Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease


Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson’s disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Configuration of implanted hardware and method of data streaming.
Fig. 2: Anatomical and physiological localization of subthalamic and cortical leads (example from RCS04).
Fig. 3: Decoding motor fluctuations from long-duration recordings at home: a single individual example (RCS01).
Fig. 4: Personalized oscillatory fingerprints: statistical significance in defined frequency bands for all individuals.
Fig. 5: Contribution of specific features and recording sites to the decoding of movement state for all five individuals.
Fig. 6: Adaptive DBS recorded at home using subcortical beta or cortical gamma control signals from two individuals.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Data were analyzed using Matlab 2019b (Mathworks). Code to process and analyze neural data recorded with Summit RC+S is available at, and code used to create the figures in this paper is available at


  1. Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15, 148–160 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Voytek, B. & Knight, R. T. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol. Psychiatry 77, 1089–1097 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ritaccio, A. L. et al. Proceedings of the Eighth International Workshop on Advances in Electrocorticography. Epilepsy Behav. 64, 248–252 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Starr, P. A. Totally implantable bidirectional neural prostheses: a flexible platform for innovation in neuromodulation. Front. Neurosci. 12, 619 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sun, F. T. & Morrell, M. J. The RNS system: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563–572 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Meidahl, A. C. et al. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov. Disord. 32, 810–819 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rouse, A. G. et al. A chronic generalized bi-directional brain-machine interface. J. Neural Eng. 8, 036018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Swann, N. C. et al. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson’s disease. J. Neurosurg. 128, 605–616 (2018).

    Article  PubMed  Google Scholar 

  9. Stanslaski, S. et al. A chronically implantable neural coprocessor for investigating the treatment of neurological disorders. IEEE Trans. Biomed. Circuits Syst. 12, 1230–1245 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kremen, V. et al. Integrating brain implants with local and distributed computing devices: a next generation epilepsy management system. IEEE J. Transl. Eng. Health Med. 6, 2500112 (2018).

    Article  PubMed  Google Scholar 

  11. Wozny, T. A. et al. Effects of hippocampal low-frequency stimulation in idiopathic non-human primate epilepsy assessed via a remote-sensing-enabled neurostimulator. Exp. Neurol. 294, 68–77 (2017).

    Article  PubMed  Google Scholar 

  12. Brittain, J. S. & Brown, P. Oscillations and the basal ganglia: motor control and beyond. NeuroImage 85, 637–647 (2014).

    Article  PubMed  Google Scholar 

  13. Swann, N. C. et al. Gamma oscillations in the hyperkinetic state detected with chronic human brain recordings in Parkinson’s disease. J. Neurosci. 36, 6445–6458 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. de Hemptinne, C. et al. Exaggerated phase-amplitude coupling in the primary motor cortex in Parkinson disease. Proc. Natl Acad. Sci. USA 110, 4780–4785 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Silberstein, P. et al. Cortico-cortical coupling in Parkinson’s disease and its modulation by therapy. Brain 128, 1277–1291 (2005).

    Article  PubMed  Google Scholar 

  16. Little, S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol. 74, 449–457 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Little, S. 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 87, 1388–1389 (2016).

    Article  PubMed  Google Scholar 

  18. Velisar, A. et al. Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients. Brain Stimul. 12, 868–876 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Swann, N. C. et al. Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing. J. Neural Eng. 15, 046006 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Herron, J. A. et al. Chronic electrocorticography for sensing movement intention and closed-loop deep brain stimulation with wearable sensors in an essential tremor patient. J. Neurosurg. 127, 580–587 (2017).

    Article  PubMed  Google Scholar 

  21. Panov, F. et al. Intraoperative electrocorticography for physiological research in movement disorders: principles and experience in 200 cases. J. Neurosurg. 126, 122–131 (2017).

    Article  PubMed  Google Scholar 

  22. Horne, M. K., McGregor, S. & Bergquist, F. An objective fluctuation score for Parkinson’s disease. PLoS ONE 10, e0124522 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Timmermann, L. et al. The cerebral oscillatory network of parkinsonian resting tremor. Brain 126, 199–212 (2003).

    Article  PubMed  Google Scholar 

  24. Qasim, S. E. et al. Electrocorticography reveals beta desynchronization in the basal ganglia-cortical loop during rest tremor in Parkinson’s disease. Neurobiol. Dis. 86, 177–186 (2016).

    Article  PubMed  Google Scholar 

  25. Graat, I. et al. Is deep brain stimulation effective and safe for patients with obsessive compulsive disorder and comorbid bipolar disorder? J. Affect. Disord. 264, 69–75 (2019).

    Article  PubMed  CAS  Google Scholar 

  26. Huang, Y., Cheeran, B., Green, A. L., Denison, T. J. & Aziz, T. Z. Applying a sensing-enabled system for ensuring safe anterior cingulate deep brain stimulation for pain. Brain Sci 9, 150 (2019).

    Article  PubMed Central  Google Scholar 

  27. Frizon, L. A. et al. Deep brain stimulation for pain in the modern era: a systematic review. Neurosurgery 86, 191–202 (2020).

