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Updated: Sep 5, 2025

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Decision thresholding on fMRI activation maps using the Hilbert-Huang transform.

Po-Chih Kuo¹, Michelle Liou²

¹Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.

Journal of Neural Engineering

|July 7, 2022

Summary

This study introduces a new method for analyzing functional magnetic resonance imaging (fMRI) data, accounting for non-stationary brain signals to improve accuracy in identifying brain activity and functional networks.

Keywords:

EMD Hilbert–Huang transform fMRI non-stationary threshold

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Area of Science:

Neuroimaging
Signal Processing
Statistical Analysis

Background:

Functional magnetic resonance imaging (fMRI) analysis requires thresholding to identify significant brain activation.
Conventional methods like Fourier transform phase randomization may yield false positives due to non-stationary brain signals.

Purpose of the Study:

To introduce a novel randomization procedure for fMRI data analysis that accounts for signal non-stationarity.
To compare the proposed method with existing techniques for improved accuracy in brain activation mapping.

Main Methods:

Utilized the Hilbert-Huang transform for a randomization procedure on fMRI time series.
Applied the method to both stationary and non-stationary fMRI datasets.
Compared the Hilbert-Huang transform method against phase-randomization and wavelet-based permutation methods.

Main Results:

The proposed Hilbert-Huang transform method effectively generated activation maps highlighting key brain regions.
The method successfully filtered out noise, particularly in white matter areas.
Demonstrated superior performance compared to conventional phase-randomization and wavelet methods.

Conclusions:

Considering the non-stationary nature of fMRI signals is crucial for accurate brain activity analysis.
The Hilbert-Huang transform-based resampling method offers a robust approach for real-life fMRI experiments.
A new statistical testing method is proposed to address non-stationarity in continuous brain signals.