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Complex-valued time-series correlation increases sensitivity in FMRI analysis.

Mary C Kociuba1, Daniel B Rowe2

  • 1Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, WI, USA.

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Summary
This summary is machine-generated.

This study introduces a complex-valued (CV) time-series analysis for functional MRI (fMRI), showing it enhances sensitivity over magnitude-only (MO) methods by including phase information. This improved correlation analysis aids in clearer identification of brain regions like the motor cortex, especially in noisy fMRI data.

Keywords:
Complex correlationFrequency correlationFunctional magnetic resonance imagingMagnetic resonance imaging

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Area of Science:

  • Neuroimaging
  • Signal Processing
  • Biophysics

Background:

  • Standard functional MRI (fMRI) analysis often discards phase information from time-series data.
  • Task-related changes in phase time-series are known but typically ignored in conventional analyses.
  • This leads to a potential loss of sensitivity in detecting neural activity.

Purpose of the Study:

  • To develop a linear matrix representation for complex-valued (CV) time-series correlation in the temporal Fourier frequency domain.
  • To demonstrate that this CV approach offers increased sensitivity compared to magnitude-only (MO) time-series correlation in fMRI.
  • To improve the characterization of functional connectivity in fMRI data.

Main Methods:

  • A real-valued isomorphism representation was used for Fourier reconstruction of CV time-series.
  • Correlation was computed in the temporal frequency domain using both CV and MO data.
  • Simulations and experimental human fMRI data were analyzed, comparing Fisher-z transformed MO and CV correlations.
  • Analysis focused on varying degrees of task-related magnitude and phase amplitude changes.
  • Correlations were examined in specific frequency bands and for voxels of interest (VOIs) in the motor cortex.

Main Results:

  • Simulations showed stronger CV correlations than MO correlations, particularly for low signal-to-noise ratio (SNR) time-series.
  • Experimental human fMRI data revealed that MO correlation maps were noisier and less distinct than CV maps.
  • The motor cortex was more difficult to identify in MO correlation maps after spatial processing compared to CV maps.

Conclusions:

  • Incorporating both magnitude and phase in spatial correlation computations more accurately defines correlated brain regions, such as the left and right motor cortices.
  • Enhanced sensitivity in correlation analysis is crucial for preserving signals in high-noise fMRI datasets.
  • This method helps avoid excessive processing-induced correlations and improves the reliability of fMRI findings.