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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Tucker Tensor Regression and Neuroimaging Analysis.

Xiaoshan Li¹, Da Xu², Hua Zhou³

¹Wells Fargo & Company.

Statistics in Biosciences

|August 6, 2021

Summary

This summary is machine-generated.

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This study introduces a flexible Tucker decomposition tensor regression model for neuroimaging data. It offers advantages over CP decomposition for analyzing complex, high-dimensional brain imaging datasets.

Area of Science:

Neuroscience
Biostatistics
Data Science

Background:

Neuroimaging data are high-dimensional tensors, requiring specialized regression models.
Vectorizing tensor data leads to high dimensionality and loss of spatial information.
Existing generalized linear tensor regression models use CP decomposition.

Purpose of the Study:

Propose a novel tensor regression model using Tucker decomposition for neuroimaging data.
Enhance analysis of high-dimensional neuroimaging data by leveraging Tucker decomposition's flexibility.
Compare the proposed Tucker model with the existing CP model.

Main Methods:

Developed a tensor regression model based on Tucker decomposition.
Applied the model to simulated and real magnetic resonance imaging (MRI) data.

Keywords:

CP decomposition Tucker decomposition magnetic resonance image multidimensional array tensor regression

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Last Updated: Oct 25, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Compared performance against CP decomposition-based tensor regression.

Main Results:

The Tucker decomposition model offers greater flexibility than CP decomposition.
Tucker regression allows for different factor numbers per mode, reducing parameters and accommodating skewed dimensions.
Demonstrated effectiveness of the Tucker model in finite sample performance on simulated and real MRI data.

Conclusions:

The Tucker decomposition tensor regression model is a flexible and effective tool for neuroimaging analysis.
This approach offers advantages in parameter reduction, handling varied image dimensions, and modeling interactions.
The Tucker model provides a principled method for image downsizing in neuroimaging studies.