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WO2025101224A2 - Stimulation cérébrale profonde adaptative pour ciblage de stade de sommeil pour traiter un dysfonctionnement du sommeil - Google Patents

Stimulation cérébrale profonde adaptative pour ciblage de stade de sommeil pour traiter un dysfonctionnement du sommeil Download PDF

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WO2025101224A2
WO2025101224A2 PCT/US2024/032980 US2024032980W WO2025101224A2 WO 2025101224 A2 WO2025101224 A2 WO 2025101224A2 US 2024032980 W US2024032980 W US 2024032980W WO 2025101224 A2 WO2025101224 A2 WO 2025101224A2
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sleep
subject
cortical
power
brain
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WO2025101224A9 (fr
WO2025101224A3 (fr
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Simon J. LITTLE
Clay SMYTH
Philip STARR
Fahim ANJUM
Timothy Denison
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University of Oxford
University of California Berkeley
University of California San Diego UCSD
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University of California Berkeley
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36135—Control systems using physiological parameters
    • A61N1/36139—Control systems using physiological parameters with automatic adjustment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
    • A61N1/0526—Head electrodes
    • A61N1/0529—Electrodes for brain stimulation
    • A61N1/0534—Electrodes for deep brain stimulation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36067—Movement disorders, e.g. tremor or Parkinson disease

Definitions

  • 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 (2016)).
  • slow waves are believed to serve multiple functions related to metabolism, cognition and synaptic homeostasis (Léger, D. et al. Sleep Med. Rev. 41, 113-132 (2016)).
  • PD reductions in slow waves are associated with faster disease progression (Schreiner, S.
  • DBS Deep Brain Stimulation
  • STN Subthalamic Nucleus
  • 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 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.
  • a method for treating sleep dysfunction in a subject 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.
  • the brain electrical signal data comprises field potential data.
  • the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region.
  • 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]
  • the sleep stage of interest is N2, N3, or REM.
  • 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.
  • the method further comprises using accelerometry in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.
  • the method further comprises using autonomic data in combination with the brain electrical signal to identify the sleep feature or sleep stage of interest.
  • the method further comprises using an electroencephalogram or a polysomnogram to identify the sleep feature or sleep stage of interest.
  • 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.
  • the method further comprises generating a hypnogram.
  • 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.
  • the control algorithm uses a machine learning algorithm for classification of sleep features and sleep stages.
  • the machine learning algorithm is a supervised machine learning algorithm.
  • the control algorithm further modulates one or more programmed stimulation parameters to maximize slow wave activity.
  • the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.
  • the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.
  • 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.
  • LDA linear discriminant analysis
  • the electrical stimulation is applied unilaterally or bilaterally.
  • the brain electrical signal comprises beta frequency, gamma frequency, delta frequency, or theta frequency neural oscillations, or individually, patient defined spectral features.
  • 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]
  • 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.
  • the second electrode is placed on a surface of the cortical sensori- motor region.
  • the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.
  • the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.
  • 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.
  • EEG electroencephalogram
  • ECG electrocorticogram
  • the ECoG electrode array spans precentral and postcentral gyri or other areas of the cortex
  • the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep.
  • the movement disorder is Parkinson’s disease.
  • the sleep dysfunction is caused by a stroke.
  • the subject is further administered daytime neurostimulation.
  • the subject is further administered dopaminergic medication.
  • the method further comprises assessing effectiveness of the treatment of the sleep dysfunction in the subject.
  • 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.
  • VAS visual-analog scale
  • SSS Stanford Sleepiness Scale
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • 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.
  • 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]
  • the method further comprises splitting the recorded brain electrical signal data into consecutive time epochs.
  • the method further comprises assigning a sleep feature or sleep stage label to each time epoch.
  • each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.
  • the method is performed while the subject is sleeping at home, in a sleep laboratory, or in a hospital.
  • 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.
  • 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.
  • 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.
  • NREM non-rapid eye movement
  • 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 Atty.
  • the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power.
  • the electrical stimulation decreases cortical-subcortical sigma spectral coherence.
  • a computer implemented method for programming a deep brain stimulation (DBS) device to treat sleep dysfunction in a subject 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.
  • DBS deep brain stimulation
  • the brain electrical signal data comprises field potential data.
