Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Association between executive functions and COMT Val108/158Met polymorphism among healthy younger and older adults: A preliminary study

  • Zoltan Apa ,

    Contributed equally to this work with: Zoltan Apa, Jessica Gilsoul

    Roles Formal analysis, Writing – original draft, Writing – review & editing

    Affiliations GIGA-CRC In Vivo Imaging, Université de Liège, Liège, Belgique, Psychology and Neuroscience of Cognition Research Unit, Université de Liège, Liège, Belgique

    ⨯
  • Jessica Gilsoul ,

    Contributed equally to this work with: Zoltan Apa, Jessica Gilsoul

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

    Affiliations GIGA-CRC In Vivo Imaging, Université de Liège, Liège, Belgique, Psychology and Neuroscience of Cognition Research Unit, Université de Liège, Liège, Belgique

    ⨯
  • Vinciane Dideberg,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Department of Medical Genetics, University Hospital, Liège, Belgium

    ⨯
  • Fabienne Collette

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing

    f.collette@uliege.be

    Affiliations GIGA-CRC In Vivo Imaging, Université de Liège, Liège, Belgique, Psychology and Neuroscience of Cognition Research Unit, Université de Liège, Liège, Belgique

    ⨯

Abstract

Background and objectives

Genetic variability in the dopaminergic system could contribute to age-related impairments in executive control. In this study, we examined whether genetic polymorphism for catechol-O-methyltransferase (COMT Val158Met) is related to performance on updating, shifting and inhibition tasks.

Methods

We administered a battery of executive tasks assessing updating, shifting and inhibition functions to 45 older and 55 younger healthy participants, and created composite z-scores associated to each function. Six groups were created based on genetic alleles (Val/Val, Val/Met, Met/Met) derived from the COMT gene and age (younger, older). Age and genotype effects were assessed with t-test and ANOVA (p<0.05).

Results

A lower performance was observed in the older group for the three executive processes, and more particularly for inhibition. Moreover, older participants homozygous for the Val allele have a lower performance on the inhibition composite in comparison to younger Val/Val.

Conclusions

These results confirm presence of executive performance decrease in healthy aging. With regard to genetic effect, older participants seem particularly disadvantaged when they have a lower baseline dopamine level (i.e., Val/Val homozygous) that is magnified by aging, and when the executive measure emphasize the need of stable representations (as in inhibition task requiring to maintain active the instruction to not perform an automated process).

Citation: Apa Z, Gilsoul J, Dideberg V, Collette F (2024) Association between executive functions and COMT Val108/158Met polymorphism among healthy younger and older adults: A preliminary study. PLoS ONE 19(5): e0303343. https://doi.org/10.1371/journal.pone.0303343

Editor: Kenji Tanigaki, Shiga Medical Center, JAPAN

Received: August 7, 2023; Accepted: April 21, 2024; Published: May 13, 2024

Copyright: © 2024 Apa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The dataset is available from the University of Liège Institutional Data Access (https://doi.org/10.58119/ULG/DGKHVL).

Funding: This work was supported by the University of Liège and grant EOS 30446199 from the Belgian National Fund for Scientific Research F.R.S-FNRS. JG and ZA are research fellows (FNRS and University of Liège, respectively) and FC is Research Director at the F.R.S-FNRS.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Aging is associated with subtle decline in cognitive functioning even in the absence of brain pathology hallmark. However, the different functions do not undergo a general decline and changes are primarily observed for high-level processes, such as in executive tasks [1,2]. Age related executive difficulties were first observed in complex and multicompound tasks involving planning, conceptualization and problem solving (for a review, see [3]). For example, lower performance by comparison to younger people is frequently reported for the three distinct functions of inhibition, shifting and updating of information identified by Miyake et al. [4] as separate cognitive processes. However, shifting and inhibition tasks are not found systematically impaired during aging [3]. For the shifting process (that also refers to flexibility abilities), the capacity to maintain and manipulate two mental sets simultaneously is more sensitive to the age-effect than the capacity to alternate between the two sets [5,6]. In the inhibition domain, there is evidence in favor of the dissociation between automatic and controlled inhibitory processes, with only the latter being sensitive to age [7–10].

Several authors proposed that changes in prefrontal cortex (PFC) structure and functions explain a large part of changes in executive functioning, [11,12]. For example, specific and global executive scores were notably found related to decreased grey matter volume in anterior brain areas [13,14], and lower executive performance was associated to decreased brain activity in prefrontal areas [15]. Presence of increased prefrontal brain activity and recruitment of bilateral regions was associated with attempts for implementation of compensatory processes (for reviews, [11,16]).

Cognitive performance during aging is also affected by diverse neurobiological factors such as neurotransmitters pathways degeneration and genetic predispositions [17,18]. Specifically, the decrease of dopamine concentration in the prefrontal cortex, [19,20] may be associated with changes in executive functioning. Catechol-O-methyltransferase (COMT) is the major enzyme involved in the degradation of released dopamine and accounts for more than 60% of the metabolic degradation of dopamine in the frontal cortex [21]. A growing body of literature reports the importance of Dopamine (DA) in human cognition [22,23], and more particularly executive functions [24]. Several authors [25–28] postulate a nonlinear relationship, namely an inverted U-shaped relationship, linking cognitive performance—notably performance to working memory (WM) tasks—with levels of cerebral dopamine. Accordingly, the hypothesis suggests that when the dopamine level is too high or too low, cognitive efficiency will decrease. The metabolic activity of the COMT enzyme is affected by polymorphism on chromosome 22q11 [29–31], resulting in Val/Val, Val/Met and Met/Met genotypes. The COMT enzyme containing Met158 is significantly less active than the Val158 enzyme, potentially resulting in a greater synaptic dopamine level [32,33]. As a consequence, individuals homozygous for Val allele (Val/Val) are those with the lowest level of cerebral synaptic dopamine, whereas homozygous for Met allele (Met/Met) have the highest level. Heterozygote (Val/Met) carriers present with an intermediate level of COMT activity [34].

Studies that investigated the effect of COMT polymorphism on executive functioning in younger adults reported discrepant results. Several studies observed, as expected, a better performance in the Met allele carriers population [35–38] while others reported a lack of association between COMT polymorphism and executive functioning, [39–43] or even a deleterious effect of the Met allele on executive and working memory performance [44–47].

Due to the decrease of dopamine availability in prefrontal cortex in aging [48], a positive effect of the homozygous Met/Met polymorphism (with the lower enzymatic activity and thus increased level of DA availability) may be expected. However, studies using the COMT Val158Met polymorphism to understand the dopaminergic modulation of age-related effects on working memory and executive functioning performance also led to divergent results. Some authors proposed that aging could magnify the Met allele advantage observed in younger adults [49–51]. On the other hand, other studies did not observe such a positive effect of carrying the Met allele in elderly [52–55] or even showed an advantage of carrying the Val allele [56–58]. However, the tasks administered vary in the involvement of executive processes, and one possible explanation to discrepancies observed is that not all executive processes are sensitive to COMT genetic polymorphism.

Consequently, the aim of this study was to investigate the potential influence of COMT genetic polymorphism on executive functioning through a battery of tasks, and to determine if allelic group mitigate the effect of aging in a general or specific way. To obtain measures of executive functioning relatively independent of non-executive processes, several tasks were administered for each function and composite scores specific to updating, shifting and inhibition functions were created. We first tested the effect of age on executive functioning by comparing the performance of younger and older participants on each composite score. Second, we tested the COMT allelic variance effects on the executive processes for which an age-effect was found. This was done on the six groups of subjects by combining age (younger, older) and genotype (Met/Met, Val/Met, Val/Val). At the age-group effect, we expected a higher overall performance among younger participants when compared to the older participants. For genetic effects, as Met phenotype carriers have a higher level of dopamine availability in prefrontal cortex [34], we expected they express a higher performance than the two other groups, and that age effect is dampened in that group.

