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Related Experiment Video

Updated: Jun 26, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Capturing functional connectomics using Riemannian partial least squares.

Matthew Ryan¹, Gary Glonek², Jono Tuke²

¹School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia. matthew.ryan@adelaide.edu.au.

Scientific Reports

|October 13, 2023

Summary

This summary is machine-generated.

This study introduces R-PLS, a novel method for analyzing brain functional connectivity matrices. R-PLS accounts for unique matrix properties, improving the identification of key neural connections in neurological disorders.

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Medical Imaging
Computational Biology

Background:

Functional and anatomical brain connectomes aid in understanding neurological disorders.
Functional magnetic resonance imaging (fMRI) measures brain function via blood-oxygen-level-dependent (BOLD) signals.
Functional connectivity matrices, derived from fMRI, represent brain network interactions.

Purpose of the Study:

To address limitations of traditional Partial Least Squares (PLS) in analyzing functional connectivity matrices.
To introduce a generalized PLS method (R-PLS) that incorporates the positive definite property of these matrices.
To apply R-PLS to neuroimaging datasets for identifying significant functional brain connections.

Main Methods:

Developed a generalized Partial Least Squares (R-PLS) method for Riemannian manifolds.
Applied R-PLS to symmetric positive definite matrices using affine invariant geometry.
Utilized the variable importance in projection (VIP) statistic to interpret R-PLS results.

Main Results:

R-PLS was applied to the COBRE and ABIDE functional imaging datasets.
Key functional connections were identified in datasets comparing schizophrenic patients with controls and individuals with autism spectrum disorder with neurotypicals.
Identified connections align with existing literature, validating the R-PLS approach.

Conclusions:

R-PLS offers a statistically robust method for analyzing brain functional connectivity matrices.
The method successfully identified relevant functional connections in clinical datasets.
R-PLS has potential for broader applications in neuroimaging and multi-modal data analysis.