[go: up one dir, main page]

WO2024124045A1 - Systèmes et méthodes de stimulation d'état cérébral - Google Patents

Systèmes et méthodes de stimulation d'état cérébral Download PDF

Info

Publication number
WO2024124045A1
WO2024124045A1 PCT/US2023/082965 US2023082965W WO2024124045A1 WO 2024124045 A1 WO2024124045 A1 WO 2024124045A1 US 2023082965 W US2023082965 W US 2023082965W WO 2024124045 A1 WO2024124045 A1 WO 2024124045A1
Authority
WO
WIPO (PCT)
Prior art keywords
brain
behavior
stimulation
network
electrodes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2023/082965
Other languages
English (en)
Inventor
Kafui DZIRASA
David Carlson
Austin TALBOT
Neil Gallagher
Yael GROSSMAN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Duke University
Original Assignee
Duke University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Duke University filed Critical Duke University
Publication of WO2024124045A1 publication Critical patent/WO2024124045A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • 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
    • A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
    • A61B5/245—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
    • 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]
    • 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
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • 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
    • 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
    • A61N1/36082—Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
    • 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
    • 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
    • A61N1/36146—Control systems specified by the stimulation parameters
    • A61N1/36167—Timing, e.g. stimulation onset
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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
    • A61N1/36078—Inducing or controlling sleep or relaxation

