Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes
Year: 2015
Type: conference-paper
Abstract: Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while early signs may emerge within the first year. White matter (WM) connectivity abnormalities have been documented in the first year of lives of ASD subjects. We introduce a novel multi-kernel support vector machine (SVM) framework to identify infants at high-risk for ASD at 6 months old, by utilizing the diffusion parameters derived from a hierarchical set of WM connectomes. Experiments show that the proposed method achieves an accuracy of 76%, in comparison to 70% with the best single connectome. The complementary information extracted from hierarchical networks enhances the classification performance, with the top discriminative connections consistent with other studies. Our framework provides essential imaging connectomic markers and contributes to the evaluation of ASD risks as early as 6 months.
Authors: Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Pew-Thian Yap, Dinggang Shen, Infant Brain Imaging Study (IBIS) Network
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,392
Awards: Infant Brain Measurement and Super-Resolution Atlas Construction, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI
OpenAlex ID: W2268769909