The review, led by Federico Garrou of the Division of Nuclear Medicine at ASST Papa Giovanni XXIII in Bergamo, Italy, together with colleagues from Novara, Exeter, and the University of Piemonte Orientale, focused on positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A, or SV2A. This protein sits in the membrane of nearly every synaptic vesicle in the brain, the tiny sacs that store and release neurotransmitters at the presynaptic terminal. Because SV2A is so abundant and so tightly linked to vesicle machinery, the amount of it that a radioactive tracer can bind to serves as a reliable proxy for the density of functioning synapses in any given region of the living brain.

The tracer story began in earnest in 2016, when researchers at Yale first demonstrated in Science Translational Medicine that the compound [11C]UCB-J could image synaptic density in the living human brain. A second-generation radioligand, [18F]SynVesT-1, followed with its first-in-human evaluation in 2021, offering the practical advantage of a fluorine-18 label, which extends the isotope’s usable half-life and makes the tracer far easier to distribute beyond on-site cyclotron facilities. Since then, a small but rapidly growing literature has applied these tools to the parkinsonian α-synucleinopathies, the family of diseases characterized by the pathological aggregation of α-synuclein protein: Parkinson’s disease itself, Parkinson’s disease dementia and dementia with Lewy bodies, and multiple system atrophy.

Garrou and colleagues systematically searched and screened the literature, ultimately including seventeen reports in their synthesis. The analysis followed established systematic review methodology, with independent screening and data extraction by multiple reviewers, and the authors explicitly note that no new datasets were generated; all evidence was drawn from previously published studies. That constraint matters, because it means the review’s conclusions are only as strong as the underlying primary studies, and the authors are candid about those limitations.

In Parkinson’s disease, the single most reproducible finding across studies was reduced SV2A binding in the substantia nigra, the midbrain structure where dopaminergic neurons die and where the disease process is known to begin. This is a technically remarkable result. The substantia nigra is small, deep in the brainstem, and notoriously difficult to image with precision, so detecting a measurable loss of synaptic terminals there in early disease validates the sensitivity of the method. But the nigra was not alone. Several studies reported additional reductions in the caudate nucleus, the broader striatum, the thalamus, the raphe nuclei, brainstem regions, and the cerebral cortex, with the extent of involvement varying considerably between cohorts.

That variability is itself informative. Parkinson’s disease is not a single uniform entity, and the spread of α-synuclein pathology follows patterns that differ between patients, potentially explaining why some cohorts show predominantly nigral synaptic loss while others reveal widespread cortical involvement. Notably, one study found that lower synaptic density in mood-related circuitry underlies depression in Parkinson’s disease, suggesting that SV2A imaging may connect specific non-motor symptoms to specific patterns of synaptic failure. Other work has linked synaptic loss to overall symptom severity, and dual-tracer studies comparing SV2A PET with dopamine transporter imaging have begun to disentangle how presynaptic terminal integrity relates to the dopaminergic deficits that conventional scans measure.

In the dementia spectrum, the abnormalities were broader and more cortical. Studies of patients with Lewy body dementia using [11C]UCB-J found reduced synaptic density relative to healthy controls, and subsequent work comparing SV2A PET with fluorodeoxyglucose PET showed relationships between synaptic density and cerebral glucose consumption. Critically, the review highlights evidence that cortical SV2A binding correlates with cognitive performance, which positions the tracer as a potential mechanistic bridge between the molecular pathology of α-synuclein and the cognitive decline that defines dementia with Lewy bodies and Parkinson’s disease dementia. If synaptic loss can be quantified before cognition deteriorates irreversibly, trials of synapse-protecting therapies would gain an outcome measure that reflects biology rather than compensation.

