comprised 30 blocks performed across three sessions (10 blocks per session). Each block consisted of 120 trials, where all possible conditions were randomly presented across trials, resulting in 3,600 total trials per participant. Please note that the current analysis included gratings and no-grating conditions (2,400 trials), while the 1,200 unilateral grating trials were not considered. During data collection, not all participants completed the 30 blocks (due to fatigue and in some cases due to technical issues). On average, participants performed 29 blocks of the task, which resulted in an average of 2,320 trials per participant actually analyzed.

*Practice.* Participants performed two practice blocks, the first one (30 trials) ensured participants understood the instructions, got familiar with the task structure and pace, and understood the responses they were requested to give (first detection and then precision/discrimination response). For this practice, feedback was presented at every trial, for both target detection (correct/incorrect) and discrimination (0° error: “perfect,” ±30° error: “close,” ±60° error: “Not too close,” ±90° error: “rather far,” or “it was at the other side”). Targets were presented at supra-threshold levels during practice. Therefore, accuracy was expected to be at, or close to, ceiling. If not, the practice was repeated until at least 80% accuracy was achieved (most of participants did not require repetition). Once participants understood the instructions, a second practice block (80 trials) was carried out to ensure that participants shifted attention as instructed. Here, no precision question was asked, and participants did not receive feedback. The validity effect was inspected by plotting the distribution of reaction times for targets appearing at validly versus invalidly cued position. The practice was repeated until a validity effect was evident, indicating participants shifted their attention according to the cue and, thus, followed the instructions. Target visibility was adjusted subsequently to maintain ~0.8 detection rate during the actual task as detailed below.

*Target titration.* In addition to practice, participants performed in a titration block (80 trials) prior to the experiment, which consisted of a
staircase procedure. Here target contrast was manipulated parametrically to obtain a psychometric curve going from zero to ceiling detection rate (only neutral attention cues were used). The data points were fitted with a sigmoidal function to obtain the 80% threshold value. Separate preitration blocks were carried out for grating and no-grating trials.

### MEG-EEG acquisition and preprocessing

Neural activity was recorded with concurrent 306-channel MEG (204 planar gradiometers and 102 magnetometers) and 64-channel EEG (Triux-Neo, MEGIN; MEG/EEG). Bipolar horizontal and vertical EOG (placed above and below the right eye) were recorded for the detection of ocular artifacts. MEG/EEG and EOG were recorded at a 1,000 Hz sampling rate, EEG was referenced to the right mastoid online, and MEG was recorded with continuous head position tracking (cHPI). EEG impedances were kept <5 kΩ during recording. Offline, MEG, and EEG data were preprocessed in parallel using MNE-python (Gramfort et al., 2013), with specific preprocessing steps applied to each method individually when necessary, as outlined below. Maxfilter with temporal signal space separation (tSSS; Elekta Neuromag) was applied to MEG data to suppress extracranial noise in sensors, to interpolate bad channels and for head movement correction. Bad channels for EEG were manually marked and interpolated. Annotations with sample points containing blinks and muscle artifacts were created and saved to exclude trials with artifacts at a later step. The data were then low-pass filtered at 249 Hz [to allow for computation of noise covariance matrices (NCMs) used for source estimation] and Notch-filtered to remove line noise and the corresponding harmonics. Independent component analysis (ICA) was applied on a separate copy of the data downsampled to 200 and 1–40 Hz bandpass filtered. All blocks performed on a given session were concatenated, and FastICA method (Hyvärinen and Oja, 2000) was applied separately to MEG and EEG data. Independent components (ICs) were then visually inspected to identify ocular and heartbeat or muscle artifacts. On average, 3.5 ± 1.2 ICs were rejected for MEG, and 4.1 ± 2.2 ICs were rejected for EEG per participant. Projections were made to attenuate the contributions of the respective artifacts, and these were applied to the MEG and EEG raw data, respectively. After ICA, EEG data was referenced to the average activity of all electrodes.

Data was then segmented into 6 s epochs, centered at cue onset. Epochs contaminated with muscle activity or blinks were excluded from subsequent analysis using the annotation files previously created. Note that MEG and EEG data were processed in parallel. Therefore, the same trials were included for both data types. In addition, only the 204 planar gradiometers were included in the subsequent analysis of the MEG data, due to their sensitivity to local sources, reduced susceptibility to environmental noise, and effective signal cancellation of common-mode noise sources, relative to magnetometers. An average number of 1,832 ± 347 clean trials per participant were included in the analysis.

### Eye-tracker acquisition and preprocessing

Fixational eye movements were recorded using an EyeLink 1000 eye tracker (SR Research), positioned ~100 cm from the eyes beneath the projection screen within the MSR. Horizontal and vertical gaze positions of the right eye were continuously sampled at 1,000 Hz (same temporal resolution as for EEG/MEG data acquisition). Prior to recording, the eye tracker underwent calibration and validation procedures using the built-in protocols from the EyeLink software. Recorded EyeLink EDF files were converted to ASCII format and then to MAT files. Saccades were detected based on velocity peaks in the eye-tracker signal using an established algorithm by Nyström and Holmqvist (2010). This algorithm adjusts saccade onset and offset thresholds to accommodate local noise variations per saccade, accounting for both within- and between-participant noise variations. Hemifield gaze direction was determined from horizontal and vertical gaze positions, and saccades were classified as directed toward the left or right hemifield, irrespective of whether the movement was toward the upper or lower visual field. Of all fixational saccades detected in the cue-target window, 86% had an amplitude <1 dva, therefore falling into the classification of microsaccades (Martinez-Conde et al., 2013).

### Behavioral data analysis (visual task performance and microsaccades)
Reaction times to correctly detected targets and accuracy in the detection response (hit rate) was analyzed using a 3 × 2 × 2 ANOVA with factors validity (valid, invalid, neutral), target position (left vs right hemifield), and grating condition (grating present vs absent). The precision response was analyzed in terms of accuracy against chance performance (using one-sample t tests) to confirm that participants reliably detected the targets.

The number of fixational microsaccades collapsed over the cue-target interval was analyzed using a 4 × 2 × 2 ANOVA with the factors attention condition (left cue, right cue, neutral cue, no-attention cue), microsaccade direction (leftward, rightward), and grating condition (grating present vs absent). Significant main effects and interactions were explored using simple tests.

