PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nppharmLink to Publisher's site
 
Neuropsychopharmacology. 2011 May; 36(6): 1237–1247.
Published online 2011 February 23. doi:  10.1038/npp.2011.9
PMCID: PMC3079849

Pattern Classification of Working Memory Networks Reveals Differential Effects of Methylphenidate, Atomoxetine, and Placebo in Healthy Volunteers

Abstract

Stimulant and non-stimulant drugs can reduce symptoms of attention deficit/hyperactivity disorder (ADHD). The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for ADHD treatment, but their differential effects on human brain function remain unclear. We combined event-related fMRI with multivariate pattern recognition to characterize the effects of MPH and ATX in healthy volunteers performing a rewarded working memory (WM) task. The effects of MPH and ATX on WM were strongly dependent on their behavioral context. During non-rewarded trials, only MPH could be discriminated from placebo (PLC), with MPH producing a similar activation pattern to reward. During rewarded trials both drugs produced the opposite effect to reward, that is, attenuating WM networks and enhancing task-related deactivations (TRDs) in regions consistent with the default mode network (DMN). The drugs could be directly discriminated during the delay component of rewarded trials: MPH produced greater activity in WM networks and ATX produced greater activity in the DMN. Our data provide evidence that: (1) MPH and ATX have prominent effects during rewarded WM in task-activated and -deactivated networks; (2) during the delay component of rewarded trials, MPH and ATX have opposing effects on activated and deactivated networks: MPH enhances TRDs more than ATX, whereas ATX attenuates WM networks more than MPH; and (3) MPH mimics reward during encoding. Thus, interactions between drug effects and motivational state are crucial in defining the effects of MPH and ATX.

Keywords: methylphenidate, atomoxetine, working memory, reward, pattern recognition

INTRODUCTION

Stimulant and non-stimulant medications that influence dopamine (DA) and noradrenaline (NA) neurotransmission can reduce symptoms of attention deficit/hyperactivity disorder (ADHD). The stimulant drug methylphenidate (MPH) has been shown to have consistently greater clinical efficacy than atomoxetine (ATX), a non-stimulant drug recently approved for the treatment of ADHD in the USA and Europe (Spencer et al, 1998; Michelson et al, 2001; Faraone et al, 2005; Kemner et al, 2005; Starr and Kemner, 2005; Newcorn et al, 2008). ATX nonetheless offers several potential advantages over MPH, including reduced abuse liability, reduced risk of motor side effects and as an alternative treatment for patients non-responsive to stimulants (Newcorn et al, 2008). However, the mechanisms underlying their differences on human brain function are unclear.

There is converging evidence that weakened prefrontal cortex (PFC) function underlies several of the hallmark deficits in ADHD (Arnsten, 2006). In particular, working memory (WM)—the ability to hold and manipulate information for future action—is impaired in ADHD (Martinussen et al, 2005; Willcutt et al, 2005) and has been strongly linked to the activity of the catecholamines (DA and NA) within the PFC (Brozoski et al, 1979; Arnsten and Goldman-Rakic, 1985). WM performance is also known to be improved with MPH (Elliott et al, 1997; Bedard et al, 2004; Mehta et al, 2004), currently understood as resulting from an increased efficiency of frontoparietal WM regions shown using PET neuroimaging studies (Mehta et al, 2000; Schweitzer et al, 2004). Studies in experimental animals suggest that ATX has a similar ability to improve WM function (Gamo et al, 2010), via effects on prefrontal cortical activity, although there are no comparative human neuroimaging studies of the effects of MPH and ATX on WM networks.

Previous studies in experimental animals have indicated that: (1) MPH inhibits both DA and NA transporters (DAT and NAT, respectively; Seeman and Madras, 1998; Han and Gu, 2006); (2) ATX is a selective inhibitor of NAT (Wong et al, 1982; Bolden-Watson and Richelson, 1993); and (3) both drugs increase concentrations of DA and NA in the PFC, but only MPH increases DA in the striatum (Bymaster et al, 2002). However, the neural consequences of these differential actions in human beings and their implications for functional brain networks are currently unknown.

Theoretically, systemically administered MPH and ATX may differentially influence distributed brain regions due to localized effects at DAT and NAT sites (Ciliax et al, 1999; Schou et al, 2005) and consequent effects on connected brain areas, in addition to the differential effects on striatal catecholamine neurotransmission shown in rodents (Bymaster et al, 2002). Thus, differential effects of MPH and ATX may be distributed across multiple brain regions. Multivariate pattern recognition (PR) methods are sensitive to such spatially distributed information by making use of the correlation between brain voxels and afford substantially greater sensitivity than conventional mass-univariate analysis methods (Haynes and Rees, 2006; Kriegeskorte et al, 2006; Norman et al, 2006). Therefore, we combined event-related fMRI with a novel whole-brain PR analytic approach to characterize and discriminate acute effects of MPH and ATX in healthy volunteers performing a WM task. Although we expected reductions in PFC activity after MPH, this study represents the first attempt to: (1) examine the effects of ATX on WM networks and (2) test potential differences between prefrontal cortical and striatal activation following administration of MPH and ATX in humans.

Finally, recent literature suggests an important contribution of reward to the regulation of WM-related brain activity (Ichihara-Takeda et al, 2010). This accords with evidence that both reward and MPH have similar effects on sustained attention task performance in ADHD (Trommer et al, 1991; Andreou et al, 2007). Therefore, we also explored the role of reward on WM function, with a focus on determining its impact on our ability to discriminate MPH and ATX.

