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Updated: May 11, 2026

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

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Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity.

Denis Rubin1, Tomer Fekete, Lilianne R Mujica-Parodi

  • 1Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York, USA.

Plos One
|May 24, 2013
PubMed
Summary
This summary is machine-generated.

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Complexity measures applied to functional MRI (fMRI) data require careful optimization. Power spectrum, Higuchi

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Brain complexity is documented at neuronal and hemodynamic scales, aiding in differentiating mental states.
  • Applying complexity measures to functional MRI (fMRI) time-series is challenging due to short, sparse, low signal-to-noise data.
  • Modality-specific optimization is crucial for reliable complexity analysis in fMRI.

Purpose of the Study:

  • To address algorithm choice and signal processing for fMRI complexity analysis.
  • To evaluate methods for resilience to fMRI artifacts and detection sensitivity.
  • To investigate complexity variations with activation, emotional content, task length, and scanner type.

Main Methods:

  • Evaluated complexity measures including power spectrum, structure function, wavelet decomposition, and fractal dimension.

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Published on: June 3, 2013

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Last Updated: May 11, 2026

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

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Published on: July 1, 2014

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Published on: June 3, 2013

  • Used simulated and real fMRI data, assessing resilience to artifacts and sensitivity (grey-white matter contrast, activation overlap).
  • Normalized results to Hurst exponents for direct comparison across methods and scanners.

    • Main Results:

    • Power spectrum, Higuchi's fractal dimension, and generalized Hurst exponent estimates showed superior performance.
    • Wavelet decomposition, detrended fluctuation analysis, aggregated variance, and rescaled range were least effective.
    • Complexity estimates are significantly impacted by algorithm choice, signal processing, time-series length, and scanner.

    Conclusions:

    • fMRI data artifacts interact uniquely with complexity calculations, differing from other physiological data (e.g., EEG).
    • Reliability and sensitivity of complexity estimates depend heavily on methodological choices.
    • Optimized complexity analysis is essential for accurate interpretation of fMRI data in mental state research.