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Adaptive deep brain stimulation in advanced Parkinson disease.
Literature Information
| DOI | 10.1002/ana.23951 |
|---|---|
| PMID | 23852650 |
| Journal | Annals of neurology |
| Impact Factor | 7.7 |
| JCR Quartile | Q1 |
| Publication Year | 2013 |
| Times Cited | 526 |
| Keywords | Adaptive Deep Brain Stimulation, Parkinson's Disease, Brain-Computer Interface |
| Literature Type | Journal Article |
| ISSN | 0364-5134 |
| Pages | 449-57 |
| Issue | 74(3) |
| Authors | Simon Little, Alex Pogosyan, Spencer Neal, Baltazar Zavala, Ludvic Zrinzo, Marwan Hariz, Thomas Foltynie, Patricia Limousin, Keyoumars Ashkan, James FitzGerald, Alexander L Green, Tipu Z Aziz, Peter Brown |
TL;DR
This study demonstrates that brain-computer interface (BCI)-controlled adaptive deep brain stimulation (aDBS) significantly improves motor function in patients with advanced Parkinson's disease compared to conventional continuous stimulation (cDBS), with a 66% improvement in unblinded assessments and a 56% reduction in stimulation time. The findings suggest that personalizing and optimizing stimulation in real-time can enhance the efficacy and efficiency of neuromodulation therapies for Parkinson's disease.
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Adaptive Deep Brain Stimulation · Parkinson's Disease · Brain-Computer Interface
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.
METHODS We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.
RESULTS Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.
INTERPRETATION BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.
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Primary Questions Addressed
- What are the long-term effects of adaptive deep brain stimulation on the progression of Parkinson's disease?
- How does the efficiency of adaptive DBS compare with other emerging therapies for Parkinson's disease?
- What specific criteria were used to select the patients for the study on adaptive DBS?
- How might adaptive DBS technology be integrated into existing treatment protocols for Parkinson's disease?
- What are the potential implications of this study's findings for future research in brain-computer interfaces and neuromodulation?
Key Findings
Research Background and Objectives
Deep brain stimulation (DBS) is a well-established treatment for advanced Parkinson's disease (PD) but is limited by its cost, side effects, and variable efficacy. This study aimed to explore a novel approach using brain-computer interfaces (BCIs) to control DBS adaptively based on real-time monitoring of pathological brain signals. The goal was to enhance the efficacy and efficiency of DBS by personalizing stimulation delivery in response to the patient's motor state.
Main Methods/Materials/Experimental Design
The study involved eight patients with advanced idiopathic PD who underwent DBS surgery targeting the subthalamic nucleus (STN). The researchers implemented a BCI-controlled adaptive DBS (aDBS) system that utilized local field potentials (LFPs) recorded from the stimulation electrodes to determine when to deliver stimulation.
Experimental Design
- Participants: 8 patients with advanced PD.
- DBS Configuration: Quadripolar macroelectrodes (Medtronic model 3389) were implanted in the STN.
- LFP Recording: Bipolar LFPs were recorded from electrode contacts, filtered between 3-37 Hz, and amplified.
- Adaptive Control: The BCI system monitored beta band oscillations (13-30 Hz) in the LFP, using a defined threshold to trigger stimulation.
- Comparative Conditions: The aDBS condition was compared against:
- No stimulation
- Conventional continuous DBS (cDBS)
- Random intermittent stimulation
Flowchart of the Methodology
Key Results and Findings
- Motor Improvement:
- aDBS led to a 66% improvement in motor scores during unblinded assessments and 50% in blinded assessments, significantly outperforming cDBS (54.3% and 30.5% improvements, respectively).
- The average improvement with aDBS compared to cDBS was 28.7% (unblinded) and 27.0% (blinded).
- Stimulation Efficiency:
- aDBS required 56% less stimulation time compared to cDBS, leading to a reduction in energy usage (132 µW for aDBS vs. 270 µW for cDBS).
- Correlation with Beta Activity:
- Clinical improvements correlated strongly with reductions in beta power, supporting the use of beta oscillations as a biomarker for PD.
Main Conclusions/Significance/Innovation
The study demonstrates that BCI-controlled adaptive DBS is not only feasible but also more effective and energy-efficient than conventional continuous stimulation. This approach allows for tailored stimulation based on real-time brain activity, potentially improving patient outcomes while reducing side effects and battery usage in implanted devices.
Research Limitations and Future Directions
- Limitations:
- The study was limited to unilateral stimulation and a small sample size, which may affect the generalizability of the findings.
- Only selected motor symptoms were assessed, and longer-term effects of aDBS were not evaluated.
- Future Directions:
- Further studies are needed to optimize aDBS parameters and explore its long-term effects and potential applications in other movement disorders and neuropsychiatric conditions.
- Development of more sophisticated algorithms for stimulation control based on multiple LFP features may enhance clinical outcomes.
Summary Table of Results
| Condition | Unblinded Improvement (%) | Blinded Improvement (%) | Stimulation Time Reduction (%) | Energy Usage (µW) |
|---|---|---|---|---|
| aDBS | 66 | 50 | 56 | 132 |
| cDBS | 54.3 | 30.5 | - | 270 |
| Random Stimulation | 33.7 | 6.7 | - | - |
This structured approach highlights the potential of adaptive DBS as a significant advancement in the treatment of Parkinson's disease, with implications for future neuromodulation therapies.
References
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Literatures Citing This Work
- Control of basal ganglia output by direct and indirect pathway projection neurons. - Benjamin S Freeze;Alexxai V Kravitz;Nora Hammack;Joshua D Berke;Anatol C Kreitzer - The Journal of neuroscience : the official journal of the Society for Neuroscience (2013)
- Bilateral functional connectivity of the basal ganglia in patients with Parkinson's disease and its modulation by dopaminergic treatment. - Simon Little;Huiling Tan;Anam Anzak;Alek Pogosyan;Andrea Kühn;Peter Brown - PloS one (2013)
- Closing the loop of deep brain stimulation. - Romain Carron;Antoine Chaillet;Anton Filipchuk;William Pasillas-Lépine;Constance Hammond - Frontiers in systems neuroscience (2013)
- Movement disorders in 2013: diagnosing and treating PD-the earlier the better? - François Tison;Wassilios G Meissner - Nature reviews. Neurology (2014)
- Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface. - Alik S Widge;Chet T Moritz - Journal of neural engineering (2014)
- Neuroscience: Tuning the brain. - Helen Shen - Nature (2014)
- Activity parameters of subthalamic nucleus neurons selectively predict motor symptom severity in Parkinson's disease. - Andrew Sharott;Alessandro Gulberti;Simone Zittel;Adam A Tudor Jones;Ulrich Fickel;Alexander Münchau;Johannes A Köppen;Christian Gerloff;Manfred Westphal;Carsten Buhmann;Wolfgang Hamel;Andreas K Engel;Christian K E Moll - The Journal of neuroscience : the official journal of the Society for Neuroscience (2014)
- Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders. - Frédéric D Broccard;Tim Mullen;Yu Mike Chi;David Peterson;John R Iversen;Mike Arnold;Kenneth Kreutz-Delgado;Tzyy-Ping Jung;Scott Makeig;Howard Poizner;Terrence Sejnowski;Gert Cauwenberghs - Annals of biomedical engineering (2014)
- Closed-loop neurostimulation: the clinical experience. - Felice T Sun;Martha J Morrell - Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics (2014)
- The highs and lows of beta activity in cortico-basal ganglia loops. - John-Stuart Brittain;Andrew Sharott;Peter Brown - The European journal of neuroscience (2014)
... (516 more literatures)
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