Definitions

  • BACKGROUND BACKGROUND [0003] Social behavior reflects the integration of sensory information with internal affective states. Many subcortical brain regions contribute to aggressive behavior in mammals including lateral septum (LSN), nucleus accumbens, lateral habenula, the ventrolateral portion of ventromedial hypothalamus, and medial amygdala. Prefrontal cortex stimulation has been shown to mitigate aggressive behavior in both humans, implicating cortical regions in regulating aggression. Finally, sensory regions, such as those underlying olfaction, also contribute to aggression. [0004] To appropriately regulate aggressive behavior, the brain must integrate information across these and other cortical and subcortical regions.
  • LSN lateral septum
  • nucleus accumbens the ventrolateral portion of ventromedial hypothalamus
  • medial amygdala Prefrontal cortex stimulation has been shown to mitigate aggressive behavior in both humans, implicating cortical regions in regulating aggression.
  • sensory regions such as those underlying olfaction, also contribute
  • the present disclosure provides a system for predicting and treating a neurological behavior in a subject.
  • the system may include a plurality of electrodes configured to receive electrical activity signals from a network of two or more regions of the subject’s brain; and a controller in communication with the plurality of electrodes, the controller having at least one processor in communication with a memory, the memory including instructions executable by the processor to: receive and record the electrical activity signals from the plurality of electrodes; generate a prediction of a time of onset of the neurological behavior from the electrical activity signals; and send a stimulation signal to the brain prior to the predicted time of onset of the neurological behavior.
  • the stimulation signal to the brain suppresses the neurological behavior.
  • the memory may include further instructions to: provide the electrical activity signals to a trained machine learning model, wherein the prediction of the time of onset is generated from the trained machine learning model.
  • the plurality of electrodes are microwire electrodes.
  • the plurality of electrodes may be implanted in the subject’s brain.
  • the two or more regions comprises 8 to 11 regions of the subject’s brain.
  • the two or more brain regions may be selected from infralimbic cortex, orbitofrontal cortex, prelimbic cortex, lateral septum, nucleus accumbens, lateral habenula, mediodorsal thalamus, ventromedial hypothalamus, medial amygdala, primary visual cortex, ventral hippocampus, and combinations thereof.
  • the neurological behavior is an aggressive state or an aggressive attack.
  • the time of onset of the neurological behavior is on a second timescale.
  • the system may further include a stimulator configured to generate the stimulation signal.
  • the stimulation signal may be light stimulation.
  • the stimulation signal may include closed loop optogenetic stimulation.
  • the stimulation signal may be directed to the prefrontal cortex of the subject’s brain.
  • a method for predicting and treating a neurological behavior in a subject including: providing a plurality of electrodes configured to receive electrical activity signals from a network of two or more regions of the subject’s brain; receiving and recording the electrical activity signals from the plurality of electrodes, via a controller in communication with the plurality of electrodes; generating a prediction of a time of onset of the neurological behavior from the electrical activity signals; and sending a stimulation signal to the 92443576.2 - 2 - Atty Docket No.23-3000-WO brain prior to the predicted time of onset of the neurological behavior, where the stimulation signal to the brain suppresses or prevents onset of the neurological behavior.
  • the method may further include providing the electrical activity signals to a trained machine learning model, wherein the prediction of the time of onset is generated from the trained machine learning model.
  • the two or more regions may include 8 to 11 regions of the subject’s brain.
  • the two or more brain regions may be selected from infralimbic cortex, orbitofrontal cortex, prelimbic cortex, lateral septum, nucleus accumbens, lateral habenula, mediodorsal thalamus, ventromedial hypothalamus, medial amygdala, primary visual cortex, and ventral hippocampus.
  • the neurological behavior is an aggressive state or an aggressive attack.
  • the plurality of electrodes may be implanted in the subject’s brain.
  • the time of onset of the neurological behavior may be on a second timescale.
  • the stimulation signal is directed to the prefrontal cortex of the subject’s brain.
  • the stimulation signal may include closed loop optogenetic stimulation.
  • FIG. 1 illustrates a schematic diagram of a system, according to an example embodiment.
  • FIG.2 illustrates example system embodiments.
  • FIG.3 illustrates an example machine learning environment.
  • FIG.4 is an example method in one embodiment.
  • FIG.5A is a schematic of optogenetic stimulation and shows social encounters utilized for testing.
  • FIG. 5A is a schematic of optogenetic stimulation and shows social encounters utilized for testing.
  • FIG.5B shows prefrontal cortex stimulation suppressed attack behavior, increased non-attack social behavior towards male mice, and suppressed non-attack social behavior towards females (*P ⁇ 0.05 for each comparison).
  • FIG.5C shows example targeted brain regions.
  • FIG. 5D shows representative local field potentials recorded during repeated exposure to social contexts that produce attack and non-attack social behavior.
  • FIG.5E shows a framework to test encoding of social states by individual brain regions.
  • FIG.5F shows all implanted regions encoded social engagement; however, only five selectively encoded the attack behavior. Pink shading indicates P ⁇ 0.05 with FDR correction.
  • FIG. 5G shows attack codes discovered from the five brain regions failed to encode aggressive behavior induced by male urine (Gray shading indicates P ⁇ 0.05 prior to but not following FDR correction).
  • FIG. 5H is a schematic of a machine-learning model used to discover network encoding attack behavior and encoding across eight learned networks.
  • FIG. 5I shows the supervised network (purple, EN-AggINH) showed the strongest encoding. Data shown as mean ⁇ SEM.
  • FIG. 6A shows neural activity was sampled while mice were socially isolated and during intervals surrounding social behavior.
  • FIG.6B shows network activity during this interval encoded attack behavior vs.
  • FIG.7A shows prominent oscillatory frequency bands composing EN-AggINH are highlighted for each brain region around the rim of the circle plot. Prominent synchrony measures are depicted by lines connecting brain regions through the center of the circle. The plot is shown at relative spectral energy of 0.4. Theta (4-11 Hz) and beta (14-30 Hz) frequency components are highlighted in blue and green, respectively.
  • FIG. 7A shows prominent oscillatory frequency bands composing EN-AggINH are highlighted for each brain region around the rim of the circle plot. Prominent synchrony measures are depicted by lines connecting brain regions through the center of the circle. The plot is shown at relative spectral energy of 0.4. Theta (4-11 Hz) and beta (14-30 Hz) frequency components are highlighted in blue and green, respectively.
  • FIG. 7B shows example relative LFP spectral energy plots for three brain regions corresponding to the circular plot in FIG.7A.
  • FIG. 7C shows Granger offset measures were used to quantify directionality within EN-AggINH. Prominent directionality was observed across the theta and beta frequency bands (shown at spectral density threshold of 0.4 and a directionality offset of 0.3).
  • FIG.7D is a schematic depicting directionality within EN-AggINH.
  • FIG.7E a representative cell showing firing activity that is negatively correlated with EN-AggINH activity. 92443576.2 - 4 - Atty Docket No.23-3000-WO [0036] FIG.
  • FIG. 7F shows EN-AggINH activity correlated with cellular firing across the brain across the brain.
  • FIG.8A shows an example strategy for validating EN-AggINH using additional aggression paradigms.
  • FIG. 8B shows an experimental approach for causally inducing aggression via direct cellular manipulation.
  • FIG.8C shows cellular activation induced attack behavior towards female mice (P ⁇ 0.001 using sign-rank test).
  • FIG.8D shows decreased EN-AggINH activity across during social interactions with female mice (P ⁇ 0.01 using one-tailed Wilcoxon sign-rank test).
  • FIG. 8E shows EN-AggINH activity and intervals surrounding these interactions (P ⁇ 0.05 using Wilcoxon sign-rank test).
  • FIG.9A is a schematic for closed loop manipulation of EN-AggINH activity.
  • FIG. 9B is an example real-time estimation of aggression. Receiver operating characteristic depicting detection of aggressive behavior in a mouse using EN-AggINH activity vs. real-time reduced encoder is shown to the right. Dashed blue line corresponds to the established detection threshold.
  • FIG.9D shows EN-AggINH activity relative to light stimulation during closed loop manipulation. [0046] FIG.
  • FIG. 9F shows behavioral effects of closed loop stimulation (**P ⁇ 0.005 using paired t-test, significance determined using FDR correction).
  • FIG. 9G is a directed graph with the inferred modes of action derived from mediation analysis. There is a causal relationship from stimulation to behavior and stimulation to EN-AggINH expression (model 1; P ⁇ 0.01 using signed rank and paired t-tests).
  • FIG.9H is a schematic for nonsynchronous control stimulation.
  • FIG.9I shows nonsynchronous stimulation does not impact aggressive behavior (P>0.05 using paired t-test). 92443576.2 - 5 - Atty Docket No.23-3000-WO
  • FIG.9J shows an impact of open loop (20Hz, fixed) and closed loop stimulation on locomotor behavior (P** ⁇ 0.01 using paired two-tailed t-test).
  • Example methods, devices, and systems are described herein. It should be understood that the words “example” and “exemplary” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or feature described herein as being an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. [0053] Articles “a” and “an” are used herein to refer to one or to more than one (i.e. at least one) of the grammatical object of the article.
  • an element means at least one element and can include more than one element.
  • “About” is used to provide flexibility to a numerical range endpoint by providing that a given value may be “slightly above” or “slightly below” the endpoint without affecting the desired result.
  • the use herein of the terms “including,” “comprising,” or “having,” and variations thereof, is meant to encompass the elements listed thereafter and equivalents thereof as well as additional elements.
  • “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations where interpreted in the alternative (“or”).
  • the transitional phrase "consisting essentially of” (and grammatical variants) is to be interpreted as encompassing the recited materials or steps "and those that do not materially affect the basic and novel characteristic(s)" of the claimed invention.
  • the term “consisting essentially of” as used herein should not be interpreted as equivalent to "comprising.”
  • the present disclosure also contemplates that in some embodiments, any feature or combination of features set forth herein can be excluded or omitted. To illustrate, if the specification states that a complex comprises components A, B and C, it is specifically intended that any of A, B or C, or a combination thereof, can be omitted and disclaimed singularly or in any combination.
  • treatment refers to the clinical intervention made in response to a disease, disorder or physiological condition manifested by a patient or to which a patient may be susceptible.
  • the aim of treatment includes the alleviation or prevention of symptoms, slowing or stopping the progression or worsening of a disease, disorder, or condition and/or the remission of the disease, disorder or condition.
  • the terms “prevent,” “preventing,” “prevention,” “prophylactic treatment” and the like refer to reducing the probability of developing a disease, disorder or condition in a subject, who does not have, but is at risk of or susceptible to developing a disease, disorder or condition.
  • the term “effective amount” or “therapeutically effective amount” refers to an amount sufficient to effect beneficial or desirable biological and/or clinical results.
  • the term “subject” and “patient” are used interchangeably herein and refer to both human and nonhuman animals.
  • the term “nonhuman animals” of the disclosure includes all vertebrates, e.g., mammals and non-mammals, such as nonhuman primates, sheep, dog, cat, horse, cow, chickens, amphibians, reptiles, and the like.
  • the methods and compositions disclosed herein can be used on a sample either in vitro (for example, on isolated cells or tissues) or in vivo in a subject (i.e. living organism, such as a patient).
  • the 92443576.2 - 7 - Atty Docket No.23-3000-WO present disclosure is based, in part, on the finding by the inventors that murine aggression is optimally encoded by a brain-wide network and that this network is organized by prominent theta (4-11 Hz) and beta (14-30 Hz) oscillations, leading from orbital frontal cortex and medial dorsal thalamus, and converging on ventral medial hypothalamus and medial amygdala.
  • the network may be a network that encodes aggression (e.g. EN-AggINH).
  • aggression e.g. EN-AggINH
  • one aspect of the present disclosure provides a method of reducing and/or eliminating aggression in a subject, the method comprising, consisting of, or consisting essentially of increasing activity in a network of brain regions (e.g. EN-AggINH activity) in the brain of the subject such that the aggression in the subject is suppressed, reduced and/or eliminated.
  • the EN-AggINH activity is increased by applying one or more light stimulations.
  • the light stimulation includes 5mW, 20Hz, 3ms pulse width. In another embodiment, the stimulation includes a 20Hz fixed frequency stimulation of infralimbic cortex.
  • Another aspect of the present disclosure provides for a closed-loop stimulation protocol based on network activity levels that suppresses aggression, but not pro-social behavior, in a subject, the method comprising, consisting of, or consisting essentially of applying to the subject one or more closed loop stimulations of the infralimbic cortex when EN-AggINH activity decreases below a threshold level as compared to a control.
  • the closed-loop stimulation includes one or more light stimulations. In one embodiment, the light stimulation includes 5mW, 20Hz, 3ms pulse width.
  • the stimulation includes a 20Hz fixed frequency stimulation of infralimbic cortex.
  • Another aspect of the present disclosure provides a designer receptor exclusively activated by designer drug (DREADD) based approach to selectively activate cells in ventral medial hypothalamus in a manner that has been shown to induce aggression.
  • DEADD designer drug
  • Treatment of such neurological behavior, psychological disorders or conditions may include a variety of interventions to reverse such changes and/or compensate for the effects of such changes. Treatments may include talk therapy, pharmaceutical intervention, surgical intervention, light stimulation, electrical stimulation, or some other procedures or therapies.
