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Inter-subject correlation in fMRI: method validation against stimulus-model based analysis.

Juha Pajula1, Jukka-Pekka Kauppi, Jussi Tohka

  • 1Department of Signal Processing, Tampere University of Technology, Tampere, Finland. juha.pajula@tut.fi

Plos One
|August 28, 2012
PubMed
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The Inter-Subject Correlation (ISC) method, a non-parametric approach for functional magnetic resonance imaging (fMRI), effectively identifies brain activation foci comparable to the traditional General Linear Model (GLM). This makes ISC suitable for naturalistic stimulus paradigms where GLM is not applicable.

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Traditional functional magnetic resonance imaging (fMRI) analysis often relies on the General Linear Model (GLM), which requires strictly controlled experimental setups and parametric activation models.
  • The Inter-Subject Correlation (ISC) method offers a non-parametric alternative by analyzing voxel-wise correlations between subject time series, making it suitable for naturalistic stimuli like movies.

Purpose of the Study:

  • To compare the analytical results of the ISC method with those of the GLM-based analysis within controlled research settings.
  • To evaluate the applicability and agreement of ISC analysis in scenarios where GLM is typically used.

Main Methods:

  • Utilized functional reference battery (FRB) fMRI data from 37 subjects.
  • Compared ISC and GLM analysis results using Pearson's correlation between test statistics and Dice index for activation regions.

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  • Performed comparisons across five distinct controlled experimental tasks.

    • Main Results:

    • Achieved an average Pearson's correlation of 0.74 between ISC and GLM statistics across the five tasks.
    • Obtained an average Dice index of 0.73, indicating significant overlap in activation regions identified by both methods.
    • Demonstrated that the data-driven ISC analysis identified similar activation foci as the model-based GLM analysis.

    Conclusions:

    • The study validates that the data-driven ISC method yields comparable results to the model-based GLM analysis in controlled fMRI experiments.
    • Highlights the significant potential of the ISC method for analyzing fMRI data from naturalistic stimulus paradigms, where GLM is often unsuitable.