Stimulant Use in Adolescent ADHD is Associated with Attenuated and Distinct Cortical Differences
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
272
Submission Type:
Abstract Submission
Authors:
L. Nate Overholtzer1, Katherine Bottenhorn1, Yara Akiel2, Alethea de Jesus1, Hedyeh Ahmadi1, Megan Herting1
Institutions:
1Keck School of Medicine of USC, Los Angeles, CA, USA, 2University of Southern California, Los Angeles, CA, USA
First Author:
Co-Author(s):
Introduction:
Over six million children have been diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) in the U.S., making it the most prevalent neurodevelopmental disorder.1,2 Pharmacologic management of ADHD is the first-line treatment approach, aimed at mitigating behavioral symptoms, and is hypothesized to "normalize" brain structure.3,4 However, research on the potential impacts of ADHD medications on brain phenotypes has been limited by small, homogeneous samples, leading to inconsistent and often null findings regarding affected brain regions.4
Methods:
We used high-quality sMRI data of grey matter brain features (e.g., cortical thickness, surface area, and volume), medication history questionnaire, and ADHD KSADS assessments from 10,511 9- and 10-year-olds enrolled in the ABCD Study®.5 MRI acquisition protocols and assessments are optimized and harmonized across the 21 study sites for 3-Tesla MRI scanners and undergo robust quality control parameters.6 ABCD uses Freesurfer version 7.1 to quantify metrics from 68 cortical regions of interest (ROIs) labeled using the Desikan-Killaney atlas.7 Participants reporting concurrent use of other neurologic or psychiatric medications were excluded. Participants were classified into three groups: typically developing controls (TDC; n= 9,198), ADHD on medication (ADHD+RX; n= 755), and unmedicated ADHD (ADHD-noRX; n = 558). First, for each grey matter feature, 1000 cross-validated elastic net regression models in R8 identified brain regions predictive of ADHD medication status (i.e., ADHD+RX vs. ADHD-noRX). These predictive regions were then used for further hypothesis testing based on selection frequency in elastic net models weighted by model performance. Second, linear mixed-effects (LME) models in R9 assessed the impact of ADHD diagnosis and medication class (i.e., amphetamine, methylphenidate, or nonstimulant) on identified regions for each grey matter feature, including for sociodemographic fixed effects (e.g., age, sex, race/ethnicity, household income, parental education) and the random effect of ABCD site.
·Table 1. Sociodemographic characteristics of ABCD sample classified into three groups: typically developing controls (TDC), ADHD on medication (ADHD+RX), and unmedicated ADHD (ADHD-noRX).
Results:
Elastic net regression identified that ADHD medication usage (i.e., ADHD+RX vs. ADHD-noRX) was related to cortical thickness in 10 regions, surface area in 2, and volume in 9. LME modeling revealed individuals with ADHD had reduced cortical thickness in the right caudal anterior cingulate and right entorhinal cortex compared to TDC, while amphetamine use attenuated deficits in the entorhinal cortex. Amphetamine and methylphenidate use were associated with greater right inferior temporal and left isthmus cingulate cortex thickness. Individuals with ADHD had greater surface area and volume in larger right banks of the superior temporal sulcus and left posterior cingulate, whereas amphetamine and methylphenidate use were associated with reductions in these regions. Additionally, amphetamine was related to larger right lingual cortex volume, while methylphenidate use was linked to larger right cuneus, inferior temporal cortex, and temporal pole volumes, but smaller left superior parietal cortex volume.
·Figure 1. LME Model significant effects of ADHD, Amphetamine, and Methylphenidate plotted by brain region. Visualized by log(P-value).
Conclusions:
Stimulant medications (i.e., amphetamine and methylphenidate) appear to normalize ADHD-related differences in surface area and volume among 9- and 10-year-olds. Additionally, stimulant use was associated with differences in cortical thickness and cortical volume within brain regions that did not exhibit significant ADHD-status differences, suggesting broader neurodevelopmental impacts beyond ADHD-specific phenotypes. In contrast, nonstimulant medication use was not associated with significant alterations in brain MRI phenotypes. Longitudinal research across adolescence is needed to clarify the long-term neurodevelopmental impacts of these medications and to determine whether they persist, intensify, or diminish over time.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Attention Deficit Disorder
Development
Machine Learning
Modeling
PEDIATRIC
Psychiatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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