Quantile Function-Based Models for Neuroimaging Classification Using Wasserstein Regression

Abstract

We propose a novel quantile function-based approach for neuroimaging classification using Wasserstein-Fr\'echet regression, specifically applied to the detection of mild traumatic brain injury (mTBI) based on the MEG and MRI data. Conventional neuroimaging classification methods for mTBI detection typically extract summary statistics from brain signals across the different epochs, which may result in the loss of important distributional information, such as variance, skewness, kurtosis, etc. Our approach treats complete probability density functions of epoch space results as functional response variables within a Wasserstein-Fr\'echet regression framework, thereby preserving the full distributional characteristics of epoch results from $L_{1}$ minimum norm solutions. The global Wasserstein-Fr\'echet regression model incorporating covariates (age and gender) allows us to directly compare the distributional patterns between healthy control subjects and mTBI patients. The classification procedure computes Wasserstein distances between estimated quantile functions from control and patient groups, respectively. These distances are then used as the basis for diagnostic decisions. This framework offers a statistically principled approach to improving diagnostic accuracy in mTBI detection. In practical applications, the test accuracy on unseen data from Innovision IP's dataset achieves up to 98\%.