Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common diseases in school aged children. In this study, we proposed a method based on social network to extract the features of the ADHD-200 resting state fMRI data between ADHD conditioned and control subjects. And the classification is done by using the support vector machine. The innovation of this paper lies in that: firstly, in the social network, the edge is defined by correlation between two voxels, where the threshold is proposed based on the optimal properties of small world; secondly, in the procedure of feature extraction, besides the traditional network features, we also exploit the new features such as assortative mixing and synchronization. We obtain an average accuracy of 63.75%, which is better than the average best imaging-based diagnostic performance 61.54% achieved in the ADHD-200 global competition. Compared with the proposed method, the result of the method based on traditional features is 61.04% , which verified that the proposed method based on new features is better than traditional one.
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Guo, X., An, X., Kuang, D., Zhao, Y., He, L. (2014). ADHD-200 Classification Based on Social Network Method. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_28
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DOI: https://doi.org/10.1007/978-3-319-09330-7_28
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