Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.01.05.697782

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

Abstract

BackgroundExisting 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. MethodsWe 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. ResultsOur 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. ConclusionsCortical 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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mahmoudi, M., Moore, L. A., Lundquist, J. T., Stier, A., Anderson, M., Fayzullobekova, B., Hermosillo, R., Houghton, A., Madison, T. J., McCollum, r., Weldon, K. B., Esler, A., Nelson, S., Miranda-Dominguez, O., Fair, D. A., Tervo-Clemmens, B., Feczko, E.. 2026-01-06. Cortical Thickness and Curvature in Autism and ADHD: A Mega-Analysis. https://doi.org/10.64898/2026.01.05.697782

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Different hippocampal subfield volumes predict source memory performance and general cognitive ability in an adult lifespan sample

Modest positive associations between episodic memory performance and whole hippocampal and hippocampal subfield volumes have been reported in numerous prior studies. A smaller number of studies have reported associations between hippocampal volume and performance on tests of non-mnemonic cognition. The present study examined whether these associations were evident in a lifespan sample of cognitively healthy adults. Of particular interest was whether any identified associations were sensitive to age, and whether associations between subfield volumes and mnemonic and non-mnemonic performance were subfield dependent. We acquired high-resolution T1- and T2-weighted structural images from 163 adults (18-87 years of age). Participants also undertook a comprehensive neuropsychological test battery and an in-scanner test of source memory. Principal components analysis was employed to reduce the neuropsychological test scores to 5 cognitive components. Two components reflected memory performance while the other three reflected different aspects of non-mnemonic cognition. Hippocampal subfields (Cornu Ammonis (CA)1, CA2-3, dentate gyrus (DG) and subiculum) were segmented and measured with the Automated Segmentation of Hippocampus Subfields (ASHS) package. Source memory performance was selectively associated across participants with CA2-3 volume. By contrast, both mnemonic and non-mnemonic component scores derived from the test battery were associated exclusively with the volume of the DG. All associations were age-invariant. The findings indicate that different cognitive domains can be dissociated by virtue of their associations with different hippocampal subfields. Of importance, these associations appear to be life-long and hence are unlikely to reflect individual differences in age-related decline in structural integrity.

neuroscience↗

Cell type specific astrocytic feedback regulates excitation inhibition balance and cortical network dynamics

Astrocytes actively regulate synaptic transmission and neuronal excitability, yet their role in orchestrating macroscopic cortical network regimes and slow-wave oscillations remains an active area of reasearch. This study investigates how bidirectional neuron astrocyte interactions shape emergent population dynamics using a computational network model of excitatory and inhibitory neurons coupled to an astrocyte. The results identify astrocytic feedback topology, rather than astrocytic coupling strength alone, as a key determinant of emergent cortical network dynamics. By systematically dissecting pathway-specific connectivity, it has been shown that the neuronal population driving astrocytic activation and the neuronal population receiving gliotransmission jointly determine whether the network occupies asynchronous irregular (AI), synchronous irregular (SI), synchronous regular(SR), asynchronous regular(AR) or quiescent regimes.Directing gliotransmission selectively onto excitatory neurons consistently promotes population synchrony regardless of the population influencing astrocytic dynamics, whereas selective modulation of inhibitory interneurons induces network quiescence via strong suppression. Under dual-target gliotransmission, network synchrony is dictated by the population driving astrocytic dynamics: excitatory-only drive promotes synchrony, while combined or inhibitory-specific drive preserves asynchronous states. Furthermore, the model reveals that astrocytic signaling kinetics provide an additional temporal control mechanism that regulates the frequency and persistence of self sustained up states.

neuroscience↗

VCP inhibition prevents cone photoreceptor degeneration in the cpfl1 mouse model of achromatopsia

Achromatopsia (ACHM) is a rare autosomal recessive retinal disorder characterized by absent cone photoreceptor function from early life, leading to severe visual impairment. Mutations in genes involved in the cone phototransduction cascade frequently result in elevated cyclic guanosine monophosphate (cGMP) levels and activation of stress pathways, including endoplasmic reticulum (ER) stress and the unfolded protein response. Targeting common downstream mechanisms rather than individual mutations may provide a broadly applicable therapeutic strategy. Here, we investigated whether pharmacological inhibition of valosin-containing protein (VCP), a key regulator of ER and protein homeostasis, can prevent cone degeneration in the spontaneous cone photoreceptor function loss 1 (cpfl1) mouse model of ACHM. Organotypic culture of retinal explants from cpfl1 mice were treated with the selective VCP inhibitor ML240. Cone survival, cell death, opsin expression and localization were assessed by TUNEL assay, immunohistochemistry, and quantitative image analysis. ML240 treatment significantly increased cone density and improved cone opsin expression and trafficking to the outer segments (OSs) in cpfl1 explants compared to controls. Importantly, rhodopsin trafficking in rod photoreceptors was unaffected, indicating that VCP inhibition did not impair normal rod phototransduction. These findings demonstrate that VCP inhibition by ML240 effectively preserves cone photoreceptors and improves cone-specific functional markers in the cpfl1 model. Targeting VCP may represent a mutation-independent therapeutic strategy for preventing cone death in ACHM.

neuroscience↗