### MEG-EEG data analysis

Time-frequency representations (TFRs) were obtained per single trial and then averaged across artifact-free epochs for each participant (using a family of 29 Morlet wavelets of 2–30 Hz in 1 Hz increments and fixed cycles of 5). In MNE-Python, the duration of the wavelet is determined by the sigma parameter, defined by the following formula:

$$ \sigma = \frac{n \text{ cycles}}{2\pi f} $$

Because we use a fixed number of cycles, the temporal smoothing of the signal will vary as a function of the frequency. For instance, the full-width at half-maximum (FWHM) of the wavelet’s Gaussian envelope (Cohen, 2019) ranged from 234.3 to 134 ms for frequencies between 8 and 14 Hz. TFRs were contrasted separately for each data type (MEG gradiometers and EEG) across conditions as follows:

To isolate neural modulations associated with spatial attention, while controlling for temporal anticipation, we created contrasts between each spatial attention conditions (left cue, right cue) and the neutral cue control condition, Attend left = power$_{att, left}$ − power$_{att, neutral}$ Attend right = power$_{att, right}$ − power$_{att, neutral}$, and this separately for trials containing gratings and no gratings. Additionally, attentional modulation indices (AMIs) were computed as follows:

$$ \text{AMI} = \frac{\text{power}_{\text{att, right}} - \text{power}_{\text{att, left}}}{\text{power}_{\text{att, right}} + \text{power}_{\text{att, left}}} $$

To isolate neural modulations associated with temporal anticipation, in the absence of spatial attention, we created contrasts between the neutral cue and no-attention cue conditions, Attend general = power$_{att, neutral}$ − power$_{att, none}$, again separately for the two grating conditions.

### Statistical analysis of time-frequency modulations.
Group statistics were performed separately for each frequency and time point on nonbaseline-corrected data. Significant differences of sensor data between conditions were estimated using a Wilcoxon signed-rank test with α = 0.05. To remove false positives (FPs) caused by multiple statistical tests, we pooled all significant observations over all sensors and then discarded as many of the least-significant comparisons as would be predicted to be false discoveries by the alpha-level and the total number of comparisons used in the corresponding test under the null hypothesis (Palva et al., 2011; Honkanen et al., 2015). We then illustrated significant power modulations across frequencies using amplitude time-frequency (TF) plots for each contrast (attend left vs attend neutral, attend right vs attend neutral, and attend neutral vs no-attention, as described above) and this separately for data type (MEG and EEG) and trials containing gratings and no gratings, as follows: For each TF point, we computed the fraction of sensors that showed a significant power increase (i.e., S$_{TF+}$, the fraction of significant positive sensors for each TF point) or significant decrease (i.e., S$_{TF-}$, the fraction of significant negative sensors for each TF point). To reveal the localization of sensors that account for significant power changes in each of the resulting time-frequency plots, we then topographically mapped onto sensor space the fraction of significant TF points within all
points of a TF area where the statistical analysis revealed a difference (here termed time-frequency region of interest, TFROI), focusing on TFROI in the alpha-band.

### Source reconstruction

Broadband continuous preprocessed sensor data (306 MEG sensors plus 64 EEG electrodes) were source transformed into 200 parcel time series as previously described in detail (Palva et al., 2010; Rouhinen et al., 2020), therefore here presented briefly. T1-weighted anatomical MRI scans for cortical surface reconstruction models were obtained for each subject at a resolution of a 1 × 1 × 1 mm (3D MPRAGE sequence) with a 3 T Trio Siemens scanner. The FreeSurfer software (surfer.nmr.mgh.harvard.edu) was used for volumetric segmentation of the individual MRI data, surface reconstruction, flattening, cortical parcellation, and neuroanatomical labeling with the 200-parcel Schaefer atlas (Schaefer et al., 2018). MNE software package (Gramfort et al., 2013, 2014) was used to construct head conductivity models and cortically constrained source models with 5 mm spacing for each subject. These models were employed for MEG-MRI colocalization and the computation of forward models. Subsequently, inverse operators where generated using the forward models along with NCMs extracted from the frequency band of 151–249 Hz, 400–100 ms before cue onset (left, right, neutral, no-attention) for each trial. Finally, fidelity-weighted inverse operators for optimized reconstruction accuracy (Korhonen et al., 2014; Siebenhühner et al., 2020) were used to create continuous broadband parcel time series. These data were then filtered into complex-valued narrowband time series using 50 Morlet wavelets with logarithmically increasing center-frequencies between 1 and 100 Hz and epoched as described above.

### Statistical analyses of source data.
The same statistical procedure described for sensor data was applied at source level. The group statistics, across subjects and between conditions (same as described above), were performed separately for each frequency, time window, and cortical parcel. To reveal the cortical sources accounting for the most prominent effects in the TFROI, we displayed the fraction of significant TF points within all points of the TFROI for each anatomical parcel, visualized on an inflated cortical surface, where areas of statistically significant reduced oscillatory activity are displayed in blue and areas of statistically significant enhanced oscillatory activity are displayed in red. TFROIs were defined by significant effects revealed at sensor-level analysis, to match TFROI included in the sensor-level topographic maps.

### Microsaccade incidence over time into cue-target interval

To be able to identify whether spectral MEG/EEG changes (as obtained through the TFR contrasts above) coincide in time with potential biases in fixational eye movement incidence across conditions, we calculated differences of microsaccade incidences per time point into the cue-target interval (i.e., attend left vs attend neutral, attend right vs attend neutral, and attend neutral vs no-attention/grating condition). To obtain time-resolved information regarding the occurrence of microsaccades, we initially generated a microsaccade time series with a sampling frequency of 1,000 Hz. Subsequently, we quantified the time courses of the number of microsaccades by employing a sliding time window of 50 ms, incremented in steps of 1 ms (as in Liu et al., 2022). For resolving directional saccade bias across time for the contrasts attend left versus attend neutral and attend right versus attend neutral, we analyzed differences in the time course of those microsaccades that were directionally biased to cued position, that is, incidence of leftward saccades when comparing attend left versus attend neutral and incidence of rightward saccades when comparing attend right versus attend neutral. For the contrast attend neutral versus no-attention (with no directional oculomotor bias toward cued position), we calculated differences between microsaccade time courses independent of microsaccade direction. Time-resolved statistical differences were identified using a Wilcoxon signed-rank test (with $\alpha = 0.05$) per time point and FP correction as used for the EEG/MEG analyses (see above).

