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

At its core, Parkinson’s disease disrupts the delicate orchestration of motor control, influencing gait patterns and leading to characteristic disturbances in posture and ambulation. Traditionally, diagnosis hinges on clinical observation of symptoms such as bradykinesia, resting tremor, and rigidity, augmented by patient history and neurological examination. Yet, these methods are inherently subjective, often leading to delays or inaccuracies in diagnosis. To bridge this gap, the authors emphasize the potential of plantar pressure analysis, a technique that intricately maps the pressure distribution beneath the foot during walking, standing, or other dynamic activities. By capturing subtle irregularities in pressure patterns and temporal gait parameters, clinicians could gain unprecedented insight into the biomechanical manifestations of Parkinson’s disease at stages far earlier than currently possible.

The human foot acts as a primary interface with the ground, and its pressure distribution reveals a wealth of information about neuromuscular integrity and motor execution. Parkinsonian gait is typified by reduced stride length, shuffling steps, and a decrease in heel strike force, all of which manifest as distinctive plantar pressure signatures measurable using pressure mats, insoles embedded with sensors, or advanced imaging modalities intertwined with force measurement technology. Wang et al. highlight how sophisticated algorithms and machine learning models can analyze multidimensional pressure data, distinguishing between subtle Parkinsonian gait abnormalities and those arising from other neurological or musculoskeletal disorders. This fine-grained differentiation carries significant clinical value, potentially enabling personalized intervention plans tailored to the unique motor profile of each patient.

This survey extensively reviews the spectrum of current plantar pressure analysis systems employed in Parkinson’s research. These range from high-resolution pressure platforms featuring piezoelectric sensor arrays that capture dynamic forces in real time, to wearable pressure sensor insoles enabling continuous ambulatory monitoring. A critical evaluation of these technologies underscores the trade-offs between data accuracy, user comfort, portability, and cost. Remarkably, the authors note that recent advances in flexible electronics and wireless data transmission have catalyzed a paradigm shift, permitting real-world gait monitoring outside clinical environments. Such ecological validity can dramatically enhance the reliability of Parkinson’s disease assessments, capturing fluctuations in motor performance throughout daily activities rather than static, clinic-based snapshots.

Furthermore, the authors explore the integration of plantar pressure metrics with other modalities, such as inertial measurement units (IMUs), electromyography (EMG), and neuroimaging, to forge robust multimodal diagnostic frameworks. Combining biomechanical data with neural signals promises a comprehensive characterization of disease progression and response to therapy. For example, detecting freezing of gait – a debilitating motor symptom in advanced Parkinson’s – can be refined by synchronizing plantar pressure data pinpointing foot placement irregularities with EMG-recorded muscle activation patterns. Such integrated approaches may unlock predictive markers of motor decline, informing timely therapeutic interventions and enhancing patient quality of life.

The survey further delves into the pathophysiological mechanisms underpinning the altered plantar pressure patterns in Parkinson’s disease. Dopaminergic neuron degeneration disrupts basal ganglia circuits integral to smooth motor function, culminating in motor deficits that manifest peripherally as impaired force modulation and proprioceptive feedback during gait. These neural abnormalities translate biomechanically into uneven pressure distribution, reduced ground reaction forces, and altered temporal sequencing of foot contact phases. By correlating these biomechanical anomalies with neurodegeneration severity, plantar pressure analysis emerges as not merely a diagnostic tool but also a biomarker reflecting underlying neuropathology.

Addressing challenges, Wang and colleagues candidly discuss the variability inherent in plantar pressure data caused by factors such as footwear differences, surface types, patient fatigue, and comorbidities like osteoarthritis. They advocate for standardizing testing protocols and data normalization techniques to mitigate confounding influences, ensuring consistency and reproducibility across studies. Moreover, large-scale population studies encompassing diverse demographics are essential to establish normative databases against which pathological deviations can be contrasted robustly.

In the realm of therapeutic monitoring, plantar pressure analysis presents exciting opportunities to quantify responses to pharmaceutical treatments, deep brain stimulation (DBS), and physiotherapy. Objective gait parameters derived from pressure sensors could serve as quantitative endpoints in clinical trials, facilitating more rapid and precise assessment of treatment efficacy. Early pilot studies discussed in the survey demonstrate measurable improvements in gait symmetry and pressure distribution post-intervention, reinforcing the technology’s potential as an indispensable clinical tool.

Importantly, patients themselves stand to benefit from these innovations through enhanced disease self-management. Wearable plantar pressure devices can provide real-time biofeedback, alerting users to gait deviations that predispose falls, a significant risk in Parkinsonian populations. Personalized gait training programs incorporating feedback loops may foster motor learning and neuroplastic adaptations, potentially slowing disease progression or improving functional independence.

Another compelling aspect explored is the potential integration of plantar pressure analysis within telemedicine frameworks. Remote patient monitoring, powered by wearable telemetry devices transmitting pressure data to healthcare providers, could facilitate continuous disease surveillance, timely adjustments in therapy, and improved access to specialist care for patients in geographically isolated regions. This aligns with the global movement toward digital health ecosystems, amplifying Parkinson’s disease management’s cost-efficiency and scalability.

Ethical considerations permeate this technological evolution. The survey underscores the necessity of safeguarding patient privacy and data security, particularly given the sensitive nature of continuous biomechanical and behavioral monitoring. Transparent consent processes and robust encryption protocols are essential to maintain trust and compliance within increasingly digitized healthcare landscapes.

Looking ahead, Wang et al. call for interdisciplinary collaborations marrying neurology, biomechanics, engineering, data science, and patient advocacy to propel plantar pressure analysis from research labs into routine clinical practice. Addressing regulatory pathways, standardization bodies, and reimbursement policies will be crucial to facilitate widespread adoption. The authors envision a future where plantar pressure measurements serve as a non-invasive, cost-effective, and precise method for Parkinson’s diagnosis, prognosis, and personalized therapeutic guidance.

In conclusion, this comprehensive survey represents a seminal contribution to Parkinson’s disease research, meticulously synthesizing technological advances, clinical applications, and future directions in plantar pressure analysis. Its insights illuminate a transformative pathway toward overcoming longstanding challenges in Parkinsonian gait assessment, enhancing diagnostic accuracy, monitoring disease progression, and ultimately improving patient outcomes. As this field rapidly evolves, plantar pressure analysis stands poised to become a cornerstone of precision medicine approaches in neurodegenerative diseases, charting new frontiers in both scientific understanding and clinical care.

Subject of Research: Diagnosis and assessment of Parkinson’s disease through plantar pressure analysis

Article Title: A comprehensive survey on diagnosis and assessment of Parkinson’s disease via plantar pressure analysis

Article References:
Wang, X., Zhao, Z., Lin, L. et al. A comprehensive survey on diagnosis and assessment of Parkinson’s disease via plantar pressure analysis. npj Parkinsons Dis. (2026). https://doi.org/10.1038/s41531-026-01416-6

Image Credits: AI Generated