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Article

Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD †

by
Johan E. Acosta-López
1,*,‡,
Isabel Suárez
2,
David A. Pineda
3,
Martha L. Cervantes-Henríquez
1,2,
Martha L. Martínez-Banfi
1,
Semiramis G. Lozano-Gutiérrez
1,
Mostapha Ahmad
1,
Wilmar Pineda-Alhucema
1,
Luz M. Noguera-Machacón
1,
Moisés De La Hoz
1,
Elsy Mejía-Segura
1,
Giomar Jiménez-Figueroa
1,
Manuel Sánchez-Rojas
1,
Claudio A. Mastronardi
4,
Mauricio Arcos-Burgos
5,‡,
Jorge I. Vélez
2,*,‡ and
Pedro J. Puentes-Rozo
1,6
1
Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia
2
Universidad del Norte, Barranquilla 081007, Colombia
3
Neuropsychology and Conduct Research Group, University of San Buenaventura, Medellín 050010, Colombia
4
INPAC Research Group, Fundación Universitaria Sanitas, Bogotá 111321, Colombia
5
Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia
6
Grupo de Neurociencias del Caribe, Universidad del Atlántico, Barranquilla 081007, Colombia
*
Authors to whom correspondence should be addressed.
†
This paper is part of Johan E. Acosta-López’s Ph.D. Thesis—Tiempos de Reacción como Probable Endofenotipo en Trastorno Por Déficit de Atención e Hiperactividad from Universidad Maimónides, Buenos Aires, Argentina, delivered in November 2017.
‡
These authors contributed equally to this work.
Brain Sci. 2021, 11(9), 1218; https://doi.org/10.3390/brainsci11091218
Submission received: 27 July 2021 / Revised: 1 September 2021 / Accepted: 2 September 2021 / Published: 15 September 2021
(This article belongs to the Section Behavioral Neuroscience)
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Versions Notes

Abstract

:
Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients’ diagnosis, follow-up, and longitudinal assessment in the clinical setting.

1. Introduction

Attention deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder that affects up to 5–16% of children and adolescents worldwide [1,2,3,4,5,6,7]. The Diagnostic Statistical Manual of Mental Disorders [8] specifies the existence of three different Presentation of ADHD, namely predominantly inattentive, predominantly hyperactive-impulsive, and combined type. Several studies based on multigenerational families, adoption, and twin studies, have provided compelling evidence that genetic factors contribute to a substantial portion of the ADHD phenotypic variance [9]. In particular, first and second degree relatives of patients with ADHD have markedly higher prevalence of symptoms [10].
Given the highly variable clinical manifestations of ADHD, it has been suggested that endophenotypes might be a better approach to dissect genetic and neurophysiological basis of ADHD. The endophenotype concept refers to markers that are more directly connected to neuropsychological, behavioral, or neuroanatomical components associated with the disease [11,12,13]. Endophenotypes might be defined as “measurable components”: (1) associated with the disorder occurring at a higher frequency in affected individuals than in the general population; (2) heritable and state-independent (that is, manifest in individuals whether the illness is active); (3) co-segregating with the disease through generations within families; and (4) enriched in the causal pathway between genes and disease [13,14,15,16]. Endophenotypes overcome the inherent difficulties of a symptoms-based clinical diagnosis, and have the potential to differentiate between potential diagnoses that present with similar symptoms [17,18], which make them suitable to study the genetic and neurophysiological basis of ADHD. It is crucial that endophenotypes are easily measurable with standardized strategies for their quantification and evaluation [19].
Several ADHD studies have identified potential cognitive endophenotypes in neuropsychological tasks [11,20,21,22], including dysfunctions in response inhibition, sustained attention, working memory, and motor control [23]. However, the cognitive mechanism boiling down all those cognitive dysfunctions is the temporal processing (TP), which is one of the principal measures reported to be affected in patients diagnosed with ADHD when different tasks and paradigms are used [11,23,24,25].
TP is associated with functions such as perception, verbal skills, temporal perspective, and future planning. These time-dependent functions are intercorrelated with other impaired functions in ADHD such as working memory, attention, and to a lesser degree inhibitory control. These key timing deficits appear to survive when these functions are controlled, suggesting independent cognitive deficits in the temporal domain [26]. Strong evidence in time deficits and behavioral measures of impulsivity and inattention suggests that timing issues are key to the clinical behavioral profile of ADHD [27]. TP is classically measured using reaction time (RT) paradigms, which provide an interesting context to study different measures of TP.
RTs are defined as the elapsed time (usually in milliseconds [ms]) between the presentation of a stimulus until the reaction or response and constitute a measure of information processing. Errors of commission provide an index of faster information activation (i.e., impulsivity or attention) [28], while omission errors and variability are classically related as indexes of sustained attention, respectively [28]. During RT-based paradigms, it is classically observed that faster TP increases the number of errors of commission while more omissions arrived at the longest RT [29].
In addition to the aforementioned deficits, deficiencies in the perception and subjective appreciation of time are equally relevant [26,27]. ADHD affected individuals show difficulties in their inner clock due to failures in sequential continuity awareness, reproduction and perception of time, leading to overly fast operation in standby periods. Thus, TP is an important neuropsychological component that has previously been proposed as an ADHD endophenotype [30,31,32]. In this regard, greater impairment of TP is found ADHD inattentive individuals [26]. Other authors found that individuals with ADHD take longer to complete tasks than healthy controls, showing a greater discrepancy between the completion times of the expected objectives [33]. Likewise, the use of TP tasks provides evidence of anticipatory and/or impulsive responses, due to failures in the discrimination of short RT intervals [34,35,36,37], which suggests that ADHD affected are prone to a greater number of commission errors. Overall, ADHD affected individuals exhibit TP dysfunction, at least as far as temporal reproduction is concerned [38].
To our knowledge, there are no reports of family-based studies in Caribbean communities, especially in those of significant African admixture, that investigate different measures of TP as possible cognitive endophenotype in patients diagnosed with ADHD. Here we explored the definition of TP endophenotypes in a sample of 232 individuals belonging to 67 nuclear families from Barranquilla, Colombia, the biggest city of the Northern Colombian Caribbean coast, who were clinically characterized using neuropsychology batteries as well as TP measures using RT-based tasks. Our overarching hypothesis was that there were specific patterns of TP and the existence of different endophenotypes in this community that represent a significant variance of the ADHD symptomatology, which could play an important role in neuropsychological models of TP [39,40,41,42] and help to better understand the etiology of this neuropsychiatric condition [10,39,40,41,42,43,44,45]. To shed some light into the role of these neuropsychological and TP measures on ADHD diagnosis, a predictive model of ADHD status that includes sex, age and potential RTs endophenotypes as predictors was constructed.

2. Subjects and Methods

2.1. Subjects

We recruited 232 individuals from 67 nuclear families segregating ADHD with at least a single ADHD-affected individual, from patients attending our research program in ADHD advertised in the Grupo de Neurociencias del Caribe’s website [46]. All individuals participated voluntarily and provided informed written consent either directly or from their parents. Initially, 124 nuclear families with at least one child affected with ADHD were sequentially ascertained. However, because of the nature of the RT task (see below), only full families with children aged 6 years and above were included. Most families were of medium socioeconomical strata and inhabit the city of Barranquilla and its metropolitan area in the Atlántico Department located at the Northern Colombian Caribbean coast. Barranquilla’s population is the result of a racial admixture between Aboriginal Amerindian communities with Spaniards and Africans, and later with Syrians-Lebanese, Sephardi Jews, Germans, Italians, and English immigrants [47,48]. The admixture composition of this community (~63% African with a vast Amerindian contribution) [49] suggests an ethnic heterogeneity [50,51] that substantially differs from that of the “Paisa” community, a genetic isolate from Colombia with a high prevalence of ADHD and a very small African ethnical background [52,53,54,55,56]. This study was approved by the Ethics Committee of Universidad Simón Bolívar at Barranquilla, Colombia (approval # 00032 of 13 October 2011).

2.2. Clinical Assessment

2.2.1. ADHD Diagnosis

We performed an extensive clinical, neurological, and neuropsychological evaluation to define ADHD status and the presence of other comorbidities as described elsewhere [20,57,58,59]. Briefly, we employed the Diagnostic Interview for Children and Adolescents version IV (DICA-IV) [60,61,62] as the gold standard to assess the diagnosis of ADHD and/or ADHD comorbidities (i.e., conduct disorder [CD] and oppositional defiant disorder [ODD]) in children, adolescents and adults. For children and adolescents, the DICA-IV structured interview was completed by their parents who reported children’s symptoms and consequences in the academic, legal and work-related areas, as well as alcohol and tobacco consumption, and its consequences [60,62,63]. Persistent symptoms impacting family, social and work-related environments were also recorded. Following the C criteria of DSM-IV, ADHD symptoms in children and adolescents were evaluated by their parents and teachers using the Colombian version of the Behavioural Assessment System for Children (BASC) [64] and the ADHD checklist [65,66].

2.2.2. Neuropsychological Assessment

Stroop’s Color and Word Test

This test assesses individual’s ability of selective attention (i.e., inhibitory control) and requires individuals to suppress automatic responses in favor of a specific response requested by the evaluator [67]. The test consists of three paper sheets: (1) administration sheet: a list of 100 words corresponding to the color’s names red, blue and green written in black ink; (2) color sheet: 100 sets of four letters “X”; and (3) interference sheet: indicates the color-word administration condition and contains 100 words with colors’ names (red, blue and green) written in color ink that mismatch the color of the actual word [68,69,70].

Cross-Out-Squares Test

Similar to Toulouse-Pierrón test [71], the cross-out-squares test (COST) assesses sustained attention with nonverbal stimulation. It consists in presenting 140 squares with a line in different positions in one of the angles or sides of each square. Individuals should cross-out as fast as possible squares that were equal to the stimuli at the top of the sheet. The total number of correct cross-out squares (maximum 48 points), as well as the number of omission and commission errors and the time to complete the task are quantified. In individuals with ADHD, these variables reflect difficulties at inhibitory level [72].

Trail Making Test (TMT)

This is a paper-and-sheet test with two parts. In Part A, numbers 1 to 25 are randomly presented in a paper sheet. Individuals should connect, as fast as possible, the numbers in consecutive order using a straight line (that is, 1-2-3-...-25). In Part B numbers 1 to 13 and letters A to L are randomly in a paper sheet. The task consists of connecting, alternating in consecutive order, numbers and letters (i.e., 1-A-2-B-3C-4D-5E-6F-…-13-L) [72,73,74,75,76,77].