  • Treatments may act to compensate for changes underlying a neurological behavior or condition (e.g., by increasing the availability of a neurotransmitter by reducing the rate of reuptake of the neurotransmitter, by increasing the intrinsic activity of a brain region, by blocking the activity 92443576.2 - 8 - Atty Docket No.23-3000-WO of a brain region and/or blocking efferents from the brain region) and/or to reverse the changes underlying the neurological behavior, psychological disorder or condition.
  • a neurological behavior or condition e.g., by increasing the availability of a neurotransmitter by reducing the rate of reuptake of the neurotransmitter, by increasing the intrinsic activity of a brain region, by blocking the activity 92443576.2 - 8 - Atty Docket No.23-3000-WO of a brain region and/or blocking efferents from the brain region
  • Stimulating a brain region based on the detected electrical activity signals from a network of two or more regions of the brain, could involve providing electrical signals (e.g., pulses or other waveforms of current and/or voltage), optical stimulation (e.g., to neurons that are sensitive to optical stimulation and/or that have been made sensitive by some intervention), chemical stimulation (e.g., by emission of timed amounts of a stimulating neurotransmitter or other substance), or providing some other excitatory stimulus in a manner that is timed according to the predicted onset time of the behavior or other information related to detected activity of the network.
  • electrical signals e.g., pulses or other waveforms of current and/or voltage
  • optical stimulation e.g., to neurons that are sensitive to optical stimulation and/or that have been made sensitive by some intervention
  • chemical stimulation e.g., by emission of timed amounts of a stimulating neurotransmitter or other substance
  • providing some other excitatory stimulus in a manner that is timed according to the predicted onset time of the behavior or other information
  • Systems and devices configured to affect the therapies and methods described herein could include implanted and/or non-implanted elements.
  • Implanted elements could include electrodes, optical fibers, leads extending beneath the skin between elements of the device or system, leads or connectors traversing the skin to permit connections between implanted and non-implanted elements, controllers, batteries, communications or power transfer coils, or other elements.
  • Non-implanted elements could include wearable electrodes or devices (e.g., an array of electroencephalogram (EEG) electrodes configured to be worn as a cap), coils for the delivery of transcranial magnetic stimulation (TMS), magnetic resonance imagers (MRI), magnetoencephalography (MEG) equipment, control interfaces (e.g., for use in setting parameters of operation of the system by a clinician or other user), power sources, or other elements.
  • EEG electroencephalogram
  • TMS transcranial magnetic stimulation
  • MRI magnetic resonance imagers
  • MEG magnetoencephalography
  • control interfaces e.g., for use in setting parameters of operation of the system by a clinician or other user
  • power sources or other elements.
  • Implanted and non-implanted elements may be in wireless or wired communication (e.g., an implanted device that is configured to detect activity in a first brain region could communicate phase or other timing information about the detected activity to a TMS stimulator that is located outside of the body) and/or may be in communication with other systems (e.g., a computer in a physician’s office).
  • the devices, systems, and methods described herein are not limited to the detection of activity in a single brain region and providing excitatory stimuli, based on the detected activity, to a single further brain region. Stimuli could be provided to one or more brain regions based on the timing or other properties of activity detected in one or more brain regions.
  • Stimuli could be provided and/or activity detected in a brain region on one side of the brain (unilaterally) or on both sides of the brain (bilaterally). Other patterns of detection from 92443576.2 - 9 - Atty Docket No.23-3000-WO one or more regions and providing stimuli to one or more further regions and/or to the regions from which the activity is detected are anticipated by the inventors.
  • II. Systems A variety of devices and systems can be used for predicting and treating a neurological behavior in a subject. Provided herein is an exemplary system for predicting and treating a neurological behavior in a subject.
  • the system can include a plurality of electrodes configured to receive electrical activity signals from a network of two or more regions of the subject’s brain and a controller in communication with the plurality of electrodes.
  • the neurological behavior may be any neurological behavior detectable from the plurality of electrodes within the network of brain regions.
  • Non-limiting examples of neurological behaviors include an aggressive state, an aggressive attack, appetitive social behavior, and depressive behavior.
  • Such systems can include elements that are implanted within a body (e.g., electrodes implanted within a brain) and/or elements that are disposed outside of the body (e.g., electrodes or other elements that may be removably mounted to the skin of the body).
  • Elements that are implanted within a body could be operably coupled to elements outside of the body (e.g., mounted to a surface of the body) by cabling, connectors, or other means passing through the skin. Additionally or alternatively, elements of such systems could be wirelessly coupled, e.g., by coils, antenna, or other means for transmitting and/or receiving wireless communications and/or wireless power. Such systems could be configured for chronic use (e.g., to provide stimulation over a protracted period of time) or for acute and/or periodic use. [0075] Such systems can be configured to detect a variety of different signals in a network of two or more brain regions.
  • such systems can be configured to detect electrical current or voltages, electrical fields, magnetic fields, chemical concentrations or gradients, acoustical waves, temperatures or temperature gradients, the intensity or wavelength of light emitted from a brain region (e.g., in response to illumination or some other applied energy or generated internally by the brain region), or some other physical variable related to the activity of a brain region.
  • the electrical signals from the network of two or more regions of the brain may provide more information for predicting an onset of the neurological behavior than electrical signals from one region alone.
  • the network can include 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more regions of the subject’s brain.
  • the network can include up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, or up to 11 regions of the subject’s brain. In at least one example, the network can include 8 to 11 regions of the subject’s brain.
  • the two or more regions of the brain in the network may include but are not limited to 92443576.2 - 10 - Atty Docket No.23-3000-WO infralimbic cortex, orbitofrontal cortex, prelimbic cortex, lateral septum, nucleus accumbens, lateral habenula, mediodorsal thalamus, ventromedial hypothalamus, medial amygdala, primary visual cortex, and ventral hippocampus.
  • the network may be a network that encodes aggression (e.g. an electome network referred to as EN-Aggression Inhibition – AggINH; “EN-AggINH”).
  • an electome network referred to as EN-Aggression Inhibition – AggINH; “EN-AggINH”.
  • EN-AggINH an electome network referred to as EN-Aggression Inhibition – AggINH; “EN-AggINH”.
  • such systems can be configured to provide a variety of different types of stimuli (e.g. stimulation signal) to excite a brain region.
  • FIG.1 illustrates a schematic diagram of a system 100, according to an example embodiment.
  • System 100 includes a plurality of electrodes 110 and a controller 120.
  • System 100 can also include a stimulator 150, a communication interface 130, and a power supply 140.
  • the plurality of electrodes 110 are configured to detect electrical activity signals from a network of two or more regions of the brain (e.g., to detect a local field potential, an electroencephalogram, an electrical field generated by the electrical activity of one or more neurons, or some other electrical voltage or current related to the electrical activity of the brain region).
  • the plurality of electrodes can be microwire electrodes.
  • the electrical activity signals can include prominent theta (4-11 Hz) and beta (14-30 Hz) oscillations from the network of two or more regions of the brain, but signals that include components at other frequencies are possible. [0078] It is understood that some or all elements of system 100 may be implantable in a patient.
  • system 100 may remain external to, and/or may be worn by, the patient.
  • individual illustrated components e.g., the plurality of electrodes 110, the stimulator 150, the controller 120
  • individual illustrated components may be distributed and/or duplicated across multiple elements of the system 100.
  • the plurality of electrodes 110, the stimulator 150, and/or elements of the controller 120 may be disposed in an implanted device while the stimulator 150 and/or other elements of the controller 120 may be disposed in an external device that is configured to be mounted to or otherwise maintained in position relative to the brain of a person, outside of the body of the person.
  • the implanted device can be configured to detect electrical activity signals from a network of two or more regions of the brain (e.g., by transmitting the detected signals) to the external device.
  • the 92443576.2 - 11 - Atty Docket No.23-3000-WO external device could then operate to provide excitatory stimulus (e.g. stimulation signal) to a brain region, in the form of optogenetic stimulation, based on the received electrical activity signals and predicted time of onset of the neurological behavior (e.g., such that the excitatory stimulus is provided at a set time or range of times prior to the predicted time of onset).
  • the plurality of electrodes 110 could be configured to detect such signals from outside of the brain.
  • the plurality of electrodes (or sensors) 110 could be configured to detect signals within the brain.
  • an electrical field and/or current within the cortex of the brain e.g., using two or more penetrating electrodes
  • the controller 120 is configured to receive and record the electrical activity signals from the plurality of electrodes 110.
  • the controller 120 can determine when there is a change in the electrical activity signals of the network that indicates a behavior change. For example, a reduction in the electrical signal from the network can indicate an onset of a neurological behavior, such as an aggressive state.
  • the reduction can be determined by comparing the signals to a threshold. Once the signals are below the threshold, the controller may predict a time of onset of the neurological behavior.
  • the threshold can be determined from a baseline of electrical activity signals from the network in the absence of the neurological behavior. A predicted time of onset of the neurological behavior may be determined from the reduction in the network electrical signal.
  • the electrical activity signals can be provided to a trained machine learning model that generates a prediction of a time of onset of the neurological behavior from the trained machine learning 92443576.2 - 12 - Atty Docket No.23-3000-WO model.
  • the predicted time of onset of the neurological behavior is on a second timescale.
  • the predicted time of onset may be provided about 1 s, about 2 s, about 3 s, about 4 s, about 5 s, about 6 s, about 7 s, about 8 s, about 9 s, or about 10 s before the neurological behavior manifests.
  • the system 100 can further include a stimulator 150.
  • the controller can further be configured to send a stimulation signal (e.g. light stimulation, electrical stimulation, etc.) to the brain prior to the predicted time of onset of the neurological behavior.
  • the stimulator 150 can be configured to generate the stimulation signal.
  • the stimulation signal to the brain suppresses the neurological behavior.
  • the stimulator 150 is configured to provide an excitatory stimulus (e.g. stimulation signal) to a brain region (e.g., an electrical voltage and/or current, an optical/light signal, channel, or other element(s) of the desired brain region).
  • the brain region that the stimulation signal is applied to can be a same or different brain region with two or more electrodes.
  • the stimulation signal can be directed to the prefrontal cortex of the subject’s brain.
  • the stimulation signal includes closed loop optogenetic stimulation.
  • the stimulator 150 is one or more of the plurality of electrodes 110. In other examples, the stimulator 150 provides optogenetic stimulation.
  • the system 100 can further include leads, optical fibers, or other means for transmitting a signal of interest from a brain region that is generating the signal to the controller 120. These means of transmitting a signal may be integral with the plurality of electrodes 110.
  • the controller can include a data acquisition system 120. Additionally or alternatively, elements of the data acquisition system or controller 120 may be disposed proximate to the brain region generating such a signal.
  • amplifiers, filters, analog- to-digital converters, photodetectors, light emitters, integrated circuits, or other electronic elements may be disposed on the backside of one or more surface electrodes, proximate the tip of a device configured to penetrate from the surface of the brain to a deep structure of the brain, or at some other location proximate a brain region of interest and/or elements of the electrodes 110 (e.g., electrodes, optical fibers, lenses, chemical transducers) configured to detect an electrical activity signal from the brain regions.
  • the plurality of electrodes 110 may include two or more electrodes configured to conduct electrical current and/or measure a local field potential (LFP) within two or more brain regions.
  • LFP local field potential
  • An LFP may be a signal generated by one or more neurons proximate to an electrode 110.
  • the LFP signal may provide information about physiology such as: synaptic currents, neuronal connectivity, average neural activity, a degree of coherence and/or synchronization of activity of a population of neurons, and/or neural 92443576.2 - 13 - Atty Docket No.23-3000-WO interaction within a given brain region.
  • the electrical activity signals may include oscillating signals having frequencies between 3-30 Hz, 3-6 Hz, 4-11 Hz, and/or 14-30 Hz, but signals that include components at other frequencies are possible.
  • the plurality of electrodes 110 may be implanted via a burr hole in a patient’s skull. Upon proper delivery/placement of the electrodes 110, a portion of the electrodes 110 may be anchored to the patient’s skull.
  • the plurality of electrodes 110 may be communicatively coupled to the controller 120 via a wireless or wired connection, which may include communication interface 130.
  • the controller 120 is configured to detect, using the electrodes 110, electrical activity signals that may be related to the activity of two or more brain regions within a predetermined network of brain regions.
  • the controller 120 can include a data acquisition system that includes a single or multi-channel recording system; that is, the data acquisition system may be configured to detect electrical activity signals (or other signals relating to such oscillatory signals) from one or more electrodes and/or elements of the electrodes (e.g., one or more electrodes of an electrode array, one or more electrodes of a tetrode).
