An Association Between Serotonin
            1A
            Receptor, Gray Matter Volume, and Sociability in Healthy Subjects and in Autism Spectrum Disorder

Background. To establish whether high-functioning children with autism spectrum disorder (ASD) have enlarged brains in later childhood, and if so, whether this enlargement is confined to the gray and/or to the white matter and whether it is global or more prominent in specific brain regions.Method. Brain MRI scans were acquired from 21 medication-naive, high-functioning children with ASD between 7 and 15 years of age and 21 comparison subjects matched for gender, age, IQ, height, weight, handedness, and parental education, but not pubertal status.Results. Patients showed a significant increase of 6% in intracranium, total brain, cerebral gray matter, cerebellum, and of more than 40% in lateral and third ventricles compared to controls. The cortical gray-matter volume was evenly affected in all lobes. After correction for brain volume, ventricular volumes remained significantly larger in patients.Conclusions. High-functioning children with ASD showed a global increase in gray-matter, but not white-matter and cerebellar volume, proportional to the increase in brain volume, and a disproportional increase in ventricular volumes, still present after correction for brain volume. Advanced pubertal development in the patients compared to the age-matched controls may have contributed to the findings reported in the present study.

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Gray matter abnormalities in autism spectrum disorder revealed by T2 relaxation

Neurology ◽

10.1212/01.wnl.0000229923.08213.1e ◽

2006 ◽

Vol 67 (4) ◽

pp. 632-636 ◽

Cited By ~ 26

Author(s):

H. Petropoulos ◽

S. D. Friedman ◽

D.W.W. Shaw ◽

A. A. Artru ◽

G. Dawson ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Gray Matter ◽

Autism Spectrum ◽

Spectrum Disorder ◽

T2 Relaxation

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Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping

European Child & Adolescent Psychiatry ◽

10.1007/s00787-017-0964-4 ◽

2017 ◽

Vol 26 (8) ◽

pp. 933-945 ◽

Cited By ~ 19

Author(s):

Jieke Liu ◽

Li Yao ◽

Wenjing Zhang ◽

Yuan Xiao ◽

Lu Liu ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Gray Matter ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Signed Differential Mapping

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Meta-analysis of Gray Matter Abnormalities in Autism Spectrum Disorder

Archives of General Psychiatry ◽

10.1001/archgenpsychiatry.2011.27 ◽

2011 ◽

Vol 68 (4) ◽

pp. 409 ◽

Cited By ~ 124

Author(s):

Esther Via ◽

Joaquim Radua ◽

Narcis Cardoner ◽

Francesca Happé ◽

David Mataix-Cols

Keyword(s):

Autism Spectrum Disorder ◽

Gray Matter ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder

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60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

Paediatrics & Child Health ◽

10.1093/pch/pxaa068.059 ◽

2020 ◽

Vol 25 (Supplement_2) ◽

pp. e25-e25

Author(s):

Sarah MacEachern ◽

Deepthi Rajashekar ◽

Pauline Mouches ◽

Nathan Rowe ◽

Emily Mckenna ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Deep Learning ◽

Gray Matter ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Classification Model ◽

Children With Asd ◽

Adc Values ◽

Deep Gray Matter ◽

The Brain

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

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