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WO2018204119A1 - Procédé et appareil de détermination d'une stimulation cérébrale optimale pour induire un comportement souhaité - Google Patents

Procédé et appareil de détermination d'une stimulation cérébrale optimale pour induire un comportement souhaité Download PDF

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Publication number
WO2018204119A1
WO2018204119A1 PCT/US2018/028962 US2018028962W WO2018204119A1 WO 2018204119 A1 WO2018204119 A1 WO 2018204119A1 US 2018028962 W US2018028962 W US 2018028962W WO 2018204119 A1 WO2018204119 A1 WO 2018204119A1
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Prior art keywords
brain
stimulation
processors
graphical representation
set forth
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Inventor
Praveen K. PILLY
Michael D. Howard
Heiko Hoffmann
Tsai-Ching Lu
Kang-Yu NI
David W. Payton
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HRL Laboratories LLC
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HRL Laboratories LLC
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Priority to CN201880016157.XA priority Critical patent/CN110392549B/zh
Priority to EP18794087.9A priority patent/EP3618707A4/fr
Publication of WO2018204119A1 publication Critical patent/WO2018204119A1/fr
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    • 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
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/375—Electroencephalography [EEG] using biofeedback
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals

Definitions

  • the present invention relates to a system for inducing a desired behavior and, more particularly, to a system for inducing a desired behavior using a determined brain stimulation.
  • THUNDER (see the List of Incorporated Literature References, Literature Reference No. 8), is mostly just capable of finding the subset of cells that are modulated by some aspect of stimulus/behavior. Simulated annealing, which is state-of-the-art for hard optimization problems with many local minima, requires tedious tuning of annealing parameters. It is slow and must be restarted from scratch when the optimization target moves.
  • Behavioral enhancement has been attempted with weak current stimulation, guided by a coarse understanding of the underlying neural processes (such as described in Literature Reference Nos. 14 and 19) and typically involves targeting the stimulation to just the prefrontal cortex (PFC) in general (see Literature Reference Nos. 5, 11, 12, and 13), or one or two brain regions known to be specialized for the particular behavior (see Literature Reference No. 17), or identified by a clinician (see Literature Reference No. 7).
  • PFC prefrontal cortex
  • the present invention relates to a system for inducing a desired behavior and, more particularly, to a system for inducing a desired behavior using a determined brain stimulation.
  • the system comprises a brain monitoring subsystem comprising a set of monitoring electrodes for sensing brain activity, and a brain stimulation subsystem comprising a set of stimulating electrodes for applying an electrical current stimulation.
  • the system further comprises one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform multiple operations.
  • a set of multi-scale distributed data is registered into a graphical representation, wherein at least a subset of the set of multi-scale distributed data is sensed brain activity.
  • a sub-graph is identified in the graphical representation and mapped onto a set of concept features, generating a concept lattice which relates the set of concept features to a behavioral effect.
  • the system determines an electrical current stimulation to be applied to produce the behavioral effect and causes the electrical current stimulation to be applied via the set of stimulating electrodes.
  • the graphical representation comprises a plurality of
  • each node representing a data item in the set of multi-scale distributed data
  • edges between the plurality of nodes representing relationships between data items, wherein the relationships are topological, statistical, and/or causal relationships.
  • the set of multi-scale distributed data comprises
  • electroencephalogram data recorded from the set of monitoring electrodes as a result of the stimulation montage applied via the set of stimulating electrodes.
  • the set of multi-scale distributed data is transformed
  • a set of stimulating electrode placements and parameters that can recreate the behavioral effect is identified.
  • the identified set of stimulating electrode placements and parameters are applied via the brain stimulation subsystem, and wherein the brain monitoring subsystem monitors the behavioral effect, wherein if the behavioral effect is unsatisfactory, then the brain monitoring subsystem updates the graphical representation and the one or more processors perform an operation of determining a new electrical current stimulation to be applied to produce a satisfactory behavioral effect.
  • self-organized criticality SOC is used to search for electrode locations or settings for brain stimulation.
  • a forward simulation is used to predict behavioral
  • the present invention also includes a computer program product and a computer implemented method.
  • the computer program product includes computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors, such that upon execution of the instructions, the one or more processors perform the operations listed herein.
