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Title:
MODULATING BRAIN ACTIVITY DURING SLEEP
Document Type and Number:
WIPO Patent Application WO/2018/053296
Kind Code:
A1
Abstract:
This disclosure relates to devices, methods and systems for the recording of electroencephalograph (EEG) signals and application of subthreshold magnetic stimulation (StMS) to modulate brain activity during sleep. Embodiments of the present disclosure provide devices, methods and systems that acquire an individual's EEG signal while simultaneously applying StMS to the individual during sleep, process the EEG signal such that electromagnetic transients associated with StMS are removed, analyze the signal to detect sleep stage, and control the application, waveform temporal parameters, strength, or other parameters of StMS based upon the detected sleep stage. The present disclosure is useful for research purposes, commercial applications and clinical interventions.

Inventors:
MADDEN MICHAEL (US)
SKVARKA JAN (US)
GODDARD ALEX (US)
BHARDWAJ JASON (US)
PANDE ATUL (US)
SAWCHAK SHARON (US)
MILLER ANDREW (US)
Application Number:
PCT/US2017/051826
Publication Date:
March 22, 2018
Filing Date:
September 15, 2017
Export Citation:
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Assignee:
TAL MEDICAL (US)
International Classes:
A61N2/00; A61B5/0476; A61B5/00; A61N2/02
Domestic Patent References:
WO2008039930A22008-04-03
Foreign References:
US9037224B12015-05-19
US20150379878A12015-12-31
US20110295142A12011-12-01
US8702582B22014-04-22
US20140235929A12014-08-21
US20070016095A12007-01-18
Other References:
BOYLE MICHAEL R ET AL: "EEG feedback-controlled transcranial alternating current stimulation", 2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), IEEE, 6 November 2013 (2013-11-06), pages 140 - 143, XP032538326, ISSN: 1948-3546, [retrieved on 20131226], DOI: 10.1109/NER.2013.6695891
RYOTA KANAI ET AL.: "Transcranial alternating current stimulation (tACS) modulates cortical excitability as assessed by TMS-induced phosphene thresholds", CLINICAL NEUROPHYSIOLOGY, vol. 121, 2010, pages 9
CAROLINE LUSTENBERGER ET AL.: "Feedback-Controlled Transcranial Alternating Current Stimulation Reveals a Functional Role of Sleep Spindles in Motor Memory Consolidation", CURRENT BIOLOGY: CB, 26 July 2016 (2016-07-26)
LISA MARSHALL ET AL.: "Nature", vol. 444, 2006, NATURE PUBLISHING GROUP, article "Boosting slow oscillations during sleep potentiates memory", pages: 7119
JULIA LADENBAUR ET AL.: "Improved memory consolidation by slow oscillatory brain stimulation during an afternoon nap in older adults", 2016, ELSEVIER B.V.
MICHAEL L. ROHAN ET AL.: "Biological Psychiatry", vol. 76, 2014, ELSEVIER, article "Rapid mood-elevating effects of low field magnetic stimulation in depression", pages: 3
NORA D VOLKOW ET AL.: "Neurolmage", vol. 51, 2010, ELSEVIER B.V., article "Effects of low-field magnetic stimulation on brain glucose metabolism", pages: 2
LISA MARSHALL ET AL., BOOSTING SLOW OSCILLATIONS DURING SLEEP POTENTIATES MEMORY
JULIA LADENBAUR ET AL., IMPROVED MEMORY CONSOLIDATION BY SLOW OSCILLATORY BRAIN STIMULATION DURING AN AFTERNOON NAP IN OLDER ADULTS
MICHAEL R. BOYLE; FLAVIO FROHLICH: "EEG feedback-controlled transcranial alternating current stimulation", INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER, 2013
JUSSI VIRKKALA ET AL.: "Automatic detection of slow wave sleep using two channel electro-oculography", JOURNAL OF NEUROSCIENCE METHODS, vol. 160, 2007, pages 1, XP005855876, DOI: doi:10.1016/j.jneumeth.2006.08.002
EDWARD S BOYDEN ET AL., INTEGRATED TRANSCRANIAL CURRENT STIMULATION AND ELECTROENCEPHALOGRAPHY DEVICE, vol. 1, pages 19
GIOVANNI SANTOSTASI ET AL.: "Journal of Neuroscience Methods", vol. 259, 2016, ELSEVIER B.V., article "Phase-locked loop for precisely timed acoustic stimulation during sleep"
BECQ ET AL.: "Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings", STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 4, February 2005 (2005-02-01)
TAREK LAJNEF ET AL.: "Journal of Neuroscience Methods", vol. 250, 2014, ELSEVIER B.V., article "Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines"
VLADIMIR MISKOVIC ET AL.: "Brain Stimulation", vol. 10, 2017, ELSEVIER INC., article "On the handling of stimulation artifacts during simultaneous electroencephalography (EEG) and transcranial low field strength magnetic stimulation (LFMS", pages: 2
Attorney, Agent or Firm:
BLUNI, Scott (US)
Download PDF:
Claims:
CLAIMS