2. Methods

2.1. Participants

Fifty-five younger (F/M 27/28; Mean age = 23.47 years old; SD = 3.12; range = 18–30) and 45 older (F/M 26/19; Mean age = 67.37 years old; SD = 4.9; range = 60–75) subjects were included in the present study. Participants were recruited from a local database at the GIGA-CRC. They were initially enrolled through advertisement in senior centers, universities, and drugstores. Possible subjects offered inclusion criteria presented at the laboratory first for DNA sampling using Isohelix® buccal swabs. For this study, all participants were Caucasian and native French-speaking, had normal or corrected-to-normal vision and hearing. They were screened for the following exclusion criteria: (1) neurological, psychological, or psychiatric disorders; (2) abusive consumption of alcohol or drugs; (3) diagnosis of neurodegenerative disease or dementia. The cognitive status of older participants was assessed using the Mattis Dementia Rating Scale (DRS) [59]. All older participants scored above 129, which constituted the cut-off threshold for risk of dementia [60], and scores ranged between 134–144. Sociocultural level was assessed by asking the number of years completed at school. Participants were finally ascribed to one of the six possible groups (2 age groups [younger vs older] * 3 genotypes [Val/Val, Val/Met, Met/Met] according to their COMT Val158Met genotype and were invited for the behavioral assessment. The selection of participants from an existing database made it possible to form groups of roughly equivalent size for each genotype (see [61] for a similar procedure).

The study was approved by the Ethic Committee of the University Hospital in Liège (approval Belgian number B707201110947). Each participant signed informed consent form before starting the study. The study was conducted in accordance with the ethical standards described in the Declaration of Helsinki (1964). The testing session lasted between 1h30 and 2h, with short breaks provided between cognitive tasks, when required by the participant.

2.2. Genotyping

Genetic DNA was collected using SK-2 Isohelix® buccal swabs (Ishohelix, Cell Projects Ltd, Kent, UK). The DDK-50 DNA isolation kits were used to stabilize (during the week of the sample collection) and extract DNA swabs. The concentration of the extracted DNA was quantified using a NanoDrop 1000® or a NanoDrop Lite® spectrophotometers (Thermo Fisher Scientific, Delaware, USA). TaqMan® polymerase-chain reactions (PCR) were performed in a 96 well reaction plate containing 25 μl reaction mixture including 2 μl of DNA sample, 12.5 μl of TaqMan® genotyping master mix (Life technologies; Thermo Fisher Scientific, Darmstadt, Germany), 1.25 μl of drug metabolism genotyping assay (rs4680; Applied Biosystems; thermos Fisher Scientific, Delaware, USA) and 9.25 μl of DNA free water. Allelic discrimination was performed using fluorescence endpoint genotyping method by means of the LightCycler® 480 Instrument (Software version 1.5, Roche Diagnostics Belgium, Vilvoorde, BE). The amplification protocol encompassed a denaturation cycle of 10 minutes at 95°C, followed by 40 cycles of amplification including 15 seconds at 92°C and 1 minute at 60°C. Genotyping analyses were performed at the Human Molecular Genetics department of the University Hospital (CHU) of Liège.

2.3. Global cognition assessment

A short battery of tasks was administered to assess that the three genotype groups did not differ on global measures of cognition, and to control for such differences in our main analyses if needed. The Raven matrices [62] and the Mill-Hill vocabulary task [63] were administered to evaluate fluid intelligence and crystallized intelligence, respectively. We also measured processing speed with the XO comparison task [64] and, for older participants, global cognitive functioning was assessed with the Mattis dementia rating scale [59], a screening tool for age-related cognitive impairment. The Mattis scale was administered before the executive tasks and the tasks assessing intelligence and processing speed were administered at the end of the testing session.

2.4. Executive functions assessment

A battery of six neuropsychological tests was administrated to assess the executive processes of updating, shifting and inhibition. Of the obtained scores several indices were derived to calculate composite scores that reflect each process solely and independently of the idiosyncratic characteristics of each task. Task administration was in the same order as presented below, and similar for all participants.

2.4.1 Stroop test.

This version of the standard Stroop test [65] used the word subtest (reading color words printed in black and white), the color subtest (naming the color of crosses [XXX], and the color-word interference subtest in which participants had to name the color of the printed color and not read the word. In each subtest, participants were required to name colors aloud as quickly as possible, and errors had to be orally corrected. Time allotted for each subtask was 45 seconds. In each condition, the number of correct responses was recorded. The interference index was calculated for each participant as follow: [(color-naming condition score—color-naming word-color score) / color-naming condition score], with a low score indicating good inhibition abilities.

2.4.2 2-Back test [66].

In this test, widely used to assess updating capacity in working memory, a list of 30 letters was presented orally. For each letter, participants had to decide whether it was identical to the one presented two steps back in the sequence. They gave their answers orally and the number of correct responses constituted the updating measure score.

2.4.3 Plus Minus test [67].

This task was administered to evaluate shifting abilities. This test comprises three distinctive subtests. In the first subtest (A), subjects were instructed to add three to each number, whereas during the second, they had to subtract three from each number. In the third subtest, participants were instructed to alternate between adding and subtracting three to each presented number. Participants were instructed to complete the task as quickly and accurately as possible. For each list, they had to note their answers and the time to complete each list was recorded. The measure used was the shifting cost, computed with the following formula: Time subtest C–((Time subtest A + Time subtest B) / 2). Higher scores denote lower shifting capacity.

2.4.4 Verbal fluency.

Phonemic (P, R, V) and category (animals, fruits, furniture) fluency tasks were administered [68]. Participants were instructed to produce as many words as possible, beginning with the target letter or belonging to the semantic category in 60 second for each letter and category. Measure of performance was the sum of words produced in the phonetic and semantic tasks, excluding repetitions and incorrect responses. Verbal fluency performance is usually considered as a measure of shifting abilities [69]. Indeed, fluency tasks require to implement and alternate between several search strategies. However, the contribution of search strategy processes may differ between phonological and semantic tasks [70,71], and consequently the shifting score used included all verbal fluency tasks.

2.4.5 Wisconsin Card Sorting Test, WCST.

In the classic version of this multi-compound executive task, participants matched the stimulus card to one of the key cards according to periodically changing rule (color, shape, number of items). Revealing sorting rule was inferred by the examiner’s verbal feedback (correct or incorrect) given to the subject. Several scores were computed based on the correct responses and errors made by the participant. We defined a flexibility score based on the percentage of perseverative errors from one category to the next one [4] and an inhibitory score corresponding to no-perseverative errors [72]. Higher scores are indicative of lower shifting and inhibition abilities.

2.4.6 Random Number Generation (RNG) task.

This is a widely used and complex task allowing to assess several executive abilities [4,73,74]. In the version we used [75], participants were asked to generate a random sequence of digits between 1 and 9, at a response pace of one second indicated by a metronome. Randomness was explained with the analogy of “picking digits out of a hat and putting them back after reading”. We emphasize that a random sequence would not contain a preponderance of repetitions or adjacent number values. The task was considered completed when the participant had achieved 100 correct answers or had achieved 110 correct answers in case of one or more errors. Flexibility and inhibition indices were computed using the RGCalc Quantifying order in response sequences Version1 software [75].

The Turning Point Index (TPI) corresponds to the number of responses that, as numerical value, correspond to a change between ascending and descending sequences. This value is compared to a theoretical random distribution over a 100-digit sequence. A higher score denoted the subject’s ability to achieve a higher random sequence, and to inhibit stereotyped responses. The Adjacency score corresponds to the number of ascending [4,5] or descending [7,8] number pairs by comparison to the total number of response pairs. As ascending and descending pairs represent typical and relatively automatic responses, a low adjacency score is indicative of good inhibition capacity. The runs Index corresponds to the variability of phase length, namely the variability in the number of items between two “turning points”. Based on the previous framework proposed by Miyake et al. [4], the inhibitory score was computed using the formula: (TPI–Adjutancy + Runs) / 3.