  • the controller 120 or data acquisition system may receive electrical signals from the plurality of electrodes 110.
  • the controller 120 or data acquisition system may convert an analog voltage waveform to a digital signal.
  • the controller 120 or data acquisition system may include one or more voltmeters or analog-to-digital converter circuits, which may provide information indicative of one or more stimulation signals in units of volts.
  • the controller 120 may be configured to operate one or more light emitters, piezoelectric transducers, electrodes, or other elements configured to emit an energy into the brain region (e.g., light energy, acoustical energy) to increase the activity within the network to suppress the neurological behavior.
  • the controller 120 may include a digital signal processing system.
  • the controller 120 may provide one or more clock and/or trigger signals to another element of system 100, such as the stimulator 150.
  • the controller 120 may be configured to record and store signals locally and/or in the memory 154.
  • the stimulator 150 may be configured to provide electrical, optical, chemical, acoustical, or some other variety of excitatory stimuli (e.g.
  • the stimulator 150 can include light emitters, electrodes, piezoelectric transducers, chemical transducers, or other tissue-stimulating elements.
  • the stimulator 150 can include amplifiers, filters, transducers, digital-to-analog converters, buffers, or other elements configured to convert, condition, or otherwise modify signals sent or generated by the controller 120 such that the stimulator 150 provides at least 92443576.2 - 14 - Atty Docket No.23-3000-WO one stimulus to a brain region.
  • the stimulator 150 can include blocking capacitors, resistors, fuses, clamping diodes, or other elements to prevent the application of deleterious stimuli to the desired brain region (e.g., to limit a maximum current and/or voltage applied, by electrodes of the stimulator 150, to the brain).
  • the stimulator 150 can include wires, optical fibers, lenses, leads, waveguides, or other elements configured to transmit a generated electrical voltage or current, a light, an acoustical energy, or some other stimulus signal to the brain region from an emitting element of the system 100 (e.g., from a light emitter, electrical buffer or amplifier, or other element(s) of the stimulator 150).
  • the brain region receiving the stimulation signal may be the same or different from the two or more brain regions in the network from which the electrical activity signals are obtained.
  • stimulator 150 may include one or more optical fibers, wires connected to one or more electrodes, or other elements that may be proximate to the desired brain region for stimulation.
  • the stimulator 150 may provide voltage waveforms, light intensity waveforms, of other signals to the brain region.
  • stimulator 150 may include circuitry such as filters, amplifiers, etc.
  • Stimulator 150 may be similar or identical to at least one of the plurality of electrodes 110.
  • stimulator 150 and electrodes 110 could both include respective one or more electrodes configured to provide an electrical interface with tissues of the brain.
  • stimulator 150 may be incorporated into at least some elements of at least one electrode of the plurality of electrodes 110.
  • stimulator 150 and at least one electrode of the plurality of electrodes 110 may be combined into a single implantable device.
  • one or more of the electrodes of the implantable device may act as either a sensing electrode or a stimulating electrode.
  • an electrode of the stimulator 150 can be used both to provide an excitatory electrical stimulus to a desired brain region and to detect an electrical signal from one or more brain regions in a network.
  • a detected electrical activity signal can be used to determine a time of onset of a behavior and to control stimulation signal provided to the desired brain region using the stimulator 150.
  • the stimulation signal is light stimulation.
  • the stimulation signal may be directed to the prefrontal cortex of the subject’s brain.
  • the stimulation signal includes closed loop optogenetic stimulation.
  • the amount of closed loop optogenetic light stimulation may be varied depending on the particular need of the subject.
  • the closed loop stimulation includes about 1 second, about 2 seconds, about 3 92443576.2 - 15 - Atty Docket No.23-3000-WO seconds, about 4 seconds, or about 5 seconds of light stimulation.
  • the closed loop stimulation includes about a one second light stimulation to the prefrontal cortex.
  • the closed loop stimulation may include a pulse of light at about 2 mW to about 10 mW and about 10 Hz to about 30 Hz for about an about 1 ms to about 5 ms pulse width. In at least one example, the closed loop stimulation may include a pulse of light at 5 mW and about 20 Hz for about a 3 ms pulse width. In some examples, the light may be a blue light. The light may have a wavelength of about 473 nm. [0091] In an embodiment, stimulator 150 may be configured to provide optical stimuli to the brain region.
  • the brain region may include photo-sensitive retinylidene proteins such as a channelrhodopsin.
  • Channelrhodopsins may provide a controllable ion channel configured to actuate based on received light.
  • Channelrhodopsin-1 (ChR1)
  • Channelrhodopsin-2 (ChR2)
  • Channelrhodopsins may provide sensitivity at various wavelengths of light.
  • the ChETA Channelrhodopsin may be opened by a blue light pulse and closed with a yellow light pulse.
  • stimulator 150 may include one or more light sources configured to actuate a channelrhodopsin or another type of light-sensitive ion channel.
  • stimulator 150 may include a blue laser diode coupled to a single-mode or multi-mode optical fiber, which may be delivered to the desired brain region.
  • the blue laser diode may provide 1.6mW/ ⁇ m 2 at 473nm wavelength.
  • Stimulator 150 may also include various optical components, such as lenses, optical filters, and apertures, among other possibilities. As such, stimulator 150 may be configured to provide light at one or more wavelengths to the brain region so as to indirectly provide an excitatory electrical signal to cells of the brain region (via ion channels of the channelrhodopsin opening and closing).
  • Controller 150 may include a processor 152 and a memory 154, such as a non- transitory computer readable medium. Memory 154 may be configured to store instructions 156. Instructions 156 may be executed by processor 152 so as to carry out various operations described herein.
  • Processor 122 may include one or more general purpose processors – e.g., microprocessors – and/or one or more special purpose processors – e.g., image signal processors (ISPs), digital signal processors (DSPs), graphics processing units (GPUs), floating 92443576.2 - 16 - Atty Docket No.23-3000-WO point units (FPUs), network processors, or application-specific integrated circuits (ASICs). Additionally or alternatively, the processor 122 may include at least one programmable in- circuit serial programming (ICSP) microcontroller.
  • ISPs image signal processors
  • DSPs digital signal processors
  • GPUs graphics processing units
  • FPUs floating 92443576.2 - 16 - Atty Docket No.23-3000-WO point units
  • ASICs application-specific integrated circuits
  • the processor 122 may include at least one programmable in- circuit serial programming (ICSP) microcontroller.
  • ICSP programmable in- circuit serial programming
  • the memory 126 may include one or more volatile and/or non-volatile storage components, such as magnetic, optical, flash, or organic storage, and may be integrated in whole or in part with the processor 122. Memory 126 may include removable and/or non-removable components.
  • Processor 122 may be capable of executing program instructions (e.g., compiled or non-compiled program logic and/or machine code) stored in memory 126 to carry out the various functions described herein. Therefore, memory 126 may include a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by system 100, cause system 100 to carry out any of the methods, processes, or operations disclosed in this specification and/or the accompanying drawings.
  • the execution of program instructions by processor 122 may result in processor 122 using data provided by various other elements of the system 100.
  • the controller 120 and the processor 122 may perform operations on based on information received from electrodes 110 and/or data acquisition system.
  • the controller 120 may include a distributed computing network and/or a cloud computing network.
  • the controller 120 may be configured to operate the elements of the system 100 to detect electrical activity signals from a network of two or more regions of the brain, to determine a predicted time of onset of a neurological behavior, and to provide a stimulation signal to the brain prior to the predicted time of onset. Such operation could be performed to provide suppression of the neurological behavior.
  • controller 120 may be configured to receive information indicative of an electrical activity signal from two or more brain regions in a network. Controller 120 may also be configured to determine, based on the received information, a predicted time of onset of the neurological behavior. Additionally, controller 120 may be configured to, in response to determining the predicted time of onset, send at least one stimulation signal to the brain. Yet further, controller 120 may be configured to, in response to the predicted time of onset, cause the stimulator 150 to provide stimulation to a brain region. [0098] Additionally or alternatively, controller 120 may be configured to carry out some or all of the method steps or blocks described in relation to method 400.
  • Two or more of the elements of system 100 may be physically and/or communicatively coupled to one another via communication interface 130.
  • 92443576.2 - 17 - Atty Docket No.23-3000-WO communication interface 130 may allow system 100 to communicate, using analog or digital modulation, with other devices, access networks, and/or transport networks.
  • communication interface 130 may facilitate circuit-switched and/or packet-switched communication, such as plain old telephone service (POTS) communication and/or Internet protocol (IP) or other packetized communication.
  • POTS plain old telephone service
  • IP Internet protocol
  • communication interface 130 may include a chipset and antenna arranged for wireless communication with a radio access network or an access point.
  • communication interface 130 may take the form of or include a wireline interface, such as an Ethernet, Universal Serial Bus (USB), or High-Definition Multimedia Interface (HDMI) port.
  • Communication interface 130 may also take the form of or include a wireless interface, such as a Wifi, BLUETOOTH®, global positioning system (GPS), or wide-area wireless interface (e.g., WiMAX or 3GPP Long-Term Evolution (LTE)).
  • GPS global positioning system
  • LTE Long-Term Evolution
  • communication interface 130 may comprise multiple physical communication interfaces (e.g., a Wifi interface, a BLUETOOTH® interface, and a wide-area wireless interface).
  • Power supply 140 may include one or more batteries.
  • the batteries may include secondary (rechargeable) or primary (non-rechargeable) cells. Additionally or alternatively, at least some elements of system 100 may be powered by a conventional wall plug outlet (e.g., 120V, 60Hz) or another type of energy source.
  • power supply 140 includes an implantable battery that may be recharged via a wireless charging system.
  • FIG.2 shows an example of computing system 200 in which the components of the system are in communication with each other using connection 205.
  • Connection 205 can be a physical connection via a bus, or a direct connection into processor 210, such as in a chipset or system-on-chip architecture. Connection 205 can also be a virtual connection, networked connection, or logical connection. [00102]
  • computing system 200 is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple datacenters, a peer network, throughout layers of a fog network, etc.
  • one or more of the described system components represents many such components each performing some or all of the function for which the component is described.
  • the components can be physical or virtual devices.
  • Example system 200 includes at least one processing unit (CPU or processor) 210 and connection 105 that couples various system components including system memory 92443576.2 - 18 - Atty Docket No.23-3000-WO 215, read only memory (ROM) 220 or random access memory (RAM) 225 to processor 210.
  • Computing system 200 can include a cache of high-speed memory 212 connected directly with, in close proximity to, or integrated as part of processor 210.
  • Processor 210 can include any general purpose processor and a hardware service or software service, such as services 232, 234, and 236 stored in storage device 230, configured to control processor 210 as well as a special-purpose processor where software instructions are incorporated into the actual processor design.
  • Processor 210 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc.
  • a multi-core processor may be symmetric or asymmetric.
  • computing system 200 includes an input device 245, which can represent any number of input mechanisms, such as a plurality of electrodes, a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc.
  • Computing system 200 can also include output device 235, which can be one or more of a number of output mechanisms known to those of skill in the art.
  • output device 235 can be one or more of a number of output mechanisms known to those of skill in the art.
  • multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 200.
  • Computing system 200 can include communications interface 240, which can generally govern and manage the user input and system output, and also connect computing system 200 to other nodes in a network.
  • communications interface 240 can generally govern and manage the user input and system output, and also connect computing system 200 to other nodes in a network.
  • Storage device 230 can be a non-volatile memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, battery backed random access memories (RAMs), read only memory (ROM), and/or some combination of these devices.
  • the storage device 230 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 210, it causes the system to perform a function.
  • a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 210, connection 205, output device 235, etc., to carry out the function.
  • FIG. 3 illustrates an example machine learning environment 300.
  • the machine learning environment can be implemented on one or more computing devices 302 (e.g., cloud computing servers, virtual services, distributed 92443576.2 - 19 - Atty Docket No.23-3000-WO computing, one or more servers, etc.).
  • the computing device(s) 302 can include training data 304 (e.g., one or more databases or data storage device, including cloud-based storage, storage networks, local storage, etc.).
  • the training data may include data from observed animals when engaged in different situations (e.g. attack behavior, non-attack behavior, appetitive social behavior, depressive behavior, etc.).
  • the training data 304 of the computing device 302 can be populated by one or more data sources 306 (e.g., data source 1, data source 2, data source n, etc.) over a period of time (e.g., t, t+1, t+n, etc.).
  • training data 304 can be labeled data (e.g., one or more tags associated with the data).
  • training data can be electrical signals from one or more networks and a label (e.g., attack behavior, non-attack behavior, appetitive social behavior, depressive behavior, etc.).
