Gauging Working Memory Capacity From Differential Resting Brain Oscillations in Older Individuals With A Wearable Device

Abstract

Working memory is a core cognitive function and its deficits is one of the most common cognitive impairments. Reduced working memory capacity manifests as reduced accuracy in memory recall and prolonged speed of memory retrieval in older adults. Currently, the relationship between healthy older individuals' age-related changes in resting brain oscillations and their working memory capacity is not clear. Eyes-closed resting electroencephalogram (rEEG) is gaining momentum as a potential neuromarker of mild cognitive impairments. Wearable and wireless EEG headset measuring key electrophysiological brain signals during rest and a working memory task was utilized. This research's central hypothesis is that rEEG (e.g., eyes closed for 90 s) frequency and network features are surrogate markers for working memory capacity in healthy older adults. Forty-three older adults' memory performance (accuracy and reaction times), brain oscillations during rest, and inter-channel magnitude-squared coherence during rest were analyzed. We report that individuals with a lower memory retrieval accuracy showed significantly increased alpha and beta oscillations over the right parietal site. Yet, faster working memory retrieval was significantly correlated with increased delta and theta band powers over the left parietal sites. In addition, significantly increased coherence between the left parietal site and the right frontal area is correlated with the faster speed in memory retrieval. The frontal and parietal dynamics of resting EEG is associated with the "accuracy and speed trade-off" during working memory in healthy older adults. Our results suggest that rEEG brain oscillations at local and distant neural circuits are surrogates of working memory retrieval's accuracy and processing speed. Our current findings further indicate that rEEG frequency and coherence features recorded by wearable headsets and a brief resting and task protocol are potential biomarkers for working memory capacity. Additionally, wearable headsets are useful for fast screening of cognitive impairment risk.

Keywords: EEG; coherence analysis; mild cognitive impairment; resting-state; working memory.

FIGURE 1

A schematic of the resting EEG (rEEG) and Bluegrass memory protocol in a right-hand dominant human participant. The participants’ eyes-opened resting-state followed by an eyes-closed resting-state followed by a memory task (a modified, delayed match-to-sample paradigm). The task includes encoding two target images in mind, then responding with the right index to indicate the memory target match (pressing “L” key on the keyboard) and left index finger for non-target in a 5-min task. During the second 5-min testing block, the participant was asked to indicate a memory target match by using the left index finger (pressing “A” key on the keyboard).

FIGURE 2

Bar plot showing group mean percent and standard deviation of the mean accuracy of retrieval of target match and non-target, non-match visual stimuli.

FIGURE 3

Bar plot showing the group mean and standard deviation of the reaction times to target match and non-target distractors during correct (green) and incorrect (red) trials. The normal older adults were significantly faster in identifying memory targets than non-targets (p < 0.05).

FIGURE 4

The distribution of (A) mean accuracy score and (B) mean reaction time (RT) across age for both male (orange) and female (red) groups.

FIGURE 5

(A) Montage of the 14-channels wireless EEG headset, (B) Bar plot showing the group average and standard deviation of the alpha band power over occipital sites (O1 and O2 electrodes) during eyes-open and eyes-closed.

FIGURE 6

The corresponding dorsal view topographic distribution of the alpha wave (8–13 Hz), during resting-state (A) eyes-closed and (B) eyes-open (Top panel). C. Average power spectral density at combined occipital sites (O1 & O2) during eyes-closed (A) and eyes-open (B); The black color curves show the average occipital power spectral density of all participants, and the blue curves show individuals’ power spectral density. (C) the point by point confidence level shown by p-value in the older adults.

FIGURE 7

Topographical plot of Pearson correlations between resting EEG (Eyes-closed) relative frequency band power and mean reaction times during working memory task. Significant negative correlation was observed at the P7 (left parieto-occipital site) at delta (δ) frequency band (p < 0.05) and approaching significant (p = 0.068) theta (θ) band power. In other words, increased δ and θ frequencies power observed in the left posterior brain region is correlated with faster reaction times. Reaction times did not correlate with higher frequency band power, i.e., α, β, nor γ.

FIGURE 8

Topographical plot of Pearson correlations between relative frequency band power of resting eyes-closed EEG and Accuracy (% correct) of the working memory task.

FIGURE 9

Brain connectivity graphs showing the significant correlations between paired EEG channels’ magnitude-squared coherence during the eye-closed resting-state and mean reaction time in (A) delta, (B) theta, (C) alpha, (D) beta, and (E) gamma bands. The color designates the level of correlation between paired coherence and the reaction times were significant (p < 0.05). Increased alpha coherence between the left frontal and right posterior sites correlates with increased reaction time.

FIGURE 10

Brain connectivity graphs showing the significant correlations between mean percent accuracy and paired rEEG channels’ coherence during the eye-closed in (A) delta, (B) theta, (C) alpha, (D) beta, and (E) gamma bands. The color designates the level of correlation between all paired coherence and the corresponding measure where the observed correlation was significant (p < 0.05). Significant increased of frontal delta and theta coherence correlates with better memory accuracy.