Additionally, three indices associated with updating of information were calculated. The Redundancy Index (RI) reflects the frequency at which each response alternative is produced. As the selection frequency among alternatives deviates from equality, the sequence can be said to have more redundancy. The Coupon score indicates (across the entire set) the mean number of responses produced before all the response alternatives are given, with a range between 9 and 100, meaning that the participant has not used up all alternatives. Higher score indicates lower updating performance. Third, the Mean Repetition Gap (MRG) Index corresponds to the mean number of responses between two productions of the same digit. A higher score represents better updating capacity. Consequently, at individual level, low redundancy and Coupon index, and high MRG score express good updating capacities. Following the previous framework proposed by Miyake et al. [4], the updating score was calculated as (- redundancy–Coupon + Mean RG)/3.

2.5. Executive composite scores

The development of the executive composite scores is based on the proposal of Miyake et al. [4] that statistically demonstrated the separability of updating, shifting and inhibition processes. Specific standardized scores were created to assess the effects of age and genotype. The procedure common to the two analyses is three-steps. (1) We first standardized the eight measures extracted from the six executive tasks. (2) A mean composite score corresponding to each executive process was created by averaging the corresponding Z-scores [mean inhibition: (“Z RNG inhibition”–“Z stroop interference”–“Z WCST no-perseverative errors)/3; mean shifting: (-Z” WCST no-perseverative errors–“Z plus-minus” + “Z total fluency”)/3; mean updating: (“Z correct responses 2-Back” + “Z RNG update score”)/2]. The minus sign was used when a low score corresponds to a high-level performance. So final values associated to each composite correspond to higher value = best performance. (3) The three mean composites scores were again Z-scored. These final scores were used in statistical analyses.

For assessment of age-related effects, at step (1), data from the older group were Z-scored based on the mean and standard deviation of the younger group. At step (3), the three mean composites scores of the older participants were again Z-scored by reference to the younger group. Consequently, final composites scores in the younger group had a mean of 0 and standard deviation of 1, and we assessed deviation of performance in the older group by comparison to the younger one [76]. The same rationale was used to assess genotype effects, except that at steps (1) and (3) the Z scores were created based on the whole sample, and we assessed deviations in each of the six subgroups.

2.6. Statistical analyses

Statistical analyses were conducted by using JASP (Version 0.11.1.0; JASP Team, 2020). Results with p < .05 were considered as statistically significant. Analyses were run on the sample size of 100 participants. Missing data (four for the whole dataset) were replaced by the mean value in the participant group for this variable.

With regard to demographic data (Sex, education) and global measures of cognition (fluid and crystallized intelligence, speed of processing, Mattis scale), the effect of age was assessed with Student t test between younger and older participants for education level and cognitive performance. Moreover, analyses of variance (ANOVA) were performed separately in younger and older participants to determine effect of COMT polymorphism on these variables in each group. A Pearson chi-square (χ2) test of independence assessed Sex distribution across age groups and, for each age group, across the three genotypes.

The effect of age on executive functioning was assessed with Student t test between younger and older participants. As detailed in the method section, measures were composite Z scores centered on mean and standard deviation of the younger group. To determine if age-related changes in executive functioning is modulated by COMT polymorphism, we performed an analysis of variance (ANOVA) with the 6 groups as an independent variable, and executive scores as an intra-subject measure. The dependent variables were composite Z-scores centered on mean and standard deviation on the whole sample. Bonferroni post-hoc tests were carried out to explore, for each executive process, the presence of difference between younger and older participants from a same genotype group or between the three genotypes within the same age group. If another comparison was statistically significant, it was reported in the result section for sake of completeness only. Data distribution and variance homogeneity for composite scores used in Student t test and ANOVA were analyzed with Shapiro-Wilk and Levene tests, respectively.

A sensitivity analysis was performed for our ANOVA including the three executive composite scores. Regarding the between effect (Group effect), with a total sample size of 100 and six groups, we were able to detect effect size of f = .30 which corresponds to an η2 = .08 and is an intermediate effect size (d Cohen = 0.60).

3. Results

3.1. Demographic data

Demographic data associated to age group are summarized in Table 1. A Pearson chi-square (χ2) test of independence showed that the Sex distribution did not significantly differ between the younger and older subgroups [Pearson χ(5)2 = 1.45, p = .92]. The two groups also did not differ on education level [t(98) = 0.11, p = 0.91]. However, older adults had a higher vocabulary level on the French adaptation of the Mill Hill test [63] [t(98) = -6.28, p < .001], and younger adults better performance on Raven’ matrices [t(98) = 7.38, p < .001] and processing speed [t(98) = 10.45, p < .001] tasks.

thumbnail
Download:
Table 1. Demographic data.

https://doi.org/10.1371/journal.pone.0303343.t001

Demographic data associated to genetic polymorphism for younger and older subjects are presented in Table 2. Pearson chi-square (χ2) test of independence showed that the Sex distribution did not significantly differ between the three genotypes for younger (Pearson χ(2)2 = 0.99, p = .61) and older adults (Pearson χ(2)2 = 1.34, p = .51). ANOVAs on educational level [F(2,52) = 0.22], fluid intelligence [F(2,52) = 0.89], vocabulary level [F(2,52) = 2.72] and speed of processing [F(2,52) = 1.75] did not show significant between-group differences in younger participants (all p>0.05). For older adults, a lower performance for the processing speed task was observed for Val/Met participants [F(2,42) = 5; p = 0.01, ƞ2 = 0.19, Bonferroni post-hoc test] while there is no significant group differences (all p>0.05) for educational level [F(2,42) = 0.63], fluid intelligence [F(2,42) = 1.56], vocabulary level [F(2,42) = 1.62] and score at the Mattis dementia rating scale [F(2,42) = 1.04].

thumbnail
Download:
Table 2. Demographic data for the three genotype groups, in younger (A) and older (B) adults.

https://doi.org/10.1371/journal.pone.0303343.t002

3.2. Age-related effects on executive functioning

The specific standardized scores created to assess age-related effects are graphically represented in Fig 1. As described in the methods section, the final composites scores in the younger group had a mean of 0 and standard deviation of 1, and we assessed deviation of performance in the older group by comparison to the younger one [76]. Results indicated lower performance in the older group for the three executive scores [Inhibition: t (98) = 6.2, p<0.001, ƞ2 = 0.28; Updating: t (98) = 2.45, p<0.05, ƞ2 = 0.06; Shifting: t(98) = 2.00, p<0.05, ƞ2 = 0.04], with the larger effect size for inhibition [77]. Scores [mean (standard deviation)] for older are -1.42 (1.28) for inhibition, -0.49 (1.01) for updating and -0.44 (1.2) for shifting.

thumbnail
Download:
Fig 1. Age-related effects on the three executive scores.

Composite Z-scores for the inhibition, shifting and updating functions. Scores of older participants were Z-scored by reference to the younger group, with higher values corresponding to better performance. Box plots show the median (horizontal line) and the interquartile (25–75%) range, and the whiskers indicate the 8 to 92% range. Dots indicate outlier participants.

https://doi.org/10.1371/journal.pone.0303343.g001

As the inhibition variable was not normally distributed and we observed slight inhomogeneity of variance for updating, the analyses were also done with Satterthwaite t and Mann-Withney U, respectively. The analyses confirmed between group differences. Inhibition: tSatterthwaite (82.06) = 6.05, p<0.001; Updating: U = 1578.5, p<0.05.