## Results

### Behavioral data: enhanced visual performance at cued relative to uncued positions

Figure 2 illustrates participant performance in the detection response for valid, invalid, and neutral cue trials for both hemifields of target presentation (left vs right) and grating condition (present vs absent). No behavioral response was required for no-attention cue trials. The data demonstrate that participants successfully shifted attention: They responded faster and with greater accuracy to targets at validly than invalidly cued position [main effect of validity for reaction time (RT): $F_{(2,62)}$ = [45.651], $p < 0.001$, and accuracy: $F_{(2,62)}$ = [26.903], $p < 0.001$]. While the effect of validity was observed in both gratings and no-grating condition (see below), these differed in strength (significant two-way interactions of validity × grating condition for RT: $F_{(2,62)}$ = [6.467], $p = 0.003$, and accuracy: $F_{(2,62)}$ = [18.728], $p < 0.001$). Hence, results are analyzed and/or illustrated separately for gratings and no-grating condition here, as well as for the microsaccade and MEG-EEG results below. Validity effects were independent of target position (no two-way interactions of validity × target position for RT: $F_{(2,62)}$ = [1.508], $p > 0.05$, or accuracy: $F_{(2,62)}$ = [2.528], $p > 0.05$, no three-way interactions of validity × target position × grating condition, for RT: $F_{(2,62)}$ = [2.183], $p > 0.05$, or accuracy: $F_{(2,62)}$ = [0.132], $p > 0.05$).

The grating condition presented with a main effect of validity for RT ($F_{(2,62)}$ = [36.479], $p < 0.001$) and accuracy ($F_{(2,62)}$ = [35.833], $p < 0.001$). Exploring the effect of validity on accuracy using simple tests revealed significant differences (M in detection rate) for all possible comparisons (valid vs invalid trials: $M = 0.22$, $SD = 0.19$, $t_{(31)}$ = 6.3, $p < 0.0001$, valid vs neutral trials: $M = 0.04$, $SD = 0.04$, $t_{(31)}$ = 5.5, $p < 0.0001$, invalid vs neutral trials: $M = -0.18$, $SD = -0.17$, $t_{(31)}$ = 5.5, $p < 0.0001$). Likewise, exploring the effect of validity on RT revealed significant differences (M in milliseconds) across all conditions (valid vs invalid trials: $M = -105$, $SD = 82$, $t_{(31)}$ = -7.09, $p < 0.0001$, valid vs neutral trials: $M = -28$, $SD = 24$, $t_{(31)}$ = -6.3, $p < 0.0001$, invalid vs neutral trials: $M = 78$, $SD = 87$, $t_{(31)}$ = 4.9, $p < 0.0001$).

The no-grating condition also presented with a main effect of validity for RT ($F_{(2,62)}$ = [41.998], $p < 0.001$) and accuracy ($F_{(2,62)}$ = [9.280], $p < 0.001$). Simple tests of detection rate revealed significant differences between valid and invalid trials ($M = 0.09$, $SD = 0.15$, $t_{(31)}$ = 3.2, $p = 0.002$) and between neutral trials and invalid trials ($M = 0.07$, $SD = 0.14$, $t_{(31)}$ = 2.82, $p = 0.008$), but only a marginal effect for valid vs neutral trials ($M = 0.01$, $SD = 0.04$, $t_{(31)}$ = 2.19, $p = 0.03$). Simple tests of RTs revealed significant differences for all contrasts (valid vs invalid trials: $M = -74$, $SD = 52$, $t_{(31)}$ = -7.36, $p < 0.0001$, valid vs neutral trials: $M = -25$, $SD = 26$, $t_{(31)}$ = -5.33, $p < 0.0001$, invalid vs neutral trials: $M = 48$, $SD = 53$, $t_{(31)}$ = 5.1, $p < 0.0001$).

In addition, we confirmed that participants reliably detected the targets by examining the precision results. When participants reported having detected the target, their gap location report was highly precise and significantly different from chance performance (>>1/8 = 0.125, as the gap could appear on one of eight positions spaced by 30° along the rings). This was observed in both grating and no-grating trials (0° error precision rate for gratings: $0.72 \pm 0.13$, $t_{(31)}$ = 25.14, $p < 0.0001$, for no-gratings: $0.53 \pm 0.11$, $t_{(31)}$ = 13.7, $p < 0.0001$). In addition, precision in trials with a detection response (“seen” gaps) was significantly higher than precision in trials with no detection response (“unseen” gaps; gratings: mean difference = 0.46, $SD = 0.12$, $t_{(31)}$ = 21.0, $p < 0.001$; no-gratings: mean difference = 0.23, $SD = 0.01$, $t_{(31)}$ = 10.9, $p < 0.0001$).
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Behavioral effects of spatial attention orienting in the detection response. A, Reaction times of correctly detected targets and B, accuracy of target detection (hit rate) for targets appearing in the left and right visual fields at validly cued, invalidly cued and neutrally cued positions (neutral cue, spatially uninformative). Data is presented separately for trials with moving gratings and without moving gratings (top vs bottom panels). Participants (n = 32) successfully shifted attention, as demonstrated by faster reaction times and higher detection rate for target presented at validly relative to neutral and invalidly cued locations both when gratings were present or absent. Post hoc tests \*p* < 0.05, \*\*p* < 0.01, \*\*\*p* < 0.001.</figcaption></figure>

## Fixational eye movements: covert spatial attention biases the direction of microsaccades

Eye-tracker data was available for 29 of the 32 participants, in whom microsaccades were detected in ~45% of all trials (three participants had to be excluded due to technical problems with eye tracking). We first analyzed the incidence of fixational microsaccades collapsed across the cue-target interval (Fig. 3). We found a significant main effect for attention condition ($F_{(3,84)}$ = [3.514], *p* = 0.019), which was independent of grating presence/absence (no interaction attention × grating condition: $F_{(3,84)}$ = [1.032], *p* = 0.382). Notably, post hoc pairwise comparisons on data collapsed across grating conditions revealed that spatial attention does not increase the number of microsaccades relative to no-attention (left cue: *n* = 104.9 ± 88.2, right cue: *n* = 105.4 ± 85.2, no-attention cue: *n* = 113.9 ± 95.1, all $t_{(28)}$ < -1.56, *p* > 0.121). Instead, neutral attention reduced the number of microsaccades, i.e., stabilized fixation, relative to both spatial attention and the no-attention conditions (neutral cue: *n* = 92.9 ± 76.11, all $t_{(28)}$ < -2.822, all *p* < 0.006). Incidence of microsaccades did not differ between left and right attention trials ($t_{(28)}$ = -0.07, *p* = 0.941).

When taking into account microsaccade direction, we observed directional microsaccade bias to depend on attention condition (interaction attention condition × microsaccade direction: $F_{(3,84)}$ = [23.941], *p* < 0.0000), again independently of grating presence/absence (attention condition × microsaccade direction × grating condition: $F_{(3,84)}$ = [1.113], *p* = 0.348). There were more microsaccades biased toward relative to away from cue direction for both left and right spatial attention conditions (left cue: left- vs rightward saccades *n* = 125.5 vs 84.5, $t_{(28)}$ = 5.034, *p* < 0.00001, right cue: right- vs leftward saccades *n* = 130.1 vs 80.8, $t_{(28)}$ = 6.845, *p* < 0.00001), but there was no directional bias for the neutral (left- vs rightward saccades *n* = 90.6 vs 95.3, $t_{(28)}$ = -1.082, *p* = 0.284) or the no-attention

cue condition (left- vs rightward saccades *n* = 111.9 vs 115.8, $t_{(28)}$ = -0.728, *p* = 0.469).

In line with prior work (Liu et al., 2024), this indicates that covert spatial attention biased the direction of associated oculomotor activity, without changing the probability of microsaccades to occur (relative to a no-attention control). Microsaccade incidences across time into the cue-target interval is resolved below relative to attention-related alpha-modulations (Fig. 6, MEG/EEG data).

## MEG/EEG data: two distinct, occipital/parietal sources of ipsi-versus contralateral alpha-modulations during covert spatial attention shifts (alpha spatial cue <> neutral cue)

Figure 4 illustrates spectral changes in the cue-target interval when participants are asked to covertly shift attention in anticipation of an upcoming target when prior information of likely target position is available versus not available (contrasts attend left vs attend neutral, attend right vs attend neutral), separately for MEG and EEG signals (Fig. 4, compare A, B), and for trials with and without gratings (top and bottom panels). We here focus on spectral changes in the alpha-band (see boxes in the time-frequency plots marking the canonical alpha-frequency band of 8-14 Hz).

Broadly, our results reveal the expected pattern of alpha-changes within the canonical alpha-band, for both grating and no-grating trials [Fig. 4A,B, dotted 8-14 Hz area in the time-frequency (tf) plots]. Alpha-amplitude decreases contralaterally and increases ipsilaterally to the attention focus for both MEG and EEG (for alpha-decreases, see Fig. 4, right tf plots in attend left and left tf plots in attend right condition/for alpha-increases, see left tf plots in attend left and right tf plots in attend right condition). This aligns well with the pattern of alpha-changes typically observed during endogenous shift of covert attention (Worden et al., 2000; Sauseng et al., 2005, 2009; Kelly et al.,
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Incidence of fixational microsaccades collapsed across the cue–target interval as measured by eye tracking, represented separately for left- and rightward microsaccades as a function of attention condition (left cue, right cue, neutral cue, no-attention cue) and for trials with and without gratings (top vs bottom panel). While microsaccades were detected in ~45% of trials, their incidence was biased toward cued direction in the spatial attention conditions (more left-/rightward microsaccades for left/right cue conditions). Post hoc tests \*p < 0.05, \*\*p < 0.01.</figcaption></figure>

2006; Thut et al., 2006; Rihs et al., 2007, Bahramisharif et al., 2010; Gould et al., 2011; Händel et al., 2011; Capilla et al., 2014; Poch et al., 2014; 2017a,b; Myers et al., 2015; Schneider et al., 2016; van Ede et al., 2017; Popov et al., 2019; Gallotto et al., 2020; Liu et al., 2022; see also Foster et al., 2017).

On closer inspection, however, our data shine new light on these well-known posterior alpha-changes in terms of their topography, timing, and spectral features, which is more evident in the MEG than EEG data (see Fig. 4A,B for amplitude changes and Fig. 6A,B for statistical results). These changes are observed irrespective of grating presence or absence (i.e., in both gratings and no-grating trials) as follows: Topographically, MEG reveals that the ipsilateral alpha-amplitude increase is observed over occipital sensors, while the contralateral alpha-amplitude decrease occurs over parietal sensors (Figs. 4A, 6A,B, MEG map insets). In terms of timing, ipsilateral alpha-increases occur early into the cue–target interval peaking at ~300–500 ms, while alpha-decreases occur much later closer to target presentation (onset at 600–800 ms; Figs. 4A, 6A,B, MEG tf plots). Spectrally, occipital alpha-increases are observed at a higher frequency within the alpha-band (10–14 Hz), whereas parietal alpha-decreases occur at a lower alpha-frequency (7–10 Hz; Figs. 4A, 6A,B, right inset, spectrogram of significant changes).

Besides illustrating significant MEG effects (in tf and topo-plots), Figure 6 depicts microsaccade incidences across time into the cue–target interval in relation to the alpha-modulations (see line graph insets below tf plots). This demonstrates that microsaccades with a directional bias to the cued position during spatial attention (relative to neutral attention) are clustered

within an early time window (~200–500 ms) postcue onset (Fig. 6A,B, bottom line graphs, see green line of difference in saccade incidence, see horizontal bar plot just above for statistics). These microsaccades in cued direction precede in onset the early, lateralized attention-related alpha-increase (by ~100–150 ms) with a subsequent overlap (Fig. 6A,B, bottom line graphs, green vs red line). This suggests that microsaccades lateralize occipital alpha-activity, as opposed to microsaccades potentially originating from posterior alpha-processes. In contrast, there was little evidence of directionally biased microsaccades during spatial as compared with neutral attention (Fig. 6A,B) in the late time window where we observed the lateralized, parietal alpha-decreases (see flat green line vs negative trend in blue line in the late window). Please note that the relative time course between oculomotor and alpha-activity in the early time window (microsaccades preceding alpha-increases) will also hold when taking into account differences in temporal smoothing factors across microsaccade and MEG/EEG analyses (both measures having been acquired simultaneously at 1,000 Hz). Specifically, while the time course representing directionally biased microsaccades have been smoothed in a forward direction using a sliding window size of 50 ms, the MEG-EEG wavelet resulted in a temporal smoothing of 187–134 ms for the microsaccade-related high alpha-frequencies (10–14 Hz; smoothing centered at each time point; see Materials and Methods). To use shorter smoothing for alpha-band activity is suboptimal (a minimum FWHM for 10 Hz would be 100 ms). Using a longer and centered sliding time window for microsaccades, on the other hand, would result in a slightly earlier microsaccade onset and a wider time course, therefore accentuating the observed relative time course of microsaccade before alpha-activity, not reverting it.

Regarding EEG, the distribution of attention-related alpha-modulations does differ somewhat from MEG (Figs. 4B, 6A,B, bottom EEG panels). As MEG, EEG is picking up the early, ipsilateral alpha-increase and the late contralateral alpha-decrease (Fig. 4B). However, in terms of topography, attention-related alpha-changes are more confined to occipital electrodes, i.e., an occipitoparietal distinction is not detectable in EEG (Fig. 4B, map insets). In addition, only the ipsilateral, occipital alpha-increase at 10–14 Hz frequency reaches significance (Fig. 6A,B, EEG topo-plots), suggesting that EEG and MEG differ as to their respective sensitivities/SNRs to capture these alpha-oscillations.

Given the above discrepancies between the MEG and EEG findings at the sensor level, which can be explained by different sensitivities to radial versus tangential sources (Goldenholz et al., 2009; Ahlfors et al., 2010a,b), we run source modeling integrating the simultaneously recorded MEG/EEG data, thereby taking advantage of their complementarities (Srinivasan et al., 2006; Ahlfors et al., 2010a,b). These source estimates of combined MEG/EEG data localized the spatial attention-driven alpha-changes to occipital and parietal areas (Fig. 7A,B), again matching in results across grating and no-grating trials (Fig. 7, compare top vs bottom panels), in analogy to sensor data. The source modeling revealed that for the contrasts attend left/right versus attend neutral, the alpha-power increase originates in occipital areas ipsilateral to the attended side, whereas the alpha-power decrease originates in contralateral parietal areas. This decrease is more localized and subtle than the increase (results only show parcels that go along with a significant modulation for the given contrast). Notably, in no occipital parcel did we find an alpha-power decrease in this contrast testing spatial attention processes.
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Sensor-level modulation of oscillatory brain activity during spatial orienting (relative to the neutral attention control) in *A*, MEG and *B*, EEG (MEG-EEG measured simultaneously). Changes are illustrated in full for two comparisons (attend left minus attend neutral and attend right minus attend neutral) both for trials with and without gratings (top vs bottom panels). The four time-frequency (tf) plots within a condition cell (spatial cue vs neutral cue/grating condition/recording modality) illustrate tf changes in the cue-target interval (zero vs cue onset) over left and right parietal sensors (top row, highlighted in pink vs cyan) and left and right occipital sensors (bottom row, highlighted in yellow and green). See colored dots in map insets for distribution of parieto-occipital sensors. Boxes within the tf plots mark the canonical alpha-frequency band (8–14 Hz), and the maps overlaying the four tf plots per condition cell depict the respective alpha-topographies. In line with previous research, alpha-amplitude increased ipsilaterally and decreased contralaterally to cued position (see topo-plots, coded in red and blue, respectively). Notably, in MEG (compare *A*) these alpha-increases versus alpha-decreases showed a triple dissociation in terms of timing into the cue-target interval (early vs late), location (occipital vs parietal), and frequency band (high vs low alpha), pointing to the presence of at least two attention-related alpha-oscillators. Also, see the differences in topography between MEG and EEG recordings (*A* vs *B*). The rightmost topographies illustrate a third comparison (attend right vs attend left), representing the AMI, calculated for the time-frequency window highlighted in the boxes within the tf plots.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Sensor-level modulation of oscillatory brain activity during temporal expectations of an upcoming target (neutral attention minus no-attention) in *A*, MEG and *B*, EEG (MEG and EEG measured simultaneously). Changes are illustrated in full for the relevant contrast for both trials with and without gratings (top vs bottom panels). The four time-frequency (tf) plots within a condition cell (neutral cue vs no-attention cue/grating condition/recording modality) illustrate tf changes in the cue–target interval (zero, cue onset) over left and right parietal sensors (top row, highlighted in pink vs cyan) and left and right occipital sensors (bottom row, highlighted in yellow and green). See colored dots in map insets for distribution of parieto-occipital sensors. Boxes within the tf plots mark the canonical alpha-frequency band (8–14 Hz), and the maps overlaying the four tf plots per condition cell depict the respective alpha-topography. Note the bilateral alpha-amplitude decrease that occurred in a late time window and a low alpha-frequency band over parietal areas with a parietotemporal spread in MEG and a more occipital localization in EEG.</figcaption></figure>

## Presence or absence of gratings does not consistently influence attention-driven alpha-modulations observed over MEG and EEG sensors

So far, we have analyzed and presented gratings and no-grating conditions separately. Overall, our data reveal that alpha-changes are replicated across both grating and no-grating conditions revealing matching topographies and source estimates of alpha-increases and alpha-decreases at corresponding frequencies and time windows for both MEG and EEG (Figs. 4–7), i.e., matching in results independently of grating presence/absence. Nonetheless, it is conceivable that presence/absence of gratings may have influenced attention processes. For instance, moving

gratings may have been distracting, hence eliciting the need for more inhibitory processes (Bonnefond and Jensen, 2024) or alternatively, may have served as attention placeholders, hence focusing spatial attention to a more confined locus, thereby reducing variability. We therefore directly compared the attention-driven alpha-modulations between grating and no-grating conditions through the following two contrasts of (1) attend left versus attend neutral in grating trials versus attend left versus attend neutral in no-grating trials and (2) attend right versus attend neutral in grating trials versus attend right versus attend neutral in no-grating trials. These contrasts revealed no to little evidence for consistent differences in attention-driven alpha-modulations between grating and no-grating trials (data not shown). Regarding MEG data, attention-driven alpha-modulations were not significantly different between gratings present versus absent trials. Regarding EEG, we found subtle differences in the alpha-band and this in the direction of the grating condition eliciting less ipsilateral alpha-increases and less contralateral alpha-decreases relative to the no-grating condition. This would not seem to correspond with any of the above interpretations (inhibition or placeholder) and importantly, represent slight quantitative deviations from otherwise qualitatively matching alpha-modulations across both grating and no-grating conditions (in terms of topographies, frequencies, and timing as illustrated above). These results hence suggest that our findings hold irrespective of whether there was a grating present or not.

