Validating a novel normalization method - 'Pscore' to assess individual deviations using diffusion MRI data from the Human Connectome Project

Motivation: Conventional methods like Zscores are often used to evaluate individual patients against a normative distribution. This can lead to biased estimations when distributions are skewed. We showed this in a pilot study using 48 controls and proposed a novel metric - 'Pscore', to address this bias. We aimed to reproduce these results systematically using a larger dataset. Goal(s): Validate the 'Pscore' approach on a large-scale high resolution neuroimaging dataset. Approach: Diffusion MRI data from the Human Connectome Project (HCP) was used to test various normalization methodsResults: Pscores demonstrate symmetric distributions and no systematic biases observed in Zscores of all diffusion MRI derived metrics. Impact: The non-Gaussian nature of neuroimaging data has implications for building normative databases and their use to assess abnormalities in individual subjects. The proposed 'Pscore' approach reliably addresses this, which implies its usefulness for individual assessments even in smaller neuroimaging datasets.