Eva Martinez Luque
BME PhD Proposal Presentation

Date: 2025-08-08
Time: 8:00 am
Location / Meeting Link: HSRB II N600/ Zoom link: https://emory.zoom.us/j/7121383662

Committee Members:
Candace C Fleischer, Ph.D. Advisor; John Oshinski, Ph.D. ; David Reiter, Ph.D.; Lisa C Krishnamurthy, Ph.D.; Vincent Marconi, MD.


Title: Improving the Assessment of Brain Metabolism through Advanced MR Spectroscopy Tools at High Fields

Abstract:
Brain metabolism offers a sensitive and underexploited window into early and potentially reversible brain dysfunction, reflecting fundamental cellular processes that often precede structural or functional changes. Despite advances in neuroimaging, magnetic resonance spectroscopy (MRS) remains clinically underutilized due to technical and translational challenges, particularly at high magnetic fields. This work addresses these limitations by enhancing the robustness and clinical relevance of high-field MRS, aiming to improve metabolic quantification, characterize spatial metabolic organization in the healthy brain, and link metabolic alterations to cognitive dysfunction in vulnerable populations such as people living with HIV (PLWH), who are at risk for HIV-associated neurocognitive disorder (HAND). Aim 1 focuses on methodological innovation by systematically comparing multi-channel coil combination algorithms at 7T, including Brown, S/N², WSVD, and OpTIMUS, to optimize spectral quality and metabolite quantification. Achieving reliable high-field MRS data is essential for all downstream analyses and clinical applications. Aim 2 leverages high-resolution whole-brain MRS imaging in healthy adults to identify spatially distributed, metabolite-specific brain networks using independent component analysis and graph theory. This aim advances our understanding of the large-scale organization of brain metabolism and its relationship to established functional and structural networks. Aim 3 integrates 7T MRS metabolic markers with multimodal neuroimaging and neuropsychological data from PLWH to evaluate their unique contribution to predicting and characterizing cognitive impairment. By applying advanced multimodal data fusion techniques, this aim seeks to uncover novel neuroimaging markers and interpretable neurobiological patterns that link metabolic, structural, and functional alterations with cognition. The significance of this work lies in developing robust technical tools, defining metabolic network architecture, and identifying metabolically specific biomarkers that enhance early detection and mechanistic understanding of brain disorders.

Wallace H. Coulter Department of Biomedical Engineering

Georgia Institute of Technology and Emory University

313 Ferst Drive

Atlanta, GA 30332

www.bme.gatech.edu