Two Spatially Distinct Posterior Alpha Sources Fulfill Different Functional Roles in Attention

Abstract

Directing attention helps to extract relevant information and suppress distracters. Alpha brain oscillations (8-12 Hz) are crucial for this process, with power decreases facilitating processing of important information and power increases inhibiting brain regions processing irrelevant information. Evidence for this phenomenon arises from visual attention studies (Worden et al., 2000); however, the effect also exists in other modalities, including the somatosensory system (Haegens et al., 2011) and intersensory attention tasks (Foxe and Snyder, 2011). We investigated in human participants (10 females, 10 males) the role of alpha oscillations in focused (0/100%) versus divided (40/60%) attention, both across modalities (visual/somatosensory; Experiment 1) and within the same modality (visual domain: across hemifields; Experiment 2) while recording EEG over 128 scalp electrodes. In Experiment 1, participants divided their attention between visual and somatosensory modality to determine the temporal/spatial frequency of a target stimulus (vibrotactile stimulus/Gabor grating). In Experiment 2, participants divided attention between two visual hemifields to identify the orientation of a Gabor grating. In both experiments, prestimulus alpha power in visual areas decreased linearly with increasing attention to visual stimuli. In contrast, prestimulus alpha power in parietal areas was lower when attention was divided between modalities/hemifields compared with focused attention. These results suggest there are two alpha sources, one of which reflects the "visual spotlight of attention" and the other reflects attentional effort. To our knowledge, this is the first study to show that attention recruits two spatially distinct alpha sources in occipital and parietal brain regions, acting simultaneously but serving different functions in attention.

Target Enhancement or Distractor Suppression? Functionally Distinct Alpha Oscillations form the Basis of Attention

“…Converging results from two recent EEG studies show that anatomically separate alpha oscillators in parietal and occipital cortex regions fulfill different roles in attention. While (dis)engagement of visual sensory attention modulated alpha power in visual cortex regions, alpha power in parietal cortex regions was modulated when participants attended versus ignored speech items (Wöstmann, Schmitt, & Obleser, 2020), and when participants divided attention between modalities or hemifields (Sokoliuk et al, 2019). The anatomical separation of different attention-modulated alpha oscillators supports attention models that posit the existence of a supra-modal hub region (in parietal cortex) that interacts with sensory areas during attentional selection (Banerjee, Snyder, Molholm, & Foxe, 2011).…”

Can the occipital alpha-phase speed up visual detection through a real-time EEG-based brain-computer interface (BCI)?

“…However, whether and how performing a perceptual task with the eyes closed jeopardized the outcome is unknown. Excluding the possibility that an eyes-closed condition could also involve sub-cortical generators of the α-activity (Lopes da Silva et al, 1973, but see Bollimunta et al, 2011Sokoliuk et al, 2019), we did not find any theoretical caveat against the eyes-closed strategy, which was instead technically convenient. Please note that others have successfully used eyes-closed preparations in the past (Lansing et al, 1959;Callaway & Yeager, 1960) and more recently (Lim et al, 2013;Hwang et al, 2015).…”

Event-related potentials reflect prediction errors and pop-out during comprehension of degraded speech

“…The dipole moments of both conditions were extracted in the post-stimulus time windows that showed significant clusters at the sensor level (time window 1: 137–207 ms; time window 2: 211–246 ms; time window 3: 250–371 ms; time window 4: 551–648 ms), and their absolute values were averaged over time points to obtain one average value per grid point (virtual electrode) and time window of interest. For clear visualization of the foci of our source estimates, we calculated t -tests at each virtual electrode and thresholded the subsequent t -images at P < 0.05 [see Supplementary data and Sokoliuk et al (2019) , for further validation of the method].…”