Title: Optimization of sensitivity to disease-associated cortical metabolic abnormality by evidence-based quantification of in vivo proton magnetic resonance spectroscopy data from 3 Tesla and 7 Tesla
Description: In vivo proton magnetic resonance spectroscopy (1H MRS) is the only method available to measure small-molecule metabolites in living human tissue, including the brain, without ionizing radiation or invasive medical procedures. Despite its attendant potential for supporting clinical diagnostics in a range of neurological and psychiatric conditions, the metabolite concentration estimates produced by 1H-MRS experiments, and therefore their sensitivity and specificity to any particular biological phenomenon under study, are readily distorted by a number of confounds. These include but are not limited to static and radiofrequency field characteristics, signal relaxation dynamics, macromolecule and lipid contributions to the spectral baseline, spectral fitting artifacts, and other uncontrolled idiosyncrasies of 1H-MRS data acquisition, processing, and quantification. Using 1H-MRS data obtained via 3-Tesla and 7-Tesla magnetic resonance (MR) scanners from healthy controls, individuals with progressive and relapsing-remitting multiple sclerosis (MS), and individuals with post-traumatic stress disorder (PTSD) and/or major depressive disorder (MDD), this work therefore aims to build and apply a framework for quantifying and thereby reducing such confounds introduced to 1H-MRS estimates of in vivo metabolite concentrations at the steps of data processing and quantification, with an ultimate aim to maximizing the potential of 1H MRS for supporting sensitive and specific clinical diagnosis of neurological or psychiatric disease.

The steps examined include spectral quantification by linear combination modeling (Chapter 2), absolute quantification by internal concentration referencing (Chapter 3), and cross-sectional statistical analysis of results (Chapters 4 and 5). Chapter 2 designs and implements a graphical user interface (GUI)-supported validation pipeline for measuring how data quality, spectral baseline, and baseline model affect the precision and accuracy of 1H-MR spectral quantification by linear combination modeling. This validation pipeline is then used to show that spectral data quality indices signal to noise ratio (SNR) and full width at half maximum (FWHM) interact with spectral baseline to influence not only the precision but also the accuracy of resultant metabolite concentration estimates, with fit residuals poorly indicative of true fit error and spectral baselines modeled as regularized cubic splines not significantly outperformed by those employing simulated macromolecules. A novel method for extending the commonly used spectral quantification precision estimate Cramér-Rao Lower Bound (CRLB) to incorporate considerations of continuous and piecewise polynomial baseline shapes is therefore presented, tested, and similarly integrated into a GUI-supported toolkit to improve the correspondence between estimated CRLB and metabolite fit error variability when this now empirically justified approach to spectral baseline modeling is used.

In Chapter 3, age- and disease-associated differences in transverse (T2) water signal relaxation measured at 7 Tesla in the prefrontal cortex of individuals with progressive (N=21) relative to relapsing-remitting (N=26) or no (N=25) multiple sclerosis are shown to influence absolute quantification of metabolite concentrations by internal referencing to water. In Chapter 4, these findings from Chapters 2 and 3 are used to justify an evidence-based 1H-MR spectral processing and quantification protocol that focuses optimization efforts on baseline modeling approach and references metabolite concentration estimates to internal creatine instead of water. When this protocol is applied to 7-Tesla prefrontal cortex 1H-MR spectra from the aforementioned multiple sclerosis and control cohorts, it supports metabolite concentration estimates that, in the absence of any additional supporting data, inform supervised-learning-enabled identification of progressive multiple sclerosis at nearly 80% held-out validation sensitivity and specificity.

Finally, in Chapter 5, the same processing, quantification, and machine-learning pipeline employed in Aim 3 is independently applied to a new set of 7-Tesla prefrontal cortex 1H-MRS raw data from an entirely different cohort of individuals with (N=20) and without (N=18) PTSD and/or comorbid or primary MDD. Here the processing, quantification, and statistics procedures designed using lessons in Chapters 2 and 3 and optimized for classifying multiple sclerosis phenotype in Chapter 4 generalize directly to metabolite-only classification of PTSD and/or MDD with sensitivity and specificity similarly near to or greater than 80%. In both Chapters 4 and 5, supervised learning avoids dimensionally reducing metabolite feature sets in order to pinpoint the specific metabolites most informative for identifying each disease group. Taken together, these findings justify the potential and continued development of 1H MRS, at least as applied in the human brain and especially as supported by multivariate approaches including supervised learning, as an auxiliary or mainstay of clinical diagnostics for neurological or psychiatric disease.