New analysis method for functional brain imaging: White noise removed T2* variation mapping using multi-echo EPI | JoVE Visualize

New analysis method for functional brain imaging: White noise removed T₂* variation mapping using multi-echo EPI

Sang-Han Choi¹, Jun-Young Chung², Dae-Hun Kang³

¹Neuroscience Convergence Center, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Journal of Neuroscience Methods|May 10, 2021

View abstract on PubMed

Summary

This study introduces a novel white noise-removed T2*-variation mapping technique for functional magnetic resonance imaging (fMRI). This new method integrates independent component analysis (ICA) and general linear model (GLM) approaches for more comprehensive brain activity analysis.

Area of Science:

Neuroimaging
Functional Magnetic Resonance Imaging (fMRI)

Background:

Standard fMRI analysis relies on independent component analysis (ICA) for resting-state imaging and the general linear model (GLM) for task-related mapping.
These conventional methods are typically applied independently, limiting their scope in analyzing complex brain states.

Purpose of the Study:

To develop a novel fMRI analysis method that integrates both task-based and resting-state analysis approaches.
To introduce white noise-removed T2*-variation mapping as a unified analysis framework for fMRI data.

Main Methods:

Derived the standard deviation to mean-square ratio of true T2* signal from multi-echo EPI (ME-EPI) data.
Removed S0 (initial signal intensity) and white noise from EPI signals using signal-coherence and slope analyses across echo times (TE).

Main Results:

The proposed method successfully generated reliable activation maps for both visual tasks and resting-state default mode networks.
Demonstrated effective removal of white noise and S0 components, enhancing map clarity.

Conclusions:

White noise-removed true T2*-variation mapping offers a unified approach for fMRI analysis, applicable to both activation and resting-state conditions.
This method is expected to facilitate studies investigating the relationship between task timing and brain activity, particularly in complex cognitive domains like emotion and awareness.