The effects of methylphenidate on cerebral activations to salient stimuli in healthy adults

Abstract

Detection of a salient stimulus is critical to cognitive functioning. A stimulus is salient when it appears infrequently, carries high motivational value, and/or when it dictates changes in behavior. Individuals with neurological conditions that implicate altered catecholaminergic signaling, such as those with attention deficit hyperactivity disorder, are impaired in detecting salient stimuli, a deficit that can be remediated by catecholaminergic medications. However, the effects of these catecholaminergic agents on cerebral activities during saliency processing within the context of the stop signal task are not clear. Here, we examined the effects of a single oral dose (45 mg) of methylphenidate in 24 healthy adults performing the stop signal task during functional magnetic resonance imaging (fMRI). Compared to 92 demographically matched adults who did not receive any medications, the methylphenidate group showed higher activations in bilateral caudate head, primary motor cortex, and the right inferior parietal cortex during stop as compared to go trials (p<0.05, corrected for family-wise error of multiple comparisons). These results show that methylphenidate enhances saliency processing by promoting specific cerebral regional activities. These findings may suggest a neural basis for catecholaminergic treatment of attention disorders.

Introduction

We are drawn to salient stimuli when we navigate through a constantly changing world. Salient stimuli appear infrequently and/or demand change from a behavioral routine. By detecting and responding to salient stimuli, individuals learn from the outcome and enrich their cognitive repertoire. A number of behavioral paradigms are used to study saliency processing. For instance, in the Stroop task, an incongruent trial requires negotiation between conflicting responses as instructed by the color and word and is more salient, compared to a congruent trial. In the stop signal or go/nogo task, a stop/nogo signal is more salient compared to a go signal, because it instructs participants to refrain from a habitual response. Although the stop signal task is typically used to study cognitive control including response inhibition, the current study focused on the contrast between stop and go trials as an index of saliency response (Farr, Hu, Zhang & Li,2012; Hendrick, Ide, Luo & Li, 2010; Hendrick, Luo, Zhang & Li, 2011). Saliency processing activates frontal and parietal cortices as well as the thalamus and striatum (Farr et al., 2012; Ptak, 2012; Ptak & Schnider, 2010; Wardak, Ben Hamed, Olivier & Duhamel, 2012).

Catecholamines play a critical role in saliency processing and related cognitive functions. In humans, individuals with neurological or psychiatric disorders that involve altered catecholaminergic signaling demonstrate deficits in detecting salient stimuli (Maccari et al., 2012; Mannan, Hodgson, Husain & Kennard, 2008; Ortega, Lopez, Carrasco, Anllo-Vento & Aboitiz, 2012). For instance, attention deficit hyperactivity disorder or ADHD is characterized by decreased dopamine D₂/D₃ receptors (Jucaite, Fernell, Halldin, Forssberg & Farde, 2005; Volkow et al., 2009) and increased dopamine transporter density (Fusar-Poli, Rubia, Rossi, Sartori & Balottin, 2012), both of which are related to dampened dopaminergic neurotransmission. Numerous studies demonstrate that children and adults with ADHD are impaired in performance and neural responses in cognitive challenges that require processing of salient stimuli (Bezdjian, Baker, Lozano & Raine, 2009; Desman et al., 2006; Fallgatter et al., 2004; Johnstone & Clarke, 2009; Karch et al., 2010; Smith, Johnstone & Barry, 2004; Spronk, Jonkman & Kemner, 2008; Tamm, Menon, Ringel & Reiss, 2004). In a go/no-go task, Tamm et al. (2004) and Fallgater et al. (2004) observed decreased activation of the cingulate cortex and supplementary motor area to no-go as compared to go stimuli in ADHD patients. In other cognitive tasks patients with ADHD show more variable reaction times, increased errors, deficient response inhibition and posterror behavioral modification (Bezdjian et al., 2009; Desman et al., 2006; Gooch, Snowling & Hulme, 2012; Mulligan et al., 2011; Shiels, Tamm & Epstein, 2012). These deficits are corrected by pharmaceuticals that increase catecholamines (Aron, Dowson, Sahakian & Robbins, 2003; Broyd et al., 2005; Jonkman, van Melis, Kemner & Marcus, 2007; Scheres et al., 2003; Tannock, Schachar, Carr, Chajczyk & Logan, 1989). For example, a common treatment for ADHD, methylphenidate increases catecholamines in the prefrontal cortex and striatum through blockade of dopamine and norepinephrine transporters (Berridge et al., 2006; 2012; Devilbiss & Berridge, 2006; Spencer, Klein & Berridge, 2012), and improves cognitive performance on various tasks, including the stop signal, go/no-go, flanker, and Stroop tasks (Aron et al., 2003; Berman, Douglas & Barr, 1999; Broyd et al., 2005; Moeller et al., in press; Tomasi et al., 2011; Zang et al., 2005). In all of these cognitive tasks, the detection of a salient stimulus – a no-go, stop or other incongruent signal – is key to an efficacious performance.