METHODS

Participant Recruitment and Data Acquisition

Fifteen healthy male university students and members of the general public (aged 20–39 years) were recruited by local advertisement and were scanned on three occasions. Participants were screened by interview and physical exam for previous or current medical, psychiatric, or neurological problems. Other exclusion criteria included any substance abuse history, smoking >5 cigarettes per day, and consuming the equivalent of >5 cups of coffee per day. Participants were trained on the WM task on the screening day and were asked to refrain from alcohol and caffeine containing products for 24 h before dosing. Participants provided written informed consent and the study was approved by South London Research Ethics Committee 3. On each scanning day, participants were screened for drugs of abuse and alcohol, and then each participant received an oral dose of MPH (30 mg), ATX (60 mg), or a placebo (PLC) according to a randomized, double-blind Latin square design. Doses of MPH and ATX were chosen to approximately match doses used in clinical practice, and doses reported in the literature (eg, Gilbert et al, 2006).

Scanning was performed on a General Electric Signa HDx 3T scanner and was timed to coincide with the peak plasma concentration for MPH and ATX (Wargin et al, 1983; Sauer et al, 2005). Between 90 and 135 min post-dose, six resting state arterial spin labelling scans were acquired, which will be reported separately. Approximately 135 min post-dose, gradient-echo (GE) echoplanar imaging was used to acquire 450 whole-brain images while participants performed a WM task (TR=2 s, TE=30 ms, FA=75°, 38 3-mm-thick near-axial slices with 0.3 mm gap, in-plane resolution=3.75 × 3.75 mm). A high-resolution GE structural scan was also acquired for each participant to assist accurate registration to a standard space (TR=3 s, TE=30 ms, FA=90°, 43 3-mm-thick near-axial slices with 0.3 mm gap, in-plane resolution=1.88 × 1.88 mm).

WM Task

During the WM task, 40 trials were presented with an inter-trial interval of 8 or 10 s, and during each trial, participants were required to remember the spatial location of a target stimulus (a dot) relative to a fixation cross. The task allowed each WM component process (encoding, delay, and retrieval) to be separately coded (Figure 1). Half the trials carried a monetary reward, indicated by the color of the stimulus and the order of trials was randomized and counterbalanced across participants. During encoding (2 s), the target stimulus was presented, followed immediately by a mask to disrupt visual iconic memory. After a variable length delay (7 or 9 s), the target and an additional distractor stimulus were presented and participants indicated which of the stimuli matched the target location by left or right button press on a two-button response box (retrieval). At the conclusion of the trial, feedback was provided, indicating success or failure and accuracy and response time (RT) were recorded. Acquisition was optimized for volume-based PR, with stimuli presented in a TR-locked manner, which ensures that data vectors were sampled from approximately the same point on the hemodynamic response curve and helps to generate a consistent response pattern for each trial. The task was written in VB.net, presented via projector to a screen at the end of the scanner bed and viewed by participants through mirrors attached to the head coil. Participants completed a visual analog scale (VAS; Bond and Lader, 1974) at four time points during each visit to record their subjective experience, which contained 16 items that were later collapsed to reflect two subjective factors: ‘alertness' and ‘tranquility' (Herbert et al, 1976; Supplementary Material). Outside the scanner, VAS responses were measured with a ruler and inside the scanner a computerized VAS was administered where participants recorded their responses by moving a sliding cursor using the two-button response box.

Figure 1
Delayed match to location working memory (WM) task. Note that the only difference between rewarded and non-rewarded trials is the color of the stimulus.

FMRI Data Pre-processing

FMRI data were realigned, spatially normalized, and smoothed with an isotropic 8 mm Gaussian kernel using statistical parametric mapping software version 5 (SPM5) (www.fil.ion.ucl.ac.uk/spm). Additional pre-processing was performed in Matlab (www.mathworks.com), which consisted of linearly detrending the data and applying a whole-brain mask to select intracerebral voxels. Classifier samples were constructed by: (1) shifting the onset of each trial by one volume to accommodate the hemodynamic delay; (2) converting brain volumes acquired during each task component to vectors; and (3) averaging two (encoding, retrieval, and shorter delay) or three (longer delay) consecutive volumes from each WM component. We averaged at least two volumes for each WM component to accommodate the temporal blurring induced by the hemodynamic response and to ensure that we captured the peak of the hemodynamic response. Trials where each participant responded incorrectly were excluded and remaining trials were averaged to construct a single mean sample per participant (averaged over approximately 16 correct trials). We constructed classifier samples for the baseline condition by extracting and averaging two volumes during the fixation period between trials (6–8 s after the end of feedback).

Classifier Implementation

We used binary Gaussian process classifiers (GPCs; Rasmussen and Williams, 2006) to classify: (1) each WM component from baseline; (2) rewarded from non-rewarded trials; and (3) each drug condition (ATX, MPH or PLC) from one another. GPCs are kernel classifiers similar to support vector machines (SVMs) that have good performance for fMRI (Marquand et al, 2010b). The main advantage of GPCs over SVMs is that GPCs provide probabilistic predictions and estimates of predictive uncertainty. Theoretical background and implementation details for GPCs have been presented elsewhere (Rasmussen and Williams, 2006; Marquand et al, 2010b), but a brief description is provided in Supplementary Material. In this work, we use linear kernel GPCs that help prevent overfitting and allow direct extraction of the weight vector as an image.

Recursive Feature Elimination

We embedded all classifiers contrasting reward or drug state in a recursive feature elimination (RFE; Guyon et al, 2002), which is a backward elimination feature selection approach that aims to find a set of features (voxels) by iteratively removing the least informative features. RFE was originally developed for SVM (SVM-RFE), and has been applied to multiple fMRI studies (eg, De Martino et al, 2008; Formisano et al, 2008; Hanson and Halchenko, 2008), but here we adapt it to GPC (‘GPC-RFE' Marquand et al, 2010a). RFE starts by creating an ‘active feature set', initially containing all cerebral voxels. A classifier is trained repeatedly on the active set and at each iteration features are ranked and a subset of the lowest ranking features is removed (2% of voxels), which continues until no features remain. Predictive performance is measured at each stage of feature removal on an independent sample, allowing an optimal number of features maximizing predictive performance to be selected (Supplementary Material). RFE is most commonly applied because it modestly increases accuracy, but here our main motivation was because it yields a spatially sparse multivariate map (akin to a thresholded statistical parametric map), which is essential to prevent falsely inferring a brain region is functionally important when in fact it is not. RFE is a principled approach to achieve this aim and is more appropriate than an arbitrary voxel-wise threshold because it: (1) validates the multivariate pattern against predictive accuracy; (2) accommodates the multivariate structure of the pattern; and (3) does not require specification of an arbitrary threshold level. We did not apply GPC-RFE to the classifiers contrasting task and baseline, because this is a trivial classification problem and the objective was only to define the brain activity pattern evoked by the task for which an unthresholded map is preferable, but for reference purposes, we provide classification accuracy for whole-brain classifiers trained to discriminate between all experimental contexts (Supplementary Material).