  • the computer implemented method includes an act of causing a computer to execute such instructions and perform the resulting operations.
  • FIG. 1 is a block diagram depicting the components of a system for inducing a desired behavior according to some embodiments of the present disclosure
  • FIG. 2 is an illustration of a computer program product according to some embodiments of the present disclosure
  • FIG. 3 is an illustration of an overview of discovering a stimulation pattern that can produce a desired behavior according to embodiments of the present disclosure
  • FIG. 4 is an illustration of a knowledge representation framework according to embodiments of the present disclosure
  • FIG. 5 is an illustration of generation of hypotheses on a lattice and Bayesian network according to embodiments of the present disclosure
  • FIG. 6 is a flow diagram illustrating the system for inducing a desired
  • FIG. 7 is an illustration of relating neuroscience data features to behavior to find the neural correlates behind memory enhancement according to
  • FIG. 8 is an illustration of a signal graph according to embodiments of the present disclosure.
  • FIG. 9 is an illustration of leveraging sparse support vector machine (SVM) and sparse canonical correlational analysis (SCCA) for dimensionality reduction according to embodiments of the present disclosure
  • FIG. 10 is an illustration of using self-organized critical (SOC) search according to embodiments of the present disclosure
  • FIG. 11 is an illustration of a subject receiving monitoring and stimulating via electrodes according to embodiments of the present disclosure.
  • the present invention relates to a system for inducing a desired behavior and, more particularly, to a system for inducing a desired behavior using a determined brain stimulation.
  • any element in a claim that does not explicitly state "means for” performing a specified function, or “step for” performing a specific function, is not to be interpreted as a "means” or “step” clause as specified in 35 U.S.C. Section 112, Paragraph 6.
  • the use of "step of or “act of in the claims herein is not intended to invoke the provisions of 35 U.S.C. 112, Paragraph 6.
  • Javadi AH Cheng P. Transcranial direct current stimulation (tDCS) enhances reconsolidation of long-term memory. Brain Stimulation, 2012. Javadi AH, Walsh V. Transcranial direct current stimulation (tDCS) of the left dorsolateral prefrontal cortex modulates declarative memory. Brain Stimulat, 5(3):231- 1, 2012.
  • transcranial direct current stimulation tDCS
  • galvanic vestibular stimulation GVS
  • the first is a system for inducing a desired behavior.
  • the system is typically in the form of a computer system operating software or in the form of a "hard- coded" instruction set. This system may be incorporated into a wide variety of devices that provide different functionalities.
  • the second principal aspect is a method, typically in the form of software, operated using a data processing system (computer).
  • the third principal aspect is a computer program product.
  • the computer program product generally represents computer-readable instructions stored on a non-transitory computer-readable medium such as an optical storage device, e.g., a compact disc (CD) or digital versatile disc (DVD), or a magnetic storage device such as a floppy disk or magnetic tape.
  • a non-transitory computer-readable medium such as an optical storage device, e.g., a compact disc (CD) or digital versatile disc (DVD), or a magnetic storage device such as a floppy disk or magnetic tape.
  • Other, non- limiting examples of computer-readable media include hard disk
  • FIG. 1 A block diagram depicting an example of a system (i.e., computer system 100) of the present invention is provided in FIG. 1.
  • the computer system 100 is configured to perform calculations, processes, operations, and/or functions associated with a program or algorithm.
  • certain processes and steps discussed herein are realized as a series of instructions (e.g., software program) that reside within computer readable memory units and are executed by one or more processors of the computer system 100. When executed, the instructions cause the computer system 100 to perform specific actions and exhibit specific behavior, such as described herein.
  • the computer system 100 may include an address/data bus 102 that is
  • processor 104 configured to communicate information. Additionally, one or more data processing units, such as a processor 104 (or processors), are coupled with the address/data bus 102.
  • the processor 104 is configured to process information and instructions.
  • the processor 104 is a microprocessor.
  • the processor 104 may be a different type of processor such as a parallel processor, application-specific integrated circuit (ASIC), programmable logic array (PLA), complex programmable logic device (CPLD), or a field programmable gate array (FPGA).
  • ASIC application-specific integrated circuit
  • PLA programmable logic array
  • CPLD complex programmable logic device
  • FPGA field programmable gate array
  • the computer system 100 is configured to utilize one or more data storage units.
  • the computer system 100 may include a volatile memory unit 106 (e.g., random access memory (“RAM”), static RAM, dynamic RAM, etc.) coupled with the address/data bus 102, wherein a volatile memory unit 106 is configured to store information and instructions for the processor 104.
  • RAM random access memory
  • static RAM static RAM
  • dynamic RAM dynamic RAM
  • the computer system 100 further may include a non-volatile memory unit 108 (e.g., read-only memory (“ROM”), programmable ROM (“PROM”), erasable programmable ROM (“EPROM”), electrically erasable programmable ROM “EEPROM”), flash memory, etc.) coupled with the address/data bus 102, wherein the nonvolatile memory unit 108 is configured to store static information and instructions for the processor 104.
  • the computer system 100 may execute instructions retrieved from an online data storage unit such as in "Cloud” computing.
  • the computer system 100 also may include one or more interfaces, such as an interface 1 10, coupled with the address/data bus 102.
  • the one or more interfaces are configured to enable the computer system 100 to interface with other electronic devices and computer systems.
  • the communication interfaces implemented by the one or more interfaces may include wireline (e.g., serial cables, modems, network adaptors, etc.) and/or wireless (e.g., wireless modems, wireless network adaptors, etc.) communication technology.
  • the computer system 100 may include an input device 112 coupled with the address/data bus 102, wherein the input device 112 is configured to communicate information and command selections to the processor 100.
  • the input device 112 is an alphanumeric input device, such as a keyboard, that may include alphanumeric and/or function keys.
  • the input device 112 may be an input device other than an alphanumeric input device.
  • the computer system 100 may include a cursor control device 114 coupled with the address/data bus 102, wherein the cursor control device 114 is configured to communicate user input information and/or command selections to the processor 100.
  • the cursor control device 114 is implemented using a device such as a mouse, a track-ball, a track-pad, an optical tracking device, or a touch screen. The foregoing notwithstanding, in an aspect, the cursor control device 114 is directed and/or activated via input from the input device 112, such as in response to the use of special keys and key sequence commands associated with the input device 112.
  • the cursor control device 114 is configured to be directed or guided by voice commands.
  • the computer system 100 further may include one or more optional computer usable data storage devices, such as a storage device 116, coupled with the address/data bus 102.
  • the storage device 116 is configured to store information and/or computer executable instructions.
  • the storage device 116 is a storage device such as a magnetic or optical disk drive (e.g., hard disk drive (“HDD”), floppy diskette, compact disk read only memory
  • HDD hard disk drive
  • floppy diskette compact disk read only memory
  • a display device 118 is coupled with the address/data bus 102, wherein the display device 118 is configured to display video and/or graphics.
  • the display device 118 may include a cathode ray tube (“CRT”), liquid crystal display (“LCD”), field emission display (“FED”), plasma display, or any other display device suitable for displaying video and/or graphic images and alphanumeric characters recognizable to a user.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • FED field emission display
  • plasma display or any other display device suitable for displaying video and/or graphic images and alphanumeric characters recognizable to a user.
  • the computer system 100 presented herein is an example computing
  • the non-limiting example of the computer system 100 is not strictly limited to being a computer system.
  • an aspect provides that the computer system 100 represents a type of data processing analysis that may be used in accordance with various aspects described herein.
  • other computing systems may also be
  • one or more operations of various aspects of the present technology are controlled or implemented using computer-executable instructions, such as program modules, being executed by a computer.
  • program modules include routines, programs, objects, components and/or data structures that are configured to perform particular tasks or implement particular abstract data types.
  • an aspect provides that one or more aspects of the present technology are implemented by utilizing one or more distributed computing environments, such as where tasks are performed by remote processing devices that are linked through a communications network, or such as where various program modules are located in both local and remote computer-storage media including memory-storage devices.
  • FIG. 2 An illustrative diagram of a computer program product (i.e., storage device) embodying the present invention is depicted in FIG. 2.
  • the computer program product is depicted as floppy disk 200 or an optical disk 202 such as a CD or DVD.
