Adaptive deep brain stimulation for sleep stage targeting to treat sleep dysfunction

Abstract

Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features or sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features or sleep stages to target sleep dysfunction. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

Description

Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 ADAPTIVE DEEP BRAIN STIMULATION FOR SLEEP STAGE TARGETING TO TREAT SLEEP DYSFUNCTION CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Patent Application No.63 / 472,191, filed June 9, 2023, and U.S. Provisional Patent Application No.63 / 522,284, filed June 21, 2023, which applications are incorporated herein by reference in their entireties. STATEMENTREGARDINGFEDERALLYSPONSOREDRESEARCH ORDEVELOPMENT

[0002] This invention was made with government support under Grant No. HR0011-20-0028 awarded by The Defense Advanced Research Projects Agency. The government has certain rights in the invention. BACKGROUND OF THEINVENTION

[0003] Sleep dysfunction is a unifying feature across many neurological and psychiatric disorders (Bassetti, C. L. et al. Eur. J. Neurol.22, 1337-1354 (2015)). In people with Parkinson’s disease (PD), non-motor symptoms significantly decrease quality of life with up to 90% reporting significant sleep dysfunction across both rapid eye movement (REM) and non REM (NREM) stages (Videnovic, A. & Golombek, D. Exp. Neurol.243, 45-56 (2013), Barone, P. et al. Mov. Disord.24, 1641-1649 (2009), Diederich, et al. Sleep Med. 6, 313-318 (2005), Martinez-Martin et al. Mov. Disord. 26, 399-406 (2011)). In healthy individuals, NREM sleep is associated with an increase in cortical brain activity in low frequencies (0.5 - 4 Hz), named slow waves, which are believed to serve multiple functions related to metabolism, cognition and synaptic homeostasis (Léger, D. et al. Sleep Med. Rev. 41, 113-132 (2018)). In PD, reductions in slow waves are associated with faster disease progression (Schreiner, S. J. et al. Annals of Neurology vol.85765-770 (2019)). Conventional, high frequency Deep Brain Stimulation (DBS) delivered to the Subthalamic Nucleus (STN) has been shown to partially improve sleep structure and NREM slow wave activity (1-4 Hz) in PD (Baumann-Vogel, H. et al. Sleep 40 (5), (2017), Arnulf, I. et al. Neurology 55, 1732-1734 (2000), Monaca, C. et al. J. Neurol.251, 214-218 (2004), Iranzo, A. et al. J. Neurol. Neurosurg. Psychiatry 72, 661-664 (2002)). However, the variability of DBS on sleep overall, the impact of DBS on the globus pallidus interna (GPi) overnight, the mechanism by which DBS improves NREM sleep, and why REM sleep disturbances appear refractory to DBS is not well understood (Zuzuárregui, J. R. P. & Ostrem, J. L. J. Parkinsons. Dis.10, 393-404 (2020), Tolleson et al. Neuromodulation 19, 724-730 (2016)).Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0004] There remains a need for improved methods of using DBS to treat sleep dysfunction, particularly for subjects who have neurological or psychiatric disorders. SUMMARY OF THE INVENTION

[0005] Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. In particular, deep brain stimulation is performed with a neural recording device that records subcortical or cortical brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep stages or sleep features. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep stages or sleep features to target sleep dysfunction at selected sleep stages or sleep features of interest. 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 from subcortical or cortical neural activity associated with one or more sleep features or sleep stages of interest and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain of the subject when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

[0006] In one aspect, a method for treating sleep dysfunction in a subject is provided, the method comprising: positioning a first electrode at a first location in a basal ganglia region or cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or cortex region; positioning a second electrode at a second location in a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; detecting a brain electrical signal associated with a sleep feature or sleep stage of interest using the second electrode; and applying electrical stimulation to the basal ganglia region or cortex region of the brain of the subject using the first electrode in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected using the second electrode.

[0007] In certain embodiments, the brain electrical signal data comprises field potential data.

[0008] In certain embodiments, the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region.

[0009] In certain embodiments, the cortical region is a cortical precentral gyrus region or postcentral gyrus region.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0010] In certain embodiments, the sleep stage of interest is N2, N3, or REM.

[0011] In certain embodiments, the sleep feature of interest is a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post-awakening period, or a sleep stage transition.

[0012] In certain embodiments, the method further comprises using accelerometry in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.

[0013] In certain embodiments, the method further comprises using autonomic data in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.

[0014] In certain embodiments, the method further comprises using an electroencephalogram or a polysomnogram to identify the sleep feature or sleep stage of interest.

[0015] In certain embodiments, the method further using a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device to identify the sleep feature or sleep stage of interest.

[0016] In certain embodiments, the method further comprises generating a hypnogram.

[0017] In certain embodiments, the method further comprises using a control algorithm to automate said applying electrical stimulation when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected.

[0018] In certain embodiments, the control algorithm uses a machine learning algorithm for classification of sleep features and sleep stages. In some embodiments, the machine learning algorithm is a supervised machine learning algorithm.

[0019] In certain embodiments, the control algorithm further modulates one or more programmed stimulation parameters to maximize slow wave activity. In some embodiments, the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz. In some embodiments, the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.

[0020] In certain embodiments, the control algorithm further uses linear discriminant analysis (LDA) or other embedded classifiers to adjust amplitude of current and / or frequency of the electrical stimulation.

[0021] In certain embodiments, the electrical stimulation is applied unilaterally or bilaterally.

[0022] In certain embodiments, the brain electrical signal comprises beta frequency, gamma frequency, delta frequency, or theta frequency neural oscillations, or individually, patient defined spectral features.

[0023] In certain embodiments, the N3 sleep stage is identified by an increase in delta power during the N3 sleep stage.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0024] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by an attenuation of beta power in a frequency range of 12 Hz to 30 Hz, an attenuation of gamma power in a frequency range of 30 Hz to 60 Hz, an increase in low frequency theta power in a frequency range of 5 Hz to 10 Hz, and / or an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz.

[0025] In certain embodiments, the second electrode is placed on a surface of the cortical sensori- motor region.

[0026] In certain embodiments, the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0027] In certain embodiments, the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0028] In certain embodiments, the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array. In some embodiments, the ECoG electrode array spans precentral and postcentral gyri or other areas of the cortex

[0029] In certain embodiments, the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep.

[0030] In certain embodiments, the movement disorder is Parkinson’s disease.

[0031] In certain embodiments, the sleep dysfunction is caused by a stroke.

[0032] In certain embodiments, the subject is further administered daytime neurostimulation.

[0033] In certain embodiments, the subject is further administered dopaminergic medication.

[0034] In certain embodiments, the method further comprises assessing effectiveness of the treatment of the sleep dysfunction in the subject. In some embodiments, assessing effectiveness of the treatment of the sleep dysfunction in the subject comprises using a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale. In some embodiments, assessing effectiveness of the treatment of the sleep dysfunction in the subject comprises monitoring the subject using actigraphy, electroencephalography, or polysomnography.

[0035] In certain embodiments, the method further comprises mapping the brain of the subject to identify an optimal location in the subcortical region or the cortical region to detect the brain electrical signal associated with a sleep feature or sleep stage.

[0036] In certain embodiments, the cortical region is a cortical precentral gyrus region or a postcentral gyrus region.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0037] In certain embodiments, the method further comprises splitting the recorded brain electrical signal data into consecutive time epochs. In some embodiments, the method further comprises assigning a sleep feature or sleep stage label to each time epoch. In some embodiments, each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.

[0038] In certain embodiments, the method is performed while the subject is sleeping at home, in a sleep laboratory, or in a hospital.

[0039] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.

[0040] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by the one or more spectral power changes in combination with detection of one or more changes in cortical- subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical- subcortical spectral coherence when the subject is awake.

[0041] In certain embodiments, a pre-awakening period or an awakening period is identified by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. In some embodiments, the increase in subcortical beta power precedes the decrease in cortical delta power.

[0042] In certain embodiments, a post-awakening period is identified by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Hz compared to average subcortical gamma power in the pre-awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

[0043] In certain embodiments, the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power.

[0044] In certain embodiments, the electrical stimulation decreases cortical-subcortical sigma spectral coherence.

[0045] In another aspect, a computer implemented method for programming a deep brain stimulation (DBS) device to treat sleep dysfunction in a subject is provided, the computer performing steps comprising: a) receiving recorded brain electrical signal data from a subcortical region or a cortical region of the brain of the subject while the subject is sleeping; b) analyzing the recorded brain electrical signal data using a classification model that identifies a pattern of electrical signals in the recorded brain electrical signal data associated with a sleep feature or sleep stage of interest; c) adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to an algorithm control law; and d) instructing the DBS device to apply an electrical stimulation to a basal ganglia region or cortex region of the brain of the subject when the sleep feature or sleep stage of interest is detected to treat the sleep dysfunction in the subject.

[0046] In certain embodiments, the brain electrical signal data comprises field potential data.

[0047] In certain embodiments, a machine learning algorithm is used to generate the classification model.

[0048] In certain embodiments, the machine learning algorithm is a supervised machine learning algorithm.

[0049] In certain embodiments, the computer implemented further comprises receiving accelerometry data for the subject while the subject is sleeping; and analyzing the accelerometry data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

[0050] In certain embodiments, the computer implemented further comprises receiving autonomic data for the subject while the subject is sleeping; and analyzing the autonomic data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

[0051] In certain embodiments, the computer implemented further comprises receiving an electroencephalogram or a polysomnogram for the subject while the subject is sleeping; andAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 analyzing the electroencephalogram or the polysomnogram using the classification model to identify the sleep feature or sleep stage.

[0052] In certain embodiments, the computer implemented further comprises receiving data from a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device; and analyzing the data using the classification model to identify the sleep feature or sleep stage.

[0053] In certain embodiments, the computer implemented further comprises generating a hypnogram.

[0054] In certain embodiments, the sleep feature or sleep stage classification model is trained by analyzing brain electrical signal data recorded over multiple nights while the subject is sleeping.

[0055] In certain embodiments, the computer implemented further comprises: a) ranking predicted stimulation effectiveness for available settings of the 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 subcortical region or the cortical region of the brain of the subject after applying electrical stimulation with the DBS device to the basal ganglia region or cortex 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 subcortical region or the cortical region of the brain of the subject after applying the electrical stimulation with the DBS device to the basal ganglia region or cortex 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.

[0056] In certain embodiments, the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.

[0057] In certain embodiments, the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity. In some embodiments, the slow wave activity is in a frequency rangeAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 of 0.5 Hz to 4 Hz. In some embodiments, different elements of sleep are targeted such as, but not limited to, N1, N2, N3, phasic and tonic REM as well as rapid sleep related physiology including slow waves, sleep spindles, K complexes and beta bursts.

[0058] In certain embodiments, the classification model identifies the N2 sleep stage or the N3 sleep stage by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.

[0059] In certain embodiments, the classification model identifies the N2 sleep stage or the N3 sleep stage by the one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical- subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake.

[0060] In certain embodiments, the classification model identifies the pre-awakening period or the awakening period by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. In some embodiments, the increase in subcortical beta power precedes the decrease in cortical delta power.

[0061] In certain embodiments, the classification model identifies the post-awakening period by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

[0062] In certain embodiments, the computer implemented method further comprises splitting the recorded brain electrical signal data into consecutive time epochs. In some embodiments, the computer implemented method further comprises assigning a sleep feature or sleep stage label to each time epoch. In some embodiments, each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.

[0063] In certain embodiments, the computer implemented method further comprises training the linear model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch by analyzing the recorded brain electrical signal data using a non-linear model during all sleep stages while the subject is sleeping. In some embodiments, canonical delta and beta power bands are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis. In some embodiments, subcortical field potentials are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis.

[0064] In certain embodiments, the brain electrical signal data comprises field potential data.

[0065] 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 associated with a sleep feature or stage.

[0066] 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 basal ganglia region or cortex region of the brain of the subject to treat the sleep dysfunction in the subject based on the recorded brain electrical signal data.

[0067] 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 computer implemented method, described herein, is provided.

[0068] In another aspect, a kit comprising the non-transitory computer-readable medium and instructions for treating sleep dysfunction in a subject with a deep brain stimulation device is provided.

[0069] In another aspect, a system for treating sleep dysfunction in a subject is provided, the system comprising: a first electrode adapted for positioning at a location in the basal ganglia region or cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or cortexAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 region; a second electrode adapted for positioning at a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; and a processor programmed according to a computer implemented method, described herein, to instruct the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region of the brain of the subject in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected using the second electrode.

[0070] In certain embodiments, the system further comprises an accelerometer to record movement of the subject while the subject is sleeping. In certain embodiments, the system further comprises a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device.

[0071] In certain embodiments, the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0072] In certain embodiments, the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array. In some embodiments, the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array. In some embodiments, the ECoG electrode array spans precentral and postcentral gyri, or other areas of the cortex.

[0073] In certain embodiments, the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep. In some embodiments, the movement disorder is Parkinson’s disease.

[0074] In certain embodiments, the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region to treat the sleep dysfunction 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

[0075] FIGS.1A-1E. Participants’ (abbreviated Part.) recording set up and intracranial cortical Field Potentials: (FIG. 1A) Clinical information for participants. UPDRS - Unified Parkinson’s Disease Rating Scale. (FIG.1B) Schematic of RC+S system. The inset provides a close-up of the cortical and subcortical leads. Adapted from Gilron et al. 2021 (Nat. Biotechnol. 39, 1078–1085). (FIG. 1C)Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Average time spent in each sleep stage (TST = Total Sleep Time), per night, classified by Dreem2 headband polysomnogram data. Error bars indicate standard deviation across nights. (FIG. 1D) Representative traces of Field Potential time series in all sleep stages from Participant 1’s left device, with stimulation on: Left column - precentral gyrus; Right Column - Subthalamic Nucleus. Columns share color legend and scale bars. (FIG. 1E) Power spectral density plots of intracranial FPs, partitioned by sleep stage, during conventional continuous stimulation. Left Quadrant - precentral gyrus; Right Quadrant - subcortical region (STN for Participant 1; GP for Participant 2). Shaded error bars indicate standard error across nights; shares color legend with FIG.1D.

[0076] FIGS. 2A-2G. Sleep stage adaptive DBS classifier performance: (FIG. 2A) Classifier performance of the participant’s (abbreviated Part.) embedded N3 classifier during validation (cDBS: no adaptive stimulation change) and test (adaptive DBS - aDBS: stimulation changes, only Participant 1) phase nights. Error bars indicate standard deviation. (FIG.2B) The proportional composition of Participant 1 and 2’s classifier outputs by ground-truth sleep stages during the validation and test nights. Utilizes the same color legend as FIGS.1D, 1E. Left plot depicts Dreem headband determined sleep stage composition of ‘N3’ embedded classifier outputs across all nights and participants. Solid bar and dotted bar correspond to left and right devices, respectively. For example, 35-40% of embedded N3 predictions in the left hemisphere device occurred during N2 sleep. Right column depicts the corresponding composition of embedded ‘Not N3’ classification. (FIG. 2C) Stacked histograms depicting number of 30 second sleep epochs with corresponding delta and beta power, color-partitioned by sleep stage (shares color legend with FIG.2B). Dotted line represents cumulative density function of embedded left and right N3 classifications as a function of band power, illustrating the proportion of N3 predictions that occurred with sleep epoch band power less than or equal to the x-axis location. Participant 1 - left column; Participant 2 - right column. This demonstrates that classification sensitivity improves for progressively deeper N3 sleep. (FIG. 2D) Sleep metrics for Participant 1’s cDBS (validation) and aDBS (test) nights. Error bars indicate standard deviation. In particular, average N3 in cDBS is 42 min, while average N3 in aDBS is 41 min. (FIG.2E) (Top) Dreem2 headband hypnogram superimposed on a spectrogram of precentral gyrus cortical FPs for one of Participant 1’s adaptive DBS test-nights. (Bottom) Stimulation amplitude as a function of time, sharing the same x-axis as the hypnogram. The stimulation amplitude was reduced (50%) during embedded classification of N3 (16.7 minute span depicting a transition into embedded classification of N3 sleep). (FIG.2F) Zoomed in depiction of the highlighted portion in FIG.2D. Black line shows the ground-truth sleep stage. Below are the raw delta power (light blue), and raw beta power (dark blue) traces, as calculated by the embedded INS device, corresponding to the highlighted portion.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Below depicts the corresponding Linear Discriminant embedded classifier output (blue) compared with the user-defined threshold (dashed). Grey line shows the resulting stimulation amplitude. All plots in FIG.2F share the same x-axis. (FIG.2G) Box plots of device-calculated band power, as described in Materials and Methods Section C, of all N3 epochs (cDBS → Left: n = 169; Right: n = 168 || aDBS → Left: n = 162; Right: n = 134) across Participant 1’s cDBS and aDBS nights. Asterisks indicate significance of independent samples t-test (Delta → Left: t = -3.5, p < 1e-3; Right: t = -5.8, p << 1e-3 || Beta → Left: t = 2.7, p < 1e-2; Right: t = 0.2, p = 0.8 ).

[0077] FIG.3. Sleep adaptive DBS pipeline.

[0078] FIGS.4A-4G. Methodology, data collection and analysis procedures: (FIG.4A) Schematic of the RC+S system setup for recording intracranial cortical Field Potentials (FP) in participants (adapted from Gilron et al. (2021) Nat. Biotechnol.39(9):1078-1085). (FIG.4B) Illustrations of the placement of RC+S sensing depth electrodes in subcortex for both STN and GPi (right) and cortical ECoG locations (left). Example data from PD2 and PD3 participants. (FIG.4C) Schematic of the Dreem2, portable headband for recording in-home polysomnography overnight (adapted from Debellemaniere et al. (2018) Front. Hum. Neurosci.12:88). (FIG.4D) Illustration of a single night of sleep in a PD patient (DBS ON) with hypnogram (purple) showing sleep stages (AW: awake; RM: REM; [N1, N2, N3]: NREM) and cortical (top 2 panels) and subcortical (bottom 2 panels) spectrogram panels from both hemispheres showing multi-frequency changes across sleep stages where the x-axis is time in hours and y-axis is frequency (Hz). FP was recorded bilaterally from cortical and subcortical regions. (FIG.4E) Flowchart of data analysis and preprocessing procedures for 10 day sleep dataset (n=5) and ON / OFF dataset (n=4). (FIG.4F) Representative traces of the RC+S FP time series in all sleep stages from cortex (left column) and subcortex (right column ;Subthalamic Nucleus). Columns share scale bars and rows share color legends (Wake, REM, N1, N2 and N3). Data from one PD participant with ON stimulation from the left hemisphere. (FIG. 4G) Comparisons of spectral powers of intracranial FPs among sleep stages in cortex (left) and subcortex (right) for a single subject, DBS ON. Shaded error bars indicate standard error. Shares color legend with FIG.4F.

[0079] FIGS.5A-5G. Spectral changes in NREM. Dynamic changes in power spectra and functional connectivity between cortical and subcortical regions during NREM sleep: (FIG.5A) Power Spectrum changes (mean ± SEM) during NREM (N2 and N3) sleep with wake stage as baseline in low frequency range (1-50 Hz) for all PD participants (n=4) during ON stimulation in cortical (top) and subcortical (bottom) areas. y-axis shows the difference in power spectra between NREM and wake stage in decibel (dB). Thick lines show mean and shaded areas show standard errors (SEM). (FIG. 5B) Power in delta (1-4 Hz) increases while beta (13-31 Hz) band power decreases during NREMAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 sleep compared to wake during ON stimulation in cortical (top) and subcortical (bottom) areas. Each bar shows the difference in spectral power for one participant averaged across multiple nights and each data point shows the average difference in spectral power across one night with data pooled from both hemispheres. (FIG.5C) During OFF stimulation conditions, delta power increases while beta power decreases in NREM compared to the wake stage in PD participants (n=4) in cortical (top) and subcortical (bottom) areas. Thick lines show means and shaded areas show standard errors. (FIG. 5D) Difference in cortical spectral power between ON and OFF stimulation conditions in 4 participants with PD in NREM sleep stages (top), showing increased delta (1-4 Hz) and decreased low-alpha and low-beta activities (8-15 Hz) while ON stimulation. Each colored line shows spectral change for one participant, thick line shows average across the participants with shaded area as SEM. The spectral power in subcortical regions didn’t show any statistically significant difference (bottom). The x-axis is frequency (Hz) and the y-axis is difference in power (ON-OFF). (FIG.5E) Changes in cortical-subcortical spectral coherence (mean ± SEM) during NREM (N2 and N3) sleep with wake stage as baseline for all participants (n=5) during ON stimulation. y-axis shows the difference in spectral coherence between NREM and wake stage. Horizontal back line at 0 represents wake stage baseline. (FIG.5F) Total difference in spectral coherence in delta (1-4 Hz, left) and beta (13-31 Hz, right) during NREM sleep compared to wake during ON stimulation. Each bar shows difference in spectral coherence for one participant averaged across multiple nights and each point shows average difference in spectral coherence across one night with data pooled from both hemispheres. (FIG.5G) During OFF stimulation conditions, delta coherence increases while beta coherence decreases in NREM compared to the wake stage in PD participants (n=4). Data from both hemispheres were pooled for all panels.

[0080] FIGS.6A-6E. Inverse relationship between subcortical beta and cortical delta activities. (FIG. 6A) Example of subcortical beta (purple) and cortical delta (green) power in a single night from one PD participant (PD3) during ON stimulation depicting the inverse relationship in temporal domain. The delta and beta powers were smoothed with a 20-point gaussian kernel. (FIG. 6B) Average Spearman’s rho correlation between subcortical beta power and cortical delta power for all 4 PD participants across multiple nights in ON (left) and in OFF (right) stimulation. Each bar shows average correlation for one participant and each point shows correlation across one night with data pooled from both hemispheres. (FIG.6C) Scatter plots depicting the correlation between subcortical FP beta (13-31 Hz) power and cortical FP delta (1-4 Hz) power during NREM sleep in 4 PD participants during ON stimulation; STN (brown and red), and GPi (blue, light blue). Each point represents data from one 5-s NREM sleep epoch. Each plot is data from one night pooled from both hemispheres for oneAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 participant. (FIG.6D) Normalized cross-correlation between subcortical beta power and cortical delta power showing the subcortical beta preceding cortical delta activities in PD participants during NREM with ON stimulation. The bar plot (left) shows lags in subcortical beta with cortical delta as reference. Each bar shows average lag for one participant and each point shows lag across one night with data pooled from both hemispheres. Example of cross correlation showing the lag in subcortical beta as a function of time (right) in one night from PD2 during ON stimulation. The vertical dashed line shows zero-lag. (FIG. 6E) Interactions between cortical delta and cortical beta activities, examined as a control for cortical delta - subcortical beta. The bar plot (left) shows average Spearman’s rho correlation between cortical delta and beta power for all 4 PD participants across multiple nights, ON stimulation. Each bar shows average correlation for one participant and each point shows correlation across one night with data pooled from both hemispheres. The scatter plots show cortical delta and beta power in 4 PD participants during ON stimulation for two representative PD participants. Each point represents data from one 5-s NREM sleep epoch.

[0081] FIGS.7A-7D. Changes in spectral power before spontaneous awakenings. Subcortical beta increases and cortical delta decreases before spontaneous awakening. (FIG.7A) Cortical delta (1 - 4 Hz) power during NREM to wake sleep transition episodes for all PD participants (n=4; mean ± SEM) during ON stimulation (left). Each data point is the average for 5-s data epochs and shadings represent SEMs for NREM to wake transitions across the recording nights for one participant. Data were pooled from both hemispheres. The vertical purple dashed line shows awakening time. x-axis (on the left) shows time in seconds since NREM sleep onset and time since awakening (middle, around vertical dashed line). The black line on top shows the norm of RC+S accelerometry data (mean ± SEM) for all NREM to wake transitions across all nights for all participants highlighting the awakening time of the episodes. The bar plots show change in cortical delta power during pre- awakening (5-s before the wake event, top) and post-awakening (15-s after the wake event, bottom) compared to the average delta power in deep NREM (average over NREM data after 40-s from NREM onset and 40-s before awakening; SWS). Each bar shows average change of power for one participant and each point shows change of power across all NREM to wake transitions in one night with data pooled from both hemispheres. Cortical delta power gradually increases as sleep deepens and decreases steadily before awakening. The average post-awakening (15s) and pre-awakening delta powers (-5s) are lower than those during SWS. The average post-awakening (15s) cortical delta power is lower than the pre-awakening delta power (-5s). (FIG.7B) Same as A, for subcortical delta power showing no significant trend across participants or recording sites. (FIG.7C) Same as A, for cortical beta power showing no significant trend across participants or recording sites for pre and postAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 awakenings. (FIG.7D) Same as A, only for subcortical beta power illustrating spontaneous rise in beta power before awakenings. The average post-awakening (15s) and pre-awakening beta power in STN (-5s) are higher than those during SWS.

[0082] FIGS.8A-8C. Wake prediction utilizing spectral power changes. Subject-specific machine- learning models utilizing spectral changes in cortex and subcortex can predict awakening. (FIG.8A) Wake prediction by subject-specific QDA models around the time of spontaneous awakenings. The vertical black dashed line shows awakening time. x-axis shows time in seconds since awakening and y-axis shows the wake prediction by individual QDA models in terms of posterior probability for NREM to wake events (mean ± SEM) for each subject (n=5). The horizontal green line (y=0.5) shows classification threshold. (FIG.8B) Receiver operating characteristic (ROC) performance for binary classification between deep NREM vs pre-wake (-5s) NREM data (blue) and deep NREM vs post- wake (+15s) data (green) for each subject. (FIG. 8C) Boxplots for the distribution of the wake predictions by QDA models in deep NREM (magenta), pre-wake (-5s) NREM (blue) and post-wake (+15s) data (green) for each participant. In all cases, Wilcoxon rank sum test p value <0.001***, <0.01**, <0.05*.

[0083] FIGS. 9A-9D. Sleep statistics in ON stimulation. Sleep statistics (mean ± SEM ) for all participants (n=5) during overnight recordings with ON stimulation. (FIG.9A) Time to sleep onset (FIG.9B) Wake after sleep onset (awakening during the sleep) in total duration and frequency over one night (FIG.9C) Durations of all sleep stages in minutes (FIG.9D) Total proportion of sleep stages.

[0084] FIGS.10A-10D. ON vs OFF stimulation sleep statistics. Sleep statistics for all PD participants (n=4) during overnight recordings during consecutive one night ON and one night OFF stimulation conditions. (FIG.10A) Time to sleep onset (FIG.10B) Wake after sleep onset (awakening during the sleep) in total duration and frequency over one night (FIG. 10C) Durations of all sleep stages in minutes (FIG.10D) Total proportion of sleep stages. x-axes are stimulation conditions (ON / OFF) and the dashed gray lines show average across all PD participants.

[0085] FIGS.11A-11D. Data processing procedures. Removal of the ECG artifact (FIG.11A) and movement-related spike artifact (FIG. 11B) from the RC+S field potential data. (FIG. 11C) Time synchronization of polysomnogram from DREEM2 with intracranial data streams from RC+S devices using accelerometry data as references (FIG.11D) Awakening time correction using accelerometry data and by finding movement events.

[0086] FIGS.12A-12E. Spectral power (mean ± SEM ) in cortex and subcortex for all sleep stages in each participant (n=5), including patient with dystonia (FIG.12A), PD3 (FIG.12B), PD9 (FIG.12C),Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 PD2 (FIG.12D), and PD7 (FIG.12E). Data from ON stimulation. Data is averaged over 10 nights and bilateral hemispheres. DETAILED DESCRIPTION OF THE INVENTION

[0087] Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records subcortical or cortical brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features and sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features and sleep stages to target sleep dysfunction at selected sleep stages of interest. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages of interest and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

[0088] 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.

[0089] 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.

[0090] 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 inAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0091] 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.

[0092] 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” or “the electrode” includes a plurality of such electrodes and reference to “an electrical signal” or “the electrical signal” includes reference to one or more electrical signals, and so forth.

[0093] It is further noted that the claims may be drafted to exclude any element which may be optional. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely”, “only” and the like in connection with the recitation of claim elements, or the use of a “negative” limitation.

[0094] 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

[0095] The term “about,” particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0096] 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, functional movement disorder, Huntington’s disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, andAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0097] The term “sleep dysfunction” is used to refer herein to a sleep-wake disorder that causes sleeplessness, insomnia, or poor sleep quality. Sleep dysfunction may include difficulty falling asleep or staying asleep, frequent nocturnal awakenings, early morning awakening, sleep fragmentation, reduced total sleep time, reduced deep sleep time, reduced non-REM or REM sleep time, and / or inability to reach deep sleep (stage N3 or delta sleep). In addition, sleep dysfunction may be associated with overnight emergence of motor symptoms, pain, nocturia, sleep disordered breathing, periodic limb movement disorder (PLMD), parasomnia, sleep apnea, REM sleep behavior disorder (RBD), circadian rhythm dysfunction, and / or excessive daytime somnolence. Tremor, rigidity and dyskinesias associated with a movement disorder such as Parkinson’s disease may occur during nocturnal awakenings and contribute to sleep dysfunction by prolonging awakenings and inability to fall back to sleep.

[0098] In some embodiments, sleep dysfunction is associated with a stroke. Insomnia may occur after a stroke, particularly in patients who have right hemispheric strokes or strokes within the thalamus or brainstem, including the pontine tegmentum and thalamo-mensencephalic region. Hypersomnia may occur after a stroke in patients who have subcortical (caudate, putamen), upper pontine, medial ponto-medullary or cortical strokes affecting the reticular activating system (RAS). Paramedian or bilateral thalamic strokes may initially induce coma, followed by hypersomnia after awakening of the patient. Supratentorial strokes may reduce non-REM sleep, total sleep time, and ipsilateral or bilateral sleep spindles. Saw-tooth waves may be reduced after a hemispheric stroke. REM sleep may be reduced after an occipital stroke. Strokes in the ponto-mesencephalic junction and the raphe nucleus may reduce the amount of non-REM sleep. Strokes in the lower pons can selectively reduce REM sleep. Paramedian thalamus and lower pontine strokes may reduce slow- wave sleep.

[0099] The terms “individual”, “subject”, “recipient”, and “patient” are used interchangeably herein and refer to any mammalian subject for whom diagnosis, 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 otherAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0100] 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 a patient being diagnosed or receiving treatment for sleep dysfunction. The user may be a health care practitioner, such as, the patient’s physician.

[0101] By “treatment” or “treating” is meant that at least an amelioration of one or more symptoms associated with the condition afflicting the subject is achieved such that the patient has a desired or beneficial clinical result, where amelioration refers to at least a reduction in the magnitude of a parameter, e.g., a symptom, associated with the condition being treated. As such, treatment includes a broad spectrum of situations ranging from lessening intensity, duration or extent of impairment caused by a condition and / or correlated with a condition, up to and including completely eliminating the condition, along with any associated symptoms. Treatment therefore includes situations where the condition, or at least a symptom associated therewith, is completely inhibited, e.g., prevented from happening, or stopped, e.g., terminated, such that the subject no longer suffers from the condition, or at least the symptoms that characterize the condition. Treatment also includes situations where the progression of the condition, or at least the progression of a symptom associated therewith, is slowed, delayed, or halted. In such cases, a subject might still have residual symptoms associated with a condition, but any increase in the severity or magnitude of the symptoms is slowed, delayed, or prevented.

[0102] The term “symptom” as used in the context of sleep dysfunction, may include, without limitation, problems with poor quality of sleep, timing of sleep, and / or the amount of sleep of a subject. A symptom of sleep dysfunction may include sleeplessness, difficulty falling asleep, difficulty staying asleep, reduced total sleep time, reduced deep sleep time, reduced non-REM or REM sleep time, and / or inability to reach deep sleep (stage N3 or delta sleep). Methods

[0103] The present disclosure provides methods for treating sleep dysfunction in a subject using nighttime deep brain stimulation. In particular, deep brain stimulation is performed with a neural recording device that records subcortical or cortical brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features and / or sleep stages. An adaptive deep brainAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep feature and / or sleep stages to target sleep dysfunction at selected sleep stages of interest. 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 from subcortical or cortical neural activity associated with one or more sleep features and / or sleep stages of interest and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the basal ganglia region or cortex region of the brain of the subject when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected. Various steps and aspects of the methods will now be described in greater detail below.

[0104] In some embodiments, the subject methods are used to treat sleep dysfunction associated with a movement disorder. Movement disorders include, but are not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, functional movement disorder, Huntington's disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, and Wilson's disease.

[0105] In some embodiments, the subject methods are used to treat sleep dysfunction associated with a neurological disorder. Neurological disorders include, but are not limited to, neurodegenerative diseases, including Alzheimer’s disease, Parkinson's disease, Huntington's disease, multiple system atrophy or dementia with Lewy bodies, and multiple system atrophy, epilepsy, stroke, bipolar disorder, a neuromuscular disorder, including amyotrophic lateral sclerosis (ALS), Charcot-Marie- Tooth disease (CMT), Chronic inflammatory demyelinating polyneuropathy (CIDP), Guillain-Barré syndrome (GBS), Lambert-Eaton syndrome, muscular dystrophy, myasthenia gravis, myopathies, and peripheral neuropathies.

[0106] In some embodiments, the subject methods are used to treat sleep dysfunction associated with a stroke. In some cases, the sleep dysfunction is insomnia, which may occur after a stroke, particularly in patients who have right hemispheric strokes or strokes within the thalamus or brainstem, including the pontine tegmentum and thalamo-mensencephalic region. In some cases, the sleep dysfunction is hypersomnia, which may occur after a stroke in patients who have subcortical (caudate, putamen), upper pontine, medial ponto-medullary or cortical strokes affecting the reticular activating system (RAS). Paramedian or bilateral thalamic strokes may initially induce coma, followed by hypersomnia after awakening of the patient. Supratentorial strokes may reduce non-REM sleep, total sleep time, and ipsilateral or bilateral sleep spindles. Saw-tooth waves may beAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 reduced after a hemispheric stroke. REM sleep may be reduced after an occipital stroke. Strokes in the ponto-mesencephalic junction and the raphe nucleus may reduce the amount of non-REM sleep. Strokes in the lower pons can selectively reduce REM sleep. Paramedian thalamus and lower pontine strokes may reduce slow-wave sleep.

[0107] The method includes positioning a first electrode in a basal ganglia region or cortex region of the brain of a subject to deliver electrical stimulation to the brain (i.e., DBS electrode) and positioning a second electrode at a subcortical region or a cortical region of the brain of the subject to detect brain electrical signals from neural activity associated with a sleep feature or sleep stage of interest while the subject is sleeping (i.e., detection electrode). In some embodiments, one or more DBS electrodes are positioned at the basal ganglia region or cortex region, and one or more detection electrodes are positioned at the subcortical region or cortical region. The DBS electrodes and the detection 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) or brain-penetrating depth electrodes. The electrical stimulation may be applied to the basal ganglia using the DBS electrode in a manner effective for treating sleep dysfunction when a brain electrical signal associated with the sleep feature or sleep stage of interest is detected from the subcortical region or cortical region of the brain using the detection electrode.

[0108] In certain embodiments, one or more detection electrodes are used to record brain electrical signals for neural activity associated with a sleep feature or sleep stage of interest in one or more brain regions. A detection electrode may be placed, for example, in a cortical precentral gyrus region and / or postcentral gyrus region to detect neural activity associated with a sleep feature or sleep stage of interest, 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 for positioning an electrode for detecting brain electrical signals from neural activity associated with a sleep feature or sleep stage of interest, as discussed further below.

[0109] 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 detection 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).Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0110] Positioning a detection 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 detection electrode may involve positioning the electrode on the surface of the specified region(s) of the brain. For example, electrodes may be placed on the surface of the brain at the cortical precentral gyrus region or postcentral gyrus region, or a combination thereof. The electrode may contact at least a portion of the surface of the brain at the cortical precentral gyrus region or postcentral gyrus region. In some embodiments, the electrode may contact substantially the entire surface area at the cortical precentral gyrus region and postcentral gyrus region. In some embodiments, the electrode may additionally contact area(s) adjacent to the cortical precentral gyrus region or postcentral gyrus region. In some embodiments, the sensing electrodes may contact any area of the cortex that allows detection of sleep features or sleep stages. In some embodiments, the electrodes may be placed extracranially, for example in the subgaleal space. In some embodiments, the sensing 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 brain electrical signals for neural activity 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, or an electroencephalography (EEG) electrode. In certain embodiments, a plurality of electrodes is positioned at one or more of the brain regions specified herein for detection of electroencephalographic signals by stereoelectroencephalography (sEEG).

[0111] In certain cases, placing the detection electrode at a target area or site (e.g., a subcortical region or a cortical 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 detection electrode may be placed in a subcortical region or a cortical region of the brain. In some embodiments, the detection electrode may additionally contact area(s) adjacent to a subcortical region or a cortical region of the brain. In some embodiments, an electrode array may be used for detecting neural activity from a cortical area, for example precentral gyrus region or postcentral gyrus region, or a combination thereof, as specified herein.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0112] The depth to which a detection 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.

[0113] Positioning an electrode in the basal ganglia region or cortex 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, a globus pallidus region, or thalamus 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.

[0114] 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 subcortical region or cortical region of the brain, or alternatively accelerometry, pulse oximetry, temperature or heart rate to treat sleep dysfunction. In some embodiments, a closed loop system is used to adjust DBS settings automatically in response to changes in neural activity in the subcortical region or cortical 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 neural activity in the subcortical region or cortical region of the brain.

[0115] The 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 basal ganglia or the cortex. 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. In certain embodiments, the region of the basal ganglia to which electrical stimulation is applied is a subthalamic nucleus region or a globus pallidus region, or other regions of the basal ganglia suitable for stimulation. The siteAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 chosen for stimulation may differ for different subjects and will depend on mapping of the basal ganglia region or cortex region of the brain of an individual subject to identify the optimal location for positioning an electrode for delivery of electrical stimulation to treat sleep dysfunction.

[0116] In some embodiments, an electrode array arranged on a planar support substrate may be used for electrically stimulating the basal ganglia. The surface area of the electrode array may be determined by the desired area of contact between the electrode array and the basal ganglia. 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.

[0117] The precise number of DBS electrodes or detection 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.

[0118] 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.