1. A method for treating a sleeping individual, the method comprising the steps of:

collecting electroencephalograph (EEG) signals from the individual;

detecting from the EEG signals when the individual is encountering slow wave sleep; and

applying subthreshold magnetic stimulation to the individual, wherein the step of applying subthreshold magnetic stimulation is initiated automatically by a processor upon detecting that the individual is encountering slow wave sleep for a predetermined period of time.

2. The method of claim 1, further comprising the step of separating biological and

stimulation-induced EEG signals from the collected EEG signals to yield processed EEG signals.

3. The method of claim 2, further comprising the step of isolating frequency bands associated with SWS to yield the processed EEG signals.

4. The method of claim 1, wherein the step of applying subthreshold magnetic stimulation comprises controlling one or more of the waveform temporal parameters and strength of the subthreshold magnetic stimulation based on the EEG signals.

5. The method of claim 2, wherein the simulation-induced signals comprise electromagnetic transients induced by the subthreshold magnetic stimulation.

6. The method of claim 2, wherein the simulation-induced signals comprise spatially and temporally correlated electrical interference from the subthreshold magnetic stimulation.

7. The method of claim 2, wherein the step of separating biological and stimulation-induced EEG signals from the collected EEG signals comprises utilizing one or more of mathematical classifiers, neural networks, component analysis, support vector machines, or machine learning algorithms.

8. The method of claim 1, wherein the subthreshold magnetic stimulation comprises low field magnetic stimulation.

9. The method of claim 8, wherein the low field magnetic stimulation comprises a pulse train comprising a series of pulses that are initiated at a frequency of 0.5Hz.

10. A system comprising:

an electroencephalograph (EEG) monitor to detect biological and stimulation-induced signals;

a coil configured to deliver subthreshold magnetic stimulation to a user; and a processor configured to:

receive the biological and stimulation-induced signals from the EEG monitor;

yield a processed EEG signal; and

control a current to the coil based on the processed EEG signal.

11. The system of claim 10, wherein the processor is configured to control the current to the coil based on the processed EEG signal to generate a time-varying magnetic field.

12. The system of claim 10, wherein the processor is configured to isolate frequency bands associated with slow wave sleep to yield the processed EEG signal.

13. The system of claim 10, wherein the processor is configured to control one or more of the waveform temporal parameters and strength of the subthreshold magnetic stimulation based on the EEG signals.

14. The system of claim 10, wherein the processor is configured to:

detect from the EEG signals when the individual is encountering a predetermined sleep stage; and

initiate delivery of the current to the coil upon detecting the predetermined sleep stage.

15. The system of claim 14, wherein the predetermined sleep stage is slow wave sleep.

16. The system of claim 10, wherein the processor is configured to detect a sleep stage based on the processed EEG signal.

17. The system of claim 16, wherein the processor is configured to control the current to the coil based on the sleep stage detected.

18. The system of claim 17, wherein the sleep stage is slow wave sleep.

19. The system of claim 10, wherein the simulation-induced signals comprise electromagnetic transients induced by the coil.

20. The system of claim 10, wherein the simulation-induced signals comprise spatially and temporally correlated electrical interference from the coil.

21. The system of claim 10, wherein the processor is configured to utilize one or more of mathematical classifiers, neural networks, component analysis, support vector machines, or machine learning algorithms to separate the biological signals and the stimulation- induced signals.

22. A method for treating a sleeping individual, the method comprising the steps of:

collecting electroencephalograph (EEG) signals from the individual;

detecting a sleep stage, or a characteristic of a sleep stage, or a combination thereof, from the EEG signals; and

applying subthreshold magnetic stimulation to the individual, wherein the step of applying subthreshold magnetic stimulation is initiated automatically by a processor upon detecting a sleep stage, or the characteristic of a sleep stage, or a combination thereof.

23. The method of claim 22, wherein the sleep stage is slow wave sleep.

24. The method of claim 23, wherein the subthreshold magnetic stimulation is applied upon detecting slow wave sleep for a predetermined period of time.

