Skip to content

Advertisement

Child and Adolescent Psychiatry and Mental Health

What do you think about BMC? Take part in

Open Access

Differential therapeutic effects of atomoxetine and methylphenidate in childhood attention deficit/hyperactivity disorder as measured by near-infrared spectroscopy

  • Yoko Nakanishi1Email author,
  • Toyosaku Ota1,
  • Junzo Iida2,
  • Kazuhiko Yamamuro1,
  • Naoko Kishimoto1,
  • Kosuke Okazaki1 and
  • Toshifumi Kishimoto1
Child and Adolescent Psychiatry and Mental Health201711:26

https://doi.org/10.1186/s13034-017-0163-6

Received: 4 December 2016

Accepted: 6 May 2017

Published: 12 May 2017

Abstract

Background

The stimulant methylphenidate (MPH) and the nonstimulant atomoxetine (ATX) are the most commonly-prescribed pharmacological treatments for attention deficit/hyperactivity disorder (ADHD). However, the drug-specific mechanism of action on brain function in ADHD patients is not well known. This study examined differences in prefrontal hemodynamic activity between MPH and ATX in children with ADHD as measured by near-infrared spectroscopy (NIRS) using the Stroop color-word task.

Methods

Thirty children with ADHD participated in the present study. We used 24-channel NIRS (ETG-4000) to measure the relative concentrations of oxyhemoglobin in the frontal lobes of participants in the drug-naïve condition and those who had received MPH (n = 16) or ATX (n = 14) for 12 weeks. Measurements were conducted every 0.1 s during the Stroop color-word task. We used the ADHD RS-IV-J (Home Version) to evaluate ADHD symptoms.

Results

Treatment with either MPH or ATX significantly reduced ADHD symptoms, as measured by the ADHD RS-IV-J, and improved performance on the Stroop color-word task in terms of number of correct words. We found significantly higher levels of oxyhemoglobin changes in the prefrontal cortex of participants in the ATX condition compared with the values seen at baseline (pre-ATX). In contrast, we found no oxyhemoglobin changes between pre- and post-treatment with MPH.

Conclusions

The present study suggests that MPH and ATX have differential effects on prefrontal hemodynamic activity in children with ADHD.

Keywords

Attention-deficit/hyperactivity disorderNear-infrared spectroscopyFunctional neuroimagingAtomoxetineMethylphenidate

Background

Attention-deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed neurodevelopmental disorders in children with lifelong deficits in a wide range of executive functions [1]. ADHD symptoms are thought to arise from dysregulation of prefrontal and subcortical catecholamine neurotransmission [2, 3]. The stimulant methylphenidate (MPH) and the nonstimulant atomoxetine (ATX) are the most frequently prescribed drugs for the treatment of ADHD. Both drugs are known to reduce clinical ADHD symptoms. The common mechanism of both drugs is that they modulate dopamine (DA) and norepinephrine (NE) neurotransmission [4]. Small changes in DA or NE concentration affect networks of pyramidal cells in the prefrontal cortex (PFC), which regulates and sustains attention [5]. It is believed that the therapeutic effects of both medications occur primarily in the PFC [5], although the exact mechanisms of their actions are unclear.

Methylphenidate acts as an indirect DA agonist, inhibiting DA reuptake by occupying the DA transporter [6]. MPH has also shown to block the norepinephrine (NE) transporter in NE transporter-rich regions, including the PFC [7]. In rodent studies, MPH has been shown to enhance the extracellular levels of both DA and NE [8]. In contrast, although ATX is a selective NE reuptake inhibitor, it also inhibits DA reuptake in the PFC. Therefore, while it does not increase DA in the striatum, it increases both DA and NE in the prefrontal cortex [8]. The partially overlapping pharmacologic profiles of these medications suggest both similarities and differences in their therapeutic mechanisms of action. In the meta-analysis focused on the comparison between MPH and ATX, MPH showed a higher response rate compared to ATX [9]. In a randomized study directly comparing MPH and ATX in adults with ADHD, the effects on executive functions were generally similar, although there was a suggestion that ATX might show a slight benefit to the immediate-release MPH in terms of improving spatial planning [10]. However, another head-to-head study comparing the two drugs found that only osmotically-released MPH improved set-shifting and verbal fluency, although osmotically-released MPH and ATX both improved executive function generally in children and adolescents with ADHD [11]. Distinct underlying pharmacological mechanisms may cause these practical differences. There are few neuroimaging studies that examined these differences [12, 13]. Cubillo et al. showed that ATX upregulated and normalized right dorsolateral prefrontal cortex under activation measured by functional magnetic resonance imaging (fMRI), while MPH upregulated left inferior frontal cortex activation [13].

Near-infrared spectroscopy (NIRS) enables the noninvasive detection of neural activity near the surface of the brain using near-infrared light [14, 15]. It measures alterations in oxygenated hemoglobin ([oxy-Hb]) and deoxygenated.

Hemoglobin ([deoxy-Hb]) concentrations in micro-blood vessels on the brain surface. Local increases in [oxy-Hb] and decreases in [deoxy-Hb] are indicators of cortical activity [15, 16]. In animal studies, [oxy-Hb] is the most sensitive indicator of regional cerebral blood flow because the direction of change in [deoxy-Hb] is determined by the degree of changes in venous blood oxygenation and volume [17]. Therefore, we decided to focus on changes in [oxy-Hb]. Furthermore, changes in [oxy-Hb] have been associated with changes in regional cerebral blood volume, using a combination of NIRS and positron emission tomography (PET) measurements [18, 19]. NIRS is a neuroimaging modality that is especially suitable for psychiatric patients for the following reasons [20]. First, because NIRS is relatively insensitive to motion artifact, it can be used in experimental scenarios in which motion may occur, such as while assessing participants who are prone to vocalization. Second, participants can be examined in a natural sitting position, without any surrounding distractions such as fMRI. Third, the cost is much lower than that of other neuroimaging modalities and the setup is very easy. Fourth, the high temporal resolution of NIRS is useful in characterizing the time course of prefrontal activity in people with psychiatric disorders [21, 22]. Fifth, functional studies of pediatric patients using single-photon emission computed tomography (SPECT) and PET are rare due to restrictions regarding the use of radioactive materials in young individuals. Accordingly, NIRS has been used to assess brain function in people with many types of psychiatric disorders, including schizophrenia, bipolar disorder, post traumatic disorder, obsessive–compulsive disorder, and ADHD [2028].

In pediatric ADHD, reduced prefrontal hemodynamic response has been measured by NIRS [23, 29, 30]. Negoro et al. examined prefrontal hemodynamic response during the Stroop color-word task in 20 children with ADHD and 20 healthy age- and sex-matched controls. They found that the oxy-Hb changes in the inferior prefrontal cortex in the control group were significantly larger than those in the ADHD group during the Stroop color-word task [23]. In an NIRS study of medication, Ota et al. examined the effects of a clinical dose of ATX on changes in prefrontal hemodynamic response during the Stroop color-word task in pediatric ADHD. They found that ATX induced an intensified prefrontal hemodynamic response [31]. In another NIRS study, Araki et al. found that the oxy-Hb concentration in the right dorsolateral PFC in the post-ATX condition was significantly increased compared to the pre-ATX condition during a continuous performance task [32]. Despite several NIRS studies with ADHD, only a few studies have examined the therapeutic effects of medication. Moreover, no studies have compared MPH with ATX directly. In this study, we examined the drug-specific effects of a clinical dose of either MPH or ATX on frontal activation as measured by NIRS in a cohort of medication-naïve pediatric ADHD subjects. We used the Stroop color-word task to assess inhibitory control and selective attention. As outlined above, there are distinct underlying pharmacological mechanisms associated with MPH and ATX. Therefore, we hypothesized that there might be a differential hemodynamic response across MPH and ATX.

