The effect of stimulant medications on reducing RT variability in patients with ADHD was evident across multiple cognitive tasks. MPH attenuated RT variability across four tasks, which required a range of cognitive abilities (eg, attentional conflict, response inhibition, and working memory). However, on a simple choice discrimination task, stimulant medication did not reduce RT variability. These effects of MPH on RT variability were detected using a range of indicators (ie, RT SD, CV, and τ). Although both RT SD and ex-Gaussian τ had comparable and moderate reliabilities, and intercorrelations between these indicators were generally quite high, the ex-Gaussian τ indicator most reliably detected effects of MPH on RT variability. This suggests that the component of variability affected by MPH is reduction of periodic-long RTs or positive distributional skew. The effects of MPH on task performance were largely specific to RT variability in this study, as MPH did not affect RT speed on any task, and only improved performance accuracy on two of the five tasks (ie, ANT and GNG).
Our primary MPH manipulation was achieved by determining each child's optimal dosage using a double blind, placebo-controlled crossover trial of MPH, and then randomizing children to either receive their optimal dosage or placebo. Our study's titration methods produced very similar outcomes to the titration outcomes reported in the Multimodal Treatment Study of Children with ADHD (MTA; Greenhill et al, 2001
) which used a similar, though more intensive daily dose titration schedule and methodology. Namely, the two studies had similar rates of stimulant response (77% of children in our study vs
77% of children in MTA), ultimate mg/kg per day (1.13
mg/kg in our study vs
mg/kg in MTA), and MPH vs
placebo effect sizes on parent (0.82 in our study vs
0.57 in MTA), and teacher (0.87 in our study vs
1.00 in MTA) ratings of behavior. The effect sizes for MPH effects on RT variability in our study were in the moderate to high range (0.60–0.95 on τ
), which is fairly consistent with the MPH effect sizes for RT variability estimates of other studies using comparable tasks (Boonstra et al, 2005
; Epstein et al, 2006
). Of note, the effect sizes for MPH effects on RT variability are of similar magnitude as effect sizes reporting differences between ADHD and typically developing controls on RT variability (Klein et al, 2006
), though some studies have reported between-group effect sizes beyond this range (de Zeeuw et al, 2008
). A comparable set of effect sizes for between-group comparisons and medication effects suggest that MPH may normalize group differences on RT variability. Such normalization would imply that RT variability, as an indicator of brain function, is highly related to the pharmacodynamic mechanisms by which MPH exerts its effect in children with ADHD. Neuroimaging studies (Bellgrove et al, 2004
; Weissman et al, 2006
) and behavioral studies with brain-damaged populations (Dockree et al, 2006
; Stuss et al, 2003
; Stuss et al, 1999
) have begun to isolate neurophysiological correlates of RT variability. Targeting regions of interest, which have been identified in this neuroscience literature, future research studies should focus on examining stimulant medication effects on brain function to better understand the mechanism of action by which stimulant medication produces robust effects on RT variability in children with ADHD.
Given the pervasiveness of MPH effects on RT variability, it is interesting to examine on which tasks and under which conditions MPH failed to attenuate RT variability. For one, we found no MPH effect on RT variability on the Choice task. This finding contradicts that of Spencer et al (2009)
who used a very similar Choice Discrimination task as used in this study. Secondly, the MPH × ER interaction on the ANT (as measured by RT SD, CV and τ
) and SST (as measured by CV) showed that MPH effects on RT variability were absent at fast ERs (ie, 1-s ISI). We believe that the source of both of these null findings is similar and can be attributed to our study's methodology. Spencer et al (2009)
used a fixed 4-s ISI compared with our use of a range of ISIs (1-, 3-, and 5-s). Additional analyses of the choice data suggest that there was essentially no mean difference in CV between the MPH and placebo conditions at the fast ER (1-s ISI; ES=0.01), whereas during the long ER conditions, there were small- to medium-sized MPH effects (3-s ISI: ES=0.44; 5-s ISI: ES=0.32). We believe that the one-third of trials that used an ISI of 1-s likely masked the overall effect of MPH on RT variability. Although the effects of MPH might have been smaller on the Choice task than on the other tasks, it seems that an effects of MPH still exists under the appropriate conditions. The MPH × ER interaction effect on the ANT and SST task also supports this explanation. On these tasks, there was no effect of MPH on RT variability at fast RTs, but there were increasing differences between children in the MPH and placebo groups as ER slowed. Most studies that have found effects of MPH on RT variability have used an ER that ranged between 2 and 4
s (Castellanos et al, 2005
; Groom et al, 2010
; Spencer et al, 2009
; Tannock et al, 1995
). At fast ERs (eg, 1-s ISI), it is possible that RT distributions become truncated because of the smaller response window. Indeed, a large percentage of responses in the N-back task during the 3-s (21.4%) and 5-s (31.0%) ISI conditions exceeded 1-s in length. This leads us to conclude that the lack of effects of MPH on RT variability on the simple Choice Discrimination task, as well as on faster ERs on the ANT task may be a result of truncated RTs. This artifact may have led to results that limited the breadth of effects of MPH on RT variability as well as the magnitude of these effects.
