Evidence map›Paper›PMID 41542489›Full record

ArticlebioRxiv : the preprint server for biology2026

Cortical Thickness and Curvature in Autism and ADHD: A Mega-Analysis.

Maryam Mahmoudi, Lucille A Moore, Jacob Lundquist, Andrew Stier, Michael Anderson, Begim Fayzullobekova, Robert Hermosillo, Audrey Houghton, Thomas J Madison, Rae McCollum and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Maryam MahmoudiMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-1796-9734
Lucille A MooreMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0002-7665-2713
Jacob LundquistMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0001-9573-1954
Andrew StierMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0009-0009-0609-6174
Michael AndersonMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-4951-0397
Begim FayzullobekovaMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0009-0000-8014-4108
Robert HermosilloMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-2959-8483
Audrey HoughtonMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0001-9426-7969
Thomas J MadisonMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-3030-6580
Rae McCollumMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0009-0002-3730-9995
Kimberly B WeldonMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-4349-9916
Steve NelsonMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.
Amy EslerMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.
Oscar Miranda-DominguezMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0002-3622-0166
Damien A FairMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0001-8602-393X
Brenden Tervo-ClemmensMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0002-9557-1126
Eric FeczkoMasonic Institute for the Developing Brain, Department of Pediatrics, University of Minnesota.ORCID 0000-0003-1337-5517

Funding

Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Jonathan Saul Carp · 2014 to 2026
$15.4M
Leveraging complementary big data methods and patient intervention designs to optimize neural markers of adolescent cannabis useK23DA057486 · NIDA · UNIVERSITY OF MINNESOTA · PI Brenden Craig Tervo-Clemmens · 2023 to 2026
$782k
NIBIB NIH HHS P41 EB018783NIDA NIH HHS K23 DA057486
6 · The paper itself

Abstract

Background: Existing evidence suggests cortical morphometric alterations occur in people with autism and ADHD. However, these findings remain tentative due to small sample sizes, heterogeneous imaging pipelines, varied statistical approaches, and limited harmonization across acquisition sites. Few studies have applied standardized processing to large, clinically enriched datasets or addressed site-related batch effects. Methods: We leveraged six large-scale brain imaging datasets (n = 9,647; male=5,835; female=3,812; ages 5-64 years), including 1,533 individuals with ADHD, 1,080 with autism spectrum disorder, and 7,034 matched controls. All imaging data were processed using the validated ABCD-HCP pipeline, with cortical parcellation into 360 regions based on the Human Connectome Project (HCP) atlas, and ComBat harmonization was applied to account for variability across 67 acquisition sites. Group-level differences in cortical thickness and sulcal curvature were examined with ANCOVAs, controlling for covariates and using Bonferroni correction for multiple comparisons. Results: Our analyses revealed distinct neuroanatomical signatures for both autism and ADHD. Individuals with autism exhibited regionally thinner cortex and curvature alterations particularly in the Cingulo-Opercular network. In contrast, individuals with ADHD displayed regionally thicker cortex, particularly in the default mode and somatomotor networks, alongside curvature differences. Control participants showed intermediate patterns, suggesting that autism and ADHD may represent diverging extremes of cortical maturation. Conclusions: Cortical thickness and curvature emerge as potential biomarkers that can advance understanding of neurodevelopmental conditions and disentangle heterogeneity across diagnostic groups. These findings highlight the value of harmonized, large-scale, standardized analyses for resolving inconsistencies in the literature.

Indexed as

ADHDAutismCortical CurvatureCortical ThicknessData HarmonizationStructural MRI

Identifiers

PMID41542489
PMCPMC12803090

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.