In recent years, the exploration of neural electrophysiological signals has catalyzed a revolution in understanding the pathophysiology of Parkinson’s disease (PD). A groundbreaking study published in npj Parkinson’s Disease has now offered unprecedented insights into the long-term stability of characteristic neural markers derived from subthalamic local field potentials (LFPs). This research, authored by Stam, van Wijk, Buijink, and colleagues, rigorously investigates the enduring consistency of both periodic and aperiodic physiomarkers recorded from the subthalamic nucleus (STN), a pivotal brain region implicated in PD motor symptoms. By unveiling the robust nature of these electrophysiological signals over extended periods, this study is poised to reshape neuromodulation strategies and biomarker development in Parkinson’s therapeutics.
The subthalamic nucleus forms part of the basal ganglia circuitry, playing a critical role in movement regulation. Parkinson’s disease, characterized by dopaminergic neuron degeneration, disrupts these basal ganglia pathways, leading to the hallmark motor impairments such as bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) targeting the STN has become a cornerstone in managing advanced PD, primarily operated via implanted electrodes that deliver electrical impulses to modulate dysfunctional neural activity. However, fine-tuning DBS parameters and maximizing treatment efficacy over years remain challenging due to variable biomarker reliability and underlying neural plasticity.
The study uniquely addresses this clinical gap by examining the stability of two distinct physiomarkers derived from LFP recordings: periodic oscillatory activities, such as beta-band rhythms, and aperiodic components, which reflect broadband spectral features potentially linked to neural excitation-inhibition balance. Traditionally, periodic beta oscillations (~13-30 Hz) have been extensively studied, with elevated beta power correlating negatively with motor performance and responsiveness to dopaminergic therapies. Yet, aperiodic neural dynamics, representing scale-free fluctuations in the frequency domain, have emerged as complementary indicators of underlying neural state and are gaining traction in neurophysiological research.
Employing an advanced longitudinal design, Stam et al. implanted directional DBS leads capable of chronic LFP monitoring in a cohort of PD patients. This approach permitted recording subthalamic signals over an extended timeframe of months to years, circumventing the limitations inherent in short-term laboratory assessments. By systematically analyzing the spectral features from these datasets, the researchers quantified the intra-individual variability of periodic beta oscillations and aperiodic broadband components, thereby assessing their temporal robustness.
A critical revelation from the analysis was the remarkable long-term consistency of both physiomarkers. Beta-band oscillations demonstrated stable oscillatory peaks in frequency and power, maintaining their spatial focality within the STN despite ongoing disease progression and therapeutic adjustments. Concurrently, the aperiodic exponent, which characterizes the slope of the power spectral density, showed minimal drift over time, suggesting that the neural excitation-inhibition balance indexed by this feature is a steadfast characteristic of subthalamic physiology in PD patients.
This constancy has profound implications for the design of adaptive DBS systems, also known as closed-loop neuromodulation. These systems rely on feedback from reliable biomarkers to dynamically adjust stimulation parameters in response to the patient’s neural state, aiming to enhance clinical outcomes and reduce side effects. The demonstration that both periodic and aperiodic features endure longitudinally argues strongly for their incorporation into real-time DBS control algorithms. Unlike biomarkers susceptible to transient fluctuations, these physiomarkers could serve as stable anchors facilitating personalized neuromodulation that adapts intelligently over the course of treatment.
Moreover, the distinction between periodic and aperiodic components opens novel vistas in understanding PD pathophysiology. While pathological beta synchrony has long been associated with motor impairment, the aperiodic spectral features may relate more fundamentally to network excitation levels and synaptic homeostasis within the STN and its broader basal ganglia context. The preserved aperiodic exponent suggests a maintained cortical-subcortical balance or a stable underlying neural noise floor, both of which could influence how the basal ganglia circuits process motor commands and respond to dopaminergic modulation.
This study also highlights the technical advancements enabling such comprehensive long-term monitoring. The use of directional DBS electrodes enhances spatial resolution, allowing precise localization of physiomarker sources and minimizing contamination from adjacent neural structures. Coupled with sophisticated signal processing pipelines capable of disentangling oscillatory and non-oscillatory signal components, these innovations are ushering in an era where nuanced understanding of brain oscillations can be integrated into everyday clinical practice.
Despite these advances, the authors also caution about inherent complexities in interpreting LFP data. Factors such as individual anatomical variability, electrode positioning, medication status, and disease heterogeneity contribute to subtle variations in the recorded signals. Therefore, while physiomarkers show resilience, developing robust algorithms capable of accommodating these inter- and intra-individual differences remains an ongoing challenge. Nonetheless, this comprehensive dataset provides an invaluable foundation for translational research aimed at refining biomarker-guided DBS paradigms.
Importantly, the findings underscore the necessity of incorporating both periodic and aperiodic signal characteristics when defining physiomarkers in PD. Prior DBS optimization strategies have predominantly fixated on beta oscillations as the primary feedback signal, which may only tell part of the story. By integrating aperiodic signal metrics, future approaches could harness complementary neurophysiological information reflective of broader circuit dynamics, potentially enhancing therapeutic precision and patient-specific customization.
Furthermore, these insights carry broader implications beyond Parkinson’s disease. The methodology and analytical framework developed in this research can be adapted to other neurological disorders where abnormal neural oscillations and altered excitation-inhibition balances play crucial roles, such as dystonia, essential tremor, and epilepsy. The notion of dissecting and tracking discrete spectral components over long periods sets a new standard for personalized neuromodulation therapies across diverse clinical contexts.
This study also invigorates discussions on the biological basis of aperiodic neural activity, a topic garnering increasing attention in systems neuroscience. Aperiodic activity has been posited to reflect fundamental aspects of cortical microcircuit function, including synaptic input distributions and membrane potential fluctuations. The stability of the aperiodic exponent in PD patients’ STN offers empirical support for its role as a trait-like neural signature, opening avenues for further investigation into how disease processes perturb these fundamental electrical properties.
From a clinical viewpoint, the ability to track physiomarker stability longitudinally enhances patient monitoring and prognosis. Stable neural markers provide clinicians with reliable indicators to evaluate disease progression, therapeutic response, and potential adjustments in DBS programming. Moreover, continuous LFP monitoring embedded within implanted devices could facilitate remote, real-time assessment of PD motor states, reducing the need for frequent clinical visits and fostering proactive disease management.
As the field moves towards precision neuromodulation, the contribution of Stam and colleagues represents a significant paradigm shift. By meticulously validating the long-term consistency of physiomarkers in the subthalamic nucleus, this work lays the groundwork for next-generation closed-loop DBS systems that are both adaptive and durable. Future studies expanding these findings to larger, more diverse patient populations will be critical in generalizing these principles and integrating them into routine clinical workflows.
In sum, this landmark investigation redefines our understanding of Parkinsonian neurophysiology, highlighting that both oscillatory beta rhythms and aperiodic spectral features are not transient artifacts but rather stable signatures embedded within the subthalamic circuitry. These findings empower researchers and clinicians alike to envision a future where tailored neuromodulation strategies leverage reliable electrophysiological physiomarkers, ultimately improving quality of life for millions affected by Parkinson’s disease worldwide.
Subject of Research:
Long-term stability of periodic and aperiodic physiomarkers in subthalamic local field potentials in Parkinson’s disease
Article Title:
Long-term consistency of aperiodic and periodic physiomarkers in subthalamic local field potentials in Parkinson’s disease
Article References:
Stam, M.J., van Wijk, B.C.M., Buijink, A.W.G. et al. Long-term consistency of aperiodic and periodic physiomarkers in subthalamic local field potentials in Parkinson’s disease. npj Parkinsons Dis. 11, 204 (2025). https://doi.org/10.1038/s41531-025-01053-5
Image Credits: AI Generated