25. The method of claim 22, wherein the step of applying subthreshold magnetic stimulation comprises controlling one or more of the waveform temporal parameters and strength of the subthreshold magnetic stimulation based on the EEG signals.

26. The method of claim 25, further comprising the step of separating biological and

stimulation-induced EEG signals from the collected EEG signals to yield processed EEG signals.

27. The method of claim 26, wherein the one or more of the waveform temporal parameters and strength of the subthreshold magnetic stimulation are controlled via closed-loop feedback from the biological EEG signals.

28. The method of claim 26, further comprising the step of isolating frequency bands

associated with SWS to yield the processed EEG signals.

29. The method of claim 26, wherein the simulation-induced signals comprise electromagnetic transients induced by the subthreshold magnetic stimulation.

30. The method of claim 26, wherein the simulation-induced signals comprise spatially and temporally correlated electrical interference from the subthreshold magnetic stimulation.

31. The method of claim 26, wherein the step of separating biological and stimulation-induced EEG signals from the collected EEG signals comprises utilizing one or more of mathematical classifiers, neural networks, component analysis, support vector machines, or machine learning algorithms.

32. The method of claim 22, wherein the subthreshold magnetic stimulation comprises low field magnetic stimulation.

33. The method of claim 32, wherein the low field magnetic stimulation comprises a pulse train comprising a series of pulses that are initiated at a frequency of 0.5Hz.

Description:
MODULATING BRAIN ACTIVITY DURING SLEEP

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority under 35 U.S.C. ยง119(e) to U.S.

Provisional Application No. 62/395,157, entitled " MODULATING BRAIN ACTIVITY DURING SLEEP ", filed September 15, 2016, which is hereby incorporated by reference in its entirety.

FIELD OF DISCLOSURE

[0002] This disclosure relates to the modulation of brain activity during sleep, and more particularly, to modulating brain activity during slow wave sleep (SWS) using Subthreshold Magnetic Stimulation (StMS) while using input from the monitoring of

electroencephalographic (EEG) signals to guide the stimulation.

BACKGROUND

[0003] A great need exists for improving the sleep of a stressed and sleep-deprived population. More efficient sleep patterns that help individuals sleep throughout the night, and/or that achieve equivalent refreshment with less time asleep, would provide tremendous advantages for health and productivity. Current sleep medications reliably address sleep onset (i.e. , "falling asleep"), but are not recommended for long term use, do not adequately enhance maintenance of sleep, and in some cases are associated with altered mental states upon waking. [0004] Sleep is generally recognized as being divided into several stages. The first stage of light sleep, achieved shortly after sleep onset, is termed "Nl ." The next stage is "N2," in which electrographic signals termed sleep spindles and K-complexes predominate. The next stage, termed "N3," is a stage in which rhythmic, large amplitude slow waves predominate in the EEG. These waves typically have an amplitude of 75 uV and a frequency of 0.5 - 1 Hz. Thus, this N3 period is often referred to as "slow wave sleep (SWS)." SWS is believed to be important for memory retention and numerous, varied health benefits.

[0005] Brain stimulation methodologies have been tried to modulate the presence of and magnitude of brain rhythms detected by electroencephalographic (EEG) signal monitoring. Both low and higher frequency methodologies have been demonstrated to modulate the properties of waking brain rhythms, slow wave sleep, and as well as sleep spindles (RYOTA KANAI ET AL., "Transcranial alternating current stimulation (tACS) modulates cortical excitability as assessed by TMS-induced phosphene thresholds," Clinical Neurophysiology 121, 9 (2010), International Federation of Clinical Neurophysiology; CAROLINE LUSTENBERGER ET AL., "Feedback-Controlled Transcranial Alternating Current Stimulation Reveals a

Functional Role of Sleep Spindles in Motor Memory Consolidation," Current biology : CB (26 july 2016); LISA MARSHALL ET AL., "Boosting slow oscillations during sleep potentiates memory," Nature 444, 7119 (2006), Nature Publishing Group; JULIA LADENB AUR ET AL., Improved memory consolidation by slow oscillatory brain stimulation during an afternoon nap in older adults, Elsevier B.V., 2016., each of which is incorporated herein by reference for all purposes.) The ability to monitor sleep stage with EEG for the purpose of controlling the application of brain stimulation, however, has been hampered by the fact that brain stimulation approaches can introduce large electrical artifacts, which corrupt EEG signal detection. Thus, the direct effects of brain stimulation are unable to be assessed, and indirect measures are required to test for efficacy.

