Abstract
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.
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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 https://github.com/openmind-consortium/Analysis-rcs-data, and code used to create the figures in this paper is available at https://github.com/roeegilron/rcsAtHome.
References
Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15, 148–160 (2019).
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).
Ritaccio, A. L. et al. Proceedings of the Eighth International Workshop on Advances in Electrocorticography. Epilepsy Behav. 64, 248–252 (2016).
Starr, P. A. Totally implantable bidirectional neural prostheses: a flexible platform for innovation in neuromodulation. Front. Neurosci. 12, 619 (2018).
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).
Meidahl, A. C. et al. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov. Disord. 32, 810–819 (2017).
Rouse, A. G. et al. A chronic generalized bi-directional brain-machine interface. J. Neural Eng. 8, 036018 (2011).
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).
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).
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).
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).
Brittain, J. S. & Brown, P. Oscillations and the basal ganglia: motor control and beyond. NeuroImage 85, 637–647 (2014).
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).
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).
Silberstein, P. et al. Cortico-cortical coupling in Parkinson’s disease and its modulation by therapy. Brain 128, 1277–1291 (2005).
Little, S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol. 74, 449–457 (2013).
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).
Velisar, A. et al. Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients. Brain Stimul. 12, 868–876 (2019).
Swann, N. C. et al. Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing. J. Neural Eng. 15, 046006 (2018).
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).
Panov, F. et al. Intraoperative electrocorticography for physiological research in movement disorders: principles and experience in 200 cases. J. Neurosurg. 126, 122–131 (2017).
Horne, M. K., McGregor, S. & Bergquist, F. An objective fluctuation score for Parkinson’s disease. PLoS ONE 10, e0124522 (2015).
Timmermann, L. et al. The cerebral oscillatory network of parkinsonian resting tremor. Brain 126, 199–212 (2003).
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).
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).
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).
Frizon, L. A. et al. Deep brain stimulation for pain in the modern era: a systematic review. Neurosurgery 86, 191–202 (2020).
Fasano, A. & Helmich, R. C. Tremor habituation to deep brain stimulation: underlying mechanisms and solutions. Mov. Disord. 34, 1761–1773 (2019).
Lo, M. C. & Widge, A. S. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness. Int. Rev. Psychiatry 29, 191–204 (2017).
Tinkhauser, G. et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease. Brain 140, 1053–1067 (2017).
Kirkby, L. A. et al. An amygdala–hippocampus subnetwork that encodes variation in human mood. Cell 175, 1688–1700 (2018).
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).
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).
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).
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).
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).
Van Gompel, J. J. et al. Anterior nuclear deep brain stimulation guided by concordant hippocampal recording. Neurosurg. Focus 38, E9 (2015).
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).
Koeglsperger, T., Mehrkens, J. H. & Botzel, K. Bilateral double beta peaks in a PD patient with STN electrodes. Acta Neurochir. 163, 205–209 (2020).
de Hemptinne, C. et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson’s disease. Nat. Neurosci. 18, 779–786 (2015).
Cole, S. R. et al. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson’s disease. J. Neurosci. 37, 4830–4840 (2017).
Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).
Pourfar, M. et al. Assessing the microlesion effect of subthalamic deep brain stimulation surgery with FDG PET. J. Neurosurg. 110, 1278–1282 (2009).
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).
Griffiths, R. I. et al. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J. Parkinsons Dis. 2, 47–55 (2012).
Braybrook, M. et al. An ambulatory tremor score for Parkinson’s disease. J. Parkinsons Dis. 6, 723–731 (2016).
Varoquaux, G. et al. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage 145, 166–179 (2017).
Rodriguez, A. & Laio, A. Machine learning. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).
Tomlinson, C. L. et al. Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov. Disord. 25, 2649–2653 (2010).
Acknowledgements
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.).
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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.
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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.
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Peer review information Nature Biotechnology thanks Ziv Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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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.
Supplementary Video 1
Adaptive DBS compared to clinically optimized open-loop DBS.
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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). https://doi.org/10.1038/s41587-021-00897-5
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DOI: https://doi.org/10.1038/s41587-021-00897-5
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