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Residuals and Least-Squares Property

Residuals and Least-Squares Property

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations.

Chen Chen¹, Xuyu Cao¹, Lixia Tian¹

¹School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

Frontiers in Neuroscience

|December 13, 2019

Summary

Partial Least Squares Regression (PLSR) effectively estimates cognitive, behavioral, and demographic (CBD) variables from MRI data. This machine learning method shows strong performance in both single and multi-label learning for neuroimaging analysis.

Keywords:

Human Connectome Project classification machine learning multi-label learning partial correlation regression resting state fMRI resting state functional connection

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Area of Science:

Neuroimaging and Machine Learning
Quantitative Trait Analysis
Brain-Behavior Relationships

Background:

Estimating cognitive, behavioral, and demographic (CBD) variables from MRI data is a growing research area.
Machine learning techniques are crucial for accurate MRI-based estimations.
Partial Least Squares Regression (PLSR) is a versatile machine learning technique with potential for multi-label learning in neuroimaging.

Purpose of the Study:

To systematically investigate the performance of PLSR for MRI-based estimation of individual CBD variables.
To evaluate PLSR's capability in simultaneous estimation of multiple CBD variables (multi-label learning).
To compare PLSR performance with existing literature findings.

Main Methods:

Utilized the Human Connectome Project (HCP) S1200 release dataset.
Employed resting-state functional connections (RSFCs) as features for estimation.
Estimated 10 CBD variables including age, gender, grip strength, and picture vocabulary using PLSR.

Main Results:

PLSR demonstrated strong performance in both single- and multi-label learning for CBD variable estimation.
Achieved higher accuracy than previously reported in literature, evidenced by stronger correlations and 97.8% gender classification accuracy.
Identified that PLSR automatically selects relevant RSFCs, and higher numbers of regions of interest (ROIs) and partial correlation improved estimation accuracy.

Conclusions:

PLSR is a highly effective, efficient, and user-friendly machine learning technique for MRI-based estimation of CBD variables.
PLSR shows significant promise for multi-label learning applications in neuroimaging.
The method's ability to automatically select relevant features and its robust performance make it a favorable choice for future research.