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Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013

Kernel selection and dimensionality reduction in SVM classification of Autism Spectrum Disorders (ASDs). Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013. Motivation . What is an ASD? 1in 88 children today What makes the research difficult?.

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Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013

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  1. Kernel selection and dimensionality reduction in SVM classification of Autism Spectrum Disorders (ASDs) ShridharanChandramouli Michelle Hromatka Yang Shen SumedhaSingla 5 December 2013

  2. Motivation • What is an ASD? • 1in 88 children today • What makes the research difficult?

  3. Problem Statement • Determine the best combination of kernel for SVM and dimensionality reduction for high dimensional data for classification of ASDs using resting state f-MRI • Goal of research: way to interpret the data

  4. Data • Reducing data to meaningful features • ABIDE dataset • 101 subjects • 34,716 features

  5. Data

  6. Basic Method • Dimensionality Reduction • For each subject S in dataset train SVM using all other subjects classify S based on model

  7. Dimensionality Reduction • PCA – principal component analysis • Fixed slope regression • Others – SVM-RFE, manual selection

  8. Fixed Slope Regression • Anderson, et. al., BRAIN: A journal of Neurology, October 2011 For data point age

  9. Kernels • RBF • Polynomial • Sigmoid • Linear

  10. Basic Method • Dimensionality Reduction? • For each subject S in dataset Regress out age in subjects scale data determine parameters for SVM train SVM using all other subjects classify S based on model store label

  11. Results • Accuracy • Sensitivity • Specificity

  12. Results (%) Using Fixed Slope Regression Using RBF Kernel

  13. Other results in the field • Anderson – fMRI, basic threshold • Ghiassian – multi-site data • Wee – structural MRI volumes

  14. Future work • Combine structural/fMRI data • Multisite extension • Medical process, multi-test process

  15. Sources • Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., ... & Lainhart, J. E. (2011). Functional connectivity magnetic resonance imaging classification of autism. Brain, 134(12), 3742-3754. • Ghiassian, S., Greiner, R., Jin, P., Brown, M.R.G. (2013). Learning to classify psychiatric disorders based on fMR Images: Autism vs. healthy and ADHD vs. Healthy. • Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., ... & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665-678. • Wee, C. Y., Wang, L., Shi, F., Yap, P. T., & Shen, D. (2013). Diagnosis of autism spectrum disorders using regional and interregional morphological features. Human Brain Mapping. • Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapnik, V. (2000, December). Feature selection for SVMs. In NIPS (Vol. 12, pp. 668-674).

  16. PCA • Data • N x d matrix, N = number of subjects, d is dimension of the data • Mapping such that >> t is the principal component “scores”

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