Abstract

Brain activity fluctuates across timescales from microseconds to decades, reflecting processes ranging from rapid synaptic transmission to slow neuromodulatory and homeostatic changes. Resting-state functional magnetic resonance imaging (rs-fMRI) captures the slow end of this spectrum (spontaneous blood oxygen level-dependent (BOLD) fluctuations over tens to hundreds of seconds), which reflect the integrated output of these underlying processes and are increasingly recognized as sensitive to both pathological and physiological brain states. The core of this thesis examines rs-fMRI as a biomarker for two clinical entities: glioblastoma and aging. Reliable biomarker development requires both methodological rigor and analytic breadth, knowing what the method assumes and knowing how to interrogate the signal. We first establish the technical and structural confounds that must be addressed when comparing these populations to healthy reference groups. We then demonstrate that spectral analysis of BOLD signals reveals a shared signature across both conditions: spectral flattening whose degree tracks clinical comorbidities and outcomes. Additionally, the topography of spectral slopes relative to youthful baselines may detect covert neuropathology not apparent by conventional measures. Together, these findings establish spectral characterization of rs-fMRI as a promising window into neurometabolic function, while underscoring that its clinical utility depends critically on careful methodological application.

Committee Chair

Eric Leuthardt

Committee Members

Joshua Shimony, Abraham Snyder; Manu Goyal; Tamara Hershey

Degree

Doctor of Philosophy (PhD)

Author's Department

Biology & Biomedical Sciences (Neurosciences)

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

4-15-2026

Language

English (en)

Author's ORCID

https://orcid.org/0000-0003-2413-9421

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