In vivo magnetic resonance spectroscopy (MRS): A critical mini-review of MRS limitations and multiscale interpretation

Magnetic Resonance Spectroscopy (MRS) provides a non-invasive means of interrogating in vivo biochemical processes, extending magnetic resonance methodologies beyond structural and hemodynamic imaging into the domain of cellular metabolism. Unlike conventional Magnetic Resonance Imaging (MRI), which primarily captures anatomical organization and indirect functional signals, MRS resolves metabolite-specific spectral features, enabling the quantification of neurochemical states within intact tissue. Advances over recent decades (including ultra-high-field systems, improvements in radiofrequency coil design, optimized pulse sequences, and increasingly sophisticated spectral modeling) have significantly enhanced signal fidelity and metabolite resolution. These developments have expanded the applicability of MRS across neurology, psychiatry, oncology, and metabolic research. However, such progress has also exposed fundamental interpretative challenges, particularly regarding spectral overlap, macromolecular contributions, and the limited specificity with which individual metabolites can be mapped onto discrete biological processes. Accordingly, the utility of MRS cannot be understood solely in terms of improved detection sensitivity or expanded clinical deployment. Rather, its interpretative power depends on how spectroscopic signals are situated within a broader multiscale framework linking molecular metabolism, cellular function, and systems-level dynamics. This mini-review, therefore, moves beyond purely technical accounts by critically examining the assumptions underlying metabolite attribution and emphasizing the necessity of multimodal integration. By synthesizing physical principles, methodological constraints, and neurobiological interpretation, this work advances a more constrained and context-sensitive framework for MRS analysis. Emerging developments (including multimodal imaging integration and machine-learning-assisted spectral decomposition) are evaluated in this light, with particular attention to their implications for multiscale neuroscience and multiscale brain imaging.