## Attention modulation index conceals alpha-subband dissociations

We also calculated the AMI in the canonical alpha-band, as used in previous studies alongside lateralization indices (Thut et al., 2006; Gould et al., 2011; Händel et al., 2011; Popov et al., 2019; Liu et al., 2022). The AMI corresponds to the contrast attend right versus attend left in MEG/EEG data. The resulting AMI topographies show the typical pattern of alpha-asymmetries for both MEG and EEG activity (Fig. 4A,B, rightmost map insets) but tend to miss the MEG-EEG differences. They conceal the spectral and topographical specificity of the present results, as the occipitoparietal, ipsicontralateral, high-low alpha-frequency distinction is not reflected in these indices across both recording modalities.

## MEG/EEG data: parietal alpha-amplitude decreases in a low alpha-frequency range (7–10 Hz) for anticipatory attention in time (alpha neutral cue < no-attention cue)

Figure 5 illustrates the spectral changes while participants prepare for an upcoming target but without spatial clues (neutral cue) as opposed to a cue that indicates that no target is upcoming and hence no-attention deployment is needed (no-attention cue). As above, this is shown separately for MEG and EEG signals and trials with and without gratings (Fig. 5A,B, top and bottom panels) and complemented by statistical tf and topo-plots (Fig. 6C, top panels for MEG, bottom panels for EEG) and source estimates (Fig. 7C). When focusing on the alpha-band, we exclusively found attention-related decreases in the late time window toward expected target onset (Fig. 5A,B). While also occurring in the lower alpha-frequency band as the late alpha-decrease during spatial orienting (~7–10 Hz), these changes showed a more widespread, bilateral distribution, including parietotemporal and occipital sensors (for MEG, see Figs. 5A, 6C, top panel; for EEG, see Figs. 5B, 6C, bottom panel). In further contrast with the late alpha-decrease during spatial orienting, the late alpha-decrease during temporal anticipation was associated with
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

**Figure 6.** Sensor-level statistical representation (in tf and topo-plots) of the effects of attentional deployment in MEG and EEG for A, attend left versus attend neutral, B, attend right versus attend neutral, and C, attend neutral versus no attention contrasts. The top panels illustrate tf statistics for trials with and without gratings separately. The bottom, panels depict map topographies in the alpha-band for early and late effects, labeled 1–3 to match the areas of tf changes with the corresponding topo-plots. Line graphs to the right of the tf plots illustrate effects summed over time (creating effect spectrograms). Note in the MEG spectrograms that alpha-increases (red line) occur in a high alpha-band (10–14 Hz), while alpha-decreases (blue line) occur in a low alpha-band (7–10 Hz). In contrast, EEG only detects the alpha-increases in the high alpha-band (at 10–14 Hz) (red line) at a significance level, indicating differential sensitivities/SNRs of EEG and MEG to capture attention-related alpha-sources. The panel below the tf plots illustrates differences in microsaccade incidence (green lines) per each of the relevant contrasts A–C, alongside alpha-increases (red lines) or decreases (blue lines), over time. For panels A, B, we calculated the differences between microsaccades that were directionally biased to cued position, while panel C illustrates differences between microsaccades independent of their direction. The horizontal green dots at the top of this panel illustrates time windows of significant
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Cortical source estimates of the high alpha-power enhancement and low alpha-band power suppression during spatial attentional deployment (contrasts A, attend left vs attend neutral and B, attend right vs attend neutral) and during general attention deployment (contrast C, attend neutral vs no attention). Numbers 1–3 refer to the areas of significant tf change in MEG and EEG sensors (Fig. 6) for which source estimates were run. Parcel-wise fractions of significant changes are shown on inflated cortical surfaces, whereby areas of reduced (low) alpha-band activity (7–10 Hz) are displayed in blue and areas of enhanced (high) alpha-band activity (10–14 Hz) are displayed in red. Source modeling has been carried out using MEG and EEG data combined. Therefore, the information contained in this figure integrates the data previously presented separately for MEG and EEG.</figcaption></figure>

differential microsaccade activity (Fig. 6C, bottom line graphs, see green line). Specifically, alongside the alpha-amplitude decrease toward expected target onset, temporal anticipation of a spatially unpredictable target (neutral cue) did stabilize saccadic activity (relative to the no-attention control; Fig. 6C, green lines show a negative trend in late time window). Please also note that for temporal anticipation, saccadic activity was observed to follow the bilateral alpha-decrease (Fig. 6C, compare green vs blue line). Source estimates localized this alpha-power decrease to parieto-occipital areas (Fig. 7C).

## MEG/EEG data: follow-up contrasts based on absence of contralateral occipital alpha-decreases during spatial attention

Based on the two main contrasts we run above, namely, (1) spatial cue versus neutral cue (Fig. 4) and (2) neutral cue versus

no-attention cue (Fig. 5), we conclude that (1) when carefully isolating the spatial component of the attention task by subtracting trials in which participants are required to deploy general anticipatory attention without a spatial component, there is little evidence for a contralateral occipital alpha-decrease specific to spatial attention with our task design (alpha-decrease only observed over parietal sensors in our case) and (2) an occipital alpha-decrease is mostly driven by general attentional (temporal) anticipation. A parietal (not occipital) source of the alpha-decrease with spatial attention is confirmed by source estimates (Fig. 7). The absence of an occipital alpha-decrease contralateral to the spatial attention focus may seem surprising, particularly in light of many studies on attention-related alpha-changes reporting alpha-decreases to include occipital sensors/areas (Sauseng et al., 2005; Thut et al., 2006; Rihs et al., 2007; Gould et al., 2011; Lobier et al., 2018; Sokoliuk et al., 2019; Gallotto et al.,

