Masters Theses

Keywords and Phrases

brain state dynamics; fMRI; functional connectivity; Hidden Markov Models; sleep staging; unsupervised learning

Abstract

Sleep is associated with systematic changes in brain activity and functional connectivity observable in functional magnetic resonance imagining (fMRI) signals. Because subjects often fall asleep during resting-state experiments, the absence of vigilance monitoring can confound the interpretation of resting-state dynamics. Although electroencephalography (EEG) is the gold standard for sleep staging, simultaneous EEG-fMRI acquisition is not always feasible.

This study investigates whether sleep stages can be inferred directly from fMRI using a probabilistic latent-state framework. Hidden Markov Models (HMMs) are applied to blood-oxygen-level-dependent (BOLD) time series to identify latent brain states and their temporal transitions. Inferred states are aligned with EEG-derived labels using Hungarian mapping and template-based matching. Temporal regularization, including majority filtering and total-variation hysteresis filtering, is applied to reduce spurious state switching.

Across subjects, inferred states exhibit patterns consistent with wakefulness and non-REM sleep, while also capturing transitional structure beyond discrete staging. Strong agreement with EEG-derived functional connectivity supports the physiological relevance of the states. These results demonstrate that unsupervised modeling of fMRI dynamics can recover meaningful vigilance-related structure and provide a practical approach for estimating sleep stages when EEG is unavailable.

Advisor(s)

Chakraborty, Nilajan
Olbricht, Gayla R.

Committee Member(s)

Wheelock, Muriah

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2026

Pagination

viii, 112 pages

Note about bibliography

Includes_bibliographical_references_(pages 108-111)

Rights

© 2026 Brileigh Jay Cates , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12588

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