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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial

Kishore Vakamudi1, Cameron Trapp1,2, Khaled Talaat1,3

  • 1Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.

Brain Connectivity
|September 7, 2020
PubMed
Summary

This study introduces a fast, real-time method for analyzing brain connectivity using functional MRI (fMRI). The new approach effectively reduces artifacts, improving the monitoring of brain network dynamics.

Keywords:
averaged sliding-windowsconnectivity dynamicsreal-time fMRIregressionresting-state fMRIseed-based correlation analysis

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Data Analysis

Background:

  • Real-time functional magnetic resonance imaging (fMRI) is of great interest for monitoring functional connectivity dynamics.
  • Traditional methods face computational challenges, especially with high-speed fMRI, limiting the analysis to fewer brain regions.
  • There is a need for computationally efficient pipelines for real-time resting-state fMRI analysis.

Purpose of the Study:

  • To describe a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline.
  • To demonstrate the pipeline's effectiveness in monitoring functional connectivity dynamics and data quality.
  • To enable real-time analysis of multiple resting-state networks.

Main Methods:

  • Developed a real-time, seed-based, resting-state fMRI pipeline using averaged sliding-windows (ASW) with partial correlations.
  • Incorporated regression of motion parameters and signals from white matter and cerebrospinal fluid.
  • Utilized a two-level sliding-window approach for monitoring intra- and internetwork correlation dynamics.

Main Results:

  • Analytical and numerical analyses confirmed ASW's selectable bandpass filter characteristics and effective suppression of artifactual correlations.
  • The pipeline is compatible with high-speed fMRI acquisition techniques (TR as short as 136 ms).
  • Demonstrated minimization of artifactual correlations in white and gray matter, comparable to conventional regression.
  • Successfully monitored dynamics of up to 12 resting-state networks with selectable temporal resolution.

Conclusions:

  • The computationally efficient and confound-tolerant pipeline is suitable for real-time monitoring of resting-state connectivity dynamics.
  • This approach enhances data quality assessment and facilitates neuroscience and clinical research.
  • The method offers a powerful, model-free way to increase tolerance to artifactual signal transients in resting-state fMRI analysis.