The Impact of BOLD Induced Linewidth Modulation on Functional 1H MRS Analysis

Functional MRS can measure the neurometabolic response to neuronal activation, therapeutic interventions and changes in physiology. Substantial technical challenges currently present a barrier to reproducible findings and broader adoption by the neuroscientific community. One such challenge is the conflation between genuine metabolic changes and bias caused by subtle spectral lineshape changes associated with the BOLD response. Previous studies have demonstrated an approximately 1% bias for glutamate estimates at 7T based on experimentally acquired data and a single conventional fitting algorithm. In this study, we use synthetic MRS data to estimate the bias for two conventional fitting methods (LCModel and ABfit-reg) at 3T and 7T and evaluate the efficacy of dynamic lineshape adjustment, during preprocessing and fitting analysis stages, to reduce bias. Using the same dataset, we also explore the potential bias in 2D fitting approaches, comparing several fitting models implemented in FSL-MRS. Bias between two conventional fitting methods without explicit linewidth correction was similar ([~]1% for glutamate) and in good agreement with previous experimental studies at 7T. Lineshape changes from the BOLD response cause similar bias in conventional and 2D fitting packages for 3T and 7T data, resulting in an overestimation of metabolic changes associated with neuronal activation. This bias may be significantly reduced (<0.2%) by incorporating a BOLD linewidth matching step for conventional analysis or by direct modelling for 2D analysis. We therefore recommend explicit BOLD lineshape correction or modelling for future task-based fMRS studies at 3T and above.