Improving deep brain stimulation: timing makes all the difference

Improving deep brain stimulation: timing makes all the difference

Jan Hirschmann1, Alfons Schnitzler1,2

1Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Duesseldorf, Germany; 2Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, Duesseldorf, Germany

Correspondence to: Alfons Schnitzler. Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, Duesseldorf, Germany. Email:

Provenance: This is an invited Editorial commissioned by the Section Editor Dr. Hongcheng Zhu, MD, PhD (Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China).

Comment on: Brocker DT, Swan BD, So RQ, et al. Optimized temporal pattern of brain stimulation designed by computational evolution. Sci Transl Med 2017;9. pii: eaah3532.

Submitted Sep 09, 2017. Accepted for publication Sep 15, 2017.

doi: 10.21037/atm.2017.09.25

Today there is an unprecedented wealth of information about the electrophysiology, the brain anatomy and the biochemical pathways relevant to Parkinson’s disease (PD). Translating this information into better treatments is a major challenge to academia and industry and requires sophisticated models of how the basal ganglia interact with the remaining motor system. With respect to electrophysiology, early models of basal ganglia function have focused on firing rates (1,2), whereas nowadays most researchers would agree that the exact timing of neuronal activity is crucial to basal ganglia function (3,4).

In a study recently published in Science Translational Medicine, Brocker and colleagues (5) capitalized on this key insight to improve deep brain stimulation (DBS), an effective neuromodulatory therapy of PD motor symptoms (6-9). They present an optimized DBS protocol which is much more energy-efficient than conventional DBS. Using less energy is desirable for several reasons. First, it reduces the amount of battery replacements and thus the amount of surgical interventions in patients treated with DBS. More importantly, the occurrence of stimulation-induced side effects, such as dysarthria or paresthesia, is related to the power applied (10). Hence, reducing stimulation power may increase the therapeutic window, leaving more options to the neurologist adapting the DBS settings. Prospectively, this improvement could increase the therapeutic benefit for a large number of patients.

Apart from its clinical relevance, the study by Bocker et al. is appealing due to its systematic approach. The authors first determined the best DBS pattern in a computational model before applying it to an animal model of PD and finally to patients. While this is the pipeline that all medical device developments should follow, it is not always adhered to in the DBS field so far.

In essence, the study by Brocker et al. is a comparison of three DBS protocols applied to the subthalamic nucleus (STN): (I) high-frequency DBS (≥130 Hz), which is known to be clinically effective but energy-intensive; (II) 45 Hz DBS, which was expected to be clinically less effective but energy-saving and (III) optimized DBS, which was designed to be both clinically effective and energy-saving. All three protocols were tested in freely moving, hemi-parkinsonian rats and in PD patients undergoing DBS surgery. In rats, clinical effects were assessed using the bar test and a methamphetamine challenge. In PD patients, clinical effects were tested using a finger-tapping paradigm and accelerometer recordings of spontaneous tremor. In addition, the authors investigated the effect of DBS on putatively pathological, neuronal oscillations (11,12). To this end, local field potentials were recorded from the globus pallidus internus (GPi) and motor cortex in rats, and from the STN in humans, while DBS was being applied.

In order to fully appreciate the results of this study, it is crucial to be clear about the difference between regular 45 Hz DBS and optimized DBS. Both had a mean pulse rate of 45 pulses per second, but in the former the DBS pulses were arranged with regular inter-pulse-interval whereas in the latter the interval was variable. In fact, the intervals were the result of an optimization process that selected those pulse sequences that were most effective in regulating basal ganglia activity in a computational model. Selection was performed using a genetic algorithm, a computational approach that mimics natural selection (13).

Interestingly, the optimized pattern of DBS pulses had quite a different effect on behavioral and electrophysiological measures than its frequency-matched, regular counterpart (45 Hz DBS). It reduced akinesia and neuronal oscillations more strongly than 45 Hz DBS in rats and humans, despite a similar rate of stimulation—an impressive proof of the relevance of timing. The difference was most evident when considering oscillations (7–10 Hz for rats, 20–33 Hz for humans), which were unaffected by regular 45 Hz DBS but strongly suppressed by optimized DBS. With respect to clinical effectiveness, optimized DBS generally performed better than 45 Hz DBS, but it did not quite reach the effect of high-frequency DBS. In particular, optimized DBS failed to suppress rest tremor when tremor was strong. This aspect raises the question whether it is possible to draw level with high-frequency DBS while maintaining the energy advantage. A reasonable strategy for further improvement suggested by the authors is to use a computational model that better represents the tremor circuitry, which has distinct electrophysiological properties (14-17).

Of course, there are limitations which need to be considered. Most importantly, the number of patients was quite low. Only four patients contributed to the results on akinesia and another four contributed to the results on tremor. Second, the time constraints during surgery limited the assessment of motor symptoms and stimulation duration. And finally, the computational model simulated neuronal activity but not motor symptoms. Hence, the authors needed to use an electrophysiological proxy for motor symptoms (fidelity of the thalamic response to a cortical input) to optimize the stimulation pattern.

Despite its limitations, the work by Brocker and colleagues is important to current DBS research. It showcases a promising alternative to closed-loop DBS, which likewise seeks to minimize stimulation energy. In closed-loop DBS, symptoms are continuously monitored and stimulation is only applied when needed. Some studies reported increased clinical benefit compared to continuous DBS (18-20), and it has been shown for a particular closed-loop system that energy costs are substantially lower despite the need for online control (21). While energy expenditure for online control is not necessary when optimizing the pulse train a priori, as Brocker et al. have done, it affords continuous adaptation of DBS settings to a continuously changing motor system in the process of degeneration. Only time can tell which advantage—low energy consumption or the ability to adapt—will prove more valuable in clinical practice. For the time being, it is re-assuring to know that neurophysiological insights keep driving clinical innovations in movement disorders, and it will be the responsibility of the field to assure that these innovations eventually reach the patients.




Conflicts of Interest: A Schnitzler has been serving as a consultant for and has received lecture fees from Medtronic Inc., Boston Scientific, and St. Jude Medical. The other author has no conflicts of interest to declare.


  1. Albin RL, Young AB, Penney JB. The functional anatomy of basal ganglia disorders. Trends Neurosci 1989;12:366-75. [Crossref] [PubMed]
  2. DeLong MR. Primate models of movement disorders of basal ganglia origin. Trends Neurosci 1990;13:281-5. [Crossref] [PubMed]
  3. Schnitzler A, Gross J. Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci 2005;6:285-96. [Crossref] [PubMed]
  4. Hammond C, Bergman H, Brown P. Pathological synchronization in Parkinson's disease: networks, models and treatments. Trends Neurosci 2007;30:357-64. [Crossref] [PubMed]
  5. Brocker DT, Swan BD, So RQ, et al. Optimized temporal pattern of brain stimulation designed by computational evolution. Sci Transl Med 2017;9:eaah3532. [Crossref] [PubMed]
  6. Rodriguez-Oroz MC, Obeso JA, Lang AE, et al. Bilateral deep brain stimulation in Parkinson's disease: a multicentre study with 4 years follow-up. Brain 2005;128:2240-9. [Crossref] [PubMed]
  7. Deuschl G, Schade-Brittinger C, Krack P, et al. A randomized trial of deep-brain stimulation for Parkinson's disease. N Engl J Med 2006;355:896-908. [Crossref] [PubMed]
  8. Okun MS, Gallo BV, Mandybur G, et al. Subthalamic deep brain stimulation with a constant-current device in Parkinson's disease: an open-label randomised controlled trial. Lancet Neurol 2012;11:140-9. [Crossref] [PubMed]
  9. Schuepbach WM, Rau J, Knudsen K, et al. Neurostimulation for Parkinson's disease with early motor complications. N Engl J Med. 2013;368:610-22. [Crossref] [PubMed]
  10. Moro E, Esselink RJ, Xie J, et al. The impact on Parkinson's disease of electrical parameter settings in STN stimulation. Neurology 2002;59:706-13. [Crossref] [PubMed]
  11. Brown P, Williams D. Basal ganglia local field potential activity: character and functional significance in the human. Clin Neurophysiol 2005;116:2510-9. [Crossref] [PubMed]
  12. Oswal A, Brown P, Litvak V. Synchronized neural oscillations and the pathophysiology of Parkinson's disease. Curr Opin Neurol 2013;26:662-70. [Crossref] [PubMed]
  13. Gen M, Cheng R. Genetic algorithms and engineering optimization. Wiley, 2000:495.
  14. Timmermann L, Gross J, Dirks M, et al. The cerebral oscillatory network of parkinsonian resting tremor. Brain 2003;126:199-212. [Crossref] [PubMed]
  15. Helmich RC, Hallett M, Deuschl G, et al. Cerebral causes and consequences of parkinsonian resting tremor: a tale of two circuits? Brain 2012;135:3206-26. [Crossref] [PubMed]
  16. Hirschmann J, Hartmann CJ, Butz M, et al. A direct relationship between oscillatory subthalamic nucleus-cortex coupling and rest tremor in Parkinson's disease. Brain 2013;136:3659-70. [Crossref] [PubMed]
  17. Hirschmann J, Butz M, Hartmann CJ, et al. Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High-Frequency Oscillations. Mov Disord 2016;31:1551-59. [Crossref] [PubMed]
  18. Rosin B, Slovik M, Mitelman R, et al. Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron 2011;72:370-84. [Crossref] [PubMed]
  19. Little S, Pogosyan A, Neal S, et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol 2013;74:449-57. [Crossref] [PubMed]
  20. Little S, Tripoliti E, Beudel M, et al. Adaptive deep brain stimulation for Parkinson's disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting. J Neurol Neurosurg Psychiatry 2016;87:1388-9. [Crossref] [PubMed]
  21. Khanna P, Stanslaski S, Xiao Y, et al. Enabling closed-loop neurostimulation research with downloadable firmware upgrades. In: IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. IEEE; 2015:1-6.
Cite this article as: Hirschmann J, Schnitzler A. Improving deep brain stimulation: timing makes all the difference. Ann Transl Med 2017;5(24):508. doi: 10.21037/atm.2017.09.25