[0119] 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 sleep dysfunction, which may include sleeplessness, difficulty falling asleep, difficulty staying asleep, inadequate total sleepAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 time, inadequate deep sleep time, inadequate non-REM or REM sleep time, and / or inability to reach deep sleep (stage N3 or delta sleep). In some embodiments, DBS is optimized to achieve a neurophysiologically defined change, for example, increasing or decreasing of slow waves or sleep spindles. In some embodiments, the subthalamic nucleus region, globus pallidus region, or thalamic region, or other regions of the brain are mapped to determine optimal positioning of DBS electrodes.

[0120] Assessment of the effectiveness of electrical stimulation at a particular site for treating sleep dysfunction may be performed using any standard method. In some embodiments, the subject is monitored while sleeping using actigraphy, electroencephalography, or polysomnography. Autonomic data may also be collected while the subject is sleeping. In some cases, an observer may monitor the subject at night to determine if the subject stays asleep or has nocturnal awakenings. In addition, a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale may be used to assess the effectiveness of electrical stimulation in treating sleep dysfunction.

[0121] In certain embodiments, the method further comprises mapping the brain of the subject to optimize positioning of a detection electrode. Positioning of the detection electrode in a subcortical or cortical region is optimized to detect brain activity features associated with sleep features or sleep stages 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 different stages of sleep. In certain embodiments, the N3 sleep stage is identified by an increase in delta power during the N3 sleep stage. In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by an attenuation of beta power in a frequency range of 12 Hz to 30 Hz, an attenuation of gamma power in a frequency range of 30 Hz to 60 Hz, an increase in low frequency theta power in a frequency range of 5 Hz to 10 Hz, and / or an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz. Thus, detection electrodes may be positioned to optimize detection of brain activity in specific frequency ranges that correlate with sleep features and / or sleep stages to be treated with electrical stimulation.

[0122] 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 subcortical, cortical, or otherAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 regions are mapped to determine optimal positioning for detection electrodes. One or more of these regions may be implanted with detection electrodes to measure electrical signals from neural activity associated with a sleep feature or sleep stage to be treated with electrical stimulation.

[0123] In certain embodiments, an accelerometer, a noninvasive sleep monitoring device, a wearable sleep monitoring device (e.g., smart ring, smartwatch, wrist band, or head band sleep tracker), a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device is used to assist sleep feature or sleep stage classification. For a description of such sleep monitoring devices see, e.g., Toften et al. (2020) Sleep Med.75:54-61, Kwon et al. (2021) IEEE J Biomed Health Inform.25(10):3844-3853, Lauteslager et al. (2020) Annu Int Conf IEEE Eng Med Biol Soc.2020:5150-5153, Chung et al. (2017) Sensors (Basel) 17(7):1685, An et al. (2022) Sci Rep. 12(1):21052, Chinoy et al. (2021) Sleep 44(5):zsaa291, Chinoy et al. (2022) Nat Sci Sleep 14:493-516, Shelgikar et al. (2016) Chest 150(3):732-43, Zhao et al. (2021) Entropy (Basel) 23(1):116; herein incorporated by reference in their entireties.

[0124] As set forth here, the subject methods involve applying electrical stimulation to a basal ganglia region (e.g., subthalamic nucleus region and / or globus pallidus region and / or thalamus region) or cortex region in a manner effective to treat sleep dysfunction in a subject when neural activity associated with a sleep feature or sleep stage in need of treatment is detected. In some embodiments, electrical stimulation is applied to the basal ganglia region (e.g., subthalamic nucleus region and / or globus pallidus region and / or thalamic region) when the N2 sleep stage and / or N3 sleep stage is detected.

[0125] Closed-loop therapy can be performed with a neurostimulator used in combination with a neural recording device that records brain electrical activity while a subject is sleeping, wherein electrical stimulation is delivered to the basal ganglia of the brain of the subject when a pattern of neural activity associated with a selected sleep feature or sleep stage 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, subcortical or cortical brain electrical signal data is recorded (e.g., from the cortical precentral gyrus region and / or postcentral gyrus region) while a subject is sleeping. Varying stimulation settings may be applied at sleep stages or when certain sleep features are detected, including baseline (stimulation off), optimal therapeutic stimulation, modified and ineffective stimulation, and maximum tolerated stimulation to identify personal neural signatures of “sleep dysfunction” and “relief of sleep dysfunction” for a patient, which are used to assist with programming of a DBS device to determine optimal therapeutic stimulation parameters for treatment of sleep dysfunction at individual sleepAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 stages or when certain sleep features are detected. 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.

[0126] 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, or 130 Hz – 150 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, 134 Hz, 136 Hz, 138 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.130.2 Hz, 130.4 Hz, etc.).

[0127] 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 the disease, 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.

[0128] 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 orAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0129] 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.

[0130] 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.

[0131] 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, 2 minutes – 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 sleep dysfunction 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.

[0132] In some embodiments, a cap on the maximum number of electrical stimulations per night can be set. For example, the maximum number of electrical stimulations per night may range from 50 therapies per night to 500 therapies per night, including any number of therapies per night in this range such as 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, or 500 therapies per night. Alternatively or addionally a cap can be set on the total amount of time of electrical stimulation per night. For example, the total amount of time of electrical stimulationAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 per night may range from 10 minutes to 100 minutes of total stimulation per night, including any amount of time within this range such as 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 minutes of total stimulation time per night.

[0133] As noted above, the treatment may ameliorate sleep dysfunction suffered by the subject. Amelioration of sleep dysfunction may include increasing non-REM or REM sleep time, increasing stage N2 sleep time, and / or increasing stage N3 sleep time. Assessment of effectiveness of the treatment may be performed using any known method for evaluating sleep dysfunction. In some embodiments, the subject is monitored while sleeping using actigraphy, electroencephalography, or polysomnography. Autonomic data may also be collected while the subject is sleeping. In some cases, an observer may monitor the subject at night to determine if the subject stays asleep or has nocturnal awakenings. In addition, a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale may be used to assess the effectiveness of the treatment of sleep dysfunction in the subject.

[0134] In certain cases, effectiveness of treatment may be assessed by detecting activity (e.g., electrical signals) associated with a sleep feature or sleep stage, which may be within a subcortical or cortical region, or another area. For example, the brain region may be the cortical precentral gyrus region and / or postcentral gyrus region and may include read outs of physiologically important variables associated with sleep such as delta / slow waves, spindles, K complexes, beta bursts and beta oscillations as well as spectral coherence. 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 detection electrode as described herein or placement of an additional electrode for measuring electrical signals at 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.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0135] Assessment of effectiveness of treatment and assessment of amelioration of sleep dysfunction 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 sleep dysfunction 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, a subject’s subcortical or cortical brain activity (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.

[0136] 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 detection electrodes may be altered before starting a second treatment regimen.

[0137] 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 sleep dysfunction (e.g., sleep dysfunction lasting at least 3 months), physical condition, medication regime, cognitive assessment, anatomical assessment, behavioral assessment and / or neurophysiological assessment. In certain cases, a subject may be further assessed to determine if deep brain stimulation will completely or partially (e.g., at least 50%) relieve the sleep dysfunction. Such a patient may undergo DBS on a temporary trial basis to determine if DBS decreases the severity of sleep dysfunction experienced by the patient. Such a patient may also be implanted with detection electrodes to identify personalized neural signatures of “sleep dysfunction” and “relief of sleep dysfunction” at selected sleep feature or sleep stages 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 improving sleep for the patient.

[0138] In certain aspects, the methods and systems of the present disclosure may include measurement of brain activity, for example, electrical activity in a subcortical region or cortical region, where the level of beta and / or delta frequency power may be measured. In certain cases, electricalAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 activity from a plurality of locations in subcortical or cortical regions may be measured and averaged. In some embodiments, electrical activity in the beta frequency range (such as 12 Hz to 30 Hz) and / or delta frequency range (such as 0.5 Hz to 4.5 Hz) may be measured from a subcortical or cortical 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. In some cases, electrical activity 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 basal ganglia region (e.g., subthalamic nucleus region, globus pallidus region, or thalamic region) or cortex region is applied, or to a period after stimulation to the basal ganglia has been applied, and monitored for an increase of decrease in the power of delta frequency range (such as 0.5 Hz to 4.5 Hz) and / or beta frequency (such as 12 Hz to 30 Hz) activity or other signals. In some cases, when the power of beta frequency (such as 12 Hz to 30 Hz) and / or delta frequency (such as .5 Hz to 4.5 Hz) activity is within a normal range (e.g., a range associated with normal sleep), the methods and systems do not apply a further stimulation to the brain. Alternatively, when the power of beta frequency (such as 12 Hz to 30 Hz) and / or delta frequency (such as .5 Hz to 4.5 Hz) activity is not within a normal range (e.g., a range associated with substantial sleep dysfunction), 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 beta frequency (such as 12 Hz to 30 Hz) and / or increase or decrease gamma frequency (such as 30 Hz to 60 Hz) activity detected at a subcortical or cortical 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.

[0139] 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 sleep dysfunction are further discussed in the Examples section and are depicted in FIG.1B. Closed-loop methods and systems for automated delivery of electrical stimulation are further described below. Closed-Loop Method for Automated Delivery of Electrical Stimulation

[0140] 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 selected sleep feature or sleep stage chosen for treatment. According to certain embodiments, the method mayAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 include receiving an electrical signal from a subcortical or cortical region (e.g., cortical precentral gyrus region or postcentral gyrus region) of the brain of the subject via a detection 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); automatically delivering electrical stimulation to the basal ganglia region (e.g., subthalamic nucleus region, globus pallidus region, or thalamic region) or the cortex region of the brain via a DBS electrode in a manner effective to treat the sleep dysfunction if the electrical signal metrics indicate that the patient is in need of treatment. For example, electrical activity in the beta frequency range (such as 12 Hz to 30 Hz) and / or delta frequency range (such as 0.5 Hz to 4.5 Hz) from a subcortical or cortical region (e.g., cortical precentral gyrus region or postcentral gyrus region) may be measured with a detection electrode, wherein the control algorithm receives the electrical activity data from the detection electrode and automates delivery of electrical stimulation via a DBS electrode to the brain when the level of beta frequency (such as 12 Hz to 30 Hz) and / or delta frequency (such as 0.5 Hz to 4.5 Hz) power indicates that the patient is at sleep stage N2 or N3. 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 improve sleep (e.g., deep sleep) of the subject.

[0141] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.

[0142] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by the one or more spectral power changes in combination with detection of one or more changes in cortical- subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical- subcortical spectral coherence when the subject is awake.

[0143] In certain embodiments, a pre-awakening period or an awakening period is identified by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movementAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. In some embodiments, the increase in subcortical beta power precedes the decrease in cortical delta power.

[0144] In certain embodiments, a post-awakening period is identified by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in a pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

[0145] In certain embodiments, the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power.

[0146] As described in the foregoing sections, effectiveness of treatment of sleep dysfunction may be assessed by detecting brain electrical activity associated with a selected sleep feature or sleep stage using a detection 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 electrical activity 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.

[0147] 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 measurement electrodes. The computing means may communicate with a control unit (also referred to as a controlAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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 measured by the detection 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 which stages of sleep are associated with sleep dysfunction, such as, a range of electrical activity that is indicative of a sleep feature or sleep stage 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 sleep dysfunction). 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 sleep dysfunction 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 basal ganglia region or cortex region of the brain via the DBS electrode.

[0148] In some cases, the computing means, via a control algorithm, may determine whether the received electrical signals are within or outside a predetermined range of neural signals indicative of a selected sleep feature or sleep stage targeted for treatment with electrical stimulation. When the received electrical signals are outside this predetermined range, then the computing means determines that the subject is at a non-targeted sleep feature or sleep stage. 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 sleep feature or sleep stage, 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 at the selected sleep stage or when a selected sleep feature 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.

[0149] 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 rangeAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 is indicative of a second sleep feature or sleep stage 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.

[0150] 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 stimulation to the brain according to the electrical stimulation program. In some embodiments, the closed-loop control system automatically delivers stimulation according to set parameters when the received electrical signals are within a predetermined range indicative of a selected sleep feature or sleep stage 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 FIG.1B.

[0151] 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 subcortical or cortical regions of the brain and compare the brain activity corresponding to one or more sleep features or sleep stages to a range indicative of sleep dysfunction. Upon detection of electrical activity indicative of sleep dysfunction, the closed loop system may automatically commence a treatment protocol of applying electrical stimulation to the brain to target sleep dysfunction at one or more sleep stages or when one or more sleep features are detected that indicate that sleep is impaired. In certain embodiments, a control algorithm modulates one or more programmed stimulation parameters to maximize slow wave activity to improve deep sleep. In certain embodiments, the closed loop system is programmed to monitor brain activity from one or more subcortical or cortical regions of the brain to determine when the subject is at the N2, N3, or REM sleep stage, and automatically commence a treatment protocol of applying electrical stimulation to the brain when a pattern of neural activity associated with the N2, N3, or REM sleep stage is detected.

[0152] In additional aspects, the closed loop system may be used as a system for monitoring brain activity and correlating the brain activity to sleep dysfunction at a particular sleep stage or when aAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 particular sleep feature is detected. For example, since the closed loop system is configured for recording electrical signals from a subject’s brain, sleep features or sleep stages may be monitored in real-time while a subject is sleeping and correlated to the measured electrical signals to provide a biomarker that is related to the subject’s sleep dysfunction at individual sleep stages or when certain sleep features are detected. For example, electrical activity measured when a subject is experiencing sleep dysfunction can be used to develop a biomarker, e.g., a range of electrical activity indicative of sleep dysfunction, and so on. As such, closed loop systems are useful for detecting sleep dysfunction.

[0153] It is understood that electrical signals that are indicative of sleep dysfunction or relief of sleep dysfunction 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 sleep dysfunction or relief of sleep dysfunction for a subject may be recorded from a subcortical or cortical 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 recorded from a subcortical or cortical 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 sleep dysfunction at particular sleep stages. 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 sleep stage, or both. For example, some of the features may be features found in a plurality of patients having sleep dysfunction at a particular sleep stage; some of the features may be features in a particular patient which may not be found in a significant number of other patients having sleep dysfunction. In some embodiments, a combination of patient-specific features and sleep dysfunction- specific features may be monitored to assess efficacy of treatment.

[0154] In a particular aspect, the closed loop system and methods provided herein may involve a recording of electrical signals from one or more subcortical or cortical regions (e.g., cortical precentral gyrus region or postcentral gyrus region, or other region) of a patient’s brain, wherein the patient has sleep dysfunction associated with a movement disorder or a neurological disorder. The patient mayAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 then be treated by application of electrical stimulation to the basal ganglia region or cortex region of the brain, and electrical signals may be recorded from a subcortical or cortical region of the brain (e.g., cortical precentral gyrus region or postcentral gyrus region, or other region) and compared to the pre-treatment recording. Features in the recorded signals that changed after the treatment would correspond to biomarkers that indicate whether the treatment is having an effect. The change in recorded signals can also optionally be correlated to the level of sleep dysfunction 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 improvement in sleep, those features would indicate to a computing means of a closed loop system that further treatment need not be performed.

[0155] 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 that distinguish sleep stages (e.g., wake, N1, N2, N3, and REM) and / or sleep features (e.g., a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post-awakening period, or a sleep stage transition) from one another. 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 sleep feature or sleep stage to be treated with deep brain electrical stimulation. In some embodiments, the level of beta frequency (such as 12 Hz to 30 Hz) and / or delta frequency (such as 0.5 Hz to 4.5 Hz) power is correlated with sleep stage N2 or N3 to determine if a patient is treated with electrical stimulation. In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by an attenuation of beta power in a frequency range of 12 Hz to 30 Hz, an attenuation of gamma power in a frequency range of 30 Hz to 60 Hz), an increase in low frequency theta power in a frequency range of 5 Hz to 10 Hz, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz. Alternatively or additionally, coherence within certain spectral frequency bands or other features of network connectivity may be correlated with a sleep feature or a sleep stage to be treated with electrical stimulation.

[0156] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0157] In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake. In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence.

[0158] In certain embodiments, a pre-awakening period or an awakening period is identified by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. In some embodiments, the increase in subcortical beta power precedes the decrease in cortical delta power.

[0159] In certain embodiments, a post-awakening period is identified by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

[0160] In some embodiments, a computer implemented method for programming a DBS device to treat sleep dysfunction in a subject is provided, the computer performing steps comprising: a) receiving recorded brain electrical signal data from a subcortical region or a cortical region of the brain of the subject while the subject is sleeping; b) analyzing the recorded brain electrical signal data using a classification model that identifies a pattern of electrical signals in the recorded brain electrical signal data associated with a sleep feature or sleep stage of interest; c) adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to an algorithm control law; and d) instructing the DBS device to apply an electricalAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 stimulation to a basal ganglia region or cortex region of the brain of the subject during the sleep feature or sleep stage of interest to treat the sleep dysfunction in the subject. See, e.g., Example 1 and FIG.3.

[0161] Analyzing the recorded brain electrical activity may comprise the use of an algorithm or classifier. In certain embodiments, a machine learning algorithm is used to generate the sleep feature or sleep stage 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.

[0162] 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.

[0163] 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 algorithmAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 may comprise Data Pre-processing. In certain embodiments, the sleep feature or sleep stage classification model is trained by analyzing brain electrical signal data recorded over multiple nights while the subject is sleeping.

[0164] In certain embodiments, the classification model identifies the N2 sleep stage or the N3 sleep stage by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.

[0165] In certain embodiments, the classification model identifies the N2 sleep stage or the N3 sleep stage by one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake.

[0166] In certain embodiments, the classification model identifies a pre-awakening period or an awakening period by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. In some embodiments, the increase in subcortical beta power precedes the decrease in cortical delta power.

[0167] In certain embodiments, the classification model identifies a post-awakening period by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

[0168] In certain embodiments, the computer implemented further comprises receiving accelerometry data for the subject while sleeping; and analyzing the accelerometry data combined with the recorded brain electrical signal data using the classification model to identify a sleep feature or sleep stage.

[0169] In certain embodiments, the computer implemented further comprises receiving data from a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device; and analyzing the data using the classification model to identify the sleep feature or sleep stage.

[0170] In certain embodiments, the computer implemented further comprises receiving autonomic data for the subject while sleeping; and analyzing the autonomic data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

[0171] In certain embodiments, the computer implemented further comprises receiving an electroencephalogram, subgaleal or burrhole / cranially mounted neurostimulator electrode recording or a polysomnogram for the subject while sleeping; and analyzing the electroencephalogram, burrhole / cranially mounted neurostimulator electrode recording, or the polysomnogram using the classification model to identify the sleep feature or sleep stage.

[0172] In certain embodiments, the computer implemented method further comprises generating a hypnogram.

[0173] In certain embodiments, the computer implemented 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 subcortical region or the cortical region of the brain of the subject after applying electrical stimulation with the DBS device to the basal ganglia region or cortex 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 linearAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 classification model; h) receiving recorded brain electrical signal data from the subcortical region or the cortical region of the brain of the subject after applying the electrical stimulation with the DBS device to the basal ganglia region or cortex 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 certain embodiments, the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.

[0174] 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 1 minute of time of the recorded brain electrical signal data, including any amount of time within this range such as 0.5, 0.75, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds of time. In certain embodiments, the computer implemented method further comprises assigning a sleep feature or sleep stage label (e.g., wake, N1, N2, N3, and REM) to each time epoch.

