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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

Guoqiang Hu¹, Deqing Wang¹, Siwen Luo²

¹School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.

Journal of Neuroscience Methods

|August 2, 2021

Summary

This summary is machine-generated.

This study introduces a new method to analyze brain activity in Parkinson's disease (PD). The sparse nonnegative tensor decomposition (SNTD) method identified specific frequency band alterations in PD patients, offering a novel diagnostic approach.

Keywords:

Co-activation pattern FMRI Sparse constrained nonnegative tensor decomposition

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Area of Science:

Neuroscience
Medical Imaging Analysis
Biomedical Engineering

Background:

Parkinson's disease (PD) diagnosis traditionally relies on clinical symptoms.
PD is associated with variations in brain activity across different frequency bands.
Frequency-specific dynamic brain alterations in PD remain underexplored.

Purpose of the Study:

To investigate frequency-specific dynamic alterations in brain activity related to Parkinson's disease.
To introduce and validate a novel sparse nonnegative tensor decomposition (SNTD) method for analyzing brain activity.
To compare the proposed SNTD method with conventional co-activation pattern (CAP) approaches.

Main Methods:

Utilized a novel sparse nonnegative tensor decomposition (SNTD) method.
Estimated frequency-specific co-activation patterns (CAPs).
Investigated differences between Parkinson's disease (PD) patients and healthy controls (HC).

Main Results:

Significant differences between PD and HC were found in the 0.04-0.1 Hz frequency band within the basal ganglia.
The average intensity in this specific frequency band in PD patients correlated significantly with the Hoehn and Yahr scale.
SNTD successfully estimated frequency-specific CAPs, revealing alterations in PD patients.

Conclusions:

Sparse nonnegative tensor decomposition (SNTD) offers an alternative to traditional K-means clustering for CAP analysis.
The proposed framework successfully extracts frequency-specific CAPs.
SNTD effectively identifies alterations in brain activity patterns associated with Parkinson's disease.