Cognitive Developmental Trajectories in Adult ADHD Patients and Controls: A Comparative Study

Abstract

Objective:

There is an updated conceptualization of whole-lifespan attention-deficit hyperactivity disorder (ADHD), promoted by awareness of probable persistence of impairment into adulthood. We investigated cognition trajectories from adolescence to mid-adulthood in ADHD.

Method:

Data of 240 patients with ADHD and 244 healthy controls (HCs) were obtained; clinical symptoms and neuropsychological functions were assessed using the various tests.

Results:

Compared to HCs, patients with ADHD except 35 to 44 age interval showed lower full scale intelligence quotient. They showed decreased verbal comprehensive scores except in the 35 to 44 age interval and working memory scores in all intervals. In the Comprehensive Attention Test, patients with ADHD showed increased working memory error frequencies except in the 15 to 17 age interval and divided attention omission error in all intervals.

Conclusion:

Adults with ADHD showed deficits not in simple attention but in complex attention, including divided attention and working memory.

Keywords

adult attention-deficit hyperactivity disorder continuous performance tasks intelligence working memory development

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurobiological disorder characterized by significant inattention, hyperactivity, and impulsivity (American Psychiatric Association [APA], 2013). Historically, ADHD has been perceived as a child-onset neurodevelopmental disorder (Doernberg & Hollander, 2016) believed to be outgrown by late adolescence or the beginning of adulthood (Hill & Schoener, 1996). Therefore, the earlier Diagnostic and Statistical Manual of Mental Disorders (DSM) versions regarded ADHD as a disease related only to children, and concerns regarding the presence of ADHD across the lifespan were limited (McCarthy et al., 2009). Over time, the increasing interest in ADHD symptoms and functional deficits persisting into adulthood has reflected in the definition of ADHD being updated in the latest, fifth edition of the DSM (DSM-5) to more precisely represent the presentation of affected adults (APA, 2013). Individuals with ADHD present with heterogeneous clinical features, including difficulty in organizing tasks, failing to pay attention to detail, distractibility, excessive talking, difficulty relaxing, impulsive behaviors, low self-esteem, problematic interpersonal relationships, and emotional dysregulation (APA, 2013; Das et al., 2012; Kirino, et al., 2015; Klassen et al., 2010; Retz et al., 2012). The most prominent deficit of adult ADHD is attentional dysfunction, especially related to impaired attention, inhibition, memory, and executive functioning (Boonstra et al., 2005; Mowinckel et al., 2015; Ossmann & Mulligan, 2003). Along with these functional and psychosocial impairments, adult ADHD is linked to significant personal and societal burdens (Faraone et al., 2015). For this reason, focus should shift to include the apprehension and management of adult ADHD.

Recently, there has been an updated conceptualization of ADHD trajectories across whole lifespans, promoted by an increased awareness about the probable persistence of impairment from childhood to adulthood (Asherson et al., 2016; Faraone et al., 2015). Meta-analyses estimate ADHD prevalence rates of 5% to 7.1% in childhood (Polanczyk et al., 2007; Spencer et al., 2007; Thomas et al., 2015; Willcutt, 2012) and 2.5% to 5% in adulthood (Fayyad et al., 2007; Polanczyk et al., 2007; Simon et al., 2009; Willcutt, 2012). Longitudinal follow-up studies show that about 65% of children with ADHD experience continuation of symptoms and functional impairment into adulthood (Faraone et al., 2006). However, there was controversy in the concept of adult ADHD as the continuum of child and adolescent ADHD (Agnew-Blais et al., 2016; Biederman et al., 2006; Caye et al., 2016; Faraone et al., 2006; Moffitt et al., 2015; Rowland et al., 2002). Considerable proportion of adults with ADHD was reported to lack a history of childhood ADHD (Agnew-Blais et al., 2016; Caye et al., 2016; Moffitt et al., 2015). These novel findings support the perspective that childhood ADHD does not resolve during early adulthood, and furthermore, that adult ADHD does not represent a continuation of childhood ADHD. However, it can be too hasty to conclude that the adult-onset ADHD is genuinely distinct from the childhood disorder considering that diagnosis of adult ADHD, contrasting child ADHD, was based mainly on self-report (Faraone & Biederman, 2016). Indeed, a recent analysis of the Multimodal Treatment of ADHD (MTA) study found that under thorough scrutiny, 95% of the control group who firstly sorted as adult-onset ADHD on screening didn’t truly meet criteria for a diagnosis, with the manifestation of symptoms only in substance abuse situations (Sibley et al., 2018) and only 2% of the group met criteria for adult-onset ADHD diagnosis when precluding cases with subthreshold symptoms of ADHD in childhood. A study tested whether those with apparent late-onset ADHD symptoms might include misclassified cases who had present high ADHD symptoms at least one point in childhood found that 75% of those with apparent late-onset ADHD, defined as low ADHD scores at age 12 and high ADHD scores at age 17, had actually shown high ADHD scores at least one point in childhood and finally, only 25% of those were classified as genuine late-onset ADHD group (Cooper et al., 2018).

Attention refers to how we actively process specific information in our surroundings and is one of the essential higher cognitive functions. Although the concept of attention cannot be explained by a single definition, clinicians and researchers have commonly classified attention into “selective attention,” “divided attention,” and “sustained attention” (Cohen & O’Donnell, 1993). “Selective attention” means focusing on a specific stimulus among several stimuli, “divided attention” means paying attention to several stimuli at once, and “sustained attention” enables continuously maintaining attention on a task for a long time. Studies have suggested that the core cognitive deficits in ADHD are based on attention-focusing abilities such as “selective attention” and “sustained attention” (Nigg et al., 2005); furthermore, problems with executive functions mediated by the frontal lobe have been highlighted for playing an important role in the pathogenesis of ADHD (Barkley, 2006; Levine et al., 1987). Executive functions are higher cognitive functions necessary to control oneself and achieve goal-oriented behavior. They include response suppression, planning, organization, set shifting, abstracting, and working memory (Lyon & Krasnegor, 1996). Children and adolescents with ADHD exhibit impaired executive functioning in areas including working memory (spatial and verbal), sustained attention, response inhibition, cognitive flexibility, planning, and organizational skills (Nigg et al., 2005). More recently, the investigation of cognitive function in adolescents and adults with ADHD has further revealed multiple neuropsychological and neurophysiological impairments including atypical brain activation during error processing, attentional allocation, and preparation-vigilance processes (Cheung et al., 2016; Groom et al., 2010; Michelini et al., 2016). Among those cognitive and physiological measures, several measures including preparation-vigilance processes (e.g., reaction time variability, omission errors of response preparation) and IQ were found to be related to ADHD outcome (persistence/remission from childhood ADHD) (Cheung et al., 2016; Michelini et al., 2016). Sibling studies have shown several familial factors underlying the mechanism of cognitive deficits in ADHD in children; reaction time variability, IQ, executive function measures such as response accuracy and working memory (Frazier-Wood et al., 2012; Kuntsi et al., 2010; Wood et al., 2011). Although diagnosis of ADHD in childhood persists in a significant number, the extent to which these cognitive deficits show a similar etiological configuration in adult ADHD is unknown. For similar reason, whether adult-onset ADHD is a delayed manifestation of the same vulnerability that underlies childhood ADHD or distinct disorder is still be investigated.

