Adaptive deep brain stimulation (aDBS) is a neuromodulation technique that delivers electrical stimulation to specific brain regions in a responsive, real-time manner, adjusting the stimulation parameters based on the patient's current brain activity.
* Closed-loop system: Unlike conventional DBS, which delivers continuous stimulation at fixed settings (open-loop), aDBS monitors neural signals (like local field potentials) and adapts stimulation accordingly.
* Personalized therapy: aDBS tailors treatment to the individual’s fluctuating needs throughout the day (e.g., during “off” or dyskinetic periods in Parkinson’s).
* Main goal: Optimize symptom control while minimizing side effects and prolonging battery life.
This technology is particularly promising for Parkinson’s disease, especially for patients who suffer from motor fluctuations and dyskinesias.
Adaptive DBS builds on the standard DBS setup but introduces feedback-driven adjustments.
1. Implantation of Electrodes Electrodes are surgically implanted into target brain regions, typically the subthalamic nucleus (STN) or globus pallidus internus (GPi), just like in conventional DBS.
2. Sensing Neural Signals Electrodes are bidirectional: they can stimulate and also record neural activity, especially local field potentials (LFPs).
The most used biomarker for Parkinson’s is the beta-band activity (13–30 Hz), which correlates with rigidity and bradykinesia.
3. Real-Time Feedback Loop An implantable pulse generator (IPG) or external controller contains algorithms that continuously analyze the recorded LFPs.
When abnormal patterns are detected (e.g., increased beta power), the system adjusts stimulation parameters (e.g., amplitude, frequency, pulse width).
4. Dynamic Stimulation Stimulation can be:
On-demand: only delivered during pathological activity.
Modulated: continuously adjusted in intensity based on symptom severity.
5. Technological Components Sensing-enabled IPGs: Like Medtronic’s Percept™ PC or AlphaDBS by Newronika.
Algorithms: Embedded or external, often machine learning-based, to interpret neural activity and trigger changes in stimulation.
Telemetry: Some systems allow remote data transfer and clinician monitoring.
6. Clinical Workflow Post-implantation:
Neural biomarkers are identified.
Algorithm thresholds are personalized.
Stimulation is titrated based on motor response and biomarker tracking.
The application of stimulators implanted directly over deep brain structures (i.e., deep brain stimulation, DBS) was developed in the late 1980s and has since become a mainstream option to treat several neurological conditions. Conventional DBS involves the continuous stimulation of the target structure, which is an approach that cannot adapt to patients' changing symptoms or functional status in real-time. At the beginning of 2000, a more sophisticated form of stimulation was conceived to overcome these limitations. Adaptive deep brain stimulation (aDBS) employs on-demand, contingency-based stimulation to stimulate only when needed. So far, aDBS has been tested in several pathological conditions in animal and human models.
While aDBS seems to be effective to treat movement disorders (Parkinson's disease and essential tremor), its role in cognitive and psychiatric disorders is to be determined, although neurophysiological assumptions are promising 1).
Although Deep Brain Stimulation (DBS) is an established treatment for Parkinson's disease (PD), there are still limitations in terms of effectivity, side-effects and battery consumption. One of the reasons for this may be that not only pathological but also physiological neural activity can be suppressed whilst stimulating. For this reason, adaptive DBS (aDBS), where stimulation is applied according to the level of pathological activity, might be advantageous. Initial studies of aDBS demonstrate effectiveness in PD, but there are still many questions to be answered before aDBS can be applied clinically. Here we discuss the feedback signals and stimulation algorithms involved in adaptive stimulation in PD and sketch a potential road-map towards clinical application 2).
Guidetti et al. asked leading DBS experts worldwide (n = 21) includin HM CINAC, Hospital Universitario HM Puerta del Sur, HM Hospitales, and Instituto Carlos III, CIBERNED, Madrid, Spain.to discuss a research agenda for aDBS research in the near future to allow full adoption. A 5-point Likert scale questionnaire, along with a Delphi method, was employed. In the next 10 years, aDBS will be clinical routine, but research is needed to define which patients would benefit more from the treatment; second, implantation and programming procedures should be simplified to allow actual generalized adoption; third, new adaptive algorithms, and the integration of aDBS paradigm with new technologies, will improve control of more complex symptoms. Since the next years will be crucial for aDBS implementation, the research should focus on improving precision and making programming procedures more accessible 3)
The study gathers insights from 21 global experts in deep brain stimulation (DBS) using a Delphi methodology to establish consensus on the future clinical viability of adaptive DBS (aDBS) in Parkinson’s disease. The panel foresees routine clinical use of aDBS within the next decade, contingent upon progress in algorithm development, patient selection criteria, and streamlined surgical/programming processes.
✅ Strengths Timely and relevant topic: The paper addresses a critical transitional phase in neuromodulation, as aDBS is emerging from pilot studies to real-world applications.
High-level expert participation: The inclusion of internationally recognized DBS leaders adds authority and scope to the findings.
Structured consensus method: The Delphi approach is appropriate for areas with limited long-term data, ensuring that consensus is not dominated by a few voices and instead iteratively refined.
Clear research roadmap: The article outlines key needs for adoption—such as simplification of implantation/programming and refinement of adaptive algorithms.
Practical orientation: The study moves beyond theory to consider implementation barriers, making it highly useful for clinical and technical stakeholders.
❌ Limitations Limited generalizability: Though the expert panel is well-qualified, its size (n=21) and selection may introduce bias. There is little discussion of geographic or healthcare system diversity among the experts.
No patient or caregiver input: The study focuses solely on clinician-experts. Inclusion of patients' perspectives on the usability and perceived benefits of aDBS would have added depth.
Vagueness in methodology: Details about the design of the questionnaire, the criteria for consensus, and how disagreements were resolved are not fully transparent.
Assumptions about timelines: The forecast that aDBS will be routine in 10 years is speculative, especially given regulatory, financial, and technical hurdles. The authors do not deeply analyze these barriers.
Lack of critical dissent: The study strongly favors aDBS adoption without exploring potential downsides, such as increased cost, complexity, or equity/accessibility issues for less-resourced settings.
🧩 Opportunities for Future Work Cost-effectiveness analyses comparing aDBS vs. conventional DBS.
Patient-centered studies to assess quality of life, adherence, and satisfaction with aDBS.
Real-world implementation research to validate how well expert consensus translates to clinical practice.
🧾 Conclusion This Delphi consensus study is a valuable, forward-looking contribution that highlights key priorities for adaptive DBS adoption. While its insights are robust and timely, its predictive nature and lack of stakeholder diversity limit its scope. Future research should incorporate empirical validation and multi-stakeholder perspectives to ensure that aDBS can be equitably and effectively integrated into Parkinson’s care.