A Mega-Analysis Of Cortical Thickness in Autistic, ADHD, and Typically Developing Individuals
Poster No:
341
Submission Type:
Abstract Submission
Authors:
Maryam Mahmoudi1, Lucile Moore1, Andrew Stier1, Robert Hermosillo2, Thomas Madison1, Michael Anderson1, Begim Fayzullobekova1, Audrey Houghton1, Jacob Lundquist3, rae McCollum1, Kimberly Weldon1, Eric Earl4, Oscar Miranda-Dominguez1, Amy Esler1, Brenden Tervo-Clemmens1, Damien Fair1, Eric Feczko1
Institutions:
1University of Minnesota, Minneapolis, MN, 2Oregon Health & Science University, Portland, OR, 3Masonic Institute for the Developing Brain, UMN, Minneapolis, MN, 4National Institute of Mental Health, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
Cortical thickness is associated with overall intelligence, visual-motor, problem-solving, and language development (Schnack et al., 2015; Menary et al., 2013 ). Reduced cortical thickness correlates with greater ADHD symptom severity (Li et al., 2022). Further, decreased gray matter cytoarchitecture in autism suggests altered neural organizations (Christensen et al., 2024). Collectively, these findings point to regional cortical thickness estimates as potential biomarkers for autism and ADHD.
However, existing literature on ADHD and autism cortical thickness is inconclusive. Compared to neurotypical individuals, autistic adults show increased thickness in the frontal (Boedhoe et al., 2020) and superior temporal cortices, while ADHD displays globally thicker and regionally thinner cortices (Bedford et al., 2024). A meta-analysis (You et al., 2024) reported reduced cortical thickness in autism and ADHD in the right temporoparietal junction subareas linked to the default mode network. Additionally, motor areas show thinner cortices in ADHD, but increased thickness in autism.
Inconclusive findings likely result from methodological heterogeneity and small sample sizes. Recent studies using large datasets have not applied consistent image processing or removed site batch effects (Monaghan et al., 2024). Therefore, we examined cortical thickness correlates with ADHD and autism using aggregated data across 66 sites processed with the same image processing pipeline and corrected for site effects. Datasets included Adolescent Brain Cognitive Development (ABCD) study, Oregon Health and Science University's (OHSU) ADHD, OHSU Autism, the Autism Brain Imaging Data Exchange (ABIDE I & II), and the Healthy Brain Network (HBN).
However, existing literature on ADHD and autism cortical thickness is inconclusive. Compared to neurotypical individuals, autistic adults show increased thickness in the frontal (Boedhoe et al., 2020) and superior temporal cortices, while ADHD displays globally thicker and regionally thinner cortices (Bedford et al., 2024). A meta-analysis (You et al., 2024) reported reduced cortical thickness in autism and ADHD in the right temporoparietal junction subareas linked to the default mode network. Additionally, motor areas show thinner cortices in ADHD, but increased thickness in autism.
Inconclusive findings likely result from methodological heterogeneity and small sample sizes. Recent studies using large datasets have not applied consistent image processing or removed site batch effects (Monaghan et al., 2024). Therefore, we examined cortical thickness correlates with ADHD and autism using aggregated data across 66 sites processed with the same image processing pipeline and corrected for site effects. Datasets included Adolescent Brain Cognitive Development (ABCD) study, Oregon Health and Science University's (OHSU) ADHD, OHSU Autism, the Autism Brain Imaging Data Exchange (ABIDE I & II), and the Healthy Brain Network (HBN).
Methods:
After data cleaning and excluding individuals with other clinical diagnoses, individuals without confirmed autism or ADHD diagnoses, or labeled controls, we had 1,527 ADHD, 1,103 autistic, 112 autism-ADHD, and 7,058 control participants. Participants with dual diagnoses were excluded from the analyses due to their small sample size.
We processed structural MRI data from the ABCD, HBN, OHSU ASD, OHSU ADHD, ABIDE I, and ABIDE II datasets using the ABCD-HCP pipeline. We parcellated cortical thickness grayordinates using the MIDB brain atlas and extracted cortical thickness values across 15 brain networks. Given MRI data collection across 66 sites, we harmonized data using NeuroCombat to reduce site effects. Statistical analyses were performed using ANOVA controlling for age and sex, pairwise t-tests, and effect size calculations using Cohen's d. P values were adjusted using Bonferroni correction for multiple comparisons.
We processed structural MRI data from the ABCD, HBN, OHSU ASD, OHSU ADHD, ABIDE I, and ABIDE II datasets using the ABCD-HCP pipeline. We parcellated cortical thickness grayordinates using the MIDB brain atlas and extracted cortical thickness values across 15 brain networks. Given MRI data collection across 66 sites, we harmonized data using NeuroCombat to reduce site effects. Statistical analyses were performed using ANOVA controlling for age and sex, pairwise t-tests, and effect size calculations using Cohen's d. P values were adjusted using Bonferroni correction for multiple comparisons.
Results:
Our findings demonstrate that data harmonization effectively decreases batch effects, as shown by cortical thickness clustering before harmonization and dispersed site effects afterward (figure 1). Pairwise comparisons for unharmonized and harmonized data (figure 2) showed that harmonized data has fewer areas that are significantly different from unharmonized data. Further, pairwise comparison showed thinner cortices in autism compared to controls and ADHD across networks including Dorsal Attention, Default Mode, Frontoparietal, Sensorimotor Medial, and Parietal Medial. In contrast, ADHD exhibited thicker cortices in these regions. The largest differences were observed between the autism and ADHD groups.
·Figure 1. PCA Plots: Unharmonized vs. Harmonized Data Corrected for Batch (Site) Effects
·Figure 2. Pairwise Comparison of Cortical Thickness: Cohen's d Effect Sizes
Conclusions:
Our results highlight the significance of data harmonization in multi-site neuroimaging studies. Our results indicate distinct cortical thickness profiles in autism and ADHD where autism shows the thinnest and ADHD shows the thickest cortices across brain networks. These results underscore the need for standard, consistent, and reproducible methodologies in large-scale neuroimaging studies to better understand neurodevelopmental conditions.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Attention Deficit Disorder
Autism
Morphometrics
MRI
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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