    PubMed  Google Scholar 

  28. Fasano, A. & Helmich, R. C. Tremor habituation to deep brain stimulation: underlying mechanisms and solutions. Mov. Disord. 34, 1761–1773 (2019).

    Article  PubMed  Google Scholar 

  29. Lo, M. C. & Widge, A. S. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness. Int. Rev. Psychiatry 29, 191–204 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tinkhauser, G. et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease. Brain 140, 1053–1067 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kirkby, L. A. et al. An amygdala–hippocampus subnetwork that encodes variation in human mood. Cell 175, 1688–1700 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Molina, R. et al. Report of a patient undergoing chronic responsive deep brain stimulation for Tourette syndrome: proof of concept. J. Neurosurg 129, 308–314 (2018).

    Article  PubMed  Google Scholar 

  33. Quinn, E. J. et al. Beta oscillations in freely moving Parkinson’s subjects are attenuated during deep brain stimulation. Mov. Disord. 30, 1750–1758 (2015).

    Article  PubMed  Google Scholar 

  34. Syrkin-Nikolau, J. et al. Subthalamic neural entropy is a feature of freezing of gait in freely moving people with Parkinson’s disease. Neurobiol. Dis. 108, 288–297 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Neumann, W. J. et al. Long term correlation of subthalamic beta band activity with motor impairment in patients with Parkinson’s disease. Clin. Neurophysiol. 128, 2286–2291 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Molina, R. et al. Neurophysiological correlates of gait in the human basal ganglia and the PPN region in Parkinson’s disease. Front. Hum. Neurosci. 14, 194 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Van Gompel, J. J. et al. Anterior nuclear deep brain stimulation guided by concordant hippocampal recording. Neurosurg. Focus 38, E9 (2015).

    Article  PubMed  Google Scholar 

  38. Vansteensel, M. J. et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375, 2060–2066 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Koeglsperger, T., Mehrkens, J. H. & Botzel, K. Bilateral double beta peaks in a PD patient with STN electrodes. Acta Neurochir. 163, 205–209 (2020).

  40. de Hemptinne, C. et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson’s disease. Nat. Neurosci. 18, 779–786 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Cole, S. R. et al. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson’s disease. J. Neurosci. 37, 4830–4840 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).

    Article  PubMed  Google Scholar 

  43. Pourfar, M. et al. Assessing the microlesion effect of subthalamic deep brain stimulation surgery with FDG PET. J. Neurosurg. 110, 1278–1282 (2009).

    Article  PubMed  Google Scholar 

  44. Mann, J. M. et al. Brain penetration effects of microelectrodes and DBS leads in STN or GPi. J. Neurol. Neurosurg. Psychiatry 80, 794–797 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Griffiths, R. I. et al. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J. Parkinsons Dis. 2, 47–55 (2012).

    Article  PubMed  Google Scholar 

  46. Braybrook, M. et al. An ambulatory tremor score for Parkinson’s disease. J. Parkinsons Dis. 6, 723–731 (2016).

    Article  PubMed  Google Scholar 

  47. Varoquaux, G. et al. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage 145, 166–179 (2017).

    Article  PubMed  Google Scholar 

  48. Rodriguez, A. & Laio, A. Machine learning. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Tomlinson, C. L. et al. Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov. Disord. 25, 2649–2653 (2010).

    Article  PubMed  Google Scholar 

Download references


We thank L. Hammer for critical reading of the manuscript and M. Olaru for proofreading. This work was funded by NIH grant UH3NS100544 (P.A.S.).

Author information

Authors and Affiliations



R.G., S.L. and P.A.S. conceived the study and experiments. J.L.O., C.A.R., P.S.L., D.D.W., N.B.G., I.O.B. and M.S.L. provided clinical care and supervision. R.P. wrote the software interface for Summit RC+S. R.G., S.L., M.S.Y. and R.W. collected data. R.G., S.L., S.S.W., C.d.H., H.E.D., G.A.W., V.K., D.A.B. and T.D. provided key analytic tools. R.G. and P.A.S. drafted the manuscript and figures.

Corresponding author

Correspondence to Ro’ee Gilron.

Ethics declarations

Competing interests

Devices were provided at no charge by Medtronic. P.A.S., C.d.H. and J.L.O. are inventors on US patent 9,295,838 ‘Methods and systems for treating neurological movement disorders’; the patent covers cortical detection of physiological biomarkers in movement disorders, which is also a topic in this manuscript.

Additional information

Peer review information Nature Biotechnology thanks Ziv Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Localization of leads in subthalamic nucleus and over precentral gyrus: all subjects.

Lead locations in all five subjects, from postoperative CT scan, computationally fused with the preoperative planning MRI. The contacts appear in white (CT artifacts from their metal content). Left column, STN leads on axial T2 weighted MRI passing through the midbrain-diencephalic junction. The STN and red nuclei are regions of T2 hypointensity. Middle and right column, quadripolar subdural paddle leads on T1 weighted MRI (oblique sagittal passing through long axis of the lead array). Red arrow indicates central sulcus. Either contact 9 (subjects 1,2,3,5) or contact 10 (subject 4) is positioned at the posterior margin of precentral gyrus (primary motor area). Horizontal white line represents 2 cm.

Extended Data Fig. 2 Over 2,600 hours of motor cortex and basal ganglia field potentials streamed in home environment.

Number of hours of eight-channel neural data recorded by each patient while awake and while asleep, prior to initiating therapeutic stimulation and also while awake during chronic therapeutic stimulation. Here, ‘asleep’ was defined as 10 PM to 8 AM.

Extended Data Fig. 3 Brief in-clinic recordings demonstrate effects of leovodopa and movement.

a, Example field potentials recorded from right hemisphere, STN (top) and motor cortex (bottom). Horizontal grey line represents 300 ms, vertical line is 200 µV. b, Example spectrogram of cortical activity (bipolar recordings contacts 8–10) showing canonical movement-related alpha-beta band (8–35 Hz) decrease, and broadband (50–200 Hz) increase, consistent with placement over sensorimotor cortex (from RCS04), recorded 27 days post-implantation (sampling rate 500 Hz). Dotted vertical line is the onset of movement. Color scale is z-scored. c, Example power spectra of STN and motor cortex field potentials, and coherence between them, showing oscillatory profile of off-levodopa (red) and on-levodopa (green) states (patient RCS01), from 30 second recordings. d, Average PSD and coherence plots across both hemispheres, both recording montages, and all five patients. STN beta amplitude is reduced in the on-medication state. Horizontal bar shows frequency bands that had significant differences between states (p < 0.05, two sided, Bonferroni corrected). Shading in group data represents standard error of the mean.

Extended Data Fig. 4 Power spectra used for Parkinsonian motor state decoding: all subjects.

Superimposed STN and motor cortex power spectra (left two columns) and STN-motor cortex coherence (right column) from averaged 10 minute nonoverlapping data segments, showing all data collected during home recordings that were used for motor state decoding (Figs. 4,5). Data are for all five subjects from both hemispheres, prior to starting therapeutic stimulation. Both recording channels for each target (0–2 and 1–3 for STN, 8–10 and 9–11 for motor cortex) are represented. Each row shows all data from one study subject. Vertical dotted lines at 13 and 30 Hz demarcate the beta band, for visual clarity.

Extended Data Fig. 5 Unsupervised clustering segregates neural data into specific behavioral states.

Example patients are RCS01 and RCS04. All raw data (recorded in the awake state) were segregated using unsupervised clustering algorithms with two different paradigms: a, Unsupervised clustering using a density based method25. b, Clustering of PSDs based on template PSDs from in clinic recording in defined on/off medication states. Black lines are the template PSD’s (dotted = off medication, solid = on medication). c, Concordance with brain states derived from wearable monitor. Barcodes compare motor state estimates derived from the wearable monitors, with the clusters derived from type of clustering algorithm (24 hour data sample).

Extended Data Fig. 6 Sleep strongly affects neural biomarkers.

Example data from RCS01,220 hours of recording during which states were segregated by bilateral wearable monitors. PKG monitor classifications were used to segregate PSD’s (10 minute averages) to ‘off’ (orange), ‘on’ (green) and ‘sleep’ (black) states. Note that the ‘sleep’ state is characterized by profound reductions in STN beta band oscillations, STN broadband activity, and all gamma band oscillations, but increases in low frequency (<12 Hz) activity in cortex, and in most of the pairwise cortex-STN coherence plots. STN = subthalamic nucleus, MC = motor cortex, coh=coherence between STN and motor cortex.

Extended Data Fig. 7 Effects of standard therapeutic DBS on oscillatory activity.

a, Power spectrum averaged over all off-stimulation and on-stimulation data in one subject (RCS01), over a total of 352 hours of recording at home during waking hours. Left plot, chronic recording from same quadripolar STN contact array (sense contacts 0–2) as utilized for therapeutic stimulation, with reduction in beta band activity during stimulation (p < 0.001, two sided) (arrow). Right plot, simultaneously collected data recorded from motor cortex (sense contacts 9–11), shows stimulation-induced frequency shift in gamma activity13 and no concomitant change in cortical beta band activity. Average PSDs for all 10 min data segments segregated by off stimulation (green), and on stimulation (gray). Shading represents one standard deviation. Differences in filters implemented during stimulation may explain the baseline shifts above 30 Hz. b, Violin plots showing the average beta power (5 Hz window surrounding peak) off/on chronic stimulation in three subjects (895 total hours of recording). In two examples, chronic open loop STN DBS both reduces median STN beta band activity, and collapses the biomodal distribution of beta activity to a unimodal one. In one example (RCS03 L side), chronic open loop DBS also reduces median STN beta band activity, but the distribution remains bimodal (arrow), suggesting persistence of motor fluctuations during DBS.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Table 1 and Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Video 1

Adaptive DBS compared to clinically optimized open-loop DBS.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gilron, R., Little, S., Perrone, R. et al. Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease. Nat Biotechnol 39, 1078–1085 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing