Personalized adaptive deep brain stimulation for treatment of parkinson's disease

Abstract

Devices, systems, software, and methods are provided for administering adaptive deep brain stimulation (DBS) to a subject who has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brain electrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre-specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom.

Description

PERSONALIZED ADAPTIVE DEEP BRAIN STIMULATION FOR TREATMENT OF PARKINSON'S DISEASE CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Patent Application No.63 / 664,871, filed June 27, 2024, which application is incorporated herein by reference in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under NS100544, F32 NS129627, K23 NS120037, R01 NS131405, and R25 NS070680 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND OF THEINVENTION

[0003] Parkinson’s disease (PD) is a common neurodegenerative movement disorder characterized by motor symptoms (e.g., bradykinesia, rest tremor, rigidity, postural instability) that are highly disabling and significantly impair quality of life (Fasano, A. et al. BMC Neurol. 19, 1-11 (2019); Sanchez-Luengos et al. Qual. Life Res.31, 3241-3252 (2022); Bloem, B. R., Okun, M. S. & Klein, C. Parkinson’s disease. Lancet 397, 2284-2303 (2021)). Deep brain stimulation (DBS) has grown to widespread use as an effective treatment for advanced Parkinson’s Disease (PD) when medication alone is ineffective. Conventional DBS (cDBS) treats motor symptoms by delivering electrical stimulation to the basal ganglia at a fixed current and frequency. Notably, however, this doesn’t account for the dynamic clinical needs of the patient, which fluctuate across a range of timescales. These fluctuations arise from sources including medication wash-in / out, activity level, and physiological motor state. The unresponsive nature of conventional DBS can result in involuntary movements (dyskinesias) when stimulation is too high with respect to the current clinical state, or slowness of movement, the cardinal symptom of the PD, when stimulation is too low.

[0004] Thus, there remains a need for better methods of delivering DBS to patients with movement disorders such as Parkinson’s disease to relieve motor symptoms and provide assistance with voluntary movement while reducing or eliminating dyskinesia. SUMMARY OF THE INVENTION

[0005] Devices, systems, software, and methods are provided for administering adaptive DBS to a subject who has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brainelectrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre- specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom. The devices and methods can be used remotely and self-directed by patients in their own homes. In one aspect, a method for treating a movement disorder in a subject is provided, the method comprising: positioning a stimulation electrode at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; positioning a first neural recording electrode at a second location in the subthalamic nucleus region of the brain of the subject and / or a second neural recording electrode at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; detecting the stimulation-entrained gamma oscillations associated with the residual motor symptom of the subject using the first neural recording electrode and / or the second neural recording electrode; and applying electrical stimulation to the subthalamic nucleus region of the brain of the subject using the stimulation electrode in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode and / or the second neural recording electrode.

[0006] In certain embodiments, the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz, 62 Hz to 68 Hz, or 64 Hz to 66 Hz, including any frequency within these ranges such as 62 Hz, 63 Hz, 64 Hz, 65 Hz, 66 Hz, 67 Hz, or 68 Hz.

[0007] In certain embodiments, the stimulation-entrained gamma oscillations are centered at half of the electrical stimulation frequency of the DBS.

[0008] In certain embodiments, the DBS shifts peak frequency of the gamma oscillations such that the gamma oscillations become entrained to a subharmonic of a stimulation frequency of the DBS.

[0009] In certain embodiments, the stimulation-entrained gamma oscillations are modulated by the medication and sleep-wake cycles.

[0010] In certain embodiments, the peak frequency of the electrical stimulation is used to predict the gamma frequency of the stimulation-entrained gamma oscillations associated with the residual motor symptom.

[0011] In certain embodiments, the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance.

[0012] In certain embodiments, the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom.

[0013] In certain embodiments, the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS.

[0014] In certain embodiments, the residual motor symptom is unilateral or bilateral.

[0015] In certain embodiments, the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region.

[0016] In certain embodiments, the movement disorder is Parkinson’s disease.

[0017] In certain embodiments, the medication is a dopaminergic medication. In some embodiments, the dopaminergic medication is levodopa.

[0018] In certain embodiments, the electrical stimulation reduces occurrence of the residual motor symptom compared to in absence of the electrical stimulation.

[0019] In certain embodiments, the amplitude of the electrical stimulation is calibrated during the treatment of the subject for both a medication on-state and a medication off-state.

[0020] In certain embodiments, the maximum amplitude of the electrical stimulation is set to avoid inducing another motor symptom or other adverse effect from said applying the electrical stimulation.

[0021] In certain embodiments, the method further comprises using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject,

[0022] In certain embodiments, the method further comprises using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject. In some embodiments, a single threshold is used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms. In other embodiments, two thresholds comprising an upper threshold and a lower threshold are used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.

[0023] In certain embodiments, the electrical stimulation is applied unilaterally or bilaterally.

[0024] In certain embodiments, the stimulation-entrained gamma oscillations are measured by recording field potentials.

[0025] In certain embodiments, the method further comprises using a control algorithm to automate said applying electrical stimulation when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected. In some embodiments, the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between presence and absence of the residual motor symptom. In some embodiments, the machine learning algorithm is a supervised machine learning algorithm. In some embodiments, the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation.

[0026] In certain embodiments, a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom.

[0027] In certain embodiments, the method further comprises using a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gamma oscillations.

[0028] In certain embodiments, the stimulation electrode is placed on a surface of the subthalamic nucleus region.

[0029] In certain embodiments, the first neural recording electrode is placed within the subthalamic nucleus region.

[0030] In certain embodiments, the second neural recording electrode is placed within the sensorimotor cortex region.

[0031] In certain embodiments, the second neural recording electrode is placed in a subdural space over the sensorimotor cortex or under the scalp.

[0032] In certain embodiments, the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0033] In certain embodiments, the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0034] In certain embodiments, the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, subdural electrode, or an electrocorticogram (ECoG) electrode array. In some embodiments, the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region.

[0035] In certain embodiments, the method further comprises assessing effectiveness of the treatment in the subject. In some embodiments, said assessing comprises using behavioral data obtained of the subject. In some embodiments, the behavioral data is accelerometry data for the subject, video-based pose kinematic data for the subject, or keylogging data from a computer used by the subject, or a combination thereof. In certain embodiments, the assessing comprises using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale.

[0036] In another aspect, a computer implemented method for programming a deep brain stimulator to treat a movement disorder in a subject is provided, the computer performing steps comprising: receiving recorded brain electrical signal data from a subthalamic nucleus region and / or a sensorimotor cortex region of the brain of the subject, wherein the brain electrical signal data comprises stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; analyzing the recorded brain electrical signal data using a motor symptom classification model that distinguishes between presence and absence of the residual motor symptom; adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to a control algorithm; and instructing the deep brain stimulator to apply an electrical stimulation to the subthalamic nucleus region of the brain when the stimulation- entrained gamma oscillations associated with the residual motor symptom are detected and said analyzing indicates the presence of the residual motor symptom.

[0037] In certain embodiments, the computer implemented method further comprises using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject. In some embodiments, a single threshold is used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low- dopaminergic symptoms. In other embodiments, two thresholds comprising an upper threshold and a lower threshold are used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.

[0038] In certain embodiments, the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between the presence and the absence of the residual motor symptom. In some embodiments, the machine learning algorithm is a supervised machine learning algorithm.

[0039] In certain embodiments, the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom.

[0040] In certain embodiments, a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom.

[0041] In certain embodiments, the computer implemented method further comprises using data from a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation- entrained gamma oscillations.

[0042] In certain embodiments, the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation.

[0043] In certain embodiments, the computer implemented method further comprises: a) ranking predicted stimulation effectiveness for available settings of a DBS device based on classifier scores for stimulation effectiveness of each setting using a linear classification model; b) selecting stimulation settings predicted to have highest stimulation effectiveness based on the linear classification model; c) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness; d) analyzing the recorded brain electrical signal data to evaluate neural response of the subject to the electrical stimulation; e) updating the linear classification model based on the neural response of the subject to the electrical stimulation to generate an updated linear classification model; f) updating the ranking of predicted stimulation effectiveness for the available settings of the DBS device using the updated linear classification model; g) selecting stimulation settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; h) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying the electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulationeffectiveness based on the updated linear classification model; and i) repeating e) - h) to adjust the available settings of the DBS device to optimize stimulation effectiveness. In some embodiments, the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.

[0044] In another aspect, a non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform a method, described herein, is provided.

[0045] In another aspect, a kit comprising the non-transitory computer-readable medium, described herein, and instructions for treating a movement disorder is provided.

[0046] In another aspect, a system for treating a movement disorder in a subject is provided, the system comprising: a stimulation electrode adapted for positioning at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; a first neural recording electrode adapted for positioning at a second location in the subthalamic nucleus region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; a second neural recording electrode adapted for positioning at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with the residual motor symptom of the subject; and a processor programmed according to a computer implemented method, described herein, to instruct the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region of the brain of the subject in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode or the second neural recording electrode and the motor symptom classification model indicates the presence of the residual motor symptom.

[0047] In certain embodiments, the brain electrical signal data comprises field potential data.

[0048] In certain embodiments, the system further comprises a wearable device to monitor the residual motor symptom.

[0049] In certain embodiments, the stimulation-entrained gamma oscillations detected by the system are in a range of 60 Hz to 70 Hz, 62 Hz to 68 Hz, or 64 Hz to 66 Hz, including any frequency within these ranges such as 62 Hz, 63 Hz, 64 Hz, 65 Hz, 66 Hz, 67 Hz, or 68 Hz.

[0050] In certain embodiments, the system further comprises the medication.

[0051] In certain embodiments, the stimulation electrode is adapted for positioning on a surface of the subthalamic nucleus region.

[0052] In certain embodiments, the first neural recording electrode is adapted for positioning within the subthalamic nucleus region.

[0053] In certain embodiments, the second neural recording electrode is adapted for positioning on within the sensorimotor cortex region.

[0054] In certain embodiments, the second neural recording electrode is adapted for positioning in a subdural space over the sensorimotor cortex or under the scalp.

[0055] In certain embodiments, the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0056] In certain embodiments, the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0057] In certain embodiments, the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, a subdural electrode, or an electrocorticogram (ECoG) electrode array. In certain embodiments, the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region.

[0058] In certain embodiments, the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region to treat the residual motor symptom in the subject. In some embodiments, the user interface is password protected and is operable by a health care practitioner. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] FIGS. 1A-1E. Configuration of implanted hardware, algorithmic model and patient demographics. FIG.1A, Illustration of the adaptive paradigm starting with real-life sensing of brain activity (blue) that reflects changes in patient’s mobility-in this example slowness of movement (bradykinesia). Neural activity is sensed continuously on-board the DBS device from either the STN or sensorimotor cortex using depth or subdural electrodes, respectively. Here, we illustrate an example of a cortical control signal for fully embedded adaptive implementation. Once a change in the brain signal across a predefined threshold is detected, the stimulation amplitude increases or decreases automatically (red) at the target brain region (STN). This adaptation of stimulation amplitude to the patient’s needs leads to improved symptoms-in this example, faster movement. FIG. 1B, Localization of depth leads in the STN with active contacts colored in red across patients innormalized Montreal Neurological Institute (MNI) space. STN is highlighted in orange and the red nucleus in red. FIG.1C, Location of cortical leads that entered the aDBS pipeline for all patients in normalized MNI space overlaid on a common brain atlas. FIG.1D, Patient characteristics including age, gender, disease duration, UPDRS-III off medication score, pre- and post-surgery levodopa equivalent daily dose (LEDD, mg) and residual motor fluctuations on clinically optimized cDBS, including the body side, the most bothersome symptom and the symptom in the opposite dopaminergic state. FIG. 1E, Timeline of the study protocol, including optimization of cDBS by a movement disorder neurologist, biomarker identification, aDBS algorithm design, and blinded comparisons between effects of aDBS and cDBS on symptoms. Stimulation conditions were applied for at least one month each in short, randomized blocks of 2-7 days.

[0060] FIG. 2. Workflow for data-driven biomarker identification and aDBS implementation. We employed a seven-step workflow, individualized for each patient: Identification of bothersome residual symptoms on cDBS and required stimulation amplitude limits for better symptom control (steps 1 and 2), in-clinic and at-home neural recordings with simultaneous symptom monitoring for biomarker identification (steps 3-4), refining parameters for patient-tailored adaptive algorithms using supervised short-term (step 5) and longer-term (step 6) at-home testing, and finally blinded, randomized comparisons between cDBS and aDBS in multiple blocks of 2-7 days per condition (total of one month per condition) in patients’ real-life environments (step 7).

[0061] FIGS.3A-3E. Examples of stimulation-entrained gamma oscillations in both in-clinic and at- home recordings. FIG.3A, Example spectrogram of cortical activity in the high dopaminergic state during systematic variations in stimulation amplitude (black dotted line), illustrating the phenomenon of stimulation-induced entrainment of gamma oscillations at half of the stimulation frequency (130 Hz, pat-1). Levodopa-induced finely-tuned gamma oscillations occur at 80-90 Hz when stimulation is off or stimulation amplitudes are low but become entrained to half the stimulation frequency (65 Hz) when stimulation exceeds a certain amplitude (1.5 mA in this example). FIGS. 3B-3C, Examples of biomarker identification using standardized in-clinic neural recordings (b, pat-2L, c, pat-1). Plots show power spectra during high and low dopaminergic states (labeled on and off medication, mean ± standard error of the mean), i.e., periods during which hyper- and hypokinetic symptoms would emerge, respectively. Recordings are collapsed across low and high stimulation amplitude conditions, which were both amplitudes at which finely-tuned gamma oscillations entrained to half the stimulation frequency. We found that medication yielded the largest effect on stimulation-entrained gamma power at half the stimulation frequency in the STN (FIG. 3B, pat-2L) and motor cortex (FIG. 3C, pat-1, anterior montage) when controlling for effects of stimulation (FIG. 8). Significant clusters are highlighted in gray. FIGS. 3D-3E, At-home recordings during constant stimulation amplitude andpatients’ normal medication schedule in the STN (FIG.3D, pat-2L) and motor cortex (e, pat-1, anterior montage). Patients marked their medication intake (red dashed line) and on- and off-set of their most bothersome symptom in their motor diary (completed in 30-minute intervals) and the streaming application9. Both patients had bothersome hypokinetic symptoms associated with low dopaminergic states, i.e., lower limb-dystonia (FIG. 3D, pat-2) and bradykinesia (FIG. 3E, pat-1). For both, stimulation-entrained gamma oscillations occur ~45 minutes after medication intake (red arrows), corresponding to a typical latency of onset for dopaminergic medication. When the patient marked their most bothersome symptom (indicated by the black dashed line) in their motor diary, stimulation- entrained gamma oscillations disappeared, indicating a transition to a low dopaminergic state.

[0062] FIGS. 4A-4E. Data-driven biomarker identification during active stimulation for all hemispheres. FIGS.4A-4B, Results of the within-subject nonparametric cluster-based permutation analysis for in-clinic recordings. FIG.4A, Graphs illustrate the effect size (Cohen’s d) of the main effect of medication on power as a function of frequency in the STN (left), the anterior cortical montage (middle), and posterior cortical montage (right) for all patients. Red and blue colors represent positive effects (high>low dopaminergic state) and negative effects (low>high dopaminergic state), respectively. For all patients, we found stimulation-entrained gamma oscillations in the STN (pat-2, both hemispheres) and cortex (pat-1, left hemisphere, pat-3, right hemisphere and pat-4, left hemisphere) at half the stimulation frequency (130 Hz) to be the optimal biomarker for medication- related fluctuations during active stimulation (FIG.8). We did not find any significant effects in the left hemisphere of pat-3 (not shown). FIG.4B, The effect sizes for cortical and STN stimulation-entrained gamma oscillations (right) were superior to those for STN beta oscillations (left) for all patients (mean ± standard error of the mean across permutations; one-sided Wilcoxon signed rank test: p=0.03; FIG. 8 and FIG.9 LDA). FIGS.4C-4E, Results of the within-subject linear discriminant analysis for at-home recordings using power spectral density at the three brain sites to predict the occurrence of the most bothersome or opposite symptom. FIG.4C, The three graphs illustrate the initial area under the curve (AUC) prior to bandwidth optimization as a function of frequency for the STN (left), the anterior cortical montage (middle), and posterior cortical montage (right) for all patients. FIG.4D, Across patients, we show that stimulation-entrained gamma oscillations in the STN (pat-2, both hemispheres) or cortex (pat-1, left hemisphere, pat-3, both hemispheres, and pat-4 left hemisphere) were the best predictors of the occurrence of the most bothersome or opposite symptom and superior to beta oscillations (mean ± standard error of the mean across permutations; one-sided Wilcoxon signed rank test: p=0.02; FIG.9). FIG.4E, The combined use of STN / cortical gamma and STN beta bands provided minimal improvement in the AUC of at-home symptom prediction using linear discriminant analysis (mean ± standard error of the mean across permutations).

[0063] FIGS.5A-5F. Characteristics and technical performance of adaptive DBS algorithms. FIG.5A, Summary of the final stimulation parameters used for blinded, randomized comparisons between stimulation conditions including the control signal for aDBS. All parameters but stimulation amplitude were identical between cDBS and aDBS. FIGS.5B, 5C, Examples of two control algorithms using subthalamic (FIG.5B, pat-2R) and cortical (FIG.5C, pat-1) stimulation-entrained gamma activity at half the stimulation frequency as control signals. In each graph, the upper subplot illustrates the control signal as a function of time with thresholds (black) that are used to determine changes in stimulation amplitude. The lower subpanel illustrates the stimulation amplitudes responding to fluctuations in the neural signal. Stimulation amplitudes were fine-tuned during algorithm optimization and might differ slightly from the final testing period. In all patients, we used a biomarker indicating high dopaminergic states, such that stimulation amplitude decreases when the biomarker amplitude exceeds a threshold. Timing of dopaminergic medication intake is marked by dashed red vertical lines. FIGS.5D, 5E, Dynamics of algorithm performance showing adaptive changes on a time course of minutes-hours. FIG. 5D, Daily percent time spent at each stimulation amplitude. Across hemispheres, the algorithm spent 70.22±17.26% of the day at the high stimulation amplitude compared to the low stimulation amplitude (29.78±17.26%). The graph is a standard box plot (center: median; box limits, upper and lower quartiles; edges of line (‘whiskers’), 1.5x interquartile range), with each dot representing one day of aDBS testing. FIG. 5E, Average duration of each stimulation amplitude state in a day. On average, high amplitude stimulation states (responding to low dopaminergic clinical states) lasted 1.2±0.58 consecutive hours and low amplitude stimulation states (responding to high dopaminergic states) lasted 0.60±0.39 hours. Every bar represents the mean in each amplitude per patient hemisphere, with each dot representing one day of aDBS testing. Pat-3’s left hemisphere’s high stimulation amplitude state has three outliers not currently plotted which include 4.32, 5.15, and 7.32 hours. FIG.5F, Mean (± standard error of the mean) total electrical energy delivered (TEED) during aDBS and cDBS. During awake hours across testing days and hemispheres, aDBS resulted in greater TEED compared to cDBS during the day (overall 15.4±18.8% increase from cDBS; linear mixed effects model, main effect stimulation condition: β=10.9, p<0.001, main effect time: β=0.005, p=0.88; Extended Data Table 1; individual two-sided, one-sample Wilcoxon signed rank tests: pat-1: p<0.001, pat-2R: p<0.01, pat-2L: p=0.23, pat-3R: p<0.001, pat-3L: p<0.001, pat-4: p<0.001).

[0064] FIGS.6A-6I. Effects of aDBS compared to cDBS on both subjective and objective metrics of motor symptoms and quality of life. FIGS. 6A-6C, Self-reported symptom duration from daily questionnaires for each subject. aDBS resulted in a significantly decreased percentage of awake hours experiencing the most bothersome symptom (FIG. 6A, pat-1, pat-2, pat-3: p<0.001, pat-4:p=0.002) without exacerbating the opposite symptom (FIG.6B, pat-1: p=0.43, pat-2: p=0.94, pat-3, p=0.02, pat-4, p=0.051, significant effects illustrated by black asterisks). Quality of life, as measured by the EQ-5D, was improved for three of four patients (pat-1: p<0.001, pat-2: p<0.001, pat-3: p=0.34, pat-4: p=0.01). Patient 3 reported very high quality of life scores, with minimal reported variance for both cDBS and aDBS. FIG.6D-6G, Effect of DBS condition across multiple motor signs illustrated in radar plots. Personalized bothersome and opposite symptoms are bold with the most bothersome symptom underlined. Control analyses showed that adaptive stimulation did not worsen any other motor symptoms, but instead patient 2 experienced decreased time with gait disturbance and dyskinesia (p<0.001 and p=0.04, respectively, blue asterisks), and patient 1 displayed a trend towards decreased time with gait disturbance (p=0.052); all other symptoms p>0.14. Further, aDBS did not exacerbate any monitored non-motor symptoms (depression, anxiety, apathy, impulsivity, pat-1: p>0.52, pat-2: p=1, pat-3: p=1, pat-4: p>0.56, data not illustrated). Note the subject-specific axis scales. FIG. 6H-6I, Wearable monitor scores demonstrating the decreases in symptom intensity fluctuations. Only patients 1, 3 and 4 are displayed, as patient 2’s bothersome and opposite symptoms were not measurable by a wearable device. Laterality refers to the brain hemisphere where aDBS was applied (and therefore contralateral motor sign measurement). Fluctuation scores represent differences between wearable scores during low- and high-dopaminergic states defined by the neural signal. aDBS decreased motor fluctuations between the high- and low-dopaminergic states compared to cDBS (fluctuation score; bradykinesia: pat-1: p<0.001, pat-3 left body: p=0.005, pat-3 right body: p=0.046, pat-4: p=0.01; dyskinesia: pat-1: p=0.04, pat-3 right body: p=0.03). Error bars in each subplot represent the standard error of the mean. All p-values are indicated as: *p<0.05, **p<0.01, ***p<0.001.

