Cortical growth patterns in relation to autism spectrum disorder in ages 1-2 years

Background and Hypothesis: Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder with a prevalence of 2.76% among children ages 3-17 in the United States1. Some studies have linked total brain volume overgrowth or gyrification changes to ASD2,3,4. However, few have attempted to relate specific growth patterns to ASD. We hypothesize that regional differences in brain growth in subjects aged 12-24 months will correlate with diagnoses from the Autism Diagnostic Observation Schedule (ADOS). Project Methods: The subjects for this study came from the Infant Brain Imaging Study (IBIS)5. The CIVET pipeline was used to segment T1-weighted magnetic resonance images (MRIs) into surfaces using a non-linear classification method5,6,7. CIVET quality control outputs were used for validation and to select parameters for the tasks along with previous recommendations5,8. Analysis of Functional NeuroImages (AFNI) was used to convert the CIVET output format, and Connectome Workbench was used to calculate surface curvature. Using cortical reconstructions and surface curvatures from 12- and 24-month brains, anatomically-constrained Multimodal Surface Matching (aMSM) was applied to achieve point correspondence and generate individual cortical growth maps9,10. Results: Within the IBIS database, we found 38 individuals with ASD and 121 controls with T1weighted scans at both 12 and 24-month time points. Once individual growth maps have been generated for all subjects, Permutation Analysis of Linear Models (PALM)11 will be used to determine statistically significant differences in the cortical growth patterns of ASD versus control groups. Conclusion and Potential Impact: Research on autism may benefit from longitudinal studies of growth, as opposed to analysis of structural differences at later ages4. We concentrate on cortical growth before 24 months, which may serve as an earlier marker of ASD, when abnormal brain growth can be seen yet social deficits are not fully established5. [1] Zablotsky B, Black LI, Blumberg SJ. Estimated Prevalence of Children With Diagnosed Developmental Disabilities in the United States, 2014–2016. NCHS Data Brief 2017. https://www.cdc.gov/nchs/data/databriefs/db291.pdf (accessed April 29, 2019). [2] Libero LE, Schaer M, Li DD, Amaral DG, Nordahl CW. A Longitudinal Study of Local Gyrification Index in Young Boys With Autism Spectrum Disorder. Cereb Cortex. 2019;29(6):2575-87. [3] Raznahan A, Toro R, Daly E, Robertson D, Murphy C, Deeley Q, et al. Cortical anatomy in autism spectrum disorder: an in vivo MRI study on the effect of age. Cereb Cortex. 2010;20(6):1332-40. [4] Duret P, Samson F, Pinsard B, Barbeau EB, Bore A, Soulieres I, et al. Gyrification changes are related to cognitive strengths in autism. Neuroimage Clin. 2018;20:415-23. [5] Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542(7641):348-51. [6] Shaw P, Malek M, Watson B, Sharp W, Evans A, Greenstein D. Development of cortical surface area and gyrification in attentiondeficit/hyperactivity disorder. Biol Psychiatry. 2012;72(3):191-7. [7] Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J.-S., Mok, K., Ivanov, O., Vincent, R.D., Lepage, C., Lerch, J., Fombonne, E., and Evans, A.C. (2006). The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping, M. Corbetta, ed. (Florence, Italy, NeuroImage). http://www.bic.mni.mcgill.ca/users/yaddab/Yasser-HBM2006-Poster.pdf [8] Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, et al. Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci. 2008;28(14):3586-94. [9] Garcia KE, Robinson EC, Alexopoulos D, Dierker DL, Glasser MF, Coalson TS, et al. Dynamic patterns of cortical expansion during folding of the preterm human brain. Proc Natl Acad Sci U S A. 2018;115(12):3156-61. [10] Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, et al. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. [11] Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 (Open Access)