Cross-Validation

RFE can be viewed as a model selection problem, where model complexity is determined by a single parameter (the number of features to retain), which must be set without using the test data set to avoid overfitting. To achieve this, we used nested leave-one-out cross-validation (LOO-CV), which uses a three-way split of the data to provide an unbiased estimate of generalization ability while also allowing unbiased parameter estimation. For each LOO-CV fold, we excluded all data for a single participant for the test set, then repeatedly repartitioned the remaining 14 participants into a validation set (one participant) and training set (13 participants). We selected the optimal number of features on the validation set before applying it to the test set.

Visualization of the Differential Activity Pattern

To visualize the differential activity patterns, we retrained each GPC-RFE classifier using all participants' data, for which the optimal number of features was the mean across all training folds. For this application, we are interested in knowing how brain activity differs between experimental classes rather than providing a representation of the decision boundary, so we did not visualize classifier weights, which is common in PR (Mourao-Miranda et al, 2005). Instead, we employed a mapping approach that enables direct visualization of the relative class distribution, where the coefficient scores at each voxel represent the relative difference between experimental classes in the context of the entire pattern (Marquand et al, 2010b; Supplementary Material).

RESULTS

Performance Measures

Repeated-measures ANOVA revealed that time (RT) did not differ between drugs (F2, 28=0.001, p=0.99), or between rewarded and non-rewarded trials (F1, 14=0.003, p=0.96), and there was no reward × drug interaction (F2, 28=0.47, p=0.63). Participants made fewer errors on rewarded relative to non-rewarded trials (F1, 14=11.54, p<0.01), but errors did not differ between drug conditions (F2, 28=0.12, p=0.89) and there was no reward × drug interaction (F2, 28=1.80, p=0.19). A summary of RT and accuracy is provided in Supplementary Table S1.

Subjective Measures

Several participants reported side effects to the administration of the drugs (eg, nausea, drowsiness), but these were mild in all cases and mostly resolved before discharge on the study day. Subjective factors of alertness and tranquility were investigated as potential confounds to any drug effect using an independent repeated-measures ANOVA for each factor. For alertness, there was no main effect of drug (F2, 28=0.59, p=0.56), but a main effect of time point was observed (F1, 14=8.83, p=0.01), whereby post-dose VAS scores were slightly lower than pre-dose scores across all drug conditions. No drug × time point interaction was found (F2, 28=0.21, p=0.81). For tranquility, there was no main effect of drug (F2, 28=1.78, p=0.19) or time point (F1, 14=0.02, p=0.88) and no interaction effect (F2, 28=0.48, p=0.62).

Task Networks

Whole-brain classifiers accurately discriminated each WM component process from baseline for all drug conditions (mean accuracy (SEM) of 18 classifiers: 97.61% (0.01); p<0.01, binomial test). As noted, the magnitude of GPC coefficients at each voxel provides a measure of the relative difference in blood oxygen level-dependent (BOLD) activation between classes in the context of the entire discriminating pattern and the sign indicates (‘favors') the class with greater mean activation (Marquand et al, 2010b). GPC distribution maps (Supplementary Figures S1 and S2) revealed a distributed network (pattern) favoring the task component processes, including bilateral intraparietal sulci (IPS; Brodmann area (BA) 7), middle frontal gyri (BA 9/46), and bilateral medial and inferior frontal gyri (BA 6 and 47, respectively) in addition to visual and motor cortical regions. The pattern favoring baseline (task-related deactivations—TRDs) included regions comprising the default mode network (DMN), that is, posterior cingulate cortex (PCC; BA 30), precuneus (BA 31), medial PFC (BA 9/10 and 32), and lateral parietal cortex (BA 39).

Classification of Reward

Classification accuracy for GPC-RFE classifiers discriminating between rewarded and non-rewarded trials exceeded chance (50%) for all WM component processes and across all three drug conditions, with the exception of the encoding component on MPH (mean (SEM) of six classifiers: 70.72% (0.04); Figure 2a). The pattern favored reward and encompassed both the WM networks and TRDs described above. Specifically, BOLD activity in lateral PFC, parietal regions, medial PFC, and PCC/precuneus was relatively increased (Figure 3; Supplementary Figure S2); in other words, the effect of reward was to attenuate TRDs and enhance activity in the WM network. TRDs were most prominently attenuated during encoding and delay components of the rewarded WM task, whereas visual and WM regions were most prominently enhanced during delay and retrieval components of the task (see Figure 3). In summary, reward produces a generalized increase in BOLD activity, including both task-related activations (which increase with reward) and TRDs (which are suppressed with reward).

Figure 2
Classification accuracy for Gaussian process classifier (GPC)-recursive feature elimination (RFE) classifiers for (a) rewarded vs non-rewarded trials, (b) atomoxetine (ATX) vs placebo (PLC), (c) methylphenidate (MPH) vs PLC, and (d) MPH vs ATX. Asterisks ...
Figure 3
Gaussian process classifier (GPC)-recursive feature elimination (RFE) distribution maps for classifiers discriminating between rewarded and non-rewarded trials for each working memory (WM) component (placebo (PLC) arm). (a) Encoding, (b) delay, and (c) ...

