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

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.

Extended Data Figure 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 2cm.

Extended Data Figure 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 Figure 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 300ms, 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 Figure 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 (Figures 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 Figure 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 method. 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 Figure 6:. Sleep strongly affects neural biomarkers.

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 Figure 7.. Effects of standard therapeutic DBS on oscillatory activity.

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 activity 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.

Figure 1.. Configuration of implanted hardware and method of data streaming.

Quadripolar leads were placed bilaterally into the subthalamic nuclei and in the subdural space over precentral gyri to cover primary motor cortex (inset provides zoomed-in view). Each DBS lead and cortical paddle pair were connected via tunneled lead extenders to the ipsilateral Summit RC+S bidirectional implantable pulse generator (IPG), placed in a pocket over the pectoralis muscle. Each RC+S uses radiofrequency telemetry in the medical implant communication spectrum (MICS) band to wirelessly communicate with a pocket sized relay device, usually worn on the patient. The relay devices transmit by Bluetooth to a single small Windows-based tablet at a distance of up to 12 m, allowing sensing of local field potentials from up to four bipolar electrode pairs for up to 30 hours per IPG, before recharge is needed. Custom software on the tablet allows remote updating of device streaming parameters or adjustment of embedded adaptive DBS algorithms, at home. Data from a wristwatch-style actigraphy monitor (Parkinson’s Kinetograph, Global Kinetics) are downloaded to a server that is synchronized off-line with neural recordings for brain-behavior correlations. RF, radio frequency.

Figure 2.. Anatomic and physiological localization of subthalamic and cortical leads (example from RCS04).

a, Localization of STN contacts with respect to the borders of STN (outlined in blue) as defined by microelectrode mapping. The microelectrode map (green line) shows the borders of STN as defined by cells (red dots) that have canonical STN single unit discharge patterns and rates. The intended depth of the DBS lead is determined by this map, and contact numbers are labelled. The middle contacts (1 and 2) are within the dorsal 4 mm of STN (motor territory). The black dot is a cell in substantia nigra, pars reticulata. b, Somatosensory evoked potential (from stimulation of the median nerve) recorded from the subdural paddle lead, montaged for three overlapping contact pairs. Reversal of the N20 potential between pairs 8–9 and 9–10 (arrow) shows localization of contact 9 to primary motor cortex, consistent with subsequent imaging. c, Location of the leads from postoperative CT computationally fused with the preoperative planning MRI. Left, STN leads on axial T2 weighted MRI which shows the STN as a region of T2 hypointensity. Right, quadripolar subdural paddle contacts on axial T1 weighted MRI showing relationship to central sulcus (red arrows) and numbering of contacts (white arrows).

Figure 3.. Decoding motor fluctuations from long duration recordings at home, single subject example (RCS01).

a, Data from wearable Personal KinetiGraph (PKG) monitor scores for bradykinesia and dyskinesia in 10 minute intervals. Example from one day. Assignment of motor state is shown in the horizontal colored bar. b, Capturing transitions between immobile (off) and mobile/dyskinetic states. Top, spectrograms for STN and motor cortex, and STN-motor cortex coherence over a 7.5 hour period (all times PM). Arrows indicate frequency bands sensitive to on-off fluctuations. Grey vertical lines show areas where the recording was discontinuous and was concatenated. Bottom, PKG dyskinesia scores indicate four transitions between off (low dyskinesia) and on with dyskinesia (patient had severe fluctuations). These are associated with transitions in beta and gamma oscillatory activity. c, Power spectra of STN and motor cortex, and STN-motor cortex coherence for all awake data from patient RCS01, segregated by mobile (on) and immobile (off) states (categorized by PKG) and averaged. Grey dashed boxes represent canonical frequencies (alpha, beta, gamma) in which an oscillation was present (see methods) and a significant difference between states was observed using the Wilcoxon ranksum test (corrected for multiple comparisons). a.u.=arbitrary units, STN=subthalamic nucleus. MC = motor cortex. MS = magnitude squared. DK = dyskinesia. BK = bradykinesia.

Figure 4.. Personalized oscillatory fingerprints: statistical significance in defined frequency bands for all subjects.

Colorplot of p-values (two sided Wilcoxon rank-sum tests) evaluating if oscillatory power and coherence in canonical bands (alpha 8–12 Hz, beta 12–30 Hz, narrow band gamma 70–90 Hz) distinguishes between mobile vs immobile states (as determined by PKG monitor) for each patient and region. Colored squares represent areas in which the computed p-value survived multiple comparisons and a peak in the PSD was present (the corrected p-value equivalent for p=0.001 on this log scale is 4.68). Average PSD/coherence values from a 4 Hz window around peak were used for tests. Schematic below the colorplot illustrates spectral peaks at canonical frequencies that may be associated with specific motor signs. STN = subthalamic nucleus, MC = motor cortex, coh=coherence between STN and motor cortex.

Figure 5.. Contribution of specific features and recording sites to the decoding of movement state for all five subjects.

Decoding was done by fitting a linear discriminant (LD) to neural features with true labels coming from a wearable sensor (PKG watch). A LD model was learned for each feature (or combination of features) and the average area under the curve (AUC) was computed. a, AUC from receiver operator curve (ROC) analysis, showing that utilizing data from both STN and cortex better discriminates mobile and immobile states (as segregated by PKG scores), than either site alone. Each symbol in each column represents a single within-subjects model from a single hemisphere, computed in a variety of ways: From neural data derived from a single brain region (STN or cortex, data columns within solid black boxes) or from combinations of data from both brain regions (columns within grey boxes). For the two data columns on the left, separate LD models were constructed for each of two recording channels within the STN or the cortex (yielding 20 symbols, two for each hemisphere), and were also separated by frequency band utilized by the model (beta or gamma). For the other four columns, both recording channels, and both frequency bands were combined for each LD model (yielding 10 symbols, one for each hemisphere). Circles represent significant AUC measurements (tested non-parametrically) while triangles did not pass multiple comparison correction. Colors segregate all scores coming from the same patient. b, Correlation between decoding accuracy (using all features from a, far right data column) and the severity of motor fluctuations, estimated by the preoperative difference between the lateralized MDS-UPDRS part III scores for akinesia and rigidity, on-medication versus 12 hours off medication (p = 0.065 for linear regression line, one sided uncorrected). Neural data from RCS01 has the most accurate state decoding and the most severe motor fluctuations.

Figure 6:. Adaptive DBS recorded at home using subcortical beta or cortical gamma control signals from two patients.

a and b, Neural data recording during two 8 hour sessions at home while running embedded adaptive STN DBS algorithms utilizing (a) subcortical STN beta band (RCS03) and (b) cortical gamma band (RCS01) as the control signals. Patients were at home undergoing activities of daily living on their habitual antiparkinsonian medications. Spectral power in the predefined frequency bands was computed on the device from the time domain signal, averaged and used to control stimulation. Blue plots show the neural control signals; green plots show the resulting stimulation current. Horizontal dotted lines on the control signal plots indicate upper and lower spectral power thresholds at which changes in stimulation amplitude were triggered. a, When the control signal is below the bottom threshold (indicating a risk a mobile state not requiring stimulation), current ramps down to 0mA. Current is held constant between thresholds, and ramps up to 1.4mA when the control signal is above the upper threshold. b, When the control signal is below the lower threshold current ramps to the highest amplitude (3.4mA). Between thresholds, current is held, and above the upper threshold, (indicating a risk for dyskinesia), current ramps down to 2.5mA. c. “Zoomed in” view of the plot in b, between noon and 1 pm, showing rapid current ramp down rates (to avoid or promptly arrest dyskinesia) and slower ramp up rates. d. Subjective and Objective evaluation of motor function for adaptive DBS. Patient ran embedded algorithm for 4 consecutive days (one month after (b) while wearing a PKG watch on the contralateral hand, and completing a motor diary. This was compared to watch and diary data collected on open loop chronic stimulation two weeks prior. Both objective (PKG wearable) and subjective (motor diary) measures indicate an increase in “on” time in comparison to open loop stimulation. aDBS = adaptive deep brain stimulation.