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Patient classification from fMRI brain activation patterns.

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Call No.	Ph.D. 2003
Author	Ford, James, 1968-
Title	Patient classification from fMRI brain activation patterns.
Date	2003
Abstract	Earlier work has suggested that various neurological or neuropsychiatric disorders may result in characteristic spatial patterns in brain activation, potentially allowing their detection from maps of brain activity under different conditions. However, standard techniques for analysis of brain activity have traditionally focused on finding the most significant areas of brain activations for a given function, and this approach does not apply well to detecting disorders in individuals. In this work, we test several simple and well-understood computational approaches to pattern recognition to attempt to detect patients with a single kind of disorder (early Alzheimer's disease, schizophrenia, or mild traumatic brain injury) from among healthy controls. The primary method uses a linear approach based on principal components analysis (PCA) and the Fisher linear discriminant (FLD) to cast the problem as one of pattern recognition within a small subspace of the very large space of possible brain activity. In addition to its simplicity and elegance, this method has the additional advantage of allowing for the integration of multiple maps of brain activity for each subject, and can correctly separate patients from controls with approximately 70-85\% accuracy for each of three different disorders. This work also considers extensions to the basic method: nonlinear dimensionality reduction (multidimensional scaling), a nonlinear version of the linear classifier (Kernel Fisher Discriminant), and incorporation of anatomic data, all of which can support useful classification when appropriately parameterized. Nearest neighbor methods, including K nearest neighbors (KNN) and nearest mean classification (NMC) are also tested and found to classify adequately in many cases. In addition, a data sharing framework for allowing indexing and exchange of sensitive data is presented with the aim of allowing integration of activation maps from multiple sites into larger studies.
Description	xii, 200 leaves : ill. 28 cm.
Format	application/pdf
Advisor	Fillia S. Makedon ; Hany Farid ; Andrew J. Saykin ; Vasilis Megalooikonomou.
Subject	Brain -- Magnetic resonance imaging. ; Brain mapping. Brain -- Localization of functions. ; Diagnostic imaging. ; Pattern perception. ; Patients -- Classification. ; Alzheimer's disease -- Diagnosis. ; Schizophrenia -- Diagnosis. ; Brain damage -- Diagnosis.
College	Dartmouth College -- Department of Computer Science.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.1655

Title	Page 1
Full Text	Patient Classi cation from fMRI Brain Activation Patterns A Thesis Submitted to the Faculty in partial ful llment of the requirements for the degree of Doctor of Philosophy in Computer Science by James Ford DARTMOUTH COLLEGE Hanover, New Hampshire July 15th, 2003 Examining Committee: (chair) Fillia S. Makedon Hany Farid Andrew J. Saykin Vasilis Megalooikonomou Carol Folt Dean of Graduate Students

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Patient classification from fMRI brain activation patterns.

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Patient classification from fMRI brain activation patterns.

Patient classification from fMRI brain activation patterns.