[0065] FIGS.7A-7H. Localization of leads over sensorimotor cortex and within subthalamic nucleus in native space. FIGS. 7A-7D, 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. FIG.7A, Cortical leads on axial T1-weighted MRI through the vertex. FIG.7B, STN leads on axial T2-weighted MRI through the region of the dorsal STN, 3 mm inferior to the intercommissural plane. FIGS.7C-7D, Cortical leads on oblique sagittal T1-weighted MRI passing through the long axis of the lead array in left (FIG.7C) and right (FIG.7D) hemispheres, respectively. FIG.7E-7H, Location of cortical leads overlayed on 3D reconstruction of cortex rendered using LeGUI. Electrodes used in the anterior and posterior cortical montages are shown in cyan and yellow, respectively. For patient 1 (FIG.7E), 2 (FIG.7F) and 4 (FIG.7H), anterior and posterior montages covered the pre- and postcentral gyrus, respectively. For patient 3 (FIG.7G), the anterior montage included one electrode on the middle frontal and one on the precentral gyrus. The posterior montagecomprised one pre- and one postcentral electrode. In all figures, red arrows indicate the location of the central sulcus.

[0066] FIGS.8A-8D. Neural biomarkers of medication effects identified in-clinic. 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. FIG. 8A, 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 three patients (four hemispheres), we found that stimulation-entrained gamma power in the STN or cortex was the best predictor of medication state. Positive Cohen’s d values highlight that the neural biomarker was higher during on-medication states. For patients 2 and 3, we did not find overlapping potentially problematic positive stimulation effects on biomarkers identified for a main effect of medication (for an illustration of problematic stimulation effects on adaptive algorithms see FIG.11). For patient 1, we excluded 63 and 67 Hz from the subsequently used control signal due to intersecting positive stimulation effects. FIG.8B, When constraining the anatomic location and frequency space to STN beta oscillations (13-30 Hz), STN beta power was only predictive for medication state in two hemispheres and smaller in effect size than cortical / STN stimulation-entrained gamma oscillations for all patients. In patient 4, the identified beta medication cluster had an additional significant interaction effect with stimulation (frequency band: 15-16 Hz, Cohen’s d: -0.49, p<0.001). FIG. 8C, Power spectral density in the STN based on in-clinic recordings off medication and off stimulation for all five hemispheres. All but one hemisphere (pat-1) exhibited a peak in the beta frequency band (illustrated in yellow). FIG.8D, Example of the suppressive effect of DBS on STN beta oscillations leading to beta power being a less adequate biomarker 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<0.001). However, this medication effect diminished during active stimulation, even at low stimulation amplitudes (1.8mA, largest effect in the beta band: 15-18 Hz, Cohens’ d=0.31, p=0.026). Data are corrected for stimulation-induced broadband shifts.

[0067] FIGS.9A-9D. Neural biomarkers of symptoms identified at-home. FIG.9Aa, Heatmaps of t- values derived from stepwise linear regressions using 1 Hz power bands between 2-100 Hz in the STN (left), the anterior cortical montage (middle) and posterior cortical montage (right) to predict the bothersome / opposite symptom measured with continuous wearable monitors for patients 1 and 3. FIG.9B, Linear regression (left) and linear discriminant analysis (LDA; right). Both methods provideconverging 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. FIG.9C, When constraining the anatomic location and frequency space to STN beta oscillations (13- 30 Hz), frequency bands identified as most predictive of bothersome / opposite symptoms were less discriminative than cortical / STN stimulation-entrained gamma oscillations (all AUC<0.7). Corresponding linear regression models also resulted in smaller magnitude coefficients with only one hemisphere, which demonstrated a significant negative association with hyperkinetic symptoms (pat- 3L). All p-values were Bonferroni-corrected for multiple comparisons (289 predictors). FIG.9D, 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 positive relationship with hypokinetic symptoms; pat-3L).

[0068] FIG.10. 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 (e.g., 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 subsequently assessing prediction accuracy based on multiple neural biomarkers.

[0069] FIGS.11A-11B. Problematic stimulation effects on neural signals. Two examples of undesired cyclic behavior of the control algorithm caused by stimulation effects. Upper sub-panels illustrate the neural signal in blue and thresholds for stimulation amplitude changes as dashed black lines, and the lower subpanel highlights the corresponding stimulation amplitude. FIG.11A, Off-state biomarkers are defined as neural signals that are higher when patients experience hypokinetic symptoms (e.g., beta oscillations). The control algorithm therefore increases stimulation amplitude when the neuralbiomarker is high. Inappropriate threshold crossings may occur when increases in stimulation amplitude suppress the control signal to a greater degree than augmentation of the control signal resulting from natural medication wear-off (in our nonparametric cluster-based analysis: a negative stimulation effect). As medication wears off and the neural signal increases over a set threshold (left green circle), the resulting increase in stimulation can subsequently suppress the biomarker until it then falls below the threshold (right green circle), leading to cyclic behavior. FIG.11B, For on-state biomarkers, as in our study, the opposite stimulation effect would be problematic. Here, control signals are higher after medication intake when patients are more prone to hyperkinetic symptoms. Stimulation amplitude increases when medication naturally wears off and hypokinetic symptoms manifest (left green circle). Inappropriate threshold crossings may occur when this change in stimulation amplitude leads to an artificial augmentation of the neural signal that exceeds the natural signal decrease observed during hypokinetic times (right green circle). Similar to the off-state biomarker, the result is cyclic behavior of the control system, independent of patients’ symptomatic state. In our nonparametric cluster-based analysis this is reported as a positive stimulation effect. For our identified on-state biomarkers in the gamma frequency range, we excluded spectral biomarkers with positive stimulation effects to avoid cyclic behavior (FIG.8).

[0070] FIGS.12A-12E. Initial and finalized adaptive stimulation parameters and example adaptive control policies. FIG.12A, 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 a delineation between the presence and absence of the most bothersome symptom, and often improved when increasing it further. The ramp rate chosen for each patient depended on the results of step 5 (we chose an example of 1 mA / s). FIG.12B, 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. FIGS. 12C-12E, 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. FIG.12C, 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. FIG.12D, A dual threshold control policy with three stimulation amplitudes (not used in this study), which may be applied to address three symptom states or approximate a proportional stimulation change in response to the biomarker. When the neural signal is below both thresholds, the stimulation amplitude is high (e.g., 4 mA). When the biomarker is between the twothresholds, stimulation adjusts to a middle amplitude (e.g., 3 mA). When the biomarker exceeds the second threshold, stimulation decreases to the low amplitude (e.g., 2 mA). FIG.12E, A control policy utilizing a middle state as a 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 (i.e., no changes are made).

[0071] FIGS.13A-13B. aDBS algorithm dynamics during nighttime. FIG.13A, Percent time spent at each stimulation amplitude during the night. Each dot represents one night of aDBS testing. Each patient spent a majority of the night in the high stimulation state. FIG.13B, Mean (±standard error of the mean) total electrical energy delivered (TEED) during aDBS and cDBS overnight, showing increased TEED during aDBS across, similar to daytime analyses (stimulation main effect: β=27.7, p<0.001, 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, all hemispheres: p<0.001).

[0072] FIGS.14A-14J. Effects of aDBS and cDBS on additional motor symptoms and sleep quality. FIG. 14A-14B, Patient self-reported motor symptom severity from daily questionnaires (1=least severe, 10=most severe). Patient 3 did not record ratings within the instructed range of 1-10 and data are therefore not reported. FIG.14A, In addition to a decrease in the amount of daily hours with the most bothersome symptom (FIG.6a), patients 1, 2, and 4 also experienced a significant improvement of symptom severity (pat-1: p<0.001, pat-2: p=0.03, pat=4: p=0.008). FIG.14B, No subject reported worsened severity of their opposite symptom (pat-1: p=0.18, pat-2: p=1, pat-4: p=0.25). FIG.14C- 14H, Comprehensive list of the self-reported duration of motor symptoms from daily questionnaires. Patients’ most bothersome and opposite symptoms are labeled as “BoSx” and “OpSx”, respectively, and are plotted instead in Fig 6a-b. No symptoms were significantly worsened by aDBS. Patients 2 also demonstrated significant improvement in the percentage of waking hours with gait disturbance and dyskinesia. Patient 1 demonstrated a trend towards significant improvement in the percentage of waking hours with gait disturbance (p=0.052). FIG.14I-14J, Self-reported sleep quality (1=poorest sleep, 10=best sleep) and duration from daily questionnaires. aDBS provided no significant change in patients’ sleep characteristics. Error bars reflect standard error of the mean. All corrected p-values are indicated as: *p<0.05, **p<0.01, ***p<0.001. DETAILED DESCRIPTION OF THE INVENTION

[0073] Devices, systems, software, and methods are provided for administering adaptive DBS to a subject who has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brainelectrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre- specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom.

[0074] Before the present devices, systems, software, and methods are described, it is to be understood that this invention is not limited to the particular devices, systems, software, and methods described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0075] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0076] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. It is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.

[0077] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0078] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example,reference to "an electrode" includes a plurality of such electrodes and reference to "the electrical signal" includes reference to one or more electrical signals, and so forth.

[0079] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed. Definitions

[0080] The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0081] The term "movement disorder" refers to any type of neurological disorder that causes either increased movements or reduced or slow movements. Movement disorders include, but are not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, essential tremor, functional movement disorder, Huntington's disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, and Wilson's disease. Symptoms may include, but art not limited to, tremor, involuntary movements, slowness of movement (bradykinesia), rigidity, postural instability, twisting movements, poor balance, irregularity of movements, stumbling, and difficulty with walking. In some cases, a movement disorder is caused by genetic and / or environmental factors, head trauma, infections, inflammation, metabolic disturbances, toxins, adverse reactions to medications, or stressful life events.

[0082] The terms “individual”, “subject”, “recipient”, and “patient” are used interchangeably herein and refer to any mammalian subject for whom treatment or therapy is desired, particularly humans. "Mammal" for purposes of treatment refers to any animal classified as a mammal, including human and non-human mammals such as non-human primates, including chimpanzees and other apes and monkey species; laboratory animals such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals such as dogs and cats; and farm animals such as sheep, goats, pigs, horses and cows.

[0083] The term “stimulation-entrained gamma oscillations” refers to gamma oscillations that are shifted in frequency as a result of treatment of a subject with DBS. The frequency of gamma oscillations typically shifts to a subharmonic of the stimulation frequency. Gamma oscillations often become entrained to half the stimulation frequency when electrical stimulation exceeds a certain amplitude. Stimulation-entrained gamma oscillations can be used as a neural biomarker of residualfluctuations in motor function and residual motor symptoms remaining after treatment of a subject with DBS.

[0084] The term “user” as used herein refers to a person that interacts with a device and / or system disclosed herein for performing one or more steps of the presently disclosed methods. The user may be the patient being receiving treatment for a movement disorder. The user may be a health care practitioner, such as the patient’s physician.

[0085] The terms "treatment", "treating", "treat" and the like are used herein to generally refer to obtaining a desired pharmacologic and / or physiologic effect. The effect can be prophylactic in terms of completely or partially preventing a disease or symptom(s) thereof and / or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and / or adverse effect attributable to the disease. The term “treatment" encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and / or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and / or symptom(s), i.e., arresting their development; or (c) relieving the disease symptom(s), i.e., causing regression of the disease and / or symptom(s). Those in need of treatment include those already inflicted (e.g., those with a movement disorder) as well as those in which prevention is desired those with a genetic predisposition to developing a movement disorder, those with increased susceptibility to developing a movement disorder, those suspected of having a movement disorder, etc.).

[0086] A therapeutic treatment is one in which the subject is inflicted prior to administration and a prophylactic treatment is one in which the subject is not inflicted prior to administration. In some embodiments, the subject has an increased likelihood of becoming inflicted or is suspected of being inflicted prior to treatment. In some embodiments, the subject is suspected of having an increased likelihood of becoming inflicted.

[0087] A "therapeutically effective dose" or “therapeutic dose” is an amount sufficient to effect desired clinical results (i.e., achieve therapeutic efficacy). A therapeutically effective dose can be administered in one or more administrations.

[0088] "Pharmaceutically acceptable excipient or carrier" refers to an excipient that may optionally be included in the compositions of the invention and that causes no significant adverse toxicological effects to the patient.

[0089] "Pharmaceutically acceptable salt" includes, but is not limited to, amino acid salts, salts prepared with inorganic acids, such as chloride, sulfate, phosphate, diphosphate, bromide, and nitrate salts, or salts prepared from the corresponding inorganic acid form of any of the preceding, e.g., hydrochloride, etc., or salts prepared with an organic acid, such as malate, maleate, fumarate,tartrate, succinate, ethylsuccinate, citrate, acetate, lactate, methanesulfonate, benzoate, ascorbate, para-toluenesulfonate, palmoate, salicylate and stearate, as well as estolate, gluceptate and lactobionate salts. Similarly salts containing pharmaceutically acceptable cations include, but are not limited to, sodium, potassium, calcium, aluminum, lithium, and ammonium (including substituted ammonium).

[0090] The term “responsive” as used herein means that the treatment is having the desired effect such as reducing symptom severity caused by a movement disorder. When the individual does not improve in response to the treatment, it may be desirable to seek a different therapy or treatment regime for the individual. Methods

[0091] The present disclosure provides methods for administering adaptive DBS to a subject who has a movement disorder that has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brain electrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre-specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom. The methods and systems can be used in performing open-loop therapy to provide clinical guidance to clinicians or technicians for adjusting deep brain stimulation programming. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the subthalamic nucleus region of the brain of the subject when pre-specified patterns of neural activity associated with a residual motor symptom are detected. In some embodiments, the subject methods are used to treat residual motor symptoms of a movement disorder such as, but not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, essential tremor, functional movement disorder, Huntington's disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, and Wilson's disease. Various steps and aspects of the methods will now be described in greater detail below.

[0092] The method includes positioning an electrode in a subthalamic nucleus region of the brain of a subject to deliver electrical stimulation to the brain (i.e., DBS electrode) and an electrode at a second location in the subthalamic nucleus region of the brain of the subject and / or an electrode ata location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations that are associated with a residual motor symptom of the subject (i.e., neural recording electrodes). In some embodiments, one or more DBS electrodes are positioned at the subthalamic nucleus region, and one or more neural recording electrodes are positioned at the subthalamic nucleus region and / or sensorimotor cortex region. In some embodiments, one or more neural recording electrodes are positioned in a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region of the sensorimotor cortex.

[0093] The DBS electrodes and the neural recording electrodes may be non-brain penetrating surface electrodes, extracranial electrodes, for example, subgaleal or skull mounted (in burrhole cap or in case of cranially mounted neurostimulator), subdural electrodes, or brain-penetrating depth electrodes. The electrical stimulation may be applied to the subthalamic nucleus using the DBS electrode in a manner effective for treating a residual motor symptom when stimulation-entrained gamma oscillations are detected from the subthalamic nucleus region and / or sensorimotor cortex region of the brain using a neural recording electrode.

[0094] In certain embodiments, one or more neural recording electrodes are used to record stimulation-entrained gamma oscillations in one or more brain regions. A neural recording electrode may be placed, for example, in a subthalamic nucleus region and / or a sensorimotor cortex region (e.g., a cortical precentral gyrus region and / or postcentral gyrus region) to detect stimulation- entrained gamma oscillations, or in other regions of the brain suitable for detection. In certain embodiments, the brain electrical signal data comprises field potential data. The site chosen for detection may differ for different subjects and may depend on mapping of the brain of an individual subject to identify the optimal location(s) for positioning an electrode for detecting stimulation- entrained gamma oscillations, as discussed further below.

[0095] As used herein, the phrases “an electrode” or “the electrode” refer to a single electrode or multiple electrodes such as an electrode array. As used herein, the term “contact” as used in the context of an electrode in contact with a region of the brain refers to a physical association between the electrode and the region. In other words, a neural recording electrode that is in contact with a region of the brain is physically touching the region of the brain. A DBS electrode can conduct electricity to specific targets in the brain. Electrodes used in the methods disclosed herein may be monopolar (cathode or anode) or bipolar (e.g., having an anode and a cathode).

[0096] Positioning a neural recording electrode for recording neural activity at specified region(s) of the brain may be carried out using standard surgical procedures for placement of intra-cranial electrodes. In certain cases, placing the neural recording electrode may involve positioning the electrode on the surface of the specified region(s) of the brain. For example, electrodes may beplaced on the surface of the brain at a subthalamic nucleus region, a sensorimotor cortex region (e.g., a cortical precentral gyrus region and / or postcentral gyrus region), or a combination thereof. The electrode may contact at least a portion of the surface of the brain at the subthalamic nucleus region or the sensorimotor cortex region (e.g., cortical precentral gyrus region or postcentral gyrus region). In some embodiments, the electrode may contact substantially the entire surface area at the subthalamic nucleus region and / or sensorimotor cortex region. In some embodiments, the electrode may additionally contact area(s) adjacent to the subthalamic nucleus region and / or sensorimotor cortex region. In some embodiments, the neural recording electrodes may contact any area of the subthalamic nucleus region and / or sensorimotor cortex region that allows detection of stimulation- entrained gamma oscillations associated with a residual motor symptom of the subject. In some embodiments, the electrodes may be placed extracranially, for example in the subgaleal space. In some embodiments, the electrodes may be placed in a subdural space over the sensorimotor cortex or under the scalp. In some embodiments, the neural recording electrode may be contained within a burr hole cap or on the case of the cranially mounted implantable neural stimulator device. In some embodiments, an electrode array arranged on a planar support substrate may be used for detecting stimulation-entrained gamma oscillations from one or more of the brain regions specified herein. The surface area of the electrode array may be determined by the desired area of contact between the electrode array and the brain. An electrode for implanting on a brain surface, such as, a surface electrode or a surface electrode array may be obtained from a commercial supplier. A commercially obtained electrode / electrode array may be modified to achieve a desired contact area. In some cases, the non-brain penetrating electrode (also referred to as a surface electrode) that may be used in the methods disclosed herein may be an electrocorticography (ECoG) electrode, a subgaleal electrode, a subdural electrode, or an electroencephalography (EEG) electrode. In certain embodiments, a plurality of electrodes is positioned in an electrode grid for detection of stimulation- entrained gamma oscillations. In certain embodiments, a plurality of electrodes is positioned at one or more of the brain regions specified herein for detection of stimulation-entrained gamma oscillations by stereoelectroencephalography (sEEG).

[0097] In certain cases, placing the neural recording electrode at a target area or site (e.g., a subthalamic nucleus region and / or sensorimotor cortex region of the brain) may involve positioning a brain penetrating electrode (also referred to as depth electrode) in the specified region(s) of the brain. For example, a neural recording electrode may be placed in a subthalamic nucleus region and / or a sensorimotor cortex region of the brain. In some embodiments, the neural recording electrode may additionally contact area(s) adjacent to a subthalamic nucleus region and / or a sensorimotor cortex region of the brain. In some embodiments, an electrode array may be used fordetecting neural activity from a cortical area, for example, a precentral gyrus region or postcentral gyrus region, or a combination thereof, as specified herein.

[0098] The depth to which a neural recording electrode is inserted into the brain may be determined by the desired level of contact between the electrode array and the brain. A brain-penetrating electrode array may be obtained from a commercial supplier. A commercially obtained electrode array may be modified to achieve a desired depth of insertion into the brain tissue.

[0099] Positioning an electrode in the subthalamic nucleus region of the brain for delivering electrical stimulation to the brain may be carried out using standard surgical procedures for placement of electrodes for deep brain stimulation. For example, the electrode may be placed in a subthalamic nucleus region or other intracranial region. Medical imaging using, for example, magnetic resonance imaging (MRI) or computerized tomography (CT) may be used to provide guidance for placement of DBS electrodes and verify correct placement of the DBS electrodes in the brain. In addition, a neurostimulator that generates electrical pulses is placed under the skin of the chest, typically below the collarbone or in the abdomen. In some embodiments the neurostimulator is cranially mounted. The surgical procedure may involve placing DBS electrodes within the brain through small holes in the skull. An electrode lead is tunneled under the skin down the neck and under the skin of the chest to connect to a chest implanted neurostimulator.

