The Effect of Methylphenidate on the Frontal-Striatal Activation Network during Response Inhibition in Children and Adults with ADHD
Karen Kuckelkorn
Date: 26/08/2017
Student number: 10336907 Course: Master thesis University of Amsterdam
Docent: Hyke Tamminga (PhD), Michelle Solleveld (MSc) Number of words: 6554
Abstract
Response inhibition is considered as one of the core cognitive deficits in Attention-Deficit/Hyperactivity Disorder (ADHD). ADHD is often treated with the stimulant medication methylphenidate. However, research to pinpoint the acute effect of
methylphenidate on neuronal mechanisms underlying response inhibition lacks. Therefore, the primary aim of this study is to investigate the acute effect of MPH on the frontal-striatal activation network during response inhibition in children and adults with ADHD. In a second research question this study assesses whether there is an acute age-related effect of MPH on the frontal-striatal activation network during response inhibition. A Go/No-Go task was used to measure response inhibition. Stimulant treatment-naive children and adult males diagnosed with ADHD executed this task in two consecutive fMRI sessions, off and on-medication. Since functional connectivity was not found between the frontal-striatal activation network, it cannot be confirmed that MPH has an enhancing effect on frontal-striatal activation during response inhibition. Findings also revealed that there was no acute age-related effect of MPH on the frontal-striatal activation network during response inhibition. Potential explanations for the unexpected findings concern the chosen regions of interest (ROIs) and the deficit in response inhibition. These and other alternative explanations are discussed.
Table of contents Introduction p. 4 Methods p. 10 Data analysis p. 13 Results p. 16 Discussion p. 22 Reference list p. 27 Appendix p. 35
Introduction
ADHD is a developmental disorder marked by inattention, hyperactivity and/or impulsivity (Scheres, Milham, Knutson, & Castellanos, 2007) with a prevalence of 2.8% in adults (Fayyad et al., 2017) and a prevalence of 7.2% in children (Thomas, Sanders, Doust, Beller, & Glasziou, 2015). In adolescence ADHD has a female to male ratio of about 1:3 (Willcutt, 2012). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V, American Psychiatric Association, 2013), ADHD can be divided into the following three subtypes: inattentive type, hyperactive/impulsive type and the combined type (Roberts, Martel, & Nigg, 2017). Symptoms in ADHD often derive from a deficit in executive
functioning (Barkley, 2010; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005), which might contribute to profound implications on interpersonal relationships and academic performance (Roberts et al., 2017). The deficit in executive functioning can be described as a set of appropriate problem-solving cognitive processes to achieve a goal, such as response inhibition (Willcutt et al., 2005).
According to different neuropsychological theories, the deficit in response inhibition is a crucial part of the executive function deficit in ADHD (Barkley, 2010). For instance, the triple pathway theory, which gives an explanation for the heterogeneity of ADHD, included the deficit in response inhibition as one of the main cognitive problems contributing to ADHD (Sonuga-Barke, Bitsakou, & Thompson, 2010). Response inhibition, also termed motor inhibition or inhibitory control of actions, is defined as the suppression of inadequate, dominant and ongoing response tendencies (Morein-Zamir et al., 2014). It can be measured with either the Stop Signal task or the Go/No-Go task, in which participants need to respond as quickly as possible to a Go-signal and need to avoid responding to a No-Go Signal, which is referred to the stopping process (Rooij et al., 2015). The correct stopping process can be considered as successful response inhibition. Compared to unaffected siblings and healthy
Signal task (Alderson, Rapport, & Kofler, 2007; Van Rooij et al., 2015), which supports the theory of a deficit in response inhibition in ADHD.
Response inhibition seems to be related to neural activation of the frontal-striatal activation network (Dambacher et al., 2014; Duann, Ide, Luo, & Li, 2009; Van Rooij et al., 2015). It involves the following brain regions: inferior frontal gyrus (IFG), the supplementary motor area (SMA), basal ganglia and the suprathalamic nucleus (Rooij et al., 2015; Rubia et al., 2011). During response inhibition the SMA, especially the pre-supplementary motor area (pre-SMA), and the basal ganglia are involved in the execution of the stopping process (Chevrier, Noseworthy, & Schachar, 2007; Zandbelt, Bloemendaal, Neggers, Kahn, & Vink, 2013) whereas the IFG is thought to play a key role in the initiation of the stopping process in response inhibition (Aron et al., 2007; Chevrier et al., 2007; Rooij et al., 2015). This can be supported by research in healthy participants, which showed that inferior frontal activation patterns are important to response inhibition (Chevrier et al., 2007; Dambacher et al., 2014). Hence, these regions seem to be important brain areas involved in response inhibition.