The computer-based continuous performance test (CPT) has been commonly used to evaluate neuropsychological abnormalities in ADHD by assessing different levels of sustained attention, vigilance, and response inhibition (Corkum & Siegel, 1993). The test is regarded as a useful tool to distinguish children with ADHD from normally developing children (Corkum & Siegel, 1993). In Korea, the computerized comprehensive attention test (CAT) has been developed, standardized for ages 4 to 49 (Huh et al., 2019; Yoo et al., 2009), and widely used to evaluate different aspects of attention and working memory (Seo et al., 2011). The CAT is composed of six subtests to measure simple visual and auditory selective attention, continuous inhibition, interference selection, divided attention, and spatial working memory. Studies on the use of the neuropsychological tests in patients with ADHD show that the sustained attention and working memory tests seemed to be effective in screening children with ADHD (Nigg et al., 2005).

There is substantial evidence to indicate that the average intelligence quotient (IQ) and executive function ability of children with ADHD are lower than those of children without ADHD, despite the fact that individuals with ADHD show the full range of IQ and executive function (Frazier et al., 2004). Likewise, adults with ADHD exhibit lower IQs than adults without ADHD, although this deviation is less marked than in children (Hervey et al., 2004; Seidman, 2006). A longitudinal study examining persistent, remitted, and late-onset ADHD groups also found that all ADHD groups exhibited lower IQ scores and executive function levels compared to healthy controls (HCs) (Agnew-Blais et al., 2019). Furthermore, the same study showed no differences in IQ trajectories between ADHD groups and control group; in other words, cognitive abilities were unchanged from childhood to young adulthood, even among all ADHD groups.

Unfortunately, studies on cognitive developmental trajectories in ADHD have generally considered the childhood and adulthood stages separately; moreover, studies on ADHD in adults are rare. Further studies on adult ADHD are required, particularly those investigating the neuropsychological parameters. In order to determine the age-related trajectories of cognition in ADHD, we examined IQ and attentional ability in a wide-range age group from adolescence to adulthood in both patients with ADHD and HCs. We hypothesized that findings of cognitive developmental trajectories in this study suggested delayed development as underlying mechanisms of developing adult ADHD.

Methods

Participants and Study Procedure

A total of 484 participants were recruited between March 2017 and February 2019 through advertisements on the bulletin boards at eight university hospitals (Kyung Hee University Hospital, Samsung Seoul Hospital, Inje University Sanggye Paik Hospital, Chung Ang University Hospital, Nowon Elji Hospital, Ewha Women’s University Hospital, Myongji Hospital, and Soonchunhyang Buchun Hospital) and two private local clinics (Pangyo and Seoul Our Child Psychiatric Clinic). All participants provided written informed consent. The inclusion criteria were a referral for assessment of ADHD and age between 15 and 65 years. Exclusion criteria were as follow: (1) treatment with medication targeting ADHD within 3 months; (2) presence of a congenital genetic disease, organic brain disease, or severe medical condition; (3) a history of autism spectrum disorder, intellectual disability, schizophrenia, bipolar I disorder, or other mental disorders within 6 months.

The psychiatric assessment was conducted by two different board-certified psychiatrists in accordance with the DSM-5. If the ADHD diagnosis of the two psychiatrists were consistent, the patients were classified into the ADHD group; if the diagnoses were inconsistent, the participants were excluded; and if the HC assessments of the psychiatrists were consistent, they were classified into the HC group. The Korean version of the Mini International Neuropsychiatry Interview (MINI) was used to exclude participants with other mental disorders. Ultimately, 240 participants with ADHD and 244 controls were included in the study. After screening and group sorting, participants were asked to visit the hospitals twice for evaluating clinical symptoms, attentional abilities, and intelligence. At the first visit, attentional abilities for all age groups were assessed using the CAT. At the second visit, the Wechsler Adult Intelligence Scale, fourth edition, Korean version (K-WAIS-IV) was administered to assess intelligence for all participants. In addition, at the second visit, all participants were asked to submit the pre-filled questionnaire, the Korean version of Adult ADHD Self-Report Scale (K-ASRS-V1.1), which was pre-distributed to assess clinical symptoms. The parents and caretakers of participants with age 15 to 17 were asked to complete Korean version of Dupaul’s ADHD rating scale (DuPaul, 1991).

Assessment Tools

K-ARS and ASRS

The severity of attention in participants with the age ranges from 15 to 17 was evaluated with K-ARS. The K-ARS is a 18-item scale for child and adolescent developed by DuPaul (1991). The Korean version of K-ARS has been validated to have adequate reliability (internal consistency ranging from 0.77 to 0.89 and test-retest reliability = 0.85) (DuPaul, 1991; So et al., 2002).

The severity of attention in participants with over the age 18 was assessed with K-ASRS-V1.1. The K-ASRS is a 18-item self-report scales based on the DSM-IV symptom criteria. The ASRS was developed by the Workgroup for Adult ADHD in cooperation with the WHO (Kessler et al., 2005). The Korean versions of ASRS have been validated (Kim et al., 2013). The scales emphasize symptom frequency rather than severity to make directions easier for subjects to understand. Total scores range from 0 to 72.

K-WAIS-IV

The WAIS-IV (The Psychological Corporation [TPC], 2008) are the gold standards for comprehensive assessment of cognitive functions for adults, respectively. Along with the full scale IQ (FSIQ) as an approximation of overall intellectual abilities, four discrete indices are sensitive measures to distinguish difficulties in cognition and have specific associations with clinical features (Mayes & Calhoun, 2006; TPC, 2008): the Verbal Comprehension Index (VCI), Perceptual Organization Index (POI), Working Memory Index (WMI), and Processing Speed Index (PSI). The verbal comprehension index (VCI) is a score derived from the administration of the “Similarities,” “Vocabulary,” and “Information” subtests and reflects verbal reasoning, verbal concept formation, and general or lexical knowledge. The perceptual organization index (POI) is derived from the administration of the “Block design,” “Matrix reasoning,” and “Visual puzzles” subtests and reflects nonverbal fluid reasoning, visual-motor integration, and visual-spatial problem-solving. The Working Memory Index (WMI) is a score derived from the administration of the “Digit span,” “Arithmetic,” and “Letter-number sequencing” subtests and reflects a subject’s ability to attend to information presented verbally, manipulate that information using short-term immediate memory, and then formulate a response. The processing speed index (PSI) is calculated using “Symbol search,” “Coding,” and “Cancellation” subtests and refers to the speed of cognitive processes and response output. Both the K-WISC-IV and the K-WAIS-IV have been validated (Goo et al., 2016; Yeom et al., 1992).

