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

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ryan Plunkett ◽  
Emily Iannopollo ◽  
Chris Basinski ◽  
Kara Garcia
Keyword(s):  
Autism Spectrum Disorder ◽  
Human Brain ◽  
The United States ◽  
Autism Spectrum ◽  
Magnetic Resonance Images ◽  
Growth Patterns ◽  
Spectrum Disorder ◽  
Individual Growth ◽  
Surface Matching ◽  
Brain Growth

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)

Exploring Growth in Expressive Communication of Infants and Toddlers With Autism Spectrum Disorder

2021 ◽  
pp. 105381512199557
Author(s):  
Jay Buzhardt ◽  
Anna Wallisch ◽  
Dwight Irvin ◽  
Brian Boyd ◽  
Brenda Salley ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Progress Monitoring ◽  
Autism Spectrum ◽  
Growth Patterns ◽  
Spectrum Disorder ◽  
Future Research ◽  
Infants And Toddlers ◽  
Early Communication ◽  
Children With Asd ◽  
Language Growth

One of the earliest indicators of autism spectrum disorder (ASD) is delay in language and social communication. Despite consensus on the benefits of earlier diagnosis and intervention, our understanding of the language growth of children with ASD during the first years of life remains limited. Therefore, this study compared communication growth patterns of infants and toddlers with ASD to growth benchmarks of a standardized language assessment. We conducted a retrospective analysis of growth on the Early Communication Indicator (ECI) of 23 infants and toddlers who received an ASD diagnosis in the future. At 42 months of age, children with ASD had significantly lower rates of gestures, single words, and multiple words, but significantly higher rates of nonword vocalizations. Children with ASD had significantly slower growth of single and multiple words, but their rate of vocalization growth was significantly greater than benchmark. Although more research is needed with larger samples, because the ECI was designed for practitioners to monitor children’s response to intervention over time, these findings show promise for the ECI’s use as a progress monitoring measure for young children with ASD. Limitations and the need for future research are discussed.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Genetic and environmental influences on structural brain measures in twins with autism spectrum disorder

2019 ◽  
Vol 25 (10) ◽  
pp. 2556-2566 ◽  
Author(s):  
John P. Hegarty ◽  
Luiz F. L. Pegoraro ◽  
Laura C. Lazzeroni ◽  
Mira M. Raman ◽  
Joachim F. Hallmayer ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Environmental Factors ◽  
Cortical Thickness ◽  
Genetic Factors ◽  
Brain Size ◽  
Environmental Influences ◽  
Autism Spectrum ◽  
Growth Patterns ◽  
Spectrum Disorder ◽  
Structural Brain

Abstract Atypical growth patterns of the brain have been previously reported in autism spectrum disorder (ASD) but these alterations are heterogeneous across individuals, which may be associated with the variable effects of genetic and environmental influences on brain development. Monozygotic (MZ) and dizygotic (DZ) twin pairs with and without ASD (aged 6–15 years) were recruited to participate in this study. T1-weighted MRIs (n = 164) were processed with FreeSurfer to evaluate structural brain measures. Intra-class correlations were examined within twin pairs and compared across diagnostic groups. ACE modeling was also completed. Structural brain measures, including cerebral and cerebellar gray matter (GM) and white matter (WM) volume, surface area, and cortical thickness, were primarily influenced by genetic factors in TD twins; however, mean curvature appeared to be primarily influenced by environmental factors. Similarly, genetic factors accounted for the majority of variation in brain size in twins with ASD, potentially to a larger extent regarding curvature and subcortical GM; however, there were also more environmental contributions in twins with ASD on some structural brain measures, such that cortical thickness and cerebellar WM volume were primarily influenced by environmental factors. These findings indicate potential neurobiological outcomes of the genetic and environmental risk factors that have been previously associated with ASD and, although preliminary, may help account for some of the previously outlined neurobiological heterogeneity across affected individuals. This is especially relevant regarding the role of genetic and environmental factors in the development of ASD, in which certain brain structures may be more sensitive to specific influences.

scholarly journals Pregnant Mothers’ Medical Claims and Associated Risk of Their Children being Diagnosed with Autism Spectrum Disorder

2021 ◽  
Vol 11 (10) ◽  
pp. 950
Author(s):  
Genevieve Grivas ◽  
Richard Frye ◽  
Juergen Hahn
Keyword(s):  
Autism Spectrum Disorder ◽  
First Trimester ◽  
The United States ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Administrative Claims ◽  
Medical Claims ◽  
Increased Risk ◽  
Geographical Regions ◽  
Tract Infections

A retrospective analysis of administrative claims containing a diverse mixture of ages, ethnicities, and geographical regions across the United States was conducted in order to identify medical events that occur during pregnancy and are associated with autism spectrum disorder (ASD). The dataset used in this study is comprised of 123,824 pregnancies of which 1265 resulted in the child being diagnosed with ASD during the first five years of life. Logistic regression analysis revealed significant relationships between several maternal medical claims, made during her pregnancy and segmented by trimester, and the child’s diagnosis of ASD. Having a biological sibling with ASD, maternal use of antidepressant medication and psychiatry services as well as non-pregnancy related claims such hospital visits, surgical procedures, and radiology exposure were related to an increased risk of ASD regardless of trimester. Urinary tract infections during the first trimester and preterm delivery during the second trimester were also related to an increased risk of ASD. Preventative and obstetrical care were associated with a decreased risk for ASD. A better understanding of the medical factors that increase the risk of having a child with ASD can lead to strategies to decrease risk or identify those children who require increased surveillance for the development of ASD to promote early diagnosis and intervention.

scholarly journals Prevalence and Treatment Patterns of Autism Spectrum Disorder in the United States, 2016

2019 ◽  
Vol 173 (2) ◽  
pp. 153 ◽  
Author(s):  
Guifeng Xu ◽  
Lane Strathearn ◽  
Buyun Liu ◽  
Matthew O’Brien ◽  
Todd G. Kopelman ◽  
...  
Keyword(s):  
United States ◽  
Autism Spectrum Disorder ◽  
The United States ◽  
Autism Spectrum ◽  
Treatment Patterns ◽  
Spectrum Disorder
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