[0100] Current is supplied by the neurostimulator to the DBS electrodes. Parameters such as pulse width, shape, frequency, amplitude, pattern, and temporal distribution can be adjusted in response to changes in neural activity in the subthalamic nucleus region and / or sensorimotor cortex region of the brain, or alternatively accelerometry, surface electromyographic data, pulse oximetry, temperature, or heart rate to treat a residual motor symptom. In some embodiments, a closed loop system is used to adjust DBS settings automatically in response to detection of stimulation-entrained gamma oscillations in the subthalamic nucleus region and / or sensorimotor cortex region of the brain. In other embodiments, an open loop system is used in which DBS settings are adjusted by a user or medical practitioner based on the detection of stimulation-entrained gamma oscillations in the subthalamic nucleus region and / or sensorimotor cortex region of the brain.

[0101] Electrical stimulation may be applied using a single electrode, electrode pairs, or an electrode array. In some embodiments, the number of electrodes used to deliver electrical stimulation to the brain ranges from 8 to 32, including any number of electrodes in this range such as 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, or 32 electrodes. In some embodiments, the electrical stimulation is applied to more than one site in the subthalamic nucleus. The site to which the electrical stimulation is applied may be alternated or otherwise spatially or temporally patterned. Electrical stimulation may be applied to the sites simultaneously or sequentially. The site chosen for stimulation may differ fordifferent subjects and will depend on mapping of the subthalamic nucleus region of the brain of an individual subject to identify the optimal location for positioning an electrode for delivery of electrical stimulation to treat a residual motor symptom.

[0102] In some embodiments, an electrode array arranged on a planar support substrate may be used for electrically stimulating the subthalamic nucleus. The surface area of the electrode array may be determined by the desired area of contact between the electrode array and the subthalamic nucleus. In some cases, cylindrical electrode arrays, paddle-style electrode arrays, or plate-style electrode arrays may be used in the methods disclosed herein for deep brain stimulation. Such DBS electrode arrays for implanting in the brain, may be obtained from a commercial supplier. A commercially obtained electrode / electrode array may be modified to achieve a desired contact area.

[0103] The precise number of DBS electrodes or neural recording electrodes contained in an electrode array (e.g., for electrical stimulation or detection of neural activity) may vary. In certain aspects, an electrode array may include two or more electrodes, such as 3 or more, including 4 or more, e.g., about 3 to 6 electrodes, about 6 to 12 electrodes, about 12 to 18 electrodes, about 18 to 24 electrodes, about 24 to 30 electrodes, about 30 to 48 electrodes, about 48 to 72 electrodes, about 72 to 96 electrodes, or about 96 or more electrodes. The electrodes may be arranged into a regular repeating pattern (e.g., a grid, such as a grid with about 1 cm spacing between electrodes), or no pattern. An electrode that conforms to the target site for optimal delivery of electrical stimulation may be used. One such example, is a single multi contact electrode with eight contacts separated by 2½ mm. Each contract would have a span of approximately 2 mm. Another example is an electrode with two 1 cm contacts with a 2 mm intervening gap. Yet further, another example of an electrode that can be used in the present methods is a 2 or 3 branched electrode to cover the target site. Each one of these three-pronged electrodes has four 1-2 mm contacts with a center to center separation of 2 of 2.5 mm and a span of 1.5 mm.

[0104] The size of each electrode may also vary depending upon such factors as the number of electrodes in the array, the location of the electrodes, the material, the age of the patient, and other factors. In certain aspects, an electrode array has a size (e.g., a diameter) of about 5 mm or less, such as about 4 mm or less, including 4 mm-0.25 mm, 3 mm-0.25 mm, 2 mm-0.25 mm, 1 mm-0.25 mm, or about 3 mm, about 2 mm, about 1 mm, about 0.5 mm, or about 0.25 mm.

[0105] In certain embodiments, the method further comprises mapping the brain of the subject to optimize positioning of an electrode for applying electrical stimulation. Positioning of a DBS electrode is optimized to maximize clinical responses to electrical stimulation to treat a residual motor symptom, which may include, without limitation, bradykinesia, dyskinesia, dysarthria, dystonia,tremor, or gait disturbance. In some embodiments, the subthalamic nucleus region, globus pallidus region, or other regions of the brain are mapped to determine optimal positioning of DBS electrodes.

[0106] In some embodiments, DBS is optimized to achieve a neurophysiologically defined change, for example, decreasing or increasing stimulation-entrained gamma oscillations. In certain embodiments, stimulation-entrained gamma oscillations at half stimulation frequency are used as a control signal for the subject, wherein the control signal is used to adjust the amplitude of electrical stimulation. In some embodiments, a single threshold is used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low- dopaminergic symptoms. In other embodiments, two thresholds comprising an upper threshold and a lower threshold are used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold. A dual threshold control policy with three stimulation amplitudes may be used, for example, to address three symptom states or provide a more proportional stimulation change in response to the level of a neural biomarker (e.g., stimulation-entrained gamma oscillations).

[0107] Assessment of the effectiveness of electrical stimulation at a particular site for treating a residual motor symptom may be performed using any standard method. In some embodiments, a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale may be used to assess the effectiveness of electrical stimulation in treating a residual motor symptom. In some embodiments, assessing effectiveness of the treatment of a residual motor symptom of a movement disorder comprises monitoring the subject using a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data. For example, a wrist-watch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG®, available from PKG Health (San Francisco, CA), can be used to monitor movement continuously to detect various motor symptoms of a movement disorder such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation.

[0108] In certain embodiments, the method further comprises mapping the brain of the subject to optimize positioning of a neural recording electrode. Positioning of the neural recording electrode ina subthalamic nucleus region and / or sensorimotor cortex region is optimized to detect brain activity features, including stimulation-entrained gamma oscillations associated with a residual motor symptom to be treated with electrical stimulation. For example, the levels of overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta) may be correlated with motor fluctuations and residual motor symptoms. In certain embodiments, a residual motor symptom is identified by an increase in gamma power in a frequency range of 60 Hz to 90 Hz. In certain embodiments, a residual motor symptom is identified by an increase in gamma power in a frequency range of 60 Hz to 70 Hz. Thus, neural recording electrodes may be positioned to optimize detection of brain activity in specific frequency ranges that correlate with a residual motor symptom to be treated with electrical stimulation.

[0109] Detection of brain activity may be performed by any method known in the art. For example, functional brain imaging of neural activity may be carried out by electrical methods such as electroencephalography (EEG), stereoelectroencephalography (sEEG), electrocorticography (ECoG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), as well as metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). In some embodiments, the subthalamic nucleus, sensorimotor cortex, or other regions are mapped to determine optimal positioning for neural recording electrodes. One or more of these regions may be implanted with neural recording electrodes to measure electrical signals from neural activity, including stimulation-entrained gamma oscillations associated with a residual motor symptom to be treated with electrical stimulation.

[0110] In some embodiments, assessing effectiveness of the treatment of a residual motor symptom of a movement disorder comprises monitoring the subject using a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data. A wrist-watch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG® is available from PKG Health (San Francisco, CA), which can monitor movement continuously and detect various motor symptoms of a movement disorder such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation.

[0111] As set forth here, the subject methods involve applying electrical stimulation to a subthalamic nucleus region in a manner effective to treat a residual motor symptom in a subject when stimulation- entrained gamma oscillations associated with the residual motor symptom are detected. In some embodiments, electrical stimulation is applied to the subthalamic nucleus region when stimulation- entrained gamma oscillations are detected. In certain embodiments, the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz, 62 Hz to 68 Hz, or 64 Hz to 66 Hz, including any frequency within these ranges such as 62 Hz, 63 Hz, 64 Hz, 65 Hz, 66 Hz, 67 Hz, or 68 Hz. Incertain embodiments, the stimulation-entrained gamma oscillations are centered at half of the electrical stimulation frequency of the DBS. For example, if electrical stimulation is applied at 130 Hz, the stimulation-entrained gamma oscillations are centered at 65 Hz.

[0112] Closed-loop therapy can be performed with a neurostimulator used in combination with a neural recording device that records brain electrical activity, wherein electrical stimulation is delivered to the subthalamic nucleus of the brain of the subject when a pattern of neural activity associated with a residual motor symptom to be treated is detected. The parameters for applying the electrical stimulation to the brain may be determined empirically during treatment or may be pre-defined, such as, from a trial study with a subject. For example, stimulation-entrained gamma oscillations associated with the residual motor symptom are recorded from a subthalamic nucleus region and / or a sensorimotor cortex region (e.g., from the cortical precentral gyrus region and / or postcentral gyrus region) of the brain of the subject. Varying stimulation settings may be applied when certain features are detected, including baseline (stimulation off), optimal therapeutic stimulation, modified and ineffective stimulation, and maximum tolerated stimulation to identify personal neural signatures of “a residual motor symptom” and “relief of a residual motor symptom” for a patient, which are used to assist with programming of a DBS device to determine optimal therapeutic stimulation parameters for treatment of a residual motor symptom. The parameters of the electrical stimulation may include one or more of frequency, pulse width / duration, duty cycle, intensity / amplitude, pulse pattern, program duration, program frequency, and the like.

[0113] Frequency refers to the pulses produced per second during stimulation and is stated in units of Hertz (Hz, e.g., 60 Hz = 60 pulses per second). The frequencies of electrical stimulation used in the present methods may vary widely depending on numerous factors and may be determined empirically during treatment of the subject or may be pre-defined. In certain embodiments, the method may involve applying electrical stimulation to the brain at a frequency of 2 Hz - 250 Hz, such as, 25 Hz - 200 Hz, 50 Hz - 250 Hz, 50 Hz -185 Hz, 50 Hz -150 Hz, 75 Hz - 200 Hz, 100 Hz - 200 Hz, 100 Hz - 180 Hz, 100 Hz - 160 Hz, 120 Hz - 150 Hz, or 130 Hz - 140 Hz. In some embodiments, the electrical stimulation to the brain is applied at a frequency of about 120 Hz to about 160 Hz, including any pulse frequency within this range such as 120 Hz, 122 Hz, 124 Hz, 126 Hz, 128 Hz, 130 Hz, 132 Hz, 133 Hz, 134 Hz, 135 Hz, 136 Hz, 137 Hz, 138 Hz, 139 Hz, 140 Hz, 142 Hz, 144 Hz, 146 Hz, 148 Hz, 150 Hz, 152 Hz, 154 Hz, 156 Hz, 158 Hz, or 160 Hz. In some embodiments, non- integer pulse frequencies are used (e.g.135.2 Hz, 135.4 Hz, etc.).

[0114] The electrical stimulation may be applied in pulses such as a uniphasic or a biphasic pulse. The time span of a single pulse is referred to as the pulse width or pulse duration. The pulse width used in the present methods may vary widely depending on numerous factors (e.g., severity of thedisease, status of the patient, and the like) and may be determined empirically or may be pre-defined. In certain embodiments, the method may involve applying an electrical stimulation at a pulse width of about 10 µsec - 500 µsec, for example, 20 µsec -450 µsec, 40 µsec -450 µsec, 60 µsec -450 µsec, 60 µsec -220 µsec, 60 µsec -120 µsec, or 60 µsec -90 µsec. In some embodiments, the electrical stimulation to the brain is applied at a pulse width of about 60 µsec to about 210 µsec, including any pulse width within this range such as 60 µsec, 65 µsec, 70 µsec, 75 µsec, 80 µsec, 85 µsec, 90 µsec, 95 µsec, 100 µsec, 105 µsec, 110v, 115 µsec, 120 µsec, 125 µsec, 130 µsec, 135 µsec, 140 µsec, 145 µsec, 150 µsec, 155 µsec, 160 µsec, 165 µsec, 170 µsec, 175 µsec, 180 µsec, 185 µsec, 190 µsec, 195 µsec, 200 µsec, 205 µsec, 210 µsec, 215 µsec, or 220 µsec.

[0115] The electrical stimulation may be applied for a stimulation period of 0.1 sec-1 month, with periods of rest (i.e., no electrical stimulation) possible in between. In certain cases, the period of electrical stimulation may be 0.1 sec-1 week, 1 sec-1 day, 10 sec-12 hours, 1 min-6 hours, 10 min- 1 hour, and so forth. In certain cases, the period of electrical stimulation may be 1 sec-1 min, 1sec- 30 sec, 1 sec-15 sec, 1 sec-10 sec, 1 sec-6 sec, 1 sec-3 sec, 1 sec-2 sec, or 6 sec-10 sec. The period of rest in between each stimulation period may be 60 sec or less, 30 sec or less, 20 sec or less, or 10 sec. In some embodiments, electrical stimulation may be applied for a year or more, 2 years or more, 3 years or more, 5 years or more, or 10 years or more. In some embodiments, electrical stimulation may be continued indefinitely as part of a long-term DBS therapy regimen.

[0116] The electrical stimulation may be applied with an amplitude of current of 0.1 mA-30 mA, such as, 0.1 mA-25 mA, such as, 0.1 mA-20 mA, 0.1 mA-15 mA, 0.1 mA-10 mA, 0.1 mA-2 mA, 0.1 mA-1 mA, 1 mA-20 mA, 1 mA-10 mA, 2 mA-30 mA, 2 mA-15 mA, 2 mA-10 mA, or 1 mA-3 mA. In some embodiments, the amplitude of current is 0.1 mA-3.5 mA, or any amplitude of current in this range such as 0.1 mA, 0.2 mA, 0.3 mA, 0.4 mA, 0.5 mA, 0.6 mA, 0.7 mA, 0.8 mA, 0.9 mA, 1.0 mA, 1.1 mA, 1.2 mA, 1.3 mA, 1.4 mA, 1.5 mA, 1.6 mA, 1.7 mA, 1.8 mA.1.9 mA, 2.0 mA, 2.1 mA, 2.2 mA, 2.3 mA, 2.4 mA, 2.5 mA, 2.6 mA, 2.7 mA, 2.8 mA, 2.9 mA, 3.0 mA, 3.1 mA, 3.2 mA, 3.3 mA, 3.4 mA, or 3.5 mA.

[0117] The electrical stimulation may be applied with an amplitude of voltage of 0.1 V-15 V, such as, 0.1 V-10 V, 0.1 V-5 V, 1 V-10 V, 1 V-5, V, or 1 V-3.5 V. In some embodiments, the amplitude of voltage is 1 V-3.5 V, or any amplitude of voltage in this range such as 1 V, 1.1 V, 1.2 V, 1.3 V, 1.4 V, 1.5 V, 1.6 V, 1.7 V, 1.8 V, 1.9 V, 2.0 V, 2.1 V, 2.2 V, 2.3 V, 2.4 V, 2.5 V, 2.6 V, 2.7 V, 2.8 V, 2.9 V, 3.0 V, 3.1 V, 3.2 V, 3.3 V, 3.4 V, or 3.5 V.

[0118] The electrical stimulation having the parameters as set forth above may be applied over a program duration of around 1 day or less, such as, 18 hours, 6 hours, 3 hours, 2 hours, 1 hour, 45 minutes, 30 minutes, 20 minutes, 10 minutes, or 5 minutes, or less, e.g., 1 minute - 5 minutes, 2minutes - 10 minutes, 2 minutes - 20 minutes, 2 minutes - 30 minutes, 5 minutes - 10 minutes, 5 minutes - 30 minutes, or 5 minutes - 15 minutes, 10 minutes - 400 minutes, 25 minutes - 300 minutes, 50 minutes - 200 minutes, or 75 minutes - 150 minutes, which period would include the application of pulses and the intervening rest period. The program may be repeated at a desired program frequency to relieve a residual motor symptom in the subject. As such, a treatment regimen may include a program for electrical stimulation at a desired program frequency and program duration. In some embodiments, the treatment regimen is controlled by a control unit in communication with a pulse generator connected to the one or more DBS electrodes in a closed-loop treatment regimen.

[0119] As noted above, the treatment may ameliorate a residual motor symptom suffered by the subject. Amelioration of a residual motor symptom may include decreasing bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance. Assessment of effectiveness of the treatment may be performed using any known method for evaluating motor symptoms. In some embodiments, efficacy of the treatment is evaluated using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale. In some embodiments, the subject is monitored for motor symptoms using a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data to evaluate the effectiveness of the treatment. A wrist-watch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG® is available from PKG Health (San Francisco, CA), which can monitor movement continuously and detect various motor symptoms of a movement disorder such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation.

[0120] In certain cases, effectiveness of treatment may be assessed by detecting brain electrical activity (e.g., stimulation-entrained gamma oscillations) associated with a residual motor symptom, which may be within a subthalamic nucleus region and / or sensorimotor cortex region, or another area. For example, the brain region may be the cortical precentral gyrus region and / or postcentral gyrus region. Detection of brain activity may be performed by functional brain imaging. Functional brain imaging may be carried out by electrical methods such as electroencephalography (EEG), chronic subgaleal recordings, burrhole or cranially mounted neurostimulator electrode recording, electrocorticography (ECoG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), as well as metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). In some embodiments, electrical methods for assessing effectiveness of treatment may involve use of a neural recording electrode as described herein or placement of an additional electrode for measuring electrical signalsat a secondary region of the brain or in the skull, or extracranially. One or more regions of the brain may be implanted with an electrode and electrical signals measured for assessment of effectiveness of the treatment. Any suitable electrodes may be used for measurements and may include one or more surface electrodes (non-brain penetrating electrode(s)) or one or more depth electrodes (brain penetrating electrode(s)) as described herein.

[0121] Assessment of effectiveness of treatment and assessment of amelioration of a residual motor symptom may be performed at any suitable time point after commencement of the treatment procedure, for example, during open-loop or closed-loop therapy or after a treatment regimen is complete. Embodiments of the subject methods include assessing effectiveness of treatment or amelioration of a residual motor symptom within seconds, minutes, hours, or days after the initial treatment regimen has been completed. In some instances, assessment may be performed at multiple time points. In some cases, more than one type of assessment may be performed at the different time points. In some embodiments, brain activity including stimulation-entrained gamma oscillations in a subthalamic nucleus region or a sensorimotor cortex region (e.g., at the cortical precentral gyrus region and / or postcentral gyrus region) may be measured prior to the application of electrical stimulation, and assessing may include comparing the subject’s brain activity after the treatment to that before the treatment and a change in the post-treatment brain activity may indicate successful treatment.

[0122] Upon completion of a treatment regimen, the patient may be assessed for effectiveness of the treatment and the treatment regimen may be repeated, if needed. In certain cases, the treatment regimen may be altered before repeating. For example, one or more of the frequency, pulse width, current amplitude, period of electrical stimulation, program duration, program frequency, and / or placement of DBS or neural recording electrodes may be altered before starting a second treatment regimen.

[0123] Application of the method may include a prior step of selecting a patient for treatment based on need as determined by clinical assessment, which may include assessment of severity of chronic motor symptoms (e.g., motor symptoms lasting at least 3 months), severity of residual motor symptoms after treatment with medication and / or DBS, physical condition, cognitive assessment, anatomical assessment, behavioral assessment and / or neurophysiological assessment. In certain cases, a subject may be further assessed to determine if adaptive deep brain stimulation will completely or partially (e.g., at least 50%) relieve a residual motor symptom. Such a patient may undergo DBS on a temporary trial basis to determine if DBS decreases the severity of a residual motor symptom experienced by the patient. Such a patient may also be implanted with neural recording electrodes to identify personalized neural signatures of “a residual motor symptom” and“relief of a residual motor symptom” to assist with deep brain stimulation programming to determine therapeutic stimulation parameters for the patient and / or evaluate whether DBS therapy will be effective for treating a residual motor symptom of the patient.

[0124] In certain aspects, the methods and systems of the present disclosure may include measurement of brain activity, for example, stimulation-entrained gamma oscillations in a subthalamic nucleus region and / or sensorimotor cortex region, wherein the level of gamma frequency power is measured. In certain cases, stimulation-entrained gamma oscillations may be measured from a plurality of locations in subthalamic nucleus and / or sensorimotor cortex regions and averaged. In some embodiments, electrical activity in the gamma frequency range (such as 60 Hz to 90 Hz) may be measured from a subthalamic nucleus region and / or sensorimotor cortex region of the brain of a subject. In some embodiments, electrical activity in the gamma frequency range (such as 60 Hz to 70 Hz) may be measured from a subthalamic nucleus region and / or sensorimotor cortex region of the brain of a subject. In some cases, data driven approaches are used to identify spectral features that are individualized and different from canonical power bands or from the gamma frequency. In some cases, stimulation-entrained gamma oscillations in one or more locations in the brain may be measured during a period extending from prior to stimulation to the period during which stimulation to the subthalamic nucleus region is applied, or to a period after stimulation to the subthalamic nucleus has been applied, and monitored for an increase of decrease in the power of gamma frequency range (such as 60 Hz to 70 Hz). In some cases, when the power of gamma frequency (such as 60 Hz to 70 Hz) activity is within a normal range (e.g., a range associated with no motor symptoms), the methods and systems do not apply a further stimulation to the brain. Alternatively, when the power of gamma frequency (such as 60 Hz to 70 Hz) activity is not within a normal range (e.g., a range associated with substantial motor symptoms), the methods and systems may apply a further stimulation to the brain. In certain cases, the application of electrical stimulation to the brain may suppress stimulation-entrained gamma oscillations (such as in a range from 60 Hz to 70 Hz) detected at a subthalamic nucleus region and / or sensorimotor cortex region. The decrease may be as compared to the power prior to the application of stimulation. In certain cases, the application of electrical stimulation to the brain may alter other neural features from one more regions of the brain. The alterations may be compared to the state of these features prior to the application of stimulation.

