Intratask Variability As a Correlate for DRD4 and SLC6A3 Variants

Abstract

Behavioral variability may be an ADHD key feature. Currently used ex-Gaussian/Fast Fourier Transform analyses characterize general distribution and oscillatory/rhythmic components of performance but are unable to demonstrate slow cumulative changes over entire tasks. Objective: To explore how performance of ADHD children and unaffected sibs gradually evolves in relation to genetic variants linked to ADHD. Method: A total of 40 kids (20 ADHD-discordant sib pairs) between 8 and 13 years resolved a visual Go/NoGo with 10% NoGo probability. Variable number tandem repeats (VNTRs) at DRD4 and SLC6A3 were identified following standard protocols. Performance changes were assessed by linear/logistic mixed-effect models. Results: Models exploring SLC6A3 effects demonstrated less accentuated increments of response time (RT) (p = .046) and cumulative increments in the correct responses to “NoGo” (p = .00027) in 10R/10R participants. Models for DRD4 showed faster decline of correct responses to “Go” (p = .0078) in 2R/7R carriers. Conclusion: Dynamical analysis of attention/inhibition measures may unravel new correlates to DRD4 and SLC6A3 variants.

Keywords

within-subject variability ADHD genetic variants

Introduction

One of the most consistent findings among ADHD patients is increased between- and within-subject behavioral variability. Although increased between-subject variability could reflect heterogeneity in the patho-physiological pathways involved in the genesis of ADHD, within-subject variability has been proposed as a key feature of the disorder and has become a topic of special interest for researchers in the last few years (Andreou et al., 2007; Bidwell, Willcutt, Defries, & Pennington, 2007; Castellanos et al., 2005; Uebel et al., 2010).

At the neuropsychological level, within-subject variability is expressed as moment-to-moment fluctuations of performance in neuropsychological tasks. To address the dynamic nature of within-subject variability, some authors have applied ex-Gaussian and Fast Fourier Transform (FFT) analyses to quantitative performance measures such as response-time (RT; Geurts et al., 2008; Hervey et al., 2006; Johnson et al., 2007; Leth-Steensen, Elbaz, & Douglas, 2000; Uebel et al., 2010; Vaurio, Simmonds, & Mostofsky, 2009). The information obtained by these approaches is complementary, as the ex-Gaussian analysis allows the division of variability into normal and exponential components, whereas FFT accounts for a detailed examination of the frequency of responses in the exponential distribution. The earliest study applying the ex-Gaussian analysis to RT in ADHD patients was published by Leth-Steensen et al. (2000), who suggested that larger mean RTs previously reported for ADHD children were not due to a generalized slowing down of all responses, but arose because the response sets of ADHD children contained a greater proportion of abnormally slow responses. Subsequent findings imply that ADHD children could present alterations in normal and exponential components of the RT distributions (Hervey et al., 2006). In a recent study, Vaurio et al. (2009) reported an increased variability in the normal/Gaussian and exponential components of RT in ADHD patients, with a significant Task × Diagnosis interaction, suggesting that the number of infrequent slow responses in ADHD children may depend on task demand.

Even when ex-Gaussian and FFT analyses are highly informative in relation to the general distribution adopted by quantitative measures and about the oscillatory/rhythmic components in such distributions, they can be less informative when analyzing more gradual components of variability, like a gradual increase of RT over the entire task, which may be better characterized by regression analysis. To the moment, little is known about gradual changes in the performance of ADHD children along medium/long-lasting neuropsychological tasks. Recently, Johnson et al. (2007) reported a gradual increase in RT and standard deviation of the response time (SDRT) over a Sustained Attention Response Task in those ADHD patients who present a rate of commission errors 1.5 SD above the control commission error mean rate. This effect was neither observed in the control group nor in the group of ADHD patients with better performance. The progressive slowing down over the course of the task reported by Johnson’s group could be reflecting an arousal deficit in a specific subgroup of ADHD participants. Unfortunately, there is still considerable inconsistency between studies exploring the long-term evolution of performance in ADHD participants (Epstein et al., 2003; Loo et al., 2003; Shallice et al., 2002).

The present study was designed to answer how does performance of ADHD children gradually evolves along a Go/NoGo task. To reach this aim, we studied a pilot sample of 20 ADHD-combined-subtype children between 8 and 13 years old and their age-matched unaffected sibs. We analyzed performance over the entire task using general and/or logistic mixed-effect models, which allow us to assess not only quantitative measurements (as RT or SDRT) but also the probability for correct/incorrect responses along the task. Mixed models importantly increase the statistical power of our analysis because they allow the inclusion of multiple observations per person at different time intervals because dependence of observation is controlled by the estimation of a random effect for each participant. Based on previous studies suggesting that within-subject variability may be associated with dopamine dysfunction (Bellgrove, Hawi, Kirley, Gill, & Robertson, 2005; Bellgrove, Hawi, Lowe, et al., 2005; MacDonald, Cervenka, Farde, Nyberg, & Backman, 2009), we decided to analyze the aforementioned results regarding the presence/absence of genetic risk variants previously linked to ADHD in the genes encoding for the dopaminergic receptor D4 (DRD4) and for the dopaminergic tranporter 1 (SLC6A3). This is one of the earliest studies exploring the phenotypic correlates of genetic risk variants for ADHD from a dynamic behavioral perspective.

Method

Participants

This study included 20 sib pairs composed by an ADHD-combined-type patient and his or her unaffected sib. Groups were comparable by age—ADHD group: M = 11.2 years, SD = 2.47 and sibs group: M = 11 years, SD = 2.35; t(38) = 0.249; p = .84—and sex distribution—ADHD group: 17 boys and 3 girls and sibs group: 13 boys and 7 girls; χ²(1) = 1.2; p = .27. Probands were originally recruited as part of an ongoing genetic association study and were referred from general psychiatric and neurological outpatient services. All participants were evaluated by a competent specialist (child psychologist or child neurologist) to confirm the following inclusion/exclusion criteria.

For probands the criteria were as follows: ADHD-combined subtype according to Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994), age between 8 and 13 years, average or higher IQ assessed by Wechsler Intelligence Scale for Children–Revised (WISC-R; Wechsler, 1974), good response to stimulant medication, and having at least one unaffected sib in the same range of age. For sibs the criteria were the following: age between 8 and 13 years and absence of ADHD or any other psychiatric morbidity according to DSM-IV criteria. As we could not evaluate IQ by mean of WISC-R in sibs, we only included children with average or higher school performance. Children with neurological deficit at physical examination and/or abnormal baseline electroencephalography (EEG) were excluded from the study.

Genotyping

Venous blood samples were obtained from all participants. Genomic DNA was isolated from lymphocytes by standard methods (Wang et al., 2004) and amplified by polymerase chain reaction (PCR) to identify the VNTR of the DRD4 and SLC6A3 loci according to the protocols described by Lichter et al. (1993) and Cook et al. (1995), respectively. Primers were as follows: For DRD4, 5′- GCG ACT ACG TGG TCT ACT CG and 5′-AGG ACC CTC ATG GCC TTG; for DAT1, 5′-TGT GGT GTA GGG AAC GGC CTG AG and 5′-CTT CCT GGA GGT CAC GGC TCA AGG. PCR products were visualized by electrophoresis in 3% agarose gel.

Task and Procedures

Participants were seated at approximately 100 cm from a monitor in a sound-attenuated chamber under low illumination and asked to perform an 8-min Go/NoGo task. The stimuli were generated using the STIM software and corresponded to green (Go) and red (NoGo) circles of 2 cm in diameter presented at the center of a black screen during 300 ms, with an intertrial period of 1,000 ms. Go stimuli had a probability of presentation of 90% and a pseudo randomization process ensured that at least 6 Go were presented before each NoGo stimulus. The participants were instructed to respond as quickly as possible with their preferred hand by clicking in a console after every Go stimulus, but to inhibit the response in front of a NoGo. The total task comprised the presentation of 360 Go and 40 NoGo stimuli. The task was preceded by a practice session and started once the participants confirmed they understood the instructions.

To exclude confounders related to the pharmacological treatment, a wash-out period of 24 hr was required in ADHD patients.

Data Analysis

To characterize the task-general performance (nondynamic) regarding clinical status and genotype, we compute the mean RT, the within-subject standard deviation of response time (wsSDRT), and proportion of correct responses to Go and NoGo stimuli in every child. Group comparisons were performed by means of parametrical methods. “Genotype” groups were defined as follows: For DRD4, the participants were classified as part of the “control” or “nonrisk” group when they were homozygous for the DRD4 4-repeat allele (n = 22; 9 of them were ADHD patients) and as part of the “risk” group when they carried at least one copy of any other alleles (n = 18; 11 of them were ADHD patients). The “risk” group was formed by 11 carriers of the 7R allele (3 of them in homozygous state) and 7 carriers of the 2R (1 of them in homozygous state). There was no association between allele possession for Exon 3 VNTR of DRD4 and clinical diagnosis of ADHD, χ²(1) = 0.9, p = .34. We defined DRD4-groups based on two lines of evidence: (a) previous reports of association between ADHD and the 2R and 7R variants of DRD4 in different populations (Faraone, Doyle, Mick, & Biederman, 2001; Leung et al., 2005; Manor et al., 2002) and (b) in vitro studies suggest that receptors encoded by 2R and 7R alleles mediate a reduced response to dopamine in comparison with the 4R allele (Asghari et al., 1995). Concerning SLC6A3, based on the high prevalence of 10R allele in Chilean population (Carrasco et al., 2004; Carrasco et al., 2006), participants were classified as homozygous for 10R or “risk” group (n = 24; 13 of them were ADHD patients) and as other genotypes or “control” group (n = 15; 6 of them were ADHD patients). The latter group comprised 14 participants with 9R/10R genotype and 1 homozygous for the 9R allele. There was no association between allele possession for 3′UTR VNTR of SLC6A3 and clinical diagnosis of ADHD, χ²(1) = 0.28, p = .59. As age and sex distributions were not comparable between the new-made genotype groups, all reported results were adjusted by age, sex, and clinical status (ADHD presence/absence) by means of linear multiple regression models, considering “ADHD status” (1 = present/0 = absence), “genotype group” (1 = risk group/0 = control group) “age,” and “sex” (1 = male/0 = female) as independent variables.

To study intratask gradual changes in “mean RT” and “wsSDRT,” we divided the complete task in 10 consecutive “time-blocks,” so we were able to compute the new ordinal variable “Time-block relative position,” which reflected the relative location of responses inside the task and took values from 1 to 10. Then, the new variables “intrablock mean response time (ibSDRT)” and the “intrablock within-subject variability of the response time” (ibwsSDRT) were estimated for each block considering correct trials only. The data obtained by the aforementioned procedure were analyzed by means of a mixed-effect model regression as described by Laird and Ware (1982). Specifically, we assigned a random effect to each one of the participants in the sample and fixed effects to variables “age,” “sex,” “genotype,” “ADHD status,” and “time-block relative position” (which took values 1-10 as mentioned before). Independent variables “ADHD status,” “genotype,” and “sex” were defined as previously described for multiple regression models in nondynamical analysis. In a second step, we explored for interactions between the aforementioned variables.

Finally, to model the probability of correct responses to Go and/or NoGo stimuli, we numbered the stimuli (trials) consecutively from 1 to 400 and included this new variable in a mixed-effect logistic regression model, including fixed effects for “age,” “sex,” “genotype,” “ADHD status,” and “trial number” and a random effect for each one of the participants in the sample.

All models were adjusted using R statistical package (Yssaad-Fesselier & Knoblauch, 2006).

Ethical Issues

All procedures performed as part of this study were approved by the Ethics Committee of the Pontificia Universidad Católica de Chile. The study was fully explained to children and their parents, and they both agreed to participate by signing written consent forms.

