Capturing functional connectomics using Riemannian partial least squares

TL;DR

This paper introduces R-PLS, a Riemannian extension of partial least squares, to analyze positive definite functional connectivity matrices from fMRI data, improving neuroimaging analysis for neurological disorders.

Contribution

The paper develops R-PLS, a novel Riemannian generalization of PLS, specifically designed for positive definite matrices in functional connectomics analysis.

Findings

R-PLS effectively identifies key brain connections in schizophrenia and autism datasets.

Results align with existing literature on functional brain differences.

Method demonstrates potential for multi-model neuroimaging analysis.

Abstract

For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through blood flow over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has potential to open up new avenues for multi-model imaging analysis linking structural and functional connectomics.