Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-011906-095333


Document Typedissertation
Author NameKulkarni, Praveen P
URNetd-011906-095333
TitleFunctional MRI Data Analysis Techniques and Strategies to Map the Olfactory System of a Rat Brain.
DegreePhD
DepartmentMechanical Engineering
Advisors
  • Prof. John M. Sullivan Jr., Advisor
  • Prof. Rainhold Ludwig, Committee Member
  • Prof. Craig F. Ferris, Committee Member
  • Prof. Brian J. Savilonis, Committee Member
  • Prof. Grétar Tryggvason, Department Head
  • Keywords
  • fMRI
  • Segmentation
  • Registration
  • Rat Brain Atlas
  • Date of Presentation/Defense2006-01-11
    Availability unrestricted

    Abstract

    Understanding mysteries of a brain represents one of the great challenges for modern science. Functional magnetic resonance imaging (fMRI) has two features that make it unique amongst other imaging modalities used in behavioral neuroscience. First, it can be entirely non-invasive and second, fMRI has the spatial and temporal resolution to resolve patterns of neuronal activity across the entire brain in less than a minute. fMRI indirectly detects neural activity in different parts of the brain by comparing contrast in MR signal intensity prior to and following stimulation. Areas of the brain with increased synaptic and neuronal activity require increased levels of oxygen to sustain this activity. Enhanced brain activity is accompanied by an increase in metabolism followed by increases in blood flow and blood volume. The enhanced blood flow usually exceeds the metabolic demand exposing the active brain area to high level of oxygenated hemoglobin. Oxygenated hemoglobin increases the MR signal intensity that can be detected in MR scanner.

    This relatively straight forward scenario is, unfortunately, oversimplified. The fMRI signal change to noise ratio is extremely small. In this work a quantitative analysis strategy to analyze fMRI data was successfully developed, implemented and optimized for the rat brain. Therein, each subject is registered or aligned to a complete volume-segmented rat atlas. The matrices that transformed the subject’s anatomy to the atlas space are used to embed each slice within the atlas. All transformed pixel locations of the anatomy images are tagged with the segmented atlas major and minor regions creating a fully segmented representation of each subject.

    This task required the development of a full 3D surface atlas based upon 2D non-uniformly spaced 2D slices from an existing atlas. A multiple materials marching cube (M3C) algorithm was used to generate these 1277 subvolumes. After this process, they were coalesced into a dozen major zones of the brain (amygdaloid complex, cerebrum, cerebellum, hypothalamus, etc.). Each major brain category was subdivided into approximately 10 sub-major zones. Many scientists are interested in behavior and reactions to pain, pleasure, smell, for example. Consequently, the 3D volume atlas was segmented into functional zones as well as the anatomical regions. A utility (program) called Tree Browser was developed to interactively display and choose different anatomical and/or functional areas.

    Statistical t-tests are performed to determine activation on each subject within their original coordinate system. Due to the multiple t-test analyses performed, a false-positive detection controlling mechanism was introduced. A statistical composite of five components was created for each group. The individual analyses were summed within groups. The strategy developed in this work is unique as it registers segments and analyzes multiple subjects and presents a composite response of the whole group. This strategy is robust, incredibly fast and statistically powerful.

    The power of this system was demonstrated by mapping the olfactory system of a rat brain. Synchronized changes in neuronal activity across multiple subjects and brain areas can be viewed as functional neuro-anatomical circuits coordinating the thoughts, memories and emotions for particular behaviors using this fMRI module.

    Files
  • Praveen_Final_Draft.pdf

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