Structural brain alterations in autism: A large-scale voxel-based morphometry mega-analysis
Poster No:
449
Submission Type:
Abstract Submission
Authors:
Emily Laltoo1, Katherine Lawrence2, Sebastian Benavidez3, Emma Gleave1, James McCracken4, Paul Thompson5, Priya Rajagopalan1
Institutions:
1University of Southern California, Los Angeles, CA, 2Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, USC, Marina del Rey, CA, 3Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, 4Department of Psychiatry, University of California San Francisco, San Francisco, CA, 5Imaging Genetics Center, Keck School of Medicine of University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Katherine Lawrence
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, USC
Marina del Rey, CA
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, USC
Marina del Rey, CA
Sebastian Benavidez
Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California
Marina del Rey, CA
Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California
Marina del Rey, CA
Paul Thompson, PhD
Imaging Genetics Center, Keck School of Medicine of University of Southern California
Los Angeles, CA
Imaging Genetics Center, Keck School of Medicine of University of Southern California
Los Angeles, CA
Introduction:
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by alterations in social communication and by repetitive behaviors. ASD is three to four times more prevalent in boys compared to girls and has a widespread impact on individuals, including diminished overall quality of life. A recent large-scale study examined gross cortical morphometry and subcortical structures in ASD (Van Rooij, 2018). Here we examined whole-brain voxel-wise volumetric differences in ASD versus neurotypical controls in an unbiased manner as a knowledge of the specific brain structural changes associated with ASD may aid in diagnosis and therapy for ASD. We investigated voxel-wise brain correlates of ASD, including subgroups stratified by sex. We used a mega-analysis approach based on voxel-based morphometry (VBM) across multiple independent publicly available databases.
Methods:
We analyzed 3D T1-weighted structural brain MRI data from 3,407 participants (1,730 with autism, 1,677 neurotypical controls; 76% male; age: 4-64 years) across 47 publicly available datasets from the Autism Brain Imaging Data Exchange (ABIDE), NIMH Data Archive (NDA) and Healthy Brain Network (HBN). Regional gray and white matter morphometry was quantified voxelwise using the CAT12 segmentation and analysis toolbox. A regression analysis was carried out at each voxel across the brain, to assess gray and white matter volume differences between the ASD and neurotypical groups, using the multiple regression equation:
y=β0+βdxdx+ βageage+βagesquaredagesquared+βsexsex+βICVICV+βSiteSite+ε
Here the dependent variable 'y' is a vector representing voxel-wise gray and white matter volumes, dx denotes diagnosis of ASD, agesquared is the squared variable for de-meaned age as recommended in prior studies, ICV denotes intracranial volume and S denotes site. The error term ε accounts for unobserved factors and measurement error.
y=β0+βdxdx+ βageage+βagesquaredagesquared+βsexsex+βICVICV+βSiteSite+ε
Here the dependent variable 'y' is a vector representing voxel-wise gray and white matter volumes, dx denotes diagnosis of ASD, agesquared is the squared variable for de-meaned age as recommended in prior studies, ICV denotes intracranial volume and S denotes site. The error term ε accounts for unobserved factors and measurement error.
Results:
Relative to neurotypical controls, the ASD group showed significantly lower gray matter volumes in the frontal lobes, bilateral putamen, amygdala, thalami and cerebellum. White matter volumes were also lower in the forceps minor, forceps major, genu and splenium of corpus callosum, and the brain stem in the ASD group.
Within the stratified group of females (smaller in number than the males), we detected no significant differences in gray matter between ASD and controls. However, we found minimally lower white matter volume (standard-FDR critical P-value=0.001; q=0.05) in the brainstem.
Within the larger male cohort, significantly smaller gray matter volumes in frontal lobes, amygdala, thalami and cerebellum were noted in ASD compared to neurotypicals (standard-FDR critical P-value=0.004; q=0.05). Males with ASD also exhibited significantly smaller white matter volumes than neurotypical males (standard-FDR critical P-value=0.02; q=0.05) in the forceps minor, forceps major, genu and splenium of the corpus callosum, and the brain stem.
Within the stratified group of females (smaller in number than the males), we detected no significant differences in gray matter between ASD and controls. However, we found minimally lower white matter volume (standard-FDR critical P-value=0.001; q=0.05) in the brainstem.
Within the larger male cohort, significantly smaller gray matter volumes in frontal lobes, amygdala, thalami and cerebellum were noted in ASD compared to neurotypicals (standard-FDR critical P-value=0.004; q=0.05). Males with ASD also exhibited significantly smaller white matter volumes than neurotypical males (standard-FDR critical P-value=0.02; q=0.05) in the forceps minor, forceps major, genu and splenium of the corpus callosum, and the brain stem.
Conclusions:
Our big-data mega-analysis demonstrates voxel-wise brain structural associations of ASD. The smaller sample size likely contributed to less significant associations within the female cohort. Lower gray matter volumes include key regions previously implicated in ASD, including the amygdala (Van Rooij, 2018), cerebellum (Wang et al., 2016) and white matter alterations within the corpus callosum (Loomba et al., 2021) and brainstem (Hanaie et al., 2016), align with findings in the ASD literature. Although limited by a cross-sectional design, our findings offer insight into regional brain differences in ASD. Our study underscores the need to recruit more female participants and evaluation of sex-specific neurobiological characteristics in ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Imaging Methods Other
Keywords:
Autism
DISORDERS
MRI
Segmentation
STRUCTURAL MRI
Other - VBM
1|2Indicates the priority used for review
Provide references using author date format
Loomba, N., et al., (2021), ‘Corpus callosum size and homotopic connectivity in Autism spectrum disorder,’ Psychiatry Research: Neuroimaging, vol. 313, p. 111301.
Van Rooij, D., et al. (2018), ‘Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results from the ENIGMA ASD Working Group,’ AJP, vol. 175, no. 4, pp. 359–369.
Wang, S.S., et al., (2016), ‘The cerebellum, sensitive periods, and autism,’ Neuron, vol. 83, no. 3, pp. 518-532.