Research on neural correlates in ADHD showed that the deficit in response inhibition is related to a deviant involvement of these frontal-striatal regions (Durston et al., 2003; Rubia et al., 2011; Scheres et al., 2007). Firstly, it has been found that a stronger connectivity within these regions was associated with decreased ADHD severity, supporting the assumption of a deviant involvement of these regions (Van Rooij et al., 2015). Secondly, a hypoactivation of these regions has been found during response inhibition in ADHD (Cubillo et al., 2010; Scheres et al., 2007). Early studies reported reduced activation of the striatum, a part of the basal ganglia, during response inhibition in ADHD (Cubillo et al., 2010; Scheres et al., 2007). Most studies on ADHD also focussed on the IFG and the SMA because hypoactivation of these regions has been specifically linked to successful response inhibition (Morein-Zamir et al., 2014; Rubia et al., 2011; Rubia et al., 2014). In the present study, it is important to focus
on regions, which are specific to the stopping process in response inhibition such as the striatum, the IFG and the SMA.
It can be suggested that the deviant involvement of frontal-striatal regions is a result of reduced striatal dopamine (DA) availability in ADHD (Spencer et al., 2007; Krause, 2008), which probably accounts for cognitive and behavioural deficits in ADHD. This difference in the dopaminergic system has been underpinned by neuroimaging studies, which found
increased striatal DA transporters (DAT) in the right caudate of drug naive adults with ADHD and in the basal ganglia of children with ADHD compared to healthy controls (Cheon et al., 2003; Spencer et al., 2007). In these studies, DAT binding ratio was investigated through positron emission tomography (PET) and single-photon emission tomography (SPET). Due to the fact that DAT is the main regulator of DA, previous studies pointed out that the increased striatal DAT levels might explain the reduced striatal DA availability in patients with ADHD (Krause, 2008; Schweri et al., 1985; Spencer et al., 2007).
To increase extracellular DA levels and to reduce ADHD symptoms, methylphenidate (MPH) is prescribed as a first choice pharmacological treatment for ADHD (MTA Group, 1999; Volkow et al., 2012). MPH is a catecholamine reuptake inhibitor, which increases extracellular DA levels by blocking the DA transporter in the basal ganglia (Arnsten, 2006; Krause, Dresel, Krause, Kung, & Tatsch, 2000; Krause, 2008). It also leads to increased concentrations of norepinephrine en dopamine in frontal regions (Arnsten, 2006; Rubia et al., 2009). Support for the enhancing effect of MPH on the dopaminergic system can also be revealed from a PET study, which assessed the binding rate to the D2/D3 DA receptor (Volkow et al., 2012). Here, they found reduced D2/D3 DA receptor availability in adults with ADHD after MPH treatment, which may signify increased DA levels in the striatum and in cortical regions. Additionally, MPH seems to improve response inhibition in both adults and children with ADHD (Aron, Dowson, Sahakian & Robbins, 2003; Coghill et al., 2014;
DeVito et al., 2009), which is probably related to its enhancing effect on the frontal-striatal activation network.
In general, a limitation often seen in the aforementioned studies is, that they lack to study functional interactions between multiple brain regions contributing to a specific symptom (Konrad & Eickhoff, 2010), such as response inhibition, when investigating the effects of MPH in ADHD. The assumption for an involvement of multiple regions in response inhibition can be supported by the cell assembly theory, which claims that cells are
co-activated throughout the whole cortex by strengthened synapses, leading to a wired activity, which is in turn linked to cognitive operations (Lansner, 2009). According to this theory, brain activity of multiple regions is involved in response inhibition, instead of local activity only (Fingelkurts, Fingelkurts, & Kähkönen, 2005). It is therefore necessary to study functional connectivity of involved regions instead of focussing on different regions separately. Since little is known about the effect of MPH on response inhibition and its underlying frontal-striatal activation network, the present study will investigate whether a single dose MPH influences the frontal-striatal activation network in adults and children with ADHD during response inhibition.
Considering the disparities of the developing brain of children and the matured adult brain, it is also important to focus on age-related effects of MPH on ADHD (Schrantee et al., 2016). Previous research on healthy participants found that frontal-striatal activation is less engaged in response inhibition in young participants compared to adults (Rubia et al., 2006; Rubia, Smith, Taylor, & Brammer, 2007; Stevens et al., 2007). Focussing on structural brain
developmental mechanisms, such as myelination, it needs to be emphasised that myelination in frontal lobes continues into adulthood (Konrad & Eickhoff, 2010), which leads to the assumption that signal transduction is slower in children than in adults due to the higher myelination forming in adults. This might be a potential explanation for the increased
frontal-Age-related disparities might lead to different effects of MPH between adults and children with ADHD, which is probably associated with age-related differences in the dopaminergic system. Previous research on the dopaminergic system of typically developing brains found that dopamine (D1/D2) receptor density in the striatum and D1 receptor density in the prefrontal cortex rises in childhood and declines after adolescence (MacDonald, Karlsson, Rieckmann, Nyberg, & Bäckman, 2012; Seeman et al., 1987). The loss of these receptors leads to a decreased DA binding rate, which probably results in stronger acute effects of MPH in children with ADHD compared to adults with ADHD. For example, preclinical research found that frontal regions of juvenile rats are highly sensitive to MPH compared to adult rats (Urban, Waterhouse, & Gao, 2012). However, human ADHD studies concentrating on the acute related effect of MPH are often inconsistent. One study found no significant age-related differences in cerebral blood flow (CBF) after a single dose of MPH whereas other studies elucidate altered CBF in adults with ADHD after MPH and increased CBF in children with ADHD (Kim, Lee, Cho, & Lee, 2001; O`Gorman et al., 2008; Schrantee et al., 2017; Schweitzer et al., 2003). Although support for the assumption that brain regions undergoing active developmental processes are more affected by drug exposure than regions, which already entered the matured stage, is slowly emerging (Andersen & Navalta, 2004), it remains to be elucidated whether there is an acute age-related effect of MPH. For the present study it is therefore important to make a comparison between the developing brain and the matured brain when considering the acute effect of MPH on the frontal-striatal activation network.
Considering the increasing number of prescribed MPH, and the poor knowledge about acute age-related effects of MPH, additional research on this effect is strongly needed (Andersen & Navalta, 2004). Early studies especially focussed on long-term effects of MPH (Schrantee et al., 2016). However, to better understand these long-term effects of MPH, it is necessary to gain more insights about the acute effects of MPH. Therefore, the aim of the
present study is to investigate the effect of MPH on the frontal-striatal activation network during response inhibition in children and adults with ADHD.
In the present study, a Go/No-Go task (Durston et al., 2003) was used to measure response inhibition. Stimulant treatment-naive children and adult males diagnosed with ADHD
executed this task in two consecutive sessions, off-medication (session 1) and on-medication (session 2). During both sessions task-related functional magnetic resonance imaging (fMRI) was conducted. Through blood oxygen-level-dependent (BOLD) contrast imaging, fMRI can detect temporal patterns of neural activity (Aguirre, Zarahn, & D'esposito, 1998). Successful response inhibition was studied on correct scores on the No-Go trials.
The primary aim of this study is to investigate whether the frontal-striatal activation network is influenced by a single dose MPH during response inhibition. To test this research question a functional connectivity analysis of regions of interest (ROIs) was assessed. The striatum, the IFG and the SMA were chosen as regions of interest of the frontal-striatal activation network. Based on previous research it can be hypothesized that functional
connectivity between frontal-striatal regions will be enhanced by a single dose of MPH during response inhibition. We expect a higher functional connectivity between the SMA and the striatum as well as between the IFG and the striatum in session 2, compared to session 1. In a second research question it is assessed whether there is an age-related acute effect of MPH on the frontal-striatal activation network during response inhibition in ADHD. In session 1, a higher functional connectivity between the striatum and the IFG and between the striatum and the SMA in adults with ADHD compared to children with ADHD, is expected. Based on the finding that brain activation in frontal-striatal regions increases with age, it is expected that an acute challenge of MPH leads to a higher increase of functional connectivity between the frontal-striatal activation network in children compared to adults with ADHD. Therefore, a higher increase in functional connectivity between the SMA and the striatum as well as
between the IFG and the striatum in children compared to adults, by comparing session 2 with session 1, is expected.
In the present study it is also important to determine whether there is an actual deficit in response inhibition. This will be done by comparing participants with ADHD with healthy controls on their performance on the Go/No-Go task. Based on previous research, it is expected that healthy controls perform better on response inhibition, compared to children and adults with ADHD. Therefore, higher scores in successful No-Go trials in healthy controls compared to children and adults with ADHD are expected.
Methods Participants
Data used in this study is obtained from the ePOD1.0 trial (Bottelier, et al., 2014). In total, 50 stimulant treatment-naive male children (10-12 years) with ADHD, and 49 stimulant treatment-naive male adults (23-40 years) with ADHD participated in the ePOD trial
(Bottelier, et al., 2014). All participants were recruited from clinical programs at De Bascule Academic Center for Child and Adolescent Psychiatry (Amsterdam), the department of Child and Adolescent Psychiatry Centre Triversum (Alkmaar) and PsyQ mental health facility (The Hague). Participants were diagnosed with ADHD (all subtypes) as defined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV, American Psychiatric Association 1994) by an experienced psychiatrist. A structured interview was used to confirm diagnosis in children (Diagnostic Interview Schedule for Children fourth edition, DISC-IV; Ferdinand et al., 1998) and in adults (DIVA; Kooij & Francken, 2010). Prior to participation, participants were screened for any contraindications for MRI (e.g. metal containing pieces implanted in their body as pacemakers or insulin pumps and patients having a permanent dental brace). All patients were in need of pharmacotherapy with MPH according to existing
guidelines and received financial compensation (100 euros) for their participation. Beforehand, participants gave their written informed consent and, in case of minors,
additionally their legal guardians gave their written informed consent. Exclusion criteria were a history of major neurological or medical disease and a history or indication for medical treatment for a Comorbid Axis I psychiatric disorder. Patients with ADHD with a history of drug treatment starting before the age of 23 and affecting the dopaminergic system, were also excluded. This included drugs such as stimulants, neuroleptics, antipsychotics and D2/D3 agonists. Participants were also screened for any contraindications to MPH treatment such as cardiovascular diseases (hypertension), hyperthyroidism, suicidality, psychosis, Tourette disorder, glaucoma and arrhythmia and were excluded if affected. Participants with an estimated IQ lower than 80 were excluded from study entry (based on subtests (block design, vocabulary) of the Wechsler Intelligence Scale for children-Revised (WISC-III NL; Wechsler, 1981) and National Adult Reading Test (NART; Nelson, 1991; Dutch translation Schmand, Bakker, Saan, & Louman, 1991)). Central committee on Human Research in the Netherlands (CCMO) and the institutional review board of the Academic Medical Center approved this study.
