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Constructing human brain-function association models from fMRI literature.

Mei-Yu Hsiao1, Der-Yow Chen, Jyh-Horng Chen

  • 1Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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A new system mines human brain function associations from medical literature. This knowledge base aids researchers in experiment design and literature comparison.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Informatics

Background:

  • Understanding human brain function is a complex challenge.
  • Existing knowledge on brain-function associations is vast and distributed.
  • Lack of consensus on cognitive function nomenclature hinders data integration.

Purpose of the Study:

  • To develop a system for mining human brain-function associations from Medline abstracts.
  • To create a web-based knowledge base for functional brain mapping.
  • To facilitate researchers' access to summarized knowledge for experimental design.

Main Methods:

  • Utilized rule-based natural language processing to extract behavioral tasks and cognitive functions.
  • Employed n-gram approximate concept mapping with the Unified Medical Language System (UMLS).

Related Experiment Videos

  • Developed an integrated system with automated PubMed MEDLINE download, entity extraction, and interactive visualization.

    • Main Results:

    • Successfully extracted and mapped brain-function associations from a large corpus of abstracts.
    • The Human Brain Functional Mapping Knowledge Base (HBFMKB) system provides a centralized resource.
    • Enabled interactive visualization of extracted knowledge.

    Conclusions:

    • The HBFMKB system effectively mines and organizes human brain-function data.
    • Provides valuable insights for neuroscientists and researchers designing experiments.
    • Facilitates comparison of new findings with existing literature.