Multiple system atrophy, the rarest and often most aggressive member of the synucleinopathy family, provided perhaps the most diagnostically intriguing signal. One study using [18F]SynVesT-1 identified a distinct pattern of synaptic density reduction concentrated in infratentorial and cerebellar regions, a distribution that differs from the patterns seen in Parkinson’s disease and Lewy body dementias and that could eventually help stratify patients phenotypically. This matters clinically because distinguishing MSA from Parkinson’s disease early in the disease course remains one of the most difficult problems in movement disorder neurology, and a biomarker that captures disease-specific spatial signatures could sharpen both diagnosis and trial enrollment.

Yet the review is equally clear about what stands between the current state of the field and clinical translation. The included studies involved small cohorts. Quantification strategies varied widely, including different reference region choices, kinetic modeling approaches, and scanning protocols, and recent methodological work has shown that reference region optimization directly affects test-retest reliability and the sensitivity with which patient-control differences can be detected. Longitudinal evidence remains scarce, with only limited follow-up data on how synaptic density evolves over time in individual patients, and the possibility of cohort overlap between publications complicates interpretation. Even the technical underpinnings are still being refined, with ongoing research into reduced injected doses, shorter scanning times, and direct four-dimensional PET reconstruction methods designed to reduce variance in synaptic density measurements.

The authors conclude that SV2A PET is a promising research biomarker for the biological characterization of synucleinopathies, but that multicentre validation and harmonized protocols are required before it can move into clinical practice. That verdict captures the field at an inflection point. The technology has proven it can see synapses fail in the living brain, and the patterns it reveals map onto what we know about how α-synucleinopathies unfold from early synaptic dysfunction to overt neurodegeneration. What remains is the unglamorous but essential work of standardization: agreeing on how to quantify binding, how to design scans, and how to pool data across centers so that the signal observed in a handful of patients in Novara, Exeter, or New Haven can become a diagnostic tool trusted everywhere. If that work succeeds, the synapse, long invisible to clinical neurology, may finally become the target that Parkinson’s disease diagnosis and drug development have been waiting for.

Subject of Research: SV2A PET imaging of synaptic density in parkinsonian α-synucleinopathies

Article Title: Synaptic vesicle glycoprotein 2A PET imaging in parkinsonian α-synucleinopathies: a systematic review

Article References: Synaptic vesicle glycoprotein 2A PET imaging in parkinsonian α-synucleinopathies: a systematic review. (n.d.). https://doi.org/10.1007/s00415-026-14123-2

Image Credits: AI Generated

DOI: 10.1007/s00415-026-14123-2

Keywords: SV2A PET, Parkinson's disease, dementia with Lewy bodies, multiple system atrophy, alpha-synucleinopathies, synaptic density, synaptic vesicle glycoprotein 2A, molecular neuroimaging, radiotracers, biomarkers, neurodegeneration, systematic review

The study, led by researchers using an unusually sensitive combination of brain imaging techniques, set out to answer a deceptively simple question. If structural plasticity in the form of new synapse formation underlies learning-related gray matter changes, then a rigorous motor learning intervention should produce detectable shifts in both measures over the same time window. Prior animal studies had shown that learning new motor skills, such as reaching tasks in rats or acrobatic training in mice, leads to synaptogenesis in the motor cortex within days to weeks. If the same biology holds in humans, the argument went, modern imaging should be able to catch it in the act.

To test this, the team recruited a small cohort of healthy adult participants and put them through a carefully controlled motor learning protocol. The task was designed to be challenging enough to drive genuine learning, with performance improvements tracked session by session to confirm that participants were actually acquiring the skill rather than simply going through the motions. Crucially, the researchers combined positron emission tomography with a synaptic vesicle glycoprotein ligand, a radiotracer that binds to proteins found abundantly in the presynaptic terminals of neurons. This class of tracer, sometimes described as offering a molecular window into synaptic density, is among the most direct non-invasive measures of synapse abundance available in living humans.

In parallel, the participants underwent high-resolution structural magnetic resonance imaging, allowing the researchers to quantify gray matter volume in regions implicated in motor learning, including the primary motor cortex, the supplementary motor area, the cerebellum, and the striatum. The logic of the design was elegant in its redundancy. If learning leaves structural fingerprints, those fingerprints should appear in the tracer signal, in the anatomical volumes, or in both, and any changes should correlate with how well individuals learned the task.