BRIEF DESCRIPTION OF THE DRAWINGS [0006] FIG. 1 illustrates a flowchart of required elements for simultaneous stimulation and EEG recording for the purpose of modulating slow wave sleep.

DETAILED DESCRIPTION

[0007] This disclosure relates to devices, methods and systems for the simultaneous recording of EEG signals and application of StMS. Embodiments of the present disclosure provide devices, methods and systems that acquire an individual's EEG signal during sleep, apply StMS to the individual, in some cases while simultaneously acquiring an individual's EEG signal, process the EEG signal such that electromagnetic transients associated with StMS are removed (i.e. , stimulation-induced EEG signals), analyze the signal to detect sleep stage, and control the application, timing, temporal parameters, strength, or other parameters of StMS based upon the detected sleep stage. The present disclosure is useful for research purposes, commercial applications and clinical interventions.

[0008] In one aspect, the disclosure constitutes devices, methods and systems that record the EEG signals and deliver StMS to a sleeping individual. In certain embodiments, EEG signals are recorded simultaneously while StMS is delivered. In certain embodiments of this aspect, the EEG signals are processed to isolate relevant frequency bands of slow wave sleep, broadly defined. In another embodiment, the EEG signals are processed to remove stimulation artifacts from the StMS (i.e. , stimulation-induced EEG signals), and then further processed to isolate relevant frequency bands. In another embodiment, an algorithm is applied to the processed EEG output so as to automatically trigger the initiation of StMS delivery based upon the detected sleep stage in real time. In certain embodiments, an algorithm is applied to the processed EEG output so as to alter the timing, strength, spatial distribution or other parameter of StMS, or to stop stimulation based upon the detected sleep stage.

[0009] Low Field Magnetic Stimulation (LFMS) is used in various embodiments of the present invention as a non-limiting example. LFMS is a form of StMS that generates a time-varying magnetic field with a maximum strength of less than about 50 G using a sequence of pulses, wherein the waveform temporal parameters include a duration of each pulse in the sequence of less than 10 milliseconds, a sequence of pulses, defined as a "pulse train," with a frequency of about 1 kHz from baseline (or 500 Hz peak-to-peak), a duration of pulses within the train lasting about 500 milliseconds, and a frequency of pulse initiation within the train every 2 seconds (i.e. , at a frequency of 0.5 Hz) over a time of 0 to 120 consecutive minutes. The resulting magnetic field induces a uniform electric field in air or tissue comprising a series of electric pulses, wherein the electric pulses are monophasic and separated by periods of substantially no electric field.

[0010] Tal Medical (Boston, MA) has created non-invasive neuromodulation devices based on a novel LFMS technology (the "Tal Medical LFMS device") to treat Major Depressive Disorder (MDD) and other psychiatric diseases as reported in MICHAEL L. ROHAN ET AL., "Rapid mood-elevating effects of low field magnetic stimulation in depression," Biological Psychiatry 76, 3 (2014), Elsevier; and U.S. Patent Nos. 8,702,582 and U.S. publication no. 2014/0235929, each of which is incorporated herein by reference for all purposes. The Tal Medical LFMS device generates a magnetic field that induces a weak (below depolarization threshold), rapidly oscillating electric field in the brain, which has the ability to modulate (normalize) neuronal circuits involved in MDD and other psychiatric disorders. Current data indicate that LFMS may exert its action by reducing brain activity in exposed regions (NORA D VOLKOW ET AL., "Effects of low-field magnetic stimulation on brain glucose metabolism.," Neurolmage 51, 2 (2010), Elsevier B.V.)

[0011] Non-invasive brain stimulation approaches other than LFMS, including transcranial direct current stimulation ("tDCS"), transcranial alternating current stimulation ("tACS") and transcranial magnetic stimulation ("TMS"), also have the potential to modulate brain activity. A key element for modulation of SWS is that the stimulation must roughly match the endogenous SWS frequency in the brain. Previous work has demonstrated that low frequency tACS and TMS (1 Hz and below) can modulate SWS (LISA MARSHALL ET AL., "Boosting slow oscillations during sleep potentiates memory"; JULIA LADENBAUR ET AL., Improved memory consolidation by slow oscillatory brain stimulation during an aftemoon nap in older adults.) As the frequency of LFMS is similar to that observed in SWS, LFMS can be a potential approach to modulate SWS.