←

differences in microsaccadic activity per each of the contrasts A–C, along with a color bar in green shades representing the values of the Wilcoxon signed-rank test statistics (W) going from lighter green, less significant *p* value, to darker green, more significant *p* value. Note that for spatial attention (A, B), alpha-increases and directionally biased microsaccades coincide early in time (green vs red line), while the late alpha-decreases cannot be explained by microsaccadic activity. For temporal attention (C), there is an association between late alpha-changes and microsaccadic activity (green vs blue line). *S*<sub>TF+</sub>, fraction of sensors showing a significant power increase. *S*<sub>TF−</sub>, fraction of sensors showing a significant power decrease. 3D color bar, shade of yellows (colors in the central square) would indicate that for a given time-frequency point increases and decreases in power coexist across different sensors, not observed in our dataset.
2020). However, some of these previous studies compared alpha-
modulations with precue baseline activity and did not control for
general (temporal) attention mechanisms that are confounded
with spatial attention in this approach (Sauseng et al., 2005;
Thut et al., 2006; Rihs et al., 2007; Gould et al., 2011; Lobier et
al., 2018; but see Sokoliuk et al., 2019; Gallotto et al., 2020). For
comparison purposes with other approaches, we therefore run
two additional contrasts on our MEG/EEG dataset: (3) spatial
cue versus no-attention cue (Fig. 8) and (4) using the more

classic, precue baseline-corrected approach (Fig. 9). We argue
that contrast 3 (against the no-attention cue) and contrast 4
(against a baseline window) can both be viewed as contrasts of
spatial attention against a no-attention control, but with contrast
3 (no-attention cue) having the advantage of maintaining the
temporal structure of the trial relative to the spatial attention
condition and allowing to subtract the strong cue-related tran-
sient, because it is present in both spatial and no-attention con-
ditions (and which will be contaminating the baseline-corrected

<figure><img src="image_10.png" /><figcaption>**Figure 8.** Sensor-level modulation of oscillatory brain activity during spatial orienting (relative to the no-attention control) in *A*, MEG and *B*, EEG (MEG-EEG measured simultaneously). Changes are illustrated in full for two comparisons (attend left minus no attention and attend right minus no attention) both for trials with and without gratings (top vs bottom panels). Note the widespread alpha-power decrease at cue onset (time zero) stronger in sensors contralateral to the attended side. Ipsilateral alpha-power increase is also observed, but not as strongly as in the contrast spatial versus neutral attention. The scale has been kept the same as in *Figure 4* for cross-comparison.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Sensor-level modulation of oscillatory brain activity during spatial orienting with precue baseline correction for A, MEG and B, EEG (MEG-EEG measured simultaneously). Changes are illustrated in full for two comparisons (attend left minus precue baseline and attend right minus precue baseline) both for trials with and without gratings (top vs bottom panels). Note the widespread alpha-power decrease at cue onset (time zero) stronger in sensors contralateral to the attended side. Ipsilateral alpha-power increase is weakest with this contrast.</figcaption></figure>

alpha-modulations). Based on our results of the two main con-
trasts 1 and 2 detailed above, we expected that the follow-up con-
trasts (i.e., spatial cue relative to no-attention cue, spatial cue
relative to precue baseline) will reveal an occipital alpha-decrease
contralateral to the spatial cue. However, we would assign this
alpha-decrease to the general attention process that is con-
founded with spatial attention in these contrasts, not to spatial
attention per se. As expected, when running these follow-up con-
trasts, alpha-power decreases became more evident, particularly
over parieto-occipital sensors, and more strongly so on the side
contralateral to the attention focus (Figs. 8, 9), as compared

with the contrast of spatial versus neutral cue (compare Fig. 4).
Note that the ipsilateral alpha-increase is maintained across these
contrasts, albeit at a weaker level (Figs. 8, 9), and that a strong
transient (in the low alpha-/theta-band) is present in the baseline
corrected approach (Fig. 9), likely reflecting a cue-related evoked
potential. We take these results as a methodological pointer that
occipital alpha-decreases with spatial attention can be overesti-
mated (and alpha-increases underestimated) in the absence of
a general attention control, but not as categoric evidence against
occipital alpha-decreases with spatial attention (see below for a
more detailed discussion).
## Discussion

Our findings show that the typical endogenous alpha-modulation by spatial attention (characterized by contralateral alpha-suppression and ipsilateral alpha-enhancement) can be topographically, temporally, and spectrally dissociated. This corroborates evidence that perceptually relevant posterior alpha-rhythms in EEG/MEG cannot be considered a single oscillator but represent a mixture of functionally distinct sources (Capilla et al., 2014; Poch et al., 2017b; Sokoliuk et al., 2019; Peylo et al., 2021; Rodriguez-Larios et al., 2022; Jensen, 2024). In addition, by revealing two spatially separate, occipital, and parietal alpha-oscillators that show distinct changes relative to attention deployment and oculomotor activity, our findings provide new pointers on the functional roles of alpha-oscillations in the context of attention tasks.

### Early ipsilateral versus late contralateral alpha-modulations in occipital and parietal areas during spatial attention: evidence of reflexive versus endogenous attentional processes

We find two sources of alpha-changes during deployment of spatial attention (contrast of spatial vs neutral cue), one over occipital and the other over parietal sites, but with opposite directions (alpha-increases versus -decreases), timings (early versus late), and relation to microsaccades.

The early effects consist of an ipsilateral, occipital alpha-enhancement at a high alpha-frequency (10–14 Hz), coinciding in time with increased incidence of microsaccades to the cued direction. This mirrors the recently reported saccade-related alpha-changes by Liu et al. (2022, 2023, 2024), both in timing and topography. Liu and colleagues found that the incidence of saccades toward the cued location increased at ~200–600 ms following cue presentation (Liu et al., 2022) and that microsaccades transiently enhance occipital alpha-amplitude ipsilateral to saccade direction (Liu et al., 2023). See also Capilla et al. (2014) and Poch et al. (2017a) for a temporal dissociation of ipsilateral from contralateral alpha-modulations but without concurrent eye tracking. In terms of frequency, we found in a recent study on oculomotor-to-visual control (Veniero et al., 2021) that activation of the frontal eye fields by TMS phase locked both occipital EEG and visual perception to a high-frequency alpha-rhythm. Based on these collective findings, we conclude that the early, lateralized, high-frequency alpha-increase during spatial orienting is likely an expression of more reflexive attention processes related to oculomotor control over occipital activity/excitability. These may be expressed both in instances of overt and covert orienting, given the evidence for the implication of oculomotor structures in both saccades and covert attention (Moore and Armstrong, 2003; Schall, 2004; Bollimunta et al., 2018) and the finding that early, lateralized occipital alpha-increases do not only occur in the presence of saccades but also in spatial orienting trials where no oculomotor activity is detected by eye tracking (Liu et al., 2022). In terms of their functional role, the view that attention-related alpha-increases may reflect selective inhibition for distractor suppression has been questioned (Noonan et al., 2016; Poch et al., 2018; Foster and Awh, 2019; van Moorselaar et al., 2020; but see Bonnefond and Jensen, 2024). Our present finding of alpha-increases in the absence of distractors supports this notion (see also Rihs et al., 2007). Because the ipsilateral, occipital alpha-increase appears to be obligatory linked to overt (and possibly covert) oculomotor activity, it is conceivable that it reflects spatiotopic inhibition of visual representation opposite to the saccadic (or covert attention) landing endpoint, possibly as part of saccadic

suppression mechanisms (Hafed et al., 2015), that would best reflect a class of indirect suppression response by alpha-modulations (Jensen, 2024). This is speculative and to be probed in future work. Please note that our data cannot be explained by oculomotor-related alpha-lateralization resulting from retinal displacement of the small central fixation element, as we would expect ipsilateral alpha-suppression not enhancement in this scenario.

