Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Mathematics
Department
Mathematics
First Advisor
Pedro Maia
Second Advisor
Andrzej Korzeniowski
Third Advisor
Xinlei "Sherry" Wang
Fourth Advisor
Souvik Roy
Abstract
Statistical and machine learning frameworks are developed for transforming dynamic time series data into static feature representations, with applications in neurophysiological signal analysis. The research utilizes automated feature extraction methods, such as TSFEL, to convert pupil diameter recordings and resting-state functional MRI (rs-fMRI) signals into high-dimensional yet interpretable feature vectors, enabling dimensionality reduction while preserving critical dynamic properties.
For ADHD diagnostics, pupillary time series are mapped into static features across statistical, temporal, and spectral domains. These features are incorporated into supervised classification models, supporting pupillometry as a non-invasive biomarker for neurodevelopmental conditions.
In neuroimaging, rs-fMRI time series from attention-related brain regions undergo similar transformation into static representations. Comparative analyses reveal nonlinear regression models more effectively capture complex brain-behavior relationships than linear models, particularly when predicting ADHD symptoms.
The research emphasizes rigorous model evaluation through cross-validation frameworks optimized for adjusted R² in regression tasks and balanced accuracy in classification tasks. By integrating dynamic-to-static feature mapping, advanced feature selection, and robust modeling strategies, this work presents a reproducible framework for analyzing complex neural signals, contributing objective biomarkers and methodological innovations to statistical learning in neuroscience.
Keywords
Statistical, Machine learning, Time series analysis, Feature extraction, Feature selection, Regression Models, Classification Model, Pupillary Dynamics, fMRI time series, ADHD
Disciplines
Applied Statistics | Cognitive Science | Data Science | Dynamic Systems | Longitudinal Data Analysis and Time Series | Neurosciences | Statistical Methodology | Statistical Models
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Eladawy, Rodina, "Statistical and Machine Learning Framework for Dynamic-to-Static Mapping: Applications in Neuroscience" (2025). Mathematics Dissertations. 268.
https://mavmatrix.uta.edu/math_dissertations/268
Included in
Applied Statistics Commons, Cognitive Science Commons, Data Science Commons, Dynamic Systems Commons, Longitudinal Data Analysis and Time Series Commons, Neurosciences Commons, Statistical Methodology Commons, Statistical Models Commons