3.3. Genetic-related effects on executive functioning

To determine if the aging effect previously observed on executive functioning is modulated by COMT polymorphism, we performed a repeated measure analysis of variance (ANOVA) with group (Younger-Met/Met, Younger-Val/Met, Younger-Val/Val, Older-Met/Met, Older Val/Met, Older -Val/Val) as an independent variable, and executive scores (Inhibition, shifting, Updating) as an intra-subject measure. Results of assumptions check showed that the repeated measures ANOVA can be applied because the data did not severely violate the conditions of application. Shapiro-Wilk tests showed a normal distribution of the data [Inhibition W = 0.984 p = 0.253; Flexibility W = 0.988 p = 0.528; Updating W = 0.976 p = 0.061]. Levene’s test indicates that variance are approximatively equal across the six groups [inhibition F(5,94) = 1.26, p = 0.29; Flexibility F(5,94) = 1.47, p = 0.21; Updating F(5,94) = 0.44, p = 0.82]. The Mauchly test revealed that sphericity was not respected χ2(5) = 12.86, p<0.005). However, the epsilon values of the Greenhouse (0.886) and Huynh-Feldt (0.949) correction being close to 1 suggest a slight violation of sphericity.

As described in the methods section, the executive indices are the composite scores values based on the means and standard deviations of the whole sample. Results show a main group effect [F(5,94) = 4.3, p<0.001, ƞ2 = 0.1], corresponding to a large effect size [77], with a higher performance in younger participants. There is no significant effect of the executive factor [F(2,188) = 0.11, p = 0.9, ƞ2 = 0.001] and no interaction between group and executive scores factors [F(10,188) = 1.46, p = 0.16, ƞ2 = 0.07]. The results are graphically presented in Fig 2 (see S1 Table for summary data).

thumbnail
Download:
Fig 2. Age-related effect on executive scores according to COMT polymorphism.

(A) Inhibition, (B) Shifting, (C) Updating. Z scores were created based on the whole sample, with higher values indicating better performance. Box plots show the median (horizontal line) and the interquartile (25–75%) range, and the whiskers indicate the 8 to 92% range. Dots indicate outlier participants.

https://doi.org/10.1371/journal.pone.0303343.g002

As we were particularly interested by the age-effect in each genotype group for each executive process separately, Bonferroni post-hoc tests were carried out on the interaction effect. These analyses indicated that Younger Val/Val participants have a higher performance than Older Val/Val on the inhibition composite z-score (p = 0.03).

Significant differences across genotype and age groups were also observed between Younger Val/Val and Older Val/Met (p = 0.04), Younger Met/Met and older Val/Val (p = 0.04), Younger Met/Met and Older Val/Met (p = 0.04). These results are reported only for sake of completeness as we do not have specific hypotheses for these differences.

4. Discussion

Our study aimed to investigate possible influence of the single nucleotide polymorphism of the Val158Met COMT gene on executive functions during normal aging. We first replicated previous studies that showed decreased executive functioning in aging. Regarding the genetic effects, we have not observed advantage of carrying Met allele in younger, while there exists age-related effect for Val homozygous individuals on inhibition, with a lower performance for older adults by comparison to younger ones.

4.1. Aging and executive functions

It is now well established that aging is associated to a decrease of executive functioning; [3,78]. Here we assessed the functions of updating, shifting and inhibition with several tasks and we computed z-scores grouping together measures from the different tasks associated to the same executive process. This allows us a measurement of executive functioning relatively independent of non-executive processes [79–81]. In that way, we observed that the effect of age encompasses the three executive processes explored, with large effect size for inhibition, while shifting and updating processes are associated to small and medium effect, respectively. A decrease in performance affecting different aspects of executive functioning simultaneously has been reported by [82]. The presence of a large age-related effect size for inhibition can seem surprising as not all inhibitory processes does seem to be impaired with aging [83]. However, the tasks used here necessitate controlled processes (i.e., to actively suppress a dominant response or routine), and not automatic processes that are generally not affected by aging.

4.2. COMT and executive functions

At this time, few genome-wide association studies (GWAs) were conducted to assess the potential genetic contribution to executive functioning. Dueker et al. [84] observed that a composite measure of executive function is associated with four SNPs located in the long intergenic non-protein coding RNA 1362 gene, LINC01362, on chromosome 1. The associated SNPs have been shown to influence expression of the tubulin tyrosine ligase like 7 gene, TTLL7 and the protein kinase CAMP-activated catalytic subunit beta gene, PRKACB, in several regions of the brain involved in executive function. Hatoum et al. [85] observed that genes related to GABAergic pathways influence executive functions (assessed by a common factor) while the effect of COMT Val/Met polymorphism was not significant at the genome-wide level.

We predicted a higher performance in carriers of Met allele, due to their higher level of dopamine availability in prefrontal cortex. However, we do not observe effect of COMT genotype between the three younger groups, neither for the three older groups. The advantage of carrying Met allele was frequently related to superior performance on a number of executive tasks, both in younger and older adults, as well as in pathological populations such as schizophrenia [35,86–88], while this is not systematic (for a metanalysis see [39]). There exists evidence that COMT polymorphism alone is not sufficient to explain genetic effects on executive performance. For example, Bertolino et al. [89] reported that both COMT and DAT genotypes have an additive effect on brain activity during an updating 2-back task, with COMT Met/Met and DAT 10/10-repeat individuals having a more focused cortical brain response (lesser brain activity for similar behavioral performance), whereas COMT Val and DAT 9-repeat alleles have the least focused brain response. In our lab, we assessed the effect of COMT rs4680, MAOA rs6323 and DRD1 rs4532 polymorphism on inhibition abilities. We observed an interactive effect of the DRD1 and COMT polymorphisms on a motor inhibitory task, and an interactive effect of the three genes on a Stroop task, according to requirement of flexibility associated to the tasks [90]. Finally, in a review of the literature, Barnes et al. [91] suggest that variants of the dopamine transporter gene (DAT1) and dopamine D4 receptor gene (DRD4) may explain individual differences in both behavioral and neural measures of inhibitory control. As our protocol was restricted to Val158Met COMT polymorphism, we could have missed an effect of dopaminergic genes in our groups of younger and older participants.

4.3. COMT, aging and executive functions

Our initial prediction was a dampening of the age-effect on executive functioning in the Met/Met group, due to their higher baseline dopamine level in prefrontal cortex [34] that should help to compensate decrease in dopamine availability associated to aging [20]. However, we observed an effect of COMT polymorphism only for older Val homozygous, by comparison to their younger counterpart. This suggests lower executive abilities in older Val/Val participants, although we were unable to identify any difference between the three genotypic groups including older participants, as previously reported by Nagel et al. [49]. These authors proposed that aging would cause individuals to migrate to the left side of the inverted U-shaped curve linking dopamine levels to cognitive performance [92,93], and Val/Val individuals would be characterized by a greater shift to the left than Met/Met ones. This greater shift of Val/Val individuals could explain the difference in cognitive performance observed here between younger Val/Val and older Val/Val subjects, whereas no significant difference was observed between younger and older Met/Met. However, this shift may be insufficient in our participants to generate a difference between the older genotype groups.

