Poster No:
261
Submission Type:
Abstract Submission
Authors:
Chen Bai1, Xingzhu Li2, Xianna Wang2, Tianyu Jin3, Yan Zhang4, Zhenbo Chen5, Tong Zhang2
Institutions:
1Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 2School of Rehabilitation Medcine, Capital Medical University, beijing, beijing, 3The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 4Department of Pediatric Rehabilitation, Beijing Bo’ai Hospital, China Rehabilitation Research Center, Beijing, Beijing, 5Department of radiology, Beijing Bo’ai Hospital, China Rehabilitation Research Center, Beijing , beijing
First Author:
Chen Bai
Shandong Provincial Hospital Affiliated to Shandong First Medical University
Jinan, Shandong
Co-Author(s):
Xingzhu Li
School of Rehabilitation Medcine, Capital Medical University
beijing, beijing
Xianna Wang
School of Rehabilitation Medcine, Capital Medical University
beijing, beijing
Tianyu Jin
The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Yan Zhang
Department of Pediatric Rehabilitation, Beijing Bo’ai Hospital, China Rehabilitation Research Center
Beijing, Beijing
Zhenbo Chen
Department of radiology, Beijing Bo’ai Hospital, China Rehabilitation Research Center
Beijing , beijing
Tong Zhang
School of Rehabilitation Medcine, Capital Medical University
beijing, beijing
Introduction:
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with atypical neural connectivity patterns. The characteristics of white matter (WM) connectivity in young children with ASD and its potential as biomarkers have not been fully revealed.
Methods:
In this study, diffusion tensor imaging (DTI) technology was employed to investigate 64 children with ASD, aged 1.9-7 years, and 35 age-matched typically developing (TD) children. WM microstructural differences between ASD and TD children were compared using tract-based spatial statistics and region of interest methods. Correlational analyses were conducted to explore the association between WM alterations in ASD children and behavioral characteristics. Furthermore, machine learning techniques were used to assess the performance of using WM microstructural features in classifying ASD and TD children.
Results:
Our findings indicate widespread WM microstructural abnormalities in young children with ASD, including reduced fractional anisotropy (FA) values in the bilateral superior longitudinal fasciculus (SLF), bilateral posterior thalamic radiation (PTR), and the right retrolenticular part of internal capsule (RIC). Increased mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) values were observed in multiple fiber tracts. In children with ASD under the age of 4 years, the primary WM abnormalities were increased MD and AD values in in fiber tracts such as left anterior corona radiata (ACR), right superior corona radiata (SCR), and right PTR. Hemispheric asymmetry was observed in both ASD and TD children, with no significant differences between the two groups. Correlational analysis revealed that increased MD and AD values in the right RIC, and increased AD values in the left external capsule were associated with social dysfunction in young children with ASD. In addition, the naive bayes (NB) model reached the best performance in classifying ASD from TD children, with an accuracy rate of 73%.
Conclusions:
Young children with ASD exhibit multiple fiber tract WM connectivity deficits. These abnormal WM microstructural features have the potential to serve as biomarkers for early diagnosis.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
Autism
Machine Learning
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
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FSL
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