Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond

An Author Correction to this article was published on 16 April 2019

This article has been updated


Subthalamic deep brain stimulation (DBS) for Parkinson disease (PD) currently requires laborious open-loop programming, which can mitigate the benefits of this treatment. Experimental closed-loop DBS systems are emerging that can sense the electrophysiological surrogates of PD motor signs and respond with delivery of an automatically adapted stimulation. Such biomarker-based neural interfaces constitute a major advance towards improving the outcomes of patients treated with DBS and enhancing our understanding of the pathophysiological mechanisms underlying PD. In this Perspectives article, we argue that closed-loop DBS, in addition to offering advantages in patients with PD, might extend the current indications for DBS to include selected psychiatric disorders in which the symptoms are similarly driven by pathological brain circuit activity. The success of closed-loop DBS in such settings will depend on the identification of symptom-specific biomarkers, which ideally should reflect causal mechanisms of the underlying pathology.

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Fig. 1: Current deep brain stimulation designs.
Fig. 2: Present and future applications of deep brain stimulation in neuropsychiatric disease.

Change history

  • 16 April 2019

    In the originally published article, one of the affiliations for Paul Krack was omitted — these should have included ‘Movement Disorders Center, Department of Neurology, University Hospital (Inselspital) and University of Bern, Bern, Switzerland.’ This error has been corrected in the HTML and PDF versions of the manuscript.


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The views expressed in this article are the authors’ own and are not an official position of their institutions. W.B. is supported by Swiss National Science Foundation grant 323530–177577. P.M. is supported by Swiss National Science Foundation grant 167836. N.B. is supported by the Deutsche Forschungsgemeinschaft (DFG) and the Wyss Center for Bio and Neuroengineering. P.K. is supported by Swiss National Science Foundation grant 310030–170271.

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Nature Reviews Neurology thanks L. Zrinzo, R. Eitan and M. Faezipour for their contribution to the peer review of this work.

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W.B., P.M. and P.K. researched the data for the manuscript, wrote the first draft of the article and prepared the display items. All authors contributed to the discussions of the article content, critically edited the article and approved its final version.

Corresponding author

Correspondence to Paul Krack.

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Competing interests

W.B. declares that he received travel and accommodation funding from Boston Scientific (unrelated to the present work). J.D. declares that he is Director of the Wyss Center, a nonprofit foundation aimed at translating neurotechnology into human application. P.K. declares that he has received research grants and personal fees from Boston Scientific, Medtronic and St Jude (manufacturers of neurostimulators and electrodes for deep brain stimulation) and from the Annemarie Opprecht Foundation, Bertarelli Foundation, Centre National Recherche Scientifique, Lily E. Safra, France Parkinson, French Ministry of Health (PHRC), INSERM, Homeperf, Orkyn, Parkinson Schweiz, Roger De Spoelberch Foundation, Swiss National Science Foundation and UCB (unrelated to the present work). The other authors declare no competing interests.

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Event-related desynchronization

(ERD). Task-related transient power decreases observed in alpha (8–13 Hz) and beta (15–30 Hz) bands during preparation for and performance of physiological movement or movement imagery. A post-movement (rebound) increase in beta power is observed following movement-related decreases in beta power. In the context of scalp EEG recordings from sensorimotor regions, ERD denotes reduced amplitude of sensorimotor rhythms and thus decreased beta power.

Event-related synchronization

(ERS). Task-related transient gamma (>30 Hz) power increases observed during self-paced and cued voluntary movements. In the context of scalp EEG recordings from sensorimotor regions, ERS denotes increased gamma power of sensorimotor rhythms.

Extracellular recordings

Electrophysiological recordings from microelectrodes aid neuronavigation by identifying the borders of target brain nuclei during functional neurosurgery. Action potentials (spikes; detected using a high-pass filter) and local field potentials (detected using a low-pass filter) represent the activity of one or a cluster of neurons, respectively, near the recording tip.

High-frequency DBS

Therapeutic deep brain stimulation (DBS) utilizes stimulation frequencies >100 Hz, the efficacy of which has been robustly reproduced. Low-frequency DBS (≤100 Hz) has been used in specific contexts but its therapeutic efficacy is debated.

Local field potential

(LFP). A signal reflecting the sum of local extracellular electrical activity of a group of neurons. Low-impedance electrodes (recording surface areas ~3–10 mm2 or more) record LFPs from large collections of neurons; high-impedance microelectrodes record LFPs from small neuronal groups.


Recurrent brain activity patterns generated by rhythmic, periodic or regular electrical activity synchronized across neurons. Focal spatiotemporal changes in oscillation signatures are linked to behaviours, mental tasks and environmental stimuli. Oscillations are divided into delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–200 Hz) frequency bands. Oscillatory activity is often represented as changes in the power of different bands over time. Filter and signal processing characteristics can also alter the power and form of oscillations.

Phase-amplitude coupling

Coupling is present when the oscillation amplitude in a high-frequency (typically gamma) band at a particular instant in time depends on the phase of oscillations in a low-frequency (typically beta) band at the same moment.

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Bouthour, W., Mégevand, P., Donoghue, J. et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat Rev Neurol 15, 343–352 (2019). https://doi.org/10.1038/s41582-019-0166-4

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