[0125] A closed-loop method allows determination of parameters of electrical stimulation based upon real-time feedback signals from the brain of the subject. Closed-loop methods and systems allow for automation of treatment of the subject including real-time need-based modulation of the treatment regimen. Exemplary closed-loop methods and associated systems for treatment of aresidual motor symptom are further discussed in the Examples section and are depicted in FIGS. 1A-1E, FIG.2, FIGS.7A-7G, and FIGS 12A-12E. Closed-loop methods and systems for automated delivery of electrical stimulation are further described below. Closed-Loop Method for Automated Delivery of Electrical Stimulation

[0126] In certain embodiments, a control algorithm is used to automate the delivery of electrical stimulation to the brain in response to detection of neural activity associated with a residual motor symptom chosen for treatment. According to certain embodiments, the method may include measuring stimulation-entrained gamma oscillations from a subthalamic nucleus region and / or sensorimotor cortex region (e.g., cortical precentral gyrus region or postcentral gyrus region) of the brain of the subject via a neural recording electrode; applying electrical signal metrics to a control algorithm that is tuned to a clinically relevant target (e.g., a range of signal indicative of effective treatment of the residual motor symptom); automatically delivering electrical stimulation to the subthalamic nucleus region of the brain via a DBS electrode in a manner effective to treat the residual motor symptom if the electrical signal metrics indicate that the patient is in need of treatment. For example, stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz from a subthalamic nucleus region and / or sensorimotor cortex region (e.g., cortical precentral gyrus region or postcentral gyrus region) may be measured with a neural recording electrode, wherein the control algorithm receives the electrical activity data from the neural recording electrode and automates delivery of electrical stimulation via a DBS electrode to the brain when the level of gamma frequency (such as 60 Hz to 90 Hz) power indicates that the patient is having the residual motor symptom. In certain embodiments, the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz, 62 Hz to 68 Hz, or 64 Hz to 66 Hz, including any frequency within these ranges such as 62 Hz, 63 Hz, 64 Hz, 65 Hz, 66 Hz, 67 Hz, or 68 Hz. In certain embodiments, the stimulation-entrained gamma oscillations are centered at half of the electrical stimulation frequency of the DBS. In some embodiments, one or more programmed stimulation parameters are modulated according to the algorithm’s control law based on the recorded electrical activity data; and modulated electrical stimulation is delivered to the brain via the DBS electrode in a manner effective to mitigate the residual motor symptom of the subject.

[0127] As described in the foregoing sections, effectiveness of treatment of a residual motor symptom may be assessed by detecting brain electrical activity, including stimulation-entrained gamma oscillations associated with a residual motor symptom using a neural recording electrode. In an open-loop system, stimulation is delivered in a pre-programmed way or manually by a user but is not automatically controlled by real-time neural feedback from the patient’s brain. The electricalactivity may be analyzed by a computing means which may output recommendations based on comparing the electrical activity to a predetermined range. A user may then carry out the recommendations, such as changing a parameter of the electrical stimulation program prior to starting another treatment regimen. In a closed-loop system, by contrast, a computing means can automatically update stimulation parameters based upon analysis of the recorded electrical signal and / or automatically deliver stimulation to the brain according to the electrical stimulation program. In some embodiments, either an open-loop or a closed-loop system may be integrated with a mechanism for user intervention, for example by allowing user-override of open-loop or closed-loop stimulation programs to enact or prevent stimulation that would ordinarily occur, or to manually change parameters of such stimulation.

[0128] In some embodiments, the computing means for directing closed-loop stimulation may be a combination of hardware / software which may be connected wirelessly or by wire to the neural recording electrodes. The computing means may communicate with a control unit (also referred to as a control module) that controls a neurostimulator pulse generator connected to the DBS electrodes. In certain embodiments, the computing means may be connected to a recorder (e.g., a neurophysiological recorder or neural recording device) that records brain activity, including stimulation-entrained gamma oscillations measured by the neural recording electrodes. The computing means may include a control algorithm that determines modification of stimulation parameters based on real-time outputs of the neurophysiological recorder. The algorithm may operate by simple on / off control of stimulation at set parameters, modifying only the on / off parameter with each evaluation cycle, or may determine sophisticated modification of a range of stimulation parameters with each cycle. In some cases, the algorithm may be based on information related to the residual motor symptom, such as, a range of electrical activity (e.g., stimulation-entrained gamma oscillations) that is indicative of the residual motor symptom to be treated with electrical stimulation. The algorithm may also include additional information such as a brain activity profile of a normal subject (not suffering from the motor symptom). Regardless of the particular control algorithm structure, the computing means may be tuned to a clinically relevant target (e.g., a range of signal indicative of effective treatment and / or a range of signal indicative of presence of the motor symptom and the need for treatment) that directs modulation of one or more programmed stimulation parameters according to the algorithm’s control law, applying the modulated electrical stimulation to the subthalamic nucleus region of the brain via the DBS electrode.

[0129] In some cases, the computing means, via a control algorithm, may determine whether the received electrical signals (e.g., stimulation-entrained gamma oscillations) are within or outside a predetermined range of neural signals indicative of the presence of the residual motor symptomtargeted for treatment with electrical stimulation. When the received electrical signals are outside this predetermined range, then the computing means determines that the residual motor symptom is absent. The computing means may then communicate with the control unit to direct stimulation shut-off by the neurostimulator pulse generator. When the received electrical signals are within the predetermined range of neural signals indicative of the targeted motor symptom, then the computing means determines that the subject should be treated with deep brain stimulation. The control algorithm within the computing means may then determine whether the initial step of applying electrical stimulation to the brain should be repeated and / or whether a parameter of the electrical stimulation should be modified prior to the step of applying electrical stimulation when brain activity indicating the presence of the residual motor symptom is detected. The computing means, via the control unit, may then communicate with the control unit to provide the appropriate instructions to the neurostimulator pulse generator.

[0130] In some embodiments, the computing means may determine whether the received electrical signals are within or outside a second predetermined range, where the second predetermined range is indicative of a second residual motor symptom targeted for treatment with electrical stimulation. When the received electrical signals are within the second predetermined range, then the computing means determines that the subject should be treated with deep brain stimulation. The computing means may then communicate with the control unit to direct stimulation switch-off by the pulse generator when the received electrical signals are outside the second predetermined range. The control algorithm within the computing means may then determine whether the initial step of applying electrical stimulation should be repeated and / or whether a parameter of the electrical stimulation modified prior to the step of applying electrical stimulation. The processor may then communicate with the control unit to provide the appropriate instructions to the pulse generator.

[0131] In certain embodiments, stimulation-entrained gamma oscillations at half the stimulation frequency are used as a control signal for a subject undergoing treatment with DBS. In some embodiments, a single threshold is used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms. In other embodiments, two thresholds comprising an upper threshold and a lower threshold are used to control amplitude of the electrical stimulation. The amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold. The amplitude of the electrical stimulation is increased when the control signal is below the lower threshold. The amplitude of the electrical stimulation is not changed when the control signal is between the upperthreshold and the lower threshold. In some embodiments, a dual threshold control policy is used with three stimulation amplitudes, for example, to address three symptom states or provide a more proportional stimulation change in response to the level of the neural biomarker associated with the residual motor symptom (e.g., stimulation-entrained gamma oscillations).

[0132] Thus, in certain aspects, the subject methods operate as a closed-loop control system which may automatically adjust one or more parameters in response to electrical activity from a region of the brain of a subject and / or automatically deliver electrical stimulation to the brain according to the electrical stimulation program. In some embodiments, the closed-loop control system automatically delivers electrical stimulation according to set parameters when the received electrical signals are within a predetermined range indicative of a residual motor symptom targeted for treatment with electrical stimulation. Exemplary closed-loop methods and associated systems are described in the Examples section of the application and are illustrated in FIGS.1A-1E, FIG.2, FIGS.7A-7G, and FIGS 12A-12E.

[0133] In some aspects, the closed loop system may be used to sense a subject’s need for treatment using the methods disclosed herein. For example, the closed loop system may be programmed to monitor brain activity from one or more subthalamic nucleus and / or sensorimotor cortex regions of the brain and compare the brain activity corresponding to a residual motor symptom to a range indicative of the presence of the motor symptom. Upon detection of electrical activity (e.g., stimulation-entrained gamma oscillations) indicative of the residual motor symptom, the closed loop system may automatically commence a treatment protocol of applying electrical stimulation to the brain to target the residual motor symptom.

[0134] In additional aspects, the closed loop system may be used as a system for monitoring brain activity and correlating the brain activity to a residual motor symptom. A wearable monitor can be used to acquire accelerometry and / or surface electromyographic (sEMG) data of the subject to detect various motor symptoms of a movement disorder such as, but not limited to, dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation. Since the closed loop system is configured for recording electrical signals from a subject’s brain, movement of the subject may be monitored in real-time continuously to detect motor symptoms and correlated with the measured electrical signals to provide a biomarker that is related to the subject’s residual motor symptoms. For example, electrical activity (e.g., stimulation-entrained gamma oscillations) measured when a subject is experiencing a residual motor symptom can be used to develop a biomarker, e.g., a range of electrical activity indicative of a residual motor symptom, and so on. As such, closed loop systems are useful for detecting residual motor symptoms and personalized neural biomarkers for an individual.

[0135] It is understood that electrical signals that are indicative of a residual motor symptom or relief of a residual motor symptom for a subject may be recorded from a subject’s brain and may be used in aspects outside of a closed loop system. For example, electrical signals indicative of a residual motor symptom or relief of a residual motor symptom for a subject may be recorded from a subthalamic nucleus region and / or sensorimotor cortex region (e.g., cortical precentral gyrus region or postcentral gyrus region), or other brain region using electrodes or another device operably coupled to the patient’s brain, which electrodes or device may or may not be part of a closed loop system. The patient may be treated as disclosed herein (e.g., by applying electrical stimulation to the brain), and electrical signals (e.g., stimulation-entrained gamma oscillations) may be recorded from a subthalamic nucleus region and / or sensorimotor cortex region, or other region in real time as the treatment is administered or after the treatment is administered. The electric signals recorded after the administration of electrical stimulation is commenced may then be compared to the electric signals recorded prior to the treatment to determine features in the recorded electric signals that change post-treatment. These features provide a feedback signal to indicate whether the treatment is having an effect on the patient’s residual motor symptom. These features can also serve as feedback signals to a closed loop system. These features may include the overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta). In some cases, these features may be patient specific or specific to a particular motor symptom, or both. For example, some of the features may be features found in a plurality of patients having a residual motor symptom; some of the features may be features in a particular patient which may not be found in a significant number of other patients having the motor symptom. In some embodiments, a combination of patient-specific features and motor symptom-specific features may be monitored to assess efficacy of treatment.

[0136] In a particular aspect, the closed loop system and methods provided herein may involve a recording of electrical signals from one or more subthalamic nucleus and / or sensorimotor cortex regions (e.g., cortical precentral gyrus region or postcentral gyrus region, or other region) of a patient’s brain, wherein the patient has a residual motor symptom associated with a movement disorder during or after receiving treatment for the movement disorder with medication, DBS, or a combination thereof. The patient may then be further treated by application of electrical stimulation to the subthalamic nucleus region of the brain, and electrical signals may be recorded from a subthalamic nucleus region and / or sensorimotor cortex region of the brain (e.g., cortical precentral gyrus region or postcentral gyrus region, or other region) and compared to a pre-treatment recording. Features in the recorded signals that change after the electrical stimulation to the subthalamic nucleus region would correspond to biomarkers that indicate whether the treatment is having aneffect. The change in recorded signals can also optionally be correlated to the level of a residual motor symptom reported by the patient after the treatment. The change can be used for modulating the treatment in a closed loop system. For example, when the change in the recorded signal correlates with absence of the residual motor symptom, those features would indicate to a computing means of a closed loop system that further treatment need not be performed.

[0137] In some embodiments, one or more pattern recognition methods can be used in analyzing recorded brain electrical activity data to automate detection of brain activity features such as stimulation-entrained gamma oscillations that are associated with a residual motor symptom. The models and / or algorithms can be provided in machine readable format and may be used to correlate the levels of overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta) with a residual motor symptom to be treated with deep brain electrical stimulation. In some embodiments, the level of gamma frequency (such as 60 Hz to 90 Hz) power is correlated with a residual motor symptom to determine if a patient is treated with electrical stimulation. In some embodiments, the level of gamma frequency (such as 60 Hz to 70 Hz) power is correlated with a residual motor symptom to determine if a patient is treated with electrical stimulation. Alternatively or additionally, coherence within certain spectral frequency bands or other features of network connectivity may be correlated with a residual motor symptom to be treated with electrical stimulation.

[0138] In some embodiments, a computer implemented method for programming a deep brain stimulator to treat a movement disorder in a subject is provided, the computer performing steps comprising: receiving recorded brain electrical signal data from a subthalamic nucleus region and / or a sensorimotor cortex region of the brain of the subject, wherein the brain electrical signal data comprises stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, DBS, or a combination thereof; analyzing the recorded brain electrical signal data using a motor symptom classification model that distinguishes between presence and absence of the residual motor symptom; adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to a control algorithm; and instructing the deep brain stimulator to apply an electrical stimulation to the subthalamic nucleus region of the brain when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected and said analyzing indicates the presence of the residual motor symptom. In certain embodiments, the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz.

[0139] Analyzing the recorded brain electrical activity (e.g., stimulation-entrained gamma oscillations) may comprise the use of an algorithm or classifier. In certain embodiments, a machine learning algorithm is used to generate a motor symptom classification model. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., I Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines (SVM), Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models.

[0140] The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include artificial neural network (recurrent or convoluted), Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP-growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering.

[0141] In some instances, the machine learning algorithms comprise a reinforcement learning algorithm. Examples of reinforcement learning algorithms include, but are not limited to, temporal difference learning, Q-learning and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Pre-processing. In certain embodiments, the motor symptom classification model is trained by analyzing brain electrical signal data recorded over multiple days.

[0142] In certain embodiments, the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrodein the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom.

[0143] In certain embodiments, the control algorithm uses a linear classifier or a binary model for classification to distinguish between the presence and the absence of the residual motor symptom. For example, linear discriminant analysis may be used for classification to distinguish between the presence and the absence of the residual motor symptom. In some embodiments, a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom.

[0144] In certain embodiments, the computer implemented method further comprises: a) ranking predicted stimulation effectiveness for available settings of a DBS device based on classifier scores for stimulation effectiveness of each setting using a linear classification model; b) selecting stimulation settings predicted to have highest stimulation effectiveness based on the linear classification model; c) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness; d) analyzing the recorded brain electrical signal data to evaluate neural response of the subject to the electrical stimulation; e) updating the linear classification model based on the neural response of the subject to the electrical stimulation to generate an updated linear classification model; f) updating the ranking of predicted stimulation effectiveness for the available settings of the DBS device using the updated linear classification model; g) selecting stimulation settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; h) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying the electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; and i) repeating e) - h) to adjust the available settings of the DBS device to optimize stimulation effectiveness. In some embodiments, the linear classification model uses linear discriminant analysis to adjust amplitude of current and frequency of the electrical stimulation.

[0145] In certain embodiments, the computer implemented method further comprises using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject to determine amplitude of the electrical stimulation to apply to the subthalamic nucleus. In some embodiments, a single threshold is used to control amplitude of the electrical stimulation,wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low- dopaminergic symptoms. In other embodiments, two thresholds comprising an upper threshold and a lower threshold are used to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold. In some embodiments, a dual threshold control policy is used with three stimulation amplitudes, for example, to address three symptom states or provide a more proportional stimulation change in response to the level of the neural biomarker.

[0146] In certain embodiments, the computer implemented method further comprises splitting the recorded brain electrical signal data into consecutive time epochs. In some embodiments, each time epoch comprises 0.5 second to 10 minutes of time of the recorded brain electrical signal data, including any amount of time within this range such as 0.5 seconds, 0.75 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, 50 seconds, 55 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, or 10 minutes of time. In certain embodiments, the computer implemented method further comprises assigning a residual motor symptom label (e.g., bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance) to each time epoch.

[0147] In some embodiments, the computer implemented method further comprises training the linear model to classify each time epoch based on whether the motor symptom is present or absent by analyzing the recorded brain electrical signal data using a non-linear model. In some embodiments, canonical gamma power bands are used as feature inputs to train the linear classification model to classify each time epoch as to whether the motor symptom is present or absent using linear discriminant analysis. In some embodiments, field potentials are used as feature inputs to train the linear classification model to classify each time epoch as to whether the motor symptom is present or absent using linear discriminant analysis. In some embodiments, the stimulation-entrained gamma oscillations are in a frequency range of 60 Hz to 70 Hz.

[0148] In certain embodiments, the computer implemented method further comprises using data from a wearable device, worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gammaoscillations. The subject may be monitored using a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data. For example, a wrist-watch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG® is available from PKG Health (San Francisco, CA), which can monitor movement continuously and detect various motor symptoms of a movement disorder such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation.

[0149] In certain embodiments, the computer implemented method further comprises storing a user profile for the subject comprising information regarding the recorded brain electrical signal data including stimulation-entrained gamma oscillations associated with the residual motor symptom. In certain embodiments, the computer implemented method further comprises storing a user profile for the subject comprising information regarding the programmed stimulation parameters used to apply electrical stimulation to the subthalamic nucleus region of the brain of the subject to treat the residual motor symptom in the subject based on the recorded brain electrical signal data.

[0150] The methods described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, a data processing apparatus. The computer readable medium can be a machine- readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or any combination thereof.

[0151] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0152] In a further aspect, the system for performing the computer implemented method, as described, may include a computer containing a processor, a storage component (i.e., memory), a display component, and other components typically present in general purpose computers. The storage component stores information accessible by the processor, including instructions that maybe executed by the processor and data that may be retrieved, manipulated or stored by the processor.

[0153] The processor and / or memory may be operably connected to a display device, for example, via a wired, such as a Universal Serial Bus (USB) connection, or wireless connection, such as a Bluetooth connection. Any convenient display device, such as a liquid crystal display (LCD), light- emitting diode (LED) display, plasma (PDP) display, quantum dot (QLED) display or cathode ray tube display device may be used. The display component may display information regarding whether the residual motor symptom is present or absent, information about brain activity associated with the residual motor protein, current stimulation parameters, or recommended changes to the stimulation parameters.

[0154] The storage component may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write- capable, and read-only memories. The processor may be a general purpose processor, a graphics processor unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein.

[0155] A general purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor can also include primarily analog components. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a graphics processor unit, a mainframe computer, a digital signal processor, a portable computing device, a personal organizer, a device controller, and a computational engine within an appliance, to name a few.

[0156] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module, engine, and associated databases can reside in memory resources such as in RAM memory, FRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of non-transitory computer-readable storage medium, media, or physical computer storage known in the art. An exemplary storage medium can be coupled to the processor such thatthe processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0157] The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions," "steps" and "programs" may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

[0158] Data may be retrieved, stored or modified by the processor in accordance with the instructions. For instance, although the system is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information which is used by a function to calculate the relevant data.

[0159] In certain embodiments, the processor and storage component may comprise multiple processors and storage components that may or may not be stored within the same physical housing. For example, some of the instructions and data may be stored on removable CD-ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a location physically remote from, yet still accessible by, the processor. Similarly, the processor may comprise a collection of processors which may or may not operate in parallel.

[0160] In some embodiments, the method can be performed using a cloud computing system. In these embodiments, the recorded brain electrical signal data from the subthalamic nucleus region and / or sensorimotor cortex region of the brain of the subject and the programming can be exported to a cloud computer, which runs the program, and returns an output to the user.

[0161] Components of systems for carrying out the presently disclosed methods are further described in the examples below. Systems

[0162] The present disclosure also provides systems which find use, e.g., in practicing the subject methods. The system may be an open-loop or closed-loop system configured for performing the methods provided herein. In some embodiments, the system may include a DBS electrode adapted for positioning at a location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region and a neural recording electrode adapted for positioning at a subthalamic nucleus region and / or a sensorimotor cortex region (e.g., cortical precentral gyrus region or postcentral gyrus region) of the brain of the subject to record brain electrical signal data, including stimulation-entrained gamma oscillations before, during, or after electrical stimulation is applied to the brain. In a closed-loop system, the system may also include a computing means and control unit programmed to instruct a DBS electrode to apply electrical stimulation to the subthalamic nucleus region of the brain of the subject in a manner effective to treat a residual motor symptom in the subject when stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the second electrode; analyze the recorded brain electrical signal data, including the stimulation-entrained gamma oscillations using a motor symptom classification model that identifies stimulation-entrained gamma oscillations in the recorded brain electrical signal data associated with the residual motor symptom; c) adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to an algorithm control law; and automatically delivering electrical stimulation to the subthalamic nucleus region of the brain of the subject via the control unit, neurostimulator pulse generator and DBS electrode in a manner effective to treat the residual motor symptom if the electrical signal metrics indicate that the patient is in need of treatment. In some embodiments, one or more programmed stimulation parameters are modulated according to the algorithm’s control law based on the recorded electrical activity data, and modulated electrical stimulation is delivered to the brain via the control unit, pulse generator and DBS electrode in a manner effective to treat a residual motor symptom at a residual motor symptom. The closed loop system may include an on-body pulse generator that is connected to the implanted DBS electrodes and hence can apply electrical stimulation to the brain automatically upon receiving a communication from the control unit or a cranially mounted neurostimulator that can also sense cortical neural signals through electrodes mounted on the case of the device.

[0163] The processor of the closed-loop system may run programming for assessing the effectiveness of treatment and modulate a parameter of the treatment as needed without user intervention. Thus, the closed-loop system may not necessarily include a user interface for a user to instruct the DBS electrode to apply an electrical stimulation to the brain to treat a residual motor symptom in the subject. However, in some embodiments, a user interface may be included in theclosed-loop system which may be used to confirm the recommendation of the closed loop system, or to override it, or to change the recommendation.

[0164] In certain aspects, a control algorithm for the methods and systems of the present disclosure may include steps of comparing an electrical signal from a region of the brain of a subject to a normal or reference electrical signal (e.g., substantially free of the residual motor symptom), wherein when the electrical signal is significantly different from the normal or reference electrical signal, the control algorithm includes steps of directing a device to apply electrical stimulation to the brain of the subject, followed by measurement of electrical signals from the region of the brain and comparing it to a normal or reference electrical signal, wherein when the measured signal is significantly different from a normal or reference electrical signal, the algorithm includes the step of applying another electrical stimulation to the brain.

[0165] In some embodiments, the control algorithm utilizes a machine learning algorithm to analyze inputted brain electrical activity data to automate detection of brain activity features, including stimulation-entrained gamma oscillations associated with the residual motor symptom. The control algorithm then directs a device to apply electrical stimulation to the brain of the subject if the brain activity features indicate the residual motor symptom is present and should be treated with electrical stimulation. For example, a machine learning algorithm may be used to correlate the levels of overall power, or power in specific frequency ranges (e.g., alpha, delta, beta, gamma, and / or theta) with a residual motor symptom that should be treated with deep brain electrical stimulation. In some embodiments, the residual motor symptom is identified by an increase of gamma power in a frequency range of 60 Hz to 90 Hz. In some embodiments, field potential data are fit to a motor symptom classification model to determine how to adjust one or more programmed stimulation parameters. In certain embodiments, the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom. In certain embodiments the algorithm provides updated optimal stimulation setting recommendations to the clinician for guiding programing and decision making.

[0166] In certain embodiments, the system further comprises a user interface comprising an input electronically coupled to a processor for instructing a DBS electrode to apply an electrical stimulation to the subthalamic nucleus region to treat a residual motor symptom in a subject. In some embodiments, the user interface is password protected and is operable by a health care practitioner.

[0167] In some embodiments, the system further comprises a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data of the subject to detect motorsymptoms such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation. Accelerometry and / or sEMG data from a wearable monitor can be combined with brain electrical signal data to assist motor symptom classification.

[0168] Components of systems for carrying out the presently disclosed methods are further described in the examples below. Administration of a Pharmacological Agent

[0169] Embodiments of the methods and systems provided in this disclosure may also include administration of an effective amount of at least one pharmacological agent. By “effective amount” is meant a dosage sufficient to treat a residual motor symptom in a subject as desired. In some embodiments, the residual motor symptom is caused by a movement disorder or a neurological disorder. The effective amount will vary somewhat from subject to subject, and may depend upon factors such as the age and physical condition of the subject, type of movement disorder or neurological disorder causing the residual motor symptom, severity of the residual motor symptom being treated, the duration of the treatment, the nature of any concurrent treatment, the form of the agent, the pharmaceutically acceptable carrier used if any, the route and method of delivery, and analogous factors within the knowledge and expertise of those skilled in the art. Appropriate dosages may be determined in accordance with routine pharmacological procedures known to those skilled in the art, as described in greater detail below.

[0170] If a pharmacological approach is employed in the treatment of a movement disorder or neurological disorder, the specific nature and dosing schedule of the agent will vary depending on the particular nature of the disorder to be treated. Representative pharmacological agents that may find use in treatment of Parkinson’s disease may include, but are not limited to, L-DOPA (l-3,4- dihydroxyphenylalanine, also known as levodopa), carbidopa (N-amino-α-methyl-3-hydroxy-L- tyrosine monohydrate), carbidopa-levodopa (Rytary, Sinemet, Duopa), a dopamine agonist, including, without limitation, pramipexole (Mirapex ER), rotigotine, apomorphine (Apokyn), and amantadine (Gocovri); a monoamine oxidase B (MAO-B) inhibitor, including, without limitation, selegiline (Zelapar), rasagiline (Azilect) and safinamide (Xadago); a catechol O-methyltransferase (COMT) inhibitor, including, without limitation, entacapone (Comtan), opicapone (Ongentys), and Tolcapone (Tasmar); an anticholinergic agent, including, without limitation, benztropine (Cogentin) and trihexyphenidyl; an adenosine receptor antagonist, including, without limitation an A2A receptor antagonist such as istradefylline (Nourianz), or an antipsychotic, including, without limitation, nuplazid (Pimavanserin), or any combination thereof.

[0171] In certain aspects, the administration of a pharmacological agent involves using a pharmacological delivery device such as, but not limited to, pumps (implantable or external devices), epidural injectors, syringes or other injection apparatus, catheter and / or reservoir operatively associated with a catheter, etc. For example, in certain embodiments a delivery device employed to deliver at least one pharmacological agent to a subject may be a pump, syringe, catheter or reservoir operably associated with a connecting device such as a catheter, tubing, or the like. Containers suitable for delivery of at least one pharmacological agent to a pharmacological agent administration device include instruments of containment that may be used to deliver, place, attach, and / or insert the at least one pharmacological agent into the delivery device for administration of the pharmacological agent to a subject and include, but are not limited to, vials, ampules, tubes, capsules, bottles, syringes and bags. Administration of a pharmacological agent may be performed by a user or by a closed loop system. Utility

[0172] The methods and systems of the present disclosure find use in the treatment of a subject, who has a movement disorder, by using adaptive deep brain stimulation. Closed-loop stimulation can be finely targeted and tuned in a personalized manner to provide relief of residual motor symptoms in subjects who have been treated with medication and / or conventional continuous DBS but still have residual motor symptoms.

[0173] In some cases, the subject methods are used to treat a residual motor symptom of a movement disorder such as, but not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, essential tremor, functional movement disorder, Huntington's disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, and Wilson's disease. Symptoms may include, but art not limited to, tremor, involuntary movements, slowness of movement (bradykinesia), rigidity, postural instability, twisting movements, poor balance, irregularity of movements, stumbling, and difficulty with walking. In some cases, a movement disorder is caused by genetic and / or environmental factors, head trauma, infections, inflammation, metabolic disturbances, toxins, adverse reactions to medications, or stressful life events.

[0174] Efficacy of the treatment of patients suffering from a movement disorder may be measured in an art accepted manner such as, by using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale. In some embodiments, assessing effectiveness of the treatment of a residual motorsymptom of a movement disorder comprises monitoring the subject using a wearable monitor that can acquire accelerometry and / or surface electromyographic (sEMG) data. A wrist-watch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG® is available from PKG Health (San Francisco, CA), which can monitor movement continuously and detect various motor symptoms of a movement disorder such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation. Examples of Non-Limiting Aspects of the Disclosure

[0175] Aspects, including embodiments, of the present subject matter described above may be beneficial alone or in combination, with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the disclosure numbered 1-96 are provided below. As will be apparent to those of skill in the art upon reading this disclosure, each of the individually numbered aspects may be used or combined with any of the preceding or following individually numbered aspects. This is intended to provide support for all such combinations of aspects and is not limited to combinations of aspects explicitly provided below. 1. A method for treating a movement disorder in a subject, the method comprising: positioning a stimulation electrode at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; positioning a first neural recording electrode at a second location in the subthalamic nucleus region of the brain of the subject and / or a second neural recording electrode at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; detecting the stimulation-entrained gamma oscillations associated with the residual motor symptom of the subject using the first neural recording electrode and / or the second neural recording electrode; and applying electrical stimulation to the subthalamic nucleus region of the brain of the subject using the stimulation electrode in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode and / or the second neural recording electrode.2. The method of aspect 1, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz. 3. The method of aspect 1 or 2, wherein the stimulation-entrained gamma oscillations are centered at half of an electrical stimulation frequency of the DBS. 4. The method of any one of aspects 1-3, wherein the DBS shifts peak frequency of the gamma oscillations such that the gamma oscillations become entrained to a subharmonic of a stimulation frequency of the DBS. 5. The method of any one of aspects 1-4, wherein the stimulation-entrained gamma oscillations are modulated by the medication and sleep-wake cycles. 6. The method of any one of aspects 1-5, wherein peak frequency of the electrical stimulation is used to predict the gamma frequency of the stimulation-entrained gamma oscillations associated with the residual motor symptom. 7. The method of any one of aspects 1-6, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance. 8. The method of any one of aspects 1-7, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom. 9. The method of any one of aspects 1-8, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS. 10. The method of any one of aspects 1-9, wherein the residual motor symptom is unilateral or bilateral. 11. The method of any one of aspects 1-10, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region.12. The method of any one of aspects 1-11, wherein the movement disorder is Parkinson’s disease. 13. The method of aspect 12, wherein the medication is a dopaminergic medication. 14. The method of aspect 13, wherein the dopaminergic medication is levodopa. 15. The method of any one of aspects 1-14, wherein the electrical stimulation reduces occurrence of the residual motor symptom compared to in absence of the electrical stimulation. 16. The method of any one of aspects 1-15, wherein amplitude of the electrical stimulation is calibrated during the treatment of the subject for both a medication on-state and a medication off- state. 17. The method of any one of aspects 1-16, wherein maximum amplitude of the electrical stimulation is set to avoid inducing another motor symptom or other adverse effect from said applying the electrical stimulation. 18. The method of any one of aspects 1-17, further comprising using stimulation- entrained gamma oscillations at half stimulation frequency as a control signal for the subject. 19. The method of aspect 18, further comprising using a single threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms. 20. The method of aspect 18, further comprising using two thresholds comprising an upper threshold and a lower threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.21. The method of any one of aspects 1-20, wherein the electrical stimulation is applied unilaterally or bilaterally. 22. The method of any one of aspects 1-21, wherein the stimulation-entrained gamma oscillations are measured by recording field potentials. 23. The method of any one of aspects 1-22, further comprising using a control algorithm to automate said applying electrical stimulation when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected. 24. The method of aspect 23, wherein the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between presence and absence of the residual motor symptom. 25. The method of aspect 24, wherein the machine learning algorithm is a supervised machine learning algorithm. 26. The method of aspect 24 or 25, wherein the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom. 27. The method of aspect 24, wherein a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom. 28. The method of any one of aspects 23-27, wherein the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation. 29. The method of any one of aspects 1-28, further comprising using a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gamma oscillations.30. The method of any one of aspects 1-29, wherein the stimulation electrode is placed on a surface of the subthalamic nucleus region. 31. The method of any one of aspects 1-30, wherein the first neural recording electrode is placed within the subthalamic nucleus region. 32. The method of any one of aspects 1-31, wherein the second neural recording electrode is placed within the sensorimotor cortex region. 33. The method of any one of aspects 1-31, wherein the second neural recording electrode is placed in a subdural space over the sensorimotor cortex or under the scalp. 34. The method of any one of aspects 1-33, wherein the stimulation electrode is a non- brain penetrating surface electrode array or a brain-penetrating electrode array. 35. The method of any one of aspects 1-34, wherein the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain- penetrating electrode array. 36. The method of any one of aspects 1-35, wherein the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, subdural electrode, or an electrocorticogram (ECoG) electrode array. 37. The method of aspect 36, wherein the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region. 38. The method of any one of aspects 1-37, further comprising assessing effectiveness of the treatment in the subject. 39. The method of aspect 38, wherein said assessing comprises using behavioral data obtained of the subject.40. The method of aspect 39, wherein the behavioral data is accelerometry data for the subject, video-based pose kinematic data for the subject, or keylogging data from a computer used by the subject, or a combination thereof. 41. The method of any one of aspects 38-40, wherein said assessing comprises using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale. 42. A computer implemented method for programming a deep brain stimulator to treat a movement disorder in a subject, the computer performing steps comprising: receiving recorded brain electrical signal data from a subthalamic nucleus region and / or a sensorimotor cortex region of the brain of the subject, wherein the brain electrical signal data comprises stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; analyzing the recorded brain electrical signal data using a motor symptom classification model that distinguishes between presence and absence of the residual motor symptom; adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to a control algorithm; and instructing the deep brain stimulator to apply an electrical stimulation to the subthalamic nucleus region of the brain when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected and said analyzing indicates the presence of the residual motor symptom. 43. The computer implemented method of aspect 42, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz. 44. The computer implemented method of aspect 42 or 43, wherein the stimulation- entrained gamma oscillations are centered at half of an electrical stimulation frequency of the DBS.45. The computer implemented method of any one of aspects 42-44, wherein the DBS shifts peak frequency of the gamma oscillations such that the gamma oscillations become entrained to a subharmonic of a stimulation frequency of the DBS. 46. The computer implemented method of any one of aspects 42-45, wherein the stimulation-entrained gamma oscillations are modulated by the medication and sleep-wake cycles. 47. The computer implemented method of any one of aspects 42-46, wherein peak frequency of the electrical stimulation is used to predict the gamma frequency of the stimulation- entrained gamma oscillations associated with the residual motor symptom. 48. The computer implemented method of any one of aspects 42-47, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance. 49. The computer implemented method of any one of aspects 42-48, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom. 50. The computer implemented method of any one of aspects 42-49, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS. 51. The computer implemented method of any one of aspects 42-50, wherein the residual motor symptom is unilateral or bilateral. 52. The computer implemented method of any one of aspects 42-51, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region. 53. The computer implemented method of any one of aspects 42-52, wherein the movement disorder is Parkinson’s disease. 54. The computer implemented method of aspect 53, wherein the medication is a dopaminergic medication.55. The computer implemented method of aspect 54, wherein the dopaminergic medication is levodopa. 56. The computer implemented method of any one of aspects 42-55, wherein the electrical stimulation reduces occurrence of the residual motor symptom compared to in absence of the electrical stimulation. 57. The computer implemented method of any one of aspects 42-56, wherein amplitude of the electrical stimulation is calibrated during the treatment of the subject for both a medication on- state and a medication off-state. 58. The computer implemented method of any one of aspects 42-57, wherein maximum amplitude of the electrical stimulation is set to avoid inducing another motor symptom or other adverse effect from said applying the electrical stimulation. 59. The computer implemented method of any one of aspects 42-58, further comprising using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject. 60. The computer implemented method of aspect 59, further comprising using a single threshold to control amplitude of the electrical stimulation, wherein amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms. 61. The computer implemented method of aspect 59, further comprising using two thresholds comprising an upper threshold and a lower threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.62. The computer implemented method of any one of aspects 42-61, wherein the electrical stimulation is applied unilaterally or bilaterally. 63. The computer implemented method of any one of aspects 42-62, wherein the brain electrical signal data comprises field potential data. 64. The computer implemented method of any one of aspects 42-63, wherein the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between the presence and the absence of the residual motor symptom. 65. The computer implemented method of aspect 64, wherein the machine learning algorithm is a supervised machine learning algorithm. 66. The computer implemented method of aspect 64 or 65, wherein the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom. 67. The computer implemented method of aspect 64, wherein a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom. 68. The computer implemented method of any one of aspects 42-67, further comprising using data from a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gamma oscillations. 69. The computer implemented method of any one of aspects 42-68, wherein the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation.70. The computer implemented computer implemented method of any one of aspects 42- 69, further comprising: a) ranking predicted stimulation effectiveness for available settings of a DBS device based on classifier scores for stimulation effectiveness of each setting using a linear classification model; b) selecting stimulation settings predicted to have highest stimulation effectiveness based on the linear classification model; c) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness; d) analyzing the recorded brain electrical signal data to evaluate neural response of the subject to the electrical stimulation; e) updating the linear classification model based on the neural response of the subject to the electrical stimulation to generate an updated linear classification model; f) updating the ranking of predicted stimulation effectiveness for the available settings of the DBS device using the updated linear classification model; g) selecting stimulation settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; h) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying the electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; and i) repeating e) - h) to adjust the available settings of the DBS device to optimize stimulation effectiveness. 71. The computer implemented method of aspect 70, wherein the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation. 72. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the method of any one of aspects 42-71.73. A kit comprising the non-transitory computer-readable medium of aspect 72 and instructions for treating a movement disorder. 74. A system for treating a movement disorder in a subject, the system comprising: a stimulation electrode adapted for positioning at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; a first neural recording electrode adapted for positioning at a second location in the subthalamic nucleus region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; a second neural recording electrode adapted for positioning at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with the residual motor symptom of the subject; and a processor programmed according to the computer implemented method of any one of aspects 42-71 to instruct the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region of the brain of the subject in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode or the second neural recording electrode and the motor symptom classification model indicates the presence of the residual motor symptom. 75. The system of aspect 74, wherein the brain electrical signal data comprises field potential data. 76. The system of aspect 74 or 75, further comprising a wearable device to monitor the residual motor symptom. 77. The system of any one of aspects 74-76, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz.78. The system of any one of aspects 74-77, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance. 79. The system of any one of aspects 74-78, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom. 80. The system of any one of aspects 74-79, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS. 81. The system of any one of aspects 74-80, wherein the residual motor symptom is unilateral or bilateral. 82. The system of any one of aspects 74-81, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region. 83. The system of any one of aspects 74-82, wherein the movement disorder is Parkinson’s disease. 84. The system of any one of aspects 74-83, wherein the medication is a dopaminergic medication. 85. The system of any one of aspects 74-84, wherein the dopaminergic medication is levodopa. 86. The system of any one of aspects 74-85, further comprising the medication. 87. The system of any one of aspects 74-86, wherein the stimulation electrode is adapted for positioning on a surface of the subthalamic nucleus region. 88. The system of any one of aspects 74-87, wherein the first neural recording electrode is adapted for positioning within the subthalamic nucleus region.89. The system of any one of aspects 74-88, wherein the second neural recording electrode is adapted for positioning within the sensorimotor cortex region. 90. The system of any one of aspects 74-89, wherein the second neural recording electrode is adapted for positioning in a subdural space over the sensorimotor cortex or under the scalp. 91. The system of any one of aspects 74-90, wherein the stimulation electrode is a non- brain penetrating surface electrode array or a brain-penetrating electrode array. 92. The system of any one of aspects 74-91, wherein the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain- penetrating electrode array. 93. The system of any one of aspects 74-92, wherein the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, a subdural electrode, or an electrocorticogram (ECoG) electrode array. 94. The system of aspect 93, wherein the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region. 95. The system of any one of aspects 74-94, wherein the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region to treat the residual motor symptom in the subject. 96. The system of aspect 95, wherein the user interface is password protected and is operable by a health care practitioner.

[0176] It will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.EXPERIMENTAL

[0177] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.

[0178] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.

[0179] The present invention has been described in terms of particular embodiments found or proposed by the present inventor to comprise preferred modes for the practice of the invention. It will be appreciated by those of skill in the art that, in light of the present disclosure, numerous modifications and changes can be made in the particular embodiments exemplified without departing from the intended scope of the invention. For example, due to codon redundancy, changes can be made in the underlying DNA sequence without affecting the protein sequence. Moreover, due to biological functional equivalency considerations, changes can be made in protein structure without affecting the biological action in kind or amount. All such modifications are intended to be included within the scope of the appended claims. Example 1 Chronic adaptive deep brain stimulation optimized with personalized neural signals is superior to conventional stimulation in Parkinson's disease Introduction

[0180] Deep brain stimulation (DBS) is a standard therapy for advanced movement disorders and is under investigation for several neuropsychiatric conditions1. Conventional DBS therapy is delivered with constant stimulation parameters (cDBS), unresponsive to patient activities or to variations in severity of symptoms during daily life. Thus, there is significant interest in adaptive DBS (aDBS) that uses real-time detection of neural signals to automatically adjust stimulation amplitude or other parameters in response to patients’ needs2,3. Fully implantable bidirectional neural interfaces, whichcan sense neural activity during stimulation and have circuitry to implement feedback control, have recently become available for investigational4,5and commercial6use. This development has catalyzed work in chronic invasive brain sensing7and now offers the technical capability to provide chronic adaptive neurostimulation8,9. However, several barriers have impeded its clinical implementation10,11, including the limited understanding of neural signatures of specific symptoms in brain disorders treatable by DBS, technical complexity of sensing brain signals during ongoing electrical stimulation12, and lack of standardized algorithms for optimizing feedback control in the setting of a large parameter space.

[0181] Parkinson’s disease (PD) is a highly prevalent neurodegenerative disease and affects ~1% of people aged 60 years or older in high-income countries13. Stimulation of the subthalamic nucleus (STN) via cDBS is widely used and supported by extensive class I evidence14-17. However, even after optimization of stimulation parameters by an expert clinician, cDBS can be associated with periods of under- and over-stimulation reflected in residual fluctuations between hypo- and hyperkinetic motor signs, such as bradykinesia and dyskinesia16,18,19. This suggests individuals with PD could further benefit from aDBS. STN local field potential oscillations in a predefined beta band (13-30 Hz) are often proposed for adaptive (closed-loop) control in PD2,20,21based on the observation that resting subthalamic beta activity is elevated in the rigid / akinetic state and reduced when motor signs are alleviated by dopaminergic medication22or neurostimulation23. Brief studies of invasive neurophysiological control signals, often using externalized brain leads or distributed control through external computers, have demonstrated that aDBS in PD can match or exceed the benefit provided by cDBS24-27. However, these results were derived from in-laboratory studies and group-level analyses, lacking individualized, data-driven approaches. Furthermore, previous studies compared the effects of aDBS to cDBS days after initial DBS lead implantation, such that cDBS stimulation parameters had not undergone standard-of-care optimization that takes months to complete28. Therefore, it remains unclear whether aDBS provides improvement beyond that of clinically optimized cDBS in real-life naturalistic settings. Finally, previous studies identified STN beta oscillations as control signals in the absence of stimulation24-26. Motor cortical signals have also shown promise in encoding motor state and controlling aDBS9,29,30, yet the effect of stimulation amplitude on both proposed control signals has not been systematically assessed. Since electrical stimulation profoundly alters oscillatory activity in the motor network23,31, it is crucial to define neural biomarkers that can still be measured and tracked during stimulation at therapeutic amplitudes, a scenario which is to date under-explored.

[0182] Here, we developed a data-driven analysis pipeline to characterize individualized neural biomarkers of PD symptoms and engineered personalized aDBS algorithms that did not pre-selectSTN beta or other frequency bands. Our pipeline identified stimulation-entrained gamma oscillations (65-70 Hz)32,33either in STN or sensorimotor cortex as the optimal biomarker of residual fluctuations in motor function that were still robust during varying stimulation amplitudes. In a blinded, randomized study across one month per condition during normal daily life, we demonstrate for the first time that adaptive stimulation reduces the time spent with bothersome motor symptoms compared to clinically optimized constant-amplitude stimulation. Results

[0183] We recruited four patients with PD from a population undergoing DBS implantation for motor fluctuations. All patients underwent bilateral placement of quadripolar DBS leads into the STN and quadripolar paddles into the subdural space over sensorimotor cortex (FIGS.1A-1C, FIG.7). Cortical recordings were performed using non-overlapping bipolar pairs with the anterior montage having at least one electrode covering the precentral gyrus and the posterior montage having at least one electrode on the postcentral gyrus (FIG. 1C, FIGS. 7A-7H). Leads were connected to an investigational bidirectional neural interface (Medtronic Summit RC+S). This device is capable of chronically streaming high-resolution time domain data in naturalistic settings while providing therapeutic stimulation and can perform aDBS using fully embedded algorithms5,9. We developed adaptive algorithms tailored to the specific clinical needs of each patient. Before patients began the aDBS trial, standard of care cDBS was optimized by a movement disorders specialist over a range of 10-31 months (mean ± standard deviation 19 ± 10), including at-home self-optimization by the patients. Each patient identified their most bothersome motor symptom persisting on clinically optimized cDBS regardless of symptom type, which could be one resulting from fluctuations in the levodopa medication cycle or from a stimulation-induced adverse effect (FIG.1D; FIG.2). In addition, we defined an opposite motor state symptom as the one most at risk of being negatively affected by therapy modifications targeting the most bothersome symptom (for example, if bradykinesia was most bothersome, dyskinesia might be the “opposite” motor symptom). This was done to ensure that aDBS did not improve the most bothersome symptoms at the expense of exacerbating other symptoms. For two patients, bothersome residual motor fluctuations on cDBS were restricted to one side of the body (pat-1, pat-4). The remaining two patients perceived persisting bilateral symptoms (pat-2, pat-3). To identify the optimal stimulation limits for symptom control during aDBS, we determined the high and low stimulation amplitudes needed to address the patients’ low and high dopaminergic symptoms, respectively (FIG. 2). As stimulation impacts neural activity within the stimulated networks, we identified neural biomarkers during active stimulation.Motor network gamma oscillations identified as optimal control signals

[0184] Establishing an aDBS algorithm involves both selection of a control signal (neural biomarker) and setting a control policy using that signal. Our workflow consisted of seven formalized steps (FIG. 2). First, patients streamed neural data in-clinic and at-home during active stimulation. We then utilized a data-driven automated pipeline to search the frequency space of field potentials in both STN and sensorimotor cortex for physiological signals that optimally predicted the occurrence of each patient’s most bothersome motor signs without a priori assumptions regarding relevant frequency bands or brain regions. Our approach employed a combination of non-parametric cluster- based permutation analysis and machine learning methods, the former for predicting medication states in clinical settings and the latter for symptom fluctuations in home environments. These methods converged on the same physiological signatures. Thereafter, we designed adaptive algorithms and optimized aDBS parameters for each patient during steps 5 and 6 (FIG.2), employing a pipeline that was standardized but not automated. This process was guided by the observed effects of parameter adjustments on symptoms and neural signals in each individual (for detailed initial and final algorithm parameters, see Methods, FIG. 12). Finally, we performed blinded, randomized crossover comparisons between cDBS and aDBS, cumulatively applied for at least one month per condition in patients’ naturalistic environments. Converging evidence from in-clinic and at-home recordings demonstrated that stimulation-entrained gamma oscillations33centered at half the stimulation rate performed optimally as a predictor of medication-related symptom state. In the setting of subthalamic stimulation above a certain amplitude, the frequency of levodopa-induced finely-tuned gamma oscillations within their typical 60-90 Hz range often shifts to a subharmonic of stimulation frequency, behaving as a “driven oscillator” (FIG. 3A)33. Despite the potential susceptibility to artifacts when sensing neural signals during active stimulation, we demonstrated that stimulation-entrained gamma oscillations were not artifactual, as they represented the entrainment of levodopa-induced narrowband gamma oscillations that were observed off stimulation (FIG.3A)29,33.

[0185] Non-parametric statistics and machine learning methods showed that stimulation-entrained gamma oscillations were prominent during high-dopamine states (FIGS.3-4, FIG.8A, FIGS.9A-9B), were reduced by 33-96% (median ± standard deviation 75±26%) when these states ended (as antiparkinsonian medications wore off), and could track symptom changes over the full range of stimulation amplitudes to be applied during aDBS (FIG.8A, FIGS.9A-9B).

[0186] Stimulation-entrained gamma activity was not limited to times with dyskinesia, but was consistently enhanced after levodopa intake (i.e., the high-dopamine state; FIGS.3D-3E)32. Similar to levodopa-induced finely-tuned gamma oscillations previously described in the absence ofstimulation29,34, entrained gamma power fluctuated with medication state in-clinic (FIGS. 3B-3C, FIGS. 4A-4B, FIG. 8A) and at-home while stimulation remained unchanged (FIGS. 3D-3E), and predicted symptoms associated with the high dopaminergic state (FIGS.4C-4D, FIGS.9A-9B).

[0187] In three of four patients, cortical entrained gamma oscillations outperformed other frequency bands across brain regions, including STN beta activity, in predicting medication and symptom states and were thus selected as the control signal (FIGS. 4A-4D). Subthalamic beta activity is often proposed as an optimal control signal for aDBS, and consistent with the literature, we did observe beta band peaks in the STN local field potential power spectrum (off stimulation and off medication) in five of six hemispheres (FIG. 8C). However, medication-related fluctuations in subcortical beta spectral power diminished when under active stimulation (FIG.8D) and were only significant in two hemispheres (FIG. 4A, FIG. 8B). Further, beta spectral power did not reliably track symptom fluctuations during chronic stimulation in the home environment with the exception of one hemisphere (FIGS.4C-4D, FIGS.9C-9D). Using STN beta activity in conjunction with subthalamic or cortical stimulation-entrained gamma activity in a linear model resulted in only a minimal change in symptom prediction accuracy (FIG. 4E, AUC increased from 0.74±0.07 to 0.75±0.07, mean ± standard deviation) and we were unable to show a significant difference between adding STN beta versus alternative random frequency bands for any subject (range of p-values: 0.08-0.82). Adaptive stimulation algorithm tracked residual motor fluctuations

[0188] During aDBS, we used STN or cortical stimulation-entrained gamma activity at half stimulation frequency as the control signal for all patients (FIG.5A). Stimulation-entrained gamma oscillations represented high-dopaminergic states; therefore, we designed aDBS algorithms that reduced stimulation amplitude when the control signal was high to either avoid hyperkinetic symptoms such as dyskinesia (pat-1, pat-3, pat-4) or relieve stimulation-induced side effects such as dysarthria (pat-2, FIGS.5B-5C). Conversely, when stimulation-entrained gamma activity was low, the control algorithms increased stimulation amplitude to the level required to mitigate low- dopaminergic symptoms. High aDBS amplitudes were on average 0.55±0.43 mA higher and low amplitudes 0.57±0.38 mA below cDBS amplitudes across hemispheres. The algorithms acted on a timescale of minutes to hours, consistent with established carbidopa-levodopa pharmacokinetics35(FIGS.5D-5E). Patient 3 received recurring short epochs of low stimulation amplitudes on aDBS due to brief but frequent bouts of dyskinesia. During awake hours, aDBS increased total electrical energy delivered (TEED) in 5 out of 6 hemispheres, allowing patients to benefit from increased stimulation delivery when it was needed (FIG. 5F, Extended Data Table 1). Across patients, the majority of nighttime was spent at the high stimulation amplitude (96.0±2.4%), which reflected suppression ofstimulation-entrained gamma activity during sleep and thereby resulted in greater nighttime TEED compared to cDBS (38.8±32.0% increase from cDBS; FIGS.13A-13B, Table 1). The alternative use of a biomarker representing low-dopaminergic states (e.g., STN beta oscillations), for which stimulation amplitude is high when the biomarker is high, would likely result in lower stimulation amplitude during sleep, since sleep usually suppresses beta oscillations36. Adaptive stimulation improved motor symptoms that persisted on clinically optimized conventional stimulation

[0189] We compared aDBS to clinically optimized cDBS for a cumulative period of one month per condition. Stimulation conditions were applied blindly in randomized brief blocks of several days and assessed using patient ratings of daily symptoms via a digitalized questionnaire. Patients switched stimulation conditions at home through a button press on their study tablet at prospectively agreed times, accommodating patients’ work and travel schedules. aDBS algorithms were tailored to each patient’s reported most bothersome residual symptom, and resulted in improvement in patient- specific motor symptoms (FIGS.6D-6G). A group-level linear mixed effects model demonstrated an improvement in the percentage of awake-time experiencing the most bothersome symptom during aDBS compared to optimized cDBS (β=-16.3±4.4%, p<0.001), without worsening the percentage of awake-time with the opposite symptom (β=-2.5±2.2%, p=0.26, Table 1). Furthermore, aDBS increased patients’ quality of life (EQ-5D37, β=6.9±1.7, p<0.001). The effects on reported bothersome / opposite symptoms were stable over time (both β estimates for main time effect ≤0.05%, p>0.17; both β estimates for time-stimulation condition interaction ≤0.04%, p>0.69). Over the course of the trial, quality of life tended to slightly decrease (β=-0.06, p=0.06), which was independent of the stimulation condition (time-stimulation condition interaction: β=-0.02±0.04, p=0.67).

[0190] Within-subject analyses confirmed these results. In each patient, aDBS reduced the time spent with bothersome motor symptoms compared to optimized cDBS (FIG.6A). This improvement did not occur at the expense of the opposite symptom, which was unaffected or improved in each subject (FIG.6B). Additionally, aDBS was associated with improved quality of life reports in three patients (FIG. 6D). Quality of life metrics for patient 3 may have exhibited a ceiling effect, as this patient reported high baseline quality of life metrics and low variability (cDBS EQ-5D range: [85, 90] vs. aDBS EQ-5D range: all responses 90 on a scale 0-100) despite reporting bothersome levels of residual bradykinesia on cDBS.

[0191] Additional assessments corroborating these findings included self-reported symptom severity (which was rated separately from duration) and motor fluctuation tracking with validated wearables38. Severity of the most bothersome symptoms also decreased during aDBS (FIG.14A; Patient 3 didnot record ratings within the instructed range of 1-10 and data are therefore not reported). The opposite symptom severity was unchanged (FIG.14B). Objective metrics confirmed the reduction in motor fluctuations in the three patients with upper limb symptoms, for whom bothersome symptoms could be tracked with wrist-watch style wearable monitors (FIGS.6H-6I). Adaptive DBS decreased the degree of motor fluctuations throughout the day as measured by the difference between symptom severity during low- versus high-dopaminergic states.

[0192] Furthermore, aDBS did not adversely affect any additionally monitored motor or non-motor symptoms (depression, anxiety, apathy, impulsivity, sleep; FIGS.6D-6G and FIG.14). For patient 4, who did not adhere to a strict medication regimen during the study, aDBS resulted in a lower levodopa equivalent daily dose (cDBS: 864±91 mg vs. aDBS: 802±57 mg, p=0.02). Throughout the trial, patients 1, 2 and 4 correctly inferred the blinded stimulation condition at a rate significantly greater than chance (pat-1: 62.3%, p=0.049, pat-2: 76.1%, p<0.001, pat-4: 68.3%, p=0.001). The reason for correctly identifying the stimulation condition was never attributed to unusual sensations, but to the perceived positive effects on motor symptoms. Discussion

[0193] We developed a data-driven pipeline for the implementation of adaptive DBS that utilized subthalamic or cortical field potentials to auto-adjust stimulation amplitudes in order to alleviate residual motor fluctuations in four individuals (6 independent hemispheres) with Parkinson’s disease. Patients were previously fully clinically optimized on conventional DBS. Adaptive algorithms were tailored to each patient's most bothersome motor symptom regardless of symptom type. Our pipeline was naïve to the spectral-frequency components and detection sites of neural signal biomarkers and revealed that, in all patients, stimulation-entrained gamma oscillations performed optimally for detecting bothersome residual motor signs. In a blinded, randomized study, we demonstrate for the first time that aDBS improved motor signs and quality of life in PD during normal daily activities at home compared to standard-of-care cDBS. Varying time scales for adaptive DBS

[0194] Adaptive neurostimulation could operate on a variety of timescales depending on the desired effects on neural circuits39. The original description of aDBS in PD, based on perioperative testing using externalized leads, operated on a subsecond timescale with the goal of shortening pathologically prolonged bursts of subthalamic beta activity24,27. Alternatively, prolonged time scales spanning weeks to months may be appropriate in some neurological or psychiatric conditions where fluctuations in network activity underlying symptoms are slow40. Here, aDBS always delivered anonzero level of stimulation and operated on an intermediate timescale of minutes to hours, consistent with the time course of levodopa-carbidopa pharmacokinetics. aDBS is unlike contingent neurostimulation-common for paroxysmal disorders-in which a neural signal triggers a brief, pre- programmed epoch of stimulation, and sensing and stimulation are not concurrent41,42.

[0195] Although conventional DBS successfully reduces the motor fluctuations of advanced PD, patients typically still require a combination of antiparkinsonian medications (albeit reduced) and stimulation for best function43. Because the effective brain concentration of levodopa has peaks and valleys, under constant stimulation patients may continue to experience symptoms that come with high- and / or low- dopaminergic states. While less severe than before device implantation, these residual fluctuations were bothersome in our study patients. aDBS therefore allowed seamless integration of medication and stimulation therapy by providing less stimulation when medication was active, associated with high-dopaminergic states, and more when medications wore off. These algorithms may be particularly well suited for patients still requiring moderate medication doses on cDBS, for instance, to maintain positive effects on mood, or to optimally treat gait disorder. Optimal frequency bands for adaptive control

[0196] We identified stimulation-entrained gamma oscillations in the STN and cortex as optimal markers of residual motor signs in all four patients. Finely-tuned gamma oscillations in the off- stimulation state were first identified in the subthalamic nucleus as a marker of the high dopaminergic state44, and much later were found in the motor cortex in rodent models of parkinsonism45and in humans29, where they are especially prominent during levodopa-induced dyskinesia34. A remarkable aspect of gamma oscillations as an aDBS control signal is that, when stimulation is turned on, gamma peak frequency shifts away from its “natural” frequency to become entrained to the nearest subharmonic of stimulation frequency, typically in a 1:2 entrainment pattern29,33(FIG.3A). Thus, the peak frequency is highly predictable—a useful property when choosing the frequency band to be utilized for adaptive control. While a signal appearing at an exact subharmonic of stimulation frequency may at first glance appear to be an electrical artifact, there are now multiple arguments against an artifactual origin. Stimulation-entrained gamma oscillations are strongly modulated by the physiological state of the brain, including dopaminergic medication cycles (FIGS.3B-3E) and sleep- wake cycles (FIG.13). They are often more prominent at a site distant from the stimulating contact (cortex) than adjacent to it (STN), have a specific topography (greater in precentral than postcentral gyrus), and, when stimulation is turned off, can require additional time to “wash out”33,46. These findings indicate a physiological origin. Although the amplitude of levodopa-induced finely-tuned gamma oscillations in the off-stimulation state has been shown to scale with the severity ofdyskinesias34, we found sustained increases in stimulation-entrained gamma oscillations throughout the medication on-state, not just during dyskinesia. Thus, we suspect that, when levodopa-induced gamma oscillations become entrained by stimulation, they are less likely to produce dyskinesia.

[0197] Most previous in-laboratory studies of aDBS in PD employed the spectral power of subthalamic beta oscillations as a feedback signal24,25,47. The choice of STN beta band activity as a feedback signal was driven by physiology studies performed in the absence of stimulation48. However, in our cohort, spectral power of STN beta oscillations did not track residual motor fluctuations in the home environment in five of six hemispheres (FIGS. 9C-9D). Therapeutic stimulation did reduce beta activity, similar to previous reports23,49,50, even at low-therapeutic stimulation levels (FIG.8D). Thus, beta band activity did not track residual motor signs within the range of stimulation amplitudes relevant for adaptive control. A critical element of our aDBS development pipeline was to evaluate the relation of oscillatory activity to bothersome motor signs over the full range of stimulation amplitudes to be used in adaptive control, rather than in the off- stimulation state. In the high-dopaminergic state, reliable entrainment of finely tuned gamma oscillations occurred within the range of therapeutic stimulation amplitudes used during adaptive implementation.

[0198] Of note, some previous studies using beta band spectral power as a control signal employed fast aDBS algorithms to shorten prolonged bursts of beta activity rather than tracking residual motor signs24,27. This required multiple stimulation amplitude changes on a subsecond time scale, using externalized leads, and is technically challenging to implement on a fully embedded system with concurrent sensing and stimulation12. A burst-trimming algorithm may prove optimal for chronic at- home aDBS if future devices are designed for very fast (subsecond) time scales. Here, subthalamic beta-band detection was also limited to a specific bipolar montage utilizing recording contacts immediately adjacent to the active stimulating contact. Other bidirectional neural interfaces that allow for multiple recording montages may be better optimized for subthalamic nucleus beta-band detection21. Cortical versus subcortical signals for adaptive DBS

[0199] Our results highlight the utility of multi-site brain recordings for aDBS algorithms. Cortical recordings did prove invaluable for three out of six hemispheres that did not have adequate neural biomarkers in the STN. Subcortical signals may be insufficient for aDBS in certain scenarios, such as when the signal is excessively contaminated by stimulation artifacts. However, we have not proven that cortical recordings are critical for aDBS in PD. To achieve high signal-to-noise recordings, we placed cortical leads in the subdural space directly on the brain surface. Commerciallyavailable neurostimulation devices can connect to two multi-contact leads, allowing the use of a stimulating lead and an additional sensing lead, which could be placed cortically, connected to the same pulse generator. Cortical sensing remains investigational, and subdural electrophysiological recordings have the disadvantage of adding invasiveness to the surgery and committing the patient to hardware that is not easy to remove. In future studies, cortical control signals might be obtained less invasively from electrodes placed under the scalp51. Adaptive DBS outperforms conventional DBS in real life

[0200] Most prior studies comparing cDBS and aDBS for the treatment of PD motor symptoms have been limited to highly-controlled in-clinic or laboratory settings24-26,30,52. Many were performed peri- operatively with limited spontaneous movement and with externalized leads24,26,52, which differs greatly from the home environment with a fully implanted stimulator and decoder. Perioperative experimentation also precludes the optimization of cDBS parameters by an expert neurologist, which usually takes several months28, and therefore does not represent a rigorous comparison to standard of care. We recently described a case report suggesting benefit of home-based embedded aDBS9, but the study was restricted to four days in one unblinded subject.

[0201] Here, we addressed these limitations and implemented aDBS algorithms in naturalistic settings for one month during patients' routine daily activities, including work, travel, and sleep. The algorithm was tested repeatedly in short blocks of several days to minimize systematic confounds from situational influences on patient ratings. We personalized each algorithm to the patients’ specific clinical needs and compared aDBS to clinically-optimized standard of care cDBS. This study is the first to systematically assess real-life adaptive stimulation for PD in a naturalistic context.

[0202] The additional improvement in PD motor signs using aDBS was achieved by delivering more total electrical energy compared to cDBS in all patients. These systems therefore delivered greater stimulation during the low-dopaminergic periods (e.g., with bradykinesia) than would be tolerated as the constant stimulation amplitude setting during cDBS. These results differ from most laboratory- based studies of aDBS whose goal has been reducing total electrical energy24,26. The recent commercialization of rechargeable pulse generators diminishes the clinical importance of conserving electrical energy. Summary

[0203] We demonstrate for the first time the benefit of aDBS for improving residual motor signs that persist in the setting of clinically optimized standard-of-care cDBS. aDBS shortened the duration of patients’ most bothersome motor signs without aggravating other motor and non-motor symptoms,and improved patients’ quality of life. For aDBS algorithms designed to reduce residual motor fluctuations, we identified stimulation-entrained gamma oscillations, in STN or motor cortex, as optimal control signals. The results were achieved by employing data-driven neural biomarker identification, controlling for independent effects of stimulation amplitude. The study supports clinically impactful aDBS in PD, highlights the benefit of multi-site brain recordings and at-home neural recordings paired with wearable monitors for configuring aDBS, and may inform the development of aDBS for other neuropsychiatric conditions. Methods Patient evaluation and DBS device Patients

[0204] We recruited four patients with PD from a population undergoing DBS implantation for motor fluctuations (male, age range: 47-68 years, disease duration: 10-15 years, pre-surgery off- medication Movement Disorder Society Unified Parkinson's Disease Rating Scale [MDS-UPDRS]-III scores: 30-49). A movement disorders neurologist evaluated and confirmed the diagnosis of PD based on established diagnostic criteria and a neuropsychologist excluded significant cognitive impairment or untreated mood disorders. The inclusion criteria for DBS surgery were: Motor fluctuations characterized by prominent rigidity and bradykinesia in the off-medication state, baseline off-medication MDS-UPDRS-III scores between 20 and 80, a greater than 30% improvement in MDS-UPDRS-III scores with medication compared to the off-medication state, and the absence of significant cognitive impairment (Montreal Cognitive Assessment score of 20 or above). We excluded patients whose primary indication for surgery was medically refractory tremor. Patients provided written consent in accordance with the Declaration of Helsinki. The Institutional Review Board of the University of California San Francisco gave ethical approval for this work. The study was registered on ClinicalTrials.gov (NCT03582891). The study protocol and the IDE application (G180097) are available through the Open Mind initiative (openmind-consortium.github.io). Surgical procedure and DBS device

[0205] All patients underwent bilateral placement of cylindrical quadripolar deep brain stimulator leads (Medtronic model 3389) into the STN and bilateral quadripolar paddles (Medtronic model 0913025) into the subdural space over the sensorimotor cortex (FIGS.1B-1C, FIG. 7). The leads were connected to an investigational sensing-enabled implantable pulse generator (MedtronicSummit RC+S model B35300R) that was placed in a pocket over the pectoralis muscle bilaterally so that each pulse generator was connected only to ipsilateral leads. STN leads were initialized as contacts 0 to 3 (0 was the deepest contact), and cortical leads were initialized as contacts 8 to 11 (8 was the most posterior contact). Two months after surgery, the locations of the leads were verified using postoperative computed tomography (CT) scans. A more detailed account of the surgical implantation can be found in a previous publication9.

[0206] Summit RC+S is an investigational rechargeable bidirectional neural interface. It is capable of streaming four bipolar time domain channels simultaneously while providing standard therapeutic stimulation on up to two quadripolar leads. It can also perform aDBS using fully embedded algorithms. The applications for sensing and aDBS were written in our laboratory in the device’s application programming interface, comply with FDA regulations for medical device software (CFR 820.30) and are available at openmind-consortium.github.io. Each RC+S system employs radiofrequency telemetry to establish wireless communication with an external compact relay device, which in turn transmits data to a Windows-based tablet using Bluetooth technology within a range of up to 12 m. This setup facilitates the capture of local field potentials and electrocorticography from a maximum of four bipolar electrode pairs, enabling continuous sensing during stimulation for up to 30 hours before requiring a recharge. The tablet is situated at patients’ homes and hosts custom software that allows for remote adjustment of streaming parameters and embedded adaptive DBS algorithms via an interface only accessible to researchers. Patients are able to initiate and stop streaming and report both medication intake and motor symptoms using a separate patient user interface. Further details on device characteristics are outlined in previous publications5,9,12. Lead Reconstruction

[0207] Electrode positions were reconstructed by linearly coregistering postoperative CT images to preoperative T1-weighted 3T magnetic resonance imaging (MRI) scans through rigid Euclidean transformation (FIGS.7A-7D). The LeGUI toolbox (Version 1.2)55was used to perform automated correction for brain shifts56and to localize electrodes to the MRI-rendered pial surfaces (FIGS.7E- 7H). Depth lead positions were reconstructed using the Lead-DBS toolbox (Version 2.6)57and, when necessary, the PACER method58was used to correct for brain shifts. For group analysis, electrode locations were first normalized into Montreal Neurological Institute (MNI) space, and STN and cortical leads were visualized using the DISTAL atlas59(FIG.1B) and the ICBM152 atlas60, respectively.Optimization of continuous DBS

[0208] For each patient, cDBS was optimized before initiating aDBS. Optimization was performed by a movement disorder neurologist over a range of 10-31 (mean ± standard deviation 19±10) months, with 5-10 (7±2) clinician visits. In addition, patients were allowed to make adjustments within a range of 1.6-4.0 mA (mean ± standard deviation amplitude span: 2.65±0.95) at home (i.e., self- optimize stimulation). Clinical optimization was attempted first with “sense friendly” contact configurations (monopolar stimulation at contacts 1 and / or 2) but clinicians were allowed to use non- sense friendly stimulation montages if those proved clinically superior. Further, the clinical neurologist modified medications as needed (FIG. 1D). The patient's participation in our study therefore did not constrain the clinical optimization of cDBS, except for ensuring the DBS system was set to the same stimulation frequency on both sides (to avoid sensing artifacts generated by the presence of two systems providing stimulation at different frequencies)61. For all patients, cDBS led to a decrease in levodopa equivalent daily dose ranging from 19% to 53% (LEDD; mean ± standard deviation 41.3±15.4%; pat-1: 50%, pat-2: 53%, pat-3: 44%, pat-4: 19%). Additionally, the median cDBS amplitude was 2.85 mA across the six hemispheres (range: 2.4-3.2 mA). Both the reduction in medication and cDBS amplitudes are in line with previous clinical trials of cDBS16,17. Medication reduction in three patients was constrained due to mood alterations (pat-1, pat-3, pat-4). In one patient, cDBS amplitudes beyond 2.0 mA tended to produce dysarthria (pat-2). For this patient, the goal was to treat off-symptoms more effectively by applying more stimulation only when needed, and reducing stimulation when not needed, to minimize dysarthria. Seven-step pipeline to design and implement adaptive DBS: overview

[0209] We devised an individualized, seven step data-driven pipeline to implement aDBS to treat residual motor fluctuations persisting after clinical optimization of cDBS (FIG.2). We first identified residual bothersome motor signs on optimized cDBS (step 1). We established appropriate high and low amplitude limits for aDBS that were typically 0.5-1.0 mA higher or lower (respectively) than the optimized cDBS amplitude (step 2). We determined neural signals or “biomarkers” that best correlated with residual motor signs (steps 3 and 4). To identify reliable biomarkers of motor state for real-world aDBS, it was critical to study neural data collected across the range of medication effects and stimulation amplitudes that would be used during aDBS, and to identify signals modulated more by the patient’s underlying motor state than by the current stimulation amplitude. This was accomplished using in-clinic (step 3) and at-home (step 4) neural recordings in which both medication state and stimulation state varied. The at-home data streaming step was important to ensure that biomarkers identified in idealized, investigator-controlled conditions in the clinic couldfunction in naturalistic settings. We then established appropriate control parameters to adjust stimulation amplitude in response to neural signals (steps 5 and 6). Finally, patients underwent a blinded, randomized comparison between cDBS and aDBS over a month per condition, conducted during patients' normal lives (including work and travel) on the schedule of medications established during cDBS optimization (step 7). Identification of patients’ residual motor signs on clinically optimized cDBS

[0210] All four patients experienced motor fluctuations on cDBS. Patients identified their most bothersome persistent motor problem while on clinically optimized cDBS (e.g., bradykinesia, dystonia) in collaboration with a movement disorder neurologist (FIG.1D and FIG.2). Additionally, the most bothersome symptom in the opposite medication state, which limited the therapeutic window during cDBS was identified (e.g., dyskinesia, dysarthria). The goal of aDBS was to improve the most bothersome symptom without exacerbating the opposite symptom, and was agnostic of the type of symptom. Prior to DBS implantation, three patients had tremor as one manifestation of their off-state, but none identified tremor as a bothersome residual problem on optimized cDBS. For two patients, residual bothersome motor fluctuations on optimized cDBS were restricted to the right side of the body; therefore, we developed a unilateral adaptive algorithm for the left hemisphere (pat-1 and pat-4). The remaining two patients received bilateral, independent, adaptive stimulation algorithms (pat-2 and pat-3). Determining individualized stimulation amplitude limits

[0211] We calibrated stimulation amplitudes for each patient, both on- and off-dopaminergic medication (i.e., in their high- and low-dopaminergic states, respectively) to determine the optimal amplitude limits for symptom control to be used for aDBS (FIG. 5A). Specifically, a movement disorders neurologist defined the low stimulation amplitude as the amplitude that mitigates adverse effects in the high-dopaminergic state without causing breakthrough hypokinetic symptoms. Similarly, the high stimulation amplitude was identified as the amplitude that effectively manages hypokinetic symptoms, such as bradykinesia, while avoiding DBS adverse effects, such as dysarthria. Monitoring motor signs

[0212] During in-clinic recordings (step 3), motor signs were assessed by a clinician using standardized rating scales. For biomarker identification at-home (step 4) and symptom monitoring during adaptive testing (step 6, step 7), we chronically monitored symptoms using wristwatch-stylewearable monitors on each wrist (Parkinson’s KinetiGraph®, PKG®, Global Kinetics). These wearables employ a proprietary algorithm38to provide validated scores of bradykinesia and dyskinesia in two-minute intervals. By synchronizing the neural data offline with these scores, we established brain-behavior correlations with higher temporal resolution than motor diaries. To validate wearable outcome measures in our cohort and determine the correspondence between wearable scores and troublesome symptoms in each individual, patients completed motor diaries every 30 minutes (or more often if symptoms occurred more frequently) during at least two days while wearing the monitors. Because the completion of motor diaries is effortful and difficult to maintain during normal daily activities, we thereafter relied on wearable data when applicable for symptom analyses to reduce patient burden. Neural recordings

[0213] At multiple times during steps 3-7, patients streamed neural data and reported both medication intake and motor symptoms using the patient graphical user interface on the streaming tablet. We sampled neural time series data from one subcortical and two cortical leads. We used sampling rates of 250-500 Hz to minimize data loss that may occur at higher sampling rates9. STN LFPs were recorded in a bipolar configuration with contacts immediately adjacent to the stimulating cathode, providing common mode rejection of the stimulus artifact during active stimulation. Cortical recordings were performed in non-overlapping bipolar pairs with an anterior montage which has at least one or both electrode contacts on precentral gyrus, and a posterior montage that has at least one or both electrode contacts on postcentral gyrus. Data were encrypted and uploaded to a secure cloud environment. Biomarker identification: details of steps 3 and 4 Data collection procedure

[0214] In step 3, we recorded neural data off- and on-dopaminergic medication in-clinic, thus in provoked low- and high-dopaminergic states, during stimulation at the previously identified low and high stimulation amplitudes. We performed off-medication recordings after at least 12 hours of overnight medication withdrawal. For each of the four combinations of medication state and stimulation amplitude, we obtained 28.3 ± 5.73 (mean ± standard deviation) minutes of recordings while patients performed a standardized set of activities including walking, resting, speaking, and eating and the MDS-UPDRS-III. We then confirmed the applicability of the in-clinic biomarker in a real-life setting at patients' homes (step 4). To that end, patients recorded neural data during theirregular daily activities while we monitored symptoms using wearable devices and motor diaries. Patients recorded at least two levodopa medication cycles of neural data at both low and high stimulation amplitudes. During streaming days, we recorded neural time-series data and onboard power of the device in the frequency bands of interest defined based on in-clinic recordings. Neural data analysis

[0215] We performed all analyses using MATLAB®2021a (The Mathworks, Natick, MA, USA) and the FieldTrip toolbox62. We first computed the power spectral density of non-interrupted time segments of the neural signal using Welch’s method with 1 s windows and 95% overlap mimicking RC+S embedded system capabilities. We calculated power spectral density over 2-100 Hz in non- overlapping 2.5 s epochs of time domain signals using a 1 Hz spectral resolution. The RC+S device streams data to a laptop computer and time stamps the neural data using the computer clock, which we used to synchronize neural data with wearable monitors. Statistical analysis for identification of biomarkers: overview

[0216] We employed a data-driven approach integrating non-parametric statistics and machine learning to identify patient-specific neural biomarkers (FIG. 10). By analyzing the entire available frequency range in both the STN and sensorimotor cortex (2-100 Hz), we identified physiological signals that reliably predicted each patient's most bothersome or opposite motor symptom. We ensured these physiological signals were not independently influenced by stimulation changes (FIG. 11) in a direction that could lead to undesired cyclic behavior in the control system unrelated to symptom fluctuations12. As residual symptom fluctuations were medication-related, we assessed the main effects of medication using in-clinic data with predetermined medication conditions in a controlled environment. To quantify and exclude confounding effects of stimulation and interactions between medication and stimulation we used a 2x2 factorial design. To this end, we employed a non-parametric cluster-based permutation analysis—a widely-used neuroscientific method that operates free of a priori assumptions regarding the data distribution. It further allows exploration of the complete frequency spectrum while effectively controlling for multiple comparisons63. At-home recordings provided a rich dataset with long-term neural time series and continuous symptom monitoring in patients’ naturalistic environment, which complemented the medication state labeled in-clinic neural signals. We used stepwise-linear regression to predict bothersome / opposite symptoms for patients with continuous symptom monitoring (i.e., upper limb symptoms measurable by wrist-watch style wearables), and a linear discriminant analysis (LDA) based method for binary- classified (rather than continuously scaled) symptoms, i.e., presence or absence of lower limbdystonia. We also expanded this LDA method to all patients by identifying data-driven mappings between continuous wearable scores and self-reported symptom labels. Both the non-parametric cluster-based analysis and the linear stepwise regression explicitly modeled the contributions of stimulation effects, whereas this effect was implicitly addressed with the LDA by using equal distribution of stimulation amplitudes within the training data sets. Statistical analysis of in-clinic data: details

[0217] To implement non-parametric cluster-based permutation, we ranked the test statistic (here sum of t-values) of the empirical in-clinic data within a permutation distribution obtained by randomly assigning condition labels to each data segment. We matched the amount of data for each condition (medication and stimulation state) by drawing equally as many samples from each of the four conditions (1000 random draws), and thereafter ranked all negative and positive clusters (adjacent data points with p<0.01) in each brain region among 1000 surrogates. We assessed the main effects of medication (across stimulation conditions) and stimulation (across medication states), as well as their interaction on power, using two-sided paired t-tests64. We determined effect size using Cohen’s d and selected the cluster (either positive or negative) with the largest main effect of medication as the optimal neural control signal. We ensured the neural signal for adaptive control was unaffected by independent contributions of stimulation to prevent adverse impact on the performance of the adaptive algorithm (FIG.11). This involved confirming the stimulation amplitudes for adaptive control did not produce any significant amplitude augmentation of on-state biomarkers (neural signals that increase after medication intake or during high-dopaminergic symptoms), nor any significant amplitude diminution of off-state biomarkers (neural signals that decrease after medication intake and increase during low dopaminergic symptoms). Statistically this is expressed as positive or negative effects of stimulation amplitude, respectively. We assessed effects in the STN and the two cortical regions separately and Bonferroni-corrected p-values for multiple comparisons. For the final ranking of clusters within the permutation distribution, we used a corrected alpha level of 0.05. Statistical analysis of at-home data: details

[0218] For at-home data, we used two analysis approaches. For patients 1,3, and 4 continuous wearable data tracked their most bothersome / opposite symptoms. Thus, we calculated a linear stepwise regression using data from all three brain regions and the frequency spectrum from 2-100 Hz as predictors for the most bothersome symptom (pat-1: bradykinesia), or the opposite symptom that limits the therapeutic window (pat-3 and pat-4: dyskinesia). Pat3-L exhibited large movement artifacts in the STN, which we addressed by excluding data points that exceeded 2 standarddeviations from the mean power across all frequencies. We z-scored predictors and modeled stimulation amplitude as an additional feature in order to extract independent contributions of power bands to symptom prediction. We excluded episodes in which the wearable score indicated an immobility level predictive of sleep (bradykinesia score >8065) or the monitor was labeled as being off-wrist. However, as we could not rule out neural data being recorded during daytime naps completely, we excluded very low frequencies as predictors that can be confounded by sleep (2-4 Hz)66. Using this method, we identified the strongest predictor of patients’ symptom states. We Bonferroni-corrected p-values for multiple comparisons (289 predictors) and used an alpha level of 0.05.

[0219] For patient 2, their most bothersome symptom, i.e., lower limb dystonia, could not be measured with wearables. Instead, this patient completed motor diaries at least every 30 minutes indicating the presence or absence of lower limb dystonia during neural streaming. We used an LDA based method to identify patient-specific neural signal biomarkers that maximized discriminability between the presence and absence of the most bothersome symptom. Initially, we identified the top five candidate 1 Hz power bands from the neural signal spectra. These bands were selected based on their ability to maximize the area under the receiver operating curve (AUC) through a 1000- repetition Monte Carlo cross-validation. The validation process involved randomly drawing an equal number of data points at both stimulation amplitudes, with a 10% subset used for testing. Within each fold of the Monte Carlo cross validation, the optimal band widths for these potential biomarkers were optimized as a hyperparameter via a nested 10-fold cross validation. The final power band width for each of the five potential biomarkers was chosen as the aggregation of 1 Hz bands that were present in 99% of the Monte Carlo folds.

[0220] Given the RC+S device’s embedded aDBS capabilities rely on discrete classification of neural signal biomarkers, we additionally assessed patient 1,3, and 4’s at-home data using the LDA method. To implement this for these patients, we used a nonlinear optimization (MATLAB fminbnd function) to identify patient-specific dichotomizing boundaries that best mapped the continuous wearable symptom scores into binary symptom labels (i.e., symptom present versus not). Data used for this optimization drew from days when patients provided simultaneous motor diaries and wearable symptom metrics. The optimization value function was the F1 score for predicting the motor diary symptom label from the contemporaneous dichotomized wearable score. Boundary values calculated for dyskinesia scores ranged from 4.0-14.2 and bradykinesia scores ranged from 11.6- 26.5. Offline assessment of biomarker performance

[0221] Given the preponderance of literature highlighting STN beta power’s potential as a neural signal biomarker for aDBS, we compared the offline prediction of medication state / symptoms (Step 3: Cohen’s d, Step 4: regression statistics and LDA AUC) by our identified biomarkers to that of subthalamic beta power bands. Beta bands used for this comparison were identified by repeating the above analyses for Steps 3 and 4 while constraining the search to subthalamic frequency bands within the beta range (13-30 Hz).

[0222] Because the RC+S system is capable of using up to four neural biomarkers as inputs into its aDBS systems, we also assessed the potential additional benefit of using beta band biomarkers in conjunction with our biomarkers. LDA identified the optimal linear combination of the two subject- specific biomarkers (data-driven and STN beta-constrained) to predict the presence / absence of bothersome symptoms. The significance of AUC changes resulting from using STN beta power (identified from Step 3) in addition to the data-driven frequency band was assessed by comparison to a surrogate distribution of AUC values. The surrogate distribution was produced by LDA classifiers using the data driven band and one of 1000 randomly selected power bands of analogous width to the beta band, but unconstrained to anatomic location or frequency range. Optimization of adaptive parameters: details of steps 5 and 6

[0223] The stimulation contact, frequency, and pulse width remained consistent between aDBS and cDBS, with only the stimulation amplitude varying in response to estimates of the patient’s clinical state (i.e., presence of symptoms). During steps 5 and 6, we identified and empirically refined additional parameters that governed both how clinical state estimates were predicted from data- driven biomarkers and the system’s temporal dynamics. To briefly summarize these parameters: the embedded aDBS platform uses windows of time domain data with frame shifts at specified intervals to compute the fast Fourier transform (FFT). Thereafter, it calculates the input signal for the control algorithm, the linear detector (LD), by averaging the signal across a researcher-specified number of FFT windows, known as the update rate. The temporal resolution of the LD is therefore determined by the sampling frequency, FFT window, FFT interval, and update rate. At each update, the input signal is compared to researcher-identified thresholds in order to predict the patient’s clinical state (i.e., whether they are having a symptom that requires stimulation adjustment). The device allows up to two thresholds to be set per detector, corresponding to three states (FIGS.12C-12E). Changes in stimulation are then governed by a look-up table for each clinical state. The temporal dynamics of the final adaptive algorithm can also be influenced by the tolerated stimulation ramping time (e.g., 0.1 mA / s ramp rate for 2.0 mA increase in stimulation amplitude=20 seconds) and the onset andtermination rate, which define the number of “updates” the LD is required to be above or below the threshold to result in a change of stimulation amplitude.

[0224] In step 5, we performed supervised testing of an initial adaptive algorithm to find individualized ramp rates and assure patient comfort with stimulation amplitudes (e.g., avoiding paresthesia). We used the predictive power bands identified in the previous steps and chose thresholds to trigger state changes based on visual inspection of the control signal. In this step of our workflow, we were interested in many state transitions during the brief testing period in-clinic in order to test adaptive parameters during stimulation changes for aDBS. We thus used an aDBS algorithm with relatively fast temporal dynamics that differed from algorithms implemented at home (update rate=1.5 s, onset and termination period=0). After identifying optimal ramp rates during which patients did not perceive stimulation changes, we moved on to unsupervised at-home testing of adaptive algorithms in step 6, the aDBS optimization phase.

[0225] During step 6, patients first streamed the selected neural biomarker with simultaneous symptom monitoring for several days while on cDBS, from which an initial set of aDBS parameters were identified by assessing the relationship of the biomarker to candidate thresholds under the fastest temporal dynamics possible. We monitored symptoms using wearables and patient comments on symptom onset and offset in the patient-facing app on the tablet. Thereafter, we conducted brief 24-hour unblinded tests of aDBS algorithms, in which we refined thresholds, onset and termination periods and, if necessary, stimulation amplitudes based on aDBS’ effects on symptoms (see Mitigating noise and artifact effects). Subsequently, we performed 24-hour blinded testing and assessed aDBS performance on daytime symptoms and sleep quality using a daily symptom application as an outcome measure (see Procedure and outcomes measures). We assured that aDBS algorithms did not decrease sleep quality, in which case we would consider developing a sleep-aware algorithm by adding a second neural biomarker for sleep, e.g., cortical low delta power67. A detailed summary of the final adaptive stimulation parameters can be found in FIG.12B. A comprehensive set of starting parameters for algorithms on a similar time scale of minutes to hours is summarized in FIG.12A. Thresholds for state transitions

[0226] We identified LD thresholds separating times with and without the most bothersome motor sign in step 6. Preliminary thresholds were calculated based on several days of streamed LDs during cDBS and thereafter fine-tuned based on results from adaptive testing. We used a binary classification algorithm aimed at balancing sensitivity and specificity while considering patient preference. We used a receiver operating characteristic curve (ROC) to identify the threshold pointson the ROC curve for optimal trade-off between true and false positive rate68. Initially, we used a single threshold algorithm for all patients and continuously monitored neural data and symptoms (FIG.12C). Mitigating noise and artifact effects

[0227] To mitigate noise that could lead to erroneous stimulation changes, we employed three strategies. Firstly, we reduced inherent noise by decreasing temporal resolution, i.e., smoothing the LD by increasing the update rate. To ensure adequate responsiveness of the algorithm, we enforced a maximal averaging of one minute. If the biomarker signal-to-noise ratio still resulted in erroneous threshold crossings, we introduced a middle state as a noise buffer zone, in which stimulation amplitude remained constant (FIG. 5C, FIG. 12E)11. Alternatively, if we observed brief and rare artifacts, we increased the onset and termination duration. Each of these parameter changes was guided by the patients’ symptoms and satisfaction as assessed by questionnaires and wearable devices for several days. To mitigate the potential effects of artifact produced by stimulation ramping12, a detector blanking period exceeding the update rate by one second was implemented such that the algorithm ignored neural signal spectral content calculated during a change in stimulation. To mitigate the potential for electrocardiogram artifacts seen in other sensing DBS devices69,70, charge at the tissue-electrode interface was actively redistributed after the stimulation impulse was delivered (“active recharge”). Blinded randomized comparisons of continuous and adaptive DBS Procedure and outcome measures

[0228] In our final step 7, we conducted a blinded, randomized comparison between the effects of aDBS and clinically optimized cDBS on motor signs and symptoms. Both stimulation conditions were applied for blocks of 2-7 days over the course of one month per condition at patients' homes. Patients changed stimulation conditions at home by pressing a button on their tablet at prospectively agreed- upon times, accommodating patients’ work and travel schedules. We used a custom MATLAB script for the computerized randomization of conditions. To assess outcomes, we utilized patients' daily symptom diaries and wearable data. We asked patients to complete the daily symptom diary implemented as a custom electronic questionnaire every night before bedtime. The questionnaire focused on the total number of hours spent with symptoms, symptom severity, and a quality of life (QoL) score validated for daily assessment of health-related QoL (EQ-5D)37. Evaluated symptoms included the most bothersome and opposite symptom as well as a range of additional common motorsymptoms (bradykinesia, dyskinesia, tremor, dystonia, dysarthria, and gait disturbance; FIG. 14). We included one question asking patients to rate their quality of sleep. Symptom severity and sleep quality were rated on a scale from 1-10. We also assessed nonmotor symptoms (depression, anxiety, apathy, and impulsivity) using the wording of the four-point scale modeled off the MDS-UPDRS-I71. In addition, we inquired daily about patients’ perceived stimulation condition (aDBS / cDBS) and the underlying rationale. They were given the choice to attribute their experience to unusual sensations related to changes in stimulation, as well as improvements or worsening of motor symptoms. One patient (pat-2) perceived paresthesias during the beginning of the testing period. We therefore excluded these data points and reduced the aDBS ramp rate for future testing.

[0229] Evaluation of the wearable monitor scores focused on quantifying changes in motor fluctuations. Logs from the DBS device during both aDBS and cDBS days indicated the time stamps at which the LD changed its estimate of clinical state, which allowed us to label wearable scores as being present during low-dopaminergic or high-dopaminergic periods. We used a symptom 'fluctuation score' as the primary metric to assess effects of aDBS on wearable data. This score was calculated for both bradykinesia and dyskinesia as the within-day difference in symptom magnitude between the low- and high-dopaminergic states (as estimated from changes in the neural biomarker recorded in the device logs). Each brain hemisphere / contralateral hemibody pair were assessed independently.

[0230] Furthermore, using logs from the DBS device, we calculated the percentage of each aDBS day and night spent at the high and low stimulation amplitudes and the daily average consecutive duration of each stimulation amplitude in hours. Finally, we calculated the total electrical energy delivered (TEED) per second during both stimulation conditions assuming an impedance of 1000 Ω for both day and night time72. Statistics of adaptive versus continuous DBS effects

[0231] A generalized linear mixed-effects model was used to perform a group-level analysis of the effects of aDBS on our three primary outcomes from the nightly questionnaires: percentage of awake hours with the patient’s bothersome symptom, the percentage of awake hours with the patient’s opposite symptom, and quality of life scores. We also used a generalized linear mixed-effects model to assess differences in daytime and nighttime TEED between aDBS and cDBS. Fixed effects included the stimulation condition (cDBS vs aDBS), time (ordinal numbering of experiment date), and their interaction. Subject number was included as a random effect. Due to our small sample size, we corroborated results using within-subject statistics. To this end, we used a two-sided Wilcoxon rank sum test to assess the effects of aDBS to cDBS on the three primary outcomes. We used thesame within-subject statistical approach to evaluate effects on wearable monitor motor fluctuation metrics for the bothersome and opposite symptom (in the three patients with upper limb symptoms). For patient 3, who had bilateral aDBS systems, the wearable metrics for the two hemibodies were evaluated independently. As a control analysis, we assessed whether aDBS affected any other motor or non-motor symptoms, including sleep, besides the personalized bothersome and opposite symptoms. To this end, we performed within-subject two-sided Wilcoxon rank sum tests for all other monitored symptoms. We also performed within-subject two-sided Wilcoxon rank sum tests for both day TEED and night TEED. P-values for within-subject control analyses and TEED evaluations were adjusted for multiple comparisons using the false discovery rate procedure. References

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Claims

What is claimed is:

1. A method for treating a movement disorder in a subject, the method comprising: positioning a stimulation electrode at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; positioning a first neural recording electrode at a second location in the subthalamic nucleus region of the brain of the subject and / or a second neural recording electrode at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; detecting the stimulation-entrained gamma oscillations associated with the residual motor symptom of the subject using the first neural recording electrode and / or the second neural recording electrode; and applying electrical stimulation to the subthalamic nucleus region of the brain of the subject using the stimulation electrode in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode and / or the second neural recording electrode.

2. The method of claim 1, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz.

3. The method of claim 1 or 2, wherein the stimulation-entrained gamma oscillations are centered at half of an electrical stimulation frequency of the DBS.

4. The method of any one of claims 1-3, wherein the DBS shifts peak frequency of the gamma oscillations such that the gamma oscillations become entrained to a subharmonic of a stimulation frequency of the DBS.

5. The method of any one of claims 1-4, wherein the stimulation-entrained gamma oscillations are modulated by the medication and sleep-wake cycles.

6. The method of any one of claims 1-5, wherein peak frequency of the electrical stimulation is used to predict the gamma frequency of the stimulation-entrained gamma oscillations associated with the residual motor symptom.

7. The method of any one of claims 1-6, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance.

8. The method of any one of claims 1-7, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom.

9. The method of any one of claims 1-8, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS.

10. The method of any one of claims 1-9, wherein the residual motor symptom is unilateral or bilateral.

11. The method of any one of claims 1-10, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region.

12. The method of any one of claims 1-11, wherein the movement disorder is Parkinson’s disease.

13. The method of claim 12, wherein the medication is a dopaminergic medication.

14. The method of claim 13, wherein the dopaminergic medication is levodopa.

15. The method of any one of claims 1-14, wherein the electrical stimulation reduces occurrence of the residual motor symptom compared to in absence of the electrical stimulation.

16. The method of any one of claims 1-15, wherein amplitude of the electrical stimulation is calibrated during the treatment of the subject for both a medication on-state and a medication off- state.

17. The method of any one of claims 1-16, wherein maximum amplitude of the electrical stimulation is set to avoid inducing another motor symptom or other adverse effect from said applying the electrical stimulation.

18. The method of any one of claims 1-17, further comprising using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject.

19. The method of claim 18, further comprising using a single threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms.

20. The method of claim 18, further comprising using two thresholds comprising an upper threshold and a lower threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.

21. The method of any one of claims 1-20, wherein the electrical stimulation is applied unilaterally or bilaterally.

22. The method of any one of claims 1-21, wherein the stimulation-entrained gamma oscillations are measured by recording field potentials.

23. The method of any one of claims 1-22, further comprising using a control algorithm to automate said applying electrical stimulation when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected.

24. The method of claim 23, wherein the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between presence and absence of the residual motor symptom.

25. The method of claim 24, wherein the machine learning algorithm is a supervised machine learning algorithm.

26. The method of claim 24 or 25, wherein the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are better measured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom.

27. The method of claim 24, wherein a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom.

28. The method of any one of claims 23-27, wherein the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation.

29. The method of any one of claims 1-28, further comprising using a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gamma oscillations.

30. The method of any one of claims 1-29, wherein the stimulation electrode is placed on a surface of the subthalamic nucleus region.

31. The method of any one of claims 1-30, wherein the first neural recording electrode is placed within the subthalamic nucleus region.

32. The method of any one of claims 1-31, wherein the second neural recording electrode is placed within the sensorimotor cortex region.

33. The method of any one of claims 1-31, wherein the second neural recording electrode is placed in a subdural space over the sensorimotor cortex or under the scalp.

34. The method of any one of claims 1-33, wherein the stimulation electrode is a non- brain penetrating surface electrode array or a brain-penetrating electrode array.

35. The method of any one of claims 1-34, wherein the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain- penetrating electrode array.

36. The method of any one of claims 1-35, wherein the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, subdural electrode, or an electrocorticogram (ECoG) electrode array.

37. The method of claim 36, wherein the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region.

38. The method of any one of claims 1-37, further comprising assessing effectiveness of the treatment in the subject.

39. The method of claim 38, wherein said assessing comprises using behavioral data obtained of the subject.

40. The method of claim 39, wherein the behavioral data is accelerometry data for the subject, video-based pose kinematic data for the subject, or keylogging data from a computer used by the subject, or a combination thereof.

41. The method of any one of claims 38-40, wherein said assessing comprises using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale.

42. A computer implemented method for programming a deep brain stimulator to treat a movement disorder in a subject, the computer performing steps comprising: receiving recorded brain electrical signal data from a subthalamic nucleus region and / or a sensorimotor cortex region of the brain of the subject, wherein the brain electrical signal datacomprises stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; analyzing the recorded brain electrical signal data using a motor symptom classification model that distinguishes between presence and absence of the residual motor symptom; adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to a control algorithm; and instructing the deep brain stimulator to apply an electrical stimulation to the subthalamic nucleus region of the brain when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected and said analyzing indicates the presence of the residual motor symptom.

43. The computer implemented method of claim 42, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz.

44. The computer implemented method of claim 42 or 43, wherein the stimulation- entrained gamma oscillations are centered at half of an electrical stimulation frequency of the DBS.

45. The computer implemented method of any one of claims 42-44, wherein the DBS shifts peak frequency of the gamma oscillations such that the gamma oscillations become entrained to a subharmonic of a stimulation frequency of the DBS.

46. The computer implemented method of any one of claims 42-45, wherein the stimulation-entrained gamma oscillations are modulated by the medication and sleep-wake cycles.

47. The computer implemented method of any one of claims 42-46, wherein peak frequency of the electrical stimulation is used to predict the gamma frequency of the stimulation- entrained gamma oscillations associated with the residual motor symptom.

48. The computer implemented method of any one of claims 42-47, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance.

49. The computer implemented method of any one of claims 42-48, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom.

50. The computer implemented method of any one of claims 42-49, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS.

51. The computer implemented method of any one of claims 42-50, wherein the residual motor symptom is unilateral or bilateral.

52. The computer implemented method of any one of claims 42-51, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region.

53. The computer implemented method of any one of claims 42-52, wherein the movement disorder is Parkinson’s disease.

54. The computer implemented method of claim 53, wherein the medication is a dopaminergic medication.

55. The computer implemented method of claim 54, wherein the dopaminergic medication is levodopa.

56. The computer implemented method of any one of claims 42-55, wherein the electrical stimulation reduces occurrence of the residual motor symptom compared to in absence of the electrical stimulation.

57. The computer implemented method of any one of claims 42-56, wherein amplitude of the electrical stimulation is calibrated during the treatment of the subject for both a medication on- state and a medication off-state.

58. The computer implemented method of any one of claims 42-57, wherein maximum amplitude of the electrical stimulation is set to avoid inducing another motor symptom or other adverse effect from said applying the electrical stimulation.

59. The computer implemented method of any one of claims 42-58, further comprising using stimulation-entrained gamma oscillations at half stimulation frequency as a control signal for the subject.

60. The computer implemented method of claim 59, further comprising using a single threshold to control amplitude of the electrical stimulation, wherein amplitude of the electrical stimulation is reduced when the control signal is higher than the threshold to mitigate hyperkinetic symptoms, and wherein the amplitude of the electrical stimulation is increased when the control signal is lower than the threshold to mitigate low-dopaminergic symptoms.

61. The computer implemented method of claim 59, further comprising using two thresholds comprising an upper threshold and a lower threshold to control amplitude of the electrical stimulation, wherein the amplitude of the electrical stimulation is decreased when the control signal is higher than the upper threshold, wherein the amplitude of the electrical stimulation is increased when the control signal is below the lower threshold, and wherein the amplitude of the electrical stimulation is not changed when the control signal is between the upper threshold and the lower threshold.

62. The computer implemented method of any one of claims 42-61, wherein the electrical stimulation is applied unilaterally or bilaterally.

63. The computer implemented method of any one of claims 42-62, wherein the brain electrical signal data comprises field potential data.

64. The computer implemented method of any one of claims 42-63, wherein the control algorithm uses a machine learning algorithm or linear discriminant analysis for classification to distinguish between the presence and the absence of the residual motor symptom.

65. The computer implemented method of claim 64, wherein the machine learning algorithm is a supervised machine learning algorithm.

66. The computer implemented method of claim 64 or 65, wherein the machine learning algorithm further determines whether the stimulation-entrained gamma oscillations are bettermeasured by the first neural recording electrode in the subthalamic nucleus region or the second neural recording electrode in the sensorimotor cortex region for use in the classification to distinguish between the presence and the absence of the residual motor symptom.

67. The computer implemented method of claim 64, wherein a receiver operating characteristic curve (ROC) is used to identify a threshold for classification of the subject as having the residual motor symptom.

68. The computer implemented method of any one of claims 42-67, further comprising using data from a wearable device, said wearable device being worn by the subject, wherein the wearable device is used to monitor the residual motor symptom in combination with measuring the stimulation-entrained gamma oscillations.

69. The computer implemented method of any one of claims 42-68, wherein the control algorithm further uses linear discriminant analysis (LDA) to determine settings that adjust stimulation amplitude or frequency of the electrical stimulation.

70. The computer implemented computer implemented method of any one of claims 42- 69, further comprising: a) ranking predicted stimulation effectiveness for available settings of a DBS device based on classifier scores for stimulation effectiveness of each setting using a linear classification model; b) selecting stimulation settings predicted to have highest stimulation effectiveness based on the linear classification model; c) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness; d) analyzing the recorded brain electrical signal data to evaluate neural response of the subject to the electrical stimulation; e) updating the linear classification model based on the neural response of the subject to the electrical stimulation to generate an updated linear classification model; f) updating the ranking of predicted stimulation effectiveness for the available settings of the DBS device using the updated linear classification model;g) selecting stimulation settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; h) receiving recorded brain electrical signal data from the sensorimotor cortex region and / or the subthalamic nucleus region of the brain of the subject after applying the electrical stimulation with the DBS device to the subthalamic nucleus region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness based on the updated linear classification model; and i) repeating e) - h) to adjust the available settings of the DBS device to optimize stimulation effectiveness.

71. The computer implemented method of claim 70, wherein the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.

72. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the method of any one of claims 42-71.

73. A kit comprising the non-transitory computer-readable medium of claim 72 and instructions for treating a movement disorder.

74. A system for treating a movement disorder in a subject, the system comprising: a stimulation electrode adapted for positioning at a first location in a subthalamic nucleus region of the brain of the subject to deliver electrical stimulation to the subthalamic nucleus region; a first neural recording electrode adapted for positioning at a second location in the subthalamic nucleus region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with a residual motor symptom of the subject, wherein the subject has been receiving a treatment for the movement disorder comprising a medication, deep brain stimulation (DBS), or a combination thereof; a second neural recording electrode adapted for positioning at a third location in a sensorimotor cortex region of the brain of the subject to measure stimulation-entrained gamma oscillations in a range from 60 Hz to 90 Hz that are associated with the residual motor symptom of the subject; anda processor programmed according to the computer implemented method of any one of claims 42-71 to instruct the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region of the brain of the subject in a manner effective to treat the residual motor symptom when the stimulation-entrained gamma oscillations associated with the residual motor symptom are detected using the first neural recording electrode or the second neural recording electrode and the motor symptom classification model indicates the presence of the residual motor symptom.

75. The system of claim 74, wherein the brain electrical signal data comprises field potential data.

76. The system of claim 74 or 75, further comprising a wearable device to monitor the residual motor symptom.

77. The system of any one of claims 74-76, wherein the stimulation-entrained gamma oscillations are in a range of 60 Hz to 70 Hz.

78. The system of any one of claims 74-77, wherein the residual motor symptom is bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance.

79. The system of any one of claims 74-78, wherein the residual motor symptom is what the subject perceives to be the most bothersome residual motor symptom.

80. The system of any one of claims 74-79, wherein the residual motor symptom continues to occur when the subject is treated for the movement disorder with continuous DBS.

81. The system of any one of claims 74-80, wherein the residual motor symptom is unilateral or bilateral.

82. The system of any one of claims 74-81, wherein the sensorimotor cortex region comprises a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region.

83. The system of any one of claims 74-82, wherein the movement disorder is Parkinson’s disease.

84. The system of any one of claims 74-83, wherein the medication is a dopaminergic medication.

85. The system of any one of claims 74-84, wherein the dopaminergic medication is levodopa.

86. The system of any one of claims 74-85, further comprising the medication.

87. The system of any one of claims 74-86, wherein the stimulation electrode is adapted for positioning on a surface of the subthalamic nucleus region.

88. The system of any one of claims 74-87, wherein the first neural recording electrode is adapted for positioning within the subthalamic nucleus region.

89. The system of any one of claims 74-88, wherein the second neural recording electrode is adapted for positioning within the sensorimotor cortex region.

90. The system of any one of claims 74-89, wherein the second neural recording electrode is adapted for positioning in a subdural space over the sensorimotor cortex or under the scalp.

91. The system of any one of claims 74-90, wherein the stimulation electrode is a non- brain penetrating surface electrode array or a brain-penetrating electrode array.

92. The system of any one of claims 74-91, wherein the first neural recording electrode and / or the second neural electrode is a non-brain penetrating surface electrode array or a brain- penetrating electrode array.

93. The system of any one of claims 74-92, wherein the first neural recording electrode and / or the second neural electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, a subdural electrode, or an electrocorticogram (ECoG) electrode array.

94. The system of claim 93, wherein the ECoG electrode array spans regions of the precentral gyrus and the postcentral gyrus region.

95. The system of any one of claims 74-94, wherein the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the stimulation electrode to apply an electrical stimulation to the subthalamic nucleus region to treat the residual motor symptom in the subject.

96. The system of claim 95, wherein the user interface is password protected and is operable by a health care practitioner.

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