  • the computing device(s) 302 can continue to receive data from the one or more data sources 306 until the neural network 308 (e.g., convolution neural networks, deep convolution neural networks, artificial neural networks, learning algorithms, etc.) of the computing device(s) 302 are trained (e.g., have had sufficient unbiased data to respond to new incoming data requests and provided an autonomous or near autonomous prediction of onset of a neurological behavior).
  • the neural network can be a convolutional neural network, for example, utilizing five layer blocks, including convolutional blocks, convolutional layers, and fully connected layers.
  • neural network 308 can be one or more neural networks of various types are not specifically limited to a single type of neural network or learning algorithm.
  • the training data 304 can be checked for biases, for example, by checking the data source 306 (and corresponding user input) verse previously known unbiased data. Other techniques for checking data biases are also realized.
  • the data sources can be any of the sources of data for providing the input electrical signals from a network of two or more brain regions as described above in this disclosure.
  • the computing device(s) 302 can receive user (e.g., physician) input 310 related to the data source.
  • the user input 310 and the data source 306 can be temporally related (e.g., by time t, t+1, t+n, etc.). That is, the user input 310 and the data source 306 can be synchronous in that the user input 310 corresponds and supplements the data source 306 in a manner of supervised or reinforced learning.
  • a data source 506 can provide an electrical signal from a network of brain regions of a subject at time t and corresponding user input 510 can be state of the subject (attack, non-attack, aggressive, non- aggressive, appetitive social behavior, depressive behavior, etc.) at time t.
  • the training data 304 can be used to train a neural network 308 or learning algorithms (e.g., convolutional neural network, artificial neural network, etc.).
  • the neural network 308 can be trained, over a period of time, to automatically (e.g., autonomously) determine what the user input 310 would be, based only on received data 312 (e.g., signals from the plurality of electrodes, etc.).
  • Trained neural network (e.g. trained machine learning model) system 316 can include a trained neural network 308 and received data 312.
  • the received data 312 can be information related to a subject/patient, as previously described above.
  • the received data 312 can be used as input to the trained neural network 308.
  • Trained neural network 308 can then, based on the received data 312, classify the received data and/or predict/determine a recommended course of action for treating the patient, based on how the neural network was trained (as described above).
  • the output from the trained neural network can send a signal for treatment.
  • the output may be a stimulation signal sent to the brain.
  • the output from the trained neural network can be provided in a human readable form, for example, to be reviewed by a physician to determine a course of action.
  • the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.
  • the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like.
  • non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.
  • Such instructions can comprise, for example, instructions and data which cause or 92443576.2 - 21 - Atty Docket No.23-3000-WO otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network.
  • the computer executable instructions may be, for example, binaries, intermediate format instruction ns such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.
  • Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. [00117] The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures. III. Methods [00118] Further provided herein are methods for predicting and treating a neurological behavior in a subject.
  • the method can be performed automatically such that the prediction of the onset of the neurological behavior occurs in a timescale that allows for treatment of the neurological behavior by applying stimulation to the subject prior to the predicted onset of the neurological behavior. This can suppress or prevent the neurological behavior from manifesting.
  • the overall framework of the method 400 for predicting and treating a neurological behavior in a subject is shown in FIG. 4.
  • the neurological behavior may an aggressive state, an aggressive attack, an appetitive social behavior, or a depressive behavior.
  • the method 400 can include providing a plurality of electrodes configured to receive electrical activity signals from a network of two or more regions of the subject’s brain. In some examples, the plurality of electrodes are implanted in the subject’s brain.
  • the two or more regions of the brain in the network may include 2 to 10, 5 to 12, or 8 to 11 regions of the subject’s brain.
  • the network may include 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 regions of the brain.
  • Non-limiting examples of the two or more brain regions include infralimbic cortex, orbitofrontal cortex, prelimbic cortex, lateral septum, nucleus accumbens, 92443576.2 - 22 - Atty Docket No.23-3000-WO lateral habenula, mediodorsal thalamus, ventromedial hypothalamus, medial amygdala, primary visual cortex, ventral hippocampus, and combinations thereof.
  • the method 400 can include receiving and recording the electrical activity signals from the plurality of electrodes, via a controller in communication with the plurality of electrodes.
  • the method 400 can optionally include providing the electrical activity signals to a trained machine learning model. In other examples, the method 400 does not utilize a trained machine learning model. Instead, the method 400 may include comparing the electrical activity signals to a threshold.
  • the method 400 can include generating a prediction of a time of onset of the neurological behavior. In some examples, the prediction is generated from the trained machine learning model. In other examples, the prediction is generated when the electrical activity signals decrease below the threshold.
  • a prediction of a time of onset of the neurological behavior can be generated.
  • the threshold can be determined from a baseline of electrical activity signals from the network in the absence of the neurological behavior.
  • a predicted time of onset of the neurological behavior can be determined from the reduction in the network electrical signal.
  • the electrical activity signals can be provided to a trained machine learning model that generates a prediction of a time of onset of the neurological behavior from the trained machine learning model.
  • the predicted time of onset of the neurological behavior is on a second timescale.
  • the predicted time of onset may be provided about 1 s, about 2 s, about 3 s, about 4 s, about 5 s, about 6 s, about 7 s, about 8 s, about 9 s, or about 10 s before the neurological behavior manifests.
  • the method 400 can include sending a stimulation signal to the brain prior to the predicted time of onset of the neurological behavior.
  • the stimulation signal to the brain suppresses or prevents the neurological behavior.
  • the stimulation signal may be sent to the desired brain region about 1 s, about 2 s, about 3 s, about 4 s, about 5 s, about 6 s, about 7 s, about 8 s, about 9 s, or about 10 s before the predicted onset of the neurological behavior.
  • the desired brain region for the stimulation signal may be the same or different than one or more of the brain regions in the network.
  • the stimulation signal is directed to the prefrontal cortex of the subject’s brain.
  • the stimulation signal can be a light signal as described herein.
  • the stimulation of the desired brain region can include closed loop optogenetic stimulation.
  • the methods, systems, and devices described herein could be used to provide a therapy to a person, e.g., to alleviate the symptoms and/or causes of one or more neurological 92443576.2 - 23 - Atty Docket No.23-3000-WO or psychological conditions or disorders.
  • the methods, systems, and devices described herein could be used to aggression, treat bipolar disorder or other mood disorders, post-traumatic stress disorder or other anxiety disorders, schizophrenia and/or memory loss, autism, or other psychological conditions or disorders.
  • Such therapies could be provided in response to a diagnosis that a patient is experiencing one or more relevant psychological disorders or conditions.
  • therapies could be provided following the application of other therapies having incomplete success or effectively in treating the causes, symptoms, or other aspects of a diagnosed psychological condition.
  • one or more devices or electrodes e.g., a cylindrical lead to access deep structures within the brain and/or penetrating or surface elements to access surface structures of the brain, along with any associated leads, implanted controllers, transcutaneous connectors, or other implanted elements
  • less invasive therapies e.g., talk therapy, pharmaceutical interventions
  • Providing such a therapy could include detecting electrical activity signals in a network of two or more brain regions and providing, to a desired brain region, one or more stimulation signals prior to a predicted onset time for a behavior that is desired to be suppressed. Electrical activity signals (oscillatory signals) can be detected from a number of different brain regions and/or stimulation signals based on such detected signals could be provided to multiple different brain regions.
  • the therapy could be provided chronically (e.g., continuously or as a number of discrete therapies over months or years) or as an acute therapy (e.g., one or more discrete sessions).
  • An acute therapy, or discrete instances of a chronic therapy could be provided by devices that are present in a physician’s office or hospital.
  • electrical activity signals could be detected using an array of EEG electrodes, a magnetoencephalogram, a magnetic resonance imager, or other devices or systems.
  • excitatory stimuli could be provided by transcranial magnetic stimulation.
  • aspects of an acute therapy e.g., detection of an oscillatory signal and/or providing excitatory stimuli
  • An acute therapy could be provided for a specified period of time, number of sessions, or amount of deliver stimulation.
  • an acute therapy could be provided until to condition is detected, e.g., until a specified degree of suppression of the neurological behavior, until a specified degree of alleviation of signs or symptoms of the neurological behavior has been observed, or until some other condition has been met.
  • 92443576.2 - 24 - Atty Docket No.23-3000-WO Providing such a therapy could include implanting one or more devices.
  • providing such a therapy could include implantation of sub-dural or supra-dural electrodes, surface electrodes, penetrating electrodes or optical fibers, leads, controllers, or other implanted elements of a device or system.
  • Such a therapy could include determining settings for provision of the therapy. Such settings could include amplitudes, dosages, pulse waveforms, frequencies, or other properties of provided stimulus. Such settings could include determining a relative timing between a predicted onset of the neurological behavior and the onset of a provided stimulus/stimulation signal (e.g., to maximize a positive effect of the stimulus on one or more brain regions).
  • Providing such a therapy can include providing adjunct therapies in combination with the provision of the stimulation signal.
  • pharmaceuticals could be provided in addition to such stimulation.
  • the amount of such pharmaceuticals provided to a patient could be decreased as the effects of the stimulation increase.
  • Social aggression is an innate behavior that can aid an organism in securing access to resources. Aggression can also disrupt group function and reduce survival under conditions of behavioral pathology. Since many brain regions contribute to multiple social behaviors, expanded knowledge of how the brain distinguishes between social states would enable the development of interventions that suppress aggression, while leaving other social behaviors intact.
  • a murine aggressive internal state is encoded by a brain-wide network.
  • This network is organized by prominent theta (4-11 Hz) and beta (14-30 Hz) oscillations, leading from orbital frontal cortex and medial dorsal thalamus, and converging on ventral medial hypothalamus and medial amygdala.
  • Activity in this network couples to brain-wide cellular firing, and the network is conserved in multiple contexts associated with aggression. Strikingly, network activity during social isolation encodes the trait aggressiveness of mice and causal cellular manipulations known to impact aggression bidirectionally regulate network activity.
  • sensory regions such as those underlying olfaction, also contribute to aggression.
  • the brain must integrate information across these and other cortical and subcortical regions.
  • the brain must ultimately utilize information from overlapping regions to segregate aggression from other social behavioral states.
  • efforts have revealed several cellular-level processes within regions that contribute to this mechanism, the complementary network level process that integrates information across regions to distinguish aggressive states from prosocial states remains unknown. Addressing this question is of major importance as 1) mammals regularly select from a repertoire of social behaviors based on external sensory cues to ensure their survival, and 2) a range of psychiatric disorders are broadly marked by a failure to appropriately match behavior with evolving social contexts.
  • mice were implanted with microwire electrodes across eleven brain regions implicated in regulating complex social behavior. Electrical activity was then recorded from these brain regions, concurrently, as mice engaged in social encounters that induced aggressive attack behavior and non-attack social behavior. After confirming that statistical models based on single brain regions could independently differentiate attack behavior from non-attack social behaviors, it was asked whether this code was also reflected at a network level, where millisecond timescale information was integrated across all the brain areas. For this analysis, a machine learning approach was used that models variations in natural patterns of activity within and between implanted brain regions across seconds of time (a timescale that we reasoned would allow us to capture socially-relevant internal states). This approach also tuned the model to optimally encode attack vs. non-attack social behavior.
  • ESR1-cre F1 male offspring were used to validate EN-AggINH. All F1 offspring were group-housed 2-5 mice per cage until they received viral injections in the ventromedial hypothalamus at 7-8 weeks. After surgery, these mice were singly housed with enrichment. All partner mice (intact males, female, and castrated males) were 7-14 weeks old. These mice were maintained on a C57BL/6J strain background. All stimulus mice were housed 5 per cage with enrichment. All behavior testing and neurophysiological recordings occurred during the dark cycle. Electrode implantation surgery [00138] Mice were anesthetized with 1% isoflurane and placed in a stereotaxic device.
  • Grounding screws were placed above the cerebellum, right parietal hemisphere, and anterior cranium.