When the data came in, the story that emerged was one of stability rather than transformation. Participants improved substantially on the motor task, confirming that the intervention was behaviorally effective. Yet comparisons of tracer binding and gray matter volumes before and after the training period revealed no statistically significant changes in any of the regions examined. Individual differences in the amount of learning did not predict changes in synaptic density measures, and there was no evidence of the kind of localized volume increases reported in some earlier motor learning studies.

The finding does not mean that nothing changed in the brains of the learners. The authors are careful to point out several plausible explanations for the null result, each of which carries important implications for how the field interprets structural plasticity research. One possibility is that the synaptic changes accompanying motor learning are either too small in magnitude or too spatially diffuse to be detected by current imaging technology, even with the sensitivity of modern synaptic tracers. Synaptogenesis in animal models tends to involve a modest net increase in synapse number, and the fraction of synapses that turn over in a given cortical region may be a tiny proportion of the total pool that the tracer signal samples.

Another possibility concerns timing and scale. Animal work shows that synapse formation can be highly transient, with newly formed spines appearing and disappearing over days, and a substantial fraction being pruned away shortly after training ends. If human motor learning follows a similar trajectory, the window between the end of training and the post-training scan could have missed a transient surge in synaptic remodeling. Gray matter volume changes, meanwhile, may reflect processes other than synaptogenesis altogether, such as changes in dendritic spines, glial cells, vasculature, or interstitial fluid, meaning that volume and synaptic density are not simply two views of the same underlying biology.

The pilot nature of the study also deserves honest scrutiny. The sample size was small, as is typical for studies combining PET imaging with repeated structural MRI, and statistical power to detect subtle within-subject changes is limited when cohort numbers run low. The authors frame the work explicitly as exploratory, designed to establish feasibility and generate effect size estimates that can inform larger, better-powered follow-up studies. Null results in small samples are notoriously ambiguous; they may reflect genuine stability, or they may reflect an inability to detect changes that a larger cohort would reveal. Both interpretations remain on the table.

Still, the study arrives at a moment when claims about rapid structural plasticity in the adult human brain have become a staple of popular science coverage and, increasingly, of clinical optimism. Exercise interventions, cognitive training programs, and rehabilitation protocols are often marketed with the promise that they can rebuild the brain, implicitly or explicitly invoking the gray matter gains reported in landmark learning studies. If those gains prove difficult to replicate with state-of-the-art measures of synaptic architecture, the field may need to recalibrate both its expectations and its explanatory language. The relationship between macroscopic volume changes and microscopic synaptic remodeling may be far looser than the standard narrative suggests.

What the study ultimately offers is a dose of methodological rigor applied to one of neuroscience’s most appealing ideas. The learning brain is undoubtedly changing, as decades of electrophysiology, animal work, and human imaging attest. But the assumption that such change must be legible in every measurable index of brain structure, on the timescale of a typical training study, is a hypothesis rather than a fact. By subjecting that hypothesis to a direct test with two complementary imaging modalities, and by publishing a transparent null result, the researchers have done the field a service that positive findings rarely provide. The next generation of plasticity studies will be better designed, better powered, and more appropriately cautious precisely because studies like this one have mapped the limits of what current tools can see.

Subject of Research: Synaptic density and gray matter volume changes following motor learning in healthy adults

Article Title: No significant changes in synaptic density and gray matter volume following motor learning—a pilot study

Article References: Hehl, M., Toyonaga, T., Carson, R. E., Dupont, P., Van Laere, K., Swinnen, S. P., & Cuypers, K. (2026). No significant changes in synaptic density and gray matter volume following motor learning—a pilot study. npj Science of Learning. https://doi.org/10.1038/s41539-026-00451-5

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00451-5

Keywords: motor learning, synaptic density, gray matter volume, brain plasticity, PET imaging, synaptic vesicle glycoprotein, structural MRI, motor cortex, cerebellum, neuroplasticity, null result, pilot study