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MULTIVARIATE STATISTICAL ANALYSIS OF MAGNETOENCEPHALOGRAPHY
DATA
by
Juan Luis Poletti Soto
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
May 2011
Copyright 2011 Juan Luis Poletti Soto
Object Description
| Title | Multivariate statistical analysis of magnetoencephalography data |
| Author | Poletti Soto, Juan Luis |
| Author email | polettis@usc.edu; juanlps@yahoo.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2010-12-09 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-02-08 |
| Advisor (committee chair) | Leahy, Richard M. |
| Advisor (committee member) |
Ortega, Antonio Pantazis, Dimitrios Tjan, Bosco S. |
| Abstract | I describe methods for the detection of brain activation and functional connectivity in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical analysis. I apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands, and I form observation matrices by putting together the power from all frequency bands and all trials. To detect changes in brain activity, I fit these observations into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables; the resulting Roy’s maximum statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. As follow-up techniques to identify individual frequencies that contribute significantly to experimental effects, I further describe protected F-tests and linear discriminant analysis. To detect functional interactions in the brain, I take these observations and compute the canonical correlation between a chosen reference voxel and every other voxel in the brain. The canonical correlation maps are also thresholded for significance, but here I use parametric asymptotic approximations. Based on collinearity properties of the vectors associated to the canonical correlations, I implement procedures to discard voxels whose interaction with the reference is due to linear mixing, and describe approximate F-tests to identify individual frequencies that contribute significantly to detected interactions. I evaluate these multivariate approaches both on simulated data and experimental data from a MEG visuomotor task study.; My results indicate that Roy’s maximum root is more powerful than univariate approaches in detecting experimental effects when correlations exist between power across frequency bands; as for canonical correlation analysis, I find that it detects experimental effects, allowing the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single test. |
| Keyword | functional connectivity; magnetoencephalography (MEG); multivariate statistics; oscillatory brain activity |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3646 |
| Rights | Poletti Soto, Juan Luis |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Soto-4248 |
| Archival file | uscthesesreloadpub_Volume32/etd-Soto-4248.pdf |
Description
| Title | Page 1 |
| Full text | MULTIVARIATE STATISTICAL ANALYSIS OF MAGNETOENCEPHALOGRAPHY DATA by Juan Luis Poletti Soto A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) May 2011 Copyright 2011 Juan Luis Poletti Soto |
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