2022
DOI: 10.1503/jpn.210074
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Subcortical volumetric alterations as potential predictors of methylphenidate treatment response in youth with attention-deficit/hyperactivity disorder

Abstract: Background: Patients with attention-deficit/hyperactivity disorder (ADHD) show structural alterations in the subcortical and dopaminergic regions of the brain. Methylphenidate is a first-line treatment for ADHD, and it is known to affect the subcortical and dopaminergic systems. The degree of pretreatment structural alterations in patients with ADHD may be an important factor in predicting methylphenidate treatment outcomes. The present study examined whether pretreatment volumetric alterations in the subcorti… Show more

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Cited by 8 publications
(4 citation statements)
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“… Encompassed Baribeau et al (2019), Bitter et al (2011), Kim et al (2022), Lee et al (2021), Tajima‐Pozo et al (2018), and Wang et al (2018). …”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“… Encompassed Baribeau et al (2019), Bitter et al (2011), Kim et al (2022), Lee et al (2021), Tajima‐Pozo et al (2018), and Wang et al (2018). …”
Section: Resultsmentioning
confidence: 99%
“…Therefore, the ES was not adjusted (Hedges's g = 0.43). In contrast, using volume-based measurement, the combined results from 10 data sets demonstrated a nonsignificant difference between participants with ADHD and their controls (Hedges' g = −0.01, p = .94, Figure 2; Baribeau et al, 2019;Bitter et al, 2011;Filipek et al, 1997;Hoogman et al, 2017;Kim et al, 2022;Lee et al, 2021;Tajima-Pozo et al, 2018;Wang et al, 2018). The mega data from Hoogman et al (2017) were divided into three data sets, namely children, adolescents, and adults.…”
Section: Quantitative Data Analysismentioning
confidence: 97%
“…Pre-treatment subcortical volumes have been associated with treatment response to methylphenidate (MPH), with responders showing smaller volumes than non-responders [ 18 ] in one study but greater gray matter concentration [ 19 ] in another study (Table 1 ). A single study using diffusion-weighted imaging found that a machine learning algorithm could predict better MPH response from higher values of local efficiency (reflecting how efficiently information can be distributed between a brain region and its neighbors) within the thalamus, precentral gyrus and superior frontal gyrus [ 20 ].…”
Section: Predictive Biomarkersmentioning
confidence: 99%
“… Hong et al [ 24 ] South Korea 83 (medication-free for >4 weeks, and with no history of long-term treatment for ADHD, defined as medicated >6 months) 22 M = 9.63 SD = 2.61 25% Not reported 8-week RCT of MPH, pre-treatment neuroimaging Resting-state connectivity assessed using striatal seeds Treatment responders ( n = 48), defined according to the CGI-I at 8-weeks, showed greater pre-treatment connectivity between striatal seeds and orbitofrontal, cingulo-opercular and middle and medial temporal regions than did non-responders. Kim et al [ 18 ] South Korea 67 (medication-free for >4 weeks, and with no history of long-term treatment for ADHD, defined as medicated >6 months) 25 M = 9.83 SD = 2.5 25% Not reported 8-week RCT of MPH, pre-treatment neuroimaging Subcortical volume Responders had smaller volumes in bilateral amygdala and hippocampal subregions and right thalamus than non-responders. Lam et al [ 29 ] UK 31 (24 subjects were receiving stable medication) 0 M = 13.90 SD = 1.58 100% Not reported 2-week real-time fMRI neurofeedback of the right IFG vs. neurofeedback of a control para-hippocampal region.…”
Section: Predictive Biomarkersmentioning
confidence: 99%