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Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial

Nature Medicine (2024)Cite this article

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Abstract

Deep brain stimulation (DBS) is a widely used therapy for Parkinson’s disease (PD) but lacks dynamic responsiveness to changing clinical and neural states. Feedback control might improve therapeutic effectiveness, but the optimal control strategy and additional benefits of ‘adaptive’ neurostimulation are unclear. Here we present the results of a blinded randomized cross-over pilot trial aimed at determining the neural correlates of specific motor signs in individuals with PD and the feasibility of using these signals to drive adaptive DBS. Four male patients with PD were recruited from a population undergoing DBS implantation for motor fluctuations, with each patient receiving adaptive DBS and continuous DBS. We identified stimulation-entrained gamma oscillations in the subthalamic nucleus or motor cortex as optimal markers of high versus low dopaminergic states and their associated residual motor signs in all four patients. We then demonstrated improved motor symptoms and quality of life with adaptive compared to clinically optimized standard stimulation. The results of this pilot trial highlight the promise of personalized adaptive neurostimulation in PD based on data-driven selection of neural signals. Furthermore, these findings provide the foundation for further larger clinical trials to evaluate the efficacy of personalized adaptive neurostimulation in PD and other neurological disorders. ClinicalTrials.gov registration: NCT03582891.

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Fig. 1: Configuration of implanted hardware, algorithmic model and patient demographics.
Fig. 2: Workflow for data-driven biomarker identification and aDBS implementation.
Fig. 3: Examples of stimulation-entrained gamma oscillations in both in-clinic and at-home recordings.
Fig. 4: Data-driven biomarker identification during active stimulation for all hemispheres.
Fig. 5: Effects of aDBS compared to cDBS on both subjective and objective metrics of motor symptoms and quality of life.
Fig. 6: Characteristics and technical performance of adaptive DBS algorithms.

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Data availability

De-identified individual participant data, including neural, wearable and digital diary data, are shared on the Data Archive for the BRAIN Initiative website (https://dabi.loni.usc.edu/; https://doi.org/10.18120/cq9c-d057). The study protocol is provided in the Supplementary Information. The Food and Drug Administration investigational device exemption is available on the Open Mind website (https://osf.io/cmndq/). Data will be available permanently with no restrictions, for purposes of replicating the findings or conducting meta-analyses.

Code availability

Code written in C# and MATLAB, which operates the investigational device and extracts raw neural data, is available on the Open Mind GitHub platform (https://openmind-consortium.github.io). The code for biomarker identification implemented in MATLAB is available in the repository Code Ocean, without restrictions59, except for code related to linear discriminant analysis (Fig. 4c–e), which will be made available after publication of a subsequent manuscript (currently in preparation) that uses this code.

References

  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. Neumann, W. -J., Gilron, R., Little, S. & Tinkhauser, G. Adaptive deep brain stimulation: from experimental evidence toward practical implementation. Mov. Disord. https://doi.org/10.1002/mds.29415 (2023).

  3. Marceglia, S. et al. Deep brain stimulation: is it time to change gears by closing the loop? J. Neural Eng. 18, 061001 (2021).

    Article  Google Scholar 

  4. Stanslaski, S. et al. Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 410–421 (2012).

    Article  PubMed  Google Scholar 

  5. 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 

  6. Thenaisie, Y. et al. Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing. J. Neural Eng. 18, 042002 (2021).

    Article  Google Scholar 

  7. 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 

  8. Nakajima, A. et al. Case report: chronic adaptive deep brain stimulation personalizing therapy based on Parkinsonian state. Front. Hum. Neurosci. 15, 702961 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gilron, 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Little, S. & Brown, P. Debugging adaptive deep brain stimulation for Parkinson’s disease. Mov. Disord. 35, 555–561 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wilkins, K. B., Melbourne, J. A., Akella, P. & Bronte-Stewart, H. M. Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson’s disease. Front. Hum. Neurosci. 17, 1310393 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ansó, J. et al. Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience. J. Neural Eng. 19, 026025 (2022).

    Article  Google Scholar 

  13. Ascherio, A. & Schwarzschild, M. A. The epidemiology of Parkinson’s disease: risk factors and prevention. Lancet Neurol. 15, 1257–1272 (2016).

