Title page for ETD etd-01312012-204254


Type of Document Master's Thesis
Author Mundle, Aditya Ramesh
Author's Email Address armundle@vt.edu
URN etd-01312012-204254
Title Blood-Oxygen-Level-Dependent Parameter Identification using Multimodal Neuroimaging and Particle Filters
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Wyatt, Christopher L. Committee Chair
Baumann, William T. Committee Member
Beex, A. A. Louis Committee Member
Keywords
  • BOLD Response
  • Nonlinear Systems
  • Overparameterization
  • System Identification
  • Particle Filter
  • Multimodal Neuroimaging
Date of Defense 2012-01-19
Availability unrestricted
Abstract
The Blood Oxygen Level Dependent (BOLD) signal provides indirect estimates of neural activity. The parameters of this BOLD signal can give information about the pathophysiological state of the brain. Most of the models for the BOLD signal are overparameterized which makes the unique identification of these parameters difficult.

In this work, we use information from multiple neu- roimaging sources to get better estimates of these parameters instead of relying on the information from the BOLD signal only. The mulitmodal neuroimaging setup consisted of the information from Cerebral Blood Volume (CBV) ( VASO-Fluid-Attenuation-Inversion-Recovery (VASO-FLAIR)), and Cerebral Blood Flow (CBF) (from Arterial Spin Labelling (ASL)) in addition to the BOLD signal and the fusion of this information is achieved in a Particle Filter (PF) framework. The trace plots and the correlation coefficients of the parameter estimates from the PF reflect ill-posedness of the BOLD model. The means of the parameter estimates are much closer to the ground truth compared to the estimates obtained using only the BOLD information. These parameter estimates were also found to be more robust to noise and influence of the prior.

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