Capturing functional connectomics using Riemannian partial least squares
- URL: http://arxiv.org/abs/2306.17371v1
- Date: Fri, 30 Jun 2023 02:24:34 GMT
- Title: Capturing functional connectomics using Riemannian partial least squares
- Authors: Matt Ryan, Gary Glonek, Jono Tuke, and Melissa Humphries
- Abstract summary: For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform interventions and treatment strategies.
One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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