Chapter title |
Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes.
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Chapter number | 21 |
Book title |
Machine Learning in Medical Imaging
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Published in |
Lecture notes in computer science, October 2015
|
DOI | 10.1007/978-3-319-24888-2_21 |
Pubmed ID | |
Book ISBNs |
978-3-31-924887-5, 978-3-31-924888-2
|
Authors |
Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Pew-Thian Yap, Dinggang Shen, Infant Brain Imaging Study (IBIS) Network, Jin, Yan, Wee, Chong-Yaw, Shi, Feng, Thung, Kim-Han, Yap, Pew-Thian, Shen, Dinggang |
Editors |
Luping Zhou, Li Wang, Qian Wang, Yinghuan Shi |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 26 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 27% |
Student > Bachelor | 4 | 15% |
Student > Postgraduate | 3 | 12% |
Student > Doctoral Student | 2 | 8% |
Student > Master | 2 | 8% |
Other | 1 | 4% |
Unknown | 7 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 4 | 15% |
Computer Science | 3 | 12% |
Neuroscience | 3 | 12% |
Agricultural and Biological Sciences | 2 | 8% |
Engineering | 2 | 8% |
Other | 4 | 15% |
Unknown | 8 | 31% |