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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain

Xiaoyu Zhao1,2, Kewei Chen3,4,5, Hailing Wang6

  • 1Department of Information Engineering, Ordos Institute of Technology, Ordos, China.

Cognitive Neurodynamics
|November 14, 2024
PubMed
Summary
This summary is machine-generated.

A new constant-block Partial Least Squares Correlation (PLSC) method effectively analyzes brain structure-function relationships, even with mismatched data dimensions. This approach enhances accuracy and robustness in identifying brain networks and their underlying structural basis.

Keywords:
Covariance structure–function networkFunctional magnetic resonance imagingPartial least squares correlationStructural magnetic resonance imagingStructure–function relationship

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Biostatistics

Background:

  • Understanding the brain's structure-function relationship is vital for neuroscience and clinical applications.
  • Traditional methods struggle to integrate static structural MRI with dynamic functional MRI due to temporal dimension mismatches.
  • Partial Least Squares Correlation (PLSC) is a common technique for analyzing joint spatial and temporal patterns.

Purpose of the Study:

  • To develop a novel variant of PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address temporal dimension mismatches.
  • To validate the proposed method using simulated and real brain data.
  • To explore the aging effect on brain structure-function relationships in healthy individuals.

Main Methods:

  • Proposed a novel 'constant-block PLSC' method to handle unmatched temporal dimensions between structural and functional MRI data.
  • Validated the method using simulated datasets with varying degrees of structure-function relationships (weak and strong).
  • Applied the method to real brain data from healthy subjects (aged 16-85), analyzing gray matter volume hubs (sMRI) and resting-state fMRI data.

Main Results:

  • The constant-block PLSC demonstrated superior performance in detecting weak structure-function relationships and improved robustness to noise compared to existing methods.
  • The method accurately identified the number of latent variables in simulated data and revealed more meaningful latent variables in real data, with significant covariance improvements (16.19-41.48% simulated, 13.29-53.68% real).
  • Analysis of real data successfully identified known brain networks (default mode, sensorimotor, auditory, dorsal attention) simultaneously in both functional and structural domains.

Conclusions:

  • The proposed constant-block PLSC is a feasible and effective tool for analyzing brain structure-function relationships, particularly when dealing with mismatched temporal data.
  • The findings suggest that gray matter volume hubs are fundamental to diverse brain functions.
  • This method offers enhanced accuracy and robustness for neuroimaging data analysis, advancing our understanding of brain organization and function across the lifespan.