  • the recording bundles were designed to target prelimbic cortex, infralimbic cortex, 92443576.2 - 27 - Atty Docket No.23-3000-WO medial amygdala, ventral hippocampus, primary visual cortex, mediodorsal thalamus, lateral habenula, lateral septum nucleus, nucleus accumbens, ventrolateral portion of the ventromedial hypothalamus, and orbitofrontal cortex were centered based on stereotaxic coordinates measured from bregma.
  • Orbital frontal cortex anterior/posterior (AP) 2.35mm, medial/lateral (ML) 1.0mm, dorsal/ventral (DV) from dura -2.75mm; infralimbic cortex and prelimbic cortex: AP 1.8mm, ML 0mm, DV -2.7mm from dura; medial amygdala: AP -1.25, ML 2.7mm, DV -4.3 from dura; lateral septum and nucleus accumbens: AP 1.0mm, ML 0mm, DV -4.0mm from dura; ventromedial hypothalamus, lateral habenula, and medial dorsal thalamus: AP - 1.47mm, ML 0mm, DV -5.4mm from dura; central hippocampus and primary motor cortex: AP -3.0mm, ML 2.6mm, DV -3.0mm from dura].
  • AP anterior/posterior
  • ML medial/lateral
  • Infralimbic cortex and prelimbic cortex were targeted by building a 0.6mm DV stagger into the bundle.
  • Lateral habenula, medial dorsal thalamus, and ventral medial hypothalamus were targeted by building a 0.3mm ML and 1.9mm and 2.5mm DV stagger into our electrode bundle microwires.
  • Primary motor cortex and ventral hippocampus were targeted using a 0.3mm ML and 2.5mm DV stagger in our electrode bundle microwires.
  • mice were unilaterally injected with AAV2- CamKII-Chr2-EYFP, based on stereotaxic coordinates from bregma (left Infralimbic cortex: AP 1.8mm, ML 0.3mm, DV -2.0mm from the brain).
  • a total of 0.5mL of virus was infused at the injection site at a rate of 0.1mL/min over five minutes and the needle left in place for ten minutes after injection.
  • CD1 mice were implanted with an optic fiber (Mono Fiberoptic Cannula coupled to a 2.5mm metal ferrule (NA: 0.22, 100mm [inner diameter], 125mm buffer [outer diameter], MFC_100/125-0.22)) 0.3mm above the injection site immediately after viral syringe was removed. These mice were allowed 3 weeks for recovery prior to behavioral testing. For the closed-loop experiments, CD1 mice were allowed 3 weeks for viral expression prior to implantation with an optrode.
  • F1 offspring were bilaterally injected with AAV2-hSyn-DIO-GqDREADD based on stereotaxic coordinates measured from bregma (AP -1.5mm, ML ⁇ 0.7mm, DV -5.7mm from the dura).
  • a total of 0.3mL of virus was infused bilaterally at a rate of 0.1mL/min and the needle left in place for five minutes after injection.
  • F1 males were screened for aggressive behavior towards females. The F1 males received i.p. injections of CNO (1mg/kg) at the start of the screening session.
  • Brains from mice used to train and validate the network were stained using NeuroTrace fluorescent Nissl Stain using standard protocol. Specifically, Nissl staining for brain tissue occurred on a shaker table at room temperature. Tissue was washed in PBST (0.1% Triton in phosphate-buffered saline solution) for 10 minutes. It was then washed for five minutes in PBS twice. The tissue was then protected from light for the remainder of the protocol. The tissue was incubated in 1:300 Nissl diluted in 2 mL PBS for 10 minutes. After the Nissl incubation, tissue was washed once in 0.1% PBST for 10 minutes, then twice in PBS for 5 minutes.
  • PBST 0.1% Triton in phosphate-buffered saline solution
  • mice and mice used for 20 Hz or closed loop stimulation were mounted in Vectashield mounting medium containing DAPI. Images were obtained at 10x using an Olympus fluorescent microscope. Of the 297 total implantation sites in the training and testing set of mice, 17 were mistargeted ( ⁇ 5.7% error rate). Of these mistargeted implants, 13 were within 200 ⁇ m of the targeted structure. Given our prior work demonstrating high LFP spectral coherence (in the 1-55Hz frequency range) across microwires separated by 250 ⁇ m, in both cortical and subcortical brain regions, it was chosen to retain these animals in our analysis. The other four mistargeted implants were within 350 ⁇ m of the targeted structure.
  • LFPs Local field potentials
  • An online noise cancellation algorithm was applied to reduce 60Hz artifact.
  • Neural spiking data was referenced online against a channel recording from the same brain area that did not exhibit a SNR>3:1.
  • cells were sorted using an offline sorting algorithm to confirm the quality of the recorded cells. Only cell clusters well- isolated compared to background noise, defined as a Mahanalobis distance greater than 3 compared to the origin, were used for the unit-Electome Factor correlation analysis. We used both single and multi-units for our analysis as our objective was to determine whether Electome Network activity was reflective of cellular activity.
  • CD1 mice used for training and testing the electome model were first subjected to screening to assess their basal level of aggressiveness. Screening occurred once a day for three consecutive days prior to surgical implantation. For each screening session, an intact male C57 was placed in the CD1’s home cage for 5 minutes and the latency to first attack was recorded.
  • mice were screened in cohorts, and we excluded 16/45 mice from further experiments. 92443576.2 - 30 - Atty Docket No.23-3000-WO [00145] All screening/testing occurred within the home cage of mice except for the quantification of cortical stimulation induced gross locomotor activity. These latter experiments were performed in a 44cm ⁇ 44cm ⁇ 35cm (L ⁇ W ⁇ H) open field arena.
  • mice (CD1 and ESR1 males) were acclimated to the recording tether for three days prior to the first recording session. Each acclimation session involved anesthetizing the mouse with 1% isoflurane, tethering the subject mouse, allowing 60 minutes to recover from isoflurane, then placing a male C57 in the home cage for 5 minutes. Mice were then anesthetized with isoflurane again and detached from the tether. The aggressive tone of experimental mice was determined based on average latency to attack partner mice during the second and third acclimation sessions. [00146] After screening, mice were implanted, and data was acquired across 1-6 behavioral testing/recording sessions following recovery. Sessions were separated by 5-7 days.
  • mice were exposed to a different pair of objects during each session. Order of exposure to stimulus mice and objects was shuffled for every session.
  • mice were injected with either saline or CNO (1mg/kg, i.p.) after the five-minute baseline recording. Thirty-five minutes after this injection, mice were exposed to an intact male C57, a castrated male C57, and a female C57, presented in pseudorandom order. Mice were subjected to six total recording sessions (three in which they were treated with saline and three in which they were treated with CNO), again in pseudorandom order. Sessions were separated by 5 days to allow an adequate washout of CNO46.
  • Behavior was scored for each second as an “attack”, “non-attack social interaction”, or “non-interaction”.
  • One second windows were identified as “aggressive” if the mouse was engaged in biting, boxing (kicking/clawing), or tussling behavior. Windows were labeled as "non-attack social interaction” if the mouse had his nose or forepaws touching the stimulus mouse (intact male/female/castrated male) or object, but was not biting, boxing, or 92443576.2 - 31 - Atty Docket No.23-3000-WO tussling.
  • non-attack social behavior examples include sniffing, grooming, or resting (placing nose or forepaws against the subject mouse, but not moving). If the stimulus mouse had his/her forepaws or nose on the CD1, but it was not reciprocated, this was labeled “non-interaction”. CD1 straight approach, sideways approach, and chasing of the stimulus mouse could result in attack (biting/kicking/tousling), non-attack social behavior (nose or paw touch), or withdrawal without any touch. Thus, while sideways approach and chasing are regularly labeled as "aggressive”, and straight approach is regularly labeled as "pro- social”, these behaviors lacked consistent resolutions.
  • mice also demonstrated these behaviors towards female and castrated mice (non-attack social context).
  • One second windows containing these behaviors were labeled "non-interaction”. All other timepoints not labeled "attack” or “non-attack social” were also labeled “non-interaction”.
  • These behavioral criteria were selected to include ethologically aggression-related behaviors and maximize the likelihood that the CD1 was aware of the presence of the stimulus mouse or object during the behavioral window, while remaining confident in the classification of "attack” and "non-attack social” window labels.
  • tail rattling is not an attack behavior like the other behaviors that were labeled as "attack”, it was consistently only demonstrated by aggressive mice towards intact male mice.
  • tail rattling is well-recognized in the literature as an aggressive behavior. Thus, this behavior was included in the “attack behavior category.” In the subset of 20 mice used for training the network, tail rattling was observed 8 ⁇ 4s out of the 135 ⁇ 26s ‘attack windows’ per mouse.
  • the videos used to generate the labels for training and testing our machine learning model was hand-scored by a trained researcher. Videos from ESR1 mice and optogenetic stimulation were automatically tracked using DeepLabCut. This information was then used for creating behavioral classifiers in SimBA. LFP preprocessing and signal artifact removal [00151] Each LFP signal was segmented into 1s non-overlapping windows.
  • Granger causality values for each window were estimated with a 20 order AR model at 1 Hz intervals to align with the power and coherence features. Granger features were processed identically to a previously reported approach.
  • Granger features were exponentiated to approximately maintain the additivity assumption made implicitly by NMF models as, exp( ⁇ ⁇ ⁇ ( ⁇ )), where ⁇ ⁇ ⁇ ( ⁇ ) is the Granger causality at frequency ⁇ from region ⁇ to region is a ratio of total power to unexplained power.
  • Exponentiated Granger feature values were truncated at 10 to prevent implausible values.
  • Data for single region and network-level machine learning analyses [00155] 21,460 seconds of data were used, pooled across the twenty mice, to train/validate our single region and network models.
  • One second windows were pooled from the twenty CD1 mice and used to generate a series of logistic models for each of the 11 brain regions.
  • the models were trained to distinguish behavioral windows from one social state exhibited by CD1 mice, from two other social states. These three social states included 1) male-directed attack, 2) female non-attack social interactions, and 3) castrated male non-attack social interactions.
  • a model was established to distinguish 4) periods where CD1 were isolated in their home cage from any of the three social states.
  • Each model was then tested on data from a holdout set of nine mice. The Area under the receiver operating curve (AUC) was calculated for each holdout mouse to determine the performance of the model. False discovery rate was used to correct for multiple hypothesis testing.
  • AUC Area under the receiver operating curve
  • Discriminative Cross-Spectral Factor Analysis – Nonnegative Matrix Factorization The network was trained to distinguish between behavioral windows when the CD1 mice showed aggressive behavior towards intact C57 males, and windows where they exhibited pro-social behavior. These latter windows comprised pro-social interactions towards intact C57 males, castrated C57 males, or C57 females.
  • dCSFA-NMF Discriminative Cross-Spectral Factor Analysis – Nonnegative Matrix Factorization
  • a 1 second window was chosen to balance capturing fine-grained transient behavior with sufficient length to properly estimate spectral features.
  • Each window was associated with a behavioral label that identified condition the CD1 mouse was subjected to during that window (intact male, castrated male, or female) and whether the CD1 mouse was engaged in aggressive or non-aggressive behavior during that window, and the aggressive behavior was coded as ⁇ ⁇ ⁇ ⁇ 0,1 ⁇ .
  • Each of the K networks is represented in vector form and combined to make a matrix ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ .
  • the electome factor scores are given by the multi-output function ⁇ ( ⁇ ; ⁇ ): ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , where ⁇ represent the parameters of the function.
  • ⁇ ⁇ ⁇ ⁇ represent the relationship between the electome factors and the behavior.
  • ⁇ is a weighting parameter used to control the relative importance of prediction.
  • Previous work has also found that the reconstruction loss can reduce overfitting and make the learned predictions more robust.
  • an elastic net loss was applied on the encoder Loss_EN with a weighting ⁇ and the ratio between the L_1 and L_2 losses set to .5.
  • the dCSFA-NMF procedure requires selection of several settings in the algorithm. Specifically, we must choose the number of electome factors K, the importance of the supervised task ⁇ , the relative importance of the power features, coherence features, and 92443576.2 - 35 - Atty Docket No.23-3000-WO Granger features, and the parameterization of the mapping function ⁇ ( ⁇ ⁇ ; ⁇ ). Besides K, these settings were chosen to match previously used values or follow heuristics.
  • a dCSFA-NMF model was estimated allowing the number of total networks to range from two to twenty.
  • the similarity between each learned encoder and decoder was compared to the model with eight networks (the final model used in this work). This was quantified using the cosine similarity, which measures the angle between two networks (or encoders), ranging from -1 to 1. A value of 1 indicates perfect alignment (pointing in the same direction), 0 is orthogonal, and -1 indicates that the vectors point in opposite directions. [00167] It was found that the supervised network maintained a strong consistency across most dimensions, particularly between 5-10 networks, as shown by the cosine similarities being greater than 0.95. The supervised encoders were virtually identical all the models except the one that utilized three networks.
  • the associated formula for this reduction in uncertainty is 1-1*(p log_2(p) +(1-p)log_2(1-p)), where p is the accuracy of the 92443576.2 - 36 - Atty Docket No.23-3000-WO model. At the extremes, an accuracy of 0.5 (random guessing) removes no uncertainty, whereas an accuracy of 1 or 0 completely eliminates uncertainty.
  • Single cell correlation to Electome Factor activity [00170] Data acquired during the third behavioral testing session was from the twenty implanted mice were used for cellular analysis. We used Spearman correlation to quantify the relationship between cellular firing windows and the activity of the electome network used to classify attack behavior.
  • This ‘fast’ model was trained on the same data.
  • the model was trained using regularized regression to best predict the output of the full encoder.
  • Optogenetic stimulation [00172] Mice were anesthetized with 1% isoflurane, then tethered to for electrophysiological recording and had an optic patch cable places over the optic fiber cannula. The mice were then allowed 60 minutes for recovery prior to session recording.
  • CD1 mice experienced two stimulation sessions.
  • For closed loop optogenetic stimulation CD1 mice experienced three sessions of behavioral screening, then two sessions of closed-loop, then two sessions of open-loop, then one session of random stimulation. Stimulation sessions were separated by 5-7 day between sessions.