[0175] In some embodiments, the computer implemented method further comprises training the linear model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch by analyzing the recorded brain electrical signal data using a non-linear model during all sleep stages while the subject is sleeping. In some embodiments, canonical delta and beta power bands are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis. In some embodiments, subcortical field potentials are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis. In certain embodiments, the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity. In some embodiments, the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.

[0176] 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 associated with a sleep feature or sleep stage.

[0177] 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 basal ganglia region or cortex region of the brain of the subject to treat the sleep dysfunction in the subject based on the recorded brain electrical signal data.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0178] 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.

[0179] 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.

[0180] 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 may be executed by the processor and data that may be retrieved, manipulated or stored by the processor.

[0181] 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 any well-known processor, such as processors from Intel Corporation. Alternatively, the processor may be a dedicated controller such as an ASIC.

[0182] 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 orAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 collections of independent source code modules that are interpreted on demand or compiled in advance.

[0183] 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.

[0184] 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. In some embodiments, a hardware accelerator is used.

[0185] In some embodiments, the method is performed using a cloud computing system. In these embodiments, the data files and the programming can be exported to a cloud computer, which runs the program, and returns an output to the user.

[0186] Components of systems for carrying out the presently disclosed methods are further described in the examples below. Systems

[0187] 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 basal ganglia region (e.g., subthalamic nucleus region, globus pallidus region or thalamic region) or cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or cortex region and a detection electrode adapted for positioning at a subcortical region or a cortical region (e.g., cortical precentral gyrus region or postcentral gyrus region) of the brain of the subject to record brain electrical signal data while theAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 subject is sleeping before, during, or after an 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 an electrical stimulation to the basal ganglia region or cortex region of the brain of the subject in a manner effective to treat sleep dysfunction in the subject when a brain electrical signal associated with a selected sleep feature or sleep stage is detected using the second electrode; analyze the recorded brain electrical signal data using a sleep feature or sleep stage classification model that identifies a pattern of electrical signals in the recorded brain electrical signal data associated with the selected sleep feature or sleep stage; 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 basal ganglia region or cortex region of the brain of the subject via the control unit, neurostimulator pulse generator and DBS electrode in a manner effective to treat sleep dysfunction if the electrical signal metrics indicate that the patient is in need of treatment. In certain embodiments, a frequency change in one brain hemisphere is introduced to create a difference between the two sides in frequency and induce / enhance endogenous brain rhythms at the frequency difference between the two stimulation frequencies. In certain embodiments, the stimulation intervention could take the form of auditory stimulation or non-invasive stimulation, including transcranial electrical stimulation or transcranial magnetic stimulation. In certain embodiments, the N2 sleep stage or the N3 sleep stage is identified by an attenuation of beta power in a frequency range of 12 Hz to 30 Hz, an attenuation of gamma power in a frequency range of 30 Hz to 60 Hz), an increase in low frequency theta power in a frequency range of 5 Hz to 10 Hz, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz. 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 sleep dysfunction at a selected sleep feature or sleep stage. 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.

[0188] 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 sleep dysfunction inAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 the subject. However, in some embodiments, a user interface may be included in the closed-loop system which may be used to confirm the recommendation of the closed loop system, or to override it, or to change the recommendation.

[0189] 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., normal sleep, substantially free of sleep dysfunction), 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.

[0190] 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 that distinguish sleep stages. The control algorithm then directs a device to apply electrical stimulation to the brain of the subject if the brain activity features indicate the subject is at a sleep stage that 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 sleep feature or sleep stage that should be treated with deep brain electrical stimulation. In some embodiments, the N2 sleep stage or the N3 sleep stage is identified by an attenuation of beta power in a frequency range of 12 Hz to 30 Hz, an attenuation of gamma power in a frequency range of 30 Hz to 60 Hz), an increase in low frequency theta power in a frequency range of 5 Hz to 10 Hz, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz. In some embodiments, field potential data are fit to a sleep feature or sleep stage classification model to determine how to adjust one or more programmed stimulation parameters including physiologically relevant events such as sleep stage, slow waves, spindles, awakenings, and sleep stage transitions. In certain embodiments the algorithm provides updated optimal stimulation setting recommendations to the clinician for guiding programing and decision making.

[0191] 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 basal ganglia region or cortex region to treat sleep dysfunction in a subject. In some embodiments, the user interface is password protected and is operable by a health care practitioner.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0192] In certain embodiments, the system further comprises an accelerometer to record movement of the subject while the subject is sleeping. Accelerometer data can be combined with brain electrical signal data to assist sleep feature or sleep stage classification.

[0193] In certain embodiments, the system further comprises a noninvasive sleep monitoring device, a wearable sleep monitoring device (e.g., smart ring, smartwatch, wrist band, or head band sleep tracker), a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device. Data from such devices can be used to assist sleep feature or sleep stage classification. For a description of such sleep monitoring devices see, e.g., Toften et al. (2020) Sleep Med.75:54-61, Kwon et al. (2021) IEEE J Biomed Health Inform.25(10):3844-3853, Lauteslager et al. (2020) Annu Int Conf IEEE Eng Med Biol Soc. 2020:5150-5153, Chung et al. (2017) Sensors (Basel) 17(7):1685, An et al. (2022) Sci Rep.12(1):21052, Chinoy et al. (2021) Sleep 44(5):zsaa291, Chinoy et al. (2022) Nat Sci Sleep 14:493-516, Shelgikar et al. (2016) Chest 150(3):732-43, Zhao et al. (2021) Entropy (Basel) 23(1):116; herein incorporated by reference in their entireties.

[0194] Components of systems for carrying out the presently disclosed methods are further described in the examples below. Administration of a Pharmacological Agent

[0195] 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 sleep dysfunction in a subject as desired. In some embodiments, the sleep dysfunction 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 sleep dysfunction, severity of the sleep dysfunction 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.

[0196] 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-Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0197] 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

[0198] The methods and systems of the present disclosure find use in the treatment of sleep dysfunction using nighttime deep brain stimulation. Closed-loop stimulation can be finely targeted and tuned in a personalized manner to achieve more reliable and / or more effective relief of sleep dysfunction at selected sleep stages compared to conventional daytime DBS techniques.

[0199] In some cases, the sleep dysfunction is caused by a movement disorder such as, but not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, 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,Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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.

[0200] In some cases, the sleep dysfunction is caused by a neurological disorder such as, but not limited to, a neurodegenerative disease, including Alzheimer’s disease, Parkinson's disease, Huntington's disease, multiple system atrophy or dementia with Lewy bodies, and multiple system atrophy, epilepsy, stroke, bipolar disorder, a neuromuscular disorder, including amyotrophic lateral sclerosis (ALS), Charcot-Marie-Tooth disease (CMT), chronic inflammatory demyelinating polyneuropathy (CIDP), Guillain-Barré syndrome (GBS), Lambert-Eaton syndrome, muscular dystrophy, myasthenia gravis, myopathies, and peripheral neuropathies.

[0201] In some embodiments, the sleep dysfunction is caused by a stroke. Insomnia may occur after a stroke, particularly in patients who have right hemispheric strokes or strokes within the thalamus or brainstem, including the pontine tegmentum and thalamo-mensencephalic region. Hypersomnia may occur after a stroke in patients who have subcortical (caudate, putamen), upper pontine, medial ponto-medullary or cortical strokes affecting the reticular activating system (RAS). Paramedian or bilateral thalamic strokes may initially induce coma, followed by hypersomnia after awakening of the patient. Supratentorial strokes may reduce non-REM sleep, total sleep time, and ipsilateral or bilateral sleep spindles. Saw-tooth waves may be reduced after a hemispheric stroke. REM sleep may be reduced after an occipital stroke. Strokes in the ponto-mesencephalic junction and the raphe nucleus may reduce the amount of non-REM sleep. Strokes in the lower pons can selectively reduce REM sleep. Paramedian thalamus and lower pontine strokes may reduce slow-wave sleep.

[0202] Efficacy of the treatment of patients suffering from sleep dysfunction may be measured in an art accepted manner such as, by using a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale. In some embodiments, assessing effectiveness of the treatment of the sleep dysfunction in the subject comprises monitoring the subject using actigraphy, electroencephalography, or polysomnography. Examples of Non-Limiting Aspects of the Disclosure

[0203] 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 limitingAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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 sleep dysfunction in a subject, the method comprising: positioning a first electrode at a first location in a basal ganglia region or a cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or the cortex region; positioning a second electrode at a second location in a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; detecting a brain electrical signal associated with a sleep feature or sleep stage of interest using the second electrode; and applying electrical stimulation to the basal ganglia region or the cortex region of the brain of the subject using the first electrode in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or the sleep stage of interest is detected using the second electrode. 2. The method of aspect 1, wherein the brain electrical signal data comprises field potential data. 3. The method of aspect 1 or 2, wherein the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region 4. The method of any one of aspects 1-3, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region. 5. The method of any one of aspects 1-4, wherein the sleep stage of interest is N2, N3, or REM. 6. The method of any one of aspects 1-5, further comprising using accelerometry in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 7. The method of any one of aspects 1-6, further comprising using autonomic data in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest. 8. The method of any one of aspects 1-7, further comprising using an electroencephalogram, a polysomnogram, a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device to identify the sleep feature or sleep stage of interest. 9. The method of any one of aspects 1-8, further comprising generating a hypnogram. 10. The method of any one of aspects 1-9, further comprising using a control algorithm to automate said applying electrical stimulation when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected. 11. The method of aspect 10, wherein the control algorithm uses a machine learning algorithm for sleep feature or sleep stage classification. 12. The method of aspect 11, wherein the machine learning algorithm is a supervised machine learning algorithm. 13. The method of any one of aspects 10-12, wherein the control algorithm further modulates one or more programmed stimulation parameters to maximize slow wave activity. 14. The method of aspect 13, wherein the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz. 15. The method of any one of aspects 10-14, wherein the control algorithm further uses linear discriminant analysis (LDA) to adjust stimulation amplitude or frequency of the electrical stimulation. 16. The method of aspect 15, wherein the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 17. The method of any one of aspects 1-16, wherein the electrical stimulation is applied unilaterally or bilaterally. 18. The method of any one of aspects 1-17, wherein the brain electrical signal comprises beta frequency, gamma frequency, delta frequency, or theta frequency neural oscillations. 19. The method of any one of aspects 5-18, wherein the N3 sleep stage is identified by an increase in delta power during the N3 sleep stage compared to when the subject is awake. 20. The method of any one of aspects 5-19, wherein the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake. 21. The method of aspect 20, wherein the N2 sleep stage or the N3 sleep stage is identified by the one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical- subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake. 22. The method of any one of aspects 1-21, wherein the second electrode is placed on a surface of the subcortical or cortical region. 23. The method of any one of aspects 1-22, wherein the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array. 24. The method of any one of aspects 1-23, wherein the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 25. The method of any one of aspects 1-24, wherein the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array. 26. The method of aspect 25, wherein the ECoG electrode array spans precentral and postcentral gyri. 27. The method of any one of aspects 1-26, wherein the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep. 28. The method of aspect 27, wherein the movement disorder is Parkinson’s disease. 29. The method of aspect 27 or 28, wherein the subject is further administered daytime neurostimulation. 30. The method of aspect 28 or 29, wherein the subject is further administered dopaminergic medication. 31. The method of any one of aspects 1-30, further comprising assessing effectiveness of the treatment of the sleep dysfunction in the subject. 32. The method of aspect 31, wherein said assessing comprises using a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale. 33. The method of aspect 31 or 32, wherein said assessing comprises monitoring the subject using actigraphy, electroencephalography, or polysomnography. 34. The method of any one of aspects 1-33, further comprising mapping the brain of the subject to identify an optimal location in the subcortical region or the cortical region to detect the brain electrical signal associated with the sleep feature or sleep stage.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 35. The method of aspect 34, wherein the cortical region is a cortical precentral gyrus region or a postcentral gyrus region. 36. The method of any one of aspects 1-35, further comprising splitting the recorded brain electrical signal data into consecutive time epochs. 37. The method of aspect 36, further comprising assigning a sleep feature or sleep stage label to each time epoch. 38. The method of aspect 36 or 37, wherein each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data. 39. The method of any one of aspects 1-38, wherein the method is performed while the subject is sleeping at home, in a sleep laboratory, or in a hospital. 40. The method of any one of aspects 1-39, wherein the sleep stage is N1, N2, N3, or phasic or tonic rapid eye movement (REM). 41. The method of any one of aspects 1-40, wherein the sleep feature is a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post- awakening period, or a sleep stage transition. 42. The method of aspect 41, wherein the pre-awakening period or the awakening period is identified by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non- rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 43. The method of aspect 42, wherein the increase in subcortical beta power precedes the decrease in cortical delta power. 44. The method of any one of aspects 41-43, wherein the post-awakening period is identified by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre- awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period. 45. The method of any one of aspects 1-44, wherein the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power. 46. The method of any one of aspects 1-45, wherein the electrical stimulation decreases cortical-subcortical sigma spectral coherence. 47. The method of any one of aspects 1-46, further comprising: detecting brain electrical signals associated with one or more additional sleep features or sleep stages of interest using the second electrode; and applying electrical stimulation to the basal ganglia region or cortex region of the brain of the subject using the first electrode in a manner effective to treat sleep dysfunction in the subject when the brain electrical signals associated with the one or more additional sleep features or sleep stages of interest are detected using the second electrode. 48. A computer implemented method for programming a deep brain stimulation (DBS) device to treat sleep dysfunction in a subject, the computer performing steps comprising: a) receiving recorded brain electrical signal data from a subcortical region or a cortical region of the brain of the subject while the subject is sleeping;Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 b) analyzing the recorded brain electrical signal data using a classification model that identifies a pattern of electrical signals in the recorded brain electrical signal data associated with a sleep feature or sleep stage of interest; c) adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to an algorithm control law; and d) instructing the DBS device to apply an electrical stimulation to a basal ganglia region or cortex region of the brain of the subject when the sleep feature or sleep stage of interest is detected to treat the sleep dysfunction in the subject. 49. The computer implemented method of aspect 48, wherein the brain electrical signal data comprises field potential data. 50. The computer implemented method of aspect 48 or 49, wherein a machine learning algorithm is used to generate the classification model. 51. The computer implemented method of aspect 50, wherein the machine learning algorithm is a supervised machine learning algorithm. 52. The computer implemented method of any one of aspects 48-51, wherein the basal ganglia region is a subthalamic nucleus (STN) region, a globus pallidus region, or a thalamic region. 53. The computer implemented method of any one of aspects 48-52, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region. 54. The computer implemented method of any one of aspects 48-53, wherein the sleep stage is N2, N3, or REM. 55. The computer implemented method of any one of aspects 48-54, further comprising: receiving accelerometry data for the subject while the subject is sleeping; and analyzing the accelerometry data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage. 56. The computer implemented method of any one of aspects 48-55, further comprising:Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 receiving autonomic data for the subject while the subject is sleeping; and analyzing the autonomic data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage. 57. The computer implemented method of any one of aspects 48-56, further comprising: receiving an electroencephalogram or a polysomnogram for the subject while the subject is sleeping; and analyzing the electroencephalogram or the polysomnogram combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage. 58. The computer implemented method of any one of aspects 48-57, further comprising receiving data from a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device; and analyzing the data using the classification model to identify the sleep feature or sleep stage. 59. The computer implemented method of any one of aspects 48-58, further comprising generating a hypnogram. 60. The computer-implemented method of any one of aspects 48-59, wherein the classification model is trained to identify the sleep feature or sleep stage by analyzing brain electrical signal data recorded over multiple nights while the subject is sleeping. 61. The computer implemented method of any one of aspects 48-60, further comprising: a) ranking predicted stimulation effectiveness for available settings of the 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 subcortical region or the cortical region of the brain of the subject after applying electrical stimulation with the DBS device to the basal ganglia region or cortex region of the brain of the subject using the settings predicted to have the highest stimulation effectiveness;Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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 subcortical region or the cortical region of the brain of the subject after applying the electrical stimulation with the DBS device to the basal ganglia region or cortex 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. 62. The computer implemented method of aspect 61, wherein the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation. 63. The computer implemented method of aspect 62, wherein the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity. 64. The computer implemented method of aspect 63, wherein the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz. 65. The computer implemented method of any one of aspects 48-64, further comprising splitting the recorded brain electrical signal data into consecutive time epochs. 66. The computer implemented method of aspect 65, further comprising assigning a sleep feature or sleep stage label to each time epoch.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 67. The computer implemented method of aspect 65, wherein each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data. 68. The computer-implemented method of any one of aspects 61-67, further comprising training the linear model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch by analyzing the recorded brain electrical signal data using a non-linear model during all sleep stages while the subject is sleeping. 69. The computer-implemented method of aspect 68, wherein canonical delta and beta power bands are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis. 70. The computer-implemented method of aspect 68, wherein subcortical field potentials are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis. 71. The computer-implemented method of any one of aspects 48-70, wherein the brain electrical signal data comprises field potential data. 72. The computer implemented method of any one of aspects 48-71, further comprising storing a user profile for the subject comprising information regarding the recorded brain electrical signal data associated with the sleep feature or sleep stage. 73. The computer implemented method of any one of aspects 48-72, further comprising storing a user profile for the subject comprising information regarding the programmed stimulation parameters used to apply electrical stimulation to the basal ganglia region or cortex region of the brain of the subject to treat the sleep dysfunction in the subject based on the recorded brain electrical signal data. 74. The computer implemented method of any one of aspects 48-73, wherein the classification model identifies the N2 sleep stage or the N3 sleep stage by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequencyAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake. 75. The computer implemented method of aspect 74, wherein the classification model identifies the N2 sleep stage or the N3 sleep stage by the one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical- subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake. 76. The computer implemented method of any one of aspects 48-75, wherein the sleep feature is a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post-awakening period, or a sleep stage transition. 77. The computer implemented method of aspect 76, wherein the classification model identifies the pre-awakening period or the awakening period by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep. 78. The computer implemented method of aspect 77, wherein the increase in subcortical beta power precedes the decrease in cortical delta power. 79. The computer implemented method of any one of aspects 76-78, wherein the classification model identifies the post-awakening period by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared toAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period. 80. 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 48-79. 81. A kit comprising the non-transitory computer-readable medium of aspect 80 and instructions for treating sleep dysfunction in a subject with a deep brain stimulation device. 82. A system for treating sleep dysfunction in a subject, the system comprising: a first electrode adapted for positioning at a location in the basal ganglia region or cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or cortex region; a second electrode adapted for positioning at a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; and a processor programmed according to the computer implemented method of any one of aspects 48-79 to instruct the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region of the brain of the subject in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected using the second electrode. 83. The system of aspect 82, wherein the brain electrical signal data comprises field potential data. 84. The system of aspect 82 or 83, wherein the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 85. The system of any one of aspects 82-84, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region. 86. The system of any one of aspects 82-85, wherein the sleep stage of interest is N2, N3, or REM. 87. The system of any one of aspects 82-86, further comprising an accelerometer to record movement of the subject while the subject is sleeping. 88. The system of any one of aspects 82-87, further comprising a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device. 89. The system of any one of aspects 82-88, wherein the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array. 90. The system of any one of aspects 82-89, wherein the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array. 91. The system of any one of aspects 82-90, wherein the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array. 92. The system of aspect 91, wherein the ECoG electrode array spans precentral and postcentral gyri. 93. The system of any one of aspects 82-92, wherein the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep. 94. The system of aspect 93, wherein the movement disorder is Parkinson’s disease.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 95. The system of any one of aspects 82-94, wherein the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region to treat the sleep dysfunction in the subject. 96. The system of aspect 95, wherein the user interface is password protected and is operable by a health care practitioner.

[0204] 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

[0205] 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.

[0206] 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.

[0207] 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.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Example 1 Adaptive Deep Brain Stimulation for Sleep Stage Targeting in Parkinson’s Disease Introduction

[0208] Sleep dysfunction is disabling in people with Parkinson’s disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. Improvements in sleep structure after DBS initiation are a fortuitous byproduct of stimulation optimized for daytime motor symptoms (inc. tremor, slowness & stiffness), rather than for overnight sleep physiology7,8,14.Furthermore, adaptive protocols for DBS in PD have primarily focused on beta activity, with minimal attention provided towards overnight slow wave activity15. Modulation of DBS stimulation parameters specifically adjusted to NREM and REM sleep stages plus neurophysiology and behavioral outcomes (e.g., RBD) would provide a critical tool to uncover the interaction between DBS and sleep neurophysiology. Identifying optimal parameters for individual sleep stages has the potential to advance new neuromodulatory therapies targeting sleep dysfunction in order to improve next day motor and non-motor symptoms, and potentially, through optimizing slow wave activity, to slow disease progression7. However, sleep physiology is multifaceted and exhibits dynamics across many frequency bands, conferring complexity beyond conventional beta-band focused adaptive DBS. Consequently, sensitive and specific modulation of stimulation parameters to individual sleep stages benefits from machine learning discrimination of sleep staging based on intracranial data.

[0209] We report a novel approach to sleep modulation in PD using a fully automated, adaptive DBS algorithm that adjusts stimulation amplitude according to sleep stage specific intracranial cortical biomarkers, demonstrated in two participants with PD. We target N3 sleep, as a proof-of-principle of sleep specific adaptive DBS, to investigate preliminary effects on slow wave activity and propose a pipeline that can be implemented fully remotely in patient’s homes to potentially target other sleep stages.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Material and methods A. RC+S System

[0210] This study was reviewed by our Institutional Review Board and registered on clinicaltrials.gov (NCT0358289; IDE G180097). We enrolled two participants diagnosed with idiopathic PD who provided written informed consent. Participants were implanted with bilateral electrodes in the STN (Participant 1) or Globus Pallidus (GP; Participant 2) nuclei. DBS targets were determined by the participant’s treating clinical team and both main targets were included in order to test the pipeline during the presence of stimulation at both STN and GPi. Implanted electrodes were connected to investigational sensing-enabled Summit RC+S (Medtronic) DBS implantable neurostimulators (INS), as part of a parent study investigating daytime closed-loop DBS for motor symptoms (FIG.1B)16. Patients were programmed for conventional DBS by a movement disorder specialist, optimizing stimulation for daytime motor symptoms. Our electrode implementation consists of bilateral sensing and stimulation-capable quadripolar leads in the basal ganglia targets as well as bilateral quadripolar subdural electrocorticogram (ECoG) arrays spanning the precentral and postcentral gyri16. Field potential (FP) time series recordings were analyzed via time frequency decomposition through the Fast Fourier Transform (FFT) embedded within the INS . All data recordings and stimulation testing were performed remotely in patients’ homes. For safety, participants could manually switch from adaptive mode with personal programmers, if needed. B. Polysomnogram and Electrocorticography Data Collection

[0211] Participants streamed overnight subcortical and cortical (precentral gyrus) FPs concomitantly with extracranial electroencephalography (EEG) data from a portable polysomnogram (PSG, Dreem2 headband, Dreem Co., Paris, France17), while on clinically optimized chronic neurostimulation and dopaminergic medication. The Dreem2 headband provides scalp electroencephalography time series as well as automated sleep stage classification hypnograms, aligned with the American Academy of Sleep Medicine sleep scoring methods, but using an automated algorithm validated on healthy adult subjects17,18. The hypnogram of the participants’ sleep stages for a given night were time-data timestamps, up to one second resolution, to the intracranial cortical and subcortical FP data during offline analysis.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 C. Sleep Stage Classifier Model Development

[0212] We recorded five and six consecutive nights of PSG plus intracranial subcortical and cortical precentral gyrus neural data for Participants 1 and 2, respectively; totaling 30 hours for Participant 1 and 36.3 hours for Participant 2 (FIG.1C). During deeper sleep stages of N2 / N3, an attenuation of beta (12-30 Hz) and gamma power (30-60 Hz), with an increase in low frequency theta (5-10 Hz) and delta power (0.5-4.5 Hz) on cortical ECoG data was found, with less pronounced differences found subcortically (FIGS.1D-1E). We therefore utilized cortical, rather than subcortical FP data for embedded RC+S sleep stage classification, in order to minimize stimulation related artifacts and to align with the cortical inputs from the automated hypnograms.

[0213] The RC+S INS has functionality to implement up to 2 linear discriminant classifiers, each using up to four spectral power bands as inputs. The INS’s embedded classifiers compute an inner product of a researcher-defined weight vector (w) with a vector of up to 4 feature inputs (x), and compares the result to a user-defined threshold (α): ^ ^(^^)^^ = ^

[0214] Above-threshold and below- of the inner product lead to control policychanges of stimulation parameters, such as predefined increases or decreases in stimulation amplitude. We implemented a single classifier per INS. The feature inputs to the classifier were power data averaged over 60 FFT interval calculations of 1 second windows (250 samples) with 50% overlap and 100% Hann filter (equivalent to one 30s sleep stage).

[0215] We leveraged the canonical sleep bands (delta and beta) as feature inputs to train the offline Linear Discriminant Analysis (LDA) model (scikit-learn; Python) to classify N3 versus non-N3 sleep epochs (FIG.2A)19,20. The entirety of the 5 and 6 nights for Participants 1 and 2, respectively, were used to train and develop the classifiers. We additionally tested inclusion of theta and gamma bands as input features for Participant 2, however this inclusion was not found to dramatically improve classification performance. LDA model weights were determined independently for each hemisphere, programmed into each INS’s embedded linear discriminant function, and validated in vivo over 2 consecutive nights (FIGS.2A-2C). On validation nights the embedded devices performed real-time continuous N3 sleep stage classification with stimulation amplitude kept continuous (cDBS). For Participant 1, two further test nights were run in which positive N3 classification resulted in a 50% reduction in stimulation amplitude for the subsequent 30 second epoch (aDBS; FIGS.2D- 2E). Stimulation amplitude reduction during N3 sleep was chosen for the safety and tolerance of the participant.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Results

[0216] We demonstrate high specificity (0.94 ± 1.4e-2) for classification of N3 sleep using intracranial cortical embedded neural classifiers, and well above chance sensitivity (0.62 ± 4e-2) across subjects and hemispheres (FIG.2A). Most false positives corresponded to misclassification of N2 sleep, which has an overlapping spectral profile to N3 (FIGS.2B-2C). N3 epochs with ‘deeper’ profiles (i.e., elevated intracranial cortical delta power and reduced beta power), further increased the sensitivity of embedded N3 classification (FIG.2C). In the two aDBS nights, there was successful reduction of stimulation amplitude for 67% and 83% of left and right recorded N3, respectively; with incorrect stimulation modulation in only 3% and 6% of left and right non-N3 (FIGS.2E-2F). Classifier performance was not affected by any potential sensing contamination from stimulation adjustments (FIG. 2A)21. No differences in time per sleep stage between the cDBS and aDBS nights were observed (FIG.2D). However, there was an increase of mean delta power on the left (11%) and right (22%) during N3 epochs for the aDBS nights when stimulation was reduced (Left: t(331) = -3.5, p < 1e-3; Right: t(302) = -5.8, p << 1e-3; FIG.2G). Discussion

[0217] We demonstrate proof-of-principle of intracranially controlled, embedded, adaptive DBS targeted to N3 NREM sleep in two participants with PD. Our approach demonstrated high specificity for stage N3 sleep and sleep stage adaptive DBS was well tolerated, with stimulation changes causing no detectable adverse effects. High specificity (low false positives) is favorable from a clinical perspective, as it reduces unnecessary changes from therapeutic stimulation in untargeted sleep stages. Sensitivity can likely be further improved through the use of subject specific features inputs as opposed to canonical power bands, and tuned to a desired mark by modulation of the LDA threshold. Although a 50% reduction in stimulation amplitude during embedded N3 classification was primarily chosen for safety reasons, the adaptive stimulation paradigm also provided evidence for an increase in slow wave activity. We propose that slow waves are likely suppressed by both intrinsic pathophysiological neural rhythms such as beta (13 - 30 Hz) oscillations as well as excessively high DBS amplitudes22. As beta is itself also suppressed by DBS, this may result in a subject-specific inverted U shaped curve relating stimulation amplitude to NREM slow wave amplitude. Additionally, there are likely other complex, non-linear interactions between DBS sub-harmonics and underlying slow wave entrainment that may allow for an increase in endogenous slow wave activity at optimal DBS amplitudes23. Slow wave activity has been linked to PD disease progression and therefore, ifAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 confirmed over larger numbers of nights and subjects, slow wave optimization through adaptive DBS may represent a promising novel potential therapeutic approach in PD7,22. This is a step towards implementing stimulation protocols to investigate the optimal overnight stimulation amplitude during N3 to support maximal slow wave activity, which we predict willspecific. Taken further, tailored DBS delivery during alternative individual sleep stages may help recover normal sleep physiology and metrics in people with PD, addressing a primary non-motor symptom of Parkinson’s Disease.

[0218] Limitations to this proof-of-principle study include ground-truth sleep stage labeling obtained through a portable polysomnogram and automated sleep-scoring algorithm

[0012] . However, simultaneous intracranial recording demonstrated expected, canonical, ECoG power band changes in different sleep stages classified by the Dreem2 band, notably an increase in delta power during N3 sleep, supporting dissociation of underlying sleep stages in our patient population. Additionally, our portable remote setup supports multi-night recordings in natural settings for improved sleep quality and classification model training, compared to single night sleep laboratory PSG. We also report a small sample size, and do not leverage subcortical data for N3 classification nor incorporate subjective measures of sleep quality. Nonetheless, multi-night, at-home recordings support within subject, individualized, sleep classification models and the proposed methods have flexibility to accommodate expanded participant cohorts, different stimulation targets and inclusion of auxiliary intracranial data streams. Additionally, subjective metrics of sleep quality can be used as an outcome measure for a more complete assessment of sleep aDBS paradigms.

[0219] Translation of the proposed pipeline for patient care might be accelerated if sleep stages could be classified from subcortical electrodes. Complementary studies have shown STN and GPi field potentials display distinct NREM vs REM physiologies in people with PD, and resulting sleep stages can be discriminated in the absence of stimulation24–26. Therefore, the proposed approach could be adjusted to include subcortical field potentials as feature inputs to the personalized linear classifier, although local stimulation related artifacts and signal distortions might reduce classification accuracy.

[0220] Personalized sleep stage adaptive DBS provides a technique to investigate sleep neurophysiology in PD. Additionally, this approach could be leveraged towards adaptive therapies that target sleep symptoms and potentially impact next day motor and non-motor functioning in PD 27–29.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 References

[0221] 1. Bassetti, C. L. et al. Neurology and psychiatry: waking up to opportunities of sleep. State of the art and clinical / research priorities for the next decade. Eur. J. Neurol.22, 1337–1354 (2015).

[0222] 2. Videnovic, A. & Golombek, D. Circadian and sleep disorders in Parkinson’s disease. Exp. Neurol.243, 45–56 (2013).

[0223] 3. Barone, P. et al. The PRIAMO study: A multicenter assessment of nonmotor symptoms and their impact on quality of life in Parkinson’s disease. Mov. Disord. 24, 1641–1649 (2009).

[0224] 4. Diederich, N. J., Vaillant, M., Mancuso, G., Lyen, P. & Tiete, J. Progressive sleep ‘destructuring’ in Parkinson’s disease. A polysomnographic study in 46 patients. Sleep Med.6, 313– 318 (2005).

[0225] 5. Martinez-Martin, P., Rodriguez-Blazquez, C., Kurtis, M. M., Chaudhuri, K. R. & NMSS Validation Group. The impact of non-motor symptoms on health-related quality of life of patients with Parkinson’s disease. Mov. Disord.26, 399–406 (2011).

[0226] 6. Léger, D. et al. Slow-wave sleep: From the cell to the clinic. Sleep Med. Rev.41, 113– 132 (2018).

[0227] 7. Schreiner, S. J. et al. Slow‐wave sleep and motor progression in Parkinson disease. Annals of Neurology vol.85765–770

[0228] 8. Baumann-Vogel, H. et al. The Impact of Subthalamic Deep Brain Stimulation on Sleep-Wake Behavior: A Prospective Electrophysiological Study in 50 Parkinson Patients. Sleep 40, (2017).

[0229] 9. Arnulf, I. et al. Improvement of sleep architecture in PD with subthalamic nucleus stimulation. Neurology 55, 1732–1734 (2000).

[0230] 10. Monaca, C. et al. Effects of bilateral subthalamic stimulation on sleep in Parkinson’s disease. J. Neurol.251, 214–218 (2004).

[0231] 11. Iranzo, A., Valldeoriola, F., Santamaría, J., Tolosa, E. & Rumià, J. Sleep symptoms and polysomnographic architecture in advanced Parkinson’s disease after chronic bilateral subthalamic stimulation. J. Neurol. Neurosurg. Psychiatry 72, 661–664 (2002).

[0232] 12. Zuzuárregui, J. R. P. & Ostrem, J. L. The Impact of Deep Brain Stimulation on Sleep in Parkinson’s Disease: An update. J. Parkinsons. Dis.10, 393–404 (2020).Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0233] 13. Tolleson, C. M., Bagai, K., Walters, A. S. & Davis, T. L. A Pilot Study Assessing the Effects of Pallidal Deep Brain Stimulation on Sleep Quality and Polysomnography in Parkinson’s Patients. Neuromodulation 19, 724–730 (2016).

[0234] 14. Deane, K. H. O. et al. Priority setting partnership to identify the top 10 research priorities for the management of Parkinson’s disease. BMJ Open 4, e006434 (2014).

[0235] 15. Little, S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol.74, 449–457 (2013).

[0236] 16. Gilron, R. et al. Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease. Nat. Biotechnol. 39, 1078–1085 (2021).

[0237] 17. Arnal, P. J. et al. The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging. Sleep 43, (2020).

[0238] 18. Berry, R. B. et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. (American Academy of Sleep Medicine, 2018).

[0239] 19. Mika, S., Ratsch, G., Weston, J., Scholkopf, B. & Mullers, K. R. Fisher discriminant analysis with kernels. in Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468) 41–48 (ieeexplore.ieee.org, 1999).

[0240] 20. Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. arXiv [cs.LG] (2012).

[0241] 21. Ansó, J. et al. Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience. J. Neural Eng.19, (2022).

[0242] 22. Mizrahi-Kliger, A. D., Kaplan, A., Israel, Z., Deffains, M. & Bergman, H. Basal ganglia beta oscillations during sleep underlie Parkinsonian insomnia. Proc. Natl. Acad. Sci. U. S. A.117, 17359–17368 (2020).

[0243] 23. Duchet, B., Sermon, J. J., Weerasinghe, G., Denison, T. & Bogacz, R. How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment. J. Neural Eng.20, (2023).

[0244] 24. Thompson, J. A. et al. Sleep patterns in Parkinson’s disease: direct recordings from the subthalamic nucleus. J. Neurol. Neurosurg. Psychiatry 89, 95–104 (2018).

[0245] 25. Chen, Y. et al. Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials. IEEE Trans. Neural Syst. Rehabil. Eng.27, 118–128 (2019).

[0246] 26. Yin, Z. et al. Pallidal activities during sleep and sleep decoding in dystonia, Huntington’s, and Parkinson's disease. Neurobiol. Dis.106143 (2023).Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0247] 27. Zahed, H. et al. The Neurophysiology of Sleep in Parkinson’s Disease. Mov. Disord. 36, 1526–1542 (2021).

[0248] 28. Verma, A. K. et al. Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings. Neurobiol. Dis.176, 105963 (2023).

[0249] 29. Amara, A. et al. Spindles and Slow Waves Predict Parkinson’s Disease-Mild Cognitive Impairment. Example 2 Additional Applications of Adaptive Deep Brain Stimulation Other Conditions

[0250] Our sleep adaptive DBS approach has been validated in Parkinson’s disease but the approach is applicable to other neurological and psychiatric conditions treated with brain stimulation. This could also be extended to patients without intracranially implanted devices, but for external stimulation in order to implement a personalized sleep specific noninvasive stimulation algorithm using transcranial stimulation or auditory / vibrotactile stimuli. Other sleep stages or micro sleep stages

[0251] Our proof of principle includes one implementation of sleep adaptive DBS – modulation of Deep Brain Stimulation amplitude according to sleep stage - including deep NREM sleep. This pipeline is generalizable to any sleep stage including N1 or REM and could also be parameterized to target more rapid sleep related dynamics to enhance or suppress sleep spindles, slow waves or features of REM. REM is important generally across neuropsychiatric disorders - for mood and memory processing. In Parkinson’s disease there is a particular feature of REM called REM behavior sleep disorder (when patients violently act out dreams) that could also be targeted with this approach. Additionally, biomarkers indicative, or predictive, of on-coming awakening events could be targeted with this approach. Alternative stimulation regimes

[0252] We have thus far shown that we can change stimulation amplitude according to intracranially defined sleep specific physiology. However, this approach is generalizable to changing anyAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 parameter of stimulation - including but not limited to stimulation frequency, pattern, pulse width, electrode contact (vertical or directional or brain-site location), cathodic or anodic stimulation.

[0253] We have also recently been testing and validating a novel approach that implements changes in stimulation frequency on only one side of the brain - in order to induce healthy / helpful (e.g., slow waves) oscillations on both sides of the brain. We believe that by using therapeutic interference related to the difference in frequencies across the two stimulation sides that we can actively enhance specific brain waves. This works by phenomena in which the difference in frequency cause a new frequency to appear. If stimulation is set at 130Hz on both sides (standard stimulation frequency), but the closed loop algorithm detects a sleep stage that warrants boosting of a particular stimulation frequency (e.g. Delta waves at 2 Hz) – then by changing the stimulation frequency on one size to 128hz or 132hz – a new oscillation at the difference between the two frequencies will be created (2Hz) that could then entrain underlying oscillations and might be therapeutic. Example 3 Algorithm Training

[0254] Additionally – rather than having a separate polysomnogram stage – it would be possible to embed a surface electrode into the case of a cranially mounted deep brain pacemaker that could itself serve as an EEG electrode for polysomnography (to provide the sleep labels), removing the necessity for either polysomnography or additional intracranial hardware (e.g. chronic electrocorticography). Other extensions to this simplification would be to have a subgaleal electrode (above the skull, under the scalp) or an electrode embedded into the electrode “cap”, which fastens the DBS electrodes into the skull. All these methods could provide chronic recording of electrocorticography, but without the need for extra hardware, within the cranium (which increases surgical risks).

[0255] We also envisage an extension to classify sleep stages from the deep, subcortical, electrodes used for stimulation. This has already shown to be possible in the absence of stimulation. With stimulation turned on - this will require mitigation of stimulation artifacts and modeling of the impact of stimulation on underlying recorded rhythms to avoid self-triggering from stimulation related artifact changes to neural recordings.

[0256] At the individual level - to achieve the best possible classifier - we would plan to incorporate reinforcement learning to further optimize stimulation parameters in order to optimize slow waves and sleep spindles. Other potential inputs to train this classifier would include 1) overnightAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 neurophysiological and sleep stage metrics 2) patient self-report of sleep quality 3) mood, motivation and cognition testing in the morning (self-report or computational behavioral paradigms) 4) resting state intracranial brain activity associated with positive or negative states 5) resting state connectivity metrics including evoked response activity, theta pattern stimulation, evoked resonant neural activity. 6) daytime behavioral metrics measured from e.g. smartwatch, exercise quantification wearables, smartphones, voice recording models, pose estimation, GPS devices to measure activity and clinical state 7) classification inaccuracy in the embedded device

[0257] Additionally, following within subject normalization, we plan to generalize the findings from single (or multiple test subjects) to a combined model that does not require individualized model training at the outset. This is likely to be more scalable than having individualized models for every subject and our hope is that, with appropriate within subject normalization and a generalizable model - that the reduction in classification accuracy would be low and acceptable.

[0258] Our current implementation is constrained by the embedded linear discriminant capabilities of the DBS device; namely, two linear discriminant equations dividing a 4-dimensional feature space into 9 partitions. We leverage a single linear discriminant analysis equation on optimized frequency power bands for sleep stage classification. We have extended embedded sleep stage classification to include support vector machines, 2-step decision tree classifier models, 2-step Gradient Boosting Machines, 2-step linear discriminant analysis classifiers, and plan to extend classification to include the estimation of parabolic or other nonlinear boundaries via two first-order taylor expansions along the nonlinear boundary, optimized via convex optimization approaches. Additionally, if one is unconstrained by embedded device capabilities, as would be the case if real-time classification is performed by a nearby tablet or computer, then our pipeline could be extended towards any nonlinear classifier of sleep stage or microstructures of sleep. This includes, but is not limited to, artificial neural networks (recurrent neural networks, transformers, long-short term memory networks, feed-forward networks), gradient boosting machines, random forests, and quadratic discriminant analysis. These nonlinear classifiers would utilize a larger feature space, including, but not limited to, entropy or slope of frequency powers, ratios of various power bands combinations, second or higher order moments of time domain and / or frequency distributions, and alternative data streams such as accelerometry, temperature, or breathing rate.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Example 4 Safety of Adaptive Deep Brain Stimulation

[0259] Safety is paramount in our studies and in developing any new stimulation approach. In the current implementation, there are a number of safety approaches already that ensure safe and tolerable adaptive DBS for sleep.

[0260] Currently, stimulation limits are set on the pacemaker that have been tested to be safe and tolerable by a clinical neurologist in a supervised manner. These stimulation limits provide guard rails against dangerous stimulation. An extension of this current practice would to implement iteratively (or using Bayesian optimization or other data driven approaches) stimulation parameter limits that start conservatively (no change) and progressively diverge towards larger and larger changes in stimulation parameters

[0261] Currently, patients have their own patient programmer, in which they can adjust stimulation amplitude or change into or out of adaptive DBS mode. Therefore, patients always have the final control of their stimulation. However, at night time when it is dark - it might be difficult to find the patient programmer or if in a worsened clinical state - have the dexterity to manage a change on this. Extensions on this might include voice or motion activation of the patient programmer so that the patient (or care giver) can easily command a switch back to conventional DBS without having to manually interact with the physical programmer. Alternatively, an application accessible via a wearable smartwatch would allow the participant adequate control of stimulation parameters in a dark environment from supine or prone position.

[0262] In addition to manual control of the stimulation through the patient programmer (by the patient) there are a significant number of automated approaches that could be employed to automatically switch the patient out of adaptive DBS back into conventional DBS. Firstly, using the classifier itself on board the device (or from an externally worn sleep sensing wearable / polysomnogram headband). These could be programmed to detect both awakenings and movement and therefore could be automatically set to switch stimulation settings back to conventional DBS if stimulation was shown to significantly increase awakenings, disrupt sleep physiology or cause overnight abnormal movements (that could be classified from the motion sensor). Additionally, it would be important for the device to analyze its own stimulation pattern and behavior. Neural systems are inherently somewhat stochastic and therefore if the stimulator control algorithm results in highly regular / stereotyped algorithm behavior - this would be suggestive that the algorithm was self triggering and would also warrant a termination of adaptive DBS and switch to conventional DBS.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Other markers that might highlight a signal to move back to adaptive DBS could use EKG or pulse metrics to analyze changes in heart rate or heart rate variability that could signal underlying physiological stress. These limits could be compared against normative reference distributions or, alternatively, against within subject, personalized distributions of heart rate, heart rate variability, electromyography, galvanic skin response, motion (accelerometer or gyroscope) on a wearable (or with a non invasive measure such as bed sensors or radar) or other personalized metrics. Example 5 Multi-Night Naturalistic Cortico-Basal Recordings Reveal Mechanisms of NREM Slow Wave Suppression and Spontaneous Awakenings in Parkinson’s Disease Introduction

[0263] Sleep disruption is one of the most prevalent non-motor symptoms of Parkinson’s disease (PD) with up to 90% of PD patients experiencing sleep dysfunction1and 60% having multiple sleep disturbance symptoms1,2. Changes in sleep patterns often predate classical neurological symptoms in PD and correlate with rates of progression and disease severity3. Sleep dysfunction in PD has a negative impact on daytime mood, cognition, fatigue, and other co-morbidities4–8, with non-motor and sleep symptoms being a greater determinant of quality of life than classical motor symptoms9–11. Therefore, understanding the neurophysiology of sleep disturbances in PD may potentially result in new principled therapies directed towards better sleep quality, mitigation of daytime symptoms and improved patients’ quality of life.

[0264] Sleep architecture in humans is broadly defined by physiologically distinct stages of rapid eye movement (REM) and non-REM (NREM) sleep. NREM sleep is further characterized by rhythmic low frequency electroencephalography (EEG) activity in the delta (0-4 Hz) and theta (4-7 Hz) ranges, increased parasympathetic activity and limited dreaming. There are currently three formally defined sub-stages of NREM: N1 (light sleep), N2 (appearance of K complexes and sleep spindles) and deep N3 (characterized by slow delta waves)13. Sleep dysfunction in PD manifests as parasomnias, fragmented sleep and disrupted sleep patterns, including notable reductions in both REM and NREM sleep11. In particular, reductions in NREM sleep slow wave activity in the delta range (< 4Hz) are associated with worsening of daytime motor symptoms and accelerated disease progression in PD3,14,15.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0265] During wakefulness, beta oscillations (13-30 Hz) are the hallmark oscillatory signature of PD and correlate with daytime motor symptoms16. Recent studies with non-human primates (NHPs) during sleep have shown that subcortical beta activity is also associated with a decrease in cortical delta activity and suggested a role for subcortical beta in spontaneous awakenings in PD17. The presence of beta oscillations has been detected during sleep in PD patients18–22. However, to date, human studies have not yet investigated mechanistic interactions between subcortical beta and cortical sleep physiology (inc. slow waves) nor spontaneous awakenings and previous studies have been completed in the absence of deep brain stimulation (DBS). Understanding the real-world contribution of the cortico-basal ganglia circuit to sleep dysfunction in PD and its interaction with DBS, has to date been limited by an inability to chronically record the intracranial activities overnight, at high resolution. This challenge has been solved by the advent of a new generation of sensing- enabled DBS devices that can stream neural data remotely from patients' own homes23. A better understanding of cortico-basal activities during sleep has the potential to reveal underlying mechanisms of sleep dysfunction in PD and could contribute to improved sleep therapies including sleep-targeted adaptive deep brain stimulation (aDBS).

[0266] In this study, we recruited four patients diagnosed with PD and one comparison patient with cervical dystonia, all with chronically implanted intracranial electrodes capable of sensing sensorimotor cortical and basal ganglia (STN / GPi) field potentials (FPs). We conducted overnight, at-home, intracranial cortical and subcortical recordings paired with portable polysomnography over multiple nights (n= 58) in the presence and absence of DBS stimulation. We demonstrate significant interactions between subcortical beta oscillations and cortical slow wave activity in the delta band during NREM, an effect modulated by DBS, and also show that subcortical beta significantly increases prior to spontaneous awakenings.Results

[0267] Four people (Table 1) with PD (x2 people with bilateral STN + sensorimotor cortical ECoG and x2 people with bilateral GPi electrodes + sensorimotor cortical ECoG) and one person with cervical dystonia (bilateral GPi electrodes + sensorimotor cortical ECOG), successfully initiated recordings from intracranial cortico-basal and external portable polysomnography (Dreem2) over 58 nights (54 ON and 4 OFF stimulation nights), remotely in their own homes. Intracranial and extracranial recordings were synchronized and artifacts removed (FIG. 4E; FIG. 11), resulting in interpretable cortical and subcortical recordings, even in the presence of DBS. A total 415 hours of sleep were recorded across all participants (Supplementary Table 1; FIG.9). PD subjects slept onAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 average 7.25±0.18 hours per night during the extended multi-night ON stimulation (n=45; duration in minutes: N1 = 34.26±1.34; N2 = 164.72±7.92; N3 = 90.06±10.53; REM = 97.64±6.54; Wake after sleep = 48.18±4.71). In the separate two night consecutive ON versus OFF DBS comparison recording nights, all four PD subjects showed an increase in time in deep NREM (N3) and REM in the ON stimulation compared to the corresponding OFF stimulation nights (Supplementary Table 1 & FIG. 5). Power spectral density plots from intracranial electrodes (FIG. 4G and FIG. 12) demonstrated expected classical changes in canonical frequency bands in NREM and REM sleep stages, supporting dissociation of different sleep stages using our portable PSG device sleep staging. Spectral power changes in NREM

[0268] We investigated spectral changes in intracranial FP activities during NREM (N2 and N3) with wakefulness as a baseline with an a priori focus on cortico-basal delta and beta24. Power spectrum analyses and Linear Mixed Effect (LME) models for average overnight band powers with a fixed effect for sleep stage (NREM vs Wake; accounting for multiple nights within participants) and a random effect for subjects (n=5) showed an increase in average delta power (1 - 4 Hz; cortex: ^ = 0.42, 95%CI= [0.36, 0.47], p-value = 3.7e-33; subcortex: ^ = 0.1, 95%CI= [0.07, 0.13], p-value = 3.3e-12; n=105; CI=confidence interval) and decrease in beta (13 - 31 Hz; cortex: ^ = -0.4, 95%CI= [-0.44, -0.37], p-value = 1.5e-41; subcortex: ^ = -0.2, 95%CI= [-0.22, -0.17], p-value = 1.4e-25) power both in cortical and subcortical regions in NREM sleep compared to wakefulness (FIGS. 5A-5B; multi-night ON stimulation). These spectral changes in NREM compared to wake were also seen during the single night of OFF DBS sleep recordings in both cortical and subcortical regions of all four PD participants (FIG.5C).

[0269] A direct comparison of our PD (n=4) vs Dystonia (n=1) analyses revealed that subcortical beta power was lower in the dystonia patients than all four of the PD patients during NREM sleep (LME model for PD vs Dystonia fixed effect: ^ = 0.18; 95%CI= [0.09, 0.27]; p-value = 0.0001). Further, band power changes between NREM sleep and wake condition in the dystonia participant were smaller compared to the PD patients (FIG. 5B). LME models demonstrated statistically significant fixed effects of disease state (PD vs Dystonia) on the changes of band power between NREM and wake stage in cortex (delta: ^ = 0.24, 95%CI= [0.14, 0.35], p-value = 1.3e-5; beta: ^ = - 0.21; p-value = 3.4e-7) and subcortex (delta: ^ = 0.08, 95%CI= [0.013, 0.14], p-value = 0.02; beta: ^ = -0.13, 95%CI= [-0.2, -0.07], p-value = 0.0001).Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0270] We also investigated how these FP activities alter with stimulation and compared power spectrums between ON and OFF stimulation conditions in our PD cohort (n=4). Spectral power comparisons revealed a relative further increase in delta and further decrease in alpha and low beta activities in cortical FP during NREM sleep in the ON versus OFF DBS conditions (FIG.5D). LME models with subjects as random effects revealed that stimulation resulted in a further increased cortical delta (1-4 Hz; ^ = 0.026, 95%CI= [0.003, 0.05], p-value = 0.03) and decreased cortical alpha (8-13 Hz; ^ = -0.0297, 95%CI= [-0.05, -0.003], p-value = 0.03), low beta (13-15 Hz; ^ = -0.026, 95%CI= [-0.042, -0.01], p-value = 0.006) in the ON versus OFF condition, for NREM versus wakefulness. No significant changes in subcortical FP during NREM were observed in ON vs OFF power spectrum comparisons, however changes in subcortical baseline power levels in the ON versus OFF DBS state may have obscured any underlying changes. Overall, these data reveal that DBS results in relatively higher cortical delta activity and reduced alpha and low beta activities in NREM sleep. Changes in functional connectivity in NREM

[0271] We next explored NREM-related changes in the functional connectivities between cortical and subcortical regions to investigate sleep-related changes in cortico-basal ganglia circuitry in PD. For this, we compared the spectral coherence in cortical and subcortical FP activities between NREM sleep and wakefulness. In all participants, LME models investigating spectral coherence with a fixed effect of sleep stage (NREM vs Wake) revealed that the total difference in spectral coherence in delta increases (^ = 0.05, 95%CI = [0.04, 0.06], p-value = 5e-11; n=104) while beta decreases (^ = -0.18; 95%CI = [-0.23, -0.14], p-value = 5.5e-13) during NREM sleep compared to wake in ON stimulation (FIGS.5E-5F). An increase in delta coherence and a decrease in beta coherence during NREM were also observed in the PD participants during their single night recordings OFF stimulation (FIG.5G). PD vs Dystonia comparison also showed that cortio-basal delta / beta coherence changes in NREM versus wakefulness were smaller in the dystonia participants compared to the PD participants (LME model with PD / Dystonia condition as a fixed effect; delta coherence: ^ = 0.05, 95%CI= [0.02, 0.08], p-value = 0.0008; beta coherence: ^ = -0.21, 95%CI= [-0.31, -0.11], p-value = 0.0001).

[0272] In our ON vs OFF DBS analysis (PD participants only; n=4), we also noted a statistically significant further decrease in low beta (13 - 15 Hz) coherence during ON stimulation compared to OFF (LME model with ON / OFF condition as fixed and subjects as random effects; ^ = -0.012, 95%CI= [-0.021, -0.003], p-value = 0.015). Collectively, these data demonstrate that functionalAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 connectivity between cortical and subcortical structures is modulated during NREM sleep versus wakefulness. There is an increase in delta coherence and a decrease in beta coherence in PD in both ON and OFF stimulation conditions, effects that are enhanced in the DBS ON condition. Interaction between cortical delta and subcortical beta activity

[0273] Spectral power and functional connectivity analyses above revealed opposing changes in delta and beta FP activities in NREM sleep versus wakefulness. To further examine for a direct relationship between these two rhythms, we investigated the interactions between cortical delta and subcortical beta FP activities specifically within NREM (N2 + N3) on short time scales. Here, we observed an inverse relationship between cortical delta power and subcortical beta power during NREM sleep (FIG. 6A). To quantify this relationship, we first used a standard correlation analysis which revealed a negative correlation between subcortical beta and cortical delta FP power (5 s epochs) in all PD participants during NREM in both ON and OFF stimulation conditions (FIGS.6B- 6C). LME modeling using band powers of NREM epochs from all participants (Cervical dystonia and PD participants; accounting for the dependency between left and right hemispheres and multiple nights within patients; n=232,064) showed an overall negative fixed effect of subcortical beta power on cortical delta power (^ = -0.24, 95%CI: [-0.28, -0.2], p-value = 3.9e-30). Additionally we found a fixed effect of PD vs Dystonia state (^ = 0.06, 95%CI: [0.01, 0.11], p-value = 0.02) during NREM sleep ON stimulation, demonstrating that this effect was greater in the PD patients that our dystonia comparison subject. Negative fixed effect of subcortical beta power on cortical delta power was also obtained through LME model in PD participants during the OFF stimulation condition (^ = -0.38, 95%CI: [-0.44, -0.32], p-value = 1.9e-32; n=17,518). These results demonstrate that there is an inverse relationship between subcortical beta and cortical delta FP power within NREM sleep in PD both during ON and OFF stimulation conditions and that this effect is significantly stronger than in our comparison dystonia patient.

[0274] Next, we utilized cross-correlation analyses to determine whether subcortical beta was leading or lagging cortical delta changes. We observed that the subcortical beta increase was leading the cortical delta decrease in 3 out of the 4 PD participants during NREM sleep (FIG.6D; average lag over multiple nights ON DBS; PD2: 4.5s, n=11; PD3: -11.4s, n=11, PD7: -7.5s, n=10, PD9: -3s, n=10). Finally, as a control analysis to rule out a prosaic inverse relationship between cortico-basal circuit delta and beta, simply reflecting depth of NREM sleep, we also correlated cortical delta and cortical beta power from the same region. If the inverse relationship between cortico-basal delta and beta was simply a function of sleep stage depth, we would expect the inverse relationship betweenAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 cortical delta and cortical beta to be strongly inverted. Unlike correlations between subcortical beta and cortical delta, which were negative for all PD participants, cortical delta and beta showed a weaker negative correlation in only 3 PD participants and a positive correlation in one PD participant (FIG. 6E) as well as in the Dystonia participant during NREM (ON stimulation). LME analysis did show an overall negative fixed effect of cortical beta power on cortical delta power (^ = -0.21, 95% CI: [-0.32, -0.1], p-value = 0.0002; n=232,064) during NREM sleep but did not show any fixed group effect of PD / Dystonia state (^ = -0.02, 95% CI: [-0.06, 0.004], p-value = 0.1), indicating that at the cortical level there was no evidence of difference between the PD / dystonia conditions, in contrast to subcortically. Additionally, in direct model comparison, the LME model for cortical delta with a fixed effect of subcortical beta showed a statistically significant improvement over the model of cortical delta with a fixed effect of cortical beta (simulated likelihood ratio test with 100 replications; p-value = 0.01). This demonstrates that subcortical beta had a stronger effect on cortical delta compared to the relationship between cortical beta activity and cortical delta activity supporting that this subcortical beta - cortical delta effect is greater than any effect of sleep stage depth. Additionally, our data showed that subcortical beta-cortical delta effect was relatively specific to PD. Changes in spectral power before spontaneous awakenings

[0275] To better understand FP activities at a finer time resolution and investigate the dynamics of intracranial FP that lead to awakenings, we analyzed the change in spectral powers in delta and beta during NREM to spontaneous wake transitions. There were on average 26.7±1.5 awakening events per night, with a total duration of 52.7±4.5 minutes for each participant ON stimulation. During NREM, cortical delta power gradually increases as sleep deepens and decreases before awakening (FIG. 7A) in all participants (multi-night ON stimulation dataset). The average cortical delta power in pre- awakening (-5s) and post-awakening (+15s) periods were both lower compared to the average spectral power found in deep NREM stage, (FIG. 7A; pre-wake: ^ = -1.3, 95%CI: [-1.9, -0.6], p- value = 7.4e-5; n=446; post-wake: ^ = -2.9, 95%CI: [-3.5, -2.3], p-value = 7.7e-20) with the post- awakening cortical delta power being lower than the pre-awakening (FIG.7A; ^ = -2.9 vs -1.3) and no fixed effects of PD / dystonia condition (pre-wake: p-value = 0.5; post-wake: p-value = 0.06). This suggests that changes in cortical delta is not PD specific, but rather a general feature of changes in neurophysiology in NREM sleep versus wakefulness. The subcortical delta (pre-wake: p-value = 0.6; post-wake: p-value = 0.002) and cortical beta power (pre-wake: p-value = 0.00004; post-wake: p- value = 0.98) did not demonstrate consistent pre and post wake changes which were significant (FIG. 7B-7C) across participants, around the time of spontaneous awakenings. However, theAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 subcortical beta power demonstrated a rise before awakenings which was sustained after awakening (FIG.7D; pre-wake: ^ = 0.6, 95%CI: [0.35, 0.84], p-value = 2.5e-6; post-wake: ^ = 1.4, 95%CI: [1.1, 1.6], p-value = 1e-19; post-awakening power was higher than pre-awakenings (^ = 1.4 vs 0.6). Disease state (PD / dystonia) showed a statistically significant fixed effect on the rise of subcortical beta power before awakenings (^ = -0.9, 95%CI: [-1.36, -0.43], p-value = 0.0002) but only a trend after awakenings (post-wake: p-value = 0.06) suggesting a that the rise of subcortical beta is PD specific.