  • a machine learning algorithm is used to generate the classification model.
  • the machine learning algorithm is a supervised machine learning algorithm.
  • 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.
  • 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.
  • the computer implemented further comprises receiving an electroencephalogram or a polysomnogram for the subject while the subject is sleeping; and Atty. 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.
  • 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.
  • PPG photoplethysmography
  • the computer implemented further comprises generating a hypnogram.
  • 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.
  • 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
  • the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.
  • LDA linear discriminant analysis
  • the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.
  • the slow wave activity is in a frequency range Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 of 0.5 Hz to 4 Hz.
  • 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.
  • 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.
  • 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.
  • 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.
  • NREM non-rapid eye movement
  • the increase in subcortical beta power precedes the decrease in cortical delta power.
  • 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.
  • the computer implemented method further comprises splitting the recorded brain electrical signal data into consecutive time epochs.
  • the computer implemented method further comprises assigning a sleep feature or sleep stage label to each time epoch.
  • each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.
  • 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.
  • 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.
  • 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.
  • the brain electrical signal data comprises field potential data.
  • 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.
  • 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.
  • 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.
  • 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.
  • a system for treating sleep dysfunction in a subject 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 Atty.
  • the system further comprises an accelerometer to record movement of the subject while the subject is sleeping.
  • 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.
  • the first electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.
  • the second electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.
  • 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.
  • EEG electroencephalogram
  • ECG electrocorticogram
  • the ECoG electrode array spans precentral and postcentral gyri, or other areas of the cortex.
  • the sleep dysfunction is caused by a movement disorder or a neurological disorder, wherein applying the electrical stimulation improves sleep.
  • the movement disorder is Parkinson’s disease.
  • 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.
  • the user interface is password protected and is operable by a health care practitioner.
  • 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.
  • TST Total Sleep Time
  • 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.
  • 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.
  • FIGS.1D, 1E 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.
  • FIG.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. (2016) 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).
  • 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.
  • FIGS.5A-5G Spectral changes in NREM. Dynamic changes in power spectra and functional connectivity between cortical and subcortical regions during NREM sleep:
  • 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).
  • y-axis shows the difference in power spectra between NREM and wake stage in decibel (dB).
  • 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.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.
  • 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.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 one Atty.
  • 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.
  • FIG. 6E 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.
  • FIGS.7A-7D Changes in spectral power before spontaneous awakenings. Subcortical beta increases and cortical delta decreases before spontaneous awakening.
  • 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 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 post Atty.
  • 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.
  • 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.
  • 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.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
  • 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.
  • 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.
  • 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.
  • 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, and Atty.
  • 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.
  • 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.
  • 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).
  • 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.
  • PLMD periodic limb movement disorder
  • RBD REM sleep behavior disorder
  • 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.
  • 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).
  • RAS reticular activating system
  • 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.
  • “Mammal” for purposes of treatment refers to any animal classified as a mammal, including human and non-human mammals such as non-human primates, including chimpanzees and other Atty.
  • 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.
  • 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.
  • 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.
  • 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).
  • Stage N3 or delta sleep methods 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/or sleep stages.
  • An adaptive deep brain Atty. 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.
  • 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.
  • 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.
  • 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.
  • ALS amyotrophic lateral sclerosis
  • CMT Charcot-Marie- Tooth disease
  • CIDP Chronic inflammatory demyelinating polyneuropathy
  • GBS Guillain-Barré syndrome
  • Lambert-Eaton syndrome muscular dystrophy, myasthenia gravis, myopathies, and peripheral neuropathies.
  • the subject methods are used to treat sleep dysfunction associated with a stroke
  • 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.
  • 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 be Atty.
  • 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.
  • 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).
  • DBS electrode a first electrode in a basal ganglia region or cortex region of the brain of a subject to deliver electrical stimulation to the brain
  • 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
  • detection electrode i.e., detection electrode.
  • 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.
  • 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.
  • 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.
  • the phrases “an electrode” or “the electrode” refer to a single electrode or multiple electrodes such as an electrode array.
  • 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.
  • 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.
  • 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.
  • placing the detection electrode may involve positioning the electrode on the surface of the specified region(s) of the brain.
  • 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.
  • 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.
  • 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.
  • the non-brain penetrating electrode also referred to as a surface electrode
  • EEG electrocorticography
  • EEG electroencephalography
  • a plurality of electrodes is positioned at one or more of the brain regions specified herein for detection of electroencephalographic signals by stereoelectroencephalography (sEEG).
  • placing the detection electrode at a target area or site may involve positioning a brain penetrating electrode (also referred to as depth electrode) in the specified region(s) of the brain.
  • a detection electrode may be placed in a subcortical region or a cortical region of the brain.
  • the detection electrode may additionally contact area(s) adjacent to a subcortical region or a cortical region of the brain.
  • 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.
  • 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.
  • 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.
  • 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.
  • MRI magnetic resonance imaging
  • CT computerized tomography
  • 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.
  • 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.
  • 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.
  • an open loop system 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.
  • the electrical stimulation may be applied using a single electrode, electrode pairs, or an electrode array.
  • 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.
  • 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.
  • 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 site Atty. 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.
  • 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.
  • 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.
  • 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.
  • 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 21 ⁇ 2 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.
  • 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.
  • 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.
  • 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.
  • 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 sleep Atty.
  • 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).
  • DBS is optimized to achieve a neurophysiologically defined change, for example, increasing or decreasing of slow waves or sleep spindles.
  • 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.
  • 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.
  • a visual-analog scale VAS
  • a Likert scale a Stanford Sleepiness Scale (SSS)
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • 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.
  • the levels of overall power, or power in specific frequency ranges e.g., alpha, beta, gamma, delta, and/or theta
  • the N3 sleep stage is identified by an increase in delta power during the N3 sleep stage.
  • 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.
  • 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. [00122] Detection of brain activity may be performed by any method known in the art.
  • 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).
  • EEG electroencephalography
  • sEEG stereoelectroencephalography
  • ECG electrocorticography
  • MEG magnetoencephalography
  • SPECT single photon emission computed tomography
  • metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET).
  • Client Ref.: SF-2023-198-3-PCT-0 regions are mapped to determine optimal positioning for detection electrodes.
  • an accelerometer e.g., 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.
  • a wearable sleep monitoring device e.g., smart ring, smartwatch, wrist band, or head band sleep tracker
  • PPG photoplethysmography
  • radar-based sleep monitoring device is used to assist sleep feature or sleep stage classification.
  • 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.
  • 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.
  • 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 sleep Atty.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • non-integer pulse frequencies are used (e.g.130.2 Hz, 130.4 Hz, etc.).
  • 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.
  • the method may involve applying an electrical stimulation at a pulse width of about 10 ⁇ sec – 500 ⁇ sec, for example, 20 ⁇ sec -450 ⁇ sec, 40 ⁇ sec -450 ⁇ sec, 60 ⁇ sec - 450 ⁇ sec, 60 ⁇ sec -220 ⁇ sec, 60 ⁇ sec -120 ⁇ sec, or 60 ⁇ sec -90 ⁇ sec.
  • the electrical stimulation to the brain is applied at a pulse width of about 60 ⁇ sec to about 210 ⁇ sec, including any pulse width within this range such as 60 ⁇ sec, 65 ⁇ sec, 70 ⁇ sec, 75 ⁇ sec, 80 ⁇ sec, 85 ⁇ sec, 90 ⁇ sec, 95 ⁇ sec, 100 ⁇ sec, 105 ⁇ sec, 110v, 115 ⁇ sec, 120 ⁇ sec, 125 ⁇ sec, 130 ⁇ sec, 135 ⁇ sec, 140 ⁇ sec, 145 ⁇ sec, 150 ⁇ sec, 155 ⁇ sec, 160 ⁇ sec, 165 ⁇ sec, 170 ⁇ sec, 175 ⁇ sec, 180 ⁇ sec, 185 ⁇ sec, 190 ⁇ sec, 195 ⁇ sec, 200 ⁇ sec, 205 ⁇ sec, 210 ⁇ sec, 215 ⁇ sec, or 220 ⁇ sec.
  • 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.
  • 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.
  • the period of electrical stimulation may be 1 sec-1 min, 1sec- 30 sec, 1 sec-15 sec, 1 sec-10 sec, 1 sec-6 sec, 1 sec-3 sec, 1 sec-2 sec, or 6 sec-10 sec.