Our results also reveal lateralized alpha-decreases during visuospatial orienting, but over parietal sites, at later time points and at the low alpha-frequency of 7–10 Hz. Because these changes were not associated with any differential eye movement activity (in the relevant spatial vs neutral cue comparison and late time window) and are sustained up to or close to expected target onset, these changes likely reflect an endogenous, top-down controlled, preparatory attention processes. Lateralized alpha-suppression over posterior sites has regularly been associated with covert spatial attention (Sauseng et al., 2005; Thut et al., 2006; Gould et al., 2011; Capilla et al., 2014; Lobier et al., 2018; Sokoliuk et al., 2019; Gallotto et al., 2020; Peylo et al., 2024). However, in contrast to our results, many of these studies report alpha-decreases over occipital areas (but see Zhigalov and Jensen, 2020). While this discrepancy may in part be explained by methodological (control) choices (see Results, MEG/EEG data: follow-up contrasts based on absence of contralateral occipital alpha-decreases during spatial attention), differences in task design may also play a role. For instance, Gallotto et al. (2020) report occipital alpha-decreases despite using an attentional (neutral cue) control. Sokoliuk et al. (2019) also using attentional controls found two sources of alpha-decreases, one in occipital and the other in parietal areas, interpreted to reflect the attentional spotlight versus attentional effort, respectively. Note that the tasks of both these studies likely engaged occipital areas, as targets consisted of to-be-discriminated gratings. In contrast, parietal areas will have been task relevant for our gap detection/discrimination task with spatial features. Hence, occipital versus parietal alpha-suppressions may not only be influenced by the chosen control condition but also task demands. Irrespective of the correct interpretation, these findings concur that more than one alpha-oscillator is at play during covert attention.

### Alpha-decreases during temporal anticipation: possible saccadic influences

Our design also allowed tracking of alpha-activity during temporal anticipation of a target without prior spatial information (neutral vs no-attention cue). Consistent with the literature (Rohenkohl and Nobre, 2011; Van Ede et al., 2017; Gallotto et al., 2020), our results reveal alpha-decreases during temporal anticipation. These modulations mirrored alpha-decreases in spatial orienting in terms of direction (decrease), timing (late window), and frequency band of change (low alpha at 7–10 Hz), but with broader spread (bilateral parieto-occipital distribution). Accordingly, parietal alpha-decreases may reflect preparatory attentional processes common to spatial and temporal anticipation. However, unlike spatial attention, the late alpha-decrease with temporal anticipation occurred alongside oculomotor changes. Temporal anticipation stabilized saccadic activity at late anticipatory stages, compared with the no-attention control. This reduction in microsaccades nearer target onset may favor target detection (Martinez-Conde et al., 2013), assuming less oculomotor activity goes along with less
saccadic suppression and hence less inhibition of visual representations. Importantly, these findings suggest that oculomotor activity should be considered not only in the context of research on alpha-oscillations and spatial attention (Liu et al., 2022, 2023) but attention in general.

## Low- versus high-frequency alpha-distinction: physiological accounts

In addition to revealing the posterior, attention-related alpha-increases versus alpha-decreases to be spatially and temporally distinct, we observed these modulations to be spectrally dissociated within the canonical alpha-band (at high- vs low alpha). Already pioneering work has pointed out that conceptualizing posterior alpha-oscillations in one fixed, canonical frequency band needs to be reconsidered given their intra- and interindividual variability and that alpha-subbands respond differentially to distinct task demands (Klimesch, 1999). However, while long-established, EEG/MEG studies reporting on a spectral heterogeneity of posterior alpha-oscillations remain sparse, possibly due to source mixing/cancelation at the sensor level (Ahlfors et al., 2010b). Recent reports of alpha-heterogeneity comprise observations of dissociable, high- versus low-frequency alpha-sources in resting-state data (Keitel and Gross, 2016; Barzegaran et al., 2017), at baseline where they differ in dynamics over time-on-task (Benwell et al., 2019), or during working memory performance, where they show distinct functional characteristics (Rodriguez-Larios et al., 2022). While there is no consistent anatomical mapping of these high- versus low-frequency alpha-rhythms across the above studies, recent research points to a systematic frequency decrease in MEG/EEG along the posterior-anterior axis as cortical thickness and structural connectivity increase with hierarchy (Mahjoory et al., 2020; Capilla et al., 2022). This accords with the frequency gradient we observed, from high to low alpha over lower-level occipital to higher-order parietal sites. It has been proposed that such a frequency gradient may reflect different timescales for the functional needs of lower-versus higher-order areas in sampling of external versus integration of internal information (Murray et al., 2014). Whether this frequency gradient maps on similar functions in the context of attention orienting could be examined in future research.

## Methodological pointers for MEG/EEG studies on perceptually relevant alpha-oscillations

Our data point to the importance of methodological choices for studying alpha-oscillations in attention. First, in line with previous recommendations (Gallotto et al., 2020; Liu et al., 2022, 2023), our results indicate that concurrent eye tracking and specific experimental controls (such as for temporal and no-attention) should be considered for isolating distinct alpha-oscillators of attention. Second, our data reveal limited sensitivity for detecting different alpha-sources, e.g., the frequency-specific, occipital-parietal dissociation, when AMIs are used (for previous applications, see Thut et al., 2006; Händel et al., 2011; Gould et al., 2011; Popov et al., 2019; Liu et al., 2022; see also Gallotto et al., 2020). Third, while both EEG and MEG picked up the typical ipsi-/contralateral alpha-asymmetry, they showed different sensitivities to alpha-activity, likely reflecting some of their known sensitivity/SNR differences in capturing radial versus tangential sources (Goldenholz et al., 2009; Ahlfors et al., 2010a,b). Rather than crediting EEG over MEG (or vice versa), we take this as further evidence that neither of these techniques is redundant (Baillet, 2017), but would profit from their combination.

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