Classification Accuracy for Drug Contrasts

For ATX vs PLC, classification accuracy exceeded chance for encoding, delay, and retrieval components of rewarded trials (p<0.05), but not during any WM component for the non-rewarded trials (Figure 2b). For MPH vs PLC, accuracy exceeded chance during encoding, delay, and retrieval of rewarded trials and during encoding of non-rewarded trials (p<0.05; Figure 2c). For MPH vs ATX, classification accuracy exceeded chance for the delay component of rewarded trials (p<0.05; Figure 2d).

For all classifiers exceeding chance, RT data were used to explore putative relationships between classifier performance and behavior. No significant correlations between RT and GPC-RFE predictive probabilities were found. Note that correlations with performance accuracy were not appropriate because all participants were well trained and made only a small number of errors, and only correct trials were included in the image analysis.

Discriminating Pattern for ATX vs PLC (Rewarded Trials)

Maps derived from classifiers trained to discriminate ATX from PLC on rewarded trials (Figure 4) contained a distributed pattern favoring PLC that included WM networks and DMN; in other words, in the reward context, ATX attenuated BOLD activity in WM networks and enhanced TRDs. During encoding, the pattern favoring PLC included the DMN (medial PFC and PCC/precuneus) and WM networks (IPS and bilateral PFC—BA 9, 46, and 47). In addition, small clusters weakly favoring ATX were observed in the cerebellum and lateral PFC during encoding. During delay and retrieval components, the pattern favoring PLC was most prominent in WM regions.

Figure 4
Gaussian process classifier (GPC)-recursive feature elimination (RFE) distribution maps for classifiers discriminating between atomoxetine (ATX) and placebo (PLC) conditions for each working memory (WM) component (rewarded trials). (a) Encoding, (b) delay, ...

Discriminating Pattern for MPH vs PLC (Rewarded Trials)

Maps derived from classifiers trained to discriminate MPH from PLC on rewarded trials (Figure 5) contained a distributed pattern favoring PLC similar to that observed for ATX, which also encompassed WM and DMN regions. During encoding, the pattern favoring PLC was mostly localized to DMN regions, but during delay and retrieval, the PLC pattern additionally included clusters in WM, motor, and visual regions, and was most widespread during retrieval. The pattern favoring MPH was restricted to encoding and was localized mostly to the cerebellum and lateral PFC.

Figure 5
Gaussian process classifier (GPC)-recursive feature elimination (RFE) distribution maps for classifiers discriminating between methylphenidate (MPH) and placebo (PLC) for each working memory (WM) component (rewarded trials). (a) Encoding, (b) delay, and ...

Discriminating Pattern for MPH vs PLC (Non-Rewarded Trials)

The map derived from the classifier trained to discriminate MPH from PLC during the encoding component of non-rewarded trials (Figure 6) contained a distributed pattern, this time favoring MPH, including DMN, WM (eg, IPS), and visual regions. Thus, in the absence of reward, MPH enhanced activity in WM networks and attenuated TRDs. Note that the map contrasting MPH and PLC shows a strong qualitative similarity to the one contrasting rewarded and non-rewarded trials in the encoding component of the PLC condition (Figure 3a).

Figure 6
Gaussian process classifier (GPC)-recursive feature elimination (RFE) distribution maps for classifiers discriminating between methylphenidate (MPH) and placebo (PLC) for the encoding working memory (WM) component (non-rewarded trials). A distributed ...

Discriminating Pattern for MPH vs ATX (Rewarded Trials)

The map derived from the classifier trained to discriminate MPH from ATX on the delay component of rewarded trials (Figure 7) contained distributed patterns favoring MPH and ATX. The pattern favoring MPH was mainly localized to WM regions (IPS and lateral PFC—BA 9/46) and the pattern favoring ATX was mainly localized to the DMN. Thus, during the delay component of rewarded trials, MPH relative to ATX resulted in greater BOLD activity in WM networks, and ATX relative to MPH resulted in greater TRDs.

Figure 7
Gaussian process classifier (GPC)-recursive feature elimination (RFE) distribution maps for classifiers discriminating between methylphenidate (MPH) and atomoxetine (ATX) for the delay component of rewarded trials. Distributed patterns of activity favoring ...

For all contrasts, the differential patterns derived from GPC-RFE show a reasonably good correspondence to those derived from an equivalent univariate SPM, except the SPM retained substantially fewer voxels (at p<0.001, uncorrected for multiple comparisons) than were retained by the classifier (data not shown).

A concise summary of the results is provided in Table 1.

Table 1
Summary of Classification Results

DISCUSSION

We have shown differential effects of MPH and ATX on brain activity patterns in healthy volunteers performing a rewarded WM task. An important conclusion from our results is that the effects of MPH and ATX on WM are context-dependent. In the rewarded context, both MPH and ATX could be accurately discriminated from PLC across all task components, showing similar patterns of attenuation across the WM networks and enhanced TRDs. During the encoding component of non-rewarded trials, MPH, but not ATX, could be discriminated from PLC; MPH increased activity in WM regions and attenuated TRDs compared with PLC. The pattern of BOLD signal changes observed during the delay component of rewarded trials also discriminated MPH from ATX. In this context, and relative to ATX, MPH produced a pattern of increased activity in WM networks, whereas ATX produced greater activity in the DMN. Overall this complex set of findings suggests that: (1) both MPH and ATX have salient effects during rewarded WM in both task-activated and deactivated networks; (2) during the delay component of rewarded trials, MPH and ATX had opposing effects on activated and deactivated networks; and (3) MPH may mimic reward during encoding.