One of the goals of this study was to examine the moderating effect of task manipulations (ER and incentive) on our MPH manipulation across the various RT variability outcomes. The ER manipulation is especially interesting in relation to the competing predictions regarding MPH × ER interaction effects that are suggested by prevailing theoretical models of ADHD. The state regulation dysfunction (SRD) model suggests that short and long ERs cause over- and underactivation in children, which detrimentally affects performance (see Sergeant and Sergeant, 2005
and van der Meere et al, 2005
for reviews). Alternatively, the delay aversion (DA) model suggests that task performance will deteriorate as delays (ie ERs) get longer (Sonuga-Barke et al, 1992
). As outlined by Sonuga-Barke et al (2010)
, according to the SRD model, MPH alters activation in patients and thus diminishes the detrimental effects of long ERs on neuropsychological performance but exacerbates performance during short ERs because of overactivation. In contrast, the DA model hypothesizes that MPH affects motivational context and increases the application of effort, and therefore attenuates the detrimental effects of longer ERs in a linear fashion. The point of difference between these two models is what occurs when children on MPH experience fast ER conditions. In the DA model, MPH does not detrimentally affect performance during short ERs. In the SRD model, performance is detrimentally affected during short ERs because of overactivation. We found that MPH did interact with ER on RT variability indicators on two of the tasks (ie, ANT and SST). The interaction showed that children taking MPH were not as affected by slower ERs as children on placebo. Also, during the fast ER conditions, there was little difference between children on MPH compared with placebo. This pattern, although it appeared for only selected RT variability measures on two of the five tasks, is more consistent with the DA model. However, the SRD model's prediction of poorer performance during the fast ER condition is based upon the assumption that MPH combined with a fast ER produces overactivation. Our 1.5-s ER condition may have been out of the range of fast ERs to produce an overactivation effect. It is possible that a faster ER may have achieved this effect. Also, given that activation states may differ across individuals, studies may need to tailor ERs on an individual by individual basis to determine thresholds for overactivation (Sonuga-Barke et al, 2010
This study also examined the moderating effects of incentive (ie, reward and response cost) on RT variability, as well as other performance outcomes. Although there was a main effect of incentive on some variables on some tasks, indicating an inconsistent and small effect of incentive on performance outcomes, there was relatively no interaction of incentive and MPH across outcomes across tasks. This is consistent with the previous literature examining MPH and incentive interactions (Groen et al, 2009
; Groom et al, 2010
) showing no synergistic effect of MPH and incentive on performance outcomes. Interestingly, MPH and incentive interactions that were observed in the current study tended to occur on more difficult tasks (ie, SST, GNG, N-back) and primarily for measures of RT speed (ie, μ
, RT mean). Examination of MPH by incentive interactions may be influenced by difficulty of task and the strength of each manipulation such that incentives may not exert an additional influence if medication is producing maximal gains or if the task is relatively easy. The current study used an optimal medication dosage, which may have reduced the opportunity for incentives to improve performance as medication already reduced RT variability substantially.
It is also important to consider the nature of the incentive manipulation, which varies greatly across studies that have examined the impact of motivation on performance in children with ADHD. The current study used a counterbalanced block design to compare performance with and without incentives, consisting of reward and response cost, and children earned a material reward (eg, toys, games), although trial-wise feedback was not provided. The counterbalanced block design and material reward are strengths of this study compared with those using a fixed block order (eg, Uebel et al, 2010
), which confounds the incentive condition and time on task, and studies that did not offer a material reward (eg, Slusarek et al, 2001
). However, completion of the incentive and no-incentive conditions sequentially may have influenced the results such that children generally associated their performance on the task with obtaining a material reward at the end of the day, thereby reducing incentive effects. Although trial-wise feedback was not provided, which would have been a more powerful incentive manipulation, as children with ADHD are particularly sensitive to immediate reinforcement (eg, Sagvolden et al, 2005
), studies reporting improved performance on cognitive tasks when incentives are provided in children with ADHD often do not include trial-wise feedback (eg, Groom et al, 2010
; Stevens et al, 2002
). In addition, Michel and colleagues (2005)
, did not find any performance differences on the SST during an immediate reinforcement condition (ie, point total accumulated on screen after each trial) compared with a delayed reinforcement condition (ie, points accumulated in the same way, but participants were not told of their points until the end). Findings such as this may occur because the actual material reward is delayed until the child completes the activity despite the presence of immediate performance feedback. In addition, incentives targeted response accuracy rather than speed or variability, possibly reducing incentive effects on RT variability. Interestingly, incentives improved response accuracy and speed on various tasks, suggesting that incentives influenced response speed despite the emphasis on accuracy. Finally, the inclusion of response cost may have reduced random responding when the participant is uncertain of the correct response, thereby increasing the number of omission errors.
