Faculty Advisor

Christopher H. Sotak, Ph.D.

Faculty Advisor

Charles W. Haynes , EdD, CCC-SLP

Faculty Advisor

Maria Mody, Ph.D., CCC-SLP

Faculty Advisor

Jean King, Ph.D.

Faculty Advisor

Blaise Frederick, Ph.D.

Faculty Advisor

Matthew Gounis, Ph.D.

Faculty Advisor

Christopher H. Sotak,Ph.D.

Abstract

The purpose of this thesis is threefold: 1) A behavioral examination of the role of diacritics in Arabic, 2) A functional magnetic resonance imaging (fMRI) investigative study of diacritics in Arabic, and 3) An optimization algorithm for brain mapping and detecting brain function. Firstly, the role of diacritics in Arabic was examined behaviorally. The stimulus was a lexical decision task (LDT) that constituted of low, mid, and high frequency words and nonwords; with and without diacritics. Results showed that the presence of vowel diacritics slowed reaction time but did not affect word recognition accuracy. The longer reaction times for words with diacritics versus without diacritics suggest that the diacritics may contribute to differences in word recognition strategies. Secondly, an Event-related fMRI experiment of lexical decisions associated with real words with versus without diacritics in Arabic readers was done. Real words with no diacritics yielded shorter response times and stronger activation than with real words with diacritics in the hippocampus and middle temporal gyrus possibly reflecting a search from among multiple meanings associated with these words in a semantic store. In contrast, real words with diacritics had longer response times than real words without diacritics and activated the insula and frontal areas suggestive of phonological and semantic mediation in lexical retrieval. Both the behavioral and fMRI results in this study appear to support a role for diacritics in reading in Arabic. The third research work in this thesis is an optimization algorithm for fMRI data analysis. Current data-driven approaches for fMRI data analysis, such as independent component analysis (ICA), rely on algorithms that may have low computational expense, but are much more prone to suboptimal results. In this work, a genetic algorithm (GA) based on a clustering technique was designed, developed, and implemented for fMRI ICA data analysis. Results for the algorithm, GAICA, showed that although it might be computationally expensive; it provides global optimum convergence and results. Therefore, GAICA can be used as a complimentary or supplementary technique for brain mapping and detecting brain function by way of fMRI.

Publisher

Worcester Polytechnic Institute

Degree Name

PhD

Department

Biomedical Engineering

Project Type

Dissertation

Date Accepted

2011-04-14

Accessibility

Unrestricted

Subjects

Diacritics, fMRI, Optimization, Brain Mapping, Neuroimaging

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