Edited by: Muthuraman Muthuraman, Johannes-Gutenberg-University Hospital, Germany
Reviewed by: Gabriel Gonzalez-Escamilla, Universitätsmedizin der Johannes Gutenberg, Universität Mainz, Germany; Rathinaswamy Bhavanandhan Govindan, Children's National Health System, United States
*Correspondence: Thalía Fernández
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During healthy aging, inhibitory processing is affected at the sensorial, perceptual, and cognitive levels. The assessment of event-related potentials (ERPs) during the Stroop task has been used to study age-related decline in the efficiency of inhibitory processes. Studies using ERPs have found that the P300 amplitude increases and the N500 amplitude is attenuated in healthy elderly adults compared to those in young adults. On the other hand, it has been reported that theta excess in resting EEG with eyes closed is a good predictor of cognitive decline during aging 7 years later, while a normal EEG increases the probability of not developing cognitive decline. The behavioral and ERP responses during a Counting-Stroop task were compared between 22 healthy elderly subjects with normal EEG (Normal-EEG group) and 22 healthy elderly subjects with an excess of EEG theta activity (Theta-EEG group). Behaviorally, the Normal-EEG group showed a higher behavioral interference effect than the Theta-EEG group. ERP patterns were different between the groups, and two facts are highlighted: (a) the P300 amplitude was higher in the Theta-EEG group, with both groups showing a P300 effect in almost all electrodes, and (b) the Theta-EEG group did not show an N500 effect. These results suggest that the diminishment in inhibitory control observed in the Theta-EEG group may be compensated by different processes in earlier stages, which would allow them to perform the task with similar efficiency to that of participants with a normal EEG. This study is the first to show that healthy elderly subjects with an excess of theta EEG activity not only are at risk of developing cognitive decline but already have a cognitive impairment.
Due to the increase in life expectancy, the diseases associated with old age, such as dementia, are becoming more frequent (Harada et al.,
According to Giaquinto and Nolfe (
Several follow-up studies in the elderly (Soininen et al.,
Taking into account this prediction, in a previous study in which we compared a group of healthy older adults with theta excess in their resting EEG to another group that had a normal EEG, we found differences in brain structure in their magnetic resonance images (MRI); we interpreted some of these differences as compensatory changes that could explain the similar cognitive performance of these two groups (Castro-Chavira et al.,
In general, healthy aging is related to a decline in specific cognitive functions, e.g., executive functions (EFs) (Buckner,
To study inhibitory control, Stroop tasks (Stroop,
The aim of this study was to explore whether healthy older adults with an electroencephalographic risk of cognitive impairment (i.e., resting EEG theta-activity excess), show lower inhibitory control than healthy older adults of the same age whose EEG was normal. This question was assessed by ERPs recorded during a Counting-Stroop task. Considering that aging is associated with cognitive deterioration, we expected to find more pronounced, age-related, cognitive deficits in the group at risk of cognitive decline, i.e., lower N500 amplitude, a lower N500 effect, and a higher Stroop effect.
Forty-four right-handed healthy older adults aged over 60 years (26 females) with a score of 1 or 2 in the Global Deterioration Scale (GDS; Reisberg et al.,
Characteristics of the samples.
Age (years) | Normal | 65.54 ± 5.21 | −1.22 | 0.22 |
Theta | 67.59 ± 5.81 | |||
IQ (WAIS) | Normal | 103.78 ± 7.95 | 0.84 | 0.40 |
Theta | 101.74 ± 8.50 | |||
Scholar Education (years) | Normal | 15.81 ± 5.13 | 0.36 | 0.71 |
Theta | 15.31 ± 3.84 | |||
Q-LES-Q score | Normal | 79.26 ± 8.04 | 0.35 | 0.72 |
Theta | 78.21 ± 10.98 | |||
AUDIT score | Normal | 2.00 ± 1.95 | −0.16 | 0.87 |
Theta | 2.09 ± 1.77 |
Furthermore, subjects did not present signs of diabetes, anemia, hypercholesterolemia, or thyroid disease in clinical blood analysis, nor did they have uncontrolled hypertensive disease. The participants and two witnesses signed informed consent.
This project was approved by the Bioethics Committee of the Neurobiology Institute of the National Autonomous University of Mexico (UNAM).
These 44 subjects were classified into two groups according to the characteristics of their EEG: the Theta-EEG group, in which subjects presented an excess of theta activity for their age in at least one electrode, and the Normal-EEG group, in which subjects presented normal EEGs, from both the qualitative and quantitative point of view.
