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Updated: May 27, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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fMRI-based data-driven brain parcellation using independent component analysis.

William D Reeves1, Ishfaque Ahmed1, Brooke S Jackson2

  • 1University of Georgia Franklin College of Arts and Sciences, Department of Physics and Astronomy, Athens, GA, USA; University of Georgia Bio-Imaging Research Center, Athens, GA, USA.

Journal of Neuroscience Methods
|February 20, 2025
PubMed
Summary
This summary is machine-generated.

The novel Independent Component Analysis-based Parcellation Algorithm (IPA) offers more reliable brain region definition and higher functional homogeneity for functional magnetic resonance imaging (fMRI) analysis compared to existing methods.

Keywords:
Data-drivenFunctional magnetic resonance imagingHypertensionMethodologicalNeuroimagingParcellation

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Mapping

Background:

  • Functional magnetic resonance imaging (fMRI) studies necessitate robust methods for parcellating the brain into regions of interest (ROIs).
  • Current parcellation approaches rely on standardized anatomical atlases (e.g., Montreal Neurological Institute - MNI) or individual functional activity patterns (e.g., Personode software).

Purpose of the Study:

  • To introduce and evaluate the Independent Component Analysis (ICA)-based Parcellation Algorithm (IPA) for creating individualized and group-level brain parcellations.
  • To assess the spatial consistency and functional homogeneity of ROIs generated by the IPA in a hypertension study cohort.

Main Methods:

  • The IPA algorithm utilizes independent components (ICs) derived from group ICA (gICA) to construct ROIs.
  • Individualized parcellations were generated by regressing ICs across all subjects, alongside a gICA-derived parcellation.
  • Spatial consistency was quantified using Dice Similarity Coefficients (DSCs), and functional homogeneity was assessed via mean Pearson correlation.

Main Results:

  • Individualized parcellations generated by IPA demonstrated a mean DSC of 0.69 ± 0.14, indicating good spatial consistency.
  • Functional homogeneity for individualized IPA parcellations averaged 0.30 ± 0.14, while gICA-derived parcellations showed 0.38 ± 0.15.
  • Comparison with Personode showed IPA's individualized parcellations had higher DSC (0.69 vs. 0.43) and homogeneity (0.30 vs. 0.28).

Conclusions:

  • The IPA method provides more reliable ROI definition and superior functional homogeneity compared to existing techniques like Personode and the MNI atlas.
  • The IPA demonstrates significant promise as an advanced parcellation technique for enhancing fMRI data analysis.
  • IPA-generated parcellations offer improved spatial consistency and functional homogeneity, crucial for accurate interpretation of fMRI findings.