  • the period of rest in between each stimulation period may be 60 sec or less, 30 sec or less, 20 sec or Atty.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a treatment regimen may include a program for electrical stimulation at a desired program frequency and program duration.
  • 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.
  • a cap on the maximum number of electrical stimulations per night can be set.
  • 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.
  • a cap can be set on the total amount of time of electrical stimulation per night.
  • the total amount of time of electrical stimulation Atty. 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.
  • 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.
  • 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.
  • 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.
  • SSS Stanford Sleepiness Scale
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • Athens insomnia scale may be used to assess the effectiveness of the treatment of sleep dysfunction in the subject.
  • 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.
  • activity e.g., electrical signals
  • 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).
  • 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.
  • 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.
  • 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.
  • a subject’s subcortical or cortical brain activity e.g., at the cortical precentral gyrus region and/or postcentral gyrus region
  • 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.
  • the patient 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.
  • 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.
  • 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.
  • 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.
  • 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, electrical Atty.
  • 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.
  • electrical activity in the beta frequency range such as 12 Hz to 30 Hz
  • delta frequency range such as 0.5 Hz to 4.5 Hz
  • data driven approaches are used to identify spectral features that are individualized and different from canonical power bands.
  • 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.
  • delta frequency range such as 0.5 Hz to 4.5 Hz
  • beta frequency such as 12 Hz to 30 Hz
  • beta frequency such as 12 Hz to 30 Hz
  • delta frequency such as .5 Hz to 4.5 Hz
  • the methods and systems do not apply a further stimulation to the brain.
  • the methods and systems may apply a further stimulation to the brain.
  • 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.
  • 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.
  • 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.
  • 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.
  • the method may Atty.
  • 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.
  • a subcortical or cortical region e.g., cortical precentral gyrus region or postcentral gyrus region
  • a control algorithm that is tuned to a clinically relevant target
  • 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.
  • beta frequency range such as 12 Hz to 30 Hz
  • delta frequency range such as 0.5 Hz to 4.5 Hz
  • 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.
  • 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.
  • 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.
  • 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 Atty.
  • 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.
  • the increase in subcortical beta power precedes the decrease in cortical delta power.
  • 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.
  • the electrical stimulation increases cortical delta power, decreases cortical alpha power, decreases cortical beta power, and decreases cortical sigma power.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 control Atty.
  • 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.
  • 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).
  • 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.
  • 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.
  • the computing means may determine whether the received electrical signals are within or outside a second predetermined range, where the second predetermined range Atty. 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.
  • 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.
  • 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.
  • 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.
  • the closed loop system may be used to sense a subject’s need for treatment using the methods disclosed herein.
  • 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.
  • 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.
  • a control algorithm modulates one or more programmed stimulation parameters to maximize slow wave activity to improve deep sleep.
  • 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.
  • 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 a Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 particular sleep feature is detected.
  • 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.
  • 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.
  • a biomarker e.g., a range of electrical activity indicative of sleep dysfunction, and so on.
  • closed loop systems are useful for detecting sleep dysfunction.
  • 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.
  • 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.
  • 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.
  • a combination of patient-specific features and sleep dysfunction- specific features may be monitored to assess efficacy of treatment.
  • 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 may Atty.
  • a subcortical or cortical region of the brain e.g., cortical precentral gyrus region or postcentral gyrus region, or other region
  • 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.
  • 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.
  • sleep stages e.g., wake, N1, N2, N3, and REM
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • a computer implemented method for programming a DBS device to treat sleep dysfunction in a subject 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 Atty.
  • Analyzing the recorded brain electrical activity may comprise the use of an algorithm or classifier.
  • 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.
  • 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.
  • AODE Average One- Dependence Estimators
  • Bayesian statistics e.g., I Bayes classifier, Bayesian network, Bayesian knowledge base
  • Case-based reasoning
  • Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN).
  • 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.
  • the machine learning algorithms may also comprise an unsupervised learning algorithm.
  • 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.
  • unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering.
  • 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.
  • the machine learning algorithm Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 may comprise Data Pre-processing.
  • 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.
  • 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.
  • 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.
  • 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.
  • NREM non-rapid eye movement
  • the increase in subcortical beta power precedes the decrease in cortical delta power.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the computer implemented method further comprises generating a hypnogram.
