TY - GEN
T1 - Identification of infants at risk for autism using multi-parameter hierarchical white matter connectomes
AU - Infant Brain Imaging Study (IBIS) Network
AU - Jin, Yan
AU - Wee, Chong Yaw
AU - Shi, Feng
AU - Thung, Kim Han
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported by the National Institute of Health grants EB006733, EB008374, EB009634, AG041721, MH100217, and AG042599. We thank the National Database for Autism Research (NDAR) for providing the data to this research.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-24888-2_21
DO - 10.1007/978-3-319-24888-2_21
M3 - Conference contribution
C2 - 26900607
AN - SCOPUS:84951952690
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 177
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
T2 - 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
Y2 - 5 October 2015 through 5 October 2015
ER -