Extracting Feature Vectors From Event-Related fMRI Data to Enable Machine Learning Analysis
| dc.contributor.author | Soldate, Jeffrey S. | en |
| dc.contributor.committeechair | LaConte, Stephen M. | en |
| dc.contributor.committeemember | VandeVord, Pamela J. | en |
| dc.contributor.committeemember | Montague, P. Read | en |
| dc.contributor.committeemember | Casas, Brooks | en |
| dc.contributor.committeemember | Vijayan, Sujith | en |
| dc.contributor.department | Department of Biomedical Engineering and Mechanics | en |
| dc.date.accessioned | 2022-10-06T08:00:29Z | en |
| dc.date.available | 2022-10-06T08:00:29Z | en |
| dc.date.issued | 2022-10-05 | en |
| dc.description.abstract | Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works. | en |
| dc.description.abstractgeneral | Machine learning is a popular tool for extracting useful information from functional MR images. One approach is classification using feature vectors derived from observations. In this work, we examine new strategies for extracting feature vectors time varying data and explore the effect these feature vectors have on the results of machine learning analysis. In a set of simulations and real data, we compare a range of standard methods for feature extraction to new methods developed for this work. We find the most effective approach for successful classification is feature extraction through the use of mixed effects models. We also find that these models preserve the selection of feature sets that are maximally important to classification. We then explore the range of considerations required to use any of the methods examined in this work for a range of cases. We hope this provides solid ground for both future expansion of feature extraction methods and helpful advice for future users of these methods. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.other | vt_gsexam:35646 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/112090 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | fMRI | en |
| dc.subject | Machine Learning | en |
| dc.subject | Feature Selection | en |
| dc.subject | Mixed Effects Models | en |
| dc.title | Extracting Feature Vectors From Event-Related fMRI Data to Enable Machine Learning Analysis | en |
| dc.type | Dissertation | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Biomedical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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