Notably, there appear to be contrasting influences of catecholaminergic signaling on saliency processing, with studies showing both increased and decreased cerebral activations. For instance, some studies show increased brain activations with methylphenidate or levodopa (Dodds et al., 2008; Hershey et al., 2004; Rubia et al., 2011; Zang et al., 2005), while others show decreased activations with these same drugs (Costa et al., 2012; Onur et al., 2011; Pauls et al., 2012). An additional observation concerns the effects on response inhibition, as indexed by the stop signal reaction time (SSRT) in the stop signal task. Some studies showed improved (decreased) SSRT with increasing catecholamines (Bari et al., 2009; 2011; Chamberlain et al., 2006; 2009; de Wit et al., 2002; Nandam et al., 2011; 2012; Turner et al., 2003), while other studies showed no effect or a baseline dependent effect on SSRT with increased catecholamines (Costa et al., 2012; Eagle et al., 2007; Fillmore et al., 2005; Nandam et al., 2011; Pauls et al., 2012).

In this study, we sought to clarify this literature by characterizing the effects of methylphenidate on saliency processing in the stop signal task. We administered 45 mg of methylphenidate orally in healthy adults and compared the results to a large cohort of demographically matched healthy participants who did not receive methylphenidate. We employed a between-subject design in order to avoid potential training or test-retest effects on behavioral performance and cerebral responses (Chao, Luo, Chang & Li, 2009; Manuel, Bernasconi & Spierer, 2012). That is, the effects of methylphenidate were not contrasted with placebo but compared to a large sample of individuals who did not receive methylphenidate. Our specific aim is to describe the effects of methylphenidate on cerebral activations during saliency processing by contrasting stop and go trials. An additional goal is to characterize the effects of methylphenidate on stop signal reaction time.

Methods

The study was performed under protocols approved by the Yale Human Investigation and Yale MRI Safety Committees. Subjects were recruited from New Haven and surrounding areas by advertisement, word of mouth and referrals. Written informed consent was obtained from all participants after a full explanation of study procedures. Twenty-five healthy adults (17 females; age 25 ± 6 years; all right-handed) were recruited and compensated for their participation in the study after a phone screening of medical including psychiatric histories, current use of medications, and MR compatibility. On the morning of the scan, a physician conducted a more thorough in-person screening and review of medical and psychiatric history to confirm eligibility. All participants were admitted as outpatients to the Yale New Haven Hospital. All participants were without medical, neurological, or psychiatric conditions and denied history of head injury and current use of prescription medications or illicit substances. One subject was eliminated from the study because of a lesion found on the structural brain image. The resulting 24 participants comprised 16 females, with a mean age of 24 ± 4 years.

On the day of fMRI, participants completed a series of questionnaires, including the Barratt Impulsiveness Scale, version 11 (BIS-11). Afterwards, participants rested in a recovery room for at least ten minutes, during which baseline heart rate, blood pressure, and anxiety measurements were taken. An hour prior to fMRI scans a physician examined and approved participants to receive either a single 45 mg oral dose of methylphenidate or placebo (single-blinded). This dosage was chosen to follow previous studies of healthy and ADHD populations with methylphenidate, so the results could best be compared to this earlier body of work. Thus, although participants did not know whether they would be receiving methylphenidate or placebo, all participants in the methylphenidate group received methylphenidate. From this time until the beginning of the structural MRI scans (approximately forty minutes), heart rate and blood pressure as well as anxiety were monitored every five minutes. During the fMRI scans, these measures were taken approximately every ten minutes between sessions. At each vital sign reading, participants also marked how anxious they felt on a visual analog scale from one (not anxious at all) to ten (extremely anxious). Data of a cohort of 92 healthy participants (58 females; age 25 ± 4 years; Farr et al., 2012; Li et al., 2008; 2010) scanned earlier under identical imaging protocols except without being given methylphenidate were used for comparison. Because of an unbalanced sample size in this comparison, we also performed a follow-up analysis and compared the current 24 participants (who received methylphenidate) with a subgroup of 24 of the 92 participants (who did not receive any medications) that were both matched individually and as a group.

Behavioral task

We employed a simple reaction time task in this stop-signal paradigm (Logan, Cowan & Davis, 1984; Li, Miliojevic, Kemp, Hong & Sinha, 2006; Li, Yan, Sinha & Lee, 2008a; Li et al., 2008b). There were two trial types: “go” and “stop,” randomly intermixed. A small dot appeared on the screen to engage attention at the beginning of a go trial. After a randomized time interval (fore-period) between 1 and 5 s, the dot turned into a circle (the “go” signal), prompting the subject to quickly press a button. The circle vanished at a button press or after 1 s had elapsed, whichever came first, and the trial terminated. A premature button press prior to the appearance of the circle also terminated the trial. A premature response was not counted as a correct or incorrect response. Approximately three quarters of all trials were go trials. The remaining one quarter were stop trials. In a stop trial, an additional “X,” the “stop” signal, appeared after and replaced the go signal. The subjects were told to withhold their button press upon seeing the stop signal. The stop signal delay (SSD) – the time interval between the go and stop signal – started at 200 ms and varied from one stop trial to the next according to a staircase procedure, increasing and decreasing by 67 ms each after a successful or failed stop trial (Levitt, 1970; De Jong, Coles, Logan & Gratton, 1990). There was an inter-trial-interval of 2s. Subjects were instructed to respond to the go signal quickly while keeping in mind that a stop signal could come up in a small number of trials. In the scanner each subject completed four 10-minute runs of the task. Depending on the actual stimulus timing (trials varied in fore-period duration) and speed of response, the total number of trials varied slightly across subjects in an experiment. With the staircase procedure, we anticipated that the subjects would succeed in withholding their response in approximately half of the stop trials. The stop signal reaction time was computed by subtracting the critical stop signal delay, or the estimated SSD required for a subject to get half of stop trials correct, from the median go reaction time (Li et al., 2008a).

Imaging protocol

Conventional T1-weighted spin echo sagittal anatomical images were acquired for slice localization using a 3T scanner (Siemens Trio). Anatomical images of the functional slice locations were next obtained with spin echo imaging in the axial plane parallel to the AC-PC line with TR = 300 ms, TE = 2.5 ms, bandwidth = 300 Hz/pixel, flip angle = 60°, field of view = 220 × 220 mm, matrix = 256 × 256, 32 slices with slice thickness = 4mm and no gap. A single high-resolution T1-weighted gradient-echo scan was applied on each participant. One hundred and seventy-six slices parallel to the AC-PC line covering the whole brain were acquired with TR=2530ms, TE=3.66ms, bandwidth = 181 Hz/pixel, flip angle = 7°, field of view = 256×256 mm, matrix = 256×256, 1mm³ isotropic voxels. Functional, blood oxygenation level dependent (BOLD) signals were then acquired with a single-shot gradient echo echo-planar imaging (EPI) sequence. Thirty-two axial slices parallel to the AC-PC line covering the whole brain were acquired with repetition time = 2,000 ms, echo time = 25 ms, bandwidth = 2004 Hz/pixel, flip angle = 85°, field of view = 220 × 220 mm, matrix = 64 × 64, 32 slices with slice thickness = 4mm and no gap. Three hundred images were acquired in each run for a total of four runs.

Data analysis and statistics

Data were analyzed with Statistical Parametric Mapping version 8 (SPM8, Wellcome Department of Imaging Neuroscience, University College London, U.K.). Images from the first five TRs at the beginning of each trial were discarded to enable the signal to achieve steady-state equilibrium between radiofrequency pulsing and relaxation. Images of each individual subject were first corrected for slice timing and realigned (motion-corrected). A mean functional image volume was constructed for each subject for each run from the realigned image volumes. These mean images were co-registered with the high resolution structural image and then segmented for normalization to an MNI (Montreal Neurological Institute) EPI template with affine registration followed by nonlinear transformation (Friston, Frith, Frackowiak & Turner, 1995a; Ashburner & Friston, 1999). The normalization parameters determined for the mean functional volume were then applied to the corresponding functional image volumes for each subject. Finally, images were smoothed with a Gaussian kernel of 8 mm at Full Width at Half Maximum.

In the first general linear model (GLM), four main types of trial outcome were distinguished: go success (G), go error (F), stop success (SS), and stop error (SE) trial. Any stop trial, SS,SE, or a combined stop (S) trial involves incongruent goals between the prepotency to respond and the intention to withhold the response. S trials are also infrequent compared to go trials, and are thus highly salient. Thus, we interpreted the contrast of S>G as reflecting saliency processing. In addition, we examined other contrasts that reflect the component processes of cognitive control, including attention monitoring and response inhibition (SS>SE, Li et al., 2006; Duann, Ide, Luo & Li, 2009), error detection (SE>SS, Ide and Li, 2011a), and post-error slowing (pSi>pSni, where pSi and pSni each represented post-error go trials that increased and did not increase in reaction time; Li et al., 2008a; Ide and Li, 2011b).

A statistical analytical design was constructed for each individual subject, using the general linear model (GLM) with the onsets of go signal in each of these trial types convolved with a canonical hemodynamic response function (HRF) and with the temporal derivative of the canonical HRF and entered as regressors in the model (Friston et al., 1995b). Realignment parameters in all 6 dimensions were also entered in the model. The data were high-pass filtered (1/128 Hz cutoff) to remove low-frequency signal drifts. Serial autocorrelation of the time series violated the GLM assumption of the independence of the error term and was corrected by a first-degree autoregressive or AR(1) model (Friston et al., 2000). The GLM estimated the component of variance that could be explained by each of the regressors.

The con or contrast (difference in β) images of the first-level analysis were used for the second-level group statistics (random effects analysis; Penny and Holmes, 2004). Brain regions were identified using an atlas (Duvernoy, 1999). All templates are in Montreal Neurological Institute (MNI) space and voxel activations are presented in MNI coordinates.

To perform a balanced sample comparison, we created a mask from the two sample t-test results between the 24 MPH participants and the 92 no-MPH participants for stop as compared to go trials. We then used this mask to perform a small volume correction for a two sample t-test between the 24 MPH and 24 matched no-MPH participants.

Results

Behavioral performance and physiological response to methylphenidate

Behavioral performance in the SST is summarized in Table 1. Go trial reaction time (RT), coefficient of variation of go trial RT, stop success rate, stop signal reaction time and the effect size of post-error slowing was not different between groups. Participants who received methylphenidate showed a trend toward a higher go success rate (t(91,23)=2.17, p<0.035, ; alpha set at 0.01, considering a total of five performance parameters compared) when compared to participants who did not receive methylphenidate.

Table 1.

Demographics and behavioral performance during the stop signal task.

	number females	age in years	BIS score	Go%	Stop%	GoRT (ms)	SSRT (ms)	PES (z- score)
MPH (n=24)	16	24±4	56.5±7.3	99.2±1.5	52.4±3.0	648.0±84.4	225.7±32.7	1.53±1.25
no-MPH (n=92)	58	25±4	59.8±9.7	97.9±3.0	53.4±3.8	669.9±119.0	218.1±59.1	1.66±1.47
no-MPH (n=24)	16	24±4	59.0±9.5	97.4±4.1	51.5±2.1	652.9±130.2	222.6±40.2	1.81±1.43
MPH vs. 92 no-MPH	χ2=.1	t=1.22	t=1.51	t=2.17	t=1.17	t=.84	t=.60	t=.40
	p<.84	p<.22	p<0.13	p<.03	p<.24	p<.40	p<.55	p<.69
MPH vs. 24 no-MPH	χ2=0	t=0.00	t=1.02	t=2.09	t=1.01	t=.70	t=.29	t=.72
	p<1.0	p<1.0	p<0.31	p<.04	p<.32	p<.49	p<.77	p<.47

BIS= Barratt Impulsivity Scale; Go% refers to the percentage of go trials to which the subject responded. Stop% refers to the percentage of successful stop trials. GoRT= mean go reaction time across trials. SSRT= stop signal reaction time, calculated by subtracting the critical stop signal delay from the median GoRT. PES= post-error slowing.

Heart rate and blood pressures were continuously recorded until 140 minutes after the administration of methylphenidate (Supplementary Figure 1). Increases were found in both heart rate (HR) (7%±13% change from baseline; t(23) = 2.48, p < 0.010) and systolic blood pressure (SBP) (10±7% change from baseline; t(23) = 6.95, p < 0.0001), but not diastolic blood pressure after administration of methylphenidate. Additionally, subjects reported increased anxiety on a ten point visual analog scale with one and ten each indicating not and extremely anxious (40±52% change from baseline; t(23) = 3.39, p < 0.001). These results validated the previously found cardiovascular and psychological effects of methylphenidate (Li et al., 2010).

Cerebral activations to saliency processing

The contrast of S>G in MPH and no-MPH participants showed activations of a wide network of cortical and subcortical structures, including the pre-supplementary motor area (pre-SMA)/anterior cingulate cortex (ACC), insula, inferior parietal cortex, and dorsolateral prefrontal cortex (Figure 1).

Figure 1.

Brain activations during stop as compared to go (S>G) trials in 92 healthy subjects who did not receive methylphenidate (a; Farr et al., 2012) and 24 healthy subjects who received methylphenidate (b) at p < 0.05, FWE corrected (one-sample t-tests). BOLD contrasts are superimposed on a T1 structural image in axial sections from z=−10 to z=62. The adjacent sections are 4mm apart. The color bar represents voxel T value. Thus, higher T value in the no-MPH group may simply reflect its sample size rather than a higher magnitude of saliency related activity. Neurological orientation: R=right; L=left.

In a two sample t-test at p < 0.05, corrected for family-wise error (FWE) of multiple comparisons, the methylphenidate group showed greater activations in the bilateral caudate, primary motor cortex, and posterior insula, as well as the right inferior parietal cortex during stop as compared to go trials (Figure 2a; Table 2). We computed the effect size of saliency activation for these clusters – caudate, cerebellum, motor cortex, and inferior parietal cortex – to further illustrate the differences between the MPH and no-MPH groups (Figure 2b).

Figure 2.

(a) Brain activations during stop as compared to go (S>G) trials in the MPH group (N=24) vs. no-MPH group (N=92) at p < 0.05, (two-sample t-test, FEW corrected.) BOLD contrasts are superimposed on a T1 structural image in axial sections from z=–30 to z=62. The adjacent sections are 4mm apart. Clusters reflect greater saliency related activations in the medicated as compared to non-medicated group. The color bar represents voxel T value. (b) Histogram of effect sizes for no-MPH and MPH subjects in five regions of interest (circled in red and labeled on a).

Table 2.

Cerebral activation during stop as compared to go (S>G) trials in the MPH group (N=24) vs. no-MPH group (N=92) at p < 0.05, FWE.

		MNI Coordinates (mm)
Cluster Size (voxels)	Voxel Z Value	X	Y	Z	Side	Identified Region
171	6.1	15	11	13	R	caudate
	4.42	27	−1	7	R	putamen
251	5.57	−21	−64	−26	L	cerebellum
307	5.5	60	−7	28	R	precentral G
	4.97	39	−7	13	R	insula
100	5.44	−45	−7	−20	L	middle temporal G
	5.09	−33	−22	1	L	superior temporal G
52	5.2	−18	−1	16	L	caudate
26	5.2	39	−7	−17	R	middle temporal G
232	5.19	−54	−10	19	L	precentral G
33	5.15	−21	−31	−14	L	parahippocampal G
33	5.05	54	−49	19	R	superior temporal G
19	5.02	36	−49	61	R	inferior parietal G
13	4.9	9	29	37	R/L	medial frontal G

Statistical threshold: p<0.05, FWE; extent, 10 voxels. G, Gyrus; S, Sulcus; L, left; R, right.

We further confirmed these findings in a smaller group of 24 matched healthy participants versus the 24 MPH participants with small volume correction for a mask of the activations (Figure 2a). The results confirmed the differences in the temporal lobe, thalamus, caudate, insula, motor cortices, inferior parietal cortex, and cerebellum (p<.05, FWE corrected; Table 3).

Table 3.

Results of the small volume correction for a mask of the activations in Figure 2a on the two sample t-test comparing the 24 MPH with 24 matched no-MPH participants during stop as compared to go trials.

		MNI Coordinates (mm)
Cluster Size (voxels)	Voxel Z Value	X	Y	Z	Side	Identified Region
33	4.82	−21	−31	−14	L	parahippocampal G
164	4.68	21	−4	19	R	thalamus
	4.67	24	−1	16	R	insula
	4.2	21	17	4	R	caudate
47	4.49	−18	−4	16	L	caudate
240	4.34	−30	−61	−32	L	cerebellum
91	4.26	−36	−16	−14	L	parahippocampal G
	3.9	−45	−7	−20	L	subtemporal G
	3.66	−27	−16	4	L	putamen
292	3.72	54	−1	25	R	inferior frontal G
	3.63	63	−7	31	R	precentral G
	3.42	30	−10	13	R	insula
24	3.56	36	−7	−17	R	parahippocampal G

Statistical threshold: p<0.05, FWE; extent, 10 voxels. G, Gyrus; S, Sulcus; L, left; R, right.

Cerebral activations to the component processes of cognitive control

We compared the methylphenidate and no-methylphenidate groups in other contrasts of cognitive control, including attentional monitoring/response inhibition (SS>SE), error processing (SE>SS), and post-error slowing (pSi>pSni, see Methods), none of which showed significant differences at a corrected threshold.

Discussion

Methylphenidate increases striatal and cortical activations to a salient stimulus

In this study, we manipulated catecholamine levels with the administration of methylphenidate in healthy individuals during the stop signal task. There were no notable changes in task performance, including the stop signal reaction time. However, participants who received methylphenidate showed higher brain activations during saliency processing in striatal and cortical regions including the caudate nuclei, primary motor cortices, inferior parietal cortex, and the cerebellum as compared to those who did not. These findings on saliency processing are consistent with contextual dependence of the effects of methylphenidate in releasing catecholamines (Volkow et al., 2004).

One of these areas, the caudate nucleus, receives direct and heavy dopaminergic projections from the midbrain (Altar & Hausar, 1987; Ohno, Sasa & Takatori, 1985; 1987; Wang, Moriwaki, Wang, Uhl & Pickel, 1997) and is widely implicated in processing salient stimuli (Crofts et al., 2001; Flagel et al., 2011; Zink, Pagnoni, Martin, Dhamala & Berns, 2003). Zink and colleagues (2003) used an attention task to investigate saliency processing independent of reward in healthy humans and showed increased caudate activity to salient events only when the distracters required a behavioral response. In an attentional shift task, dopaminergic depletion of the caudate through 6-hydroxydopamine infusions resulted in reduced distraction from task-irrelevant stimuli in monkeys (Crofts et al., 2001). Lesions of the caudate nucleus in humans disrupt attention and the extent of the lesion corresponds to the severity of cognitive impairments (Benke, Delazer, Bartha & Auer, 2003). In rats, a food cue induced c-fos mRNA expression in the caudate only when rats attributed salience to it during classical conditioning (Flagel et al., 2011). Additionally, genetically altered mice with reduced striatal dopamine have impairments in responding to novel objects which can be corrected with methylphenidate or levodopa (Brown et al., 2010). Together, these findings support a catecholamine-mediated attentional mechanism of the caudate nucleus.

The caudate nucleus in particular has been implicated in the pathogenesis of ADHD in both morphological and functional studies (Hynd et al., 1993; Castellanos et al., 1994; Volkow et al., 2007; Igual et al., 2012). Children with ADHD have small volumes of the caudate nucleus which seems to normalize by puberty (Carrey et al., 2012). Caudate nucleus was also found to be underactivated during the interference condition in an oddball task in patients with ADHD (Rubia et al., 2011a). Our current result that methylphenidate increases activation of the caudate nucleus during saliency processing parallels findings of increased caudate activation in a stop signal (Rubia, Cubillo, Woolley, Brammer & Smith, 2011b) and a rewarded continuous performance task (Rubia et al., 2009) with administration of methylphenidate versus a placebo in children with ADHD. The result is also broadly consistent with normalized activations of the right caudate in attention-related tasks in meta-analysis of long-term stimulant medication use in ADHD (Hart, Radua, Nakao, Mataix-Cols & Rubia, 2012).

As part of the ventral attention system, the inferior parietal cortex (IPC) responds to detection of a target stimulus (Bunge, Hazeltine, Scanlon, Rosen & Gabrieli, 2002; Corbetta & Shulman, 1998). The IPC showed underactivation during interference processing in a Simon task in patients with ADHD (Rubia et al., 2011a). The IPC increased activations with methylphenidate in ADHD participants during stop errors as compared to go trials in the stop signal task and during non-rewarded as compared to non-target trials in the continuous performance task (Rubia et al., 2009; 2011b) as well as in healthy participants during nonswitch errors as compared to correct trials in a reversal learning task (Dodds et al., 2008), all of which involve saliency processing. Positron emission tomography imaging showed significant reductions in cerebral activity in the IPC after methylphenidate administration in healthy participants, suggesting that methylphenidate causes significant changes in available dopamine in this brain area (Udo de Haes, Maguire, Jager, Paans & den Boer, 2007). Taken altogether, these results suggest that increased catecholamines in the IPC caused by methylphenidate may confer enhanced attention-related activation during the stop signal task.

Primary motor cortical activity is influenced by dopaminergic signaling (Ge et al., 2012; Hosp, Pekanovic, Rioult-Pedotti & Luft, 2011; Ostock et al., 2011). Dopaminergic projection to the primary motor cortex is necessary for motor learning; lesioning of the ventral tegmental area in rats does not inhibit previously learned motor skills, but prevents the learning of new motor tasks (Hosp et al., 2011). Administration of a moderate dose of levodopa in humans promotes plasticity of the primary motor cortex as monitored by evoked electric potentials (Monte-Silva, Liebetanz, Grundey, Paulus & Nitsche, 2010). Thus, the effects of methylphenidate on higher primary motor cortical activity are broadly consistent with these earlier results.

The cerebellum is critical to motor coordination (Manto & Oulad Ben Taib, 2013) and may compensate for the loss of basal ganglia inputs to cortex in Parkinson’s disease (Martinu & Monchi, 2012). Lesions of the cerebellum caused upregulation of dopamine D₁ receptors in the basal ganglia and electrical stimulation of the cerebellum affects dopaminergic signaling in the midbrain and striatum (Dempsey & Richardson, 1987; Nieoullon & Dusticier, 1980), suggesting a dopaminergic process in this cerebellar mechanism. Recent research also indicates an important role of the cerebellum in cognitive functioning (Koziol, Budding & Chidekel, 2012; Leiner, Leiner & Dow, 1986; Stoodley, 2012), such as “training” frontal cortices in anticipating behavioral outcomes (Koziol et al., 2012) and facilitating prefrontal cortical processes in decision-making (Cisek & Kalaska, 2005), processes that involve catecholamines and saliency processing (Hershey et al., 2004; Kelly et al., 2009; Rogers et al., 2011).

Methylphenidate did not appear to alter stop signal reaction time

We did not observe any notable changes in task performance, including stop signal reaction time, consistent with many previous studies (Bari, Eagle, Mar, Robinson & Robbins, 2011; Costa et al., 2012; Eagle et al., 2011; Fillmore, Rush & Hays, 2005; Hamidovic, Kang & de Wit, 2008; Hershey et al., 2004; Kratz et al., 2009; Pauls et al., 2012), but at odds with others (Bari et al., 2009; Chamberlain et al., 2006; 2009; de Wit, Enggasser & Richards, 2002; Nandam et al., 2011; Li et al., 2010; Turner et al., 2003). It may be that the healthy participants in our study are already performing optimally and do not provide room for improvement with methylphenidate. For instance, in a stop signal task, methylphenidate decreased go reaction times in rats and showed a baseline-dependent effect on response inhibition, improving inhibitory control in slow but not fast responders (Eagle, Tufft, Goodchild & Robbins, 2007). This consideration may also account for the differences between healthy and clinical populations.

Catecholamines and saliency processing: other pre-clinical and clinical studies

In rodents, microinfusion of a dopamine agonist in the medial prefrontal cortex enhanced the salience of normally nonsalient stimuli in a fear conditioning task (Lauzon, Bishop & Laviolette, 2009). Selective depletion of norepinephrine in the mouse prefrontal cortex abolished saliency related signaling in the nucleus accumbens as tested by conditioned place preference to both rewarding and aversive stimuli (Ventura, Morrone & Puglisi-Allegra, 2007). In humans, neurological conditions other than ADHD also involve deficits in saliency processing. For instance, patients with Parkinson’s disease (PD) are impaired in visual search for salient but not non-salient targets among distractors (Cormack, Gray, Ballard & Tovee, 2004; Horowitz, Choi, Horvitz, Cote & Mangels, 2006; Lubow, Dressler & Kaplan, 1999; Mannan et al., 2008). While age-matched individuals without PD benefit from the saliency of target stimulus, PD patients demonstrate similar reaction times identifying salient and non-salient target among distractors. Dopaminergic agents remediate this deficit by facilitating cognitive and emotive processing of salient stimuli in PD (Goerendt, Lawrence & Brooks, 2004; Nagy et al., 2012; Subramanian, Hindle, Jackson & Linden, 2010). Thus, the current findings may further our understanding of attention deficits in clinical conditions other than ADHD (Arnsten, 2006; Arnsten & Rubia, 2012).

Conclusions and limitations of the study

In summary, methylphenidate enhanced saliency processing during the stop signal task in healthy adult individuals. More research to understand the functional implications of methylphenidate-evoked striatal cortical activations during saliency processing could elucidate the neural bases of catecholaminergic treatment of attention disorders.

There are a few important limitations to this study. First and most significantly, we did not have a placebo control for the individuals who received methylphenidate. The placebo effect is thus a potential confound for the differences that we observed between the methylphenidate and no-methylphenidate group. Thus, although previous studies suggested that the effects of methylphenidate on cerebral activations can be distinguished from placebo in a number of different behavioral paradigms (Marquand et al., 2011; Volkow et al., 2006), the current results need to be replicated in a placebo-controlled study. Second, the contrast of stop versus go trials may involve motor response inhibition, although participants were successful only half of the time. It could not be ruled out that the observed neural changes were related to response inhibition but not captured by stop signal reaction time. On the other hand, while the stop signal task (SST) is mostly used to examine the psychological constructs and neural processes of cognitive control, including response inhibition (Duann et al., 2009; Ide et al., 2013; Li et al., 2006), it was known to involve a saliency, infrequency, or odd-ball effect. In fact, many imaging studies of the SST have attempted to account for this saliency effect in identifying the component processes of cognitive control. For instance, Chikazoe and colleagues included infrequent go trials, in addition to frequent go and infrequent nogo trials, in a behavioral task, in order to isolate neural surrogates independent of a saliency response (Chikazoe et al., 2009). Similarly, to disambiguate the role of the right inferior frontal cortex in the SST, Hampshire and colleagues introduced a stop trial to which participants did not need to respond (by withholding the button press) and showed that the right inferior frontal cortex is recruited when a salient cue is detected (Hampshire et al., 2010). Thus, saliency processing is intrinsic to the SST. Third, methylphenidate influences both dopaminergic and noradrenergic neurotransmission. While there is heavy dopaminergic innervation of the caudate nucleus, the cortical mantle receives both dopaminergic and noradrenergic inputs. Thus, it remains to be determined whether and how blockade of dopaminergic and/or noradrenergic transporters by methylphenidate accounts for the current findings. Fourth, the methylphenidate group represents a small sample, which limits the power to detecting changes in behavioral and neural measures of the component processes of cognitive control. Thus, although we did not observe any effects on response inhibition, error processing, and post-error slowing, these negative results need to be confirmed with larger samples in future work. In addition, the effects of methylphenidate may depend on individual characteristics such as impulsivity, which cannot be adequately addressed in a small sample with limited heterogeneity. Fifth, we did not use any standardized measure to screen the psychiatric status of the healthy participants. Finally, this study involved only healthy adult participants. Thus, the implications of the current results cannot be generalized to patient populations including ADHD or Parkinson’s disease.