Iterative dual LDA: A novel classification algorithm for resting state fMRI

Abstract

Resting-state functional MRI (rfMRI) provides valuable information about functional changes in the brain and is a strong candidate for biomarkers in neurodegenerative diseases. However, commonly used analysis techniques for rfMRI have undesirable features when used for classification. In this paper, we propose a novel supervised learning algorithm based on Linear Discriminant Analysis (LDA) that does not require any decomposition or parcellation of the data and does not need the user to apply any prior knowledge of potential discriminatory networks. Our algorithm extends LDA to obtain a pair of discriminatory spatial maps, and we use computationally efficient methods and regularisation to cope with the large data size, high-dimensionality and low-sample-size typical of rfMRI. The algorithm performs well on simulated rfMRI data, and better than an Independent Component Analysis (ICA)-based discrimination method on a real Parkinson’s disease rfMRI dataset.