Abstract
Neuroimaging studies of rare disorders, such as dyslexia, require long term, multi-centre data collection in order to create representative disease specific cohorts. However, multi-site data have inherent heterogeneity caused by site specific acquisition protocols, scanner setup, etc. The aim of this study was the analysis of the influence of the two confounding factors: site location and field strength on feature selection procedure. We propose two methods: site-dependent whitening and site-dependent extension and compare with naive approach using classification accuracy as a quality measure of selected features subset. The proposed methods outperform the naive approach, and significantly improves the classification performance of developmental dyslexia.
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Płoński, P., Gradkowski, W., Marchewka, A., Jednoróg, K., Bogorodzki, P. (2014). Dealing with the Heterogeneous Multi-site Neuroimaging Data Sets: A Discrimination Study of Children Dyslexia. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_43
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DOI: https://doi.org/10.1007/978-3-319-09891-3_43
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