To identify which subjects belonged to the Normal-EEG and the Theta-EEG groups, an EEG was recorded from each participant. The digital EEG was recorded at rest with eyes closed using a Medicid™ IV system (
Twenty-four artifact-free segments of 2.56 s each were selected, and the QEEG analysis was performed offline using the Fast Fourier Transform to obtain the power spectrum every 0.39 Hz; also the geometric power correction (Hernández et al.,
The Normal-EEG group, which included 22 participants with normal EEGs (65.54 ± 5.21 y.o., 13 females), and the Theta-EEG group, with 22 participants with abnormally high
Series of one, two, three, or four words that denote numbers (“one,” “two,” “three,” “four”) were presented in the center of a 17-inch computer screen. Time presentation of the stimuli was 500 ms, and the inter-stimulus interval was 1,500 ms. An incongruent condition consisted in a trial where the number of presented words did not correspond with the meaning of the word; the congruent condition consisted in a trial in which the number of presented words and the meaning of the word that was presented matched. A total of 120 incongruent and 120 congruent stimuli were randomly presented.
Subjects were asked to indicate the number of times that the word appeared in each trial, using a response pad that they held in their hands. One-half of the participants used their left thumbs to answer “one” or “two” and their right thumbs to indicate “three” or “four”; the other half of the participants used their opposite hand to counterbalance the motor responses. The participants were asked to answer as quickly and accurately as possible. We ensured that the participants understood the instructions by presenting a brief practice task before the experimental session.
The EEGs were recorded with 32 Ag/AgCl electrodes mounted on an elastic cap (Electrocap), using NeuroScan SynAmps amplifiers (Compumedics NeuroScan) and the Scan 4.5 software (Compumedics NeuroScan). Electrodes were referenced to the right earlobe (A2), and the electrical signal was collected from the left earlobe (A1) as an independent channel. Recordings were re-referenced off-line in two ways: (a) to the averaged earlobes, as usually was performed in previous studies, and (b) to the average reference (see
ERPs were obtained for each subject and experimental condition (i.e., congruent and incongruent). Epochs of 1,500 ms were obtained for each trial that consisted of 200-ms pre-stimulus and 1,300-ms post-stimulus intervals. An eye movement correction algorithm (Gratton et al.,
Three mixed, 2-way ANOVAs were separately performed for the mean reaction time, reaction time variability, and percentage of correct answers. The between-subject factor was group (Normal-EEG and Theta-EEG), and the within-subject factor was condition (congruent and incongruent). The percentages of correct responses were transformed using the function {ARCSINE [Square Root (percentage/100)]} to ensure normal distribution of the data (McDonald,
Visual inspection of the grand averages of the ERPs (Figure
Grand average ERPs per experimental condition by group. Colored shadow boxes indicate significant differences between the conditions in the same group. Positive amplitude is plotted upward. Red lines represent ERPs of the Normal-EEG group, and blue lines represent the Theta-EEG group. Solid and dotted lines represent congruent and incongruent conditions, respectively.
Difference waves (i.e., incongruent minus congruent condition) on the midline electrodes. Colored boxes indicate significant differences between the groups.
Four mixed, 4-way ANOVAs were independently performed on the mean amplitude data, one per each ERP component. Group [Normal-EEG and Theta-EEG] was included as the between-subjects factor. The within-subjects factors were: (a) Condition [Congruent and Incongruent], (b) Coronal [Frontal (F7, F3, Fz, F4, and F8), Fronto-central (FT7, FC3, FCz, FC4, and FT8), Central (T3, C3, Cz, C4, and T4), Central-parietal (TP7, CP3, CPz, CP4, and TP8), and Parietal (T5, P3, Pz, P4 and T6)]; and (c) Sagittal [Left (F7, FT7, T3, TP7, and T5), Medial-left (F3, FC3, C3, CP3, and P3), Medial (Fz, FCz, Cz, CPz, and Pz), Medial-right (F4, FC4, C4, CP4, and P4), and Right (F8, FT8, T4, TP8, and T6)]. The Huynh-Feldt correction was applied to the degrees of freedom of those analyses with more than one degree of freedom in the numerator. Degrees of freedom are reported uncorrected, but the epsilon value was included. Only differences that involved the main effect of Group and any interaction of Group by Condition are reported. Tukey's HSD
The same analysis was also performed when the average reference was used, and it is described in the Supplementary Material.
The mean reaction times (RTs), RTs variability, and percentage of correct responses for the congruent and incongruent conditions of the two groups are shown in Table
Mean and standard deviation (SD) of the reaction times and percentage of correct responses.
Normal EEG | 22 | Incongruent | 721.80 ± 53.83 | 11462.90 ± 2410.50 | 79.05 ± 12.05 |
Congruent | 652.88 ± 52.61 | 10569.93 ± 2853.51 | 85.94 ± 8.69 | ||
Theta EEG | 22 | Incongruent | 702.12 ± 72.60 | 11501.90 ± 2941.84 | 77.72 ± 13.07 |
Congruent | 649.62 ± 65.25 | 10901.97 ± 4454.62 | 86.17 ± 9.37 |
Although there was no significant main effect of Group (
When common average re-referencing was used, the most critical results remained unchanged: no differences were found in N200, P300 effect was robust in both groups, and in the Normal-EEG group a great N500 effect was observed while in the Theta-EEG group this effect was absent. Therefore, we think, in general terms, that our results are almost not affected by the volume conduction. Taking into account that the global average from a limited number of channels might not be a good estimate of volume conduction, we only present the results when A1A2 reference was used.
No significant main effect of Group was observed for this time window [
The analysis of the amplitude in this time window showed a significant main effect of Group [
Amplitude maps and wave difference map in both groups at the three windows analyzed. The Normal-EEG group is on the left, and the Theta-EEG group is on the right. Note in the difference maps (Incon-Con) that
In this time window, the amplitude of the negative wave showed a significant main effect of Group [
Group and Condition did not interact significantly with the Coronal distribution factor [
Table
N500 (500–700 ms)
Frontal | Left | F7 | −0.48 | −0.42 |
Left-medial | F3 | −1.26 |
0.20 | |
Medial | Fz | −1.86 |
0.03 | |
Right medial | F4 | −1.62 |
0.28 | |
Right | F8 | −0.99 | 0.57 | |
Fronto–central | Left | FT7 | −0.94 | −0.35 |
Left-medial | FC3 | −1.85 |
−0.26 | |
Medial | FCz | −2.59 |
−0.30 | |
Right-medial | FC4 | −1.89 |
−0.10 | |
Right | FT8 | −1.03 |
−0.65 | |
Central | Left | T3 | −1.08 |
−0.32 |
Left-medial | C3 | −2.34 |
−0.45 | |
Medial | Cz | −3.03 |
−0.50 | |
Right-medial | C4 | −2.97 |
−0.29 | |
Right | T4 | −1.15 |
−0.64 | |
Centro-parietal | Left | TP7 | −1.40 |
−0.09 |
Left-medial | CP3 | −2.40 |
−0.42 | |
Medial | CPz | −2.74 |
−0.54 | |
Right-medial | CP4 | −2.35 |
−0.39 | |
Right | TP8 | −1.23 |
−0.41 | |
Parietal | Left | T5 | −1.57 |
−0.25 |
Left-medial | P3 | −2.07 |
−0.21 | |
Medial | Pz | −2.54 |
−0.28 | |
Right-medial | P4 | −2.01 |
−0.21 | |
Right | T6 | −1.43 |
−0.44 |
The aim of this study was to explore the inhibitory control process, using ERPs, of healthy older adults with an electroencephalographic risk of cognitive impairment (i.e., an excess of theta activity in their EEG), considering a control group of older adults with normal EEG, during the performance of a Counting-Stroop task. We expected to find that adults with an excess of theta activity would show a greater detriment of inhibitory control with respect to healthy older adults of the same age with a normal EEG, displaying a greater Stroop effect (in behavioral measures) and smaller N500 amplitude and N500 effect (i.e., larger differences between ERP amplitude of the incongruent vs. congruent condition, with a greater amplitude for the incongruent condition).
Considering that all our subjects were physically healthy without signs of cognitive impairment and that the only difference between the groups was their EEG activity (normal or theta excess), one would expect that (a) both groups would show Stroop effects, and (b) the cognitive processing would be equal in the two groups or, if different, that there were advantages for the Normal-EEG group; that is, an increased Stroop effect would occur in the Theta-EEG group.
Our results confirm the first assumption: both groups displayed a Stroop effect; however, in contrast to the hypothesis that individuals at risk for cognitive decline would present higher Stroop effects (greater RTs in the incongruent than in the congruent trials as an effect of the interference), our results showed a significantly greater Stroop effect in the Normal-EEG group than in the Theta-EEG group. This result is not in accordance with the results obtained by other authors (Spieler et al.,
Surprisingly, the Theta-EEG group had smaller mean RTs than the Normal-EEG group, which was more evident in the incongruent condition, although a significant level was not reached. The normal group appeared to perform worse on the task; however, this performance will be discussed further in the subsequent sections.