Computerized CAT

The CAT takes a total of 40 to 45 minutes and comprises six subtests: the simple selective attention (visual and auditory), continuous inhibition, interference selection, divided attention, and working memory tests. The number of stimuli and time required for each subtest are 300 stimuli/10 minutes, 300 stimuli/10 minutes, 300 stimuli/10 minutes, 150 stimuli/5 minutes, 100 stimuli/3 minutes and 20 seconds, and all you can/5 minutes respectively. The tests have been standardized for children aged 4 to 49 and established to be valid and reliable (Huh et al., 2019; Seo et al., 2011; Yoo et al., 2009). All subtests are performed using a computer and at the start of each subtest, voice and text guides are presented and trained researches checked whether participants understood. In the selective attention test, subjects press the button (assigned keyboard button; space bar) quickly when they see/hear the specified targets (e.g., circle figure, bell). In the continuous inhibition test, subjects press the button when they see any figure except an X. In the interference selection test, the subject pays attention to the target in the middle position while ignoring the interference stimuli. In the divided attention test, the subject remembers the figure and sound of the previous stimuli (e.g., circle, triangle, square, bell, buzzer) and presses the button when the previous stimuli reappear. In the working memory test, the subject remembers the orders and positions of the highlighted boxes, then clicks the boxes in the order they are highlighted and in the reverse order. Except for the working memory test, each subtest has five indicators (commission errors [CE], omission errors [OE], mean reaction time [RT mean], standard deviation of reaction time [RT sd], and sensitivity coefficient [d′]). The CE refers to the number of wrong responses, the OE to the number of missed responses, the RT mean indicates the average time taken to respond to the stimuli, the RT sd reveals the response time variability, and d′ indicates how successfully the target stimuli are differentiated from the non-target stimuli. A high d′ score means a higher target-noise differentiation. The working memory task evaluates the ability of recall of the stimuli in order and in reverse order and has two indicators (correct response number and memory span).

Statistical Analysis

All computations were performed using the Statistical Package for the Social Science (SPSS) version 24 (IBM SPSS statistics). In preliminary assumption testing for the normality, linearity, univariate and multivariate outliers, homogeneity of covariance matrices, and multicollinearity, there were no serious violations.

The differences in the age, K-ARS scores and ASRS scores between ADHD and healthy comparison groups were analyzed using independent t-test. The number of age category, sex distribution, and education category between ADHD and healthy comparison groups were analyzed using chi-square test. The IQ total and IQ subscales were analyzed suing MANOVA with covariate of age.

CAT score analyses were performed as follows: Firstly, we tried to analyze the differences in the sub-categories scores of CAT between all ADHD group and all healthy control group using MANCOVA with covariates of age and IQ total. In those MANCOVA results, we found that the age and IQ could affect the results of subset scores of CAT. In second analyses, all participants were classified into four age groups. Because the age range of recruited participants was from 15 to 44, the age interval was classified into 15 to 17, 18 to 24, 25 to 34, and 35 to 44 based on the adult ADHD prevalence report of the National Comorbidity Survey (Kessler et al., 2006). In each age group, the differences of subset scores of CAT between ADHD group and healthy control group using MANCOVA with covariates of IQ total.

The effect size in each assessment was calculated using the partial eta squared (ŋ2) in MANCAOVA, Cohen’s d in independent t test, and Cramer’s V in chi-square test. The effect size of Cohen’s d was interpreted as follows: 0.0 < d < 0.2, small; 0.3 < d < 0.5, medium; d > 0.6, large (Cohen, 1988). The effect size of Cramer’s V was interpreted as follows: V > 0, no or very weak; V > 0.05, weak; V > 0.10, moderate; V > 0.15, medium; and V > 0.25, very strong (Cramér, 1946). The effect size of partial eta-squared was interpreted as follows: partial ŋ2 = 0.01–0.09, small; ŋ2 = 0.09–0.25, medium; and ŋ² > 0.25, large (Bakeman, 2005).

Results

Demographic characteristics

There were no significant differences in age, sex, and education between the ADHD group and HC group. Of the 240 patients with ADHD, 175 (72.9%) were diagnosed with combined, 59 (24.6) with inattentive, and six (2.5%) with hyperactive type ADHD (Table 1).

Table 1.

Demographic Characteristics.

	ADHD (n = 240)	Healthy control (n = 244)	Statistics
Age (mean ± SD)	23.5 ± 8.6	24.2 ± 7.3	t = 1.03, p = .30, d = 0.087
15–17 (n, (%))	79 (32.9)	76 (31.1)	χ² = 0.43, p = .51, v = 0.002
18–24 (n, (%))	71 (29.6)	70 (28.7)
25–34 (n, (%))	58 (24.2)	61 (25.0)
35–44 (n, (%))	32 (13.3)	37 (15.2)
Sex (male/female)	184 (76.7)/56 (23.3)	179 (73.4)/65 (26.6)	χ² = 0.46, p = .23, v = 0.002
15–17 (n, (%))	60 (75.9)/19 (24.1)	56 (73.7)/20 (26.3)
18–24 (n, (%))	55 (77.5)/16 (22.5)	49 (70.0)/21 (30.0)
25–34 (n, (%))	43 (74.1)/15 (25.9)	44 (72.1)/17 (27.9)
35–44 (n, (%))	26 (81.3)/6 (18.7)	30 (81.1)/7 (18.9)
Education (n, (%))
Junior high	87 (36.2)	84 (34.4)	χ² = 0.38, p = .56, v = 0.002
High	99 (41.3)	98 (40.2)
Undergraduate	38 (15.8)	45 (18.4)
Graduate	16 (6.7)	17 (7.0)
ADHD type
Inattention	59 (24.6)
Hyperactivity–impulsivity	6 (2.5)
Combined	175 (72.9)
K-ARS scores (age 15–17; n = 155)	(n = 79)	(n = 76)
Inattentive score*	14.2 ± 5.1	6.9 ± 3.8	t = 10.5, p < .01, d = 1.623
Hyperactivity scores*	15.6 ± 5.6	6.6 ± 3.7	t = 13.6, p < .01, d = 1.896
Total score*	28.7 ± 9.4	11.4 ± 6.6	t = 21.4, p < .01, d = 2.130
ASRS scores (age 18–44; n = 329)	(n = 161)	(n = 168)
Part A*	12.5 ± 5.5	6.8 ± 4.9	t = 9.45, p < .01, d = 1.094
Part B*	21.4 ± 11.9	11.2 ± 8.8	t = 6.91, p < .01, d = 0.975
Total scores*	34.3 ± 16.1	18.2 ± 14.5	t = 6.78, p < .01, d = 1.051

Note. K-ARS = Korean Dupauls attention-deficit hyperactivity disorder (ADHD) rating scale scores; ASRS = Adult attention-deficit hyperactivity disorder (ADHD) self-report scale; d = Cohen’s d; v = Cramer’s V.

