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When structure affects function--the need for partial volume effect correction in functional and resting state

Juergen Dukart1, Alessandro Bertolino2

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Area of Science:

  • Neuroimaging
  • Brain Network Analysis
  • Magnetic Resonance Imaging (MRI)

Background:

  • Functional and resting-state MRI are key tools for studying brain networks.
  • Current studies often overlook structural variations that can influence functional MRI results.
  • Partial volume effects, arising from mixed tissue types within a voxel, are a significant confound.

Purpose of the Study:

  • To investigate the impact of partial volume effects on functional and resting-state MRI analyses.
  • To assess how underlying structural differences affect the detection of functional brain network changes.
  • To evaluate the necessity of controlling for structural information in MRI studies.

Main Methods:

  • Utilized a simulation approach to model partial volume effects.
  • Evaluated real resting-state MRI data to validate simulation findings.
  • Applied degree centrality as a key resting-state connectivity measurement.

Main Results:

  • Structural differences significantly increase detected functional differences in both fMRI and rs-fMRI.
  • Partial volume effects have the largest impact at high signal-to-noise ratios.
  • Structural information explains approximately 25% of the variance in degree centrality.
  • Controlling for structure substantially alters group-level correlational maps.

Conclusions:

  • Partial volume effects stemming from structural variations are a critical confound in MRI-based functional brain network analyses.
  • Current analytical approaches may overestimate functional differences, particularly in populations with known structural changes (e.g., neurological diseases).
  • Incorporating structural information into the analysis is essential for robust and accurate functional connectivity assessments.