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Abstract #3781

Using ‘P-scores’: a novel percentile-based normalization method to accurately assess individual deviation in heavily skewed neuroimaging data

Rakibul Hafiz1, Amritha Nayak1,2, M. Okan Irfanoglu1, Leighton Chan3, and Carlo Pierpaoli1
1National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health (NIH), Bethesda, MD, United States, 2Henry Jackson Foundation for Advancement of Military Medicine, Bethesda, MD, United States, 3Rehabilitation Medicine Department, National Institutes of Health (NIH), Bethesda, MD, United States

Synopsis

Keywords: Data Analysis, Diffusion/other diffusion imaging techniques, Quantitative Medical ImagingWe propose a novel quantity to correctly assess the extent individuals deviate from the median of a heavy-tailed distribution. We compute a percentile-based score, we call ‘P-score’, which normalizes the deviation of an individual from the sample median by incorporating the individual’s position in the left/right tail of the sample distribution and the corresponding length between the sample median and the 5th/95th percentile edge values of the sample distribution, respectively. We demonstrate the skewness present in diffusion MRI data and the bias introduced when Z-scores are used and further show the control of this bias using the proposed ‘P-scores’ approach.

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