Results

General Performance by Clinical Status and Genotype (Nondynamic Analysis)

Table 1 Part A summarizes the participant’s general (nondynamic) performance regarding clinical status (ADHD presence/absence). Analysis is presented in terms of “Percentage of Correct Responses to Go and NoGo stimuli,” “mean RT,” and “SDRT,” in the complete task as a single block. As observed, ADHD patients presented less correct responses to Go stimuli compared with sibs (80.5% ADHD group vs. 89.8% in sibs; p = .0006). Trends for a longer mean RT (p = .07) and a bigger wsSDRT (p = .08) in the ADHD group are also noted.

Table 1.

Nondynamical (General) Analysis of Performance Measures.

	Part A		Part B		Part C
	ADHD	Sibs	DRD4 4R/4R	Other than DRD4 4R/4R	DAT 1 10R/10R	Other than DAT1 10R/10R
Correct to Go (%)	80.5	89.8	90.5	76	81	85.1
Correct to NoGo (%)	q	69.2	64.9	59	55.9	65.2
Mean RT (msec)	312.55	290.05	283.98	324.23	310.26	300.28
wsSDRT (msec)	134.31	116.51	116.6	123.6	138.66	129.21

Note. Part A—ADHD patients compared with sibs; Part B—Analysis in the DRD4-risk group (2R and/or 7R carriers) and DRD4-nonrisk group (homozygous for 4R). Part C—Analysis in SLC6A3-risk group (homozygous for 10R) and SLC6A3-nonrisk group (at least one 9R allele). Reported significance levels were adjusted by age, sex, and ADHD presence/absence.

p < .05. **p < .01. ***p > .001.

Table 1 Part B shows performance analysis regarding genotype (DRD4 and SLC6A3 groups as defined in “Method” section). When compared with 7R/2R-DRD4 carriers, participants from the nonrisk DRD4 group (homozygous for 4R variant) had a better performance in the task, reflected as a greater percentage of correct responses in front to Go stimuli (90.5% in the nonrisk group vs. 76% in the risk group; p = .011), independently of the presence/absence of ADHD. No significant results arose from the analysis of SLC6A3 effect after controlling by age, sex, and clinical status (presence/absence of ADHD).

Intratask Evolution of Performance (Dynamic Analysis)

Models exploring de effect of DRD4 genotype

When applying mixed-effect models to characterize intratask gradual changes of performance and their eventual relationship to DRD4 genetic variants, we observed that all participants presented a decrease in the “Probability for Correct Responses to Go stimuli” over the task, independently of their clinical and/or genotype status. This was expressed in the logistic models as a statistically significant effect (p < .0001) of the number of already presented stimuli on the probability for correct responses (trial effect). Interestingly, the decline was more accentuated in the group of “DRD4-risk genotype,” with a significant interaction between the number of already presented stimuli and “DRD4 genotype” (p = .0078). The effect of this interaction was statistically significant even after controlling by sex, age, and ADHD status (see Table 2 for details). Figure 1 shows an example of the probabilities modeled for each genotype (risk group and control group) in ADHD and control boys at age 11. We did not find significant net effects of DRD4 on the gradual evolution of the variables “ibRT” (p = .9) and “ibwsSDRT” (p = .64).

Table 2.

Estimated Fixed Effects for All Dependent Variables Included in Logistic/Linear Mixed-Effect Regressions.

Model for . . .	Dependent variable	Estimated effect	SE	p
Probability for correct responses to NoGo stimuli (analysis by SLC6A3)	Age	0.074	0.061	0.223
	Sex	−0.111	0.298	0.71
	ADHD status	0.43	0.263	0.103
	SLC6A3-risk group	1.313	0.363	3e-04
	Trial	0.004	0.001	2.38e-05
	SLC6A3-risk group × Trial	−0.004	0.001	3e-04
Probability for correct responses to Go stimuli (analysis by SCL6A3)	Age	0.228	0.016	2e-16
	Sex	0.695	0.072	2e-16
	ADHD status	1.014	0.07	2e-16
	SLC6A3-risk group	0.373	0.127	0.003
	Trial	−0.003	0.001	2.27e-09
	SLC6A3-risk group × Trial	−2e-04	5e-04	0.644
Probability for correct responses to Go stimuli (analysis by DRD4)	Age	0.032	0.071	7e-06
	Sex	0.707	0.358	0.049
	ADHD status	0.95	0.339	0.005
	DRD4-risk group	−0.513	0.347	0.139
	Trial	−0.002	4e-04	1.42e-07
	DRD4-risk group × Trial	−0.001	0.0005	0.008
Intrablock mean reaction time (ibRT; analysis by SLC6A3)	Age	−11.921	5.569	0.041
	Sex	−39.086	28.144	0.176
	ADHD status	57.983	24.857	0.027
	SLC6A3-risk group	19.376	28.364	0.500
	“Time-block” relative position	4.420	1.739	0.012
	SLC6A3-risk group × Trial	−4.175	2.083	0.046

Note. Significant effects are in bold font. Only models with statistically significant effects for “SLC6A3/DRD4-risk groups” or for the interaction “SLC6A3/DRD4-risk Groups × Trial/time-block relative position” are shown.

Figure 1.

Graphical simulation of the effect of DRD4 genotype in the probability of correct responses to Go stimuli over the complete task in ADHD patients (A) and sibs (B), as modeled by our best fit mixed-effect logistic model.

Models exploring the effect of SLC6A3/DAT-genotype

Although the nondynamic analysis failed to show significant effects of SLC6A3 genotype on the summary performance measures of the Go/NoGo task, the dynamic approach enabled us to demonstrate significant net effects of SLC6A3 genotype on the dependent variables ibRT and Probability for Correct Responses to both Go and NoGo stimuli (Table 2).

First of all, in agreement with some previous reports suggesting a better performance in attention/inhibition tasks among carriers of the 10R allele, the model exploring the effects of “SLC6A3 genotype” in temporal evolution of responses to NoGo stimuli showed a significant interaction between the number of already presented trials and genotype (p = .00027), with a gradual increase of the probability for correct responses over the task in the group homozygous for the 10R variant. Age, sex, and ADHD status were not significant in this model. Figure 2 shows a graphic example of this interaction. In the same line, the models exploring the effects of “SLC6A3 genotype” on the probability for correct responses to Go stimuli showed a significant net effect of SLC6A3 genotype over the probability of correct responses to Go stimuli (p = .0032), with participants homozygous to the 10R variants presenting a general better performance in the task. No Genotype × Trial interaction was present in this model (p = .6).

Figure 2.

Graphical simulation of the effect of SLC6A3/DAT1 genotype in the probability of correct responses to NoGo stimuli over the complete task in ADHD patients (A) and sibs (B), as modeled by our best fit mixed-effect logistic model.

However, the linear mixed-effect model for ibRT, designed to characterize eventual increments/decrement in the mean RT over the task, showed a significant interaction between “Time-block relative position” and “SCL6A3 genotype” (p = .046) with carriers of at least one 9R allele presenting a greater increment of RT over the task. As an example, Figure 3 shows the RT modeled along the task for ADHD boys at age 11 carrying the “10R/10R genotype” (continuous line) or “at least one copy of 9R” (fragmented line). As observer, Response Times for carriers of at least one 9R allele gradually increase from 298 ms to 338 ms, whereas RTs for 10R homozygous keep nearly constant values around 315 ms.

Figure 3.

Graphical representation of the effect of SLC6A3 genotype in the temporal evolution of the reaction time in ADHD boys at 11 years old.

Finally, we did not observe significant effect of SLC6A3 genotype nor significant Genotype × Time interaction when exploring models explaining “ibwsSDRT” as the dependent variable.

Discussion

This study was designed as a pilot research exploring the potential utility for new dynamical approaches to characterize the complex behavior of ADHD children and its correlation with some genetic variants related to the dopamine signaling process. Our intention was to overcome the pitfalls inherent to more traditional statistical approaches, mainly based on central tendency measurements, which generally loses information in regard to the temporal domain of the behavioral measurements.

Models exploring the cognitive-dynamical correlates for DRD4 variants revealed that carriers of at least one copy of the 2R or 7R-DRD4 alleles present a faster deterioration in the chance for correct responses to Go and NoGo stimuli, with statistically significant effects in the case of Go stimuli (p = .0078) and marginally significant effects in the case of NoGo stimuli (p = .07). Such deterioration was observed in ADHD patients and control sibs and was independent of potential confounders (age, sex, and clinical status). Noteworthy, the net effect of clinical status (ADHD presence/absence) was significant but counterintuitive, with better performances for ADHD children in comparison with sibs over the complete task when isolating the effects of age, sex, and genotype. This suggests that previously reported underachievement of ADHD patients could be explained by the genotype contribution to the dynamical evolution of performance over the task. At a cognitive level, these results may be reflecting alterations in the arousal maintenance (Bunce, MacDonald, & Hultsch, 2004) and/or defective effort control mechanism in 7R/2R carriers. Both deficits have been considered in theoretical models for ADHD (Sergeant, 2000, 2005).

Our findings are in agreement with some previous studies exploring the neurocognitive phenotype associated to DRD4 variants, which reported poorer inhibition/attention outcomes in patients and healthy population carrying the 2R/7R DRD4 variants (Auerbach, Benjamin, Faroy, Geller, & Ebstein, 2001; Congdon et al., 2008; Kieling et al., 2006; Kim et al., 2009). Noteworthy, the results from Auerbach et al. (2001) suggested that alterations in the attention process may be observable in carriers of 7R allele as young as 1 year old (Auerbach et al., 2001). In a related line, in a cohort study including 105 ADHD children and their healthy controls, Shaw et al. (2007) demonstrated a significant effect of the presence of the 7R-DRD4 allele in the cortical thickness of the right orbitofrontal/inferior frontal and right parietooccipital regions (previously linked to response inhibition and within-subject variability of performance) with ADHD-7R carriers presenting the thinnest cortex, followed by ADHD noncarriers, healthy 7R carriers, and finally, healthy noncarriers. All together, these results (including ours) are consistent with the neurocomputational work of Li and Sikstrom (2002), where dopamine deregulation is suggested to alter the signal-to-noise ratio of neural information processing, leading to noisier information (i.e., signal) processing and impaired cognitive functioning.

However, not all previous findings are in agreement with our results. Some studies report no effect of DRD4-risk variants on attention/inhibition or even a better performance of 7R carries in Continuous Performance Test (CPT)/inhibition tasks, mainly in terms of RT and SDRT (Barkley et al., 2006; Bellgrove, Hawi, Lowe, et al., 2005; Swanson et al., 2000). Among them, the only previous study exploring the correlation between dynamic within-subject variability and the 48-bp VNTR of DRD4 (by means of FFT models) has shown a significant Genotype × Diagnostic interaction for omission errors and for the slow frequency domain of within-subject variability, reflected as a poorer performance of the 7R absent group of ADHD patients in comparison with the 7R present group of ADHD patients (Johnson et al., 2008). This incongruence with our results may be explained by differences in task design. In particular, our study was designed to achieve highly automated responses. Based on the recent findings reported by Vaurio et al. (2009), under such conditions, increased variability is principally due to infrequent slow responses, which suggest alterations in arousal regulation. In opposition, task conditions that require higher degrees of response control may be particularly sensitive to explore eventual alterations in the mechanisms involved in engaging a state of preparedness to respond.