  • For behavioral screening CD1 mice were exposed to intact C57 males, females, and castrated male mice for 5 minutes each. Screening session two and three were used to determine reduced network threshold at which 40% of aggressive behavioral windows could be detected. For each session, mice were recorded for 3 minutes of baseline in their home cage, then during the three social encounters. Mice were recorded in an open field for 5 minutes after each session.
  • LFP preprocessing and signal artifact removal was used to preprocess the data and remove data with significant artifacts.
  • EN-AggINH expression was calculated by projecting the data into the learned model. The remaining data was then fit into two logistic regression models to predict behavior using the statsmodel package in python. The first model used only the stimulation to predict behavior (behavior ⁇ const + stimulation), and the second model used stimulation and network expression to predict behavior (behavior ⁇ const + network_expression + stimulation). These two models were compared by using a likelihood ratio test to evaluate whether the second model was significantly better. [00176] For the causal mediation analysis, we again need to roughly balance treatment and control groups. We used the same data as described above in the classic mediation analysis.
  • the treatment was blue versus yellow light stimulation, the mediator as EN-AggINH expression, and the outcome as aggressive versus non-aggressive behavior.
  • Data was screened to remove windows with missing data. Multiple brain regions fail to independently encode attack behavior across mice and contexts 92443576.2 - 38 - Atty Docket No.23-3000-WO [00177]
  • the initial analysis was focused on the medial prefrontal cortex (i.e., prelimbic and infralimbic cortex in mice), since this brain region had been implicated in social behaviors.
  • optogenetic stimulation of the medial prefrontal cortex was sufficient to suppress attack behavior and increase non-aggressive social behaviors in CD1 strain mice.
  • CD1 mice show periods of attack behavior, defined by biting, boxing (kicking/clawing), or tussling behavior, when a male C57BL6/J (C57) mouse is introduced into their home cage.
  • C57 C57BL6/J
  • Optogenetic stimulation experiments were performed during these social encounters using a protocol modeled after prior work, where blue light stimulation is used to activate channelrhodopsin in the medial prefrontal cortex for the entirety of a social encounter (473nm, 5mW, 20Hz, 3ms pulse width, FIG. 5A-5B).
  • medial prefrontal cortex stimulation was unable to selectively suppress attack behavior. Rather, stimulation suppressed multiple types of social behavior.
  • CD1 mice were implanted with electrodes across multiple cortical and subcortical brain regions known to contribute to social behavior, including infralimbic cortex, orbitofrontal cortex, prelimbic cortex, lateral septum, nucleus accumbens, lateral habenula, mediodorsal thalamus, ventromedial hypothalamus, medial amygdala, primary visual cortex, and ventral hippocampus, as illustrated in FIG. 5C. Following surgical recovery, neural activity was recorded while the CD1 mice freely interacted with an intact male C57 mouse and a female C57 mouse for 300 seconds each.
  • dCSFA-NMF discriminative cross spectral factor analysis non-negative matrix factorization
  • the electrical functional connectomes networks generated from dCSFA-NMF integrate LFP power (oscillatory amplitude across 1-56 Hz; a correlate of cellular and synaptic activity within brain regions), LFP synchrony (how two regions’ LFP frequencies synchronize across time; a correlate of brain circuit function), and LFP Granger synchrony (Granger causality testing; a correlate of directional transfer of information across a brain circuit).
  • dCSFA-NMF generates electome network activity scores (an indicator of the strength of each network) at a temporal resolution sufficient to capture brain states underlying the external behavior under observation (in this instance, a resolution of one second).
  • the electome networks are designed to learn patterns that explain interpretable correlates of neural activity whose expression relate to measured behavior, facilitating an overall interpretable model. Any given brain region can belong to multiple electome networks and each electome network may incorporate any number of brain regions.
  • dCSFA-NMF thus integrates spatially distinct brain regions and circuits into electome networks that encode behavior. [00181] To explore whether there was a generalized activity pattern within individual regions that encoded social behavior, a series of dCSFA-NMF models were designed based on local field potential (LFP) oscillatory power in frequencies from 1-56Hz.
  • FIG. 5E shows Framework to test encoding of social states by individual brain regions.
  • Each model was trained using observations pooled from twenty CD1 mice to separate periods where mice were socially isolated from periods where they were engaged in social behavior (e.g., attack behavior towards the intact males and non-attack social behavior towards male and female mice).
  • the number of networks for each model was chosen by a validation procedure to balance how much brain activity is explained by the model, how much the model predicts behavior, and the parsimoniousness of the model (see methods).
  • one network was encouraged to preferentially encode isolation from attack and non- attack social behavior (i.e., supervision).
  • N 9 mice).
  • the model generalizability to decode each class of social behavior from the other three for each of the 11 implanted brain areas (i.e., 33 additional models) was trained and tested.
  • the model was trained and tested.
  • five of the brain region-based statistical models decoded attack behavior versus non-attack social behavior: infralimbic cortex, lateral habenula, ventral hippocampus, medial amygdala, and medial dorsal thalamus.
  • V1 successfully decoded the non-attack male interaction from the other social conditions as well (P ⁇ 0.05 using one-tailed Wilcoxon rank-sum test, significance determined by FDR correction for 44 comparisons, see FIG. 5F). None of the other implanted brain regions showed this selectivity.
  • This network-level encoding 92443576.2 - 41 - Atty Docket No.23-3000-WO mechanism generalized to multiple new contexts associated with aggression.
  • the network also encoded attack behavior with a predictive efficacy that exceeded independent ventral hippocampus activity.
  • a new dCFSA-NMF model was trained using data from all the implanted brain regions.
  • the model utilized eight electome networks (see methods), one of which was trained to encode periods of attack-behavior (positive class) from social behavior in castrated male and female social context (negative class).
  • Non-attack social behavior towards intact males were also included in the negative class to discourage the network from simply learning non-aggressive sensory cues specific to the intact male (i.e., supervision, electome network #1; see FIG.5H).
  • This network was then validated using the set of nine holdout CD1 mice from the single area coding test analysis. Again, none of these mice were used to train the electome networks.
  • Attack behavior is indicative of an aggressive brain state.
  • electome network #1 (hereafter referred to as EN-Aggression Inhibition - AggINH) represented a network that putatively inhibited aggression when its activity was highest.
  • EN-AggINH generalized to a second aggression context. 92443576.2 - 42 - Atty Docket No.23-3000-WO [00188]
  • the model was tested for ventral hippocampus since we observed a trend towards decoding attack behavior in the urine context. This model failed to encode the aggressive state.
  • EN-Aggression Inhibition maps to interpretable biology [00189] EN-AggINH was composed of prominent theta frequency activity (4-11 Hz) in medial amygdala and beta frequency activity (14-30 Hz) in medial amygdala and prelimbic cortex (FIG. 7A-7B). Prominent synchrony and directionality were also observed in the theta and beta frequency bands. Indeed, the network showed strong directionality that led from orbital frontal cortex and primary visual cortex, relayed through medial dorsal thalamus, and infralimbic cortex, and converged on medial amygdala and ventral hippocampus (FIG.7C-7D).
  • EN-AggINH activity reflects the dynamics of cellular activity across the brain.
  • EN-Aggression Inhibition generalizes to new biological contexts related to aggression [00191] To further validate EN-AggINH, it was established that activity in this network was modulated by orthogonal biological conditions that have been shown to induce or suppress aggressive behavior in mice.
  • An excitatory DREADD (AAV-hsyn-DIO-hM3Dq) was expressed in the ESR1+ cells of ventromedial hypothalamus, since it has been shown that direct excitation of these cells induces aggressive behavior towards female mice.
  • Experiments were performed in the male F1 offspring of female CD1 strain mice crossed with ESR1-Cre male mice on a C57 strain background. Subsequently, the mice were implanted with recording electrodes to target the same brain regions as the initial experiment used to train the network model. Following recovery, behavioral and neural recording were performed when mice were exposed to a female mouse.
  • mice were either treated with saline or CNO (Clozapine N-oxide, which activates the excitatory DREADD), in a pseudorandomized order, prior to the repeated testing sessions.
  • CNO Clozapine N-oxide, which activates the excitatory DREADD
  • the network model generalized to a second aggression context induced by a cellular manipulation, and robust to different genetic backgrounds.
  • EN-AggINH does not simply encode sensory cues associated with male intruders since the network responses observed in the CNO treated mice were induced by a female intruder.
  • EN-Aggression Inhibition mediates attack behavior [00194] It was tested whether a cellular manipulation known to causally decrease aggression increased network activity.
  • the intermediate variable is viewed, at least in part, as a mechanistic route (a mediator) for how the treatment impacts the outcome.
  • a mechanistic route a mediator for how the treatment impacts the outcome.
  • the classic Baron and Kenny approach was used to determine whether EN-AggINH activity mediates the effect of neurostimulation on aggressive behavior. According to this statistical approach, there is a mediated effect of network activity on behavior if three conditions are met: 1) stimulation modulates network activity, 2) network activity correlates with behavior, and 3) modeling the behavior from network activity and stimulation together is better than modeling behavior from stimulation alone.
  • a significant direct effect of stimulation on attack behavior P ⁇ 0.005, FIG. 9F
  • network activity P ⁇ 0.0005, FIG.9D
  • this aggressive brain state was encoded by decreased activity in the network. This observation does not indicate that overall brain activity is suppressed during aggressive states. Rather, the findings argue that the aggressive state is encoded by a network that decreases its activity relative to when mice are socially isolated or engaged in pro-social behavior. Indeed, the data suggested that several common regions/circuits were activated during aggressive and pro-social behavior. These common circuits need not be reflected in the network since the model was trained to differentiate attack vs. non-attack social behavior. Nevertheless, the discovery of a network that decreased its activity during aggression raises the intriguing hypothesis that this brain activity inhibits aggression during pro-social engagement. When activity in this inhibition network is suppressed, aggressive behavior emerges.
  • a loud sound can cause an animal to transition from sleeping to a hyper aroused internal state.
  • many modulatory strategies that regulate attack behavior could mediate their effect by driving the brain out of the brain state represented by low EN-AggINH activity.
  • delivering a bright visual cue or a strong sensory cue (i.e., air puff) timed to decreases in EN-AggINH activity may be used suppress attack behavior, since many circuits and sensory inputs likely converge onto the internal state represented by EN-AggINH.
  • the closed loop stimulation approach was developed using a neural-network based approximation technique that was substantially constrained relative to dCSFA-NMF.
  • the reduced encoder was developed solely using power and coherence features. This approach was needed because the processing time to calculate granger directionality was prohibitive for real time implementations. As such, the reduced encoder sacrifices some predictive efficacy for speed. Nevertheless, it was found that the reduced encoder was sufficient to identify the precise time windows where the brain transitioned into aggression, as marked by a decrease in EN-AggINH activity.
  • convolutional neural networks may be used to bypass the feature extraction step to further improve the precision of the real time stimulation approach.
  • neural network encoders may be altered to predict both aggressive and pro-social states, such as the generalized social appetitive network.
  • aggressive behavior could be further suppressed relative to pro-social behavior.
  • the findings also pointed to a network that exhibits increased activity during aggressive behavior (Electome Network 6, see FIG. 6B).
  • imitation encoders for both Electome Network 6 and EN-AggINH may be integrated to further optimize closed loop approaches to selectively suppress aggression.
  • Multiple neuropsychiatric disorders including mood disorders, psychotic disorders, neurodevelopmental disorders, and neurodegenerative disorders are associated with deficits in regulating social behavior, including aggression. While multiple pharmacological approaches have been instituted to suppress aggressive behavior towards self and others, many 92443576.2 - 49 - Atty Docket No.23-3000-WO of these strategies act by sedating the individual and can disrupt aspects of pro-social function.
  • a step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data).
  • the program code can include one or more instructions executable by a processor for implementing specific logical functions or actions in the method or technique.
  • the program code and/or related data can be stored on any type of computer readable medium such as a storage device including a disk, hard drive, or other storage medium.
  • the computer readable medium can also include non-transitory computer readable media such as computer-readable media that store data for short periods of time like register memory, processor cache, and random access memory (RAM).
  • the computer readable media can also include non-transitory computer readable media that store program code and/or data for longer periods of time.
  • the computer readable media may include secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, compact-disc read only memory (CD-ROM), for example.
  • the computer readable media can also be any other volatile or non-volatile storage systems.
  • a computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Molecular Biology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Surgery (AREA)
  • Psychiatry (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Neurology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Neurosurgery (AREA)
  • Theoretical Computer Science (AREA)
  • Radiology & Medical Imaging (AREA)
  • Psychology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Physiology (AREA)
  • Developmental Disabilities (AREA)
  • Databases & Information Systems (AREA)
  • Hospice & Palliative Care (AREA)
  • Computing Systems (AREA)
  • Child & Adolescent Psychology (AREA)
  • Signal Processing (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)