  • 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 linear Atty.
  • the linear classification model uses linear discriminant analysis (LDA) to adjust amplitude of current and frequency of the electrical stimulation.
  • LDA linear discriminant analysis
  • the computer implemented method further comprises splitting the recorded brain electrical signal data into consecutive time epochs.
  • 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.
  • 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.
  • a sleep feature or sleep stage label e.g., wake, N1, N2, N3, and REM
  • 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.
  • 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.
  • 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.
  • the stimulation amplitude is optimized during the N3 sleep stage to maximize slow wave activity.
  • the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.
  • 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.
  • 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.
  • 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.
  • 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.
  • a computer program also known as a program, software, software application, script, or code
  • 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.
  • 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.
  • 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.
  • the instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions,” “steps” and “programs" may be used interchangeably herein.
  • the instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or Atty.
  • Data may be retrieved, stored or modified by the processor in accordance with the instructions.
  • 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.
  • 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.
  • the processor and storage component may comprise multiple processors and storage components that may or may not be stored within the same physical housing.
  • 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.
  • the processor may comprise a collection of processors which may or may not operate in parallel.
  • a hardware accelerator is used.
  • the method is performed using a cloud computing system.
  • the data files and the programming can be exported to a cloud computer, which runs the program, and returns an output to the user.
  • Components of systems for carrying out the presently disclosed methods are further described in the examples below.
  • 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.
  • 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 the Atty.
  • a basal ganglia region e.g., subthalamic nucleus region, globus pallidus region or thalamic region
  • 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 the Atty
  • 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
  • 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.
  • the stimulation intervention could take the form of auditory stimulation or non-invasive stimulation, including transcranial electrical stimulation or transcranial magnetic stimulation.
  • 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.
  • 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.
  • 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.
  • 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 in Atty. Dckt.: UCSF-739WO Client Ref.: SF-2023-198-3-PCT-0 the subject.
  • 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.
  • a normal or reference electrical signal e.g., normal sleep, substantially free of sleep dysfunction
  • 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.
  • 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.
  • 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.
  • 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.
  • the algorithm provides updated optimal stimulation setting recommendations to the clinician for guiding programing and decision making.
  • 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.
  • the user interface is password protected and is operable by a health care practitioner.
  • 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.
  • 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.
  • a wearable sleep monitoring device e.g., smart ring, smartwatch, wrist band, or head band sleep tracker
  • PPG photoplethysmography
  • radar-based sleep monitoring device e.g., radar-based sleep monitoring devices.
  • data from such devices can be used to assist sleep feature or sleep stage classification.
  • 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.
  • Embodiments of the methods and systems provided in this disclosure may also include administration of an effective amount of at least one pharmacological agent.
  • effective amount is meant a dosage sufficient to treat sleep dysfunction in a subject as desired.
  • 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. [00196] 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.
  • 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.
  • 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 [00198] 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.
  • 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.
  • 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.
  • 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.
  • 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-T
  • 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).
  • RAS reticular activating system
  • 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.
  • 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.
  • VAS visual-analog scale
  • SSS Stanford Sleepiness Scale
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • Athens insomnia scale assessing effectiveness of the treatment of the sleep dysfunction in the subject comprises monitoring the subject using actigraphy, electroencephalography, or polysomnography.
  • a method for treating sleep dysfunction in a subject 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.
  • the brain electrical signal data comprises field potential data.
  • the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region 4.
  • the cortical region is a cortical precentral gyrus region or postcentral gyrus region.
  • 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.
  • 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.
  • 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.
  • PPG photoplethysmography
  • the method of any one of aspects 1-8 further comprising generating a hypnogram. 10.
  • the control algorithm uses a machine learning algorithm for sleep feature or sleep stage classification.
  • the machine learning algorithm is a supervised machine learning algorithm.
  • the control algorithm further modulates one or more programmed stimulation parameters to maximize slow wave activity.
  • the slow wave activity is in a frequency range of 0.5 Hz to 4 Hz.
  • the control algorithm further uses linear discriminant analysis (LDA) to adjust stimulation amplitude or frequency of the electrical stimulation. 16.