Trial design
In this study, we used an adaptation of the Go/No-Go task, in which Pokémon cartoon series were displayed on the screen (Fig. 1). Participants were instructed to press a button in response to presented Pokémon cartoons (Go trials) and were instructed to avoid pressing the button when the Pokémon cartoon “Meowth” was displayed on the screen (No-Go trials). In each session, three runs, each lasting 3 minutes and 56 seconds, were used. Each run
consisted of a total of 57 trials, with 75% of Go trails and 25% of No-Go trials. In total, all three runs contained 171 trials. This resulted in a total of 42 No-Go trials and 129 Go trials. The sequence of presentation of the different types of No-Go trials was pseudorandomized for
each run. To avoid a learning effect of the pattern, No-Go trials after 2 or 4 preceding Go trials were included in all runs, but were not included in the statistical analysis. Each trial had a duration of 4000 ms, with a stimulus duration of 500 ms and an interstimulus interval of 3500 ms. Based on previous behavioural studies, the Go/No-Go task has strong test-retest reliability and modest validity (Langenecker, Zubieta, Young, Akil, & Nielson, 2007).
Figure 1. Trial design of the Go/No-Go task: “Meowth” was displayed for the No-Go trials; participants were instructed to avoid pressing the button when “Meowth” was displayed. This figure is adapted from Durston et al., 2002.
Procedure
This study took place in the department of Radiology at the Academic Medical Center (AMC, Amsterdam). Beforehand, participants gave their written informed consent and, in case of minors, additionally their legal guardians gave their written informed consent.
Preceding to the MRI scans, participants underwent a practice of the Go/No-Go task, to make sure that they understood the task correctly. In a first session the Go/No-Go task was
conducted during the MRI scan without administration of MPH, followed by an oral MPH challenge (0.5 mg/kg, children max. 20 mg; adults max 40 mg) and a 90 minute interval, after which they underwent a second MRI scan. Maximum plasma serum concentrations are reached around 90 to 120 minutes after an acute challenge of MPH (Volkow & Swanson,
2003). Hence, measuring the therapeutic effects of MPH should be done 90 minutes after administration. In the post-MPH fMRI scan participants again executed the Go/No-Go task. Scan Acquisition
A 3.0 T Philips Achieva MR scanner (Philips Medical Systems, Best, Netherlands) in the AMC was used in the present study. For the T1-weighted anatomical brain scan, which lasted 4.37 minutes, the following scan parameters were used: TR=9.8 ms, TE=4.6ms, voxel size= 0.875 x 0.875 x 1.2mm. The fMRI scan during the Go/No-Go task lasted 10.53 minutes. Here, scan parameters were: TR=2300 ms; TE=30 ms; slice thickness of 3mm; voxel size= 2.29 x 2.29 x 3mm. To acquire Blood Oxygenation Level Dependent (BOLD), a gradient echo fast single shot echo planar image (GE-EPI) was included.
Data analysis
For the data analysis, we conducted a psycho-physiological interaction (PPI)
connectivity analysis. PPI analysis is defined as a functional brain imaging method, which has been used to detect activity of brain regions, depending on the interaction between a
psychological state (in this case the Go/No-Go task) and a physiological state (the extracted time course of the region of interest) (Kim & Horwitz, 2008; Rooij et al., 2015). For the psychological state, magnetic resonance image onset times of unsuccessful and successful Go and No-Go trials were extracted from E-Prime data files (E-Prime 2.0, Psychology Software Tools, Pittsburgh, PA) and were processed using Unix. For the physiological state, three regions of interest (ROIs) have been defined as seed regions for task-dependent resting state connectivity analysis. These regions, derived from previous research, are the IFG (Fig. 2), the striatum (Fig. 3) and the SMA (Fig. 4). MNI152 standard-space T1-weighted average
structural template image was used as a template brain image and the Harvard-Oxford cortical and subcortical structural atlases were used to define regions. Pre-processing and motion correction was done using FSL (Version 6, FMRIB's Software Library,
www.fmrib.ox.ac.uk/fsl) (Smith et al., 2004). In a first step, by using FSL, Feat FMRI analysis was conducted as a first level analysis. Here, we used the striatum as a ROI. All further analyses were therefore based on a first level analysis with the striatum as the ROI. Voxels with a similar time course to the region of interest (striatum) output a positive correlation as a function of task condition (Van Rooij et al., 2015). In a next step, by using Featquerry in FSL, functional connectivity of the three ROIs was calculated. Here, we chose to analyse connectivity between striatum and IFG and between striatum and SMA. Linear Pearson correlation coefficients between time series for each ROI were computed followed by the transformation of the correlation coefficients into Fishers z-scores. This output gave the test significant connections at subject level. In this analysis, we chose the successful No-Go trials as the task condition, which correspond with a successful response inhibition. Four variables, two for session 1 and two for session 2, comprising z-scores of functional
connectivity between the striatum and the SMA and between the striatum and IFG, were the output.
Figure 2. Inferior Frontal Gyrus (IFG) mask; IFG labelled in red; a region comprised of the pars opercularis (Brodmann´s area 44) and the pars triangularis (Brodmann´s area 45);
involved in the initiation of the stopping process of an action (Van Rooij et al., 2015); Images are shown in an axial view, coronal view and a sagittal view).
Figure 3. Striatum mask; striatum labelled in blue; a region comprised of nucleus caudate, putamen and globus pallidus; the striatum is part of the basal ganglia; execution of the stopping process in response inhibition (Rubia et al., 2011; Van Rooij et al., 2015).
Figure 4. Supplementary Motor Area (SMA) mask; SMA labelled in yellow; action execution of the stopping process in response inhibition (Rubia et al., 2011; Van Rooij et al., 2015).
By using IBM Statistics SPSS 22, analyses on group level were done. For the first research question, which studied whether the frontal-striatal activation network is influenced by a single dose of MPH in response inhibition, Wilcoxon signed-rank test was used as a non-parametric test. This test was used because our data was not significantly normal distributed. In case of normally distributed data a paired t-test would have been used.
For the data analysis of the second research question, which studied whether there is an age-related effect of MPH on the frontal-striatal activation network during response inhibition in ADHD, a mixed design analysis of variance (ANOVA) (GLM) was used on the
transformed z-scores (one independent variable, using different groups and different times). Age was the independent variable and participants were split into two groups, one group consisting of adults with ADHD and one consisting of children with ADHD. A comparison according to age has been made, between and within the sessions. Due to the violation of the assumption of sphericity, the Greenhouse-Geisser tests were used to report findings of the mixed design ANOVA.
To compare performance on the Go/No-Go task between healthy controls and
participants with ADHD, an independent sample t-test was used for session 1. To control for variation between healthy controls and participants with ADHD a dependent sample t-test was used. A comparison for age, BMI and IQ was made. A repeated measures ANOVA was used to compare performance on the Go/No-Go task between adults with ADHD and children with ADHD in both sessions.
A sample size of 99 participants was required given a standard effect size of 0.7, a significance level of 5% and a power of 90%. This was computed for an ANOVA: Repeated measures, within-between interaction by using G*power (G*power 3.1, www.gpower.hhu.de).
Results
Sample Characteristics
70 participants, 42 adults and 28 children were included in the analysis. 29 participants were excluded either due to lacking fMRI scans and behavioural data of the task (n = 13) or due to the detection of high levels of motion during fMRI scans (n = 16). Acceptable limits of motion were based on the primary feat analysis (below 3mm) and on visual inspection of the scans and motion graphs. For all participants demographic data was assessed, as is shown in Table 1. Demographics show that there was no significant difference between adult healthy
controls and adults with ADHD in IQ, t(55) = -.12, p > .05, age, t(59) = 2.5, p > .05, and BMI, t(64) = 1.1, p > .05. There was also no significant difference between children healthy
controls and children with ADHD in IQ, t(74) = .07, p > .05, and age, t(59) = -.05, p > .05. A significant difference in BMI between children healthy controls and children with ADHD was found, t(31) = -7.16, p < .0001.
Table 1
Demographics for Participants with ADHD and Healthy Controls ADHD Children Mean (SD) ADHD Adults Mean (SD) Healthy Controls Children Mean (SD) Healthy Controls Adults Mean (SD) Age 11,35 (0,86) 28,54 (4,6) 11,36 (0,84) 25,18 (1,86) IQ* 101,32 (18,63) 107,80 (7,5) 100,88 (8,48) 108,08 (5,52) BMI 17,18 (1,94) 23,83 (4,62) 21,72 (1,1) 22,36 (1,13) Severity ADHD** ADHD-RS VvGK attention VvGK hyperactivity 22,18 (3,32) 15,51(5,8) 32,17 (9,66) 3,22 (2,11) 3,44 (2,96) 12,09 (5,97) Note.
* estimated IQ is measured through subtests (block design, vocabulary) of the Wechsler Intelligence Scale for children-Revised (WISC-III NL; Wechsler, 1981) and National Adult Reading Test (NART)
** severity ADHD for children is assessed through the VvGK (Oosterlaan, Scheres, Antrop, Roeyers & Sergeant, 2000) with the discrimination between hyperactivity symptoms and inattention problems (VvGK= Vragenlijst voor Gedragsproblemen bij kinderen (ADHD); questionnaire for behavioral problems in children); severity of ADHD for adults is assessed through the ADHD-RS= ADHD-Rating Scale.
Performance on the Go/No-Go task
Independent t-test showed that there was no significant difference between healthy controls and participants with ADHD on their scores on the Go/No-Go task, t(92) = -2.76, p > .05 (Fig. 5).
Figure 5. Behavioural data: Correct scores on the No-Go trials. (grey = healthy controls; striped = participants with ADHD).
For the integration of age, the same test was done for adults and children separately. Referring to children, it was found that healthy controls and participants with ADHD did not differ significantly in their scores on the task, t(38) = -1.67, p = .103. For the adult group, a significant difference between healthy control adults and adults with ADHD on their scores on the task was found, t(52) = -4.01, p < .0001. On average, adult healthy controls scored higher on the task (M = 35.09, SE = 4.99) than adult participants with ADHD (M = 27.79, SE = 5.48), as is shown in Fig. 5 and in Table 2.
Table 2 Go/No-Go Scores ADHD Children Mean (SD) ADHD Adults Mean (SD) Healthy Controls Children Mean (SD) Healthy Controls Adults Mean (SD) Accuracy No-Go session 1 18,24
(4,73) 27,95 (5,44) 21,44 (6,98) 35,09 (4,98) Accuracy No-Go session 2 21,48
(6,37) 33,31 (5,03) - - Reactiontime Go session 1* 432,45 (45,86) 414,74 (47,91) 388,86 (43,04) 398,16 (24,48) Reactiontime Go session 2* 397,49 (39,59) 408,23 (39,93) - -
Repeated measures ANOVA showed a significant main effect for session on the Go/No-Go scores, F(1, 86) = 61.26, p > .001, as is shown in Fig 6. On average, in session 1, adults with ADHD scored higher on the task, (M = 27.94, SE = 0.77), than children with ADHD (M = 17.69, SE = 0.81). In session 2, adults with ADHD also scored higher (M = 33.28, SE = 0.32), than children with ADHD (M = 21.24, SE = 0.85). However, there was no interaction effect between age and session, F(1, 86) = 2.509, p = .117.
Figure 6. Correct scores on the No-Go trials.
Within group differences in functional connectivity
Wilcoxon signed-rank test showed that there was no significant difference in functional connectivity between the striatum and the SMA when comparing session 2 with session 1, z = -.606, p = .545, r = -.072. There was also no significant difference in functional connectivity between the IFG and the SMA when comparing session 2 with session 1, z = -.1.06, p = .291, r = .126.
Between group differences in functional connectivity
age (adult vs children) on PPI functional connectivity of time series extracted from the striatum and the IFG, F(1, 68) = .303, p = .584 (Fig. 7). There was also no significant main effect for session on functional connectivity between the striatum and the IFG, F(1, 68) = .003, p = .96 (Fig. 7). No interaction effect between age and these time series was found, F(1, 68) = 2,42, p = .125. A main effect for age on functional connectivity between striatum and SMA was observed, F(1, 68) = 4.006, p = .049, as is shown in Fig. 8. On average, children with ADHD compared to adults with ADHD showed higher degrees of functional connectivity between the striatum and the SMA in session 2 and in session 1, as is shown in Fig. 8. There was also no main effect for session on functional connectivity between the striatum and the SMA, F(1, 68) = 1.195, p = .278. No interaction effect between age and functional connectivity between the striatum and the SMA was found, F(1, 68) = 1.03, p = .313 (Fig. 8).
Figure 8. Functional connectivity between SMA and striatum.
Explorative Analysis
Additionally, a whole brain functional connectivity analysis for session 1 and session 2 was conducted for adults and children separately. Connectivity patterns were depicted from the striatum seed region for the correct No-Go trails. Significant connectivity was reached at p < 0.05 at cluster level (uncorrected for multiple comparison). MNI152 standard-space T1-weighted average structural template image was used as a template brain image and the Harvard-Oxford cortical and subcortical structural atlases were used to define regions. For adults with ADHD, 6 clusters of activation patterns were detected for session 1 (Table 3, see appendix). Functional connectivity was found for the striatum seed region with the left postcentral gyrus, left/right cerebral cortex, right middle temporal gyrus, left/right subcallosal cortex, right precuneous cortex and the right accumbens. For the second session, 7 clusters were detected (Table 4, see appendix). Functional connectivity was found for the striatum seed region with the right precentral gyrus, right precuneous cortex, right/left cerebral cortex, left postcentral gyrus, right planum polare, right angular gyrus and the right superior temporal gyrus. For children with ADHD, one cluster was detected in session 2 (Table 5, see
putamen. In session 1 no clusters were detected for this group. For all sessions, no
connectivity patterns between the striatum and the IFG nor between the striatum and the SMA were detected.
Discussion
The present study was designed to determine whether there is an effect of MPH on the frontal-striatal activation network during response inhibition in children and adults with ADHD. Returning to the hypothesis posed at the beginning, it cannot be confirmed that an acute challenge of MPH has an enhancing effect on frontal-striatal activation during response inhibition. This conclusion can be drawn from the fact that we did not find a significant difference in functional connectivity in this network, before and after an acute challenge of MPH. A higher functional connectivity between time series of regions extracted from the striatum, the SMA and the IFG, after an acute challenge of MPH, was not found.
Another objective was to investigate whether there is an acute age-related effect of MPH on the frontal-striatal activation network during response inhibition in ADHD. The hypothesis of an acute-age related effect of MPH on the frontal-striatal activation network during response inhibition, cannot be confirmed, since the acute response of MPH did not lead to a higher increase in functional connectivity between the striatum, the SMA and the IFG in children compared to adults with ADHD. However, our expectation of a higher functional connectivity in the frontal-striatal activation network in adults compared to
children can be partially confirmed. This conclusion can be drawn from the fact that we found a higher functional connectivity between the striatum and the SMA in adults compared to children in the session without the MPH challenge. Interestingly, results did not show a difference in functional connectivity between the striatum and the IFG, when comparing adults with children, in this session.
Our explorative whole brain functional connectivity analysis showed that MPH has some effect on activation patterns throughout the brain. More activation clusters have been detected after an acute challenge of MPH in adults and children with ADHD during response inhibition. Based on this finding, MPH seems to enhance functional connectivity between the striatum and other subcortical and cortical brain regions.
In line with our expectation, findings on the performance of the Go/No-Go task showed that adult healthy controls performed better on response inhibition than adults with ADHD. Against our expectation, this difference was not found in children.
The most likely explanation for the unexpected findings of the functional connectivity analysis concerns the choice for the striatum as a region of interest (ROI), because response inhibition is probably not confined to the whole striatum. Even though previous research confined the whole striatum to this process (Cubillo et al., 2010; Scheres et al., 2007), in our study, no difference in functional connectivity in the frontal-striatal activation network, before and after an acute challenge of MPH, was found. Therefore, one may speculate whether there actually was functional connectivity between the chosen regions of interest. Functional connectivity between the striatum and the other chosen regions of interest was not found by our explorative whole brain functional connectivity analysis. However, results of this analysis showed functional connectivity between striatum and putamen in children with ADHD, after an acute challenge of MPH, which might indicate that smaller parts of the striatum are more specific to response inhibition. Hence, subdividing the striatum into smaller parts might have yielded different results. Further support for this assumption can be obtained from previous studies, which showed that striatal subregions are associated with different connectivity patterns during different cognitive operations (Di Martino et al., 2008). For example, some studies indicated that specifically the dorsal striatum, subdivided into the putamen and the nucleus caudate, has been activated during response inhibition (Rubia et al., 2009; Vaidya et
al., 1998) and that MPH enhanced activation in this region in patients with ADHD during response inhibition (Vaidya et al., 1998). This in turn also affected frontal regions (Vaidya et al., 1998). Based on this, it can be concluded that subdividing the striatum into smaller parts might have led to more accurate research findings. For future studies, it would be useful to select regions of interest based on results obtained from whole brain analysis instead of selecting regions of interest based on previous research findings.
Another potential explanation for the fact that we did not find functional connectivity in the frontal-striatal activation network can be explained by the deficit in response inhibition. The question came up whether adults and children with ADHD, participated in the current study, differed from healthy controls in response inhibition. Although, on average, healthy controls performed better on response inhibition than participants with ADHD, this difference was not significant. This leads to the conclusion that in this study not all participants with ADHD might have a deficit in response inhibition. This might be supported by the triple pathway theory, which highlights the heterogeneity of ADHD by dissociating between three different pathways in ADHD (Sonuga-Barke et al., 2010): the deficit in response inhibition, timing impairments and a motivational deficit in delay aversion (motivation to avoid delay) (Smith, Taylor, Warner Rogers, Newman, & Rubia, 2002). According to this theory and previous research findings, it can be assumed that deficits in response inhibition, timing and delay aversion are all important characteristics in some, but not in all patients with ADHD. In that case, it seems likely that not all participants of our study had a dysfunctional frontal-striatal activation network. Hence, although a subgroup of our sample had a dysfunctional network, in some no dysfunctional network was measured. This may explain why we did not find a difference in functional connectivity before and after an acute challenge of MPH. Further studies should therefore only include participants who perform significantly worse on response inhibition in comparison with healthy controls.
MPH did not have an age-related effect on the frontal-striatal activation network during response inhibition in ADHD. Against our expectations, an acute challenge of MPH did not increase functional connectivity of frontal-striatal regions in children over adults. This may be explained, in part, by differences in neural contributions to response inhibition
between adults and children with ADHD (Lei et al., 2015). This is supported by the fact that whole brain functional connectivity analysis of our study found that adults with ADHD have different and more pattern activation than children with ADHD. As most researchers (Rooij et al., 2015; Rubia et al., 2011), in the present study, it has been suggested that identical brain regions are involved in response inhibition in adults and children. However, other researchers (Casey, Giedd, & Thomas, 2000; Lei et al., 2015) found that neural contributions to response inhibition differ in children and adults with ADHD (Lei et al., 2015). For example, the anterior cingulate cortex (ACC), a region within the cingulate gyrus, which is considered to be also important in response inhibition (Agam, Joseph, Barton, & Manoach, 2010; Braver, Barch, Gray, Molfese, & Snyder, 2001), was hypoactivated during response inhibition in children with ADHD compared to adults with ADHD (Lei et al., 2015). This gives support for an age-related effect of MPH in this area. Our whole brain functional connectivity results are in line with this finding. Here, it is shown that the striatum predicts activity in the ACC in adults but not in children with ADHD. Results of this study already pointed out that adults with ADHD have different and more pattern activation than children with ADHD. From this it follows, that different brain regions might contribute to response inhibition in adults and children. Differences in involved brain regions make it difficult to compare the age-related effect of MPH on a specific neural network. Even though it remains unclear how neural contributions differ between the developing and the matured brain, taking these differences into account is necessary for future studies. From this point of view, testing the effect of MPH on age-related brain networks would be a more reliable approach. As mentioned above, by
insights about activation differences and consistencies in adults compared to children.
A limitation of our study is the small sample size used in this study, which might have increased the risk of a type II error. Therefore, replicating the current study with a larger sample size is necessary. Furthermore, statistical assumptions were not met consistently. The assumption of a normal distribution was violated. However, after visual inspection of the data histograms, which seemed to be nearly normal distributed, we still conducted a mixed design ANOVA, which is relatively robust to violations of normality (Schmider, Ziegler, Danay, Beyer, & Bühner, 2010). The results thus need to be interpreted carefully.
Another potential limitation is that MRI indirectly measures the effect of MPH on the dopaminergic system. To better understand this indirect measure, a process named
neurovascular coupling, defined by coupling between neuronal activity and the resulting hemodynamic response, is important (Choi, Chen, Hamel, & Jenkins, 2006). Although changes in hemodynamic responses, measured with MRI, are related to neuronal activity, vascular activation also seems to mediate the hemodynamic response to dopaminergic stimulation (Choi et al., 2006). It thus needs to be questioned whether it is sufficient to exclusively focus on neuronal activity.
We can conclude that the effect of MPH on the frontal-striatal activation network during response inhibition in children and adults with ADHD remains to be established. Considering the age-related effect of MPH, we provide some evidence that the frontal-striatal activation network differ between children with ADHD and adults with ADHD. However, the exact differences still need to be investigated. This stresses the need for further studies that address the acute (age-related) effect of MPH on this brain network during response inhibition.
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Appendix
Table 3
Results of Whole Brain Functional Connectivity Analysis for Session 1 for Adults with ADHD
Cluster Voxel Z-value Max x
voxel
Max y voxel
Max z voxel Left Postcentral Gyrus;
Left Cerebral Cortex
279 2.8 54 41 62.9
Right Middle Temporal Gyrus; Right Cerebral Cortex
210 3.02 23 35 43.3
Right Subcallosal Cortex; Right Accumbens
107 2.72 39 71 31
Left Subcallosal Cortex; Left Cerebral Cortex
93 2.48 50 70 27.3
Right Middle Temporal Gyrus, temporooccipital part;
Right Cerebral Cortex
59 2.33 17 37 40.5
Right Precuneous Cortex; Right Cerebral Cortex
55 2.63 42 42 59.9
Note. Connectivity patterns were depicted from the striatum seed region for the correct No-Go trails. Significant connectivity was reached at p < 0.05 at cluster level (uncorrected multiple comparison). For adults with ADHD, 6 clusters of activation patterns were detected for session 1.
Table 4
Results of the Whole Brain Functional Connectivity Analysis for Session 2 for Adults with ADHD
Cluster Voxel Z-value Max x
voxel
Max y voxel
Max z voxel Right Precentral Gyrus;
Right Cerebral Cortex
670 3.38 27 53 66.6
Right Postcentral Gyrus; Right Cerebral Cortex
616 2.73 28 44 70.3
Left Postcentral Gyrus; Left Cerebral Cortex
153 2.94 53 43 63.1
Right Planum Polare; Right Cerebral Cortex
69 2.63 22 59 31.6
Right Precuneous Cortex; Right Cerebral Cortex
65 2.61 44 43 62.2
Right Angular Gyrus; Right Cerebral Cortex
57 2.33 19 35 42.7
Right Superior Temporal Gyrus; Right Cerebral Cortex
51 2.25 19 45 39.3
Note. Connectivity patterns were depicted from the striatum seed region for the correct No-Go trails. Significant connectivity was reached at p < 0.05 at cluster level (uncorrected multiple comparison). For adults with ADHD, 7 clusters of activation patterns were detected for
Table 5
Results of Whole Brain Functional Connectivity Analysis for Session 2 for Children with ADHD
Cluster Voxel Z-value Max x
voxel Max y voxel Max z voxel Right Putamen 177 2.95 30 67 31.9
Note. Connectivity patterns were depicted from the striatum seed region for the correct No-Go trails. Significant connectivity was reached at p < 0.05 at cluster level (uncorrected multiple comparison). For children with ADHD, 1 cluster of activation pattern was detected for session 2.
