Watching Brain Metabolism Move: Spectroscopic Imaging Shifts From Static Snapshots to Living Flux

For decades, clinicians studying neurodegenerative diseases have relied on imaging techniques that capture the brain in something like a photograph: a single moment, frozen in time. A major review published in the Journal of Translational Medicine argues that this era of static metabolic snapshots is giving way to something far more revealing. A team led by Marta M. Pokotylo and Jannik Prasuhn of the University of Luebeck, together with colleagues at Kiel University and Johns Hopkins University, maps out how magnetic resonance spectroscopy and spectroscopic imaging, collectively known as MRS and MRSI, are evolving from tools that measure the resting concentrations of brain metabolites into platforms capable of watching metabolism itself flow through the living brain. The shift, the authors contend, could transform how Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and related disorders are detected, monitored, and ultimately treated.

The clinical motivation is straightforward. Dysregulated brain metabolism is increasingly recognized as a central player in the development and progression of neurodegenerative diseases, not merely a byproduct of neuronal death. The workhorse technique for probing this metabolism in patients has been positron emission tomography, or PET, most famously with the glucose analog 18F-fluorodeoxyglucose. PET can show where the brain takes up a radioactive tracer, and reduced uptake in certain regions is a well-established signature of neurodegeneration. But PET has a fundamental blind spot: it reveals only the initial uptake of the tracer and offers no insight into its downstream metabolic fate. It cannot tell clinicians whether glucose that enters a neuron is being funneled into energy production, diverted into biosynthetic pathways, or left to accumulate. It also provides no window into dynamic metabolic fluxes and turnover, the actual rates of biochemical reactions that define how a cell is functioning.

MRS and MRSI approach the problem from an entirely different physical angle. Rather than detecting radioactive decay, these techniques exploit nuclear magnetic resonance, the same phenomenon underlying MRI, to identify specific chemical compounds based on the characteristic resonance frequencies of their atomic nuclei. The result is a non-invasive, spatially resolved, in vivo measurement of metabolite levels, achieved without any radioactive tracer. Proton MRS and MRSI, which detect hydrogen nuclei, are the most widely utilized variants because they can be implemented on standard clinical MRI scanners and require no special hardware beyond what most hospitals already possess. In neurodegenerative research, proton MRS has long been used to track markers such as N-acetyl-L-aspartate, a proxy for neuronal health, and the combined signal of glutamate and glutamine, which reflects excitatory neurotransmitter metabolism. These measurements, however, remain essentially equilibrium measurements: they report how much of each metabolite is present, not how quickly it is being made, consumed, or exchanged.

The review’s central conceptual contribution is a framework for moving beyond this equilibrium limitation toward dynamic metabolic imaging. In a dynamic experiment, a labeled substrate is introduced into the body and the scanner watches as the label is incorporated, step by step, into downstream metabolites. The rate at which labeled products appear encodes the flux through specific enzymatic pathways, such as the tricarboxylic acid cycle that powers oxidative phosphorylation in mitochondria. This is the kind of information that reveals metabolic reprogramming and metabolic bottlenecks, the mechanistic signatures that static concentration measurements cannot capture. The authors lay out the key considerations for designing such experiments, including the choice of tracer, the method of signal enhancement, the acquisition hardware, the image reconstruction and preprocessing pipeline, and the metabolic modeling needed to convert time-course data into meaningful biochemical rate constants.

Tracer selection is where the physics gets interesting. Proton MRSI can be extended to heteronuclear approaches that detect less abundant nuclei, including deuterium, carbon-13, nitrogen-15, and oxygen-17. Each of these broadens the scope of accessible metabolites and pathways. Deuterium, the heavy isotope of hydrogen, has emerged as a particularly attractive option. Deuterium-labeled glucose can be administered safely, and the resulting deuterium metabolic imaging, or DMI, tracks its conversion into products such as glutamate and lactate. The revival of deuterium MRSI, the review notes, has opened new avenues for dynamic metabolic investigation in the human brain without requiring hyperpolarization, making it one of the most practical routes to clinical translation. Because deuterium resonates at a frequency far from that of protons, the technique can be implemented with relatively modest hardware additions to existing scanners.

For metabolites present at very low concentrations, however, even heteronuclear detection struggles against the intrinsically poor sensitivity of magnetic resonance. This is where nuclear spin hyperpolarization enters the picture. Hyperpolarization techniques, including dissolution dynamic nuclear polarization and parahydrogen-based methods such as parahydrogen-induced polarization and signal amplification by reversible exchange, can transiently boost the nuclear polarization of a tracer by four to five orders of magnitude before it is injected. The result is a signal strong enough to detect low-abundance metabolites in vivo in real time, enabling hyperpolarized metabolic imaging of reactions that would otherwise be invisible. Dissolution DNP, for example, can hyperpolarize carbon-13-labeled pyruvate, allowing researchers to watch the enzyme lactate dehydrogenase convert it to lactate within seconds, a direct readout of a key step in cellular energy metabolism. The review discusses these hyperpolarization methods in detail, along with their technical demands, including the specialized dissolution equipment and rapid injection systems they require.

None of this is trivial to implement. The authors are candid about the major methodological and translational challenges standing between dynamic MRSI and routine clinical use. Signal-to-noise ratio remains the persistent adversary, since metabolic signals are orders of magnitude weaker than the water signal used in conventional MRI. Higher magnetic field strengths improve sensitivity and spectral resolution but bring their own problems, including increased specific absorption rate and greater hardware cost. Vendor hardware variability compounds the difficulty, as scanners from different manufacturers differ in gradient performance, coil configurations, and pulse sequence availability. Perhaps most consequentially, there is currently a lack of standardization across acquisition, reconstruction, preprocessing, and modeling strategies, which makes it difficult to compare results between sites or to pool data across studies. The review also points to the role of modern computational tools, including machine learning and deep learning approaches, in accelerating image reconstruction and improving the extraction of quantitative information from noisy spectroscopic data.

What could dynamic MRSI actually deliver for patients with neurodegenerative diseases? The authors argue that the technique provides a promising foundation for investigating brain metabolism in vivo, offering mechanistic insights into metabolic fluxes, reprogramming, and bottlenecks that could reshape biomarker development and diagnostic practice. Mitochondrial dysfunction is a recurring theme across neurodegeneration, and dynamic measurements of TCA cycle flux or oxidative phosphorylation could reveal disease mechanisms years before structural changes appear on conventional MRI. In disorders such as multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration, where distinguishing between overlapping clinical syndromes is notoriously difficult, metabolic fingerprints might aid patient stratification for clinical trials. Longitudinal disease monitoring is another compelling application: because MRSI involves no ionizing radiation, it can be repeated safely, making it well suited to tracking disease progression and treatment response over time in a way that repeated PET scans cannot easily match.

The translational path, however, requires honest acknowledgment of where the field stands. Dynamic MRSI is currently limited to a handful of research institutions with the specialized hardware, chemistry infrastructure, and analytical expertise to pull it off. Hyperpolarized tracers must be produced under good manufacturing practice conditions, and regulatory pathways under medical device regulation frameworks add further complexity. The authors emphasize that ongoing technical and methodological advances are steadily improving feasibility, biochemical specificity, and the prospects for broader implementation, but they are equally clear that standardized and harmonized acquisition, reconstruction, and preprocessing protocols are necessary before these approaches can achieve broad clinical implementation. Without such harmonization, the field risks fragmenting into incompatible local practices that cannot generate the large, comparable datasets needed for clinical validation.

The bigger picture is one of a field at an inflection point. The shift from static to dynamic MRSI, the review concludes, has great potential to advance biomarker and diagnostic tool development, patient stratification, and longitudinal disease monitoring in neurodegenerative disorders, provided that current challenges and limitations are sufficiently addressed. In an era when most experimental therapies for Alzheimer’s and Parkinson’s diseases have stumbled, partly because patients are enrolled too late in a disease process that begins decades before symptoms, a technology that can measure the metabolic fluxes of the living brain, non-invasively and repeatedly, offers exactly the kind of window that drug developers and neurologists have been seeking. The photograph is becoming a movie, and what it shows may change how neurodegeneration is understood.

Subject of Research: Dynamic magnetic resonance spectroscopic imaging of brain metabolism in neurodegenerative disorders

Article Title: From equilibrium to dynamic metabolic imaging: the evolving role of magnetic resonance spectroscopic imaging in neurodegenerative disorders

Article References: Pokotylo, M. M., Peters, J. P., Diercks, A., Mohamad, F. H., Brüggemann, N., Pravdivtsev, A. N., Hövener, J.-B., & Prasuhn, J. (2026). From equilibrium to dynamic metabolic imaging: the evolving role of magnetic resonance spectroscopic imaging in neurodegenerative disorders. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-09084-z

Image Credits: AI Generated

DOI: 10.1186/s12967-026-09084-z

Keywords: magnetic resonance spectroscopy, MRSI, hyperpolarization, deuterium metabolic imaging, neurodegenerative diseases, brain metabolism, PET, metabolic flux, Alzheimer's disease, Parkinson's disease, biomarkers, metabolic modeling