Attention Deficit/Hyperactivity Disorder: A Focused Overview for Children's Environmental Health Researchers

Andréa Aguiar; Paul A. Eubig; Susan L. Schantz

Disclosures

Environ Health Perspect. 2010;118(12):1646-1653. 

In This Article

Neuropsychological Functions Affected in ADHD

Attention

Attention is a multidimensional construct (Stefanatos and Baron 2007) that can be broadly defined as the facilitated processing of one piece of information over others (Nigg and Nikolas 2008). Attention consists of several interrelated processes including alertness and vigilance (Oken et al. 2006; Posner 1995). In psychology and cognitive neuroscience the term "alertness" is described as the ability to obtain an alert state by focusing rapidly on new or unexpected information or stimuli (Nigg and Nikolas 2008). Similarly, vigilance or sustained attention is described as the ability to maintain attention on a task for a period of time once the alert state is entered (Oken et al. 2006).

Research indicates that children with ADHD have problems with alertness as well as with vigilance. These two attentional functions can both be assessed with continuous performance tasks (CPTs), which measure the ability to respond to a rare target (e.g., the letter "X" when it is preceded by the letter "A" but not by other letters) over an extended period of time (usually ≥ 15 min).

Table 1 lists the two attention functions that are impaired in ADHD individuals, the neuropsychological tasks used to assess the functions, the behavioral findings obtained with ADHD individuals, and meta-analyses that estimated the strength of association between deficits in these functions and ADHD based on Cohen's d, which is defined as the difference in means divided by the pooled standard deviation across study populations. Cohen's d is a standardized measure often used to compare the effects of variables measured on different scales and to estimate effect size across different studies. Cohen (1988) categorizes effect sizes around 0.2 as small, around 0.5 as moderate, and around 0.8 as large. Meta-analyses that focused only on ADHD adults, were published before 2004, or did not measure effect sizes in terms of Cohen's d are not included.

Alertness. Alertness can be measured by the subject's reaction time or how quickly the individual responds to the target stimuli (Posner 1995). Based on a meta-analysis of 13 studies of CPT performance in individuals diagnosed with ADHD, Frazier et al. (2004) reported that those with an ADHD diagnosis were slower than non-ADHD controls in responding to the target, with a small to moderate pooled effect size across studies (d = 0.39) (Table 1). Slower reaction times in ADHD children are not constrained to CPT tasks. For example, a recent study (Albrecht et al. 2008) compared boys diagnosed with ADHD with their unaffected siblings and with non-ADHD controls using a computerized reaction time task in which the stimuli were either congruent or incongruent with a previous target stimulus. ADHD boys were slower in their correct responses on both congruent and incongruent trials than were the non-ADHD controls. Interestingly, the unaffected siblings of the ADHD boys were midway between the two other groups; they did not differ significantly from either their ADHD siblings or the controls.

Vigilance. Vigilance is commonly assessed by errors of omission (misses) on CPTs. Two meta-analyses, one of 30 and the other of 33 studies that were published in 2004 or later, found that on CPTs, ADHD children made more errors of omission than non-ADHD controls did (Frazier et al. 2004; Willcutt et al. 2005). Both meta-analyses reported moderate effect sizes. The two meta-analyses did not employ completely unique data sets. Unfortunately, not enough information was available in Frazier et al. (2004) to ascertain the degree of overlap.

Executive Functions

Executive function refers to a set of abilities including working memory, response inhibition, and error correction that are involved in goal-directed problem solving (Marcovitch and Zelazo 2009). Executive function allows an individual to plan a series of steps necessary to achieve a desired goal, keep these steps in mind while acting on the goal, monitor progress through these steps, and have the cognitive flexibility to adjust or change the steps if progress is not being made toward the original goal. Table 2 lists the executive functions that have been identified as impaired in a number of meta-analytic studies of ADHD children and adolescents published since 2004 (Alderson et al. 2007; Frazier et al. 2004; Homack and Riccio 2004; Lansbergen et al. 2007; Lijffijt et al. 2005; Martinussen et al. 2005; Romine et al. 2004; van Mourik et al. 2005; Walshaw et al. 2010; Willcutt et al. 2005). Table 2 includes the neuropsychological tasks commonly used to assess these functions, the behavioral findings obtained with ADHD individuals, and the strength of the association, with the resulting effect sizes expressed as Cohen's d. The inclusion criteria for Table 2 are similar to those for Table 1: The table lists only meta-analyses published since 2004 that reported effect sizes as Cohen's d and that analyzed studies whose samples included children. As in Table 1, there is overlap in the studies included in the various meta-analyses in Table 2.

Working Memory. Working memory is the ability to hold something in mind momentarily while doing something else or while using the information to perform an action (Baddeley 1986). Research indicates that there are separate neural circuits for working memory processes that involve verbal information (verbal working memory) versus spatial information (spatial working memory) (Baddeley 1996). Myriad neuropsychological tasks index verbal and spatial working memory function. Since 2004, three meta-analyses (Martinussen et al. 2005; Walshaw et al. 2010; Willcutt et al. 2005) evaluated studies on working memory in ADHD children and adolescents. These studies found moderate effect sizes ranging from 0.55 to 0.63 for impairments in ADHD children and adolescents compared with non-ADHD controls on seven different verbal working memory tasks: Digits Backward, Sentence Span, Color/Digit Span, Children's Memory Scale Numbers Backward, Counting Span, Paced Auditory Serial Addition Task, and Self-Ordered Pointing Task (SOPT)-Objects. Table 2 gives short descriptions of each of these tasks. Larger effect sizes ranging from 0.63 to 1.04 were observed for impairments in children and adolescents diagnosed with ADHD compared with non-ADHD controls in five spatial working memory tasks: spatial span, a spatial working memory task from the Cambridge Neuropsychological Test Automated Battery (CANTAB); Finger Windows Test Backward; SOPT-Abstract; and the Spatial Span Backward task from the Wechsler Adult Intelligence Scale (WAIS). Table 2 also provides brief descriptions of these spatial working memory tasks.

Response Inhibition. Response inhibition refers to the ability to inhibit or interrupt a response during dynamic moment-to-moment behavior (Nigg and Nikolas 2008). Key paradigms that tap this ability and have shown significant deficits in ADHD children are the go/no-go task, the stopping or stop signal time (SST) task (Aron and Poldrack 2005; Winstanley et al. 2006), the fixed interval schedule of reinforcement (Sagvolden et al. 1998), and CPTs. Only meta-analyses of studies of response inhibition in SST and CPT tasks met the criteria for inclusion in Table 2, so this discussion focuses on these two response inhibition measures.

As Huizenga et al. (2009) describe, in the SST task subjects are typically required to make rapid choice responses to "go" signals (e.g., press a button with the right hand if they see an X and a button with the left hand if they see an O). At random and occasional time intervals, a stop signal (e.g., the letter A or a tone) is presented shortly after the go signal, instructing the subject to inhibit the already initiated response activated by the go signal. As listed in Table 2, since 2004, five meta-analyses (Alderson et al. 2007; Frazier et al. 2004; Lijffijt et al. 2005; Walshaw et al. 2010; Willcutt et al. 2005) estimated Cohen's d effect size for SST studies that included or were limited to children. The analyses indicated that, compared with non-ADHD individuals, those diagnosed with ADHD were consistently slower in stopping an ongoing response, suggesting difficulty in response inhibition. Effect sizes for stop signal reaction times in ADHD samples were in the moderate range (d = 0.54–0.63).

Commission errors (or false alarms) in CPTs are also often used as a marker of response inhibition deficits in ADHD children. Since 2004, three meta-analyses (Frazier et al. 2004; Walshaw et al. 2010; Willcutt et al. 2005) have examined the strength of the association between CPT commission errors and ADHD diagnosis in studies that included children and teens and calculated Cohen's d effect sizes (Table 2). As in the SST analyses, the results for CPT commission errors were in the moderate range (d = 0.51–0.56).

Cognitive Flexibility. The ability to switch attention from one aspect of an object to another, or to adapt and shift one's response based on situational demands, such as changes in the rules, schedule, or type of reinforcement in a task, is defined as cognitive flexibility or set shifting (Monsell 2003; Stemme et al. 2007). Tests used to assess cognitive flexibility in children include the Wisconsin Card Sorting Test (WCST), the Stroop Color-Word test (Stroop task), and the Trail Making Test Part B (Trails-B).

On the WCST, subjects are asked to sort into two different piles a series of cards with figures that can differ in color, shape, and/or number. Each time a card is sorted, the subject receives feedback as to whether the choice was correct or incorrect, and based on this feedback the subject must infer the correct category (color, shape, or number) for sorting (Romine et al. 2004). After the subject correctly sorts the cards in a series of consecutive trials, the sorting category is changed and the subject must learn the new sorting category by trial and error. An indicator of impairments in cognitive flexibility is the tendency to make perseverative errors or persist in sorting the cards by the previously correct category, even after being told the sorting strategy is incorrect. Four recent meta-analyses (Frazier et al. 2004; Romine et al. 2004; Walshaw et al. 2010; Willcutt et al. 2005) computed small (0.35) to medium (0.52) effect sizes for the differences in mean perseverative errors between ADHD individuals and non-ADHD controls on the WCST (Table 2). ADHD individuals made more perseverative errors on the WCST than did non-ADHD controls, suggesting that ADHD is associated with impaired cognitive flexibility.

In the Stroop task, problems in cognitive flexibility are measured by the degree of difficulty subjects have in naming the color of the ink used to print color words when the two are mismatched (e.g., when the word "green" is printed in blue ink). Interference scores quantify subjects' difficulty in the task, with higher scores indicating greater difficulty. Effect sizes for Stroop interference scores reported in five recent meta-analyses (Frazier et al. 2004; Homack and Riccio 2004; Lansbergen et al. 2007; van Mourik et al. 2005; Walshaw et al. 2010) vary widely from small (0.35) to large (1.11), making it hard to characterize the findings (Table 2). This inconsistency may be at least partially due to variation in the method used to calculate the interference score across studies. [For a description of different ways of deriving interference scores, see Homack and Riccio (2004).]