Abstract

La présente invention concerne des systèmes et des méthodes pour prédire et traiter un comportement neurologique chez un sujet. Le système peut comprendre une pluralité d'électrodes conçues pour recevoir des signaux d'activité électrique provenant d'un réseau constitué par au moins deux régions du cerveau du sujet, ainsi qu'un dispositif de commande en communication avec la pluralité d'électrodes. Le système peut être configuré pour recevoir et enregistrer les signaux d'activité électrique provenant de la pluralité d'électrodes, transmettre les signaux d'activité électrique au dispositif de commande, générer une prédiction de moment d'apparition du comportement neurologique, et envoyer un signal de stimulation au cerveau avant le moment prédit d'apparition du comportement neurologique. Le signal de stimulation envoyé au cerveau peut empêcher le comportement neurologique.
PCT/US2023/082965 2022-12-07 2023-12-07 Systèmes et méthodes de stimulation d'état cérébral Ceased WO2024124045A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263430691P 2022-12-07 2022-12-07
US63/430,691 2022-12-07

Publications (1)

Publication Number Publication Date
WO2024124045A1 true WO2024124045A1 (fr) 2024-06-13

Family

ID=91380223

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/082965 Ceased WO2024124045A1 (fr) 2022-12-07 2023-12-07 Systèmes et méthodes de stimulation d'état cérébral

Country Status (1)

Country Link
WO (1) WO2024124045A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119541886A (zh) * 2024-11-07 2025-02-28 中国人民解放军军事科学院军事医学研究院 基于前扣带回皮质和背内侧前额叶皮层的注意力预测方法、计算机设备及存储介质
CN119850040A (zh) * 2025-03-18 2025-04-18 浙江创享仪器研究院有限公司 一种仪器服务单位仪器使用绩效评估方法及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090306741A1 (en) * 2006-10-26 2009-12-10 Wicab, Inc. Systems and methods for altering brain and body functions and for treating conditions and diseases of the same
US20120238835A1 (en) * 2011-03-16 2012-09-20 Searete Llc, A Limited Liability Corporation Of The State Of Delaware System, devices, and methods for real-time monitoring of cerebrospinal fluid for markers of progressive conditions
US20190143119A1 (en) * 2016-03-28 2019-05-16 Duke University Systems and Methods for Brain Stimulation
US20200337631A1 (en) * 2013-10-09 2020-10-29 Nedim T Sahin Systems, environment and methods for identification and analysis of recurring transitory physiological states and events using a portable data collection device
US20210398357A1 (en) * 2016-06-20 2021-12-23 Magic Leap, Inc. Augmented reality display system for evaluation and modification of neurological conditions, including visual processing and perception conditions

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090306741A1 (en) * 2006-10-26 2009-12-10 Wicab, Inc. Systems and methods for altering brain and body functions and for treating conditions and diseases of the same
US20120238835A1 (en) * 2011-03-16 2012-09-20 Searete Llc, A Limited Liability Corporation Of The State Of Delaware System, devices, and methods for real-time monitoring of cerebrospinal fluid for markers of progressive conditions
US20200337631A1 (en) * 2013-10-09 2020-10-29 Nedim T Sahin Systems, environment and methods for identification and analysis of recurring transitory physiological states and events using a portable data collection device
US20190143119A1 (en) * 2016-03-28 2019-05-16 Duke University Systems and Methods for Brain Stimulation
US20210398357A1 (en) * 2016-06-20 2021-12-23 Magic Leap, Inc. Augmented reality display system for evaluation and modification of neurological conditions, including visual processing and perception conditions

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119541886A (zh) * 2024-11-07 2025-02-28 中国人民解放军军事科学院军事医学研究院 基于前扣带回皮质和背内侧前额叶皮层的注意力预测方法、计算机设备及存储介质
CN119850040A (zh) * 2025-03-18 2025-04-18 浙江创享仪器研究院有限公司 一种仪器服务单位仪器使用绩效评估方法及系统

Similar Documents

Publication Publication Date Title
Gehrlach et al. Aversive state processing in the posterior insular cortex
Kitanishi et al. Robust information routing by dorsal subiculum neurons
US20190143119A1 (en) Systems and Methods for Brain Stimulation
WO2024124045A1 (fr) Systèmes et méthodes de stimulation d'état cérébral
Essig et al. Inhibitory neurons in the superior colliculus mediate selection of spatially-directed movements
Carlson et al. Dynamically timed stimulation of corticolimbic circuitry activates a stress-compensatory pathway
Mouchati et al. Optogenetic “low‐theta” pacing of the septohippocampal circuit is sufficient for spatial goal finding and is influenced by behavioral state and cognitive demand
Blumberg et al. Efficacy of nonselective optogenetic control of the medial septum over hippocampal oscillations: the influence of speed and implications for cognitive enhancement
Arieli et al. Temporally‐precise basolateral amygdala activation is required for the formation of taste memories in gustatory cortex
Callahan et al. Movement-related increases in subthalamic activity optimize locomotion
Zhang et al. Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility
Grossman et al. Brain-wide oscillatory network encodes an aggressive internal state
Singer Hebbian Modification of Synaptic Transmission as a Common Mechanism in Experience-Dependent Maturation of Cortical Functions.
Walters Neural computations underpinning anxiety in health and disease
Driessen et al. Induction of cortical ON/OFF periods in awake mice fulfills sleep functions
Gregory et al. The brain architecture of punishment learning
Sawtell et al. Cerebellum-like structures
GoodSmith Firing Correlates and Mnemonic Functions of Dentate Gyrus Granule Cells and Mossy Cells
Wilmerding Contributions of the dentate gyrus to episodic and spatial memory
Sawtell et al. Cerebellum-Like Structures
González Combining a real-time closed-loop system with neuromodulation: an integrative approach to prevent pathological repetitive behaviors
Espejo et al. Sensory context for coding of natural sounds in auditory cortex
Garrett Study guide to accompany bob garrett’s brain & behavior: An introduction to biological psychology
Zahler A neural mechanism for coordinating the head and eyes in mice
Calvi FACULTAD DE CIENCIAS BIOLÓGICAS

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23901595

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23901595

Country of ref document: EP

Kind code of ref document: A1