Although the effects of MPH on RT variability were evident across multiple tasks and indicators, the effects of MPH on other performance indicators (ie, RT mean, accuracy) were few and inconsistent. Despite the fact that RT mean was highly correlated with RT variability indicators (eg, τ
), we observed no significant effects of MPH on RT speed across tasks despite the fact that other studies have found that MPH speeds RT (Epstein et al, 2006
). In addition, MPH effects on accuracy were evident on only two of the tasks (ie, ANT and GNG). Moreover, there was no effect of MPH on SSRT, a commonly used indicator of response inhibition, which has been found previously to improve with medication (ie, get smaller) among children with ADHD (Tannock et al, 1989
). On all these indicators, it is interesting that no significant effects were observed in this study, which in many ways used robust methodology compared with many previous studies, including a large sample, a titrated optimized dosing procedure, and a wide variety of RT tasks. A straightforward explanation of these results is that MPH primarily affects RT variability. However, it may be that the inclusion of task manipulations of ER and incentive introduced variance to the mean estimates and diminished our ability to detect MPH effects on these other outcome measures. Indeed, ER had very large effects across all performance indicators. However, the lack of any interaction effects between these manipulations and MPH argues against this explanation. Alternatively, it may be that introducing our within-task manipulations intrinsically altered the task characteristics enough to change the task demands and alter performance of task. For example, our ER manipulation may have inadvertently introduced a ‘jittering effect,' similar to that produced by changing ER on a trial-by-trial basis, which has been shown to improve task performance in children with ADHD (Ryan et al, 2010
). Such an effect may have improved performance in children across both groups and diminished our ability to find MPH effects.
There are limitations of this study which may have affected the results of the study. We have already noted some limitations of our incentive and ER manipulations. In addition to the noted limitation to the lower bound of our ER manipulation, our ER manipulation was also limited in terms of the upper bound. The 5.5-s upper bound may not have fully tested the interactive effects of MPH on ER, had slower rates been included. In addition, the MPH dosages used in this study also may have led to attenuated MPH effects. Although we used a placebo-controlled, double-blind titration trial to determine optimal dosage, the highest dosage used in the trial was 54
mg for children
kg and 36
mg for children <25
kg. Other studies (eg, Spencer et al, 2009
) have used higher dosages. In our study, a considerable number of children received the highest dosage as their optimal dosage (ie, 24%). Indeed, it could be that for some of these children, a higher dosage would have been optimal. Also, a significant minority of children (24%) exited the titration trial, with the placebo dosage as their optimal dosage, which is comparable to stimulant response rate in other studies (eg, Greenhill et al, 2001
). The fact that 19% of the children in the optimal-dose condition received the same stimulant dosage (ie, placebo) as that received by the placebo-control group may have affected this study's ability to detect between group differences on some of the study outcomes (eg, accuracy).
In summary, RT variability, which appears to be one of the most ubiquitous and robust indicators of cognitive deficit in patients with ADHD (Castellanos and Tannock, 2002
), appears to be significantly reduced by MPH. Moreover, this study's results suggest that MPH effects on neuropsychological tasks are largely specific to RT variability and also largest in magnitude on RT variability indicators. Our use of a medication-naïve sample helps in attributing observed effects of MPH to acute medication effects rather than cumulative and chronic effects of long-term MPH treatment (Andersen, 2005
). Future research should examine how effects of MPH on RT variability relate to effects of MPH on ADHD behavioral outcomes. Behavior analog studies examining correspondences between RT variability and behavioral manifestations of attention (eg, Rapport et al, 2009
), as well as neurophysiological studies examining neural patterns during periods of enhanced RT variability (eg, Bellgrove et al, 2004
; Weissman et al, 2006
) might help in elucidating the behavioral and neural correlates of RT variability and its response to MPH.