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Optimizing affinity measures for parcellating brain structures based on resting state fMRI data: a validation on

Hewei Cheng1, Hong Wu2, Yong Fan1

  • 1Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Journal of Neuroscience Methods
|September 17, 2014
PubMed
Summary

This study introduces a novel optimization method for brain parcellation using resting-state fMRI. The approach enhances functional homogeneity and spatial contiguity, improving the robustness of brain region segmentation.

Keywords:
Affinity measureBrain parcellationConstrained bi-level programming optimizationFunctional connectivityNormalized cutfMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Mapping

Background:

  • Resting-state fMRI enables brain parcellation into functionally homogeneous subregions via clustering algorithms.
  • Current methods often use empirically specified parameters for voxel affinity, risking bias towards spatial smoothness.

Purpose of the Study:

  • To develop an optimized brain parcellation method yielding functionally homogeneous and spatially contiguous results.
  • To address limitations of parameter sensitivity in existing clustering-based parcellation techniques.

Main Methods:

  • A constrained bi-level programming optimization method was employed to refine voxel affinity measures.
  • Identified parameter spaces ensuring spatially contiguous parcellations.
  • Searched within these spaces for optimal functional homogeneity and spatial smoothness.

Main Results:

  • Successfully parcellated the medial superior frontal cortex into supplementary motor area (SMA) and pre-SMA for 106 subjects.
  • Validated results using functional connectivity analysis and meta-analysis.
  • Demonstrated superior performance compared to state-of-the-art brain parcellation methods.

Conclusions:

  • The proposed method generates brain parcellations consistent with established functional anatomy.
  • Optimizing the affinity measure enhances parcellation robustness and functional homogeneity.