Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions

Abstract

Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.

Keywords: alpha; hemispheric asymmetry; human; neuroscience; oscillations; spatial attention; subcortical structures.

Figure 1.. Schematic of experimental design.

(A) Two face stimuli were presented simultaneously in the left and right hemifield. After baseline, a directional cue indicated the location of the target. After a variable delay interval (1000–2000ms) the eye-gaze of each stimulus (independent of the other) shifted randomly to the right or left. Subjects had to indicate the direction of the target eye movement after the delay interval. (B) Examples of visual stimuli for each of the four conditions. (C) Table with the labels of the four load/salience conditions. Adapted from Figure 1 of Gutteling et al., 2022.

Figure 2.. Alpha power decreases contralaterally and increases ipsilaterally with respect to the cued hemifield.

(A) Time-frequency representations of power demonstrate the difference between attended right versus left trials (t=0 indicate the target onset). (B) Topographical plot of the relative difference between attend right versus left trials. Regions of Interest sensors (ROIs) are marked with white circles. (C) The alpha band modulation MI(α) averaged over ROI sensors within the left and right hemispheres, respectively. The absolute MI(α) increased gradually during the delay interval until the onset of the target stimuli.

Figure 3.. Hemispheric lateralization modulation (HLM(α)) grand average and basal ganglia volumes across all participants.

(A) The HLM(α) distribution across participants. While there was considerable variation across participants, we observed no hemispheric bias in lateralized modulation values across participants (p-value = 0.39). (B) Histograms of the lateralization volumes of subcortical regions. We found that caudate nucleus was right lateralized (p-value = 0.021), whereas, putamen, nucleus accumbens, and thalamus volumes showed left lateralization (p-value = 0.004, p-value <0.001 and p-value <0.001, respectively). Th = Thalamus, CN = Caudate nucleus, Put = Putamen, GP = Globus pallidus, Hipp = Hippocampus, Amyg = Amygdala, Acc = Nucleus accumbens.

Figure 4.. Lateralization volume of thalamus, caudate nucleus, and globus pallidus in relation to hemispheric lateralization modulation of alpha (HLM(α)) in the task.

(A) The β coefficients for the best model (containing three regressors) associated with a generalized linear model (GLM) where lateralization volume (LV) values were defined as explanatory variables for HLM(α). The model significantly explained the HLM(α) (p-value = 0.0007). Error bars indicate standard errors of mean (SEM), n = 33. Asterisks denote statistical significance; *p-value <0.05. (B) Partial regression plot showing the association between LV_Th and HLM(α) while controlling for LV_GP and LV_CN (p-value = 0.01). (C) Partial regression plot showing the association between LV_GP and HLM(α) while controlling for LV_Th and LV_CN (p-value = 0.061). (D) Partial regression plot showing the association between LV_CN and HLM(α) while controlling for LV_Th and LV_GP (p-value = 0.008). Negative (or positive) LVs indices denote greater left (or right) volume for a given substructure; similarly negative HLM(α) values indicate stronger modulation of alpha power in the left compared with the right hemisphere, and vice versa. The dotted curves in B, C, and D indicate 95% confidence bounds for the regression line fitted on the plot in red.

Figure 4—figure supplement 1.. Lateralization volume of thalamus, caudate nucleus, and globus pallidus in relation to hemispheric lateralization modulation of rapid invisible frequency tagging (HLM(RIFT)) on the right and behavioural asymmetry on the left.

(A and E) The β coefficients for the best model (having three regressors) associated with a generalized linear model (GLM) where lateralization volume (LV) values were defined as explanatory variables for HLM(RIFT) (A) and behavioural asymmetry (E). Error bars indicate standard errors of mean (SEM), n = 33. (B and F) Partial regression plot showing the association between LV_Th and HLM(RIFT) (B, p-value = 0.59) and behavioural asymmetry (F, p-value = 0.38) while controlling for LV_GP and LV_CN. (C and G), Partial regression plot showing the association between LV_GP and HLM(RIFT) (C, p-value = 0.16) and behavioural asymmetry (G, p-value = 0.80) while controlling for LV_Th and LV_CN. (D and H) Partial regression plot showing the association between LV_CN and HLM(RIFT) (D, p-value = 0.53) and behavioural asymmetry (H, p-value = 0.74) while controlling for LV_Th and LV_GP. Negative (or positive) LVs indices denote greater left (or right) volume for a given substructure; similarly negative HLM(RIFT) values indicate stronger modulation of RIFT power in the left compared with the right hemisphere, and vice versa; positive behavioural asymmetry value shows higher accuracy when the target was on the right as compared with left, and vice versa for negative behavioural asymmetry values. The dotted curves in (B, C, D, F, G, and H) indicate 95% confidence bounds for the regression line fitted on the plot in red.

Figure 5.. β estimates of subcortical nuclei from a multivariate regression model predicting HLM(α) in the four perceptual load conditions.

Here, the HLM(α) values for the four load conditions are the dependent variables and the lateralization volume of subcortical structures are the explanatory variables. The model significantly explains HLM(α) variability (p-value = 0.001) in comparison with null model. Error bars indicate SEM, n = 33. Asterisks denote statistical significance; *p-value <0.05.

Author response image 1.

Author response image 2.