Statistically significant.

Compared to 76 HC adolescent group, 79 adolescent ADHD group showed increased K-ARS total (t = 21.4, p < .01, d = 2.130), inattentive (t = 10.5, p < .01, d = 1.623), and hyperactivity (t = 13.6, p < .01, d = 1.896) scores. Compared to 168 HC adult group, 161 adult ADHD group showed increased ASRS total (t = 6.78, p < .01, d = 1.051), part A (t = 9.45, p < .01, d = 1.094), and part B (t = 6.91, p < .01, d = 0.975) scores.

Comparisons of Intelligence Quotient and CAT Scores

In MANCOVA analyses between all ADHD group and all healthy comparison group, all IQ sub-scale scores, including verbal comprehension index (VCI)(F = 27.50, p < .01, ŋ² = 0.044), perceptual organization index (POI)(F = 22.96, p < .01, ŋ² = 0.045), working memory index (WMI)(F = 15.13, p < .01, ŋ² = 0.022), processing speed index (PSI)(F = 7.94, p < .01, ŋ² = 0.134), and full scale IQ (FSIQ)(F = 75.01, p < .01, ŋ² = 0.113) scores were lower in the ADHD group than in the HC group (Table 2). The covariate of age could affect the results of 3 sub-scales of IQ: VCI (F = 1.03, p = .31, ŋ² = 0.002), POI (F = 4.53, p = .03, ŋ² = 0.007), WMI (F = 8.54, p < .01, ŋ² = 0.014), PSI (F = 11.61, p < .01, ŋ² = 0.019), and FSIQ (F = 1.66, p = .19, ŋ² = 0.003).

Table 2.

Comparisons of Intelligence Quotient and Comprehensive Attention Test scores.

	ADHD (n = 240)	Healthy control (n = 244)	Statistics
Intelligence quotient^a	Mean ± SD	Mean ± SD	Statistics
VCI*	95.2 ± 15.9	104.0 ± 13.3	F = 27.50, p < .01, ŋ² = 0.044
POI*	97.6 ± 16.5	105.5 ± 14.1	F = 22.96, p < .01, ŋ² = 0.045
WMI*	94.8 ± 16.3	105.7 ± 15.2	F = 15.13, p < .01, ŋ² = 0.022
PSI*	91.6 ± 17.1	98.5 ± 14.6	F = 7.94, p < .01, ŋ² = 0.134
FSIQ*	93.9 ± 15.9	104.2 ± 13.3	F = 75.01, p < .01, ŋ² = 0.113
CAT^b	Mean ± SD	Mean ± SD
Simple visual OE	0.9 ± 2.7	0.5 ± 1.0	F = 0.39, p = .53, ŋ² = 0.016
Simple visual CE	2.0 ± 1.8	1.3 ± 1.4	F = 2.01, p = .15, ŋ² = 0.032
Simple auditory OE	1.1 ± 2.1	0.5 ± 0.8	F = 0.80, p = .37, ŋ² = 0.001
Simple auditory CE	1.1 ± 2.3	0.9 ± 1.6	F = 0.04, p = .84, ŋ² < 0.001
Continuous inhibition OE*	3.3 ± 3.0	1.3 ± 2.9	F = 7.03, p < .01, ŋ² = 0.124
Continuous inhibition CE*	11.1 ± 9.3	6.8 ± 5.8	F = 11.90, p < .01, ŋ² = 0.196
Interference selection OE*	3.9 ± 6.0	0.8 ± 2.5	F = 5.21, p < .01, ŋ² = 0.105
Interference selection CE*	5.8 ± 7.1	4.3 ± 3.8	F = 4.85, p = .01, ŋ² = 0.084
Divided OE*	9.1 ± 9.6	5.8 ± 6.1	F = 9.47, p < .01, ŋ² = 0.152
Divided CE*	5.7 ± 6.8	4.1 ± 5.8	F = 4.43, p < .01, ŋ² = 0.093
Working memory forward*	6.3 ± 2.9	7.8 ± 1.2	F = 32.30, p < .01, ŋ² = 0.501
Working memory backward*	6.2 ± 4.2	7.4 ± 2.2	F = 24.18, p < .01, ŋ² = 0.382

Note. VCI = Verbal comprehension index; POI = Perceptual organization index; WMI = working memory index; PSI = Processing speed index; FSIQ = full scale intelligence quotient; OE = omission errors; CE = commission errors.

MANCOVA, covariate: age.

MANCOVA, covariates: age and IQ total.

Statistically significant.

In MANCOVA analyses between all ADHD group and all healthy comparison group, there were no significant differences in simple visual OEs (F = 0.39, p = .53, ŋ² = 0.045), simple visual CEs (F = 2.01, p = .15, ŋ² = 0.032), simple auditory OEs (F = 0.80, p = .37, ŋ² = 0.001), simple auditory CEs (F = 0.04, p = .84, ŋ² = 0.001) between the groups. However, compared to the HC group, the OEs (F = 7.03, p < .01, ŋ² = 0.124) and CEs (F = 11.90, p < .01, ŋ² = 0.196) in continuous inhibition, the OEs (F = 5.21, p < .01, ŋ² = 0.105) and CEs (F = 4.85, p = .01, ŋ² = 0.152) in interference selection, and the OEs (F = 9.47, p < .01, ŋ² = 0.152) and CEs (F = 4.43, p < .01, ŋ² = 0.093) in divided attention were increased in the ADHD group. The forward (F = 32.30, p < .01, ŋ² = 0.501) and backward (F = 24.18, p < .01, ŋ² = 0.382) working memory scores in the ADHD group were decreased compared to those in the HC group (Table 2). The covariate of IQ total could affect the results of all subsets of CAT analyses; simple visual OE (F = 5.42, p = .02) and CE (F = 8.78, p < .01), simple auditory OE (F = 10.09, p < .02) and CE (F = 14.36, p = .02), continuous inhibition OE (F = 10.21, p < .01) and CE (F = 7.76, p < .01), interference selection OE (F = 9.36, p < .01) and CE (F = 7.771, p < .01), divided attention OE (F = 21.32, p < .01) and CE (F = 16.29, p < .01), working memory forward (F = 9.58, p < .01) and backward (F = 16.94, p < .01). The covariate of age could affect the results of eight subsets of CAT analyses; simple visual OE (F = 3.26, p = .03) and CE (F = 3.13, p = .03), simple auditory OE (F = 4.52, p = .02) and CE (F = 4.13, p = .02), continuous inhibition OE (F = 1.35, p = .09) and CE (F = 2.24, p = .07), interference selection OE (F = 2.43, p = .07) and CE (F = 2.17, p = .07), divided attention OE (F = 10.01, p < .01) and CE (F = 12.589, p < .01), working memory forward (F = 7.21, p < .01) and backward (F = 8.02, p < .01).