It is also important to consider that no differences between the groups were found in terms of the number of correct answers. This fact and the high percentages of correct answers suggest that both groups performed well on the task.
Another fact that should be considered is the greater standard deviation in the RTs observed in the Theta-EEG group than in the Normal-EEG group, which indicates less response-consistency between the subjects of the Theta-EEG group, which could be a sign of failures in the activation of automatic mechanisms underlying the conflict in several subjects at risk for cognitive decline. However, no differences in terms of RTs variability were observed between groups. Previous studies regarding intra-individual cognitive variability have pointed out that, as age increases so does the RTs variability (Bielak et al.,
An interference effect was manifested on the ERPs as an ERP effect (i.e., the magnitude resulting from subtracting the amplitude of the incongruent stimulus minus the amplitude of the congruent stimulus) in several time windows that are linked to different processing stages in the Stroop effect, which is specifically discussed below for each time window.
The amplitude differences between conditions that have been reported in this window have been related to a cerebral response to a non-perceptual conflict that has been described in flanker tasks (van Veen and Carter,
Although some authors exclude differences in the P300 component as a brain response related to the conflict (Larson et al.,
In terms of the topographical distribution of the P300 component (Figure
In contrast to what was observed in the previously discussed time window, the N500 deflections had more amplitude in the Normal-EEG group than in the Theta-EEG group for the two conditions. The effect observed between 500 and 700 ms corresponds with the beginning of mechanisms involved in inhibitory processes to solve the conflict when this exists (West and Alain,
Another important finding observed in the 500–700 ms time window was the presence of an N500 effect in almost all regions in the Normal-EEG group, as opposed to the lack of an N500 effect in the Theta-EEG group. This N500 effect has been associated with the inhibition of the competence between semantic and counting information (Rebai et al.,
In the early stages of processing in the Counting-Stroop task, there did not appear to be significant differences between the groups, as both groups seemed to recruit the same amount of resources for attention processing. Differences started to occur with more complex and demanding processes, such as the categorization process. Older adults with a risk of cognitive decline appeared to require more resources to categorize words as congruent or incongruent, and they did not perform the categorization process as well as older adults without a risk.
This finding was supported by the most dramatic and unexpected result of the present study: the lack of an N500 effect in the Theta-EEG group, while the normal EEG group exhibited a robust N500 effect distributed throughout the entire scalp. The N500 in the Stroop tasks has been related to response interference processing (West and Alain,
Another plausible mechanism that could explain the lack of conflict between reading and counting is that in the Theta-EEG group, the reduction in processing speed, a characteristic of aging (Salthouse,
All of these findings indicate that people with an excess of theta activity show a peculiar ERP pattern that can be observed across multiple levels of the neurocognitive system before any clinical sign of cognitive impairment is observed, supporting the idea that aging is not a homogeneous process; therefore, there are probably several subgroups within the “healthy” elderly population. This study is the first to clearly exhibit a preclinical cognitive deficit in healthy elderly subjects with an excess of theta in their EEG.
TF made substantial contributions to the conception of the work, EEG analysis, and interpretation of the data; she drafted parts of the manuscript and revised it critically; SS-M made substantial contributions to design of the work, ERPs acquisition and analysis, and interpretation of data; he drafts the first version of the manuscript and revised subsequent versions; GA-C made substantial contributions to the ERPs acquisition and analysis, and interpretation of data; she revised the manuscript critically; JS-P made substantial contributions to the statistical analysis and interpretation of data; he drafted parts of the manuscript and revised it critically; SG-S made substantial contributions to the interpretation of data; she revised the manuscript critically; JS-L made substantial contributions to design of the work and to interpretation of data; he made the figures and revised the manuscript critically; GO-O made substantial contributions to the interpretation of data and revised the manuscript critically; SS-M, GA-C, JS-P, SG-S, JS-L, GO-O, and TF give their final approval of the version to be published and agree to be responsible for all aspects of the work so that questions about the accuracy or integrity of any part of the work are adequately investigated and solved.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer GG and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.
The authors acknowledge Lourdes Lara, Leonor Casanova, and Teresa Alvarez for administrative support; Susana Angelica Castro Chavira, Héctor Belmont, Erick Pasaye, Ramón Martínez, and Sandra Hernández for technical assistance; Marbella Espino, MD for performing the neurological and psychiatric assessments; and Mauricio González-López for proofreading.
The Supplementary Material for this article can be found online at: