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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Coclustering for cross-subject fiber tract analysis through diffusion tensor imaging.

Cui Lin1, Darshan Pai, Shiyong Lu

  • 1Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. cuilin@wayne.edu

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|February 4, 2010
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Summary

This study introduces a new computational tool, the business model-based coclustering algorithm (BCA), for analyzing brain connectivity. BCA accurately maps anatomical connections in the human brain using diffusion tensor imaging data.

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Area of Science:

  • Computational neuroscience
  • Neuroimaging
  • Brain connectivity analysis

Background:

  • Understanding brain function relies on studying anatomical and functional connectivity.
  • Current computational methods lack robustness for quantitative assessment of cortical connectivity.
  • Brain plasticity and development are linked to connectivity patterns.

Purpose of the Study:

  • To present a novel computational framework for quantitative assessment of anatomical connectivity patterns.
  • To introduce the business model-based coclustering algorithm (BCA) for brain connectivity analysis.
  • To enable automated and reproducible assessment of human brain connectivity.

Main Methods:

  • Application of the business model-based coclustering algorithm (BCA).
  • Utilizing diffusion tensor imaging (DTI) data for analysis.
  • Quantitative analysis and modeling of cortical connectivity.

Main Results:

  • BCA effectively partitions the cortical mantle into distinct clusters.
  • The algorithm maximizes connectivity strength between identified clusters.
  • BCA demonstrated robust performance in identifying major white matter fiber bundles.
  • Outlier detection and parameter-independent cluster determination were achieved.

Conclusions:

  • BCA provides a robust and accurate computational framework for assessing cortical connectivity.
  • The algorithm facilitates the detection of abnormal connectivity patterns in neurological diseases.
  • BCA enhances our understanding of brain organization and its alterations in disease states.