Age Interval-Based Comparison of IQ Between ADHD and HC Groups

Verbal comprehension scores of the 15 to 17 (F = 4.12, p = .04, ŋ² = 0.016), 18 to 24 (F = 12.6, p < .01, ŋ² = 0.090), and 25 to 34 (F = 7.86, p < .01, ŋ² = 0.0476) ages in the ADHD group were lower than those in the HC group. Compared to the HC group, perceptual organization scores of only the 15 to 17 age group (F = 4.13, p = .04, ŋ² = 0.016) in the ADHD group were decreased. Working memory scores of all age intervals were lower than those in the HC group. Processing speed of the 15 to 17 (F = 4.38, p = .03, ŋ² = 0.017) and 18 to 24 (F = 8.55, p < .01, ŋ² = 0.063) ages were lower than those in the HC group. Full scale scores of the 15 to 17 (F = 21.38, p < .01, ŋ² = 0.077), 18 to 24 (F = 24.93, p < .01, ŋ² = 0.164), and 25 to 34 (F = 18.72, p < .01, ŋ² = 0.104) ages in the ADHD group were lower than those in the HC group (Table 3) (Figure 1).

Table 3.

Age Interval-Based Comparison of Intelligence Quotient Between ADHD and Healthy Control Groups.

	Age category (number of total, control, ADHD)	15–17 (155,76, 79)	18–24 (141, 70, 71)	25–34 (119, 61, 58)	35–44 (69, 37, 32)
	Age category (number of total, control, ADHD)	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
VCI	Control	103.3 ± 13.2	106.3 ± 14.2	103.8 ± 13.6	100.3 ± 11.2
	ADHD	91.3 ± 16.9*	97.4 ± 11.8*	97.2 ± 17.1*	93.6 ± 19.4
	F	4.12	12.60	7.86	1.78
	p	.04	<.01	<.01	.19
	ŋ²	0.016	0.090	0.047	0.030
POI	Control	104.2 ± 13.2	104.1 ± 13.9	107.1 ± 13.8	104.9 ± 9.6
	ADHD	95.0 ± 15.8*	99.4 ± 12.8	103.3 ± 16.6	97.7 ± 17.9
	F	4.13	9.91	3.24	2.89
	p	.04	<.01	.07	0.94
	ŋ ²	0.016	0.072	0.020	0.048
WMI	Control	103.1 ± 14.6	107.5 ± 15.6	105.4 ± 15.4	103.9 ± 10.7
	ADHD	93.2 ± 17.9*	95.3 ± 13.7*	94.7 ± 14.7*	95.8 ± 17.2*
	F	6.59	9.47	6.29	4.32
	p	.01	<.01	<.01	.04
	ŋ ²	0.025	0.069	0.041	0.069
PSI	Control	98.1 ± 14.4	102.5 ± 15.6	99.0 ± 14.8	96.2 ± 12.9
	ADHD	87.8 ± 16.5*	91.1 ± 16.1*	94.3 ± 14.7	94.6 ± 19.6
	F	4.38	8.55	1.29	0.98
	p	.03	<.01	.26	.41
	ŋ ²	0.017	0.063	0.008	0.004
FSIQ	Control	103.2 ± 12.4	105.7 ± 14.4	105.1 ± 12.9	102.6 ± 9.4
	ADHD	91.4 ± 15.7*	93.3 ± 11.3*	95.6 ± 16.6*	94.7 ± 20.1
	F	21.38	24.93	18.72	2.87
	p	<.01	<.01	<.01	.08
	ŋ ²	0.077	0.164	0.104	0.048

Note. MANCOVA analyses with covariate of age; ADHD = Attention-deficit/hyperactivity disorder; VCI = Verbal comprehension index; POI = Perceptual organization index; WMI = working memory index; PSI = Processing speed index; FSIQ = full scale intelligence quotient.

Statistically significant.

Figure 1.

Age interval-based comparison of IQ between ADHD and HC groups.

Age Interval-Based Comparison of CAT Between ADHD and Healthy Control Groups

Regarding simple attention modules including simple visual OEs (F = 5.97, p < .01, ŋ² = 0.045) and CEs (F = 4.11, p ≤ .02, ŋ² = 0.037) as well as simple auditory OEs (F = 4.15, p = .02, ŋ² = 0.0314) and CEs (F = 4.01, p = .02, ŋ² = 0.0354), the ADHD group showed increased number of errors only in the 15 to 17 age interval. There were no differences in simple attention modules in other age intervals (Table 4) (Figure 2).

Table 4.

Age Interval-Based Comparison of Comprehensive Attention Test Between ADHD and Healthy Control Groups.