Methods

Participants

Thirty patients aged 6–14 years and diagnosed with ADHD according to the DSM-5 criteria [33] participated in the present study. Participants had no history of treatment for a developmental disorder, and had consulted an experienced pediatric psychiatrist at the Department of Psychiatry at Nara Medical University. These participants underwent a standard clinical assessment comprising a psychiatric evaluation, a semi-structured diagnostic interview (the kiddie schedule for affective disorders and schizophrenia for school-age children-present and lifetime version [34]), and a medical history assessment. Two experienced pediatric psychiatrists confirmed the diagnosis of ADHD according to the DSM-5 criteria [33]. Intellectual level was assessed using the Wechsler intelligence scale for children-fourth edition (WISC-IV), and individuals with full-scale IQ (FIQ) scores below 70 were excluded. We also excluded those who presented with a comorbid Axis I diagnosis, a neurological disorder, a head injury, a serious medical condition, or a history of substance abuse/dependence because these influenced the prefrontal hemodynamic response [2022, 24, 26, 35, 36]. In total, 30 participants with ADHD who had no previous medication history were enrolled in the present study. All participants were right-handed and of Japanese descent.

We used NIRS to measure the relative concentrations of oxy-Hb in participants in the drug-naïve condition (pre-treatment) and after 12 weeks of treatment with either osmotically released MPH (n = 16) or ATX (n = 14) (post-treatment). The participants were assigned either MPH or ATX by the decision of an experienced pediatric psychiatrist. All measurements were conducted at the same time of day (10.00–11.00 h). All the participants were MPH and ATX naïve and started to take MPH 18 mg/day or ATX 0.5 mg/kg/day in the morning, respectively. They were titrated up as needed to the lowest effective dose by the decision of an experienced pediatric psychiatrist every 2 weeks. The mean dose of MPH was 0.87 mg/kg (SD = 0.23), and the mean dose of ATX was 1.30 mg/kg (SD = 0.44). The characteristics of the participants are shown in Table 1. This study was approved by the Institutional Review Board at Nara Medical University. Written informed consent was obtained from all participants and/or their parents prior to the study.
Table 1

Participant characteristics

 

MPH (n = 16)

ATX (n = 14)

p value

Mean

SD

Mean

SD

Sex (male/female)a

14/2

 

11/3

 

0.642

Age (years)

8.19

2.46

9.50

2.03

0.125

Medication dose (mg/kg)

0.87

0.23

1.30

0.44

NA

FIQ (WISC-IV)

94.19

13.46

96.64

14.43

0.634

ARF

30.63

10.65

32.29

13.46

0.709

ARI

16.75

5.52

18.29

6.84

0.502

ARH

13.88

6.72

14.00

7.99

0.963

SCWC-1

18.31

7.66

25.86

8.76

0.018

SCWC-2

19.81

9.56

29.36

11.11

0.017

SCWC-3

21.19

9.17

25.36

10.35

0.252

MPH methylphenidate, ATX atomoxetine, NA not applicable, FIQ full-scale IQ, WISC-IV Wechsler Intelligence Scale for children-fourth edition, ARF ADHD RS IV-J full scores, ARI ADHD RS IV-J inattention subscale scores, ARH ADHD RS IV-J hyperactivity subscale scores, SCWC-1 Stroop color-word task number of correct answers first time, SCWC-2 Stroop color-word task number of correct answers second time, SCWC-3 Stroop color-word task number of correct answers third time

aThe Chi square test was used; otherwise t-tests were used

Assessment of ADHD symptoms

We used the ADHD Rating Scale-IV-Japanese version (ADHD RS-IV-J) (Home Version) [37] to evaluate ADHD symptoms in the participants. A higher ADHD RS-IV-J score is associated with more severe ADHD symptoms. All participants underwent ADHD RS-IV-J assessment pre- and post-treatment which were rated by parents (Table 3).

The Stroop color-word task

The traditional Stroop task was combined with the word-reading task, incongruent color-naming task, and the color-naming task. However, we reconstructed the Stroop task according to previously described methods [38]. The Stroop color-word task consisted of two pages stapled together: each page had 100 items in five columns of 20 items each and the page size was 210 × 297 mm. On the first page, the words RED, GREEN, and BLUE were printed in black ink. On the second page, the words RED, GREEN, and BLUE were printed in red, green, or blue ink, with the limitation that the word meaning and ink color could not match. The items on both pages were randomly distributed, with the exception that no item could appear directly after the same item within a column. Before the task, the examiners instructed the participants as follows: ‘This is to test how quickly you can read the words on the first page, and say the colors of the words on the second page. After we say “begin”, please read the words in the columns, starting at the top left, and say the words/colors as quickly as you can. After you finish reading the words in the first column, go on to the next column, and so on. After you have read the words on the first page for 45 s, we will turn the page. Please repeat this procedure for the second page.’ The entire Stroop color-word task sequence consisted of three cycles of 45 s spent reading the first page and 45 s spent reading the second page (the color-word task). The task ended with 45 s spent reading the first page, which we designated as the baseline task. We recorded the number of correct answers in each cycle, and refer to them as follows:

Stroop color-word task number of correct answers first time (SCWC-1), second time (SCWC-2), and third time (SCWC-3). Examiners who were blind to the diagnoses of the participants administered the Stroop color-word task. The Stroop task used in this study was different from the traditional Stroop task. We made the Stroop color-word task simple because the participants were school-aged children. Furthermore, we excluded the color-naming task (part of the traditional Stroop task) because we wanted to have only two tasks (baseline task and activation task) for our NIRS study.

NIRS measurements

We measured [oxy-Hb] using a 24-channel NIRS machine (Hitachi ETG-4000, Hitachi Medical Corporation, Tokyo, Japan). We measured the absorption of two wavelengths of near-infrared light (760 and 840 nm). [Oxy-Hb] was calculated as previously described [39]. The inter-probe intervals of the machine were 3.0 cm, and previous reports have established that the machine measures at a point 2–3 cm beneath the scalp, that is, the surface of the cerebral cortex [36, 40]. The participants were asked to adopt a natural sitting position for the NIRS measurement. The distance between the participants’ eyes and the paper on which items were listed was between 30 and 40 cm. The NIRS probes were placed on the scalp over the prefrontal brain regions, and arranged to measure the relative changes in Hb concentration at 24 measurement points that made up an 8 × 8-cm2. The lowest probes were positioned along the Fp1–Fp2 line according to the international 10/20 system commonly used in electroencephalography. The correspondence between the probe positions and the measurement points in the cerebral cortex were confirmed by superimposing the probe positions onto a three-dimensionally reconstructed cerebral cortex of a representative participant in the control group, obtained via MRI (Fig. 1). The absorption of near-infrared light was measured with a time resolution of 0.1 s. The data were analyzed using the ‘integral mode’: the pre-task baseline was determined as the mean across the 10 s just before the task period, the post-task baseline was determined as the mean across the 25 s immediately after the task period, and linear fitting was performed on the data between the two baselines. Moving average methods were used to exclude short-term motion artifacts in the analyzed data (moving average window, 5 s). We attempted to exclude motion artifacts by closely monitoring artifact-evoking body movements, such as neck movements, biting, and blinking (identified as being the most influential in a preliminary artifact-evoking study), and by instructing the participants to avoid these movements during the NIRS measurements. Examiners were blind to the treatment condition of the participants.
Fig. 1

Location of the 24 channels on the near-infrared spectroscopy instrument

Statistical analysis

We used the Chi square (χ2) test to examine group differences for categorical variables (e.g. gender). Clinical variables with a normal distribution were compared using Student’s t tests. Correlations between SCWC and characteristics of the subjects were tested with Spearman’s correlation test. For statistical comparison of the participant characteristics between the pre- and post-treatment conditions, we used a two-tailed paired t test. Specifically, we compared oxy-Hb changes between the pre- and post-treatment conditions. To conduct a more detailed comparison of oxy-Hb changes along the time course of the task, we used MATLAB 6.5.2 (Mathworks, Natick, MA, USA) and Topo Signal Processing type-G version 2.05 (Hitachi Medical Corporation, Tokyo, Japan).

Analyses of variance were performed to examine treatment (with two levels, i.e. MPH and ATX) × condition (with two levels, i.e. pre- and post-treatment) interactions. The threshold for statistical significance was set at p < 0.05. Bonferroni-adjusted p values are reported (i.e. corrected for multiple comparisons). We used PASW Statistics18.0J for Windows (SPSS, Tokyo, Japan) for statistical analyses.

Results

Demographic data

The demographic characteristics of the study participants are presented in Table 1. The participant groups did not differ in terms of mean age, sex, FIQ, ADHD-RS-IV-J scores including the ARF, ARI and ARH subscale scores, and SCWC-3 scores (p > 0.125 for all 7 variables). We found significant differences in the SCWC-1, SCWC-2 scores between the MPH and ATX groups (t = −2.52, p = 0.018; t = −2.53, p = 0.017).

Correlation between Stroop task performance and participant characteristics

Because the MPH and ATX groups varied considerably in terms of SCWC-1 SCWC-2 scores, we calculated Spearman’s correlations for the SCWC scores, age, FIQ, and ADHD-RS-IV-J, as shown in Table 2. In the ATX group, the SCWC-1, SCWC-2 and SCWC-3 scores were positively correlated with age (ρ = 0.866, p < 0.000, ρ = 0.798, p < 0.001 and ρ = 0.718, p < 0.004), while none of SCWC scores significantly correlated with FIQ and ADHD-RS-IV-J scores. In the MPH group, the SCWC2 score were positive correlated with age (ρ = 0.522, p < 0.038), and SCWC1 score were positive correlated with FIQ (ρ = 0.557, p < 0.025), whereas none of the SCWC scores were significantly associated with ADHD-RS-IV-J scores.
Table 2

Correlation between Stroop task performance and participant characteristics

 

MPH (n = 16)

ATX (n = 14)

SCWC-1

SCWC-2

SCWC-3

SCWC-1

SCWC-2

SCWC-3

Age

0.483

0.522*

0.417

0.866**

0.798**

0.718**

FIQ

0.557*

0.304

0.307

−0.159

−0.023

0.982

ARF

−0.368

−0.292

−0.351

−0.248

−0.237

−0.306

ARI

−0.001

−0.076

−0.162

−0.085

−0.116

−0.227

ARH

−0.448

−0.364

−0.335

−0.422

−0.376

−0.402

MPH methylphenidate, ATX atomoxetine, FIQ full-scale IQ, WISC-IV Wechsler Intelligence Scale for children-fourth edition, ARF ADHD RS IV-J full scores, ARI ADHD RS IV-J inattention subscale scores, ARH ADHD RS IV-J hyperactivity subscale scores, SCWC-1 Stroop color-word task number of correct answers first time, SCWC-2 Stroop color-word task number of correct answers second time, SCWC-3 Stroop color-word task number of correct answers third time

* p < 0.05

** p < 0.01

Clinical and behavioral improvement

Both treatments were associated with statistically significant improvements in terms of both ADHD-RS-IV-J scores and SCWC scores, as shown in Table 3. In both groups, the ADHD-RS-IV-J scores including the ARF, ARI and ARH subscale scores in the post-treatment condition were significantly lower than scores in the pre-treatment condition (p < 0.01 for all 6 variables). Additionally, the SCWC-1, SCWC-2 and SCWC-3 scores in the post-treatment condition were significantly higher than those in the pre-treatment condition (p < 0.033 for all 6 variables). There were no significant main effects of treatment condition × medication interactions for any of the performance measures (p > 0.098 for all 6 variables).
Table 3

Clinical outcome and task performance

 

MPH (n = 16)

ATX (n = 14)

p value

Pre-

Post-

Pre-

Post-

MPH

ATX

 

Mean (SD)

Mean (SD)

Mean (SD)

Mean (SD)

Pre vs posta

Pre vs posta

Time × Drug Interactionb 

ARF

30.63 (10.65)

17.06 (10.51)

32.29 (13.46)

22.71 (10.54)

0.000

0.001

0.208

ARI

16.75 (5.52)

10.13 (6.01)

18.29 (6.84)

13.71 (6.27)

0.000

0.010

0.272

ARH

13.88 (6.72)

6.94 (5.47)

14.00 (7.99)

9.00 (4.84)

0.000

0.002

0.295

SCWC-1

18.31 (7.66)

27.75 (11.70)

25.86 (8.76)

33.79 (13.57)

0.000

0.000

0.520

SCWC-2

19.81 (9.56)

28.13 (10.54)

29.36 (11.11)

33.50 (12.82)

0.000

0.033

0.098

SCWC-3

21.19 (9.17)

26.44 (10.91)

25.36 (10.35)

34.07 (12.16)

0.006

0.002

0.210

MPH methylphenidate, ATX atomoxetine, ARF ADHD RS IV-J full scores, ARI ADHD RS IV-J inattention subscale scores, ARH ADHD RS IV-J hyperactivity subscale scores, SCWC-1 Stroop color-word task number of correct answers first time, SCWC-2 Stroop color-word task number of correct answers second time, SCWC-3 Stroop color-word task number of correct answers third time

aTwo-tailed paired t test

bTwo-way factorial ANOVA

Comparison of NIRS measurements between pre- and post- treatment

We calculated the grand average waveforms of [oxy-Hb] concentration changes during the Stroop color-word task in the pre- and post-treatment condition (Figs. 2, 3).
Fig. 2

Grand average waveforms showing changes in oxyhemoglobin(oxy-Hb) during the Stroop color-word task pre- and post-MPH. Cyan lines indicate pre-MPH and blue lines indicate post-MPH. Yellow lines indicate the beginning and end of each trial. Ch channel

In the MPH group, the grand waveforms of [oxy-Hb] concentration showed little change in both pre- and post-conditions (Fig. 2). We did not find any differences in mean [oxy-Hb] measurements between pre- and post-MPH in any of the 24 channels that were recorded in Table 4. By contrast, in the ATX group, the grand waveforms of [oxy-Hb] concentration change appeared to increase substantially during task performance in the post- rather than in the pre-condition (Fig. 3). On channel 21, the mean oxy-Hb measurement was significantly larger in the post-condition relative to the pre-condition, as displayed in Table 5.
Fig. 3

Grand average waveforms showing changes in oxyhemoglobin(oxy-Hb) during the Stroop color-word task pre- and post-ATX. Pink lines indicate pre-ATX and red lines indicate post-ATX. Yellow lines indicate the beginning and end of each trial. The statistically significant region is shown within navy frames (Ch21). Ch channel

Table 4

Difference in mean oxyhemoglobin between the task and post-task periods pre- and post-MPH

 

Pre-MPH (mMmm)

Post-MPH (mMmm)

Student’s t test

Mean

SD

Mean

SD

Ch1

0.0136

0.1101

−0.0319

0.1463

NS

Ch2

−0.0254

0.1011

−0.0739

0.0782

NS

Ch3

−0.0047

0.0878

−0.0689

0.1241

NS

Ch4

0.0097

0.0759

−0.0062

0.0981

NS

Ch5

−0.0209

0.0746

−0.0225

0.0585

NS

Ch6

−0.0245

0.1071

−0.1513

0.2498

NS

Ch7

−0.0354

0.1242

−0.0337

0.1116

NS

Ch8

0.0019

0.0623

−0.0018

0.1245

NS

Ch9

−0.0009

0.0707

−0.0173

0.1256

NS

Ch10

−0.0601

0.1518

−0.0436

0.0936

NS

Ch11

0.0174

0.0686

0.0426

0.2025

NS

Ch12

−0.0092

0.0494

−0.013

0.1055

NS

Ch13

−0.0371

0.0739

−0.023

0.0948

NS

Ch14

−0.0001

0.1577

−0.0349

0.1056

NS

Ch15

−0.0033

0.0667

0.0233

0.2365

NS

Ch16

−0.0356

0.0745

−0.0476

0.0712

NS

Ch17

−0.0154

0.0741

−0.0415

0.0804

NS

Ch18

0.0207

0.1593

−0.0691

0.1423

NS

Ch19

0.0006

0.138

−0.035

0.1215

NS

Ch20

−0.0571

0.091

−0.0582

0.1153

NS

Ch21

−0.00569

0.1034

−0.0432

0.1346

NS

Ch22

−0.0553

0.0996

−0.0371

0.0891

NS

Ch23

−0.0369

0.0936

−0.037

0.0688

NS

Ch24

−0.0414

0.0836

−0.0326

0.1047

NS

Group differences tested with t test

NS not significant

Table 5

Difference in mean oxyhemoglobin measurements between the task and post-task periods pre- and post-ATX

 

Pre-ATX (mMmm)

Post-ATX (mMmm)

Student’s t test

Bonferroni correction

Mean

SD

Mean

SD

Ch1

0.013

0.0717

−0.0389

0.0706

NS

NS

Ch2

−0.0092

0.1313

−0.0309

0.0681

NS

NS

Ch3

0.0294

0.0837

0.0194

0.0848

NS

NS

Ch4

0.0259

0.0595

0.018

0.0571

NS

NS

Ch5

−0.0008

0.0656

−0.0191

0.0542

NS

NS

Ch6

0.0213

0.0789

0.0000

0.0477

NS

NS

Ch7

−0.0209

0.0634

0.0133

0.0821

NS

NS

Ch8

0.0107

0.0767

0.0508

0.0761

NS

NS

Ch9

−0.0049

0.0734

−0.0095

0.0968

NS

NS

Ch10

−0.0182

0.0419

−0.0312

0.0667

NS

NS

Ch11

0.0042

0.0941

0.0483

0.0747

NS

NS

Ch12

−0.0337

0.0558

−0.0159

0.0621

NS

NS

Ch13

−0.0363

0.0865

−0.0059

0.0565

NS

NS

Ch14

−0.0039

0.0695

0.0042

0.0731

NS

NS

Ch15

−0.0278

0.0949

0.0061

0.084

NS

NS

Ch16

−0.0119

0.1077

0.0109

0.0606

NS

NS

Ch17

−0.0013

0.057

0.0314

0.0438

NS

NS

Ch18

0.046

0.0746

0.0456

0.1136

NS

NS

Ch19

−0.0391

0.0727

0.0178

0.0652

p = 0.049

NS

Ch20

0.0585

0.0871

0.0883

0.1638

NS

NS

Ch21

−0.0341

0.0753

0.0956

0.0954

p = 0.000

*

Ch22

0.006

0.0844

0.0156

0.0813

NS

NS

Ch23

−0.0101

0.0803

0.0202

0.0899

NS

NS

Ch24

0.006

0.101

−0.0123

0.0773

NS

NS

Group differences tested with t test

NS not significant

* Significant with Bonferroni correction for multiple comparisons

Comparison of NIRS measurements between two groups

Channel 21 showed significant treatment-by-condition interactions (F = 13.102, p = 0.002). However, there were no main effects for either treatment or condition on channel 21 (F = 2.260, p = 0.147; F = 3.99, p = 0.058). We did not find any differences in mean [oxy-Hb] measurements between the pre-ATX and the pre-MPH (t = 0.756, p = 0.458) on this channel. However, the mean oxy-Hb measurement for channel 21 was significantly larger for post-ATX relative to post-MPH (t = −0.2802, p = 0.009).

Correlation between degree of clinical improvement and hemodynamic change in Channel 21

We conducted Spearman’s rank correlation analyses between hemodynamic change in channel 21 with scores of SCWC and ADHD-RS-IV-J scores, shown in Table 6. There were no correlations between hemodynamic change and these scores for either ATX or MPH (all p > 0.2).
Table 6

Correlation between degree of clinical improvement and hemodynamic change in channel 21

 

MPH

ATX

ARF

−0.006

−0.196

ARI

−0.072

−0.406

ARH

−0.017

−0.087

SCWC-1

−0.331

0.147

SCWC-2

−0.213

−0.014

SCWC-3

−0.349

−0.100

Tested using Spearman’s correlation test

MPH methylphenidate, ATX atomoxetine, ARF ADHD RS IV-J full scores, ARI ADHD RS IV-J inattention subscale scores, ARH ADHD RS IV-J hyperactivity subscale scores, SCWC-1 Stroop color-word task number of correct answers first time, SCWC-2 Stroop color-word task number of correct answers second time, SCWC-3 Stroop color-word task number of correct answers third time

All p > 0.216

Discussion

To our knowledge, this is the first NIRS study to compare the effectiveness of MPH with ATX directly in children with ADHD by measuring hemodynamic responses during the Stroop color-word task. ATX significantly increased activation in the prefrontal cortex, especially in left lateral frontal pole cortex (FPC), after 12 weeks of administration. MPH did not increase activation in the prefrontal cortex, but it did make comparable improvements in terms of ADHD symptoms and Stroop color-word task performance to those seen in ATX.

Some studies have referred indirectly to differences in the neurobiological actions between MPH and ATX. Event-related potential studies of oddball tasks in pediatric ADHD have shown that MPH can normalize low P300 or mismatch negativity amplitudes [41], while ATX can normalize long P300 latencies and low MMN amplitudes, at least partially [42]. In a fMRI study of adult ADHD using a multi-source interference task, ATX did not activate dorsal anterior midcingulate cortex [43], as MPH has been shown to do [44]. However, few studies have directly investigated how the pharmacological mechanisms of action differ between the two drugs, and little is known about the mechanisms by which they exert their therapeutic effects. In one fMRI study that used a go/no-go task with 36 participants with pediatric ADHD, comparable improvements in response inhibition and ADHD symptoms were seen after 6 to 8 weeks of daily treatment with MPH vs ATX. Symptomatic improvement was associated with gains in task-related activation for ATX and reductions in activation for MPH in the right inferior frontal gyrus, left anterior cingulate/supplementary motor area, and bilateral posterior cingulate cortex [45]. In another fMRI study using a counting Stroop paradigm, 12 weeks of ATX pharmacotherapy decreased activity in the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in 42 participants with pediatric ADHD, which correlated with improvement in focused attention. In contrast, MPH increased activity in the inferior frontal gyrus, which correlated with decreasing severity of impulsivity [46]. Comparing effects of acute doses of both drugs and a placebo with boys with ADHD during a stop task, MPH had a drug-specific effect of normalizing the right ventrolateral prefrontal and cerebellar under-activation observed under both placebo and ATX [47]. Taken together, these reports indicate that the mechanisms by which MPH and ATX exert their therapeutic effects are different: this is consistent with the findings from the present study. Nevertheless, the concept of drug-specific laterality effects on prefrontal regions is still controversial. Our data showed that ATX upregulated the frontal cortex during Stroop interference, at least partially. The present findings suggest that frontal mechanisms serve an important role in the therapeutic actions of ATX. However, despite the fact that MPH did not increase activation in the PFC, there were still comparable improvements in terms of ADHD symptoms for those taking this medication. One parsimonious explanation is that MPH increases activation in other brain regions, which might contribute to the improvement in ADHD symptoms.

Volkow et al. [48, 49] found that in healthy adults, MPH enhanced the salience of a reward task, increased levels of extra-cellular dopamine, and induced reductions in glucose metabolism within the default mode network (DMN). The DMN is a distributed brain system, comprising medial pre-frontal cortex and medial and lateral parietal regions. It is anti correlated with the attentional networks activated by goal-directed behavior, and is thought to reflect intrinsic activity [50]. Recently, influential new brain network models [51, 52] have proposed that proper sustained attention functioning requires both engagement of task-positive networks (TPNs), including a frontoparietal control network and dorsal and ventral attention networks, and suppression of the DMN [50, 53, 54]. A failure of the anti-phase synchronization between DMN and TPN may be involved in the manifestation of ADHD. There is evidence suggesting that the striatal DA system plays a role in the modulation of the DMN [55, 56]. MPH produces robust increases in extracellular dopamine levels [57], which potentiate corticostriatal inputs [58] and have been found to enhance striatal activation in child ADHD [59, 60]. Furthermore, some studies have shown that MPH may normalize DMN deactivation patterns [61, 62]. Therefore, we speculate that MPH might tend to activate DMN regions rather than TPN during task-related activation. In contrast, an increase of prefrontal activation has been reported after MPH treatment in several studies using different neuroimaging modalities, including NIRS [44, 59, 63, 64]. The variability in findings across studies is likely related to different cognitive tasks, dosage, patients’ ages, and treatment duration.

Increases in left lateral FPC activity were observed after ATX treatment in our study. However, we found no significant correlations between the hemodynamic changes in this area and degree of the clinical improvements. The FPC is the most anterior part of the cerebral cortex, and has reciprocal connections with most prefrontal areas [65, 66]. Tsujimoto et al. suggested that the FPC has a role in monitoring and evaluating decisions, especially those with a self-generational component [67]. Arai et al. found that children with ADHD show abnormalities in functional maturation of the frontal pole [68]. Based on these findings, a direction for future research will be to assess participants using another battery associated with self-generated behavior, separate to NIRS recordings.

The results of the present study suggest that multi-channel NIRS systems may have potential in the pharmacotherapeutic evaluation in children with ADHD for clinical practice. It is very significant for patients that an effect of the pharmacotherapy is visualized. In the future, it is need to predict the effect of the pharmacotherapy using the NIRS for clinical practice.

The present study has several potential limitations. First, methodological limitations include the relatively small number of participants, non-randomised study, and lack of a double-blind, placebo-controlled design. At baseline, the ATX group had higher mean SCWC1 and SCWC2 scores than the MPH group. Although scores were not correlated with degree of clinical severity with ADHD-RS, the two groups were not quite entirely equivalent in their characteristics. Future work seeking to compare MPH, ATX and/or placebo should consider a double-blind randomized or crossover design with larger samples. Second, we had no healthy control as a comparison cohort. Our study showed that ATX significantly increased activation in channel 21. In one previous NIRS study using the Stroop, Negoro et al. reported a lower increase of oxy-changes in channels 8, 18, 19, 21, and 22 in individuals with child ADHD compared with controls [23]. Considering the above findings, we predicted that improvement of ADHD symptoms with ATX treatment would be associated with increased activation in those regions; our findings were consistent with these predictions. Third, NIRS does not detect activity in deeper cortical structures, such as the medial pre-frontal cortex, which is part of the DMN. Fourth, the spatial resolution for the detection of hemodynamic responses from the scalp surface using NIRS is lower than that of fMRI, SPECT, or PET. However, the spatial resolution may be within an acceptable range because previous NIRS studies have also found clear distinctions in hemodynamic responses between diagnostic groups [2326, 28, 69].

Conclusions

In conclusion, this is the first NIRS study using the Stroop interference task to examine how the pharmacological mechanisms of action differ between MPH and ATX. Findings suggest that effective treatment with MPH and ATX is produced by distinct mechanisms in frontal regions.

Abbreviations

MPH: 

methylphenidate

ATX: 

atomoxetine

ADHD: 

attention deficit/hyperactivity disorder

fMRI: 

functional magnetic resonance imaging

NIRS: 

near-infrared spectroscopy

DA: 

dopamine

NE: 

norepinephrine

PFC: 

prefrontal cortex

SPECT: 

single-photon emission computed tomography

PET: 

positron emission tomography

FIQ: 

full-scale IQ

WISC-IV: 

Wechsler intelligence scale for children-fourth edition

ARF: 

ADHD RS IV-J full scores

ARI: 

ADHD RS IV-J inattention subscale’s scores

ARH: 

ADHD RS IV-J hyperactivity subscale’s scores

SCWC-1: 

Stroop color-word task number of correct answers first time

SCWC-2: 

Stroop color-word task number of correct answers second time

SCWC-3: 

Stroop color-word task number of correct answers third time

FPC: 

frontal pole cortex

DMN: 

default mode network

TPNs: 

task-positive networks

Declarations

Authors’ contributions

YN, TO and JI developed the study protocol. YN, TO, KY, NK and KO performed the experiments. YN and TO analyzed the data. JI and TK administered and supervised programs and overall conduct of the study. All authors read and approved the final manuscript.

Acknowledgements

We wish to thank the participants for their invaluable participation in the study. The authors would also like to thank Hitachi Medical Corporation for the ETG-4000 equipment, and the skilled technical and methodical support.

Competing interests

YN, TO, KY, NK and KO have no competing interests, respectively. JI and TK have received honoraria for lecturing from Eli Lilly and Janssen Pharmaceutica.

Consent for publication

Written informed consent was obtained from all participants and/or their parents prior to the study.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board at Nara Medical University.

Funding

This study was supported by JSPS KAKENHI Grant Number 22591285 without a further role in the study design, collection, analysis, and interpretation of the data, drafting the report, or the decision to submit the paper for publication.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Psychiatry, Nara Medical University School of Medicine
(2)
Faculty of Nursing, Nara Medical University School of Medicine

References

  1. Seidman LJ. Neuropsychological functioning in people with ADHD across the lifespan. Clin Psychol Rev. 2006;26(4):466–85.View ArticlePubMedGoogle Scholar
  2. Chamberlain SR, Del Campo N, Dowson J, Müller U, Clark L, Robbins TW, Sahakian BJ. Atomoxetine improved response inhibition in adults with attention deficit/hyperactivity disorder. Biol Psychiatry. 2007;62(9):977–84.View ArticlePubMedGoogle Scholar
  3. Del Campo N, Chamberlain SR, Sahakian BJ, Robbins TW. The roles of dopamine and noradrenaline in the pathophysiology and treatment of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2011;69(12):e145–57.View ArticlePubMedGoogle Scholar
  4. Makris N, Biederman J, Monuteaux MC, Seidman LJ. Towards conceptualizing a neural systems-based anatomy of attention-deficit/hyperactivity disorder. Dev Neurosci. 2009;31(1–2):36–49.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Arnsten AF. Toward a new understanding of attention-deficit hyperactivity disorder pathophysiology: an important role for prefrontal cortex dysfunction. CNS Drugs. 2009;23(Suppl 1):33–41.View ArticlePubMedGoogle Scholar
  6. Volkow ND, Wang GJ, Fowler JS, Gatley SJ, Logan J, Ding YS, Hitzemann R, Pappas N. Dopamine transporter occupancies in the human brain induced by therapeutic doses of oral methylphenidate. Am J Psychiatry. 1998;155(10):1325–31.View ArticlePubMedGoogle Scholar
  7. Hannestad J, Gallezot JD, Planeta-Wilson B, Lin SF, Williams WA, van Dyck CH, Malison RT, Carson RE, Ding YS. Clinically relevant doses of methylphenidate significantly occupy norepinephrine transporters in humans in vivo. Biol Psychiatry. 2010;68(9):854–60.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Bymaster FP, Katner JS, Nelson DL, Hemrick-Luecke SK, Threlkeld PG, Heiligenstein JH, Morin SM, Gehlert DR, Perry KW. 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(5):699–711.View ArticlePubMedGoogle Scholar
  9. Liu Q, Zhang H, Fang Q, Qin L. Comparative efficacy and safety of methylphenidate and atomoxetine for attention-deficit hyperactivity disorder in children and adolescents: meta-analysis based on head-to-head trials. J Clin Exp Neuropsychol. 2017. doi:10.1080/13803395.2016.1273320.PubMedGoogle Scholar
  10. Ni HC, Shang CY, Gau SS, Lin YJ, Huang HC, Yang LK. A head-to-head randomized clinical trial of methylphenidate and atomoxetine treatment for executive function in adults with attention-deficit hyperactivity disorder. Int J Neuropsychopharmacol. 2013;16(9):1959–73.View ArticlePubMedGoogle Scholar
  11. Yang L, Cao Q, Shuai L, Li H, Chan RC, Wang Y. Comparative study of OROS-MPH and atomoxetine on executive function improvement in ADHD: a randomized controlled trial. Int J Neuropsychopharmacol. 2012;15(1):15–26.View ArticlePubMedGoogle Scholar
  12. Smith A, Cubillo A, Barrett N, Giampietro V, Simmons A, Brammer M, Rubia K. Neurofunctional effects of methylphenidate and atomoxetine in boys with attention-deficit/hyperactivity disorder during time discrimination. Biol Psychiatry. 2013;74(8):615–22.View ArticlePubMedGoogle Scholar
  13. Cubillo A, Smith AB, Barrett N, Giampietro V, Brammer M, Simmons A, Rubia K. Drug-specific laterality effects on frontal lobe activation of atomoxetine and methylphenidate in attention deficit hyperactivity disorder boys during working memory. Psychol Med. 2014;44(3):633–46.View ArticlePubMedGoogle Scholar
  14. Boas DA, Dale AM, Franceschini MA. Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy. Neuroimage. 2004;23(Suppl 1):S275–88.View ArticlePubMedGoogle Scholar
  15. Strangman G, Boas DA, Sutton JP. Non-invasive neuroimaging using near-infrared light. Biol Psychiatry. 2002;52(7):679–93.View ArticlePubMedGoogle Scholar
  16. Obrig H, Villringer A. Beyond the visible–imaging the human brain with light. J Cereb Blood Flow Metab. 2003;23(1):1–18.View ArticlePubMedGoogle Scholar
  17. Hoshi Y, Kobayashi N, Tamura M. Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model. J Appl Physiol. 2001;90(5):1657–62.PubMedGoogle Scholar
  18. Ohmae E, Ouchi Y, Oda M, Suzuki T, Nobesawa S, Kanno T, Yoshikawa E, Futatsubashi M, Ueda Y, Okada H, Yamashita Y. Cerebral hemodynamics evaluation by near-infrared time-resolved spectroscopy: correlation with simultaneous positron emission tomography measurements. Neuroimage. 2006;29(3):697–705.View ArticlePubMedGoogle Scholar
  19. Villringer K, Minoshima S, Hock C, Obrig H, Ziegler S, Dirnagl U, Schwaiger M, Villringer A. Assessment of local brain activation. A simultaneous PET and near-infrared spectroscopy study. Adv Exp Med Biol. 1997;413:149–53.View ArticlePubMedGoogle Scholar
  20. Matsuo K, Kato T, Taneichi K, Matsumoto A, Ohtani T, Hamamoto T, Yamasue H, Sakano Y, Sasaki T, Sadamatsu M, Iwanami A, Asukai N, Kato N. Activation of the prefrontal cortex to trauma-related stimuli measured by near-infrared spectroscopy in posttraumatic stress disorder due to terrorism. Psychophysiology. 2003;40(4):492–500.View ArticlePubMedGoogle Scholar
  21. Kameyama M, Fukuda M, Yamagishi Y, Sato T, Uehara T, Ito M, Suto T, Mikuni M. Frontal lobe function in bipolar disorder: a multichannel near-infrared spectroscopy study. Neuroimage. 2006;29(1):172–84.View ArticlePubMedGoogle Scholar
  22. Suto T, Fukuda M, Ito M, Uehara T, Mikuni M. Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study. Biol Psychiatry. 2004;55(5):501–11.View ArticlePubMedGoogle Scholar
  23. Negoro H, Sawada M, Iida J, Ota T, Tanaka S, Kishimoto T. Prefrontal dysfunction in attention-deficit/hyperactivity disorder as measured by near-infrared spectroscopy. Child Psychiatry Hum Dev. 2010;41(2):193–203.View ArticlePubMedGoogle Scholar
  24. Ota T, Iida J, Sawada M, Suehiro Y, Yamamuro K, Matsuura H, Tanaka S, Kishimoto N, Negoro H, Kishimoto T. Reduced prefrontal hemodynamic response in pediatric obsessive-compulsive disorder as measured by near-infrared spectroscopy. Child Psychiatry Hum Dev. 2013;44(2):265–77.View ArticlePubMedGoogle Scholar
  25. Okada K, Ota T, Iida J, Kishimoto N, Kishimoto T. Lower prefrontal activity in adults with obsessive-compulsive disorder as measured by near-infrared spectroscopy. Prog Neuropsychopharmacol Biol Psychiatry. 2013;43:7–13.View ArticlePubMedGoogle Scholar
  26. Yamamuro K, Ota T, Iida J, Nakanishi Y, Uratani M, Matsuura H, Kishimoto N, Tanaka S, Negoro H, Kishimoto T. Prefrontal dysfunction in pediatric Tourette’s disorder as measured by near-infrared spectroscopy. BMC Psychiatry. 2015;15:102.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Yamamuro K, Makinodan M, Kimoto S, Kishimoto N, Morimoto T, Toritsuka M, Matsuoka K, Takebayashi Y, Takata T, Takahashi M, Tanimura Y, Nishihata Y, Matsuda Y, Ota T, Yoshino H, Iida J, Kishimoto T. Differential patterns of blood oxygenation in the prefrontal cortex between patients with methamphetamine-induced psychosis and schizophrenia. Sci Rep. 2015;5:12107.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Yamamuro K, Kimoto S, Iida J, Kishimoto N, Nakanishi Y, Tanaka S, Ota T, Makinodan M, Kishimoto T. Reduced prefrontal cortex hemodynamic response in adults with methamphetamine induced psychosis: relevance for impulsivity. PLoS ONE. 2016;11(4):e0152373.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Weber P, Lütschg J, Fahnenstich H. Cerebral hemodynamic changes in response to an executive function task in children with attention-deficit hyperactivity disorder measured by near-infrared spectroscopy. J Dev Behav Pediatr. 2005;26(2):105–11.View ArticlePubMedGoogle Scholar
  30. Inoue Y, Sakihara K, Gunji A, Ozawa H, Kimiya S, Shinoda H, Kaga M, Inagaki M. Reduced prefrontal hemodynamic response in children with ADHD during the Go/NoGo task: a NIRS study. NeuroReport. 2012;23(2):55–60.View ArticlePubMedGoogle Scholar
  31. Ota T, Iida J, Nakanishi Y, Sawada S, Matsuura H, Yamamuro K, Ueda S, Uratani M, Kishimoto N, Negoro H, Kishimoto T. Increased prefrontal hemodynamic change after atomoxetine administration in pediatric attention-deficit/hyperactivity disorder as measured by near-infrared spectroscopy. Psychiatry Clin Neurosci. 2015;69(3):161–70.View ArticlePubMedGoogle Scholar
  32. Araki A, Ikegami M, Okayama A, Matsumoto N, Takahashi S, Azuma H, Takahashi M. Improved prefrontal activity in AD/HD children treated with atomoxetine: a NIRS study. Brain Dev. 2015;37(1):76–87.View ArticlePubMedGoogle Scholar
  33. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. (DSM-5). 5th ed. Arlington: APA; 2013.View ArticleGoogle Scholar
  34. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D, Ryan N. Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36(7):980–8.View ArticlePubMedGoogle Scholar
  35. Roberts CA, Montgomery C. fNIRS suggests increased effort during executive access in ecstasy polydrug users. Psychopharmacology. 2015;232(9):1571–82.View ArticlePubMedGoogle Scholar
  36. Hock C, Villringer K, Müller-Spahn F, Wenzel R, Heekeren H, Schuh-Hofer S, Hofmann M, Minoshima S, Schwaiger M, Dirnagl U, Villringer A. Decrease in parietal cerebral hemoglobin oxygenation during performance of a verbal fluency task in patients with Alzheimer’s disease monitored by means of near-infrared spectroscopy (NIRS)–correlation with simultaneous rCBF-PET measurements. Brain Res. 1997;755(2):293–303.View ArticlePubMedGoogle Scholar
  37. Ymazaki K. ADHD-RS-IV Japanese version. In: Kanbayashi Y, Saito K, Kita M, editors. Japanese guideline for the diagnosis and treatment of attention deficit hyperactivity disorder (ADHD). Tokyo: Jiho; 2013. p. 48–54 (In Japanese).Google Scholar
  38. Golden CJ. A group version of the Stroop color and word test. J Pers Assess. 1975;39(4):386–8.View ArticlePubMedGoogle Scholar
  39. Schweitzer JB, Faber TL, Grafton ST, Tune LE, Hoffman JM, Kilts CD. Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. Am J Psychiatry. 2000;157(2):278–80.View ArticlePubMedGoogle Scholar
  40. Toronov V, Webb A, Choi JH, Wolf M, Michalos A, Gratton E, Hueber D. Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging. Med Phys. 2001;28(4):521–7.View ArticlePubMedGoogle Scholar
  41. Sawada M, Iida J, Ota T, Negoro H, Tanaka S, Sadamatsu M, Kishimoto T. Effects of osmotic-release methylphenidate in attention-deficit/hyperactivity disorder as measured by event-related potentials. Psychiatry Clin Neurosci. 2010;64(5):491–8.View ArticlePubMedGoogle Scholar
  42. Yamamuro K, Ota T, Iida J, Nakanishi Y, Matsuura H, Uratani M, Okazaki K, Kishimoto N, Tanaka S, Kishimoto T. Event-related potentials reflect the efficacy of pharmaceutical treatments in children and adolescents with attention deficit/hyperactivity disorder. Psychiatry Res. 2016;242:288–94.View ArticlePubMedGoogle Scholar
  43. Bush G, Holmes J, Shin LM, Surman C, Makris N, Mick E, Seidman LJ, Biederman J. Atomoxetine increases fronto-parietal functional MRI activation in attention-deficit/hyperactivity disorder: a pilot study. Psychiatry Res. 2013;211(1):88–91.View ArticlePubMedGoogle Scholar
  44. Bush G, Spencer TJ, Holmes J, Shin LM, Valera EM, Seidman LJ, Makris N, Surman C, Aleardi M, Mick E, Biederman J. Functional magnetic resonance imaging of methylphenidate and placebo in attention-deficit/hyperactivity disorder during the multi-source interference task. Arch Gen Psychiatry. 2008;65(1):102–14.View ArticlePubMedGoogle Scholar
  45. Schulz KP, Fan J, Bédard AC, Clerkin SM, Ivanov I, Tang CY, Halperin JM, Newcorn JH. Common and unique therapeutic mechanisms of stimulant and nonstimulant treatments for attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2012;69(9):952–61.View ArticlePubMedGoogle Scholar
  46. Chou TL, Chia S, Shang CY, Gau SS. Differential therapeutic effects of 12-week treatment of atomoxetine and methylphenidate on drug-naïve children with attention deficit/hyperactivity disorder: a counting Stroop functional MRI study. Eur Neuropsychopharmacol. 2015;25(12):2300–10.View ArticlePubMedGoogle Scholar
  47. Cubillo A, Smith AB, Barrett N, Giampietro V, Brammer MJ, Simmons A, Rubia K. Shared and drug-specific effects of atomoxetine and methylphenidate on inhibitory brain dysfunction in medication-naive ADHD boys. Cereb Cortex. 2014;24(1):174–85.View ArticlePubMedGoogle Scholar
  48. Volkow ND, Wang GJ, Fowler JS, Telang F, Maynard L, Logan J, Gatley SJ, Pappas N, Wong C, Vaska P, Zhu W, Swanson JM. Evidence that methylphenidate enhances the saliency of a mathematical task by increasing dopamine in the human brain. Am J Psychiatry. 2004;161(7):1173–80.View ArticlePubMedGoogle Scholar
  49. Volkow ND, Fowler JS, Wang GJ, Telang F, Logan J, Wong C, Ma J, Pradhan K, Benveniste H, Swanson JM. Methylphenidate decreased the amount of glucose needed by the brain to perform a cognitive task. PLoS ONE. 2008;3(4):e2017.View ArticlePubMedPubMed CentralGoogle Scholar
  50. 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(2):676–82.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Sonuga-Barke EJ, Castellanos FX. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci Biobehav Rev. 2007;31(7):977–86.View ArticlePubMedGoogle Scholar
  52. Castellanos FX, Proal E. Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends Cogn Sci. 2012;16(1):17–26.View ArticlePubMedGoogle Scholar
  53. Li CS, Yan P, Bergquist KL, Sinha R. Greater activation of the “default” brain regions predicts stop signal errors. Neuroimage. 2007;38(3):640–8.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, Schweitzer JB. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 2009;1273:114–28.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Kelly C, de Zubicaray G, Di Martino A, Copland DA, Reiss PT, Klein DF, Castellanos FX, Milham MP, McMahon K. L-dopa modulates functional connectivity in striatal cognitive and motor networks: a double-blind placebo-controlled study. J Neurosci. 2009;29(22):7364–78.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Tomasi D, Volkow ND, Wang R, Telang F, Wang GJ, Chang L, Ernst T, Fowler JS. Dopamine transporters in striatum correlate with deactivation in the default mode network during visuospatial attention. PLoS ONE. 2009;4(6):e6102.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Volkow ND, Wang GJ, Fowler JS, Logan J, Franceschi D, Maynard L, Ding YS, Gatley SJ, Gifford A, Zhu W, Swanson JM. Relationship between blockade of dopamine transporters by oral methylphenidate and the increases in extracellular dopamine: therapeutic implications. Synapse. 2002;43(3):181–7.View ArticlePubMedGoogle Scholar
  58. Bamford NS, Zhang H, Schmitz Y, Wu NP, Cepeda C, Levine MS, Schmauss C, Zakharenko SS, Zablow L, Sulzer D. Heterosynaptic dopamine neurotransmission selects sets of corticostriatal terminals. Neuron. 2004;42(4):653–63.View ArticlePubMedGoogle Scholar
  59. Rubia K, Halari R, Cubillo A, Smith AB, Mohammad AM, Brammer M, Taylor E. Methylphenidate normalizes fronto-striatal under activation during interference inhibition in medication-naïve boys with attention-deficit hyperactivity disorder. Neuropsychopharmacology. 2011;36(8):1575–86.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Epstein JN, Casey BJ, Tonev ST, Davidson MC, Reiss AL, Garrett A, Hinshaw SP, Greenhill LL, Glover G, Shafritz KM, Vitolo A, Kotler LA, Jarrett MA, Spicer J. ADHD- and medication-related brain activation effects in concordantly affected parent-child dyads with ADHD. J Child Psychol Psychiatry. 2007;48(9):899–913.View ArticlePubMedGoogle Scholar
  61. Peterson BS, Potenza MN, Wang Z, Zhu H, Martin A, Marsh R, Plessen KJ, Yu S. 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(11):1286–94.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Liddle EB, Hollis C, Batty MJ, Groom MJ, Totman JJ, Liotti M, Scerif G, Liddle PF. Task-related default mode network modulation and inhibitory control in ADHD: effects of motivation and methylphenidate. J Child Psychol Psychiatry. 2011;52(7):761–71.View ArticlePubMedGoogle Scholar
  63. Ishii-Takahashi A, Takizawa R, Nishimura Y, Kawakubo Y, Hamada K, Okuhata S, Kawasaki S, Kuwabara H, Shimada T, Todokoro A, Igarashi T, Watanabe K, Yamasue H, Kato N, Kasai K, Kano Y. Neuroimaging-aided prediction of the effect of methylphenidate in children with attention-deficit hyperactivity disorder: a randomized controlled trial. Neuropsychopharmacology. 2015;40(12):2676–85.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Matsuura N, Ishitobi M, Arai S, Kawamura K, Asano M, Inohara K, Fujioka T, Narimoto T, Wada Y, Hiratani M, Kosaka H. Effects of methylphenidate in children with attention deficit hyperactivity disorder: a near-infrared spectroscopy study with CANTAB®. Child Adolesc Psychiatry Ment Health. 2014;8(1):273.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Petrides M, Tomaiuolo F, Yeterian EH, Pandya DN. The prefrontal cortex: comparative architectonic organization in the human and the macaque monkey brains. Cortex. 2012;48(1):46–57.View ArticlePubMedGoogle Scholar
  66. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Tsujimoto S, Genovesio A, Wise SP. Evaluating self-generated decisions in frontal pole cortex of monkeys. Nat Neurosci. 2010;13(1):120–6.View ArticlePubMedGoogle Scholar
  68. Arai S, Okamoto Y, Fujioka T, Inohara K, Ishitobi M, Matsumura Y, Jung M, Kawamura K, Takiguchi S, Tomoda A, Wada Y, Hiratani M, Matsuura N, Kosaka H. Altered frontal pole development affects self-generated spatial working memory in ADHD. Brain Dev. 2016;38(5):471–80.View ArticlePubMedGoogle Scholar
  69. Shinba T, Nagano M, Kariya N, Ogawa K, Shinozaki T, Shimosato S, Hoshi Y. Near-infrared spectroscopy analysis of frontal lobe dysfunction in schizophrenia. Biol Psychiatry. 2004;55(2):154–64.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement