2026-09-19T07:08:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/180642022-01-13T07:53:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
John W. Belliveau.
Lin, Fa-Hsuan, 1972-
Harvard University--MIT Division of Health Sciences and Technology.
Harvard University--MIT Division of Health Sciences and Technology
2005-06-02T19:49:54Z
2005-06-02T19:49:54Z
2003
2004
http://hdl.handle.net/1721.1/18064
57509396
Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2004.
Includes bibliographical references.
This thesis integrates hardware development, data analysis, and mathematical modeling to facilitate our understanding of brain cognition. Exploration of these brain mechanisms requires both structural and functional knowledge to (i) reconstruct the spatial distribution of the activity, (ii) to estimate when these areas are activated and what is the temporal sequence of activations, and (iii)to determine how the information flows in the large-scale neural network during the execution of cognitive and/or behavioral tasks. Advanced noninvasive medical imaging modalities are able to locate brain activities at high spatial and temporal resolutions. Quantitative modeling of these data is needed to understand how large-scale distributed neuronal interactions underlying perceptual, cognitive, and behavioral functions emerge and change over time. This thesis explores hardware enhancement and novel analytical approaches to improve the spatiotemporal resolution of single (MRI) or combined (MRI/fMRI and MEG/EEG) imaging modalities. In addition, mathematical approaches for identifying large-scale neural networks and their correlation to behavioral measurements are investigated. Part I of the thesis investigates parallel MRI. New hardware and image reconstruction techniques are introduced to improve spatiotemporal resolution and to reduce image distortion in structural and functional MRI. Part II discusses the localization of MEG/EEG signals on the cortical surface using anatomical information from AMTRI, and takes advantage of the high temporal resolution of MEG/EEG measurements to study cortical oscillations in the human auditory system. Part III introduces a multivariate modeling technique to identify "nodes" and "connectivity" in a
(cont.) large-scale neural network and its correlation to behavior measurements in the human motor system.
by Fa-Hsuan Lin.
Ph.D.
256 p.
15708001 bytes
15742150 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Harvard University--MIT Division of Health Sciences and Technology.
Spatiotemporal brain imaging and modeling
Thesis
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