[0276] Finally, we investigated whether machine-learning models can predict the transition to spontaneous awakening. For this, we trained subject-specific quadratic discriminant analysis (QDA) classifiers with cortical delta, cortical low gamma, subcortical beta and low gamma spectral powers during ON stimulation as features. For each participant (4 PD and 1 Dystonia), we performed 10-fold cross-validation in a 5s epoch window from deep NREM to spontaneous awakening events (FIG. 8A). We found that the wake prediction provided by subject-specific models increases its prediction of the awake state, before full awakening has occurred. Specifically, the QDA models were able to differentiate between deep NREM and pre-wake NREM (-5s) with reasonable accuracy (~70%) in the majority of the participants (deep vs pre-wake NREM accuracy: Dystonia = 63.7%, PD3=73.7%, PD9=55.6%, PD2=68.9%, PD7=69.8%; Supplementary Table 2; FIGS. 8A-8B). Deep NREM vs awake stage (+15s) showed higher accuracies compared to deep vs pre-wake NREM as expected. Area under the curve (AUC) performances were promising among PD participants (>70% in PD3, PD2 and PD7; Supplementary Table 2; FIG.8B) in deep vs pre-wake NREM classification indicating that ROC-based optimization of the classification thresholds may further improve the prediction of awakenings in PD. The performance of the QDA models, despite having only four spectral power features as inputs, suggests the viability and potential applications of machine-learning algorithms for identifying micro-stages of sleep and designing adaptive DBS therapies that can modulate stimulation to prevent awakening.Discussion

[0277] We collected multi-night intracranial brain recordings from four PD and one dystonia participant from cortical and subcortical regions, paired with polysomnography for both DBS ON and OFF conditions, remotely in patients’ own homes over 58 nights. We found increased slow wave activity in the delta band and decreased beta power and connectivity in the cortio-basal network during NREM, an effect that was enhanced by DBS. Within NREM, there was a direct inverse relationship between subcortical beta and cortical delta activity and further, we found that subcorticalAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 beta power rises before spontaneous awakenings. These data strengthen the hypothesis that subcortical beta is related to overnight sleep disruptions and spontaneous awakenings in PD.

[0278] Our study advances understanding of sleep neurophysiology in a number of areas. First, technically, we recorded high resolution intracranial cortical and subcortical neural activity during sleep, over multiple nights (n=58), using a fully embedded, sensing-enabled DBS device in the naturalistic setting from participants in their own homes. Second, we provided evidence of subcortical beta and cortical delta interaction during NREM in PD participants and its modulation by DBS. This effect has been previously noted in a primate model of PD17, but to our knowledge, our study demonstrated this interaction in humans with PD for the first time. Third, this is supported by analyses of NREM sleep neurophysiology in both ON and OFF stimulation which disclosed both stimulation- dependent (increased cortical delta and decreased alpha plus low beta in NREM) and stimulation- independent (subcortical beta and cortical delta interaction) effects. Our cortico-basal delta beta interaction finding and pre-awakening subcortical beta rise were significantly stronger effects in the PD cohort in our comparison with our dystonia subject.

[0279] It is now established that during the daytime, subcortical beta oscillations are excessive in PD and potentially contribute to circuit disruption and motor symptoms25,26. Here, we show that subcortical beta oscillations also disrupt cortical slow oscillations during NREM sleep in humans with PD and are partially responsible for awakenings during the night, validating findings from PD models in primates17. Further, we show that DBS stimulation, known to reduce subcortical beta oscillations during wakefulness27, here resulted in the increase in cortical delta power and a decrease in cortical alpha and low beta during NREM sleep. This finding aligns with previous studies where an increased accumulation of EEG delta power during NREM sleep was found as a result of subthalamic DBS in PD28. One current hypothesis is that DBS therapy improves subjective sleep by reducing overnight discomfort through improved motor movements. Data in our study indicates that DBS therapy appears to additionally improve sleep in PD through direct modulation of beta and delta oscillations.

[0280] Although previous studies have documented the presence of subcortical beta oscillations during sleep in STN and GPi18–22, these studies to date have been single night studies and have not probed interactions between beta and delta or spontaneous awakenings during the night nor investigated DBS stimulation effects on sleep physiology. Here, we show that within NREM sleep, subcortical beta inversely correlates with cortical delta power and precedes spontaneous awakenings on a fast time scale. Our findings on the mechanisms of cortical-subcortical interactions during sleep provide a foundation for the development of closed-loop adaptive DBS approaches for restoring normal sleep patterns in people with PD.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0

[0281] The link between sleep dysfunction and daytime motor, mood and cognitive symptoms makes sleep an enticing potential target for further investigation6–8. Moreover, sleep disturbances, and particularly reductions in cortical slow wave activity during NREM has been linked to faster disease progression3,14. Therefore, targeting beta oscillations during NREM sleep has the potential to reduce overnight insomnia, increase cortical slow waves and improve waking motor and non - motor symptoms. This supports the proposal that daytime neural activities and overnight sleep physiology are notably dissociable and require different strategies for aDBS to optimize rhythms during these two distinct phases. Implementing different aDBS algorithms around the circadian cycle could be achieved by the introduction of daytime (versus sleep) neural classifiers, circadian (clock) based algorithms and combined feedforward and feedback controllers that optimize both daytime and nighttime neurophysiology29,30.

[0282] Our study has limitations that warrant discussion. First, our ground-truth sleep stage labelings were obtained through a portable polysomnogram and automated sleep-scoring algorithm, validated on healthy controls31, instead of a conventional laboratory based PSG. However, we note that our intracranial recordings, grouped according to sleep stages defined from our portable PSG, revealed anticipated and classical changes in ECoG activities across various stages (FIG.12). In particular, the observed elevation in delta power during N3 sleep and reduction in beta power provides evidence of the differentiation of underlying sleep stages within our group of patients using this scheme (FIG. 4F). Furthermore, our portable remote setup enabled us to collect multi-night recordings in a natural setting which compares favorably to single-night PSG recordings (from a sleep laboratory) which can be subject to first night acclimatization effects. We also report a small sample size of participants, although notably, we collected many nights of recordings per subject (n=58 total) which supported highly statistically powered LME analyses that modelled within as well as across subject effects, similar to the strengths of primate research. Our comparison participant was a single cervical dystonia patient (rather than a formal control group) reflecting the uniqueness of this patient cohort, with high resolution sensing-enabled pulse generators and chronically implanted ECoG electrodes. However, despite this, and in view of the large within-subject dataset size and linear mixed modeling, we were able to show a difference between the dystonia patient and the PD group, which should though be supported in future by larger and more balanced cohorts. Finally, we here restricted our analysis to NREM and canonical power bands with a focus on beta and delta24. We did not examine changes in other sleep stages or specifically analyze sleep spindles (which overlap in frequency with low beta) or other frequency bands which will be reported separately.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Conclusions

[0283] In this study, we recorded and analyzed intracranial FPs with extracranial polysomnography at-home over multiple nights in PD participants, in the presence and absence of DBS. Our data revealed that cortico-basal network power and connectivity in the delta and beta bands are increased and decreased in NREM versus awakening respectively, an effect that was enhanced by DBS. Further, within NREM, cortical delta band slow wave activity was inversely related to subcortical beta, which also rises prior to spontaneous awakenings. These findings uncover a role of subcortical beta in sleep dysfunction in PD and provide targets for future personalized sleep-specific adaptive DBS. Methods Participants, demography, and ethics

[0284] We recruited 4 participants with idiopathic PD for this study (Table 1). A movement disorders physician diagnosed each individual with PD according to the Movement Disorder Society PD diagnostic criteria32. The motor component of the United Parkinson’s Disease Rating Scale (UPDRS) scores were administered by trained raters. We also recruited one participant with cervical dystonia as a comparison subject. Participants were recruited from a parent study focused on investigating closed-loop DBS for daytime motor symptoms. Implanted electrodes were connected to an investigational sensing-enabled Summit RC+S DBS implantable pulse generator provided by Medtronic (FIG.4A)23. This study was reviewed by our Institutional Review Board and registered on clinicaltrials.gov (NCT0358289; IDE G180097). The study was also reviewed by the Human Resources Protection Office (HRPO) at Defense Advanced Research Projects Agency (DARPA). Written informed consent was provided by all participants. All subjects had chronic bilateral cortical ECoG electrodes and two PD participants were implanted with bilateral electrodes in the Subthalamic Nucleus (STN; PD2 and PD7) and two PD and 1 dystonia subject were implanted with bilateral electrodes in the Globus Pallidus (GPi; PD3, PD9 and dystonia subject) nuclei (FIG. 4B). DBS electrode implantation targets were determined by the clinical team. A movement disorder specialist programmed the patients with conventional DBS settings, optimizing stimulation to address daytime motor symptoms.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Experimental design and protocols

[0285] We collected data using two protocols: long-term multi-night data collection ON stimulation plus separate two night comparison recordings, one night ON DBS and one night OFF DBS. During the long-term overnight data collection, each participant (n=5) was equipped with a portable PSG (Dreem2) headset and overnight intracranial data as well as polysomnography data were recorded for ~10 nights (Supplementary Table 1) that were predominantly consecutive. In the ON / OFF protocol, overnight data from the PD participants (n=4) were collected for two consecutive days. On the first day DBS was ON (3.075 ± 0.65 mA) and the next day DBS was OFF. During both data collection protocols, the PD participants were on their regular clinical dopaminergic replacement medications. ON / OFF recordings were not completed in the cervical dystonia patient at the patient’s request. All data recordings were performed remotely in patients’ homes. Polysomnography acquisition

[0286] Extracranial polysomnography (PSG) was recorded through the Dreem headband which includes an automated sleep staging algorithm with extracranial electroencephalography (EEG) data (Dreem2 headband, Dreem Co., Paris, France)31,33. The Dreem2 headband provided sleep stage classification hypnograms according to scoring methods (NREM: N3, N2 N1 and REM) of the American Academy of Sleep Medicine (AASM) which has been validated on healthy subjects (FIG. 4C)31,33. The sleep staging was performed using EEG data at every 30-s epoch. Sleep onset was defined as the start of the NREM sleep (3 consecutive epochs were required to classify N1). Wakefulness after sleep onset (WASO) was calculated as the total waking time after sleep onset and before the last epoch of sleep. As N1 is difficult to detect and physiologically distinct, we focused our analysis on N2 and N3 stages for NREM sleep. Intracranial data collection

[0287] For each participant, the Summit RC+S device was implanted bilaterally and connected to bilateral sensing and stimulation-capable quadripolar leads in the basal ganglia targets (STN in 2 PD patients or GPi in 2 PD patients and 1 cervical dystonia patient) plus quadripolar sensorimotor chronic electrocorticography (ECoG), sensing only strips, with 4 electrode contacts spanning the central gyrus (FIG. 4B). Overnight intracranial data were collected from cortical and subcortical structures in both left and right hemispheres (FIG. 4D) in addition to data from bilateral accelerometers embedded within the chest mounted pulse generator devices. The time series FP data were recorded at either a 250 Hz or 500 Hz sampling rate.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Data Preprocessing

[0288] The intracranial recordings were validated and synchronized to the PSG recordings using accelerometry data. Cross-correlation was applied to accelerometry data from both the Dreem2 band and the RC+S neurostimulator in order to ascertain the delay between PSG and RC+S timeseries (FIG.11). As PSG hypnogram sleep stage estimates were performed on 30-s epochs, we also used post waking movement (measured via accelerometry) to further re-align to waking at a sub 30-s scale (FIG.11). All intracranial data were downsampled to 250 Hz and filtered through a 0.8-100 Hz zero-phase IIR elliptic bandpass filter with 1dB passband ripple and 100 dB attenuation (‘filtfilt’ and ‘designfilt’ function in Matlab). Large artifactual spikes in the subcortical intracranial data were removed along with the corresponding cortical data (FIG.11). To identify artifacts, absolute squared subcortical data were first smoothed with a gaussian kernel with 1s window then any period larger than 5 times the median over the whole night was considered artifactual spikes. The ECG artifacts in the subcortical data were removed using a combination of two ECG data remover algorithms (‘PerceptHammer’ and ‘Perceive’ library; Matlab; FIG.11)34,35. Power spectrum analysis

[0289] To calculate the power spectra, the intracranial data from each night were z-scored for each location. Then, the NREM data segments (N2+N3) were collected together according to the PSG hyponogram labels. The selected data were segmented into 5-s epochs and power spectra were calculated for each epoch using a Hamming window of 1-s, 512 point FFT with 50% overlap by Welch’s method (‘pwelch’ in Matlab) which was normalized by the total power in 0-50 Hz. The calculated power spectrums for each epoch were then pooled over both hemispheres within subjects. For calculating the change in power spectrum in NREM with wake as the baseline, the power spectrum for wake epochs were calculated in a similar manner as during NREM and the difference between the average wake power spectrum and NREM power spectrum for each night was calculated. For calculating the ON vs OFF power spectrum, average power spectra were calculated for ON and OFF nights and their difference was taken. The averages were calculated on log- transformed power spectra. Spectral coherence analysis

[0290] To compute the spectral coherence, the intracranial data obtained from each night were normalized using z-scoring for each location. Subsequently, NREM data segments comprising N2Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 and N3 sleep stages were extracted and then divided into 5-s epochs. For each epoch, 5-s of cortical and subcortical data were utilized for estimating the one-sided magnitude squared coherence using the multitaper method (‘mscohere’; Matlab) with Hamming window of 1-s and 512 point FFT. The epoch-wise spectral coherences were then pooled over both hemispheres. Similarly to the power spectral analysis, spectral coherence for wake epochs were calculated and the difference between the average of wake and NREM spectral coherence for each night was calculated in order to obtain the change in spectral coherence in NREM with wake as the baseline. Beta-delta correlation analysis

[0291] To analyze the interaction between subcortical beta and cortical delta activity during NREM sleep in intracranial signals, we applied z-scoring, power spectrum calculation and normalization techniques as previously described. However, there was one exception regarding the normalization of the cortical power spectrum where instead of normalizing it by dividing the total power (0-50 Hz), we divided it by the total power excluding the beta range (0-13 and 31-50 Hz). This adjustment was necessary to avoid detecting spurious negative correlations that could be caused through the normalization procedure itself. Both subcortical beta and cortical delta were calculated for 5-s epochs which were log-transformed for each night and each hemisphere. The band powers were then pooled over both hemispheres. Subsequently, for each participant, we calculated the Spearman’s rho correlation coefficient between subcortical beta and cortical delta power across all 5-s epochs for each night. Similar results were obtained when employing various other normalization methods. For calculating the delay between subcortical beta and cortical delta power, normalized cross-correlation (‘xcorr’ function in Matlab) was calculated between these band powers from the 5s epochs from above for each night. Lag was calculated by finding the minimum (trough, reflecting a negative relationship) normalized cross-correlation between the two band powers. Epoch band powers for each night were smoothed using a 20-point Gaussian kernel. Data for each night were mean- subtracted and pooled from both hemispheres. To investigate interactions between delta and beta powers from cortex, we applied the same power spectrum calculation techniques on 5s epochs as previously described in beta-delta correlation analyses. The only exception was the normalization step of the power spectrum which was not applied to avoid detecting artificial negative correlations that could be imposed by the normalization of the power spectrum. Spearman's rho correlation coefficient was calculated between the cortical delta and beta power in all 5s epochs throughout each night for all participants.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 NREM to wake transition analysis

[0292] To investigate the changes in spectral power during NREM to wake events, all intracranial data were bandpassed using the zero-phase IIR elliptic bandpass filters. Next the data were z-scored for each night for each hemisphere at all locations. Hilbert transform was applied to the z-scored data and the absolute square of the results were converted into a decibel scale for band-specific power. After detecting all NREM to wake transitions, events with NREM sleep less than 85-s and Wake period of less than 25-s were ignored. Maximum 1-s total discontinuity in the sleep event was allowed and band-power for each 5-s epoch was averaged. All epochs in NREM data after 40-s from NREM onset and 40-s before awakening were averaged to calculate deep NREM (slow-wave sleep; SWS) power. All epochs in awake stage data after 25-s from wake onset were averaged to calculate awake stage power. All data were analyzed from the ON stimulation multi-night dataset. Wake prediction models

[0293] Individual QDA models (‘fitcdiscr’ in Matlab) were trained for each participant with four intracranial spectral power features: cortical delta, subcortical beta, cortical low gamma (31-50 Hz) and subcortical low gamma powers from data of NREM to wake transitions. The data processing was identical to NREM to wake transition analyses previously described. During each NREM to wake transition, NREM data after 40-s from NREM sleep onset up to the awake state and all data after the wake stage were utilized for the model training. Average powers were calculated for deep NREM and awake stage as described previously. Five QDA models were trained for all 5 participants. Uniform prior distribution was assumed during training. No score transform was applied. 10-fold cross-validations were performed for observing the performances. To bias the models towards predicting pre-wake events, the data were labeled as follows: 0 for all data before 5s of awakening and 1 for the rest of the data and the data at -5s (5s before awakening) were given more weight (10x) compared to all other data during QDA training. Threshold for binary classification was 0.5 and no further threshold optimization was conducted. Statistical methods

[0294] A significance threshold of 0.05 was employed to determine statistical significance. Linear mixed effect models (LME) were utilized (‘fitlme’ in Matlab) for investigating the spectral power and coherence differences, the interactions between cortical and subcortical beta with cortical delta powers. Theoretical likelihood ratio test (‘compare’ in Matlab) was used for comparing LME models.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Wilcoxon rank sum test (‘ranksum’ in Matlab) was utilized for measuring group-level differences in wake predictions. All analyses were performed using Matlab 2022a (Mathworks). Table 1: Participant demographics Subject ID PD2 PD3 PD7 PD9 Dystonia Age 58 66 40 48 65 Gender M M M M M Diagnosis PD PD PD PD Dystonia Dx 11 13 9 13 30 RCS Target STN GPi STN GPi GPi Pulse Width 60 60 60 90 60 Stimulation 130.2 178.6 130 150 130.2 Frequency L contact C+2- C+1- C+2- C+2- C+1- R contact C+1- C+1- C+2- C+2- C+2- Medication A-HCL 100mg C-Ldopa 25- C-Ldopa 25- Rytary 195mg NA (3 times daily) 100mg CR (1-2 100mg (1 time (3 times daily) C-Ldopa 25-100 tabs at bedtime) daily) Rasagaline mg IR (5 times and 25-100mg IR (Azilect) 1mg (1 daily) (3 times daily) time daily) UPDRS-III (OFF) 49 66 41 39 NA UPDRS-III (ON) 5 24 14 16 NA UPDRS 1.7 No sleep Slight sleep Slight sleep Mild sleep NA symptoms symptoms symptoms symptoms UPDRS 1.8 No daytime Mild daytime Moderate Mild daytime NA sleepiness sleepiness daytime sleepiness sleepiness Sleep diagnosis No sleep Nocturia, RBD Daytime OSA, Restless Leg sx / conditions sleepiness Insomnia SyndromeAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Neuropsych No reported Mild sleep Day time Had Good sleep. report (pre-op) sleep disorder or difficulties, with sleepiness difficulties No conditions nocturia and RBD (strongest 4- sleeping movements / dy 5pm), usually before stonia at night. sleeps late and PD.Occasiona Restless Leg sleeps very little lly couldn't fall Syndrome at overnight asleep at night. night. UPDRS scores were pre-op, Dx= Disease duration (Years), RBD= REM sleep behaviour disorder, Obstructive sleep apnea = OSA, PD = Parkinson’s disease, C-Ldopa = Carbidopa-Levodopa (Sinemet), A-HCL = Amantadine HCL (Symmetrel). None of them suffered from Dementia. Supplementary Table 1: Sleep statistics ON stimulation OFF stimulation DYS PD3 PD9 PD2 PD7 PD3 PD9 PD2 PD7 SO (min) 18.8±2.4 29.9±3.7 32.2±3.4 19.2±1.9 25.3±2.9 22.4 106.4 34.9 22.5 N1 (min) 23.5±2.7 33.5±2.7 28.9±2.3 35.7±3.0 39.8±1.3 17.5 33 47.9 25 N2 (min) 143.0±13.0 99.2±9.9 184.6±11.3 181.8±10.0 192.4±14.9 92.8 169 236.9 232.6 N3 (min) 99.5±6.1 204.9±10.2 35.8±4.8 54.5±5.8 71.4±3.9 150.1 22.5 75.8 60.9 REM (min) 55.8±6.3 112.8±8.0 125.6±14.8 59.5±5.4 93.2±13.1 140.6 72.5 62.8 92.8 N2+N3 (min) 242.5±12.9 304.2±14.4 220.4±12.1 236.3±9.1 263.8±14.6 242.9 191.5 312.8 293.5 WASO (min) 62.0±12.2 73.5±8.2 17.7±3.2 63.1±9.2 39.0±4.8 132.7 67.5 102.2 28.9 Wake event 24.7±2.5 36.4±2.0 16.5±2.2 20.7±2.4 38.8±3.4 21 14 35 23 TST (hours) 6.4±0.2 8.7±0.1 6.5±0.2 6.6±0.3 7.3±0.3 8.9 6.1 8.8 7.3 Total nights 9 11 12 12 10 1 1 1 1 SO = Time to Sleep onset; WASO= Wake after sleep onset; N1, N2, N3, REM, N2+N3 total duration times per night in minutes; TST= total sleep time; Wake event = total wake events during one night. ON stimulation includes average sleep metrics for 11 nights of recordings at home. OFF stimulation includes a single night of at home recording in the absence of stimulation. Supplementary Table 2: Performance of wake prediction Deep vs pre-wake (-5s) NREM Deep NREM vs post-wake (+15s) Subject Dys PD3 PD9 PD2 PD7 Dys PD3 PD9 PD2 PD7 Accuracy 63.7 73.7 55.6 68.9 69.8 65.3 78.9 69.4 84 77.4 AUC 63.3 73.4 59 77.4 71.3 60.6 83.9 74.8 94.9 80.7 Sensitivity 33.9 63.2 36.1 41.5 50.9 37.1 73.7 63.9 71.7 66Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 Specificity 93.5 84.2 75 96.2 88.7 93.5 84.2 75 96.2 88.7 PPV 84 80 59.1 91.7 81.8 85.2 82.3 71.9 95 85.4 NPV 58.6 69.6 54 62.2 64.4 59.8 76.2 67.5 77.3 72.3 Odds ratio 7.4 9.1 1.7 18.1 8.1 8.5 14.9 5.3 64.6 15.2 U-test p-value 0.01 0.01 0.2 1.2e-6 1.6e-4 0.04 3.7e-4 3e-4 1e-15 5e-8 Individual QDA model performance for binary classification. PPV=positive predictive value, NPV=negative predictive value, U-test= Wilcoxon rank sum test, AUC= Area under the receiver operating characteristic curve References

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Claims

Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 What is claimed is:

1. A method for treating sleep dysfunction in a subject, the method comprising: positioning a first electrode at a first location in a basal ganglia region or a cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or the cortex region; positioning a second electrode at a second location in a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; detecting a brain electrical signal associated with a sleep feature or sleep stage of interest using the second electrode; and applying electrical stimulation to the basal ganglia region or the cortex region of the brain of the subject using the first electrode in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or the sleep stage of interest is detected using the second electrode.

2. The method of claim 1, wherein the brain electrical signal data comprises field potential data.

3. The method of claim 1 or 2, wherein the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region 4. The method of any one of claims 1-3, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region.

5. The method of any one of claims 1-4, wherein the sleep stage of interest is N2, N3, or REM.

6. The method of any one of claims 1-5, further comprising using accelerometry in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.

7. The method of any one of claims 1-6, further comprising using autonomic data in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 8. The method of any one of claims 1-7, further comprising using an electroencephalogram, a polysomnogram, a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device to identify the sleep feature or sleep stage of interest.

9. The method of any one of claims 1-8, further comprising generating a hypnogram.

10. The method of any one of claims 1-9, further comprising using a control algorithm to automate said applying electrical stimulation when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected.

11. The method of claim 10, wherein the control algorithm uses a machine learning algorithm for sleep feature or sleep stage classification.

12. The method of claim 11, wherein the machine learning algorithm is a supervised machine learning algorithm.

13. The method of any one of claims 10-12, wherein the control algorithm further modulates one or more programmed stimulation parameters to maximize slow wave activity.

14. The method of claim 13, wherein the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.

15. The method of any one of claims 10-14, wherein the control algorithm further uses linear discriminant analysis (LDA) to adjust stimulation amplitude or frequency of the electrical stimulation.

16. The method of claim 15, wherein the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.

17. The method of any one of claims 1-16, wherein the electrical stimulation is applied unilaterally or bilaterally.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 18. The method of any one of claims 1-17, wherein the brain electrical signal comprises beta frequency, gamma frequency, delta frequency, or theta frequency neural oscillations.

19. The method of any one of claims 5-18, wherein the N3 sleep stage is identified by an increase in delta power during the N3 sleep stage compared to when the subject is awake.

20. The method of any one of claims 5-19, wherein the N2 sleep stage or the N3 sleep stage is identified by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.

21. The method of claim 20, wherein the N2 sleep stage or the N3 sleep stage is identified by the one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical-subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake.

22. The method of any one of claims 1-21, wherein the second electrode is placed on a surface of the subcortical or cortical region.

23. The method of any one of claims 1-22, wherein the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

24. The method of any one of claims 1-23, wherein the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

25. The method of any one of claims 1-24, wherein the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 26. The method of claim 25, wherein the ECoG electrode array spans precentral and postcentral gyri.

27. The method of any one of claims 1-26, wherein the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep.

28. The method of claim 27, wherein the movement disorder is Parkinson’s disease.

29. The method of claim 27 or 28, wherein the subject is further administered daytime neurostimulation.

30. The method of claim 28 or 29, wherein the subject is further administered dopaminergic medication.

31. The method of any one of claims 1-30, further comprising assessing effectiveness of the treatment of the sleep dysfunction in the subject.

32. The method of claim 31, wherein said assessing comprises using a visual-analog scale (VAS), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale.

33. The method of claim 31 or 32, wherein said assessing comprises monitoring the subject using actigraphy, electroencephalography, or polysomnography.

34. The method of any one of claims 1-33, further comprising mapping the brain of the subject to identify an optimal location in the subcortical region or the cortical region to detect the brain electrical signal associated with the sleep feature or sleep stage.

35. The method of claim 34, wherein the cortical region is a cortical precentral gyrus region or a postcentral gyrus region.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 36. The method of any one of claims 1-35, further comprising splitting the recorded brain electrical signal data into consecutive time epochs.

37. The method of claim 36, further comprising assigning a sleep feature or sleep stage label to each time epoch.

38. The method of claim 36 or 37, wherein each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.

39. The method of any one of claims 1-38, wherein the method is performed while the subject is sleeping at home, in a sleep laboratory, or in a hospital.

40. The method of any one of claims 1-39, wherein the sleep stage is N1, N2, N3, or phasic or tonic rapid eye movement (REM).

41. The method of any one of claims 1-40, wherein the sleep feature is a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post- awakening period, or a sleep stage transition.

42. The method of claim 41, wherein the pre-awakening period or the awakening period is identified by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non- rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep.

43. The method of claim 42, wherein the increase in subcortical beta power precedes the decrease in cortical delta power.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 44. The method of any one of claims 41-43, wherein the post-awakening period is identified by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre- awakening period, and an increase in subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

45. The method of any one of claims 1-44, wherein the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power.

46. The method of any one of claims 1-45, wherein the electrical stimulation decreases cortical-subcortical sigma spectral coherence.

47. The method of any one of claims 1-46, further comprising: detecting brain electrical signals associated with one or more additional sleep features or sleep stages of interest using the second electrode; and applying electrical stimulation to the basal ganglia region or cortex region of the brain of the subject using the first electrode in a manner effective to treat sleep dysfunction in the subject when the brain electrical signals associated with the one or more additional sleep features or sleep stages of interest are detected using the second electrode.

48. A computer implemented method for programming a deep brain stimulation (DBS) device to treat sleep dysfunction in a subject, the computer performing steps comprising: a) receiving recorded brain electrical signal data from a subcortical region or a cortical region of the brain of the subject while the subject is sleeping; b) analyzing the recorded brain electrical signal data using a classification model that identifies a pattern of electrical signals in the recorded brain electrical signal data associated with a sleep feature or sleep stage of interest; c) adjusting one or more programmed stimulation parameters based on the recorded brain electrical signal data according to an algorithm control law; andAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 d) instructing the DBS device to apply an electrical stimulation to a basal ganglia region or cortex region of the brain of the subject when the sleep feature or sleep stage of interest is detected to treat the sleep dysfunction in the subject.

49. The computer implemented method of claim 48, wherein the brain electrical signal data comprises field potential data.

50. The computer implemented method of claim 48 or 49, wherein a machine learning algorithm is used to generate the classification model.

51. The computer implemented method of claim 50, wherein the machine learning algorithm is a supervised machine learning algorithm.

52. The computer implemented method of any one of claims 48-51, wherein the basal ganglia region is a subthalamic nucleus (STN) region, a globus pallidus region, or a thalamic region.

53. The computer implemented method of any one of claims 48-52, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region.

54. The computer implemented method of any one of claims 48-53, wherein the sleep stage is N2, N3, or rapid eye movement (REM).

55. The computer implemented method of any one of claims 48-54, further comprising: receiving accelerometry data for the subject while the subject is sleeping; and analyzing the accelerometry data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

56. The computer implemented method of any one of claims 48-55, further comprising: receiving autonomic data for the subject while the subject is sleeping; and analyzing the autonomic data combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

57. The computer implemented method of any one of claims 48-56, further comprising:Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 receiving an electroencephalogram or a polysomnogram for the subject while the subject is sleeping; and analyzing the electroencephalogram or the polysomnogram combined with the recorded brain electrical signal data using the classification model to identify the sleep feature or sleep stage.

58. The computer implemented method of any one of claims 48-57, further comprising receiving data from a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device; and analyzing the data using the classification model to identify the sleep feature or sleep stage.

59. The computer implemented method of any one of claims 48-58, further comprising generating a hypnogram.

60. The computer-implemented method of any one of claims 48-59, wherein the classification model is trained to identify the sleep feature or sleep stage by analyzing brain electrical signal data recorded over multiple nights while the subject is sleeping.

61. The computer implemented method of any one of claims 48-60, further comprising: a) ranking predicted stimulation effectiveness for available settings of the 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 subcortical region or the cortical region of the brain of the subject after applying electrical stimulation with the DBS device to the basal ganglia region or cortex 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;Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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 subcortical region or the cortical region of the brain of the subject after applying the electrical stimulation with the DBS device to the basal ganglia region or cortex 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.

62. The computer implemented method of claim 61, wherein the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.

63. The computer implemented method of claim 62, wherein the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.

64. The computer implemented method of claim 63, wherein the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.

65. The computer implemented method of any one of claims 48-64, further comprising splitting the recorded brain electrical signal data into consecutive time epochs.

66. The computer implemented method of claim 65, further comprising assigning a sleep feature or sleep stage label to each time epoch.

67. The computer implemented method of claim 65, wherein each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.

68. The computer-implemented method of any one of claims 61-67, further comprising training the linear model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleepAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 stage epoch by analyzing the recorded brain electrical signal data using a non-linear model during all sleep stages while the subject is sleeping.

69. The computer-implemented method of claim 68, wherein canonical delta and beta power bands are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis.

70. The computer-implemented method of claim 68, wherein subcortical field potentials are used as feature inputs to train the linear classification model to classify each time epoch as an N3 sleep stage epoch or a non-N3 sleep stage epoch using linear discriminant analysis.

71. The computer-implemented method of any one of claims 48-70, wherein the brain electrical signal data comprises field potential data.

72. The computer implemented method of any one of claims 48-71, further comprising storing a user profile for the subject comprising information regarding the recorded brain electrical signal data associated with the sleep feature or sleep stage.

73. The computer implemented method of any one of claims 48-72, further comprising storing a user profile for the subject comprising information regarding the programmed stimulation parameters used to apply electrical stimulation to the basal ganglia region or cortex region of the brain of the subject to treat the sleep dysfunction in the subject based on the recorded brain electrical signal data.

74. The computer implemented method of any one of claims 48-73, wherein the classification model identifies the N2 sleep stage or the N3 sleep stage by one or more spectral power changes selected from a decrease in beta power in a frequency range of 12 Hz to 30 Hz compared to the beta power when the subject is awake, a decrease in gamma power in a frequency range of 30 Hz to 60 Hz compared to the gamma power when the subject is awake, an increase in theta power in a frequency range of 5 Hz to 10 Hz compared to the theta power when the subject is awake, and an increase in delta power in a frequency range of 0.5 Hz to 4.5 Hz compared to the delta power when the subject is awake.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 75. The computer implemented method of claim 74, wherein the classification model identifies the N2 sleep stage or the N3 sleep stage by the one or more spectral power changes in combination with detection of one or more changes in cortical-subcortical spectral coherence selected from an increase in delta cortical-subcortical spectral coherence compared to the delta cortical-subcortical spectral coherence when the subject is awake and a decrease in beta cortical- subcortical spectral coherence compared to the beta cortical-subcortical spectral coherence when the subject is awake.

76. The computer implemented method of any one of claims 48-75, wherein the sleep feature is a slow wave, a sleep spindle, a K complex, a beta burst, a pre-awakening period, an awakening period, a post-awakening period, or a sleep stage transition.

77. The computer implemented method of claim 76, wherein the classification model identifies the pre-awakening period or the awakening period by one or more spectral power changes selected from a decrease of cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power during deep non-rapid eye movement (NREM) sleep, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power during deep NREM sleep, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power during deep NREM sleep, and an increase of subcortical beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power during deep NREM sleep.

78. The computer implemented method of claim 77, wherein the increase in subcortical beta power precedes the decrease in cortical delta power.

79. The computer implemented method of any one of claims 76-78, wherein the classification model identifies the post-awakening period by one or more spectral power changes selected from a decrease in cortical delta power in a frequency range of 1 Hz to 4 Hz compared to average cortical delta power in the pre-awakening period, an increase in cortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average cortical gamma power in the pre-awakening period, an increase in subcortical gamma power in a frequency range of 31 Hz to 50 Hz compared to average subcortical gamma power in the pre-awakening period, and an increase in subcorticalAtty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 beta power in a frequency range of 13 Hz to 31 Hz compared to average subcortical beta power in the pre-awakening period.

80. 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 48-79.

81. A kit comprising the non-transitory computer-readable medium of claim 80 and instructions for treating sleep dysfunction in a subject with a deep brain stimulation device.

82. A system for treating sleep dysfunction in a subject, the system comprising: a first electrode adapted for positioning at a location in the basal ganglia region or cortex region of the brain of the subject to deliver electrical stimulation to the basal ganglia region or cortex region; a second electrode adapted for positioning at a subcortical region or a cortical region of the brain of the subject to record brain electrical signal data while the subject is sleeping; and a processor programmed according to the computer implemented method of any one of claims 48-79 to instruct the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region of the brain of the subject in a manner effective to treat sleep dysfunction in the subject when the brain electrical signal associated with the sleep feature or sleep stage of interest is detected using the second electrode.

83. The system of claim 82, wherein the brain electrical signal data comprises field potential data.

84. The system of claim 82 or 83, wherein the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region.

85. The system of any one of claims 82-84, wherein the cortical region is a cortical precentral gyrus region or postcentral gyrus region.

86. The system of any one of claims 82-85, wherein the sleep stage of interest is N2, N3, or REM.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 87. The system of any one of claims 82-86, further comprising an accelerometer to record movement of the subject while the subject is sleeping.

88. The system of any one of claims 82-87, further comprising a noninvasive sleep monitoring device, a wearable sleep monitoring device, a photoplethysmography (PPG)-based sleep monitoring device, or a radar-based sleep monitoring device.

89. The system of any one of claims 82-88, wherein the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

90. The system of any one of claims 82-89, wherein the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

91. The system of any one of claims 82-90, wherein the second electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted neurostimulator electrode, or an electrocorticogram (ECoG) electrode array.

92. The system of claim 91, wherein the ECoG electrode array spans precentral and postcentral gyri.

93. The system of any one of claims 82-92, wherein the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep.

94. The system of claim 93, wherein the movement disorder is Parkinson’s disease.

95. The system of any one of claims 82-94, wherein the system further comprises a user interface comprising an input electronically coupled to the processor for instructing the first electrode to apply an electrical stimulation to the basal ganglia region or cortex region to treat the sleep dysfunction in the subject.Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 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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