Another widely used tool for assessing cognitive flexibility is Trails-B, in which subjects are presented with numbers and letters inside circles that are randomly arranged on a sheet of paper. Subjects are asked to connect in ascending order the numbers and letters while alternating between them (e.g., 1–A–2–B–3–C–4); they are asked to do this as quickly as possible (Lezak et al. 2004). Time to complete the task is measured, with longer response times indicative of difficulties in cognitive flexibility. Two meta-analyses (Frazier et al. 2004; Willcutt et al. 2005) have reported medium effect sizes (d = 0.55 and 0.59 respectively, as shown in Table 2) as evidence of reduced cognitive flexibility in ADHD versus control children based on Trails-B scores.

Planning. Some researchers have found that deficits in planning and strategy development discriminate well between children with ADHD and those without (Papadopoulos et al. 2005). ADHD children have been found to perform poorly in four tasks that are commonly used to assess planning ability: Tower of Hanoi (TOH) task and its variant the Tower of London (TOL) task, Porteus Maze, and Rey-Osterrieth Complex Figure Task (ROCF).

Tower tasks such as TOH and TOL are a popular neuropsychological measure of planning (Riccio et al. 2004). The many variations of this task basically involve moving stacked beads or disks of different sizes to new positions that match the model provided. This must be accomplished in a minimum number of moves and while following rules for moving the objects (e.g., only one disk can be moved at a time, no disk can be placed on top of a smaller disk) (Papadopoulos et al. 2005; Riccio et al. 2004). It is assumed that subjects will generate a more efficient solution if they plan a series of moves before actually beginning to move the beads or disks (Riccio et al. 2004).

In the Porteus Maze task, subjects are presented with mazes of increasing difficulty. They must find a solution (i.e., the way out) while following a number of rules (e.g., no entering a dead end, no backtracking) (Levin et al. 2001). Planning the movement through the maze increases the subjects' ability to adhere to the rules. In the ROCF, individuals are asked to copy and later recall a complex figure composed of 64 segments. In both stages the examiner can rate the accuracy of the different lines as well as the level of organization when clustering lines during the copying and recall phases (Sami et al. 2003). Higher levels of organization are indicative of better strategic planning. Three recent meta-analyses (Frazier et al. 2004; Walshaw et al. 2010; Willcutt et al. 2005) indicate effect sizes in the low to medium range (d = 0.24–0.69) (Table 2) for the differences between ADHD individuals and non-ADHD controls in these four planning tasks.

Summary of Meta-analytic Studies

In summary, meta-analyses indicate that performance is impaired in ADHD individuals on a large number of attention and executive function tasks. Within the attention and executive function domains, larger deficits are found on tasks measuring vigilance, working memory (especially spatial working memory), and response inhibition abilities, whereas smaller but significant deficits are also seen on tasks measuring alertness, cognitive flexibility, and planning abilities. There is overlap in the studies included in some of the meta-analyses discussed herein. Thus, the individual analyses cannot be taken as totally independent indicators of the effect. Also, deficits on any single test of attention or executive function are not sufficient for a diagnosis of ADHD (e.g., Homack and Riccio 2004) or for differentiating ADHD from other mental or learning disorders (e.g., Walshaw et al. 2010). This should not be surprising given the great heterogeneity of symptoms across affected individuals. Finally, meta-analyses to date have lacked in-depth analyses of the associations between patterns of behavioral deficits on the various neuropsychological tasks and the three different ADHD diagnoses (ADHD-C, ADHD-PI, and ADHD-PH), primarily because most ADHD studies, especially older studies, have not evaluated ADHD subtypes.

Temporal Information Processing and Responses to Reinforcement

Two other types of deficits related to the processing of temporal information and to responses to the reinforcing properties of rewards have been reported in ADHD children but have not been subjected to meta-analysis. These deficits could contribute to the difficulties ADHD children have in executive function tasks. Recent studies have focused increasingly on temporal information processing, which is believed to be key to the control and modulation of behavior (Barkley 1997; Nigg and Casey 2005). Toplak et al. (2006) reviewed 38 studies that measured temporal information processing in ADHD children. Most of these studies used tasks in which the child was asked to indicate the end of a specific time interval, either by holding down a response key for the specified interval or by responding verbally to indicate the end of the interval. There were no external cues by which the child could estimate the interval. Most studies found poor time estimation in children with ADHD, especially when longer time intervals were employed.

In terms of responses to reinforcement, Luman et al. (2005) reviewed 22 studies comparing the responses of children with and without an ADHD diagnosis to reinforcement contingencies in a variety of tasks. The authors concluded that ADHD is associated with increased weighting of near-term over long-term (but larger) rewards, positive response to high-intensity reinforcement, and a lack of a physiological response, such as heart rate acceleration, to potential rewards. The pattern of results in these studies suggests that ADHD children have difficulty reasoning about rewards and, as a result, do not respond appropriately to reinforcements. Although abnormalities in responses to reinforcement have been studied in the context of motivation, they could be related to impairments in executive functioning, especially in the case of difficulties in weighing near-term versus long-term rewards.

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