Concerning the phenotypic correlates for SLC6A3 genetic variants, the logistic model fitting for correct responses to NoGo stimuli showed a significant interaction between “SLC6A3 genotype” and the number of already presented trials, reflected as a gradual increase of the probability for correct responses along the task in the group of homozygous for the 10R variant (risk genotype). Such increment resembles a “learning effect” and is not observed in carriers of at least one copy of the 9R variant. In the same line, the logistic model fitting for the correct responses to Go stimuli showed a significant effect of SLC6A3 genotype, with an overall better performance for 10R/10R carriers. In addition, 10R homozygous presented significantly smaller increments of RT along the task, which implies that the aforementioned improvement in correct responses did not affect overall RTs in the 10R group. These results are counterintuitive because 10R allele has demonstrated to be associated with increased expression of DAT1, which should lead to lower levels of dopamine in the synaptic space. Even when counterintuitive, they are in agreement with previous reports of a better performance in the 10R/10R participants in tasks assessing motor inhibition and/or sustained attention (Barkley et al., 2006; Boonstra et al., 2008; Kim et al., 2006). To explain this kind of observations, Fossella et al. (2002) proposed that both higher and lower than average levels of synaptic dopamine may lead to cognitive impairment, and in conclusion, alleles leading to higher levels of dopamine may not be associated with better performance. Alternatively, Mill et al. (2006) suggested that the combination of specific genotypes (rather than the presence/absence of a single risk allele) could lead to cognitive/behavioral dysfunction, emphasizing the role of Gen × Gen interactions in ADHD and other neuropsychiatric disorders (Carrasco et al., 2006). Unfortunately, due to sample size constrains, we were unable to explore how Gene × Gene interaction could explain the aforementioned results.

The presented results suggest that dynamical gradual evolution of selected attention/inhibition measures over a Go/NoGo task could be related at a phenomenological level to dopaminergic function and may represent phenotypic correlates to DRD4 and SLC6A3 genetic variants. Noteworthy, the use of mixed-effect model to characterize participant’s performance from a dynamical perspective has unrevealed net genotype effects previously masked by the traditional nondynamic analysis, based on central-tendency measurements, which is generally blind to temporal-related changes in behavior such as learning effects. New studies overcoming our sample size limitations are mandatory to replicate these initial findings.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financed by the project 1080219, from the National Fund for Science and Technology, FONDECYT, Chile.

Author Biographies

Marcela Henríquez-Henríquez finished her MD studies at School of Medicine, Pontificia Universidad Católica de Chile, in 2003 and obtained her PhD in Medical Sciences in the same institution in 2008. Her doctoral thesis was based on the search of new endophenotypes for ADHD. Currently, she is finishing her clinical specialization in Clinical Pathology and Laboratory Medicine at the School of Medicine of Pontificia Universidad Católica de Chile.

Luis Villarroel obtained his PhD in Statistics at Faculty of Mathematics of Pontificia Universidad Católica de Chile in 2005. Currently he is an Associated Professor of the Public Health Department, School of Medicine of Pontificia Universidad Católica de Chile.

Hugo Henríquez studied Clinical Chemistry and Laboratory Medicine in Universidad de la Frontera, Temuco, Chile. Nowadays he is finishing his Master in Human Genetics at Universidad de Chile. Currently he is an active member of the Expert Committee in Immune-hematology at the Public Health Institute of Chile.

Francisco Zamorano finished his studies in Biological Sciences at Pontificia Universidad Católica de Chile in 2006. Currently he is a PhD. C. in Medical Sciences, working at the Cognitive Neuroscience Laboratory at the Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile.

Francisco Rothhammer has a DDS from the University of Chile (1964) and a PhD in Biomedical Sciences from the University of Chile (1977). He is the Director of the “Instituto de Alta Investigación” at the Universidad de Tarapacá, Chile.

Francisco Aboitiz studied Biology at the Faculty of Sciences, University of Chile (1983), obtained a PhD in Neuroscience at the University of California, Los Angeles (1991), became Professor at the Faculty of Medicine, University of Chile (1992-2002) and as of 2002 is Professor of the Department of Psychiatry at the Faculty of Sciences at the Pontifical Catholic University of Chile. As of 2010, he is also Chairman of the Interdisciplinary Center for Neuroscience at the same University.

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