Abstract
In this study, we tested whether the disturbed structural connectivity in whole brain cortex could be discriminating biomarker for schizophrenia. The anatomical fiber streamlines constructed on AAL template by diffusion tenor image were selected as potential features and a linear SVM pattern classifier was used to categorize the schizophrenia and healthy controls. We randomly divided the whole data into two groups, a training set which contained 32 patients and 25 controls and a test set had 31 patients and 24 controls. We compared two kinds of feature selection methods 1) Univariate t-test based filtering; 2) sparse regression based filtering. The sparse regression features correctly identified 97% cases in test dataset (96% sensitivity and 98% specificity), while the t-test significant impaired connectivity achieved 94% accuracy (92% sensitivity and 96% specificity). The sparse regression selected structural connectivities were consistent in 90% individuals 10 percent more than the t-test filtered features.
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Zheng, J., Wang, Y., Chen, H., Chen, H. (2013). Sparse Brain anatomical Network Based Classification of Schizophrenia Patients and Healthy Controls. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_102
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DOI: https://doi.org/10.1007/978-3-642-42057-3_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
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