The results in this study were determined by applying recently developed PR techniques to the neuroimaging data, which afford substantially greater sensitivity than conventional mass-univariate techniques (Haynes and Rees, 2006; Norman et al, 2006) by making use of spatial correlation between voxels, lending themselves well to whole-brain inference. These properties make PR ideally suited to drug discrimination studies, where drugs administered systemically can theoretically influence distributed brain regions owing to direct effects at target receptor sites and consequent effects on connected brain regions. It is important to emphasize that multivariate brain maps derived from PR analysis provide a different perspective to mass-univariate analysis and should be interpreted differently. In particular, multivariate brain maps describe a pattern of activity, and coefficients should not be interpreted as representing focal effects because many brain regions potentially contribute to the accuracy of the classifier.

The WM networks identified in this study agree well with previous studies (Curtis et al, 2004; Gibbs and D'Esposito, 2005) and were sensitive to reward. During rewarded trials participants performed the task more accurately, which was reflected as a generalized pattern of increased brain activity throughout WM networks and in the DMN. Indeed, increased activity in WM brain regions is a known effect of reward on WM tasks (Pochon et al, 2002; Taylor et al, 2004; Pessoa and Engelmann, 2010) and may reflect an increase in neuronal effort.

MPH and ATX did not alter performance accuracy or response latency during the WM task. However, previous studies using MPH and amphetamine have suggested that reductions in BOLD activation accompanied by equivalent behavioral performance reflect an increased efficiency of WM networks (Mattay et al, 2000; Mehta et al, 2000). Thus, for our data in a rewarded context, this would seem to be the most parsimonious explanation for the effects of MPH and ATX on task activation and deactivation networks. This effect is probably mediated by increased catecholamine concentrations in WM regions (Bymaster et al, 2002), which is known to focus neuronal activity by enhancing responses to task-relevant stimuli while suppressing background noise (Foote et al, 1975; Seamans et al, 2001). Historically, DA has been linked with WM performance through increasing the efficiency of PFC neurons by decreasing delay-related response to ‘noise' (Arnsten, 2007; Vijayraghavan et al, 2007) and the stabilization of their sustained activity (Durstewitz et al, 2000). However, NA is probably also important as therapeutic doses of MPH increase PFC extracellular concentrations of NA substantially more than DA (Berridge et al, 2006), and the beneficial effects of MPH and ATX on WM can be blocked by either DA D1 or NA α2 receptor antagonists (Arnsten and Dudley, 2005; Gamo et al, 2010). NA is also known to increase delay-related activity of PFC neurons in response to ‘signals' (Arnsten, 2007) and increase the salience of novel stimuli, leading to the suggestion that it serves as an alarm system for contextual changes (Yu and Dayan, 2005).

The PCC, precuneus, and ventromedial PFC are known to show decreased activity during a wide range of goal-directed tasks (Shulman et al, 1997). These regions have been proposed to underlie a ‘default mode' of brain function (Raichle et al, 2001) and it is thought that to facilitate goal-directed action, task-irrelevant mental activity in these regions must be suppressed. Indeed, failure to suppress default mode activity reflects momentary lapses in attention (Weissman et al, 2006), resulting in increased probability of error (Eichele et al, 2008). There is also preliminary evidence that ADHD may be characterized by deficiencies in attentional focus and insufficient suppression of brain activity in focal regions of the DMN (Fassbender et al, 2009) and that MPH may normalize the amplitude of TRDs in treatment-responsive ADHD participants (Peterson et al, 2009). Our results are consistent with this interpretation and further show that suppression of task-irrelevant mental activity may be a mechanism common to both MPH and ATX. Importantly, this effect was context-dependent, as it was only observed during rewarded trials.

In a rewarded context, classification accuracy was equivalent for classifiers discriminating MPH or ATX from PLC for each WM component, although accuracy was slightly higher for both drugs during retrieval relative to encoding and delay. Qualitatively, the effects of MPH and ATX were comparable, with both drugs producing a generalized decrease in brain activity in WM networks and DMN (ie, attenuation of activity in WM networks and enhancement of TRDs). Nonetheless, the extent of these effects separated the drugs during the delay component of rewarded trials: ATX attenuated BOLD activity in WM networks more than MPH and MPH enhanced TRDs more than ATX.

Microdialysis studies in rodents have shown that MPH and ATX increase DA concentration in the PFC, but only MPH increases DA in the striatum (Bymaster et al, 2002), and that therapeutic doses of MPH increase catecholamine concentration in the PFC substantially more than that in the striatum (Berridge et al, 2006). However, in our study we did not observe increased striatal activity following MPH, similar to other neuroimaging studies in healthy volunteers (Mehta et al, 2000; Udo de Haes et al, 2007). This may be because the WM task we employed does not substantially engage the striatum, even for rewarded trials (Supplementary Figure S2), which is consistent with a recent review of the effects of reward on WM (Pessoa and Engelmann, 2010) or simply because the consequential effects of MPH on striatal DA levels are expressed in connected brain regions. Thus, subcortical effects of MPH on DA remain a candidate mechanism for the differential effects of MPH and ATX, as the PFC and striatum are strongly connected by parallel corticostriatal circuits (Alexander et al, 1986), and there is emerging evidence suggesting that the striatal DA system plays a role in the modulation of the DMN (Kelly et al, 2009; Tomasi et al, 2009). However, studies concurrently measuring striatal DA release and its functional consequences on brain activity are required to test this hypothesis explicitly.

In a non-rewarded context, it was only possible to discriminate MPH from PLC during encoding. In this case, the differential pattern (Figure 6) bears a strong qualitative resemblance to that differentiating rewarded from non-rewarded trials (Figure 3), suggesting that while MPH did not improve performance at the dose administered, MPH nevertheless mimics the reward effect. Discrimination accuracies for classifiers contrasting rewarded and non-rewarded trials were also consistently lower on MPH than on ATX or PLC and did not exceed chance for encoding, indicating that activity patterns discriminating reward and non-rewarded trials were less distinguishable on MPH (Figure 2a), which is consistent with the suggestion that MPH increases task salience (Volkow et al, 2004). This effect is probably mediated by DA, because a learned association between a cue and a reward results in increased phasic dopaminergic firing during cue presentation not reward delivery (Schultz et al, 1993) and increased dopaminergic firing, often followed by immediate depression, is also associated with stimuli that resemble the rewarded stimulus (Schultz and Romo, 1990). Catecholaminergic signalling has also been associated with an ‘inverted-U' dose–response relationship in the PFC (Arnsten, 2006; Levy, 2009) with optimal PFC function at intermediate concentrations and too much or too little DA or NA resulting in impaired PFC function. Although speculative at this stage, such a relationship may underlie different contextual effects of MPH, where rewarded and non-rewarded contexts may engage curves with different optimal dosing. Also, we only administered one dose of each drug here, so it is possible that ATX shares the reward-emulating effect at a different dose, which could additionally account for the classifier's inability to discriminate MPH and ATX during encoding of non-rewarded trials (Supplementary Figure S4).

Individual differences in response to drug administration may be an interesting line of future research. In particular, genetic factors influence responses to stimulants (Mattay et al, 2003), and although we did not collect genetic information here, inclusion of genetic factors can only be expected to improve predictive performance. As noted, only one dose of each drug was administered so that dose effects cannot be excluded as confounds, but three lines of evidence speak against this possibility: first, administered doses were matched according to doses used in clinical practice. Second, motor-evoked potentials were altered to a similar extent for both drugs using identical doses to those administered here (Gilbert et al, 2006). Third, opposing effects of MPH and ATX on activated and deactivated task networks during the delay component of rewarded trials are difficult to explain by a simple dose effect.

In summary, we accurately discriminated the effects of MPH and ATX on rewarded and non-rewarded WM networks using multivariate PR. We suggest that this method is ideal for drug discrimination studies because for most psychotropic medications subtle distributed effects probably predominate over strong focal effects. More importantly, our results show that MPH and ATX have effects on WM function that are context-dependent and suggest that the interaction between drug effects and motivational state will be crucial in defining the beneficial effects of MPH and ATX in ADHD.

Acknowledgments

We wish to acknowledge the support of the King's College London Centre of Excellence in Medical Engineering, funded by the Wellcome Trust and EPSRC under grant no. WT088641/Z/09/Z and would like to thank Bill Vennart, Paul Maguire and Caroline Wooldridge from Pfizer Global Research and Development. JMM was funded by the Wellcome Trust (grant no. WT086565/Z/08/Z) and AM gratefully acknowledges support from King's College Annual Fund.

Notes

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies the paper on the Neuropsychopharmacology website (http://www.nature.com/npp)

Supplementary Material

Supplementary Information

References

  • Alexander GE, Delong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357–381. [PubMed]
  • Andreou P, Neale BM, Chen W, Christiansen H, Gabriels I, Heise A, et al. Reaction time performance in ADHD: improvement under fast-incentive condition and familial effects. Psychol Med. 2007;37:1703–1715. [PMC free article] [PubMed]
  • Arnsten A, Dudley A. Methylphenidate improves prefrontal cortical cognitive function through α2 adrenoreceptors and dopamine D1 receptor actions: relevance to therapeutic effects in attention-deficit hyperactivity disorder. Behav Brain Funct. 2005;1:2–10. [PMC free article] [PubMed]
  • Arnsten AFT. Stimulants: therapeutic actions in ADHD. Neuropsychopharmacology. 2006;31:2376–2383. [PubMed]
  • Arnsten AFT. Catecholamine and second messenger influences on prefrontal cortical networks of ‘representational knowledge': a rational bridge between genetics and the symptoms of mental illness. Cerebral Cortex. 2007;17:I6–I15. [PubMed]
  • Arnsten AFT, Goldman-Rakic PS. Alpha-2-adrenergic mechanisms in prefrontal cortex associated with cognitive decline in aged non-human primates. Science. 1985;230:1273–1276. [PubMed]
  • Bedard AC, Martinussen R, Ickowicz A, Tannock R. Methylphenidate improves visual–spatial memory in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2004;43:260–268. [PubMed]
  • Berridge CW, Devilbiss DM, Andrzejewski ME, Arnsten AFT, Kelley AE, Schmeichel B, et al. Methylphenidate preferentially increases catecholamine neurotransmission within the prefrontal cortex at low doses that enhance cognitive function. Biol Psychiatry. 2006;60:1111–1120. [PubMed]
  • Bolden-Watson C, Richelson E. Blockade by newly-developed antidepressants of biogenic-amine uptake into rat-brain synaptosomes. Life Sci. 1993;52:1023–1029. [PubMed]
  • Bond AJ, Lader MH. The use of analogue scales in rating subjective feelings. Br J Med Psychol. 1974;47:211–218.
  • Brozoski TJ, Brown RM, Rosvold HE, Goldman PS. Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science. 1979;205:929–932. [PubMed]
  • Bymaster FP, Katner JS, Nelson DL, Hemrick-Luecke SK, Threlkeld PG, Heiligenstein JH, et al. Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: a potential mechanism for efficacy in attention deficit/hyperactivity disorder. Neuropsychopharmacology. 2002;27:699–711. [PubMed]
  • Ciliax BJ, Drash GW, Staley JK, Haber S, Mobley CJ, Miller GW, et al. Immunocytochemical localization of the dopamine transporter in human brain. J Comp Neurol. 1999;409:38–56. [PubMed]
  • Curtis CE, Rao VY, D′Esposito M. Maintenance of spatial and motor codes during oculomotor delayed response tasks. J Neurosci. 2004;24:3944–3952. [PubMed]
  • De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage. 2008;43:44–58. [PubMed]
  • Durstewitz D, Seamans JK, Sejnowski TJ. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. Jo Neurophysiol. 2000;83:1733–1750. [PubMed]
  • Eichele T, Debener S, Calhoun VD, Specht K, Engel AK, Hugdahl K, et al. Prediction of human errors by maladaptive changes in event-related brain networks. Proc Natl Acad Sci USA. 2008;105:6173–6178. [PubMed]
  • Elliott R, Sahakian BJ, Matthews K, Bannerjea A, Rimmer J, Robbins TW. Effects of methylphenidate on spatial working memory and planning in healthy young adults. Psychopharmacology. 1997;131:196–206. [PubMed]
  • Faraone SV, Biederman J, Spencer T, Michelson D, Adler L, Reimherr F, et al. Efficacy of atomoxetine in adult attention-deficit/hyperactivity disorder: a drug–placebo response curve analysis. Behav Brain Funct. 2005;1:16. [PMC free article] [PubMed]
  • Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, et al. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 2009;1273:114–128. [PubMed]
  • Foote S, Freedman R, Oliver AP. Effects of putative neurotransmitters on neuronal activity in monkey auditory cortex. Brain Res. 1975;86:229–242. [PubMed]
  • Formisano E, De Martino F, Bonte M, Goebel R. ‘Who' Is Saying ‘What'? Brain-based decoding of human voice and speech. Science. 2008;322:970–973. [PubMed]
  • Gamo NJ, Wamg M, Arnsten AF. Methylphenidate and atomoxetine enhance prefrontal function through alpha-2 adrenergic and dopamine D1 receptors. J Am Acad Child Adolesc Psychiatry. 2010;49:1011–1023. [PMC free article] [PubMed]
  • Gibbs SEB, D'Esposito M. A functional MRI study of the effects of bromocriptine, a dopamine receptor agonist, on component processes of working memory. Psychopharmacology. 2005;180:644–653. [PubMed]
  • Gilbert DL, Ridel KR, Sallee FR, Zhang J, Lipps TD, Wassermann EM. Comparison of the inhibitory and excitatory effects of ADHD medications methylphenidate and atomoxetine on motor cortex. Neuropsychopharmacology. 2006;31:442–449. [PubMed]
  • Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422.
  • Han DD, Gu HH. 2006. Comparison of the monoamine transporters from human and mouse in their sensitivities to psychostimulant drugs BMC Pharmacology6 Article No. 6. pp 1–7.7. [PMC free article] [PubMed]
  • Hanson SJ, Halchenko YO. Brain reading using full brain support vector machines for object recognition: there is no ‘Face' identification area. Neural Comput. 2008;20:486–503. [PubMed]
  • Haynes JD, Rees G. Decoding mental states from brain activity in humans. Nat Rev Neurosci. 2006;7:523–534. [PubMed]
  • Herbert M, Johns MW, Dore C. Factor-analysis of analog scales measuring subjective feelings before and after sleep. Br J Med Psychol. 1976;49:373–379. [PubMed]
  • Ichihara-Takeda S, Takeda K, Funahashi S. Reward acts as a signal to control delay-period activity in delayed-response tasks. Neuroreport. 2010;21:367–370. [PubMed]
  • Kelly C, de Zubicaray G, Di Martino A, Copland DA, Reiss PT, Klein DF, et al. -Dopa modulates functional connectivity in striatal cognitive and motor networks: a Double-Blind Placebo-Controlled Study. J Neurosci. 2009;29:7364–7378. [PMC free article] [PubMed]
  • Kemner JE, Starr HL, Ciccone PE, Hooper-Wood CG, Crockett RS. Outcomes of OROS (R) methylphenidate compared with atomoxetine in children with ADHD: a multicenter, randomized prospective study. Adv Ther. 2005;22:498–512. [PubMed]
  • Kriegeskorte N, Goebel R, Bandettini P. Information-based functional brain mapping. Proc Natl Acad Sci USA. 2006;103:3863–3868. [PubMed]
  • Levy F. Dopamine vs noradrenaline: inverted-U effects and ADHD theories. Austr NZ J Psychiatry. 2009;43:101–108. [PubMed]
  • Marquand A, De Simoni S, O'Daly O, Mourao-Miranda J, Mehta M. International Conference on Pattern Recognition. Istanbul, Turkey; 2010a. Quantifying the information content of brain voxels using target information, Gaussian processes and recursive feature elimination Qu.
  • Marquand A, et al. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. NeuroImage. 2010b;49:2178–2189. [PubMed]
  • Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2005;44:377–384. [PubMed]
  • Mattay VS, Callicott JH, Bertolino A, Heaton I, Frank JA, Coppola R, et al. Effects of dextroamphetamine on cognitive performance and cortical activation. NeuroImage. 2000;12:268–275. [PubMed]
  • Mattay VS, Goldberg TE, Fera F, Hariri AR, Tessitore A, Egan MF, et al. Catechol O-methyltransferase val(158)-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci USA. 2003;100:6186–6191. [PubMed]
  • Mehta MA, Goodyer IM, Sahakian BJ. Methylphenidate improves working memory and set-shifting in AD/HD: relationships to baseline memory capacity. J Child Psychol Psychiatry. 2004;45:293–305. [PubMed]
  • Mehta MA, Owen AM, Sahakian BJ, Mavaddat N, Pickard JD, Robbins TW. Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain. J Neurosci. 2000;20:6. [PubMed]
  • Michelson D, Faries D, Wernicke J, Kelsey D, Kendrick K, Sallee FR, et al. Atomoxetine in the treatment of children and adolescents with attention-deficit/hyperactivity disorder: a randomized, placebo-controlled, dose–response study. Pediatrics. 2001;108:E83. [PubMed]
  • Mourao-Miranda J, Bokde AL, Born C, Hampel H, Stetter M. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage. 2005;28:980–995. [PubMed]
  • Newcorn JH, Kratochvil CJ, Allen AJ, Casat CD, Ruff DD, Moore RJ, et al. Atomoxetine and osmotically released methylphenidate for the treatment of attention deficit hyperactivity disorder: acute comparison and differential response. Am J Psychiatry. 2008;165:721–730. [PubMed]
  • Norman KA, Polyn SM, Detre GJ, Haxby JV. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn Sci. 2006;10:424–430. [PubMed]
  • Pessoa L, Engelmann J. Embedding reward signals into perception and cognition. Front Neurosci. 2010;4:1–8. [PMC free article] [PubMed]
  • Peterson BS, Potenza MN, Wang ZS, Zhu HT, Martin A, Marsh R, et al. An fMRI study of the effects of psychostimulants on default-mode processing during stroop task performance in youths with ADHD. Am J Psychiatry. 2009;166:1286–1294. [PMC free article] [PubMed]
  • Pochon JB, Levy R, Fossati P, Lehericy S, Poline JB, Pillon B, et al. The neural system that bridges reward and cognition in humans: an fMRI study. Proc Natl Acad Sci USA. 2002;99:5669–5674. [PubMed]
  • Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA. 2001;98:676–682. [PubMed]
  • Rasmussen C, Williams CKI. Gaussian Processes for Machine Learning. The MIT Press: Cambridge, MA; 2006.
  • Sauer JM, Ring BJ, Witcher JW. Clinical pharmacokinetics of atomoxetine. Clin Pharmacokinet. 2005;44:571–590. [PubMed]
  • Schou M, Halldin C, Pike VW, Mozley PD, Dobson D, Innis RB, et al. Post-mortem human brain autoradiography of the norepinephrine transporter using (S,S)-F-18 FMeNER-D-2. Eur Neuropsychopharmacol. 2005;15:517–520. [PubMed]
  • Schultz W, Apicella P, Ljungberg T. Responses of monkey dopamine neurons to reward and conditioned-stimuli during successive steps of learning a delayed-response task. J Neurosci. 1993;13:900–913. [PubMed]
  • Schultz W, Romo R. Dopamine neurons of the monkey midbrain—contingencies of responses to stimuli eliciting immediate behavioural reactions. J Neurophysiol. 1990;63:607–624. [PubMed]
  • Schweitzer JB, Lee DO, Hanford RB, Zink CF, Ely TD, Tagamets MA, et al. Effect of methylphenidate on executive functioning in adults with attention-deficit/hyperactivity disorder: normalization of behavior but not related brain activity. Biol Psychiatry. 2004;56:597–606. [PubMed]
  • Seamans JK, Gorelova N, Durstewitz D, Yang CR. Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons. J Neurosci. 2001;21:3628–3638. [PubMed]
  • Seeman P, Madras BK. Anti-hyperactivity medication: methylphenidate and amphetamine. Mol Psychiatry. 1998;3:386–396. [PubMed]
  • Shulman GL, Fiez JA, Corbetta M, Buckner RL, Miezin FM, Raichle ME, et al. Common blood flow changes across visual tasks.2. Decreases in cerebral cortex. J Cogn Neurosci. 1997;9:648–663. [PubMed]
  • Spencer T, Biederman J, Wilens T, Prince J, Hatch M, Jones J, et al. Effectiveness and tolerability of tomoxetine in adults with attention deficit hyperactivity disorder. Am J Psychiatry. 1998;155:693–695. [PubMed]
  • Starr HL, Kemner J. Multicenter, randomized, open-label study of OROS methylphenidate versus atomoxetine: treatment outcomes in African-American children with ADHD. J Natl Med Assoc. 2005;97:11S–16S. [PMC free article] [PubMed]
  • Taylor SF, Welsh RC, Wager TD, Phan KL, Fitzgerald KD, Gehring WJ. A functional neuroimaging study of motivation and executive function. NeuroImage. 2004;21:1045–1054. [PubMed]
  • Tomasi D, Volkow ND, Wang RL, Telang F, Wang GJ, Chang L, et al. Dopamine transporters in striatum correlate with deactivation in the default mode network during visuospatial attention. PLoS One. 2009;4 [PMC free article] [PubMed]
  • Trommer BL, Hoeppner JAB, Zecker SG. The go-no go test in attention-deficit disorder is sensitive to methylphenidate. J Child Neurol. 1991;6:S128–S131. [PubMed]
  • Udo de Haes JI, Maguire RP, Jager PL, Paans AMJ, den Boer JA. Methylphenidate-induced activation of the anterior cingulate but not the striatum: a 15O H2O PET study in healthy volunteers. Hum Brain Mapp. 2007;28:625–635. [PubMed]
  • Vijayraghavan S, Wang M, Birnbaum SG, Williams GV, Arnsten AFT. Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory. Nat Neurosci. 2007;10:376–384. [PubMed]
  • Volkow ND, Wang GJ, Fowler JS, Telang F, Maynard L, Logan J, et al. Evidence that methylphenidate enhances the saliency of a mathematical task by increasing dopamine in the human brain. Am J Psychiatry. 2004;161:1173–1180. [PubMed]
  • Wargin W, Patrick K, Kilts C, Gualtieri CT, Ellington K, Mueller RA, et al. Pharmacokinetics of methylphenidate in man, rat and monkey. J Pharmacol Exp Therap. 1983;226:382–386. [PubMed]
  • Weissman DH, Roberts KC, Visscher KM, Woldorff MG. The neural bases of momentary lapses in attention. Nat Neurosci. 2006;9:971–978. [PubMed]
  • Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry. 2005;57:1336–1346. [PubMed]
  • Wong DT, Threlkeld PG, Best KL, Bymaster FP. A new inhibitor of norepinephrine uptake devoid of affinity for receptors in rat-brain. J Pharmacol Exp Therap. 1982;222:61–65. [PubMed]
  • Yu AJ, Dayan P. Uncertainty, neuromodulation, and attention. Neuron. 2005;46:681–692. [PubMed]

Articles from Neuropsychopharmacology are provided here courtesy of Nature Publishing Group