L'identification des enfants à risque élevé de troubles du spectre de l'autisme en utilisant des réseaux de connectivité de matière blanche multiparamètre et multiéchelleles multiparamétriques

Traduction: G.M.

Hum Brain Mapp. 2015 Sep 14. doi: 10.1002/hbm.22957.

Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

Jin Y¹, Wee CY¹, Shi F¹, Thung KH¹, Ni D², Yap PT¹, Shen D^1,3; Infant Brain Imaging Study (IBIS) network.

Author information

• ¹Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina.
• ²The Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Shenzhen University, China.
• ³Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

Abstract

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
© 2015 Wiley Periodicals, Inc.

KEYWORDS:

Autism spectrum disorder; classification; connectivity networks; diffusion weighted imaging; infant

PMID:

26368659