  • the computer program product generally represents computer-readable instructions stored on any compatible non-transitory computer-readable medium.
  • the term "instructions” as used with respect to this invention generally indicates a set of operations to be performed on a computer, and may represent pieces of a whole program or individual, separable, software modules.
  • Non-limiting examples of "instruction” include computer program code (source or object code) and "hard-coded" electronics (i.e. computer operations coded into a computer chip).
  • the "instruction" is stored on any non-transitory computer-readable medium, such as in the memory of a computer or on a floppy disk, a CD-ROM, and a flash drive. In either event, the instructions are encoded on a non-transitory computer-readable medium.
  • Described is a system to discover the relationships between neural activity, applied current stimulation, and behavioral performance (such as memory enhancement) from neural data (e.g., produced by programs such as DARPA (Defense Advanced Research Projects) RAM (Restoring Active Memory) and SUBNETS (Systems-Based Neurotechnology for Emerging Therapies) and to compute the optimal brain stimulation montage for an individual subject, or across the general population, that will produce a desired behavioral effect.
  • desired behavioral effects include enhancement of selected memories (e.g., beneficial for task performance) and weakening of selected memories (e.g., those that cause trauma or harm task performance).
  • the system runs closed-loop, monitoring the brain, computing and applying new stimulation patterns, and improving the behavioral effect on each round until the desired effect is achieved.
  • a unique element of the system described herein is a forward model to predict behavioral outcomes for different stimulations, and the use of a search method as an optimization technique to identify the ideal stimulation pattern to produce a desired behavioral outcome.
  • the forward model upon which the invention is based can also provide answers to clearly definable queries, such as "What are the essential brain attributes underlying a given behavioral outcome?" and "Which brain stimulation patterns lead to an improvement of a specific behavior?"
  • embodiments of the present disclosure includes techniques to register, across subjects and within subjects across trials, the multi-scale distributed neural data as well as the induced current flow distributions in the brain volume.
  • SOC self-organized critical
  • FIG. 3 illustrates how the system described herein is applied to discover a stimulation pattern that can produce a desired behavior.
  • Datafication i.e., domain-specific data ingestion 300 extracts and ingests relevant experimental and computational data, comprising both quantitative and qualitative elements, and integrates them into a knowledge representation (KR) 302.
  • KR knowledge representation
  • Datafication includes making pairwise topological, statistical, and causal relations within the data and composing them into multilayer graphs, each annotated with qualitative and quantitative information of experimental designs.
  • Datafication tools are capable of extracting time-varying multi-scale correlational and causal interactions among the identified neural entities, to facilitate a deeper discovery of the functional connectivity underlying behavior.
  • Datafication refers to converting phenomena or data into a computable format that aids in the extraction of knowledge.
  • the KR 304 outputs ranked-ordered hypotheses to domain-specific
  • Non-limiting examples of domains include complex domains with large datasets like neuroscience, climate science, gene-protein disease networks, smart power grids, wireless communication systems, and autonomous systems. These tools analyze and perform function inferences for fast brain classification to predict memory behavior (element 308) as well as optimization of stimulation patterns for memory enhancement (element 310).
  • Multimodal data can be described within a signal graph, such as shown in FIG. 8, or across a multitude of annotated graphs, such as shown in FIG. 4.
  • FIG. 8
  • autoencoders compute concept features for the concept lattice 512 and Bayesian networks 514.
  • An autoencoder as known to those skilled in the art, is a recurrent network used for unsupervised learning of efficient codings. The aim of an encoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Hypotheses (element 516) can then be generated on the concept lattice 512 and the Bayesian network 514 and retrieve corresponding entities, relations, and functions from the multilayer graphs (FIG. 5).
  • hypotheses may be which neural signatures (among the huge amount of available data from EEG and implanted electrodes, over time and across subjects) underlie successful memory encoding.
  • the hypotheses may be which set of material properties can be synthesized to achieve the ideal behavior of each, to discover new polymeric materials with desired properties from historical experimental and computational data of polymer formulation and monomer constituents.
  • FIG. 4 depicts source data from EEG or invasive electrodes (converted into spatiotemporal graphs 400) compiled into a graph-based representation 402 of causal (element 404), statistical (element 406), and topological (element 408) relationships between neural data.
  • