Neuropharmacological effect of methylphenidate on attention network in children with attention deficit hyperactivity disorder during oddball paradigms as assessed using functional near-infrared spectroscopy

Abstract

Abstract. The current study aimed to explore the neural substrate for methylphenidate effects on attentional control in school-aged children with attention deficit hyperactivity disorder (ADHD) using functional near-infrared spectroscopy (fNIRS), which can be applied to young children with ADHD more easily than conventional neuroimaging modalities. Using fNIRS, we monitored the oxy-hemoglobin signal changes of 22 ADHD children (6 to 14 years old) performing an oddball task before and 1.5 h after methylphenidate or placebo administration, in a randomized, double-blind, placebo-controlled, crossover design. Twenty-two age-and gender-matched normal controls without methylphenidate administration were also monitored. In the control subjects, the oddball task recruited the right prefrontal and inferior parietal cortices, and this activation was absent in premedicated ADHD children. The reduced right prefrontal activation was normalized after methylphenidate but not placebo administration in ADHD children. These results are consistent with the neuropharmacological effects of methylphenidate to upregulate the dopamine system in the prefrontal cortex innervating from the ventral tegmentum (mesocortical pathway), but not the noradrenergic system from the parietal cortex to the locus coeruleus. Thus, right prefrontal activation would serve as an objective neurofunctional biomarker to indicate the effectiveness of methylphenidate on ADHD children in attentional control. fNIRS monitoring enhances early clinical diagnosis and the treatment of ADHD children, especially those with an inattention phenotype. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Neuropharmacological effect of atomoxetine on attention network in children with attention deficit hyperactivity disorder during oddball paradigms as assessed using functional near-infrared spectroscopy

“…fNIRS, which is convenient and robust, is suitable for the functional monitoring of children with ADHD who have difficulty performing active cognitive tasks in the enclosed environments of other imaging modalities. In fact, the cumulative postscan exclusion rate of our previous fNIRS studies (all studies) [25][26][27][28] was <5% of a total of 66 right-handed ADHD children and 54 control subjects including six year olds. [25][26][27][28] This is far lower than the rejection rate of functional magnetic resonance imaging (fMRI) studies, which is typically 50% for ADHD subjects six years old and older and 30% for the corresponding normal controls.…”

Exploring effective multiplicity in multichannel functional near-infrared spectroscopy using eigenvalues of correlation matrices

“…Experimental data used in this study were obtained from three of our previous, separate studies [29][30][31] and reanalyzed from different perspectives. Here, we briefly describe the experimental procedures.…”

“…This was in order to cover a wide range of activation patterns from a focused and lateralized activation pattern evoked by an OBT in healthy children, 29 to a moderately focused and lateralized activation pattern evoked by a CFT in healthy adults, 30 and to a broad and less lateralized activation pattern evoked by an NMT in healthy adults. 31 Our first expectation was to detect changes in M eff associated with degrees of focus.…”

“…For the resampling simulations, we selected, from published studies, three kinds of experimental data with different activation profiles: OBT data from children with lateralized focused activation (task 1), 29 CFT data from adults with lateralized moderately focused activation (task 2), 30 and NMT data from adults with bilateral broad activation (task 3). 31 The β-values of a general linear model (GLM) with regression to a hemodynamic response function or the average values of oxy-Hb signals were used as summary data.…”

“…Specifically, after the blocks with considerable artifacts were removed, for each channel of each subject the averaged differences in oxy-Hb values between each task and the preceding baseline periods were further averaged across the task blocks. 29 To find the relationship between the number of channels and the M eff , we randomly resampled a given number channels (m∶ð1 ≤ m ≤ 44Þ) from each dataset and calculated M eff . For each m, resampling was performed 1000 times, and the average value and standard deviation were calculated.…”