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Author ORCID Identifier


Campus-Only Access for Five (5) Years

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Public Health

Year Degree Awarded


Month Degree Awarded


First Advisor

Edward J. Stanek III

Second Advisor

Jing Qian

Third Advisor

Jacquie Kurland

Subject Categories

Biostatistics | Public Health


Guidance is needed to choose the optimal algorithms to predict spoken word groups (Actions or Objects) from brain activation patterns using repeated fMRI (functional Magnetic Resonance Imaging) data.

A set of predictive classifier models for categorical outcomes were evaluated including logistic regression, Lasso, Ridge Regression, Elastic Net, and Support Vector Machine (SVM), Gaussian Process (GP), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), K-nearest Neighbors (KNN), and Random Forest (RF). The evaluation was conducted on simulated high-dimensional fMRI scenarios. Comparisons were made based on predictive performance (Accuracy, AUC, Precision, Recall, F1 Score, Time). Through the study design and simulation, we identified patterns between the predictive model performance and key fMRI parameters including Signal-to-noise Ratio (SNR), Spatial Resolution, Temporal Correlation, Number of Regions of Interest (ROIs), and Spacing between Regions. By comparison of different simulation scenarios, better prediction was possible with higher SNR, Spatial Resolution and Temporal Correlation. The best-performing algorithms were chosen by the model performance metrics and consistency, and included SVM and Elastic Net, followed by Logistic Regression and Ridge. RF and GB performed the worst.

The best-performing classification algorithms from the simulation study were fitted to real-application fMRI data collected from healthy participants in a study of object and action picture naming in aphasia. The application evaluated the model performance and enabled the potential for clinically meaningful interpretation by identifying and visualizing multivariate voxel patterns.


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Friday, September 01, 2023