2.2.3. Reaction Time Tasks Assessment

Conner’s Continuous Performance Test-II (CPT-II)®

This is a computerized test in which stimuli consists of letters presented every 250 ms [78,79,80]. A total of 360 letters in six blocks are randomly presented. The time interval of stimuli presentation (1, 2 or 4 s) varies within blocks. Individuals are told to press the spacebar when all letters but letter “X” are shown. The total completion time of the CPT is 14 min and can only be administered to individuals aged 6 years or more [81].

Go/No-Go Tasks

We administered a two-part Go/No-Go (GNG) task using visual signals [82,83]. Part A consists of 50 randomized trials of 2000 ms long and 1000 ms inter stimulus interval, with a usual GNG naturalistic dominant stimuli (green [go] and red [no-go] signals). Individuals had to react the Go signal (green light) and had to stop the response to the known No-Go signal (red light). Part B consists of 50 trials of reversal Go/No-Go stimuli (red [go] and green [no-go] signals) with the same randomized and automatically scored conditions of Part A. Here, the Go signal was changed to red light and the No-Go or stop signal was changed to green light [83]. RTs were obtained and recorded for both Part A and Part B using a multi-operational apparatus for reaction times (MOART) system model 35600, with PsymSoft II Psychomotor Control Software model 35800. Individuals performed the GNG tasks between 8 and 11 am, in a light-, temperature-, and noise-controlled environment [82,83].

2.3. Statistical Analysis

Means and standard deviations (SDs) were used to summarize numerical variables. These variables were compared among ADHD affected and unaffected individuals using two-sided Mann–Whitney U test for two independent samples. Uncorrected Cohen’s d was calculated to measure the effect size for all variables [84,85]. To avoid the effect of potential confounding variables such as age and sex, p-values were corrected using analysis of covariance (ANCOVA). For categorical variables, frequencies and proportions were calculated; comparisons were performed using a χ2 test of independence. All statistical analyses were performed in R version 3.4.3 (https://www.R-project.org/, accessed on 6 December 2017).

2.4. Heritabilty Estimation

Heritability is defined as the proportion of the total phenotypic variation explained by genetic factors [86]. To estimate the heritability of neuropsychological and RT-based variables in our set of families, we used the ASSOC module in the Statistical Analysis of Genetic Epidemiology (SAGE) software [86,87,88], which evaluates the association between a continuous trait and one or more covariates from pedigree data in the presence of familial correlations, and simultaneously estimates familial variance components (i.e., familial correlations and heritability) [87]. Parameters in the segregation model evaluated by ASSOC are estimated by maximum likelihood under the assumption that all parameters, including polygenic heritability and other familiar correlations, follow multivariate normality [87,88,89,90].

2.5. Predictive Model for ADHD

Following previous work by our group and others [20,91,92,93,94,95], we applied Advanced Recursive Partitioning Approach (ARPA) using the Classification and Regression Tree (CART) [96,97] and TreeNet [98] modules implemented in the Salford Predictive Modeller® software suite (Salford Systems, San Diego, CA, USA; https://www.salford-systems.com/, accessed 10 December 2017) to construct a predictive tree-based model of ADHD status in our cohort. A short description of CART, Random Forest, and TreeNet is provided elsewhere [20,91,92,94,95]. Sex, age, and potential TP endophenotypes were used as predictors. ARPA is widely used in predictive analyses as (1) it accounts for non-linear hidden interactions, (2) is independent of the type of data and of the data distribution type, (3) offers fast solutions to reveal hidden complex substructures and (4) provides non-biased statistical analyses of high dimensional data [99]. The performance of this predictive model was assessed by calculating the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC), as well as different performance measures for binary classifiers including the sensitivity (Se), specificity (Sp), and correct classification rate (accuracy), among others (Supplementary Material).

3. Results

3.1. Subjects

Two-hundred and thirty-two individuals (104 [44.8%] females, 128 [55.2%] males) from 67 nuclear families were included in this study (Table 1). From this group, 124 (53.4%) individuals were diagnosed with ADHD (40 [32.3%] females, 84 [67.7%] males). No children or adults were under medication for ADHD. The male-to-female ratio was 2.1 (95%CI = 1.46–3.16) among ADHD affected individuals; as expected, the ADHD diagnosis distribution differed by sex (χ2 = 15.94, degrees of freedom [df] = 1, p = 6.53 × 10−5). The mean ± SD for age at diagnosis in the whole sample was 28.24 ± 15.45 (range: 8–60); statistically significant differences were found by ADHD status (affected: 23.13 ± 15.6; not affected: 34.11 ± 13.06) but not by sex (female: 28.99 ± 13.88; male: 27.64 ± 16.64, W = 6715.5, p = 0.9075). The average family size was 3 ± 0.64 individuals (range 3–6); a total of 40 (59.7%) trios, 24 (35.8%) quartets, two (3%) families with five members, and one (1.5%) family with six members were recruited. Of note, this sample differs from that used in previous studies of ADHD in this Caribbean Community [20,59,70] because the CPT can only be applied to individuals aged 6 years or more.

3.2. Differences in TP Neuropsychological Tests between Affected and Unaffected Individuals

Our main results are presented in Table 2. After controlling for age and sex, we found statistically significant differences between ADHD affected and unaffected individuals in tasks of temporal processing of information, which assess the RT and individual’s performances. In particular, ADHD affected individuals showed more omission errors than unaffected individuals in MOART-based tasks (0.27± 0.64 vs. 0.07 ± 0.3, p = 0.029) and COST (6.32 ± 5.71 vs. 4.77 ± 4.34, p = 6.02 × 10−12), and more lecture errors in the Stroop test (1.3 ± 2.01 vs. 0.6 ± 1.12, p = 0.049) (Figure 1a), which refer to the number of incorrectly identified colors. We also found differences between ADHD affected and unaffected individuals in the typical scores of the omission and commission errors (Punctuation 1; 62.22 ± 32.63 vs. 58.48 ± 20.17, p = 0.015) and perseverance (Punctuation 8; 60.77 ± 23.63 vs. 58.6 ± 22.16, p = 0.020) of the CPT. Furthermore, individuals with ADHD obtained a lower hit rate than unaffected individuals in the COST (41.62 ± 5.71 vs.42.88 ± 4.48, p = 0.026) (Figure 1a).

3.3. Heritability Estimates

We found strong statistical evidence supporting hereditary transmission (i.e., heritability parameter, h2) in neuropsychological tests assessing RT in our sample (Table 2 and Figure 1b). These variables include the omissions in Part A (h2 = 0.349, p < 0.004) and Part B (h2 = 0.369, p < 0.009) of the GNG task, the number of hits (h2 = 0.626, p < 2.14 × 10−4) and omissions (h2 =0.66, p < 1 × 10−7) in the COST, and the lecture ( =0.705, p < 1 × 10−7) and denomination errors (h2 = 0.661, p < 1 × 10−7) in the Stroop test. We also found that the RT prepotent response standard deviation (PRSS) (h2 = 0.499, p < 1.38 × 10−5) and Punctuation 3 (h2 = 0.506, p < 6.87 × 10−4) of CPT’s Part B are heritable. Similarly, Punctuation 12 (h2 = 0.246, p < 0.043) of the HSEISICH showed evidence genetics effects and hereditary transmission. No genetics effects and hereditary transmission in the Trail Making Test were found (Table 2 and Figure 1b).

3.4. Potential Endophenotypes

Following our results, the omission errors in the GNG task Part A, the number of hits and omissions in the COST as well as the number of lecture errors of the Stroop test, can be considered TP endophenotypes in ADHD (Table 2 and Figure 1c).
Table
Table 2. Performance of 232 individuals from the Colombian Caribbean on TP and RT-based neurological and neuropsychological tasks.
Table 2. Performance of 232 individuals from the Colombian Caribbean on TP and RT-based neurological and neuropsychological tasks.
#TaskAffected (n = 124)Unaffected (n = 108)dpHeritability
h2 (SD)p
MOART
Part A (Go)Mean (SD)Mean (SD)
1RT (ms)532.54 (130.41)472.22 (87.7)0.5360.118 b0.188 (0.154)0.111
2Commission errors0.95 (1.39)0.52 (1.09)0.3400.929 baa
3Omission errors0.27 (0.64)0.07 (0.38)0.3620.029b0.349 (0.131)0.004
4Early responses1.46 (2.64)0.59 (1.51)0.4000.445aa
Part B (No-Go)
5RT (ms)513 (119.63)463.84 (97.32)0.4480.264 b0.369 (0.157)0.009
6Commission errors1.4 (1.93)0.69 (1.29)0.4300.263 baa
7Omission errors0.31 (0.77)0.11 (0.37)0.3200.160 baa
8Early responses2.48 (4.33)0.83 (1.76)0.4850.159 baa
Cross-out-squares test
9Hits41.62 (5.71)42.88 (4.48)0.2440.0260.626 (0.178)2.14 × 10−4
10Omissions6.32 (5.71)4.77 (4.34)0.3036.02 × 10−12b0.66 (0.106)<1 × 10−7
11Commissions1.76 (5.6)1.82 (5.08)0.0110.440 baa
12RT (ms)209.35 (112.12)156.67 (64.32)0.5670.082 baa
Trail Making Test
13Hits in Part A23.62 (0.81)23.89 (0.31)0.4260.071aa
14RT in Part A (ms)54.02 (64.61)38.33 (21.86)0.3170.410 baa
15Hits in Part B22.43 (3.32)22.96 (2.05)0.1890.398aa
16RT in Part B (ms)128.28 (91.06)96.4 (64.05)0.4010.254 baa
Stroop test
Lecture
17RT (ms)76.94 (48.01)60.43 (20.42)0.4380.041baa
18Errors1.3 (2.01)0.6 (1.12)0.4190.049b0.705 (0.088)<1 × 10−7
Denomination
19RT (ms)124.64 (76.05)96.85 (49.74)0.4270.059 baa
20Errors3.77 (4.43)2.4 (4.19)0.3180.1550.661 (0.086)<1 × 10−7
Mismatch
21RT (ms)112.4 (80.55)95.8 (70.05)0.2190.827 baa
22Errors6.32 (6.35)3.75 (5.29)0.4370.155 b0.207 (0.165)0.105
CPT
23Omissions14.66 (15.88)8.09 (9.89)0.4900.121 b0.1 (0.178)0.287
24Omissions (%)4.55 (4.92)2.5 (3.07)0.4920.117 b0.119 (0.179)0.254
25Punctuation 162.22 (32.63)58.48 (20.17)0.1360.015baa
26Commission20.55 (14.93)13.24 (7.84)0.6020.134 caa
27Commission (%)53.74 (22.68)36.71 (21.58)0.7680.147 c0.076 (0.165)0.323
28Punctuation 251.02 (8.36)49.41 (8.01)0.1960.1960.2 (0.167)0.116
29Average hit RT (ms)479.32 (83.98)477.7 (66.43)0.0210.764aa
30Punctuation 361.34 (10.92)62.77 (9.5)0.1390.851 b0.506 (0.158)6.87 × 10−4
31Standard deviation of hit RT (ms)10.84 (7.03)8.23 (3.97)0.4490.513 baa
32Punctuation 457.17 (12.13)58.37 (9.93)0.1070.376 baa
33Variability20.71 (20.55)12.95 (10.48)0.4670.380 baa
34Punctuation 556.48 (12.27)56.75 (9.7)0.0240.216 baa
35Detectability0.5 (0.68)0.69 (0.4)0.3470.856 baa
36Punctuation 652.09 (8.37)50.22 (8.75)0.2190.2100.203 (0.162)0.105
37PR standard deviation0.95 (0.94)1.05 (1.17)0.0950.714 b0.499 (0.119)1.38 × 10−5
38Punctuation 753.76 (12.73)53.5 (11.69)0.0210.765aa
39Perseverations7.48 (14.21)2.33 (4.66)0.4750.272 baa
40Punctuation 860.77 (23.63)58.6 (22.16)0.0940.020baa
41HRTBCH0.01 (0.03)0 (0.02)0.1040.399 baa
42Punctuation 949.77 (9.85)49.68 (9.94)0.0090.979aa
43HSEBCH0.06 (0.1)0.04 (0.07)0.2510.527 baa
44Punctuation 1050.56 (12.31)53.14 (11.23)0.2180.248aa
45HRTISICH0.07 (0.07)0.04 (0.05)0.4560.517 baa
46Punctuation 1150.49 (12.76)47.08 (11.93)0.2760.445 b0.163 (0.163)0.159
47HSEISICH0.09 (0.12)0.06 (0.09)0.3000.566 baa
48Punctuation 1249.87 (10.3)48.4 (10.97)0.1390.992 c0.246 (0.144)0.043
a Parameter could not be maximized in SAGE. b Corrected for age using ANCOVA. c Corrected for sex and age using ANCOVA. CPT: Conners’ continuous performance test; d: Cohen’s effect size; h2: heritability estimated value; HRTBCH: Hit RT to block change; HSEBCH: Hit RT standard error to block change; HRTISICH: Hit RT interstimulus interval; HSEISICH: Hit standard error of the interstimulus interval; MOART: Multi-operational apparatus for reaction times; PR: Prepotent response; RT: Reaction time; SD: Standard deviation. p-values < 0.05 are shown in bold. Task numbers in italics are included in the predictive model (see Figure 1).

3.5. Predictive Model for ADHD Diagnosis

A seven-level tree with seven terminal nodes was derived by CART to differentiate ADHD affected from unaffected individuals in our cohort (Figure 2a). Splitting nodes include age at diagnosis, sex and traits 3 (omissions in the GNG task), 10 (omissions in COST), and 18 (lecture errors in Stroop test). It is noteworthy that these variables defining splitting nodes were also found to be endophenotypes (see Table 2 and Figure 1c). This predictive model yields sensitivity, specificity and correct classification rate (yields Se, Sp and accuracy values) values of 79.6% (95% CI = 71.6–86.9%), 79.8 % (95% CI = 72.4–86.6%) and 79.7% (95% CI = 74.6–84.9%), respectively, suggesting that these RT endophenotypes allow the accurate prediction of ADHD status in our cohort. Although this CART-based predictive model yields similar results when validated using RF and TreeNet (data not shown), it outperforms that including only sex and age to predict ADHD status (Supplementary Material).
Our sample includes 232 clinically evaluated individuals (node 1, 58% ADHD affected; Figure 2a). In the first split, children aged 14 or younger (35% of the total sample) have 84% chance of being ADHD affected regardless of sex (terminal node 3). Individuals in node 5 (33%, n = 232), that is, males aged 14 years or older, have a 58% chance of being diagnosed with ADHD. This chance increases to 86% when 9.5 or more omissions in the COST are made (18% of the total sample, node 11, n = 18). Twenty-five percent of the sample (n = 57) is in node 10 and have a 53% chance of being diagnosed as ADHD unaffected.
In node 4, 32% of the sample (n = 73) are females aged 14 years or older, and have 74% of being ADHD unaffected (Figure 2a). Node 20 comprises 23% of the total sample (n = 52); these individuals are male aged 14 years or less that made less than 2.5 lecture errors in the Stroop test and less than 9.5 omissions in the COST. Node 40 gathers 51 male individuals (22% of the total sample) aged more than 14 but less than 48 years that made <0.5 omissions in the GNG tasks, <2.5 lecture errors in the Stroop test and <9.5 omissions in the COST. Individuals with these characteristics have a 61% chance of being diagnosed as ADHD unaffected.

4. Discussion

In this study, we aimed to further investigate measures of temporal processing (TP) as possible cognitive endophenotype in ADHD. Different measures of TP were assessed in 232 individuals from nuclear families segregating ADHD from a Caribbean Community in Barranquilla, Colombia and dissect potential TP endophenotypes. TP was measured using RT-based paradigms such as the CPT and Go/No-Go tasks. Additionally, neuropsychological assessment was carried out using instruments such as the Stroop test, the cross-out-squares test (COST) and the Trail Making test (TMT) parts A and B (Table 2).
First of all, we identified important phenotypic differences between ADHD-affected and unaffected individuals in the TP measures (Table 2) [100]. In the CPT task, individuals with ADHD committed more omission and commission errors than controls; similar results were observed for the number of commission errors in the COST task. Omission errors in TP are one of the most reported findings in RT-based studies [67,101,102]. This type of error is associated with the absence of accelerated responses, has been proposed as one of the main components of the gradual and trial-by-trial variability [67,101,103,104,105] and is considered responsible for the fast (or slow) but ineffective performance fluctuations typical in individuals with ADHD [103,104,106,107]. Furthermore, omission and commission errors are associated with occasional lapses as well as fluctuations and/or neural oscillations in circuit calibration during timely repetitive tasks [108]. Evidence suggests that this non-response may be due to an underestimation of time intervals resulting in a delay in information processing [109]. In this sense, alterations are mainly related to activation, control in the preparation and/or pre-adjustment of the motor response [110]. Thus, deficits in motor response processing and preparation make it difficult to perform tasks involving sustained effort, activation and use of time, which lead to a diminished performance that is sufficiently severe to produce omission errors [108,111].
We found that ADHD affected individuals differed from unaffected individuals in omissions and perseverance of the CPT (Table 2). Omissions represent a deficit in stimulus detection, which leads to difficulties to sustain a correct response. Children with ADHD are more imprecise than unaffected children due to problems in vigilance and sustained attention, which lead to an increased number of incorrect responses [112]. Perseverance reflects a slow reaction to problems in the selection criterion, and an inappropriate engagement control. Altogether, perseverance yields difficulties in the mobility of cognitive resources when permanent changes occur due to compromised alertness, sustained and selective attention, as well as difficulties with time-limited testing in ADHD [113]. This suggests a series of slower and more variable responses to visually presented stimuli, which lead to failures in the simultaneous processing of a second stimulus in ADHD affected individuals [114]. On the other hand, the differences found in errors of commission (Table 2) are in line with those proposing this trait as an ADHD-specific TP deficit [110]. Another aspect is that slower information processing and the higher variability in the RT could produce more omission errors which highlights the existence of differences in execution performance between ADHD affected and unaffected individuals [31,115,116,117]. Most importantly, these measures are heritable, thus commission and omission errors can be considered TP endophenotypes in ADHD (Table 2 and Figure 1).
It is noteworthy that the COST showed a differential pattern between ADHD affected and unaffected individuals in our sample (Table 2). Indeed, ADHD affected individuals have a lower number of hits compared to unaffected individuals. This difference is associated with precision difficulties in visual-search tasks and slow processing rates, which lead to a lower speed of processing information [118]. The speed of visual processing is related with omission errors and has been found to be different between ADHD affected and unaffected individuals [119,120].
Our findings also indicate that ADHD affected individuals committed more lecture errors in the Stroop task compared to unaffected individuals and this measure was also heritable and there is a tendency for ADHD affected to spend more time to complete the task (Table 2). This diminution in the speed across phases may be due to the occurrence of conflicts in information processing (i.e., errors) [121], leading to difficulties in the changes from the most automatic to the least automatic tendencies [122]. Some studies indicate that this phenomenon is related to a low sensitivity and difficulties for hit-error-hit changes [123,124,125,126], manifested in an alternation of the excitability and inhibition, as well as in the motor sensorial control. These alterations might be associated with cortical hypoactivation [127,128,129,130] and functional connectivity reduction [131].
In addition to genetic aspects, the aforementioned neuropsychological alterations have been proposed as working models in ADHD for the development of more precise diagnostic tools, and to obtain better treatment responses [11,31,132,133]. Erroneous responses and RT variability are important for the identification of intermediate phenotypes useful in genetic molecular studies of ADHD [134,135,136]. Finding strong evidence of heritability in several neuropsychological variables assessing RT and errors (Table 2) is consistent with previous studies, indicates an important genetic component in RT and neuropsychological features in families segregating ADHD [133,137,138,139], and supports future genetic association studies of TP endophenotypes in our cohort.
The idea of a TP deficit in ADHD is coherent with different models, where TP is a complex cognitive process that involves multiple components to modulate time-associated tasks. In ADHD, TP highlights the importance of time perception and task-specific demands and coincides with current ADHD models. The ARPA-based predictive model of ADHD diagnosis includes, in addition to demographic variables such as age and sex, three TP endophenotypes derived from neuropsychological instruments used to assess TP (Table 2 and Figure 2). This predictive model performs reasonably well in terms of sensitivity, specificity, correct classification rate, the area under the ROC curve and lift, which makes it a plausible alternative for the diagnosis of ADHD compared to the DSM-IV criteria. Considering that (1) impulsive and omission errors are associated with occasional lapses in attention (i.e., failures of sustained attention that are severe enough to produce an error of omission) [140,141,142,143,144], (2) using RT-based task helps to establish differential variability patterns between ADHD affected and unaffected individuals [103,104,140,141,142,143,144] associated to fluctuations in fast and/or slow, but ineffective responses (i.e., errors of omission and/or commission) [106,107], this predictive model could be used in the clinical practice for ADHD diagnosis and follow-up, as well as to contribute to personalized interventions by elucidating the clinical heterogeneity of the disorder in this Caribbean community.
Future research directions could potentially include the study of TP measures, RT variability and the trial-by-trial performance variability using entropy-based measures and transitions between states (i.e., correct-incorrect-correct responses and early response followed by timeouts) that might be useful to better understand, from a different perspective, the differential evolution of pattern of individuals with ADHD when compared to controls. Using our family-based cohort, these findings could be subsequently integrated to complex segregation, and genetic linkage and association studies of TP towards the development of more precise and differential ADHD diagnosis [31,133,137,145,146]. Linkage and association genetic studies could, in parallel, help to establish TP and RT-based endophenotypes as neurobiological markers in ADHD that could contribute to better understand its etiology [21] and support the contribution of the ADGRL3, SNAP-25, FGF1 and DRD4 genes, which were previously reported to be associated with ADHD [4,5,147,148,149,150,151,152]. In the future, these results might be important to improve the attention, characterization, diagnosis and follow-up of individuals with ADHD through translational and precision medicine approaches [112,153,154,155].