[0012] The present disclosure allows for the simultaneous recording of EEG signals and control of StMS throughout a night of sleep. A flowchart describing an embodiment of the present disclosure is shown in Figure 1. It should be noted that, although the embodiment shown in Figure 1 is specific to LFMS, the present invention is equally applicable to other forms of StMS. To achieve the goal of modulating SWS, EEG signals must be recorded from a sleeping individual and analyzed. EEG recording requires the use of electrodes on the head, including, but not restricted to, the forehead, mastoid area, and scalp, as is known in the art. Electrodes can be either adherent or physically positioned with the use of a cap or band worn on the head. The signal from these electrodes is passed through an amplifier. Though acquired continually, the signal can be passed for analysis in 5-30 second epochs. Following amplification, the EEG is filtered into relevant frequency bands, including slow/delta band (0.1-4 Hz) and higher frequencies (> 4 Hz), which can be further subdivided into narrower frequency bands as needed. The output of this filtering is passed to a processor, broadly defined, which can implement a detection algorithm to determine sleep stage (wake, REM, Nl, N2, and N3). This detection algorithm can employ analyses of spectral content (MICHAEL R. BOYLE, FLAVIO FROHLICH, "EEG feedback-controlled transcranial alternating current stimulation," International IEEE/EMBS Conference on Neural Engineering, NER (2013); JUSSI ViRKKALA ET AL., "Automatic detection of slow wave sleep using two channel electro- oculography.," Journal of neuroscience methods 160, 1 (2007); EDWARD S BOYDEN ET AL., "Integrated transcranial current stimulation and electroencephalography device" 1, 19.), spectral phase (GIOVANNI SANTOSTASI ET AL., "Phase-locked loop for precisely timed acoustic stimulation during sleep," Journal of Neuroscience Methods 259 (2016), Elsevier B. V ), or use mathematical classifiers (BECQ ET AL., "Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings," Studies in Computational Intelligence 4, February (2005); PHILLIP LOW, TERRENCE J SEJNOWSKI, "US20070016095A1- Automated detection of sleep and waking states."), neural networks, principal or independent components analysis, support vector machines (TAREK LAJNEF ET AL., "Learning machines and sleeping brains:

Automatic sleep stage classification using decision-tree multi-class support vector machines," Journal of Neuroscience Methods 250 (2014), Elsevier B.V.; VLADIMIR MISKO Vic ET AL., "On the handling of stimulation artifacts during simultaneous electroencephalography (EEG) and transcranial low field strength magnetic stimulation (LFMS)," Brain Stimulation 10, 2 (2017), Elsevier Inc.), adaptive and machine learning algorithms, or any combination of these methods to determine sleep state.

[0013] Upon detecting the desired sleep stage, which in a preferred embodiment is SWS, a course of StMS is initiated in an automated fashion. Upon initiation of StMS, a specific signal separation algorithm is applied to allow for the continued simultaneous monitoring of EEG state. The signal separation algorithm is necessary because the StMS will induce

electromagnetic transients (stimulation-induced signals) that will interfere with the biological EEG signal. The signal separation algorithm can use standard frequency filtering techniques, mathematical classifiers, neural networks, principal or independent components analysis, support vector machines, adaptive and machine learning algorithms, or any combination of these methods to determine spatially and temporally correlated electrical interference from the StMS device. The signal separation algorithm can be engaged on the same computer performing filtering, and is introduced upstream of the EEG filtering. Once the signal separation algorithms have been employed, filtering and detection algorithms can be applied as previously described.

[0014] As described, embodiments of the present disclosure are used to guide the automated initiation of an StMS treatment. Likewise, embodiments of the present disclosure may be used to automatically terminate StMS or alter StMS properties to yield a fully closed-loop system. The rules for initiation and termination may differ. For example, initiation may require 15 seconds, 30 seconds, 45 seconds, 60 seconds or longer of SWS detected, while termination may require 15 seconds, 30 seconds, 45 seconds, 60 seconds or longer of no SWS detected. Other criteria may be used to determine whether to initiate or terminate StMS, including, but not restricted to, time of the night, cumulative amount of SWS detected, body movement as measured by actigraphy/accelerometers. Furthermore, aspects of the detection algorithm may be used to modulate the strength, timing, temporal parameters of pulses and pulse trains, spatial distribution or other parameter of StMS.

[0015] In certain embodiments, the present disclosure makes use of an algorithm that calibrates the signal separation algorithm periodically, including upon first use by an individual, and possibly as frequently as every epoch of EEG acquisition. This calibration is required to adjust for an individual's head size, for the location of the EEG electrodes on the head, for any movements of StMS field or EEG electrode induced by the subject's movement.