Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults

Abstract

Attentional selection in the context of goal-directed behavior involves top-down modulation to enhance the contrast between relevant and irrelevant stimuli via enhancement and suppression of sensory cortical activity. Acetylcholine (ACh) is believed to be involved mechanistically in such attention processes. The objective of the current study was to examine the effects of donepezil, a cholinesterase inhibitor that increases synaptic levels of ACh, on the relationship between performance and network dynamics during a visual working memory (WM) task involving relevant and irrelevant stimuli. Electroencephalogram (EEG) activity was recorded in 14 healthy young adults while they performed a selective face/scene working memory task. Each participant received either placebo or donepezil (5mg, orally) on two different visits in a double-blinded study. To investigate the effects of donepezil on brain network dynamics we utilized a novel EEG-based Brain Network Activation (BNA) analysis method that isolates location-time-frequency interrelations among event-related potential (ERP) peaks and extracts condition-specific networks. The activation level of the network modulated by donepezil, reflected in terms of the degree of its dynamical organization, was positively correlated with WM performance. Further analyses revealed that the frontal-posterior theta-alpha sub-network comprised the critical regions whose activation level correlated with beneficial effects on cognitive performance. These results indicate that condition-specific EEG network analysis could potentially serve to predict beneficial effects of therapeutic treatment in working memory.

Keywords: BNA; Cognitive enhancement; Donepezil; EEG; Working memory.

Copyright © 2013 Elsevier Inc. All rights reserved.

Figures

Fig. 1

Delay-recognition paradigm. The paradigm consisted of three tasks in which aspects of visual information were held constant while task-demands were manipulated. During each trial, participants observed sequences of two faces and two natural scenes (Cue stimuli) presented in a randomized order. Participants were given the following instructions: remember faces (ignore scenes, hereafter, Face Memory), remember scenes (ignore faces, hereafter, Scene Memory) or passively view all stimuli (hereafter, Passive View). The task was presented in 3 separate runs (20 trials each) with each of the three conditions in randomorder. Each of the Cue stimuli was presented for a period of 800 ms. After a delay of 9 s. participants were asked to indicate with a button press whether a face or a scene stimulus (i.e., the probe stimulus, presented for a duration of 1 s)matched the previously presented relevant cues, yielding performance measures of the WM task (reaction time and percent-correct). In the passive view response period, an arrow was presented, and participants were required to make a button press indicating the direction of the arrow. In the current study, only the electrophysiological responses to the irrelevant distractor face stimuli appearing in the Cue period of the Scene Memory condition were analyzed. The lines below the stimuli are used to highlight task-relevance in this illustration and were not present in the actual task. Reproduced from Fig. 1 in Gazzaley et al. (2008).

Fig. 2

Outline of steps in the functional network analysis. The BNA analysis involves two independent processes — group pattern analysis (blue arrows) and individual participant evaluation (red arrows). For the group analysis, the raw EEG of each participant undergoes three separate processing stages: (1) preprocessing (artifact removal, band-passing); (2) salient event extraction (discretization, normalization) and (3) network analysis (unitary event extraction, pair-pattern extraction) on all salient events gathered from all of the participants. The single participant level process involves three stages — the first two are identical to the first two stages of the group level process. In the third stage, the single participant activity is compared to the set of patterns collected during the group analysis stage. See text for further details. The multiple arrows stemming from the normalization stage represent the pooling of each participant's salient event to form the database on which the pattern analysis stage was performed. Reproduced from Fig. 1 in Shahaf et al. (2012).

Fig. 3

Shown is a bi-regional point plane, the X and Y axes of which represent latencies of individual salient events and inter-event intervals, respectively. A sliding adjustable rectangular window searches for clusters containing a minimum number of subjects. A cluster within the plane defines an event pair satisfying both absolute and relative time constraints. For further details see text. Reproduced from Fig. A1 in Shahaf et al. (2012).

Fig. 4

Grand-average ERP responses evoked by the irrelevant face stimuli in placebo and donepezil. The middle inset depicts the 64-channel cap (10–20 system) electrode placement (Biosemi Active-two system). For each of the overlaid ERP waveforms the arrows point to the corresponding position of the recording channel. PLB = placebo; DPZ = donepezil.

Fig. 5

BNA networks characterizing the processing of irrelevant faces in placebo (A) and donepezil (B). Upper images (white background) display characteristic network activity overlaid on scalp electrode montage. Discrete time-frame activations depicting the evolution in time (in ms) of each network activation are displayed for placebo and donepezil within the gray background columns beneath each characteristic network (C and D, respectively). A node is represented as a dot on the scalp, while a colored circle at a specific node denotes activity within a specific frequency band/s. The frequency band of the electrode activity is color coded (see top-left color legend). A colored circle at a node will receive its full hue when the time frame coincides with the group-mean latency of the activity peak and lighter shades when away from it, the spectrum width being dependent on the spread of individual latencies. Gray-shaded lines connect between pair of nodes.

Fig. 6

Correlations between BNA/sBNA scores and behavioral data. (A) Correlation between BNA scores and performance (under placebo). The graph shows that the donepezil network BNA scores in placebo significantly correlatewith performance on the WM task in placebo. (B) The theta–alpha sub-network (sBNA) extracted from the donepezil characteristic network. See colored legend for node activation in Fig. 5. (C) Correlation between sBNA scores and performance improvement. The graph depicts a significant correlation between the donepezil sBNA scores in placebo and the difference of % correct between placebo and donepezil (% correct donepezil-minus-% correct placebo).