@inproceedings{ca3d2e57ebdd430a8be0efe59c75c674,
title = "Identification of infants at risk for autism using multi-parameter hierarchical white matter connectomes",
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.",
author = "{Infant Brain Imaging Study (IBIS) Network} and Yan Jin and Wee, {Chong Yaw} and Feng Shi and Thung, {Kim Han} and Yap, {Pew Thian} and Dinggang Shen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 ; Conference date: 05-10-2015 Through 05-10-2015",
year = "2015",
doi = "10.1007/978-3-319-24888-2_21",
language = "English",
isbn = "9783319248875",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "170--177",
editor = "Luping Zhou and Yinghuan Shi and Li Wang and Qian Wang",
booktitle = "Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings",
}