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Related Experiment Video

Updated: May 7, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Integrated strategy for improving functional connectivity mapping using multiecho fMRI.

Prantik Kundu1, Noah D Brenowitz, Valerie Voon

  • 1Section on Functional Imaging Methods, Functional MRI Core Facility, and Statistical and Scientific Computing Core, National Institute of Mental Health, Bethesda, MD 20814.

Proceedings of the National Academy of Sciences of the United States of America
|September 17, 2013
PubMed
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A new method using multiecho echo planar imaging and independent component analysis improves functional connectivity analysis in resting state fMRI. This approach effectively removes motion artifacts, enhancing signal-to-noise ratio and statistical inference for brain networks.

Area of Science:

  • Neuroimaging
  • Brain Network Analysis
  • Functional MRI

Background:

  • Resting state fMRI (functional Magnetic Resonance Imaging) is crucial for studying brain networks.
  • Head motion during scanning significantly biases functional connectivity estimates.
  • Existing preprocessing methods struggle to fully mitigate motion-related artifacts.

Purpose of the Study:

  • To present an integrated strategy for acquiring, denoising, and estimating functional connectivity in resting state fMRI data.
  • To address the challenges posed by head motion and its impact on BOLD (Blood Oxygen Level-Dependent) signal analysis.
  • To improve the accuracy and reliability of brain network analysis.

Main Methods:

  • Utilizing multiecho (ME) echo planar imaging for data acquisition.
Keywords:
human neuroimagingresting state fMRItime series

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Functional Mapping with Simultaneous MEG and EEG
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Related Experiment Videos

Last Updated: May 7, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

  • Applying spatial independent component analysis (ICA) to differentiate BOLD signals from non-BOLD artifacts based on echo-time dependence.
  • Implementing independent components regression for robust functional connectivity estimation.

    • Main Results:

    • The ME-ICA method effectively removes motion artifacts in resting state fMRI data.
    • The proposed strategy demonstrated a fourfold improvement in signal-to-noise ratio compared to traditional methods.
    • Independent components regression simplified statistical inference and controlled for type 1 errors in group comparisons.

    Conclusions:

    • The integrated strategy offers a physically principled and operator-independent approach to artifact removal in resting state fMRI.
    • This method enhances the specificity of functional connectivity analysis.
    • The findings support the use of this technique for more reliable and valid brain network research, especially in the presence of subject motion.