	Age category (number of total, control, ADHD)	15–17 (155, 76, 79)	18–24 (141, 70, 71)	25–34 (119, 61, 58)	35–44 (69, 37, 32)
	Age category (number of total, control, ADHD)	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Simple visual omission errors	Control	0.3 ± 0.8	0.6 ± 1.4	0.5 ± 0.9	0.3 ± 0.5
	ADHD	2.4 ± 3.2*	0.8 ± 2.5	0.7 ± 0.9	0.5 ± 0.3
	F	5.97	0.09	0.01	1.97
	p	<.01	.76	.92	.17
	ŋ ²	0.045	0.001	<0.001	0.034
Simple visual commission errors	Control	1.6 ± 1.7	1.1 ± 1.9	1.4 ± 1.7	0.9 ± 1.2
	ADHD	2.7 ± 4.4*	1.5 ± 2.0	1.7 ± 2.4	1.0 ± 1.9
	F	4.11	0.09	0.19	0.03
	p	.02	.75	.66	.86
	ŋ ²	0.037	0.001	0.001	0.001
Simple auditory omission errors	Control	0.4 ± 0.8	0.7 ± 1.3	0.3 ± 0.4	0.7 ± 1.2
	ADHD	2.1 ± 4.5*	0.9 ± 2.9	0.7 ± 0.7	0.7 ± 1.4
	F	4.15	1.21	0.10	0.05
	p	.02	.27	.75	.83
	ŋ ²	0.031	0.009	0.001	0.001
Simple auditory commission errors	Control	1.2 ± 2.0	1.0 ± 1.7	1.1 ± 4.0	0.7 ± 2.1
	ADHD	2.3 ± 3.5*	1.3 ± 3.6	1.3 ± 3.3	0.7 ± 2.9
	F	4.01	1.92	0.60	0.10
	p	.02	.17	.44	.75
	ŋ ²	0.035	0.015	0.004	0.002
Continuous inhibition omission errors	Control	0.9 ± 3.7	1.9 ± 3.8	1.2 ± 2.9	1.2 ± 3.4
	ADHD	3.9 ± 4.0*	4.0 ± 4.1*	3.1 ± 2.9*	2.2 ± 3.0
	F	3.63	4.21	8.35	0.64
	p	.03	.03	<.01	.43
	ŋ ²	0.027	0.031	0.049	0.011
Continuous inhibition commission errors	Control	8.1 ± 8.7	8.0 ± 7.5	5.5 ± 5.5	5.5 ± 3.6
	ADHD	12.0 ± 12.3*	10.2 ± 10.7*	11.8 ± 7.4*	7.1 ± 4.9
	F	3.77	3.87	10.12	0.44
	p	.03	.04	<.01	.51
	ŋ ²	0.028	0.022	0.059	0.008
Interference selection omission errors	Control	0.9 ± 3.6	1.1 ± 2.9	0.6 ± 0.8	0.9 ± 1.8
	ADHD	5.0 ± 11.7*	2.8 ± 4.1*	1.2 ± 1.5	2.1 ± 2.6
	F	4.36	3.32	0.03	2.43
	p	.01	.04	.87	.12
	ŋ ²	0.033	0.035	<0.001	0.041
Interference selection commission errors	Control	6.1 ± 7.1	4.1 ± 4.2	2.8 ± 2.9	2.1 ± 2.3
	ADHD	7.9 ± 10.4*	6.0 ± 8.3*	4.4 ± 4.2	3.2 ± 3.9
	F	3.71	3.33	1.93	1.24
	p	.03	.04	.17	.27
	ŋ ²	0.028	0.024	0.012	0.021
Divided omission errors	Control	5.7 ± 9.0	6.7 ± 6.1	4.9 ± 6.5	4.9 ± 3.9
	ADHD	11.8 ± 12.6*	8.5 ± 8.3*	7.5 ± 7.4*	8.6 ± 7.5*
	F	4.79	3.37	3.39	3.98
	p	.01	.04	.04	.04
	ŋ ²	0.031	0.026	0.027	0.042
Divided commission errors	Control	5.5 ± 7.5	3.9 ± 3.6	3.3 ± 4.7	4.1 ± 3.7
	ADHD	6.4 ± 8.8	5.1 ± 5.3*	5.2 ± 8.3*	6.0 ± 6.4*
	F	1.03	3.79	3.88	4.37
	p	.31	.03	.03	.03
	ŋ ²	0.004	0.029	0.030	0.071
Working memory forward	Control	7.3 ± 4.0	8.0 ± 3.7	7.7 ± 5.7	8.1 ± 6.0
	ADHD	6.5 ± 4.8	6.4 ± 3.9*	6.3 ± 6.0*	6.2 ± 6.6*
	F	1.88	4.12	5.59	5.11
	p	.17	.02	.02	.03
	ŋ ²	0.007	0.032	0.034	0.082
Working memory backward	Control	6.5 ± 3.0	7.4 ± 4.1	7.6 ± 5.3	7.6 ± 5.8
	ADHD	6.2 ± 4.4	6.2 ± 1.4*	6.3 ± 5.7*	5.9 ± 5.2*
	F	0.18	3.99	8.37	7.41
	p	.67	.03	<.01	<.01
	ŋ ²	0.001	0.030	0.049	0.041
Interference selection omission errors	Control	0.9 ± 3.6	1.1 ± 2.9	0.6 ± 0.8	0.9 ± 1.8
	ADHD	5.0 ± 11.7*	2.8 ± 4.1*	1.2 ± 1.5	2.1 ± 2.6
	F	4.36	3.32	0.03	2.43
	p	.01	.04	.87	.12
	ŋ ²	0.033	0.035	<0.001	0.041
Interference selection commission errors	Control	6.1 ± 7.1	4.1 ± 4.2	2.8 ± 2.9	2.1 ± 2.3
	ADHD	7.9 ± 10.4*	6.0 ± 8.3*	4.4 ± 4.2	3.2 ± 3.9
	F	3.71	3.33	1.93	1.24
	p	.03	.04	.17	.27
	ŋ ²	0.028	0.024	0.012	0.021
Divided omission errors	Control	5.7 ± 9.0	6.7 ± 6.1	4.9 ± 6.5	4.9 ± 3.9
	ADHD	11.8 ± 12.6*	8.5 ± 8.3*	7.5 ± 7.4*	8.6 ± 7.5*
	F	4.79	3.37	3.39	3.98
	p	.01	.04	.04	.04
	ŋ ²	0.031	0.026	0.027	0.042
Divided commission errors	Control	5.5 ± 7.5	3.9 ± 3.6	3.3 ± 4.7	4.1 ± 3.7
	ADHD	6.4 ± 8.8	5.1 ± 5.3*	5.2 ± 8.3*	6.0 ± 6.4*
	F	1.03	3.79	3.88	4.37
	p	.31	.03	.03	.03
	ŋ ²	0.004	0.029	0.030	0.071
Working memory forward	Control	7.3 ± 4.0	8.0 ± 3.7	7.7 ± 5.7	8.1 ± 6.0
	ADHD	6.5 ± 4.8	6.4 ± 3.9*	6.3 ± 6.0*	6.2 ± 6.6*
	F	1.88	4.12	5.59	5.11
	p	.17	.02	.02	.03
	ŋ ²	0.007	0.032	0.034	0.082
Working memory backward	Control	6.5 ± 3.0	7.4 ± 4.1	7.6 ± 5.3	7.6 ± 5.8
	ADHD	6.2 ± 4.4	6.2 ± 1.4*	6.3 ± 5.7*	5.9 ± 5.2*
	F	0.18	3.99	8.37	7.41
	p	.67	.03	<.01	<.01
	ŋ ²	0.001	0.030	0.049	0.041

Note. MANCCOA analyses with covariate of IQ total; ADHD = Attention-deficit/hyperactivity disorder.

Statistically significant

Figure 2.

Age interval-based comparison of CAT between ADHD and healthy control groups.