    Article  PubMed  Google Scholar 

  14. Vitek, J. L. et al. Subthalamic nucleus deep brain stimulation with a multiple independent constant current-controlled device in Parkinson’s disease (INTREPID): a multicentre, double-blind, randomised, sham-controlled study. Lancet Neurol. 19, 491–501 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Okun, M. S. et al. Subthalamic deep brain stimulation with a constant-current device in Parkinson’s disease: an open-label randomised controlled trial. Lancet Neurol. 11, 140–149 (2012).

    Article  PubMed  Google Scholar 

  16. Weaver, F. M. et al. Bilateral deep brain stimulation vs best medical therapy for patients with advanced Parkinson disease: a randomized controlled trial. JAMA 301, 63–73 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Deuschl, G. et al. A randomized trial of deep-brain stimulation for Parkinson’s disease. N. Engl. J. Med. 355, 896–908 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Follett, K. A. et al. Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease. N. Engl. J. Med. 362, 2077–2091 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Odekerken, V. J. et al. Subthalamic nucleus versus globus pallidus bilateral deep brain stimulation for advanced Parkinson’s disease (NSTAPS study): a randomised controlled trial. Lancet Neurol. 12, 37–44 (2013).

    Article  PubMed  Google Scholar 

  20. Bronte-Stewart, H. et al. Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease: ADAPT-PD clinical trial methodology and early data (P1-11.002). Neurology https://doi.org/10.1212/WNL.0000000000203099 (2023).

  21. Marceglia, S. et al. Double-blind cross-over pilot trial protocol to evaluate the safety and preliminary efficacy of long-term adaptive deep brain stimulation in patients with Parkinson’s disease. BMJ Open 12, e049955 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kühn, A. A., Kupsch, A., Schneider, G.-H. & Brown, P. Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson’s disease. Eur. J. Neurosci. 23, 1956–1960 (2006).

    Article  PubMed  Google Scholar 

  23. Kühn, A. A. et al. High-frequency stimulation of the subthalamic nucleus suppresses oscillatory β activity in patients with Parkinson’s disease in parallel with improvement in motor performance. J. Neurosci. 28, 6165–6173 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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 

  26. Bocci, T. et al. Eight-hours conventional versus adaptive deep brain stimulation of the subthalamic nucleus in Parkinson’s disease. NPJ Park. Dis. 7, 88 (2021).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  28. Bronstein, J. M. et al. Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch. Neurol. 68, 165 (2011).

    Article  PubMed  Google Scholar 

  29. 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 

  30. 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 

  31. Bove, F., Genovese, D. & Moro, E. Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson’s disease. Expert Rev. Neurother. 22, 789–803 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Wiest, C. et al. Finely-tuned gamma oscillations: spectral characteristics and links to dyskinesia. Exp. Neurol. 351, 113999 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sermon, J. J. et al. Sub-harmonic entrainment of cortical gamma oscillations to deep brain stimulation in Parkinson’s disease: model based predictions and validation in three human subjects. Brain Stimul. 16, 1412–1424 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Olaru, M. et al. Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson’s disease. Brain J. Neurol. https://doi.org/10.1093/brain/awae004 (2024).

  35. Herdman, M. et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual. Life Res. 20, 1727–1736 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  37. Nutt, J. G., Woodward, W. R., Hammerstad, J. P., Carter, J. H. & Anderson, J. L. The “on–off” phenomenon in Parkinson’s disease: relation to levodopa absorption and transport. N. Engl. J. Med. 310, 483–488 (1984).

    Article  CAS  PubMed  Google Scholar 

  38. van Rheede, J. J. et al. Diurnal modulation of subthalamic beta oscillatory power in Parkinson’s disease patients during deep brain stimulation. NPJ Parkinsons Dis. 8, 88 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Tinkhauser, G. & Moraud, E. M. Controlling clinical states governed by different temporal dynamics with closed-loop deep brain stimulation: a principled framework. Front. Neurosci. 15, 734186 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Alagapan, S. et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 622, 130–138 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Heck, C. N. et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS System Pivotal trial. Epilepsia 55, 432–441 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Scangos, K. W. et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat. Med. 27, 1696–1700 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vizcarra, J. A. et al. Subthalamic deep brain stimulation and levodopa in Parkinson’s disease: a meta-analysis of combined effects. J. Neurol. 266, 289–297 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Brown, P. et al. Dopamine dependency of oscillations between subthalamic nucleus and pallidum in Parkinson’s disease. J. Neurosci. 21, 1033–1038 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Halje, P. et al. Levodopa-induced dyskinesia is strongly associated with resonant cortical oscillations. J. Neurosci. 32, 16541–16551 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wiest, C. et al. Subthalamic deep brain stimulation induces finely-tuned gamma oscillations in the absence of levodopa. Neurobiol. Dis. 152, 105287 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Arlotti, M. et al. Eight-hours adaptive deep brain stimulation in patients with Parkinson disease. Neurology 90, e971–e976 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Foffani, G. & Alegre, M. Brain oscillations and Parkinson disease. Handb. Clin. Neurol. 184, 259–271 (2022).

    Article  PubMed  Google Scholar 

  49. Feldmann, L. K. et al. Toward therapeutic electrophysiology: beta-band suppression as a biomarker in chronic local field potential recordings. NPJ Parkinsons Dis. 8, 44 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen, Y. et al. Neuromodulation effects of deep brain stimulation on beta rhythm: a longitudinal local field potential study. Brain Stimul. 13, 1784–1792 (2020).

    Article  PubMed  Google Scholar 

  51. Olson, J. D. et al. Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans. Clin. Neurophysiol. 127, 277–284 (2016).

    Article  PubMed  Google Scholar 

  52. Piña-Fuentes, D. et al. Acute effects of adaptive deep brain stimulation in Parkinson’s disease. Brain Stimul. 13, 1507–1516 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Busch, J. L. et al. Single threshold adaptive deep brain stimulation in Parkinson’s disease depends on parameter selection, movement state and controllability of subthalamic beta activity. Brain Stimul. 17, 125–133 (2024).

    Article  PubMed  Google Scholar 

  54. Merk, T. et al. Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Exp. Neurol. 351, 113993 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Davis, T. S. et al. LeGUI: a fast and accurate graphical user interface for automated detection and anatomical localization of intracranial electrodes. Front. Neurosci. 15, 769872 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Horn, A. et al. Lead-DBS v2: towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage 184, 293–316 (2019).

    Article  PubMed  Google Scholar 

  57. Oehrn, C. R. et al. Chronic adaptive deep brain stimulation is superior to conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial [Source Data]. Data Archive for the Brain Initiative https://doi.org/10.18120/cq9c-d057 (2024).

  58. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. -M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).

    Article  PubMed  Google Scholar 

  59. Oehrn, C. R. et al. Chronic adaptive deep brain stimulation is superior to conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial. Code Ocean. https://doi.org/10.24433/CO.5656158.v1 (2024).

  60. Oehrn, C. R. et al. Direct electrophysiological evidence for prefrontal control of hippocampal processing during voluntary forgetting. Curr. Biol. 28, 3016–3022 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    Article  PubMed  Google Scholar 

  62. Gilron, R. et al. Sleep-aware adaptive deep brain stimulation control: chronic use at home with dual independent linear discriminate detectors. Front. Neurosci. 15, 732499 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Cernera, S. et al. Wearable sensor-driven responsive deep brain stimulation for essential tremor. Brain Stimul. 14, 1434–1443 (2021).

    Article  PubMed  Google Scholar 

  64. Hammer, L. H., Kochanski, R. B., Starr, P. A. & Little, S. Artifact characterization and a multipurpose template-based offline removal solution for a sensing-enabled deep brain stimulation device. Stereotact. Funct. Neurosurg. 100, 168–183 (2022).

    Article  PubMed  Google Scholar 

  65. Neumann, W. -J. et al. The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces. Brain Stimul. 14, 1301–1306 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): process, format, and clinimetric testing plan. Mov. Disord. 22, 41–47 (2007).

    Article  PubMed  Google Scholar 

  67. McAuley, M. D. Incorrect calculation of total electrical energy delivered by a deep brain stimulator. Brain Stimul. 13, 1414–1415 (2020).

    Article  PubMed  Google Scholar 

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Acknowledgements

The study was supported by National Institute of Neurological Disorders and Stroke (NINDS) UH3NS100544 (to P.A.S.), the Parkinson Fellowship of the Thiemann Foundation (to C.R.O.), NINDS F32NS129627 (to S.C.), NINDS R25NS070680 (to L.H.H.) and TUYF Charitable Trust Fund (to J.Y.). Research reported in this publication was also partly supported by R01 NS090913 (to P.A.S.), NINDS K23NS120037 (to S.L.) and R01 NS131405 (to S.L.). Investigational devices were provided at no charge by the manufacturer, but the manufacturer had no role in the conduct, analysis or interpretation of the study. The Open Mind consortium for technology dissemination, funded by NINDS U24 NS113637 (to P.A.S.), provided technical resources for the use of the Summit RC+S neural interface. We thank T. Wozny for lead localization, W. Chiong for neuroethical input, C. Smyth, R. Gilron, R. Wilt and C. de Hemptinne for technical contributions and K. Probst for medical art (Fig. 1a). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Carina R. Oehrn, Stephanie Cernera, Lauren H. Hammer.

  2. These authors jointly supervised this work: Simon Little, Philip A Starr.

Authors and Affiliations

  1. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA

    Carina R. Oehrn, Stephanie Cernera, Maria Shcherbakova, Jiaang Yao, Amelia Hahn & Philip A. Starr

  2. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Lauren H. Hammer, Sarah Wang, Jill L. Ostrem & Simon Little

  3. Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, San Francisco, CA, USA

    Jiaang Yao, Simon Little & Philip A. Starr

  4. Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Sarah Wang, Jill L. Ostrem, Simon Little & Philip A. Starr

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Contributions

P.A.S., S.L., J.L.O., C.R.O., S.C. and L.H.H. designed the study and analysis pipeline. C.R.O., S.C., L.H.H., M.S. and J.Y. collected and analyzed the data. A.H. facilitated patient communication and coordination throughout the study. S.W. oversaw study administration, including institutional review board approval and regulatory compliance. C.R.O., S.C., L.H.H., S.L. and P.A.S. drafted the manuscript, and all authors reviewed, commented on and approved the final version.

Corresponding author

Correspondence to Carina R. Oehrn.

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Competing interests

S.L. consults for Iota Biosciences. J.L.O. reports support from Medtronic and Boston Scientific for research and education and consults for AbbVie and Rune Labs. P.A.S. receives support from Medtronic and Boston Scientific for fellowship education. C.R.O., S.C., L.H.H., M.S., J.Y., A.H. and S.W. declare no competing interests.

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Nature Medicine thanks Jaimie Henderson, Andrea Kühn and Theoden Netoff for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Localization of leads over sensorimotor cortex and within subthalamic nucleus in native space.

a–d, Example localization of cortical and subcortical leads in patient 2, generated by fusing postoperative CT with preoperative MRI scans. Contacts appear as white CT artifacts due to metal content and are labeled with red arrows. a, Cortical leads on axial T1-weighted MRI through the vertex. b, STN leads on axial T2-weighted MRI through the region of the dorsal STN, 3 mm inferior to the intercommissural plane. c,d, Cortical leads on oblique sagittal T1-weighted MRI passing through the long axis of the lead array in left (c) and right (d) hemispheres, respectively. e–h, Location of cortical leads for each patient overlayed on 3D reconstruction of cortex rendered using the Locate Electrodes Graphical User Interface (LeGUI). Electrodes used in the anterior and posterior cortical montages are shown in cyan and yellow, respectively. For patient 1 (e), 2 (f) and 4 (h), anterior and posterior montages covered the pre- and postcentral gyrus, respectively. For patient 3, right side (g), the anterior montage included one electrode on the middle frontal and one on the precentral gyrus. The posterior montage comprised one pre- and one postcentral electrode. In all figures, red arrows indicate the location of the central sulcus.

Extended Data Fig. 2 Initial and finalized adaptive stimulation parameters and example adaptive control policies.

a, Suggested initial parameters for algorithms developed for time scales of minutes to hours, as identified during steps 5 and 6 of the pipeline. An update rate of 10 s typically provided a signal to noise ratio that allowed for adequate discrimination between the presence and absence of the most bothersome symptom, and this could often be improved with a further increase in update rate. The ramp rate chosen for each patient depended on the results of step 5 (we chose an example of 1 mA/s). b, Detailed final adaptive stimulation parameters including control signals, thresholds, FFT interval, update rates, blanking periods, onset and termination duration, and ramp rates used for each patient and hemisphere. c–e, Examples of potential control policies that can be used for an adaptive algorithm, using artificial data. The upper subpanels of each subfigure illustrate an on-state biomarker (blue), as used in our study, along with thresholds (red). Lower subpanels demonstrate the adjustment of stimulation amplitude based on the relationship of the neural signal to the thresholds. c, A single threshold control policy with two stimulation amplitudes. When the biomarker is above the threshold, stimulation amplitude decreases and once below threshold, stimulation amplitude increases. d, A dual threshold control policy with three stimulation amplitudes (not used in this study), which may be applied to address three symptom states. When the neural signal is below both thresholds, the stimulation amplitude is high (for example, 4 mA). When the biomarker is between the two thresholds, stimulation adjusts to a middle amplitude (for example, 3 mA). When the biomarker exceeds the second threshold, stimulation decreases to the low amplitude (for example, 2 mA). e, A control policy utilizing a middle state as noise buffer. Stimulation is high when the control signal is below the bottom threshold and stimulation is low when the control signal is above the top threshold. When the control signal is between the two thresholds, it remains at the level of the stimulation amplitude prior to crossing the threshold (that is, no changes are made).

Extended Data Fig. 3 Neural biomarkers of medication effects identified in-clinic.

a,b, All tables show the results from our within-patient non-parametric cluster-based permutation analyses using in-clinic recordings during two medication states (off vs. on) and stimulation conditions (low vs. high stimulation amplitude). P-values were Bonferroni-corrected for multiple comparisons. Note that p < 10−3 indicates that the cluster was found in all 1000 permutations. This means the probability of observing this effect by chance is less than 1 in 1000. a, Statistics for the largest main effect of medication, stimulation, and their interaction for each patient and hemisphere when searching the whole frequency space (2–100 Hz) across brain regions. Frequencies represent the center frequency of 1-Hz wide power spectral density bins. For all four patients (five out of six hemispheres), we found that gamma power (specifically, stimulation-entrained gamma in four hemispheres) in the STN or cortex was the best predictor of medication state (in pat-3L, there was no significant effect of medication in any frequency band in clinic, but at home symptom monitoring identified cortical stimulation-entrained gamma power as neural biomarker; Extended Data Fig. 4). Positive Cohen’s d values for the medication effect highlight that the neural biomarker was higher during on-medication states. Positive Cohen’s d values for the stimulation effect indicate that the neural biomarker was higher during on-stimulation states (independent of medication), which could result in undesirable self-triggering of the algorithm (threshold crossing of the neural biomarker linked to stimulation change itself, rather than true fluctuations of medication states and symptoms). Therefore, for patient 1, we excluded 63 and 67 Hz from the subsequently used control signal (positive Cohen’s d main effect of stimulation). For patients 2, 3 and 4, we did not find stimulation effects that positively modulated biomarkers and therefore were unrestricted in biomarker selection. b, When constraining the anatomic location and frequency space to STN beta oscillations (13–30 Hz), STN spectral beta power was only predictive for medication state in two hemispheres (pat-2R and pat-4) and smaller in effect size than cortical/STN stimulation-entrained gamma oscillations for all patients.

Extended Data Fig. 4 Neural biomarkers of symptoms identified at-home.

We identified predictors of the most bothersome symptom (pat-1: bradykinesia, pat-2: lower limb dystonia), or the opposite symptom that limits the therapeutic window (pat-3 and pat-4: dyskinesia). a, Heatmaps of t-values derived from stepwise linear regressions using 1 Hz power bands between 2–100 Hz in the STN (left), anterior cortical montage (middle) and posterior cortical montage (right) to predict symptoms continuously measured with upper extremity wearable monitors for patients 1, 3 and 4 (patient 2’s bothersome symptom did not involve the upper extremity). b–d, Results from the linear regression (left) and linear discriminant analysis (LDA; right). P-values were Bonferroni-corrected for multiple comparisons (289 predictors). b, Both methods provide converging evidence that stimulation-entrained gamma power centered at half the stimulation frequency (65 Hz) in the STN and cortex optimally distinguishes hypo- and hyperkinetic symptoms. c, When constraining the anatomic location and frequency space to STN beta oscillations (13–30 Hz), frequency bands identified as most predictive were less discriminative than cortical/STN stimulation-entrained gamma oscillations (LDA: AUC < 0.7). Regression models resulted in smaller magnitude coefficients, with only one hemisphere demonstrating a significant negative association with hyperkinetic symptoms (pat-3L). d, STN beta frequency bands were also poorly predictive of wearable bradykinesia scores (AUC < 0.6), again with only one hemisphere demonstrating a significant effect in the regression model (corresponding to a positive relationship with hypokinetic symptoms; pat-3L). e, Comparison of LDA results for STN and cortical gamma activity in predicting bothersome symptoms. Neural signals selected for adaptive stimulation are shaded in grey. In three out of six hemispheres (pat-2L, pat-2R, pat-4), stimulation-entrained gamma activity in the STN distinguished between hypo- and hyperkinetic symptoms. For pat-2, STN stimulation-entrained spectral gamma power was the optimal biomarker used for aDBS in both hemispheres. In pat-4, stimulation-entrained gamma activity in the STN was a strong predictor of residual motor signs but slightly underperformed compared to cortical signals. f, Visual illustration of AUC values comparing STN and cortical gamma activity in predicting bothersome symptoms. For pat-4, the predictive value of stimulation-entrained spectral gamma power was only slightly reduced compared to cortical signals.

Extended Data Fig. 5 Beta oscillations in the STN.

a, Power spectral density in the STN based on in-clinic recordings off medication and off stimulation for all six hemispheres. All but one hemisphere (pat-1) exhibited a peak in the beta frequency band (illustrated in yellow). b, Example of the suppressive effect of DBS on STN beta oscillations precluding use of beta band activity as a biomarker of medication state during active stimulation (pat-2L, all data collected during the same in-clinic recording session). Off stimulation, the spectral peak in the beta frequency range was suppressed by medication (13–21 Hz, Cohens’ d = −1.09, p < 10−3). However, this medication effect diminished during active stimulation, even at low stimulation amplitudes (1.8 mA, largest effect in the beta band: 15–18 Hz, Cohens’ d = 0.31, p = 0.026). Data are corrected for stimulation-induced broadband shifts.

Extended Data Fig. 6 Effects of aDBS and cDBS on most bothersome symptom severity, additional motor symptoms, and sleep quality.

a–j, Bar plots illustrating the mean (±s.e.m.) self-reported symptoms, aside from the most bothersome symptoms, across testing days. Each dot represents the rating for one testing day (blue: cDBS, red: aDBS). These ratings constituted secondary outcome measures to ensure that we are not aggravating other motor and non-motor symptoms. a,b, Patient self-reported motor symptom severity from daily questionnaires (1 = least severe, 10 = most severe). Note that patients rated symptom severity (shown here) independently of symptom duration; bar graphs for the latter are in Fig. 5a,b. Patient 3 did not record ratings within the instructed range of 1–10 and their data are therefore not reported. a, In addition to a decrease in the amount of daily hours with the most bothersome symptom (symptom duration, shown in Fig. 5a), patients 1, 2, and 4 also experienced a significant improvement of symptom severity (pat-1: p < 10−5, pat-2: p = 0.018, pat = 4: p = 0.003). b, No subject reported worsened severity of their opposite symptom (pat-1: p = 0.18, pat-2: p = 1, pat-4: p = 0.19). c–h, Comprehensive list of the self-reported duration of motor symptoms from daily questionnaires. These bar graphs illustrate only symptoms that were not identified by the patient as the most bothersome or as the opposite symptom. For each patient’s most bothersome symptom, results are displayed in Fig. 5a and panel a of this figure; and are labeled in c–h as not applicable (n/a). None of these “other” motor symptoms were worsened by aDBS, and patient 2 demonstrated significant improvement in the percentage of waking hours with dyskinesia (d, p = 0.044) and gait disturbance (h, p < 10−4). i,j, Self-reported sleep quality (1 = poorest sleep, 10 = best sleep) and duration from daily questionnaires. aDBS provided no change in patients’ sleep characteristics. The number of testing days for each patient and condition used for statistical tests are summarized in Fig. 6a. Asterisks illustrate results from two-sided Wilcoxon rank sum tests. P-values for all within-subject control analyses were adjusted for multiple comparisons using the false discovery rate procedure and are indicated as: *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 7 aDBS algorithm dynamics during nighttime.

a, Percent time spent at each stimulation amplitude during the night. Each dot represents the mean values of one night of aDBS testing across high stimulation states (orange) and low stimulation states (blue) in one hemisphere. Graphs are standard box plots (center: median; box limits: upper and lower quartiles; whiskers: minima = 25th percentile-1.5 times the interquartile range, maxima = 75th percentile+1.5 times the interquartile range). Each patient spent most of the night in the high stimulation state. b, Mean (±s.e.m.) total electrical energy delivered (TEED) during aDBS and cDBS overnight, showing increased TEED during aDBS, similar to daytime analyses (stimulation main effect: β = 27.7, p < 10−25, time main effect: β = 0.05, p = 0.377). Individually, TEED was increased in all hemispheres during aDBS (two-sided, one-sample Wilcoxon signed rank test, pat-1: p < 10−6, pat-2R: p < 10−5, pat-2L: p < 10−5, pat-3R: p < 10−6, pat-3L: p < 10−6, pat-4: p < 10−4). The number of testing nights for each patient and condition used for both illustrations are stated in Fig. 6a and are equivalent to the testing days. Asterisks illustrate results from two-sided one-sample Wilcoxon signed rank tests. P-values for TEED evaluations were adjusted for multiple comparisons using the false discovery rate procedure and are indicated as: *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 8 Flowchart of biomarker identification analyses.

We identified neural biomarkers using standardized in-clinic and at-home recordings in patients’ naturalistic environments. Non-parametric cluster-based permutation analysis identified candidate spectral biomarkers from in-clinic data by assessing main effects of medication state, stimulation amplitude, and the interaction. Next, the predictability of neural biomarkers as robust aDBS control signals of symptom state was tested using at-home recordings. For patients where the most bothersome symptom was monitored by a wearable device (for example, upper extremity bradykinesia or dyskinesia), linear stepwise regression was used to take advantage of the continuous nature of the symptom measurements. The most predictive frequency bands and recording sites were selected based on t-values. If the patient’s most bothersome symptom could not be captured by wearable monitors, the patient’s motor diaries and streaming app entries instead labeled the presence of symptoms. A linear discriminant analysis (LDA) based method identified the most predictive frequency band and recording site from these discretely labeled neural signal data, as measured by the area under the receiver operating curve (AUC). We also applied the LDA-based approach to symptoms measured by wearable monitors by mapping the continuous wearable scores to discrete symptom labels using a patient-specific dichotomization. This dichotomization allowed for subsequent offline assessment of the prediction accuracy based on multiple neural biomarkers combined as shown in Fig. 4e (note for online aDBS only single power band classifiers were implemented, as multiple power band classifiers were not found to be superior).

Extended Data Table 1 Group-level generalized linear models characterizing the effect of aDBS on the duration (% awake time) of the most bothersome symptom, opposite symptom, quality of life, and both daytime and nighttime total electrical energy delivered
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Extended Data Table 2 Summary of adverse events by patient
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Oehrn, C.R., Cernera, S., Hammer, L.H. et al. Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial. Nat Med (2024). https://doi.org/10.1038/s41591-024-03196-z

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