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/brainsci11091218/s1, Figure S1: ROC curves of predictive models for ADHD diagnosis; Table S1: Evaluation of a binary system for ADHD prediction.

Author Contributions

Conceptualization: J.E.A.-L. and J.I.V.; methodology: M.A.-B. and J.I.V.; validation: J.E.A.-L., I.S., D.A.P., M.L.C.-H., M.L.M.-B., S.G.L.-G., E.M.-S., M.S.-R., C.A.M., M.A.-B., J.I.V. and P.J.P.-R.; formal analysis: J.E.A.-L., M.A.-B. and J.I.V.; investigation: J.E.A.-L. and D.A.P., M.L.C.-H., M.L.M.-B., S.G.L.-G., M.A., W.P.-A., L.M.N.-M., G.J.-F., M.D.L.H., E.M.-S., M.S.-R. and P.J.P.-R.; resources: J.E.A.-L., P.J.P.-R. and J.I.V.; data curation: J.E.A.-L., M.A.-B. and J.I.V.; writing—original draft preparation: J.E.A.-L., I.S., M.A.-B. and J.I.V.; writing—review and editing: J.E.A.-L., I.S., D.A.P., M.L.C.-H., M.L.M.-B., S.G.L.-G., M.A., W.P.-A., L.M.N.-M., G.J.-F., M.D.L.H., E.M.-S., M.S.-R., C.A.M., M.A.-B., J.I.V. and P.J.P.-R.; visualization: J.E.A.-L. and J.I.V.; supervision: M.A.-B. and J.I.V.; project administration: J.E.A.-L. and J.I.V.; funding acquisition: J.E.A.-L., M.L.C.-H., M.S.-R., P.J.P.-R. and J.I.V. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed by COLCIENCIAS, project “Fenotipos Complejos y Endofenotipos del Trastorno por Déficit de Atención e Hiperactividad y su Asociación con Genes Mayores y de Susceptibilidad”, grant 1253-5453-1644, contract RC 384-2011, conferred to Grupo de Neurociencias del Caribe, Universidad Simón Bolívar, Barranquilla. M.L.C.-H., J.E.A.-L. and J.I.V. were partially supported by research grant FOFICO 32101 PE0031 from Universidad del Norte, Barranquilla, Colombia. The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the paper. The APC was funded by Universidad Simón Bolívar, Barranquilla, Colombia.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Universidad Simón Bolívar, Barranquilla, Colombia. The study was approved by the Ethics Committee of Universidad Simón Bolívar, Barranquilla, Colombia (approval # 00032, 13 October 2011).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Acknowledgments

We express our highest appreciation to the families enrolled in this study. M.L.C.-H. and E.M.-S. are doctoral students at Universidad del Norte, Barranquilla, Colombia, and Universidad De Flores in Buenos Aires, Argentina, respectively. Some of this work is to be presented in partial fulfilment of the requirements for the Ph.D. degree. J.E.A.-L., M.A.-B., J.I.V. and P.J.P.-R. have full access to all the data in the study and are responsible for submitting this work for publication.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Thomas, R.; Sanders, S.; Doust, J.; Beller, E.; Glasziou, P. Prevalence of attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Pediatrics 2015, 135, e994–e1001. [Google Scholar] [CrossRef] [Green Version]
  2. Polanczyk, G.V.; Willcutt, E.G.; Salum, G.A.; Kieling, C.; Rohde, L.A. ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. Int. J. Epidemiol. 2014, 43, 434–442. [Google Scholar] [CrossRef] [PubMed]
  3. Polanczyk, G.; de Lima, M.S.; Horta, B.L.; Biederman, J.; Rohde, L.A. The worldwide prevalence of ADHD: A systematic review and metaregression analysis. Am. J. Psychiatry 2007, 164, 942–948. [Google Scholar] [CrossRef] [PubMed]
  4. Jain, M.; Vélez, J.I.; Acosta, M.T.; Palacio, L.G.; Balog, J.; Roessler, E.; Pineda, D.; Londoño, A.C.; Palacio, J.D.; Arbelaez, A.; et al. A cooperative interaction between LPHN3 and 11q doubles the risk for ADHD. Mol. Psychiatry 2012, 17, 741–747. [Google Scholar] [CrossRef] [Green Version]
  5. Arcos-Burgos, M.; Jain, M.; Acosta, M.T.; Shively, S.; Stanescu, H.; Wallis, D.; Domené, S.; Vélez, J.I.; Karkera, J.D.; Balog, J.; et al. A common variant of the latrophilin 3 gene, LPHN3, confers susceptibility to ADHD and predicts effectiveness of stimulant medication. Mol. Psychiatry 2010, 15, 1053–1066. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Bukstein, O.G. Attention deficit hyperactivity disorder and substance use disorders. Behav. Neurosci. Atten. Deficit. Hyperact. Disord. Its Treat. 2011, 145–172. [Google Scholar] [CrossRef]
  7. Pelham, W.E., Jr.; Wheeler, T.; Chronis, A. Empirically supported psychosocial treatments for attention deficit hyperactivity disorder. J. Clin. Child. Psychol. 1998, 27, 190–205. [Google Scholar] [CrossRef]
  8. APA. Diagnostic and Statistical Manual of Mental Disorders (DSM), 4th ed.; APA: Washington, DC, USA, 2000. [Google Scholar]
  9. Sibley, M.H.; Pelham, W.E.; Molina, B.S.G.; Gnagy, E.M.; Waschbusch, D.A.; Garefino, A.C.; Kuriyan, A.B.; Babinski, D.E.; Karch, K.M. Diagnosing ADHD in adolescence. J. Consult. Clin. Psychol. 2012, 80, 139–150. [Google Scholar] [CrossRef] [Green Version]
  10. Faraone, S.V.; Mick, E. Molecular genetics of attention deficit hyperactivity disorder. Psychiatr. Clin. N. Am. 2010, 33, 159–180. [Google Scholar] [CrossRef] [PubMed]
  11. Castellanos, F.X.; Tannock, R. Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nat. Rev. Neurosci. 2002, 3, 617–628. [Google Scholar] [CrossRef] [PubMed]
  12. Miller, G.A.; Rockstroh, B.S. Progress and prospects for endophenotypes for schizophrenia in the time of genomics, epigenetics, oscillatory brain dynamics, and the Research Domain Criteria. Neurobiol. Schizophr. 2016, 17–38. [Google Scholar] [CrossRef]
  13. Walters, J.T.R.; Owen, M.J. Endophenotypes in psychiatric genetics. Mol. Psychiatry 2007, 12, 886–890. [Google Scholar] [CrossRef]
  14. Lee Gregory, M.; Burton, V.J.; Shapiro, B.K. Developmental Disabilities and Metabolic Disorders. In Neurobiology of Brain Disorders: Biological Basis of Neurological and Psychiatric Disorders; Michael, J.Z., Lewis, P.R., Joseph, T.C., Eds.; Elsevier: Amsterdam, The Netherlands, 2015; pp. 18–41. [Google Scholar]
  15. Falconer, D.S.; Mackay, T.F.C. Introduction to Quantitative Genetics; Longman: Essex, UK, 1996. [Google Scholar]
  16. Flint, J.; Munafò, M.R. The endophenotype concept in psychiatric genetics. Psychol. Med. 2007, 37, 163–180. [Google Scholar] [CrossRef] [PubMed]
  17. Brotman, M.A.; Rich, B.A.; Guyer, A.E.; Lunsford, J.R.; Horsey, S.E.; Reising, M.M.; Thmas, L.A.; Fromm, S.J.; TOwbin, K.; Pine, D.S.; et al. Amygdala activation during emotion processing of neutral faces in children with severe mood dysregulation versus ADHD or bipolar disorder. Am. J. Psychiatry 2010, 167, 61–69. [Google Scholar] [CrossRef] [Green Version]
  18. Gottesman, I.I.; Gould, T.D. The endophenotype concept in psychiatry: Etymology and strategic intentions. Am. J. Psychiatry 2003, 160, 636–645. [Google Scholar] [CrossRef]
  19. Tsuang, M.T.; Faraone, S.V. The frustrating search for schizophrenia genes. Am. J. Med. Genet. 2000, 97, 1–3. [Google Scholar] [CrossRef]
  20. Cervantes-Henríquez, M.L.; Acosta-López, J.E.; Martínez-Banfi, M.L.; Vélez, J.I.; Mejía-Segura, E.; Lozano-Gutiérrez, S.G.; Sánchez-Rojas, M.; Zurbarán, M.A.; Zurek, E.E.; Arcos-Burgos, M.; et al. ADHD Endophenotypes in Caribbean Families. J. Atten. Disord. 2018, 2114. [Google Scholar] [CrossRef] [Green Version]
  21. Mastronardi, C.A.; Pillai, E.; Pineda, D.A.; Martinez, A.F.; Lopera, F.; Velez, J.I.; Palacio, J.D.; Patel, H.; Easteal, S.; Acosta, M.T.; et al. Linkage and association analysis of ADHD endophenotypes in extended and multigenerational pedigrees from a genetic isolate. Mol. Psychiatry 2016, 21, 1434–1440. [Google Scholar] [CrossRef]
  22. Pineda, D.A.; Lopera, F.; Puerta, I.C.; Trujillo-Orrego, N.; Aguirre-Acevedo, D.C.; Hincapie-Henao, L.; Arango, C.P.; Acosta, M.T.; Holzinger, S.I.; Palacio, J.D.; et al. Potential cognitive endophenotypes in multigenerational families: Segregating ADHD from a genetic isolate. Atten. Defic. Hyperact. Disord. 2011, 3, 291. [Google Scholar] [CrossRef] [Green Version]
  23. Gau, S.S.; Shang, C.Y. Executive functions as endophenotypes in ADHD: Evidence from the Cambridge Neuropsychological Test Battery (CANTAB). J. Child Psychol. Psychiatry 2010, 51, 838–849. [Google Scholar] [CrossRef] [PubMed]
  24. Sweet, L.H. N-Back Paradigm. In Encyclopedia of Clinical Neuropsychology; Kreutzer, J.S., DeLuca, J., Caplan, B., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 1718–1719. [Google Scholar]
  25. Woods, S.P.; Lovejoy, D.W.; Stutts, M.L.; Ball, J.D.; Fals-Stewart, W. Comparative efficiency of a discrepancy analysis for the classification of Attention-Deficit/Hyperactivity Disorder in adults. Arch. Clin. Neuropsychol. 2002, 17, 351–369. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Noreika, V.; Falter, C.M.; Rubia, K. Timing deficits in attention-deficit/hyperactivity disorder (ADHD): Evidence from neurocognitive and neuroimaging studies. Neuropsychologia 2013, 51, 235–266. [Google Scholar] [CrossRef] [PubMed]
  27. Rubia, K.; Halari, R.; Cubillo, A.; Mohammad, A.-M.; Brammer, M.; Taylor, E. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naive children with ADHD during a rewarded continuous performance task. Neuropharmacology 2009, 57, 640–652. [Google Scholar] [CrossRef] [PubMed]
  28. Kofler, M.J.; Rapport, M.D.; Sarver, D.E.; Raiker, J.S.; Orban, S.A.; Friedman, L.M.; Kolomeyer, E.G. Reaction time variability in ADHD: A meta-analytic review of 319 studies. Clin. Psychol. Rev. 2013, 33, 795–811. [Google Scholar] [CrossRef] [PubMed]
  29. Heitz, R.P. The speed-accuracy tradeoff: History, physiology, methodology, and behavior. Front. Neurosci. 2014, 8, 150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Rommelse, N.N.J.; Oosterlaan, J.; Buitelaar, J.; Faraone, S.V.; Sergeant, J.A. Time reproduction in children with ADHD and their nonaffected siblings. J. Am. Acad. Child Adolesc. Psychiatry 2007, 46, 582–590. [Google Scholar] [CrossRef]
  31. Castellanos, F.X.; Lee, P.P.; Sharp, W.; Jeffries, N.O.; Greenstein, D.K.; Clasen, L.S.; Blumenthal, J.D.; James, R.S.; Ebens, C.L.; Walter, J.M.; et al. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 2002, 288, 1740–1748. [Google Scholar] [CrossRef]
  32. Valko, L.; Schneider, G.; Doehnert, M.; Müller, U.; Brandeis, D.; Steinhausen, H.-C.; Drechsler, R. Time processing in children and adults with ADHD. J. Neural Transm. 2010, 117, 1213–1228. [Google Scholar] [CrossRef] [Green Version]
  33. Prevatt, F.; Proctor, B.; Baker, L.; Garrett, L.; Yelland, S. Time estimation abilities of college students with ADHD. J. Atten. Disord. 2011, 15, 531–538. [Google Scholar] [CrossRef]
  34. Meck, W.H. Neuropsychology of timing and time perception. Brain Cogn. 2005, 58, 1–8. [Google Scholar] [CrossRef]
  35. Gutiérrez-García, A.G.; Reyes-Platas, D.I.; Picazo, O. Percepción del tiempo en la neuropsicopatología: Una revisión sistemática. Psiquiatría Biológica 2017, 24, 85–96. [Google Scholar] [CrossRef]
  36. Mette, C.; Grabemann, M.; Zimmermann, M.; Strunz, L.; Scherbaum, N.; Wiltfang, J.; Kis, B. No clear association between impaired short-term or working memory storage and time reproduction capacity in adult ADHD patients. PLoS ONE 2015, 10, e0133714. [Google Scholar] [CrossRef]
  37. Barkley, R.A.; Murphy, K.R.; Bush, T. Time perception and reproduction in young adults with attention deficit hyperactivity disorder. Neuropsychology 2001, 15, 351–360. [Google Scholar] [CrossRef] [PubMed]
  38. Narbona, N.; Leal-Campanario, R.; Gruart, A. El procesamiento temporal en el Trastorno por Déficit de Atención e Hiperactividad. Rev. Psicol. Clínica Niños Adolesc. 2021, 8, 9–15. [Google Scholar]
  39. Barkley, R.A.; Koplowitz, S.; Anderson, T.; McMurray, M.B. Sense of time in children with ADHD: Effects of duration, distraction, and stimulant medication. J. Int. Neuropsychol. Soc. 1997, 3, 359–369. [Google Scholar] [CrossRef] [PubMed]
  40. Barkley, R.A. Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychol. Bull. 1997, 121, 65. [Google Scholar] [CrossRef] [Green Version]
  41. Barkley, R.A. Attention-deficit hyperactivity disorder. Sci. Am. 1998, 279, 66–71. [Google Scholar] [CrossRef]
  42. Barkley, R.A. Attention-deficit/hyperactivity disorder, self-regulation, and time: Toward a more comprehensive theory. J. Dev. Behav. Pediatr. 1997, 18, 271–279. [Google Scholar]
  43. Faraone, S.V.; Perlis, R.H.; Doyle, A.E.; Smoller, J.W.; Goralnick, J.J.; Holmgren, M.A.; Sklar, P. Molecular genetics of attention-deficit/hyperactivity disorder. Biol. Psychiatry 2005, 57, 1313–1323. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Larsson, H.; Chang, Z.; D’Onofrio, B.M.; Lichtenstein, P. The heritability of clinically diagnosed attention deficit hyperactivity disorder across the lifespan. Psychol. Med. 2014, 44, 2223–2229. [Google Scholar] [CrossRef]
  45. Lopera, F.; Palacio, L.G.; Jimenez, I.; Villegas, P.; Puerta, I.C.; Pineda, D.; Jiménez, M.; Arcos-Burgos, M. Discrimination between genetic factors in attention deficit. Rev. Neurol. 1999, 28, 660–664. [Google Scholar]
  46. Puentes Rozo Acosta-Lopez, J.E.; Cervantes-Henriquez, M.; Martinez-Banfi, M.; Lozano-Gutierrez, S.; Jimenez-Figueroa, G.; Pineda-Alhucema, W.; Mejia-Segura, E.; Zurbaran, M.A.; Zurek, E.E.; Sanchez-Rojas, M.; et al. Attention Deficit /Hyperactivity Disorder and Comorbidities in 120 Nuclear Families from a Caribbean Community. Unpublished work. 2017. [Google Scholar]
  47. Villalón, J. Colonias Extranjeras en Barranquilla; Ediciones Uninorte: Barranquilla, Colombia, 2008. [Google Scholar]
  48. Wabgou, M.; Vargas, D.; Carabali, J.A. Las migraciones internacionales en Colombia. Investig. Desarro. 2012, 20, 142–167. [Google Scholar]
  49. Barragán-Duarte, J.L. Mapa genético de los colombianos. UN Periódico 2007, 105. Available online: http://historico.unperiodico.unal.edu.co/ediciones/105/15.html (accessed on 15 February 2017).
  50. Martinez, B.; Caraballo, L.; Gusmao, L.; Amorim, A.; Carracedo, A. Autosomic STR population data in two Caribbean samples from Colombia. Forensic Sci. Int. 2005, 152, 79–81. [Google Scholar] [CrossRef]
  51. Usaquén Martinez, W. Validación y Consistencia de Información en Estudios de diversidad Genética Humana a Partir de Marcadores Microsatélites. Tesis de Doctorado en Ciencias-Biología, Universidad Nacional de Colombia, Bogota, Colombia, 2012. [Google Scholar]
  52. Arcos-Burgos, M.; Muenke, M. Genetics of population isolates. Clin. Genet. 2002, 61, 233–247. [Google Scholar] [CrossRef] [PubMed]
  53. Arcos-Burgos, M.; Castellanos, F.X.; Pineda, D.; Lopera, F.; Palacio, J.D.; Palacio, L.G.; Rapoport, J.L.; Berg, K.; Bailey-Wilson, J.; Muenke, M.; et al. Attention-deficit/hyperactivity disorder in a population isolate: Linkage to loci at 4q13.2, 5q33.3, 11q22, and 17p11. Am. J. Hum. Genet. 2004, 75, 998–1014. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Bravo, M.L.; Valenzuela, C.Y.; Arcos-Burgos, O.M. Polymorphisms and phyletic relationships of the Paisa community from Antioquia (Colombia). Gene Geogr. 1996, 10, 11–17. [Google Scholar] [PubMed]
  55. Arcos-Burgos, M.; Castellanos, F.X.; Lopera, F.; Pineda, D.; Palacio, J.D.; Garcia, M.; Henao, G.C.; Palacio, L.G.; Berg, K.; Bailey-Wilson, J.E.; et al. Attention-deficit/hyperactivity disorder (ADHD): Feasibility of linkage analysis in a genetic isolate using extended and multigenerational pedigrees. Clin. Genet. 2002, 61, 335–343. [Google Scholar] [CrossRef] [PubMed]
  56. Arcos-Burgos, M.; Castellanos, F.X.; Konecki, D.; Lopera, F.; Pineda, D.; Palacio, J.D.; Rapoport, J.L.; Berg, K.; Bailey-Wilson, J.; Muenke, M. Pedigree disequilibrium test (PDT) replicates association and linkage between DRD4 and ADHD in multigenerational and extended pedigrees from a genetic isolate. Mol. Psychiatry 2004, 9, 252–259. [Google Scholar] [CrossRef] [Green Version]
  57. Pineda, D.A.; Acosta López, J.; Cervantes-Henríquez, M.L.; Jimenez-Figueroa, G.; Sánchez-Rojas, M.; Pineda-Alhucema, W.; Mejía-Segura, E.; Puentes-Rozo, P.J. Conglomerados de clases latentes en 408 miembros de 120 familias nucleares de Barranquilla con un caso índice afectado de trastorno de atención hiperactividad (TDAH). Acta Neurol. Colomb. 2016, 32, 275–284. [Google Scholar] [CrossRef] [Green Version]
  58. Cervantes-Henríquez, M.L.; Acosta-López, J.E.; Martinez, A.F.; Arcos-Burgos, M.; Puentes-Rozo, P.J.; Vélez, J.I. Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25. J. Atten. Disord. 2021. [Google Scholar] [CrossRef]
  59. Puentes-Rozo, P.J.; Acosta-López, J.E.; Cervantes-Henríquez, M.L.; Martínez-Banfi, M.L.; Mejia-Segura, E.; Sánchez-Rojas, M.; Anaya-Romero, M.; Anaya-Romero, M.E.; Acosta-Hoyos, A.; García-Llinás, G.A.; et al. Genetic Variation Underpinning ADHD Risk in a Caribbean Community. Cells. 2019, 8, 907. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Palacio, J.D.; Castellanos, F.X.; Pineda, D.A.; Lopera, F.; Arcos-Burgos, M.; Quiroz, Y.T.; Henao, G.C.; Puerta, I.C.; Ramírez, D.L.; Rapoport, J.L.; et al. Attention-deficit/hyperactivity disorder and comorbidities in 18 Paisa Colombian multigenerational families. J. Am. Acad. Child Adolesc. Psychiatry 2004, 43, 1506–1515. [Google Scholar] [CrossRef]
  61. Llanos Lizcano, L.J.; García Ruiz, D.J.; González Torres, H.J.; Puentes Rozo, P. Trastorno por déficit de atención e hiperactividad (TDAH) en niños escolarizados de 6 a 17 años. Pediatría Atención Primaria 2019, 21, e101–e108. [Google Scholar]
  62. Reich, W. Diagnostic interview for children and adolescents (DICA). J. Am. Acad. Child Adolesc. Psychiatry 2000, 39, 59–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Tacchini, G.; Coppola, M.T.; Musazzi, A.; Altamura, A.C.; Invernizzi, G. Multinational validation of the Composite International Diagnostic Interview (CIDI). Minerva Psichiatr. 1994, 35, 63–80. [Google Scholar] [PubMed]
  64. Pineda, D.A.; Kamphaus, R.W.; Mora, O.; Restrepo, M.A.; Puerta, I.C.; Palacio, L.G.; Jiménez, I.; Mejía, S.; García, M.; Arango, J.C.; et al. A system of multidimensional behavior assessment. A scale for parents of children from 6 to 11 years of age. Colombian version. Rev. Neurol. 1999, 28, 672–681. [Google Scholar] [PubMed]
  65. Pichot, P.; López-Ibor Aliño, J.J.; Valdés Miyar, M. DSM-IV: Manual Diagnóstico y Estadístico de los Trastornos Mentales; Masson, S.A.: Milano, Italy, 2001. [Google Scholar]
  66. APA. Diagnostic and Statistical Manual of Mental Disorders (DSM); APA: Washington, DC, USA, 1994; pp. 143–147. [Google Scholar]
  67. Bekker, E.M.; Kenemans, J.L.; Hoeksma, M.R.; Talsma, D.; Verbaten, M.N. The pure electrophysiology of stopping. Int. J. Psychophysiol. 2005, 55, 191–198. [Google Scholar] [CrossRef]
  68. Golden, C. Stroop Color and Word Test: A Manual for Clinical and Experimental Uses; Stoelting Company: Wood Dale, IL, USA, 1978. [Google Scholar]
  69. Golden, F.S. A Manual for the Adult Stroop Color and Word Test; Stoelting Company: Wood Dale, IL, USA, 2002; pp. 1–11. [Google Scholar]
  70. Puentes, P. Neuropsicología de las Funciones Ejecutivas; Ediciones Universidad Simón Bolívar: Barranquilla, Colombia, 2009. [Google Scholar]
  71. Pineda, D.; Ardila, A.; Rosselli, M.; Arias, B.E.; Henao, G.C.; Gomez, L.F.; Mejía, S.E.; Miranda, M.L. Prevalence of attention-deficit/hyperactivity disorder symptoms in 4- to 17-year-old children in the general population. J. Abnorm. Child Psychol. 1999, 27, 455–462. [Google Scholar] [CrossRef]
  72. Acosta-López, J.; Cervantes-Henríquez, M.; Sánchez-Rojas, M.; Núñez-Barragán, M.; Puentes Rozo, P.; Aguirre-Acevedo, D.C.; Pineda, D.A. Alteraciones del Control Inhibitorio Conductual en Niños de 6 A 11 Años Con TDAH Familiar de Barranquilla. Psicogente 2010, 13, 274–291. [Google Scholar]
  73. Reitan, R.M. The relation of the trail making test to organic brain damage. J. Consult. Psychol. 1955, 19, 393–394. [Google Scholar] [CrossRef] [PubMed]
  74. Reitan, R.M. The validity of the trail making test as an indicator of organic brain damage. Percept. Mot. Skills 1958, 8, 271–276. [Google Scholar] [CrossRef]
  75. Reitan, R.M.; Wolfson, D. Category Test and Trail Making Test as measures of frontal lobe functions. Clin. Neuropsychol. 1995, 9, 50–56. [Google Scholar] [CrossRef]
  76. Reitan, R.M.; Wolfson, D. The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation, 1st ed.; Neuropsychology Press: Tucson, AZ, USA, 1985. [Google Scholar]
  77. Reitan, R.M.; Wolfson, D. The Trail Making Test as an initial screening procedure for neuropsychological impairment in older children. Arch. Clin. Neuropsychol. 2004, 19, 281–288. [Google Scholar] [CrossRef]
  78. Conners, C.K.; Epstein, J.N.; Angold, A.; Klaric, J. Continuous performance test performance in a normative epidemiological sample. J. Abnorm. Child Psychol. 2003, 31, 555–562. [Google Scholar] [CrossRef] [PubMed]
  79. Conners, C.K.; Sitarenios, G. Conners’ Continuous Performance Test (CPT). In Encyclopedia of Clinical Neuropsychology; Springer: Berlin/Heidelberg, Germany, 2011; pp. 681–683. [Google Scholar]
  80. Conners, C.K.; Staff, M.H.S.; Connelly, V.; Campbell, S.; MacLean, M.; Barnes, J. Conners’ Continuous Performance Test II (CPT II v. 5); Multi-Health Systems Inc.: North Tonawanda, NY, USA, 2000; Volume 29, pp. 175–196. [Google Scholar]
  81. Cornblatt, B.A.; Risch, N.J.; Faris, G.; Friedman, D.; Erlenmeyer-Kimling, L. The Continuous Performance Test, identical pairs version (CPT-IP): I. New findings about sustained attention in normal families. Psychiatry Res. 1988, 26, 223–238. [Google Scholar] [CrossRef]
  82. Jiménez-Figueroa, G.; Vidarte Claros, J.A.; Restrepo de Mejía, F. Interference Control in Attention Deficit and Hyperactivity Disorder (ADHD). CES Psicol. 2020, 13, 104–124. [Google Scholar] [CrossRef]
  83. Jimenez-Figueroa, G.; Ardila-Duarte, C.; Pineda, D.A.; Acosta-Lopez, J.E.; Cervantes-Henriquez, M.L.; Pineda-Alhucema, W.; Cervantes-Gutiérrez, J.; Quintero-Ibarra, M.; QSánchez-Rojas, M.; Vélez,, J.I.; et al. Prepotent response inhibition and reaction times in children with attention deficit/hyperactivity disorder from a Caribbean community. Atten. Defic. Hyperact. Disord. 2017, 9, 199–211. [Google Scholar] [CrossRef]
  84. Brand, A.; Bradley, M.T.; Best, L.A.; Stoica, G. Accuracy of effect size estimates from published psychological research. Percept. Mot. Skills 2008, 106, 645–649. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Cohen, J. Statistical Power Analysis for the Behavioural Sciences; Lawrence Erlbaum Associates: Hillside, NJ, USA, 1988. [Google Scholar]
  86. Visscher, P.M.; Hill, W.G.; Wray, N.R. Heritability in the genomics era--concepts and misconceptions. Nat. Rev. Genet. 2008, 9, 255–266. [Google Scholar] [CrossRef]
  87. Elston, R.C.; Gray-McGuire, C. A review of the “Statistical Analysis for Genetic Epidemiology” (S.A.G.E.) software package. Hum. Genom. 2004, 1, 456–459. [Google Scholar] [CrossRef] [PubMed]
  88. Elston, R.C.; Satagopan, J.M.; Sun, S. Genetic terminology. Methods Mol. Biol. 2012, 850, 1–9. [Google Scholar] [PubMed] [Green Version]
  89. Elston, R.C.; Satagopan, J.M.; Sun, S. Statistical Human Genetics; Humana Press: New York, NY, USA, 2012. [Google Scholar]
  90. Bochud, M. Estimating heritability from nuclear family and pedigree data. Stat. Hum. Genet. Methods Protoc. 2012, 171–186. [Google Scholar] [CrossRef]