Regarding continuous inhibition OEs and CEs, the ADHD group showed increased number of errors in 15 to 17 (OEs: F = 3.63, p = .03, ŋ² = 0.027; CEs: F = 3.77, p = .03, ŋ² = 0.028), 18 to 24 (OEs: F = 4.21, p = .03, ŋ² = 0.031; CEs: F = 3.87, p = .04, ŋ² = 0.022), and 25 to 34 (OEs: F = 8.35, p < .01, ŋ² = 0.049; CEs: F = 10.12, p < .01, ŋ² = 0.057) age intervals, compared to the HC group. In interference selection OEs and CEs, the ADHD group showed increased number of errors in 15 to 17 (OEs: F = 4.36, p = .01, ŋ² = 0.033; CEs: F = 3.71, p = .03, ŋ² = 0.028) and 18 to 24 (OEs: F = 3.32, p = .04, ŋ² = 0.035; CEs: F = 3.33, p = .04, ŋ² = 0.024) age intervals, compared to the HC group. In divided OEs, the ADHD group showed increased number of errors in all ages of 15 to 17 (F = 4.79, p = .01, ŋ² = 0.031), 18 to 24 (F = 3.31, p = .04, ŋ² = 0.026), 25 to 34 (F = 3.39, p = .04, ŋ² = 0.042), and 35 to 44 (F = 3.98, p = .04, ŋ² = 0.042), compared to the HC group. In divided CEs, the ADHD group showed increased number of errors only in 18 to 24 (F = 3.79, p = .03, ŋ² = 0.029), 25 to 34 (F = 4.37, p = .03, ŋ² = 0.071), and 35 to 44 (F = 4.37, p = .03, ŋ² = 0.071) age intervals, compared to the HC group. In all working memory modules including forward and backward, the ADHD group showed increased number of errors in 18 to 24 (forward: F = 4.12, p = .02, ŋ² = 0.032; backward: F = 3.99, p = .03, ŋ² = 0.030), 25 to 34 (forward: F = 5.59, p = .02, ŋ² = 0.034; backward: F = 8.37, p < .01, ŋ² = 0.049), and 35 to 44 (forward: F = 5.11, p = .03, ŋ² = 0.082; backward: F = 7.41, p < .01, ŋ² = 0.041) age intervals, compared to the HC group (Table 4) (Figure 2).

Discussion

We obtained the trajectory data on various kinds of intelligence, attention, and working memory from adolescence to adulthood for both the ADHD and HC groups. In this study, patients with ADHD had lower scores on all IQ subscales, including the verbal comprehension index (VCI), perceptual organization index (POI), working memory index (WMI), processing speed index (PSI), and full scale IQ (FSIQ) compared to HCs. This pattern was similar to results from previous studies (Frazier et al., 2004; Theiling & Petermann, 2016) which showed lower scores on IQ scales in ADHD groups compared to controls. On CAT test, most results, including OEs and CEs of continuous inhibition, interference selection, divided attention, and working memory measures, were lower in the ADHD group compared to the HC group.

To investigate more sophisticated age-related cognition trajectories, comparisons of cognitive abilities among age groups between the ADHD patients and HCs were done in post hoc analyses.

Age Interval-Based Comparison of IQ Between ADHD and HC Groups

A comparison of IQ between patients with ADHD and HCs by age groups revealed different patterns according to subtests. For perceptual organization index (POI), statistically significant superiority in HCs seen in the 15 to 17 age group was not observed in the 18 to 24, 25 to 34, and 35 to 44 age groups. For processing speed index (PSI), statistically significant superiority in HCs seen in the 15 to 17 and 18 to 24 age groups was not observed in the 25 to 34 and 35 to 44 age groups. For verbal comprehension index (VCI), statistically significant superiority in HCs seen in the 15 to 17, 18 to 24, and 25 to 34 age groups was not observed in the 35 to 44 age group. For working memory index (WMI), statistically significant superiority in HCs was seen in all age groups. Lastly, for full scale IQ (FSIQ), patients with ADHD in the 15 to 17, 18 to 24, and 25 to 34 age intervals showed lower scores compared to HCs, while the scores were not significantly different between the two groups in the 35 to 44 age group. These results indicate that working memory deficits in ADHD last the longest with respect to various cognitive functions measured using the IQ test.

Previous studies comparing cognitive performance between adults with and without ADHD showed similar patterns of deficits across the domains of the WAIS-IV (Theiling & Petermann, 2016). Theiling and Petermann (2016) found that WAIS-IV reliably distinguished patients from controls, and a reduction of the full scale IQ (FSIQ) was most likely owing to deficits in working memory and processing speed. These results were also replicated in studies using different versions of the WAIS and other neuropsychological tests (Dige & Wik, 2005; Downey et al., 1997; Goodwin et al., 2011; Marchetta et al., 2008) and in studies investigating childhood ADHD (Hervey et al., 2004; Seidman, 2006; Thaler et al., 2013). In addition, working memory deficits are particularly noticeable in ADHD patients (Baddeley, 2012; Barkley, 1997), and brain dysfunctions involving reduced activation in working memory-related brain regions, including in the dorsolateral prefrontal cortex and parieto-occipital region, and dysfunctional connectivity between the frontal and subcortical areas have been found in fMRI studies (Burgess et al., 2010; Sheridan et al., 2007).

Working memory performance, the capability to retain and operate information to guide goal-directed behaviors, is determined by prefrontal cortex (PFC) function in the harmonization of information processing in associated networks (Crone et al., 2006). Research has shown that working memory capability includes not only the information gathering and maintenance abilities but also executive functions related to the active regulative elements, which are related to the attention process through top-down control mechanisms (Gazzaley & Nobre, 2012; Luck & Vogel, 2013). The WM functions undergo continuous changes during the lifespan, and this pattern is due to age-dependent trajectories of the posterior and frontal brain regions. WM performance increases across childhood (Conklin et al., 2007; Gathercole, 1999; Luciana et al., 2005), and decreases in a flattened curve pattern during adulthood, with accelerated decline in old age (Borella et al., 2008; Cowan et al., 2006; Zanto & Gazzaley, 2019). In the present study, the pattern of increase in childhood and monotonical decrease during adulthood was found to be periodically delayed in ADHD groups. In other words, peak age groups presenting the highest WM subscores in WAIS-IV was early adulthood (ages 25 to 34) in our study and this finding indicates a maturational delay in brain development.

Age Interval-Based Comparison of CAT Test Results Between ADHD and HC Groups

In a comparison of CAT subtest scores between patients with ADHD and HCs by age groups, the error rates in the ADHD group was higher than that in the HC group in the overall context. However, differences were observed in that when the error rates of the ADHD group were not statistically different from that of HC group in each subtest. For the OE and CE of the simple visual attention and simple auditory attention tests, statistically significant superiority in HCs seen in the 15 to 17 age group was not observed in the 18 to 24, 25 to 34, and 35 to 44 age groups. For the OE and CE of interference selection test, statistically significant superiority in HCs seen in the 15 to 17 and 18 to 24 age groups was not observed in the 25 to 34 and 35 to 44 age groups. For the OE and CE of continuous inhibition test, statistically significant superiority in HCs seen in the 15 to 17, 18 to 24, and 25 to 34 age groups was not observed in the 35 to 44 age group. For OE and CE of divided attention test and scores of working memory test, statistically significant superiority in HCs was persisted in the highest age group, 35 to 44. These results indicate that, similar to the results of the IQ test, working memory deficits in ADHD last the longest among various types of attention tested using the CAT test.

We cautiously infer that the sequential normalization of attentional abilities seen in this study provide a support for the “brain maturation delay theory for ADHD” (Casey et al., 2005; Whitford et al., 2007). In ADHD research, neurodevelopmental mechanisms that cause the changes in ADHD have received significant attention, especially during childhood (Shaw et al., 2006; Spronk et al., 2008; Vaidya, 2012), and longitudinal and cross-sectional structural MRI studies showed that children with ADHD underwent delayed maturation in many brain regions, especially in the frontal cortex (Castellanos et al., 2002; Gogtay et al., 2002; Shaw et al., 2007). The most noticeable delay in structural development arising in the frontal regions aligns well with the neuropsychological findings that ADHD is characterized by the late development of the cognitive control abilities that are facilitated by the prefrontal regions (Rubia et al., 2005, 2007; Shaw et al., 2007; Smith et al., 2006). In this context, the late developing continuous inhibition, divided attention, and working memory observed in this study support the maturational delay theory, because continuous inhibition, divided attention, and working memory are higher cognitive functions mainly controlled by the frontal regions. In that a substantial portion of this maturational delay was found to be made up by young adulthood (Shaw et al., 2007) and that the neurobiological mechanisms that underlie the deviations in adult ADHD remain mostly uncovered before our study, these results can provide a wider perspective on a maturational delay across the lifespan in ADHD.

Taken together, we can cautiously suggest that among various kinds of cognition, working memory deficits may be crucial and long-lasting factors in the pathogenesis of ADHD; this is consistently reflected in the IQ and CAT subtests and leads to a lower FSIQ, which is a comprehensive reflection of higher cognition. In turn, these findings support a frontal dysfunction and maturational delay theory in ADHD.

Limitations

There are several limitations to this study. First, there was a lack of representation in terms of age, because subject data were collected only from adolescence to mid-adulthood. If full-range age group data from early-childhood to late-adulthood were obtained, the results would show a more complete lifetime trajectory. Second, there was a lack of representation in terms of disease course. As the participants enrolled in this study were patients currently diagnosed with ADHD, patients in remission, who had been diagnosed with ADHD in their childhood and had recovered in adulthood, were excluded. Furthermore, whether the participants had persistent ADHD (met diagnostic criteria of ADHD from childhood to adulthood) or adult-onset ADHD (did not meet diagnostic criteria of ADHD in childhood but did in adulthood) was unclear. To compensate for this, conducting longitudinal cohort studies would be necessary, and the results would help determine whether cognitive function differs among groups with different ADHD courses. Third, there might be a lack of representation in terms of severity of ADHD symptoms. In order to completely rule out the effects of medication targeting ADHD, we excluded individuals who had been treated with ADHD medication within 3 months. This would result in selection bias with higher portion of high-functioning individuals with ADHD who didn’t need medication. It might be reflected in the results that almost as may ADHD participants as HC were undergraduates and graduates, contrary to previous studies (Loe & Feldman, 2007). Finally, the potential that IQ effects would fully or partly of explain executive function deficits on the CAT (Mahone et al., 2002) was not considered in this study and it may also be a limitation.

Conclusion

This study provides developmental trajectories for varying types of cognition in adolescents and adults with and without ADHD in Korea. In summary, the simple attention deficits in ADHD may be recovered from late adulthood onwards, and IQ scores in ADHD normalize after the mid-30s. On the other hand, deficits in higher cognition, including divided attention and working memory, last until mid-adulthood.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Doug Hyun Han

Author Biographies

Ji Sun Hong obtained an MD and PhD from Chung-Ang University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training at Chung-Ang University Hospital, South Korea.

Young Sik Lee obtained an MD and PhD from Seoul National University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training at Seoul National University Hospital. He is currently a professor of psychiatry at Chung-Ang University College of Medicine, South Korea.

Minha Hong obtained an MD and PhD from Kyung Hee University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training at Kyung Hee University Hospital. She is currently an assistant professor of psychiatry at Myongji Hospital, South Korea.

Bongseog Kim obtained an MD from Seoul National University College of Medicine and PhD from Inje University, completed psychiatry residency and child-adolescent psychiatry fellowship training at Seoul National University Hospital. He is currently a professor of psychiatry at Inje University Paik Hospital, South Korea.

Yoo Sook Joung obtained an MD from Yonsei University College of Medicine and PhD from Ajou University, completed psychiatry residency and child-adolescent psychiatry fellowship training. She is currently a professor of psychiatry at Sungkyunkwan University College of Medicine, South Korea.

Hanik K Yoo obtained an MD and PhD from Seoul National University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training at Seoul National University Hospital. He is currently the director of Seoul Brain Research Institute and Seoul Our Child Psychiatric Clinic, South Korea.

Eui-Jung Kim obtained an MD and PhD from Ewha Womans University College of Medicine, completed psychiatry residency at Ewha Womans University Medical Center and child-adolescent psychiatry fellowship training at Samsung Seoul Hospital. She is currently a professor of psychiatry at Ewha Womans University College of Medicine, South Korea.

Soyoung Irene Lee obtained an MD and PhD from Korea University College of Medicine, completed child-adolescent psychiatry fellowship training at Samsung Seoul Hospital. She is currently a professor of psychiatry at Soonchunhyang University College of Medicine, South Korea.

Soo Young Bhang obtained an MD from Chonnam National University and PhD from University of Ulsan, completed psychiatry residency at Asan Medical center and child-adolescent psychiatry fellowship training at Seoul National University Hospital. She is currently a assistant professor of psychiatry at Eulji University School of Medicine, South Korea.

Seung Yup Lee obtained an MD from Kyung Hee University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training at Kyung Hee University Hospital, South Korea.

Geon Ho Bahn obtained an MD and PhD from Kyung Hee University University College of Medicine, completed psychiatry residency and child-adolescent psychiatry fellowship training. He is currently a professor of psychiatry at Kyung Hee University School of Medicine, South Korea.

Doug Hyun Han obtained an MD and PhD from Chung-Ang University College of Medicine, completed psychiatry residency at Chung-Ang University Hospital, and child-adolescent psychiatry fellowship training at Seoul National University Hospital. He is currently the head professor of psychiatry at Chung-Ang University College of Medicine, South Korea.

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