  • LDA linear discriminant analysis
  • the brain electrical signal comprises beta frequency, gamma frequency, delta frequency, or theta frequency neural oscillations.
  • 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 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.
  • 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.
  • EEG electroencephalogram
  • 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.
  • VAS visual-analog scale
  • SSS Stanford Sleepiness Scale
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • Athens insomnia scale 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.
  • MTT maintenance of wakefulness test
  • ESS Epworth sleepiness scale
  • MSLT multiple sleep latency test
  • Athens insomnia scale comprises using a visual-analog scale (VAS),
  • 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.
  • 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.
  • the sleep stage is N1, N2, N3, or phasic or tonic rapid eye movement (REM).
  • 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 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.
  • NREM non- rapid eye movement
  • 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.
  • 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.
  • DBS deep brain stimulation
  • the cortical region is a cortical precentral gyrus region or postcentral gyrus region.
  • 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.
  • 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.
  • 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.
  • 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.
  • PPG photoplethysmography
  • the computer implemented method of any one of aspects 48-58 further comprising generating a hypnogram. 60.
  • 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.
  • LDA linear discriminant analysis
  • 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.
  • the computer implemented method of aspect 65 further comprising assigning a sleep feature or sleep stage label to each time epoch. Atty.
  • each time epoch comprises 0.5 second to 1 minute of time of the recorded brain electrical signal data.
  • 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.
  • 71. The computer-implemented method of any one of aspects 48-70, wherein the brain electrical signal data comprises field potential data. 72.
  • 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 Atty.
  • 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 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.
  • NREM non-rapid eye movement
  • 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.
  • 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 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.
  • the system of aspect 82 wherein the brain electrical signal data comprises field potential data.
  • the basal ganglia region is a subthalamic nucleus region, a globus pallidus region, or a thalamic region.
  • the sleep stage of interest is N2, N3, or REM. 87.
  • 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.
  • 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.
  • EEG electroencephalogram
  • EoG electrocorticogram
  • 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.
  • the movement disorder is Parkinson’s disease. Atty.
  • 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.
  • the user interface is password protected and is operable by a health care practitioner.
  • DBS stimulation parameters specifically adjusted to NREM and REM sleep stages plus neurophysiology and behavioral outcomes 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 progression 7 .
  • sleep physiology is multifaceted and exhibits dynamics across many frequency bands, conferring complexity beyond conventional beta-band focused adaptive DBS.
  • 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 gyri 16 .
  • Field potential (FP) time series recordings were analyzed via time frequency decomposition through the Fast Fourier Transform (FFT) embedded within the INS .
  • FFT Fast Fourier Transform
  • 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 subjects 17,18 .
  • 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.
  • Client Ref. SF-2023-198-3-PCT-0 C.
  • the RC+S INS has functionality to implement up to 2 linear discriminant classifiers, each using up to four spectral power bands as inputs.
  • w weight vector
  • x a vector of up to 4 feature inputs
  • ⁇ user-defined threshold
  • Above-threshold and below- of the inner product lead to control policy changes of stimulation parameters, such as predefined increases or decreases in stimulation amplitude.
  • 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).
  • the embedded devices performed real-time continuous N3 sleep stage classification with stimulation amplitude kept continuous (cDBS).
  • cDBS stimulation amplitude kept continuous
  • 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.
  • High specificity 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 amplitudes 22 .
  • 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.
  • Translation of the proposed pipeline for patient care might be accelerated if sleep stages could be classified from subcortical electrodes.
  • 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 2 7–29 . Atty.
  • Example 2 Additional Applications of Adaptive Deep Brain Stimulation Other Conditions [00250]
  • 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 [00251]
  • 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.
  • 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.
  • biomarkers indicative, or predictive, of on-coming awakening events could be targeted with this approach.
  • Alternative stimulation regimes [00252] 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 any Atty.
  • 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.
  • stimulation is set at 130Hz on both sides (standard stimulation frequency)
  • 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.
  • a particular stimulation frequency e.g. Delta waves at 2 Hz
  • Example 3 Algorithm Training [00254] 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).
  • 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.
  • Safety of Adaptive Deep Brain Stimulation [00259] Safety is paramount in our studies and in developing any new stimulation approach.
  • 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.
  • 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.
  • NREM 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.