Capitalizing on previous studies, we tentatively propose that age of our participants and the use of only one genetic polymorphism can be partly responsible for the negative result we observed. Indeed, some data suggest that age-related genetic effects on cognition are observed in relatively younger participants. Two studies that assessed longitudinal decline in cognition reported an effect of COMT polymorphism only in participants younger than 60 [93,94]. Our participants are 60–75 years old, and therefore may have been outside the age range to detect a deleterious influence of the Val allele in aging if such an influence is specific to the 5th decade of life [94]. Moreover, it is also possible that the effect of COMT genotype in aging is expressed only in interaction with other genes not necessarily linked to the dopaminergic system. For example, Nagel et al. [49] observed that older COMT Val homozygotes have particularly low levels of performance if they were also BDNF Met carriers. Erickson et al. [95] reported that the COMT polymorphism does not affect the trajectory of age-related executive control decline, whereas the Val/Val polymorphism for BDNF may promote faster rates of cognitive decay in older age. Sapkota et al. [96] observed that additive effects of COMT and BDNF polymorphisms predicted lower executive performance. In a subsequent study, the authors also showed that APOEε4+ carriers with increased allelic risk for COMT and BDNF genes had lower composite executive score [97]. Finally, an interactive effect was also observed for both COMT and APOE for semantic fluency, with Val/Met heterozygous individuals performing better only in the context of an ApoE Ɛ4 allele [98].

4.4. COMT, aging and inhibition

We observed an age-related effect for Val homozygous carriers only for inhibition abilities. One possible interpretation is in line with the findings of Bilder et al. ([48]; see also [99]) that COMT polymorphism has allelic specific and opposite effects on the stability and flexibility of cognitive processes. According to these authors and our results, the COMT Met allele supports the stability of representations necessary for the ongoing cognitive task while the Val allele favors switching abilities and consequently updating/flexibility of these representations. Consequently, the Val carriers might be advantaged on tasks requiring flexibility and updating mechanisms and the Met carriers by tasks requiring stable maintenance abilities [48,99–101]. In other words, Val/Val individuals would have lower capacity for tasks requiring stability of representations, which underlies inhibition processes [102]. This would explain why we observed and effect of age for inhibitory abilities, but not updating nor shifting, in Val homozygous carriers. A similar interpretation was proposed by de Frias et al. [103] to explain differences in pattern of brain activity during an 2-back task: increased transient activity in the medial temporal lobe was observed in the Met/Met group and interpreted as a decrease of updating abilities while increased sustained activity in the prefrontal cortex was reported for the Val/Val group and attributed to less efficient maintenance processes (see also [101,104]).

4.5. Strengths and limitations

To the best of our knowledge this is the first study to incorporate and analyze the three executive processes of inhibition, shifting and updating among younger and older participants in the context of COMT allelic variance. We demonstrated that inhibition, shifting and updating performances are distinctly affected by COMT polymorphism in each age group. Another strength of our study is the use of composite scores which enabled us to obtain a purer and more distinguishable measure of the executive processes core components, thus taking further the prominent model proposed by Miyake et al. [4].

As previously discussed, one possible limitation is the restricted age range of our older sample which does not allow us to determine whether the effects of COMT polymorphism differ in young-old and old-old participants [94]. Finally, our sample size is well in line with other behavioral studies which were aiming to investigate cognitive processes, but slightly smaller, however adequately sized, compared to the previous studies on executive functioning and COMT effect in aging [105]. Consequently, presence of a lower performance during aging on the composite score of inhibition (but not on shifting or updating scores) for homozygous Val carriers needs replication with protocols targeting for example one specific executive process. This will allow inclusion of a larger number of participants than in this study.

5. Conclusion

This study used a battery of tasks to explore the effect of COMT polymorphism on executive performance in aging by using compound scores that allow a purer measure of the executive processes of updating, shifting and inhibition. We showed, as expected, lower performance in the older group for the three executive processes. However, older participants seem disadvantaged only when the executive measure emphasize the need of stable representations (as in inhibition task requiring the instruction to not perform an automated process) and when they are homozygous for Val allele, with a lower baseline dopamine level that is magnified by aging. Until now, studies on the effect of COMT polymorphism on executive functioning (and more generally cognition) in aging led to divergent results, however we can conclude here on the evidence of COMT allelic variance effect on the inhibitory component of executive functioning in older participants. Further studies are needed to investigate and disentangle the effect of task characteristics, interaction with other genes (e.g., BDNF) or if the effect is specific to a very narrow age range.

Supporting information

S1 Table. Summary data for executive composite scores.

https://doi.org/10.1371/journal.pone.0303343.s001

(DOCX)