  91. Londono, A.C.; Castellanos, F.X.; Arbelaez, A.; Ruiz, A.; Aguirre-Acevedo, D.C.; Richardson, A.M.; Easteal, S.; Lidbury, B.A.; Arcos-Burgos, M.; Lopera, F. An 1H-MRS framework predicts the onset of Alzheimer’s disease symptoms in PSEN1 mutation carriers. Alzheimers Dement. 2014, 10, 552–561. [Google Scholar] [CrossRef]
  92. Vélez, I.J.; Correa, C.J.; Arcos-Burgos, M. A new method for detecting significant p-values with applications to genetic data. Rev. Colomb. Estadística 2014, 37, 67–76. [Google Scholar] [CrossRef]
  93. Velez, J.I.; Lopera, F.; Sepulveda-Falla, D.; Patel, H.R.; Johar, A.S.; Chuah, A.; Tobón, C.; Rivera, D.; Villegas, A.; Cai, Y.; et al. APOE*E2 allele delays age of onset in PSEN1 E280A Alzheimer’s disease. Mol. Psychiatry 2016, 21, 916–924. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Wong, M.L.; Dong, C.; Andreev, V.; Arcos-Burgos, M.; Licinio, J. Prediction of susceptibility to major depression by a model of interactions of multiple functional genetic variants and environmental factors. Mol. Psychiatry 2012, 17, 624–633. [Google Scholar] [CrossRef] [Green Version]
  95. Wong, M.L.; Dong, C.; Flores, D.L.; Ehrhart-Bornstein, M.; Bornstein, S.; Arcos-Burgos, M.; Licinio, J. Clinical outcomes and genome-wide association for a brain methylation site in an antidepressant pharmacogenetics study in Mexican Americans. Am. J. Psychiatry 2014, 171, 1297–1309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Olshen, R.; Breiman, L. A Conversation with Leo Breiman. Stat. Sci. 2001, 16, 184–198. [Google Scholar] [CrossRef]
  97. Breiman, L. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat. Sci. 2001, 16, 199–231. [Google Scholar] [CrossRef]
  98. Friedman, J.H. SMART User Guide; Technical Report; Stanford University: Stanford, CA, USA, 1984; Available online: https://statistics.stanford.edu/research/smart-users-guide (accessed on 8 September 2021).
  99. Rao, D.C. CAT scans, PET scans, and genomic scans. Genet. Epidemiol. 1998, 15, 1–18. [Google Scholar] [CrossRef]
  100. Cervantes-Henríquez, M.L.; Acosta-López, J.; Aguirre-Acevedo, D.C.; Pineda-Álvarez, D.; Puentes Rozo, P. Fenotipo comportamental evaluado con una escala multidimensional de la conducta en niños y adolescentes de 30 familias con trastorno de atención-hiperactividad. Acta Neurol. Colomb. 2008, 24, 53–62. [Google Scholar]
  101. Schachar, R.; Mota, V.L.; Logan, G.D.; Tannock, R.; Klim, P. Confirmation of an inhibitory control deficit in attention-deficit/hyperactivity disorder. J. Abnorm. Child Psychol. 2000, 28, 227–235. [Google Scholar] [CrossRef] [PubMed]
  102. Willcutt, E.G.; Doyle, A.E.; Nigg, J.T.; Faraone, S.V.; Pennington, B.F. Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biol. Psychiatry 2005, 57, 1336–1346. [Google Scholar] [CrossRef]
  103. Stuss, D.T.; Murphy, K.J.; Binns, M.A.; Alexander, M.P. Staying on the job: The frontal lobes control individual performance variability. Brain 2003, 126, 2363–2380. [Google Scholar] [CrossRef]
  104. Stuss, D.T.; Binns, M.A.; Murphy, K.J.; Alexander, M.P. Dissociations within the anterior attentional system: Effects of task complexity and irrelevant information on reaction time speed and accuracy. Neuropsychology 2002, 16, 500–513. [Google Scholar] [CrossRef] [PubMed]
  105. Wood, A.C.; Rijsdijk, F.; Johnson, K.A.; Andreou, P.; Albrecht, B.; Arias-Vasquez, A.; Buitelaar, J.K.; McLoughlin, G.; Rommelse, N.N.; Sergeant, J.A.; et al. The relationship between ADHD and key cognitive phenotypes is not mediated by shared familial effects with IQ. Psychol. Med. 2011, 41, 861–871. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Johnson, K.A.; Barry, E.; Bellgrove, M.A.; Cox, M.; Kelly, S.P.; Daibhis, A.; Daly, M.; Keavey, M.; Watchorn, A.; Fitzgerald, M.; et al. Dissociation in response to methylphenidate on response variability in a group of medication naive children with ADHD. Neuropsychologia 2008, 46, 1532–1541. [Google Scholar] [CrossRef]
  107. Johnson, K.A.; Robertson, I.H.; Kelly, S.P.; Silk, T.J.; Barry, E.; Daibhis, A.; Watchorn, A.; Keavey, M.; Fitzgerald, M.; Gallagher, L.; et al. Dissociation in performance of children with ADHD and high-functioning autism on a task of sustained attention. Neuropsychologia 2007, 45, 2234–2245. [Google Scholar] [CrossRef] [Green Version]
  108. Gupta, D.S.; Bahmer, A. Increase in Mutual Information During Interaction with the Environment Contributes to Perception. Entropy 2019, 21, 365. [Google Scholar] [CrossRef] [Green Version]
  109. Corkum, P.V.; Siegel, L.S. Is the Continuous Performance Task a valuable research tool for use with children with Attention-Deficit-Hyperactivity Disorder? J. Child Psychol. Psychiatry 1993, 34, 1217–1239. [Google Scholar] [CrossRef]
  110. Van der Meere, J.; Vreeling, H.J.; Sergeant, J. A motor presetting study in hyperactive, learning disabled and control children. J. Child Psychol. Psychiatry 1992, 33, 1347–1351. [Google Scholar] [CrossRef]
  111. Gupta, D.S.; Banerjee, A.; Roy, D.; Piras, F. Editorial: Temporal Structure of Neural Processes Coupling Sensory, Motor and Cognitive Functions of the Brain. Front. Comput. Neurosci. 2020, 14, 73. [Google Scholar] [CrossRef]
  112. Sarkar, I.N. Biomedical informatics and translational medicine. J. Transl. Med. 2010, 8, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Capdevila-Brophy, C.; Artigas-Pallarés, J.; Ramírez-Mallafré, A.; López-Rosendo, M.; Real, J.; Obiols-Llandrich, J.E. Fenotipo neuropsicológico del trastorno de déficit atencional/hiperactividad:¿ existen diferencias entre los subtipos. Rev. Neurol. 2005, 40, 17–23. [Google Scholar] [CrossRef] [Green Version]
  114. Alderson, R.M.; Rapport, M.D.; Kofler, M.J. Attention-deficit/hyperactivity disorder and behavioral inhibition: A meta-analytic review of the stop-signal paradigm. J. Abnorm. Child Psychol. 2007, 35, 745–758. [Google Scholar] [CrossRef] [PubMed]
  115. Slachevsky, C.A.; Pérez, C.; Silva, J.R.; Ruiz-Tagle, A.; Mayol, R.; Muñoz-Neira, C.; Núñez-Huasaf, J. Descomponiendo el síndrome de déficit atencional en el adulto: Hacia un entendimiento de su heterogeneidad pronóstica. Rev. Med. Chile 2012, 140, 379–385. [Google Scholar] [CrossRef] [Green Version]
  116. Bluschke, A.; Broschwitz, F.; Kohl, S.; Roessner, V.; Beste, C. The neuronal mechanisms underlying improvement of impulsivity in ADHD by theta/beta neurofeedback. Sci. Rep. 2016, 6, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Bluschke, A.; Roessner, V.; Beste, C. Specific cognitive-neurophysiological processes predict impulsivity in the childhood attention-deficit/hyperactivity disorder combined subtype. Psychol. Med. 2016, 46, 1277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Waber, D.P.; Weiler, M.D.; Forbes, P.W.; Bernstein, J.H.; Bellinger, D.C.; Rappaport, L. Neurobehavioral factors associated with referral for learning problems in a community sample: Evidence for an adaptational model for learning disorders. J. Learn. Disabil. 2003, 36, 467–483. [Google Scholar] [CrossRef] [PubMed]
  119. Rosch, K.S.; Hawk, L.W., Jr. The effects of performance-based rewards on neurophysiological correlates of stimulus, error, and feedback processing in children with ADHD. Psychophysiology 2013, 50, 1157–1173. [Google Scholar] [CrossRef] [Green Version]
  120. Caspersen, I.D.; Petersen, A.; Vangkilde, S.; Plessen, K.J.; Habekost, T. Perceptual and response-dependent profiles of attention in children with ADHD. Neuropsychology 2017, 31, 349–360. [Google Scholar] [CrossRef] [PubMed]
  121. Yáñez-Téllez, G.; Romero-Romero, H.; Rivera-García, L.; Prieto-Corona, B.; Bernal-Hernández, J.; Marosi-Holczberger, E.; Guerrero-Juárez, V.; Rodríguez-Camacho, M.; Silva-Pereyra, J.F. Cognitive and executive functions in ADHD. Actas Esp. Psiquiatr. 2012, 40, 293–298. [Google Scholar]
  122. Peterson, T.R.; Laplante, M.; Thoreen, C.C.; Sancak, Y.; Kang, S.A.; Kuehl, W.M.; Gray, N.S.; Sabatini, D.M. DEPTOR is an mTOR inhibitor frequently overexpressed in multiple myeloma cells and required for their survival. Cell 2009, 137, 873–886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. Shiels, K.; Hawk, L.W., Jr. Self-regulation in ADHD: The role of error processing. Clin. Psychol. Rev. 2010, 30, 951–961. [Google Scholar] [CrossRef] [Green Version]
  124. Mirsky, A.F.; Pascualvaca, D.M.; Duncan, C.C.; French, L.M. A model of attention and its relation to ADHD. Ment. Retard. Dev. Disabil. Res. Rev. 1999, 5, 169–176. [Google Scholar] [CrossRef]
  125. MacDonald, S.W.S.; Li, S.-C.; Bäckman, L. Neural underpinnings of within-person variability in cognitive functioning. Psychol. Aging 2009, 24, 792. [Google Scholar] [CrossRef] [PubMed]
  126. Heaton, R.K.; Avitable, N.; Grant, I.; Matthews, C.G. Further crossvalidation of regression-based neuropsychological norms with an update for the Boston Naming Test. J. Clin. Exp. Neuropsychol. 1999, 21, 572–582. [Google Scholar] [CrossRef] [PubMed]
  127. Van Rooij, D.; Hoekstra, P.J.; Mennes, M.; von Rhein, D.; Thissen, A.J.A.M.; Heslenfeld, D.; Zwiers, M.P.; Faraone, S.V.; Oosterlaan, J.; Franke, B.; et al. Distinguishing adolescents with ADHD from their unaffected siblings and healthy comparison subjects by neural activation patterns during response inhibition. Am. J. Psychiatry 2015, 172, 674–683. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  128. Satterfield, J.H.; Cantwell, D.P.; Satterfield, B.T. Pathophysiology of the hyperactive child syndrome. Arch. Gen. Psychiatry 1974, 31, 839–844. [Google Scholar] [CrossRef] [PubMed]
  129. Morein-Zamir, S.; Dodds, C.; van Hartevelt, T.J.; Schwarzkopf, W.; Sahakian, B.; Müller, U.; Robbins, T. Hypoactivation in right inferior frontal cortex is specifically associated with motor response inhibition in adult ADHD. Hum. Brain Mapp. 2014, 35, 5141–5152. [Google Scholar] [CrossRef] [PubMed]
  130. Lou, H.C.; Henriksen, L.; Bruhn, P. Focal cerebral hypoperfusion in children with dysphasia and/or attention deficit disorder. Arch. Neurol. 1984, 41, 825–829. [Google Scholar] [CrossRef]
  131. Silberstein, R.B.; Pipingas, A.; Farrow, M.; Levy, F.; Stough, C.K. Dopaminergic modulation of default mode network brain functional connectivity in attention deficit hyperactivity disorder. Brain Behav. 2016, 6, e00582. [Google Scholar] [CrossRef] [Green Version]
  132. Bellgrove, M.A.; Hawi, Z.; Kirley, A.; Fitzgerald, M.; Gill, M.; Robertson, I.H. Association between dopamine transporter (DAT1) genotype, left-sided inattention, and an enhanced response to methylphenidate in attention-deficit hyperactivity disorder. Neuropsychopharmacology 2005, 30, 2290–2297. [Google Scholar] [CrossRef]
  133. Doyle, A.E.; Faraone, S.V.; Seidman, L.J.; Willcutt, E.G.; Nigg, J.T.; Waldman, I.D.; Pennington, B.F.; Peart, J.; Biederman, J. Are endophenotypes based on measures of executive functions useful for molecular genetic studies of ADHD? J. Child Psychol. Psychiatry 2005, 46, 774–803. [Google Scholar] [CrossRef]
  134. Nigg, J.T. Neuropsychologic theory and findings in attention-deficit/hyperactivity disorder: The state of the field and salient challenges for the coming decade. Biol. Psychiatry 2005, 57, 1424–1435. [Google Scholar] [CrossRef]
  135. Kuntsi, J.; Wood, A.C.; Rijsdijk, F.; Johnson, K.A.; Andreou, P.; Albrecht, B.; Arias-Vasquez, A.; Buitelaar, J.K.; McLoughlin, G.; Rommelse, N.N.; et al. Separation of cognitive impairments in attention-deficit/hyperactivity disorder into 2 familial factors. Arch. Gen. Psychiatry 2010, 67, 1159–1167. [Google Scholar] [CrossRef]
  136. Kuntsi, J.; Stevenson, J. Psychological mechanisms in hyperactivity: II The role of genetic factors. J. Child. Psychol. Psychiatry 2001, 42, 211–219. [Google Scholar] [CrossRef]
  137. Andreou, P.; Neale, B.M.; Chen, W.; Christiansen, H.; Gabriels, I.; Heise, A.; Meidad, S.; Muller, U.C.; Uebel, H.; Banaschewski, T.; et al. Reaction time performance in ADHD: Improvement under fast-incentive condition and familial effects. Psychol. Med. 2007, 37, 1703–1715. [Google Scholar] [CrossRef] [Green Version]
  138. Pineda, D.A.; Palacio, L.G.; Puerta, I.C.; Merchan, V.; Arango, C.P.; Galvis, A.Y.; Gómez, M.; Aguirre, D.C.; Lopera, F.; Arcos-Burgos, M. Environmental influences that affect attention deficit/hyperactivity disorder: Study of a genetic isolate. Eur. Child Adolesc. Psychiatry 2007, 16, 337–346. [Google Scholar] [CrossRef]
  139. Rommelse, N.N.; Altink, M.E.; Arias-Vasquez, A.; Buschgens, C.J.; Fliers, E.; Faraone, S.V.; Buitelaar, J.K.; Sergeant, J.A.; Oosterlaan, J.; Franke, B. Differential association between MAOA, ADHD and neuropsychological functioning in boys and girls. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2008, 147B, 1524–1530. [Google Scholar] [CrossRef] [PubMed]
  140. Epstein, J.N.; Hwang, M.E.; Antonini, T.; Langberg, J.M.; Altaye, M.; Arnold, L.E. Examining predictors of reaction times in children with ADHD and normal controls. J. Int. Neuropsychol. Soc. 2010, 16, 138–147. [Google Scholar] [CrossRef] [Green Version]
  141. Epstein, J.N.; Langberg, J.M.; Rosen, P.J.; Graham, A.; Narad, M.E.; Antonini, T.N.; Brinkman, W.B.; Froehlich, T.; Simon, J.O.; Altaye, M. Evidence for higher reaction time variability for children with ADHD on a range of cognitive tasks including reward and event rate manipulations. Neuropsychology 2011, 25, 427–441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Henriquez-Henriquez, M.P.; Billeke, P.; Henriquez, H.; Zamorano, F.J.; Rothhammer, F.; Aboitiz, F. Intra-Individual Response Variability Assessed by Ex-Gaussian Analysis may be a New Endophenotype for Attention-Deficit/Hyperactivity Disorder. Front. Psychiatry 2014, 5, 197. [Google Scholar] [CrossRef] [Green Version]
  143. Hervey, A.S.; Epstein, J.N.; Curry, J.F.; Tonev, S.; Eugene Arnold, L.; Keith Conners, C.; Hinshaw, S.P.; Swanson, J.M.; Hechtman, L. Reaction time distribution analysis of neuropsychological performance in an ADHD sample. Child Neuropsychol. 2006, 12, 125–140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Leth-Steensen, C.; Elbaz, Z.K.; Douglas, V.I. Mean response times, variability, and skew in the responding of ADHD children: A response time distributional approach. Acta Psychol. 2000, 104, 167–190. [Google Scholar] [CrossRef]
  145. Hongsermeier, T.; Kashyap, V. A Knowledge Management platform for Translational Medicine. AMIA Ann. Symp. Proc 2005, 2005, 984. [Google Scholar]
  146. Salamanca-Ortíz, D.N.; Vergara-Vergara, J.Y.; Escobar-Córdoba, F.; Rodríguez-Gama, Á.; Caminos-Pinzón, J.E. Genetic and molecular advances in the study of mental disorders. Rev. Fac. Med. 2014, 62, 319–324. [Google Scholar]
  147. Gómez Sánchez, C.I. Genética y Farmacogenética del Trastorno por Déficit de Atención e Hiperactividad en Niños de la Población Española. Ph.D. Thesis, Universidad Autónoma de Madrid, Madrid, Spain, 2017. [Google Scholar]
  148. Ribases, M.; Ramos-Quiroga, J.A.; Sanchez-Mora, C.; Bosch, R.; Richarte, V.; Palomar, G.; Gastaminza, X.; Bielsa, A.; Arcos-Burgos, M.; Muenke, M.; et al. Contribution of LPHN3 to the genetic susceptibility to ADHD in adulthood: A replication study. Genes Brain Behav. 2011, 10, 149–157. [Google Scholar] [CrossRef] [PubMed]
  149. Hwang-Gu, S.L.; Gau, S.S. Interval timing deficits assessed by time reproduction dual tasks as cognitive endophenotypes for attention-deficit/hyperactivity disorder. PLoS ONE 2015, 10, e0127157. [Google Scholar] [CrossRef] [PubMed]
  150. Bruxel, E.M.; Salatino-Oliveira, A.; Akutagava-Martins, G.C.; Tovo-Rodrigues, L.; Genro, J.P.; Zeni, C.P.; Polanczyk, G.V.; Chazan, R.; Schmitz, M.; Arcos-Burgos, M.; et al. LPHN3 and attention-deficit/hyperactivity disorder: A susceptibility and pharmacogenetic study. Genes Brain Behav. 2015, 14, 419–427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  151. Arcos-Burgos, M.; Muenke, M. Toward a better understanding of ADHD: LPHN3 gene variants and the susceptibility to develop ADHD. Atten. Defic. Hyperact. Disord. 2010, 2, 139–147. [Google Scholar] [CrossRef] [Green Version]
  152. Acosta, M.T.; Vélez, J.I.; Bustamante, M.L.; Balog, J.Z.; Arcos-Burgos, M.; Muenke, M. A two-locus genetic interaction between LPHN3 and 11q predicts ADHD severity and long-term outcome. Transl. Psychiatry 2011, 1, 1–8. [Google Scholar] [CrossRef]
  153. Payne, P.R.; Embi, P.J.; Sen, C.K. Translational informatics: Enabling high-throughput research paradigms. Physiol. Genom. 2009, 39, 131–140. [Google Scholar] [CrossRef] [Green Version]
  154. Payne, P.R.; Johnson, S.B.; Starren, J.B.; Tilson, H.H.; Dowdy, D. Breaking the translational barriers: The value of integrating biomedical informatics and translational research. J. Investig. Med. 2005, 53, 192–200. [Google Scholar] [CrossRef]
  155. Vélez, J.I. Machine Learning based Psychology: Advocating for A Data-Driven Approach. Int. J. Psychol. Res. 2021, 14, 6–11. [Google Scholar] [CrossRef]
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Figure 1. Neuropsychological tasks where (a) ADHD affected individuals differed from controls; (b) genetic effects and hereditary transmission are present; and (c) fulfil the requirements to be considered as potential endophenotypes are shown in orange. Displayed numbers correspond to task # in Table 2.
Figure 1. Neuropsychological tasks where (a) ADHD affected individuals differed from controls; (b) genetic effects and hereditary transmission are present; and (c) fulfil the requirements to be considered as potential endophenotypes are shown in orange. Displayed numbers correspond to task # in Table 2.
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Figure 2. (a) Classification tree for predicting ADHD status in individuals from the Colombian Caribbean. Numbers within white squares represent the node number, the first line corresponds to the most frequent class (no: unaffected; yes: ADHD affected), the second line to the probability of each class within the node, and the third line to the percentage of the total sample size (n = 232) within each node. Nodes where ADHD affected individuals are more likely to be classified are shown in blue. (b) Variable importance (left) and ROC curve for the CART strategy. Displayed numbers correspond to task # in Table 2. (c) Performance measures for the learning (blue) data set; the grey line represents a naïve classifier. AUC: Area under the curve; CART: Classification and regression tree; CI: confidence interval; CR: Classification rate; ROC: Receiver operating characteristic.
Figure 2. (a) Classification tree for predicting ADHD status in individuals from the Colombian Caribbean. Numbers within white squares represent the node number, the first line corresponds to the most frequent class (no: unaffected; yes: ADHD affected), the second line to the probability of each class within the node, and the third line to the percentage of the total sample size (n = 232) within each node. Nodes where ADHD affected individuals are more likely to be classified are shown in blue. (b) Variable importance (left) and ROC curve for the CART strategy. Displayed numbers correspond to task # in Table 2. (c) Performance measures for the learning (blue) data set; the grey line represents a naïve classifier. AUC: Area under the curve; CART: Classification and regression tree; CI: confidence interval; CR: Classification rate; ROC: Receiver operating characteristic.
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Table
Table 1. Demographic characteristics of 232 individuals included in this study.
Table 1. Demographic characteristics of 232 individuals included in this study.
UnaffectedAffectedStatistic IndexpCohen’s Effect Size
(n = 108)(n = 124)
SexFrequency (%)Frequency (%)χ2
Female64 (59.25)40 (32.26)15.942<0.00001-
Male44 (40.75)84 (67.74)
Mean (SD)Mean (SD)Mann–Whitney’s U
Age34.11 (13.06)23.14 (15.60)9239.5<0.00010.758
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Acosta-López, J.E.; Suárez, I.; Pineda, D.A.; Cervantes-Henríquez, M.L.; Martínez-Banfi, M.L.; Lozano-Gutiérrez, S.G.; Ahmad, M.; Pineda-Alhucema, W.; Noguera-Machacón, L.M.; Hoz, M.D.L.; et al. Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD. Brain Sci. 2021, 11, 1218. https://doi.org/10.3390/brainsci11091218

AMA Style

Acosta-López JE, Suárez I, Pineda DA, Cervantes-Henríquez ML, Martínez-Banfi ML, Lozano-Gutiérrez SG, Ahmad M, Pineda-Alhucema W, Noguera-Machacón LM, Hoz MDL, et al. Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD. Brain Sciences. 2021; 11(9):1218. https://doi.org/10.3390/brainsci11091218

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Acosta-López, Johan E., Isabel Suárez, David A. Pineda, Martha L. Cervantes-Henríquez, Martha L. Martínez-Banfi, Semiramis G. Lozano-Gutiérrez, Mostapha Ahmad, Wilmar Pineda-Alhucema, Luz M. Noguera-Machacón, Moisés De La Hoz, and et al. 2021. "Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD" Brain Sciences 11, no. 9: 1218. https://doi.org/10.3390/brainsci11091218

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