Meeting Banner
Abstract #3920

Topography High Sensitivity of Discriminative Fibers in ASD Children with Parcellation-Free Machine Learning Method

Qi Qi1, Litong Ni1, Junyi Wang2, Wei Zhang2, Xi Zhu2, Yijie Li2, Doudou Cao1, Xujun Duan2, Fan Zhang2, and Shijun Li1
1Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China, 2University of Electronic Science and Technology of China, Chengdu, China

Synopsis

Keywords: Diffusion Analysis & Visualization, White Matter, Autism Spectrum Disorder

Motivation: Abnormalities in white matter fibers are associated with the onset of Autism Spectrum Disorder(ASD) though early diagnosis of ASD is difficult.

Goal(s): This study aims to establish a Parcellation-Free model for ASD with Diffusion Tensor Imaging that can accurately classify and identify abnormal white matter fibers.

Approach: The Parcellation-Free model preserve fiber-level white matter information of the ASD children without brain region parcellation and averaging, and input it into the Vision Transformer classifier, then identify the white matter fibers contribute to the classification.

Results: The classification model achieved an sensitivity of 90.4%, and output fibers that contribute to classification, confirming previous research.

Impact: We aim to establish a reliable classification model for early diagnosis for ASD, which can output white matter fibers that contribute to classification at the fiber level,which may be meaningful for exploring the pathogenesis and potential intervention targets of ASD.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords