Human Brain Function Mapping Knowledge-base: Methodology, System, and Applications
Date Issued
2010
Date
2010
Author(s)
Hsiao, Mei-Yu
Abstract
With a rapid progress in the field, a great many functional magnetic resonance imaging (fMRI) studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue.
The purpose of this dissertation is to develop and validate an information extraction algorithm for extracting information from the fMRI literature. It is divided into two parts. First, we developed a generalized hierarchical concept-based dictionary of brain functions for named entity extraction based on the Unified Medical Language System (UMLS), which integrates many terminologies such as MeSH, Psychological Index Terms and similar vocabulary sources. Second, a hybrid method that combined a dictionary and a rule-based approach for recognizing and classifying concepts related to human brain studies. To the best of our knowledge, this is the first study to extract brain functions and experimental tasks from the fMRI literature automatically. The generalized hierarchical concept-based dictionary of brain functions we have developed is the first generalized dictionary of this kind. It can be helpful for further studies in text mining, as can algorithms for automatic retrieval of brain functions and their hierarchical relationships for cross-referencing. The precision and recall of our information extraction algorithm was on par with that of human experts. Our approach presents an alternative to the more laborious, manual entry approach to information extraction.
In addition, to demonstrate the possible applications of the extracted terms, we present a human brain function mapping knowledge-base system (BrainKnowledge) that combines fMRI datasets with the published literature in a comprehensive framework for studying human brain activities. BrainKnowledge not only contains indexed literature, but also provides the ability to compare experimental data with those derived from the literature. In this dissertation, we will describe BrainKnowledge, which provides concept-based queries organized by brain structures and functions and also mines results to support or explain the experimental fMRI results, and we will present application examples with the studies of affect, and studies of language to illustrate its capabilities.
In summary, this dissertation successfully demonstrated the ability of information extraction from the fMRI literature. Furthermore, BrainKnowledge, which combines fMRI experimental results with Medline abstracts, may be of great assistance to scientists not only by freeing up resources and valuable time, but also by providing a powerful tool that collects and organizes over ten-thousand abstracts into readily usable and relevant sources of information for researchers.
Subjects
Neuroinformatics
information extraction
literature mining
brain structure-function model
knowledge bases
Type
thesis
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