References

  1. 1. Crawford JR, Bryan J, Luszcz MA, Obonsawin MC, Stewart L. The Executive Decline Hypothesis of Cognitive Aging: Do Executive Deficits Qualify as Differential Deficits and Do They Mediate Age-Related Memory Decline? Aging Neuropsychol Cogn. 2000 Mar;7(1):9–31.
  2. 2. Salthouse TA, Atkinson TM, Berish DE. Executive Functioning as a Potential Mediator of Age-Related Cognitive Decline in Normal Adults. J Exp Psychol Gen. 2003;132(4):566–94. pmid:14640849
  3. 3. Collette F, Salmon E. Les modifications du fonctionnement exécutif dans le vieillissement normal. Psychol Fr. 2014 Mar;59(1):41–58.
  4. 4. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognit Psychol. 2000 Aug;41(1):49–100. pmid:10945922
  5. 5. Verhaeghen P, Cerella J. Aging, executive control, and attention: a review of meta-analyses. Neurosci Biobehav Rev. 2002 Nov;26(7):849–57. pmid:12470697
  6. 6. Wasylyshyn C, Verhaeghen P, Sliwinski MJ. Aging and task switching: A meta-analysis. Psychol Aging. 2011;26(1):15–20. pmid:21261411
  7. 7. Andrés P, Guerrini C, Phillips LH, Perfect TJ. Differential Effects of Aging on Executive and Automatic Inhibition. Dev Neuropsychol. 2008 Mar 6;33(2):101–23. pmid:18443972
  8. 8. Collette F, Germain S, Hogge M, Van der Linden M. Inhibitory control of memory in normal ageing: Dissociation between impaired intentional and preserved unintentional processes. Memory. 2009 Jan;17(1):104–22. pmid:19105088
  9. 9. Collette F, Schmidt C, Scherrer C, Adam S, Salmon E. Specificity of inhibitory deficits in normal aging and Alzheimer’s disease. Neurobiol Aging. 2009 Jun;30(6):875–89. pmid:18029058
  10. 10. Hogge M, Salmon E, Collette F. Interference and Negative Priming in Normal Aging and in Mild Alzheimer’s Disease. Psychol Belg. 2008 Jan 1;48(1):1.
  11. 11. Dennis N, Cabeza R. Neuroimaging of Healthy Cognitive Aging. Fundam Informaticae—FUIN. 2008 Jan 1;3.
  12. 12. Park DC, Reuter-Lorenz P. The Adaptive Brain: Aging and Neurocognitive Scaffolding. Annu Rev Psychol. 2009 Jan 1;60(1):173–96. pmid:19035823
  13. 13. Manard M, Bahri MA, Salmon E, Collette F. Relationship between grey matter integrity and executive abilities in aging. Brain Res. 2016 Jul;1642:562–80. pmid:27107940
  14. 14. Head D, Kennedy KM, Rodrigue KM, Raz N. Age differences in perseveration: Cognitive and neuroanatomical mediators of performance on the Wisconsin Card Sorting Test. Neuropsychologia. 2009 Mar;47(4):1200–3. pmid:19166863
  15. 15. Mattay Venkata S, Fera F, Tessitore A, Hariri AR, Berman KF, Das S, et al. Neurophysiological correlates of age-related changes in working memory capacity. Neurosci Lett. 2006 Jan;392(1–2):32–7. pmid:16213083
  16. 16. Turner GR, Spreng RN. Executive functions and neurocognitive aging: dissociable patterns of brain activity. Neurobiol Aging. 2012 Apr;33(4):826.e1–826.e13. pmid:21791362
  17. 17. Ding X, Barban N, Tropf FC, Mills MC. The relationship between cognitive decline and a genetic predictor of educational attainment. Soc Sci Med. 2019 Oct;239:112549. pmid:31546143
  18. 18. Xu Y, Yan J, Zhou P, Li J, Gao H, Xia Y, et al. Neurotransmitter receptors and cognitive dysfunction in Alzheimer’s disease and Parkinson’s disease. Prog Neurobiol. 2012 Apr;97(1):1–13. pmid:22387368
  19. 19. Bäckman L, Nyberg L, Lindenberger U, Li SC, Farde L. The correlative triad among aging, dopamine, and cognition: Current status and future prospects. Neurosci Biobehav Rev. 2006 Jan;30(6):791–807. pmid:16901542
  20. 20. Volkow ND, Fowler JS, Wang GJ, Baler R, Telang F. Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology. 2009 Jan;56:3–8. pmid:18617195
  21. 21. Karoum F, Chrapusta SJ, Egan MF. 3-Methoxytyramine Is the Major Metabolite of Released Dopamine in the Rat Frontal Cortex: Reassessment of the Effects of Antipsychotics on the Dynamics of Dopamine Release and Metabolism in the Frontal Cortex, Nucleus Accumbens, and Striatum by a Simple Two Pool Model. J Neurochem. 1994 Feb 2;63(3):972–9. pmid:7914228
  22. 22. Nieoullon A. Dopamine and the regulation of cognition and attention. Prog Neurobiol. 2002 May;67(1):53–83. pmid:12126656
  23. 23. Ott T, Nieder A. Dopamine and Cognitive Control in Prefrontal Cortex. Trends Cogn Sci. 2019 Mar;23(3):213–34. pmid:30711326
  24. 24. Khanthiyong B, Thanoi S, Reynolds GP, Nudmamud-Thanoi S. Association study of the functional Catechol-O-Methyltranferase (COMT) Val158Met polymorphism on executive cognitive function in a Thai sample. Int J Med Sci. 2019 Sep 20;16(11):1461–5. pmid:31673237
  25. 25. Arnsten AFT. Catecholamine modulation of prefrontal cortical cognitive function. Trends Cogn Sci. 1998 Nov;2(11):436–47. pmid:21227275
  26. 26. Cools R, D’Esposito M. Inverted-U–shaped dopamine actions on human working memory and cognitive control. Biol Psychiatry. 2011 Jun;69(12):e113–25. pmid:21531388
  27. 27. Cools R, Robbins TW. Chemistry of the adaptive mind. Thompson JMT, editor. Philos Trans R Soc Lond Ser Math Phys Eng Sci. 2004 Dec 15;362(1825):2871–88. pmid:15539374
  28. 28. Goldman-Rakic P. D1 receptors in prefrontal cells and circuits. Brain Res Rev. 2000 Mar;31(2–3):295–301. pmid:10719156
  29. 29. Grossman MH, Emanuel BS, Budarf ML. Chromosomal mapping of the human catechol-O-methyltransferase gene to 22q11.1→q11.2. Genomics. 1992 Apr;12(4):822–5. pmid:1572656
  30. 30. Lachman HM, Papolos DF, Saito T, Yu YM, Szumlanski CL, Weinshilboum RM. Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders: Pharmacogenetics. 1996 Jun;6(3):243–50.
  31. 31. Männistö PT, Kaakkola S. Catechol-O-methyltransferase (COMT): biochemistry, molecular biology, pharmacology, and clinical efficacy of the new selective COMT inhibitors. Pharmacol Rev. 1999 Dec;51(4):593–628. pmid:10581325
  32. 32. Chen J, Lipska BK, Halim N, Ma QD, Matsumoto M, Melhem S, et al. Functional Analysis of Genetic Variation in Catechol-O-Methyltransferase (COMT): Effects on mRNA, Protein, and Enzyme Activity in Postmortem Human Brain. Am J Hum Genet. 2004 Nov;75(5):807–21. pmid:15457404
  33. 33. Lotta T, Vidgren J, Tilgmann C, Ulmanen I, Melen K, Julkunen I, et al. Kinetics of Human Soluble and Membrane-Bound Catechol O-Methyltransferase: A Revised Mechanism and Description of the Thermolabile Variant of the Enzyme. Biochemistry. 1995 Apr 4;34(13):4202–10. pmid:7703232
  34. 34. Weinshilboum RM, Otterness DM, Szumlanski CL. METHYLATION PHARMACOGENETICS: Catechol O-Methyltransferase, Thiopurine Methyltransferase, and Histamine N-Methyltransferase. Annu Rev Pharmacol Toxicol. 1999 Apr;39(1):19–52. pmid:10331075
  35. 35. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci. 2001 Jun 5;98(12):6917–22. pmid:11381111
  36. 36. Mattay VS, Goldberg TE, Fera F, Hariri AR, Tessitore A, Egan MF, et al. Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci. 2003 May 13;100(10):6186–91. pmid:12716966
  37. 37. Rosa EC, Dickinson D, Apud J, Weinberger DR, Elvevåg B. COMT Val158Met polymorphism, cognitive stability and cognitive flexibility: an experimental examination. Behav Brain Funct. 2010;6(1):53. pmid:20836853
  38. 38. Stefanis NC, van Os J, Avramopoulos D, Smyrnis N, Evdokimidis I, Stefanis CN. Effect of COMT Val 158 Met Polymorphism on the Continuous Performance Test, Identical Pairs Version: Tuning Rather Than Improving Performance. Am J Psychiatry. 2005 Sep;162(9):1752–4.
  39. 39. Barnett JH, Scoriels L, Munafò MR. Meta-Analysis of the Cognitive Effects of the Catechol-O-Methyltransferase Gene Val158/108Met Polymorphism. Biol Psychiatry. 2008 Jul;64(2):137–44. pmid:18339359
  40. 40. Blanchard MM, Chamberlain SR, Roiser J, Robbins TW, Müller U. Effects of two dopamine-modulating genes (DAT1 9/10 and COMT Val/Met) on n-back working memory performance in healthy volunteers. Psychol Med. 2011 Mar;41(3):611–8.
  41. 41. Degen C, Zschocke J, Toro P, Sattler C, Wahl HW, Schönknecht P, et al. The COMTp.Val158Met Polymorphism and Cognitive Performance in Adult Development, Healthy Aging and Mild Cognitive Impairment. Dement Geriatr Cogn Disord. 2016;41(1–2):27–34.
  42. 42. Ihne JL, Gallagher NM, Sullivan M, Callicott JH, Green AE. Is less really more: Does a prefrontal efficiency genotype actually confer better performance when working memory becomes difficult? Cortex. 2016 Jan;74:79–95. pmid:26649915
  43. 43. Wardle MC, de Wit H, Penton-Voak I, Lewis G, Munafò MR. Lack of Association Between COMT and Working Memory in a Population-Based Cohort of Healthy Young Adults. Neuropsychopharmacology. 2013 Jun;38(7):1253–63. pmid:23337869
  44. 44. Colzato LS, Waszak F, Nieuwenhuis S, Posthuma D, Hommel B. The flexible mind is associated with the catechol-O-methyltransferase (COMT) Val158Met polymorphism: Evidence for a role of dopamine in the control of task-switching. Neuropsychologia. 2010 Jul;48(9):2764–8. pmid:20434465
  45. 45. Krugel LK, Biele G, Mohr PNC, Li SC, Heekeren HR. Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions. Proc Natl Acad Sci. 2009 Oct 20;106(42):17951–6. pmid:19822738
  46. 46. Wang Y, Li J, Chen C, Chen C, Zhu B, Moysis RK, et al. COMT rs4680 Met is not always the ‘smart allele’: Val allele is associated with better working memory and larger hippocampal volume in healthy Chinese: COMT rs4680 Met is not always the ‘smart allele.’ Genes Brain Behav. 2013 Apr;12(3):323–9.
  47. 47. Weiss EM, Schulter G, Fink A, Reiser EM, Mittenecker E, Niederstätter H, et al. Influences of COMT and 5-HTTLPR Polymorphisms on Cognitive Flexibility in Healthy Women: Inhibition of Prepotent Responses and Memory Updating. Homberg J, editor. PLoS ONE. 2014 Jan 20;9(1):e85506. pmid:24465579
  48. 48. Bilder RM, Volavka J, Lachman HM, Grace AA. The Catechol-O-Methyltransferase Polymorphism: Relations to the Tonic–Phasic Dopamine Hypothesis and Neuropsychiatric Phenotypes. Neuropsychopharmacology. 2004 Nov;29(11):1943–61. pmid:15305167
  49. 49. Nagel IE. Human aging magnifies genetic effects on executive functioning and working memory. Front Hum Neurosci [Internet]. 2008 [cited 2018 Dec 5];2. Available from: http://journal.frontiersin.org/article/10.3389/neuro.09.001.2008/abstract. pmid:18958202
  50. 50. Nyberg L, Andersson M, Kauppi K, Lundquist A, Persson J, Pudas S, et al. Age-related and Genetic Modulation of Frontal Cortex Efficiency. J Cogn Neurosci. 2014 Apr;26(4):746–54. pmid:24236764
  51. 51. Sambataro F, Reed JD, Murty VP, Das S, Tan HY, Callicott JH, et al. Catechol-O-Methyltransferase Valine158Methionine Polymorphism Modulates Brain Networks Underlying Working Memory Across Adulthood. Biol Psychiatry. 2009 Sep;66(6):540–8. pmid:19539269
  52. 52. Bruder GE, Keilp JG, Xu H, Shikhman M, Schori E, Gorman JM, et al. Catechol-O-Methyltransferase (COMT) Genotypes and Working Memory: Associations with Differing Cognitive Operations. Biol Psychiatry. 2005 Dec;58(11):901–7. pmid:16043133
  53. 53. Heim AF, Coyne MJ, Kamboh MI, Ryan C, Jennings JR. The catechol-o-methyltransferase Val158Met polymorphism modulates organization of regional cerebral blood flow response to working memory in adults. Int J Psychophysiol. 2013 Nov;90(2):149–56. pmid:23810996
  54. 54. O’Hara R, Miller E, Liao CP, Way N, Lin X, Hallmayer J. COMT genotype, gender and cognition in community-dwelling, older adults. Neurosci Lett. 2006 Dec;409(3):205–9. pmid:17029783
  55. 55. Potter GG, Taylor WD, McQuoid DR, Steffens DC, Welsh-Bohmer KA, Krishnan KRR. The COMT Val158Met polymorphism and cognition in depressed and nondepressed older adults. Int J Geriatr Psychiatry. 2009 Oct;24(10):1127–33. pmid:19296553
  56. 56. Bellander M, Bäckman L, Liu T, Schjeide BMM, Bertram L, Schmiedek F, et al. Lower baseline performance but greater plasticity of working memory for carriers of the val allele of the COMT Val158Met polymorphism. Neuropsychology. 2015 Mar;29(2):247–54.
  57. 57. Fiocco AJ, Lindquist K, Ferrell R, Li R, Simonsick EM, Nalls M, et al. COMT genotype and cognitive function: An 8-year longitudinal study in white and black elders. Neurology. 2010 Apr 20;74(16):1296–302. pmid:20404311
  58. 58. Solís-Ortiz S, Pérez-Luque E, Morado-Crespo L, Gutiérrez-Muñoz M. Executive functions and selective attention are favored in middle-aged healthy women carriers of the Val/Val genotype of the catechol-o-methyltransferase gene: a behavioral genetic study. Behav Brain Funct. 2010;6(1):67. pmid:21029471
  59. 59. Mattis S. Mental Status Examination for Organic Mental Syndrome in the Elderly Patient. In: Geriatric Psychiatry. In: Bellack L. and Karusu T.B., Eds.,. New York: Grune & Stratton; 1976. p. 77–121.
    • 60. Monsch AU. Clinical Validity of the Mattis Dementia Rating Scale in Detecting Dementia of the Alzheimer Type: A Double Cross-Validation and Application to a Community-Dwelling Sample. Arch Neurol. 1995 Sep 1;52(9):899.
    • 61. Reuter M, Peters K, Schroeter K, Koebke W, Lenardon D, Bloch B, et al. The influence of the dopaminergic system on cognitive functioning: A molecular genetic approach. Behav Brain Res. 2005 Oct;164(1):93–9. pmid:16026865
    • 62. Raven JC. Advanced Progressive Matrices. Sets I and II. London: HK Lewis & Co. San Antonio, Texas; 1965.
      • 63. Deltour JJ. Echelle de vocabulaire Mill Hill de JC Raven: Adaptation française et normes comparées du Mill Hill et du Standard Progressive Matrices (PM38). Man Annex Braine Château Belg Appl Tech Mod. 1993;
      • 64. Sorel O. Approche développementale du raisonnement bayésien: analyse quantitative et qualitative selon le format de présentation et le niveau scolaire. Doctoral Dissertation, University of Tours, France. 2009.
        • 65. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18(6):643–62.
        • 66. Kirchner WK. Age differences in short-term retention of rapidly changing information. J Exp Psychol. 1958;55(4):352–8. pmid:13539317
        • 67. Spector A, Biederman I. Mental Set and Mental Shift Revisited. Am J Psychol. 1976 Dec;89(4):669.
        • 68. Cardebat D, Doyon B, Puel M, Goulet P, Joanette Y. [Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level]. Acta Neurol Belg. 1990;90(4):207–17.
        • 69. Eslinger PJ, Grattan LM. Frontal lobe and frontal-striatal substrates for different forms of human cognitive flexibility. Neuropsychologia. 1993 Jan;31(1):17–28. pmid:8437679
        • 70. Nutterupham K, Saykin A, Rabin L, Roth R, Wishart H, Pare N, et al. Verbal fluency performance in amnestic MCI and older adults with cognitive complaints. Arch Clin Neuropsychol. 2008 May;23(3):229–41. pmid:18339515
        • 71. Troyer AK, Moscovitch M, Winocur G. Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology. 1997;11(1):138–46. pmid:9055277
        • 72. Steinmetz JP, Houssemand C. What About Inhibition in the Wisconsin Card Sorting Test? Clin Neuropsychol. 2011 May;25(4):652–69. pmid:21547856
        • 73. Friedman NP, Miyake A. The Relations Among Inhibition and Interference Control Functions: A Latent-Variable Analysis. J Exp Psychol Gen. 2004 Mar;133(1):101–35. pmid:14979754
        • 74. Peters M, Giesbrecht T, Jelicic M, Merckelbach H. The random number generation task: Psychometric properties and normative data of an executive function task in a mixed sample. J Int Neuropsychol Soc [Internet]. 2007 Jul [cited 2021 May 27];13(04). Available from: http://www.journals.cambridge.org/abstract_S1355617707070786.
        • 75. Towse JN, Neil D. Analyzing human random generation behavior: A review of methods used and a computer program for describing performance. Behav Res Methods Instrum Comput. 1998 Dec;30(4):583–91.
        • 76. Keefe R. The Brief Assessment of Cognition in Schizophrenia: reliability, sensitivity, and comparison with a standard neurocognitive battery. Schizophr Res. 2004 Jun 1;68(2–3):283–97. pmid:15099610
        • 77. Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences. Cohen J. (1988). Hillside, NJ: Lawrence Erlbaum Associates; 1998.
          • 78. McDonald AP, D’Arcy RCN, Song X. Functional MRI on executive functioning in aging and dementia: A scoping review of cognitive tasks. Aging Med. 2018 Sep;1(2):209–19. pmid:31942499
          • 79. Lehto J. Are Executive Function Tests Dependent on Working Memory Capacity? Q J Exp Psychol Sect A. 1996 Feb;49(1):29–50.
          • 80. Rabbitt P, editor. Methodology Of Frontal And Executive Function [Internet]. 0 ed. Routledge; 2004 [cited 2022 Sep 21]. Available from: https://www.taylorfrancis.com/books/9781135472030.
            • 81. Robbins TW, James M, Owen AM, Sahakian BJ, Lawrence AD, Mcinnes L, et al. A study of performance on tests from the CANTAB battery sensitive to frontal lobe dysfunction in a large sample of normal volunteers: Implications for theories of executive functioning and cognitive aging. J Int Neuropsychol Soc [Internet]. 1998 Sep [cited 2022 Sep 21];4(05). Available from: http://www.journals.cambridge.org/abstract_S1355617798455073.
            • 82. Gilsoul J, Simon J, Hogge M, Collette F. Do attentional capacities and processing speed mediate the effect of age on executive functioning? Aging Neuropsychol Cogn. 2019 Mar 4;26(2):282–317. pmid:29405831
            • 83. Andres P, Van der Linden M. Age-Related Differences in Supervisory Attentional System Functions. J Gerontol B Psychol Sci Soc Sci. 2000 Nov 1;55(6):P373–80. pmid:11078107
            • 84. Dueker N, Wang L, Gardener H, Gomez L, Kaur S, Beecham A, et al. Genome-wide association study of executive function in a multi-ethnic cohort implicates LINC01362: Results from the northern Manhattan study. Neurobiol Aging. 2023 Mar;123:216–21. pmid:36658081
            • 85. Hatoum AS, Morrison CL, Mitchell EC, Lam M, Benca-Bachman CE, Reineberg AE, et al. Genome-wide Association Study Shows That Executive Functioning Is Influenced by GABAergic Processes and Is a Neurocognitive Genetic Correlate of Psychiatric Disorders. Biol Psychiatry. 2023 Jan;93(1):59–70. pmid:36150907
            • 86. Goldberg TE, Egan MF, Gscheidle T, Coppola R, Weickert T, Kolachana BS, et al. Executive Subprocesses in Working Memory: Relationship to Catechol- O -methyltransferase Val158Met Genotype and Schizophrenia. Arch Gen Psychiatry. 2003 Sep 1;60(9):889.
            • 87. Malhotra AK, Kestler LJ, Mazzanti C, Bates JA, Goldberg T, Goldman D. A Functional Polymorphism in the COMT Gene and Performance on a Test of Prefrontal Cognition. Am J Psychiatry. 2002 Apr;159(4):652–4. pmid:11925305
            • 88. Joober Ridha, Boksa Patricia, Benkelfat Chawki, Rouleau Guy. Genetics of schizophrenia: from animal models to clinical studies. J Psychiatry Neurosci. 2002 Jun 11;27(5):336–47. pmid:12271789
            • 89. Bertolino A. Additive Effects of Genetic Variation in Dopamine Regulating Genes on Working Memory Cortical Activity in Human Brain. J Neurosci. 2006 Apr 12;26(15):3918–22. pmid:16611807
            • 90. Jaspar M. The impact of dopaminergic genes on inhibitory processes and cognitive control [Internet]. Poster presented at: British Neuropsychological Society with la Société de Neuropsychologie de Langue Française; 2016 Mar 18; London United kingdom. Available from: https://orbi.uliege.be/handle/2268/201342.
              • 91. Barnes JJM, Dean AJ, Nandam LS, O’Connell RG, Bellgrove MA. The Molecular Genetics of Executive Function: Role of Monoamine System Genes. Biol Psychiatry. 2011 Jun;69(12):e127–43. pmid:21397212
              • 92. Harris SE, Wright AF, Hayward C, Starr JM, Whalley LJ, Deary IJ. The functional COMT polymorphism, Val158Met, is associated with logical memory and the personality trait intellect/imagination in a cohort of healthy 79 year olds. Neurosci Lett. 2005 Sep;385(1):1–6. pmid:15979789
              • 93. Starr JM, Fox H, Harris SE, Deary IJ, Whalley LJ. COMT genotype and cognitive ability: A longitudinal aging study. Neurosci Lett. 2007 Jun;421(1):57–61. pmid:17548151
              • 94. de Frias CM, Annerbrink K, Westberg L, Eriksson E, Adolfsson R, Nilsson LG. COMT Gene Polymorphism Is Associated with Declarative Memory in Adulthood and Old Age. Behav Genet. 2004 Sep 1;34(5):533–9. pmid:15319576
              • 95. Erickson KI. Genetic contributions to age-related decline in executive function: a 10-year longitudinal study of COMT and BDNF polymorphisms. Front Hum Neurosci [Internet]. 2008 [cited 2022 Nov 3];2. Available from: http://journal.frontiersin.org/article/10.3389/neuro.09.011.2008/abstract. pmid:18958211
              • 96. Sapkota S, Vergote D, Westaway D, Jhamandas J, Dixon RA. Synergistic associations of catechol-O-methyltransferase and brain-derived neurotrophic factor with executive function in aging are selective and modified by apolipoprotein E. Neurobiol Aging. 2015 Jan;36(1):249–56. pmid:25107496
              • 97. Sapkota S, Bäckman L, Dixon RA. Executive function performance and change in aging is predicted by apolipoprotein E, intensified by catechol-O-methyltransferase and brain-derived neurotrophic factor, and moderated by age and lifestyle. Neurobiol Aging. 2017 Apr;52:81–9. pmid:28131014
              • 98. Pizzonia KL, Suhr JA, Clark LA, Clark BC. The relation of ApoE and COMT gene–gene interactions to cognitive and motor function in community-dwelling older adults: a pilot study. Front Aging Neurosci. 2023 Aug 31;15:1206473. pmid:37744392
              • 99. Durstewitz D, Seamans JK. The Dual-State Theory of Prefrontal Cortex Dopamine Function with Relevance to Catechol-O-Methyltransferase Genotypes and Schizophrenia. Biol Psychiatry. 2008 Nov;64(9):739–49. pmid:18620336
              • 100. Ettinger U, Kumari V, Collier DA, Powell J, Luzi S, Michel TM, et al. Catechol-O-Methyltransferase (COMT) Val158Met Genotype is Associated with BOLD Response as a Function of Task Characteristic. Neuropsychopharmacology. 2008 Dec;33(13):3046–57. pmid:18235427
              • 101. Nolan KA, Bilder RM, Lachman HM, Volavka J. Catechol O -Methyltransferase Val 158 Met Polymorphism in Schizophrenia: Differential Effects of Val and Met Alleles on Cognitive Stability and Flexibility. Am J Psychiatry. 2004 Feb;161(2):359–61.
              • 102. Hasher L, Zacks R, May C. Inhibitory control, circadian arousal and age. In: Attention and Performance. 1999. p. 653–75.
              • 103. de Frias CM, Marklund P, Eriksson E, Larsson A, Oman L, Annerbrink K, et al. Bäckman. Influence of COMT gene polymorphism on fMRI-assessed sustained and transient activity during a working memory task. J Cogn Neurosci. 2010 Jul;22(7):1614–22.
              • 104. Morris KA, Grace SA, Woods W, Dean B, Rossell SL. The influence of COMT rs4680 on functional connectivity in healthy adults: A systematic review. Eur J Neurosci. 2020 Oct;52(8):3851–78. pmid:32306439
              • 105. Liu ME, Hong CJ, Liou YJ, Tsai YL, Hsieh CH, Tsai SJ. Association study of a functional catechol-O-methyltransferase polymorphism and executive function in elderly males without dementia. Neurosci Lett. 2008 May;436(2):193–5. pmid:18387741