  • EEG rhythmic low frequency electroencephalography
  • N1 light sleep
  • N2 appearance of K complexes and sleep spindles
  • deep N3 characterized by slow delta waves
  • Sleep dysfunction in PD manifests as parasomnias, fragmented sleep and disrupted sleep patterns, including notable reductions in both REM and NREM sleep 11 .
  • 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 PD 3,14,15 .
  • beta oscillations 13-30 Hz
  • 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).
  • cortical delta is not PD specific, but rather a general feature of changes in neurophysiology in NREM sleep versus wakefulness.
  • 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 s timulation to prevent awakening.
  • 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 primates 17 .
  • DBS stimulation known to reduce subcortical beta oscillations during wakefulness 27 , 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 PD 28 .
  • DBS therapy improves subjective sleep by reducing overnight discomfort through improved motor movements.
  • sleep disturbances, and particularly reductions in cortical slow wave activity during NREM has been linked to faster disease progression 3,14 . 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.
  • daytime neural activities and overnight sleep physiology are notably dissociable and require different strategies for aDBS to optimize rhythms during these two distinct phases.
  • 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).
  • 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.
  • a movement disorder specialist programmed the patients with conventional DBS settings, optimizing stimulation to address daytime motor symptoms.
  • Extracranial polysomnography 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 [00287] 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.
  • EoG quadripolar sensorimotor chronic electrocorticography
  • the time series FP data were recorded at either a 250 Hz or 500 Hz sampling rate.
  • 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).
  • 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.
  • Beta-delta correlation analysis 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.
  • Wake prediction models [00293] 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.
  • Table 1 Participant demographics Subject ID PD2 PD3 PD7 PD9 Dystonia Age 58 66 40 48 65 Gender M 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- 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)
  • 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 66 Atty.
  • PPV positive predictive value
  • NPV negative predictive value
  • U-test Wilcoxon rank sum test
  • AUC Area under the receiver operating characteristic curve References [00295] 1. Videnovic A, Golombek D. Circadian and sleep disorders in Parkinson’s disease. Exp Neurol.5/2013;243:45-56. [00296] 2. Barone P, Antonini A, Colosimo C, et al. The PRIAMO study: A multicenter assessment of nonmotor symptoms and their impact on quality of life in Parkinson’s disease. Mov Disord.2009;24(11):1641-1649. [00297] 3. Schreiner SJ, Imbach LL, Werth E, et al. Slow ⁇ wave sleep and motor progression in Parkinson disease.

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Abstract

L'invention concerne des dispositifs, des systèmes, un logiciel et des procédés pour traiter un dysfonctionnement du sommeil chez un sujet à l'aide d'une stimulation cérébrale profonde nocturne. Une stimulation cérébrale profonde est effectuée avec un dispositif d'enregistrement neuronal qui enregistre des données de signal électrique cérébral pendant que le sujet dort. Des modèles de calcul d'apprentissage automatique sont utilisés pour détecter et classifier des motifs d'activité neuronale associés à différentes caractéristiques de sommeil ou à différents stades de sommeil. L'invention concerne un algorithme de stimulation cérébrale profonde adaptative qui module des paramètres de stimulation à l'aide de caractéristiques de sommeil ou de stades de sommeil classés par voie intracrânienne pour cibler un dysfonctionnement du sommeil. L'invention concerne également des procédés et des systèmes pour effectuer une thérapie en boucle fermée avec un stimulateur cérébral profond qui enregistre des signaux électriques cérébraux à partir d'une activité neuronale sous-corticale ou corticale associée à des caractéristiques ou stades de sommeil sélectionnés et ajuste automatiquement des réglages de stimulateur cérébral profond et/ou délivre une stimulation électrique cérébrale profonde lorsque des motifs prédéfinis d'activité neuronale associés à une caractéristique de sommeil ou à un stade de sommeil sélectionné sont détectés.
PCT/US2024/032980 2023-06-09 2024-06-07 Stimulation cérébrale profonde adaptative pour ciblage de stade de sommeil pour traiter un dysfonctionnement du sommeil Pending WO2025101224A2 (fr)

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US5335657A (en) * 1991-05-03 1994-08-09 Cyberonics, Inc. Therapeutic treatment of sleep disorder by nerve stimulation
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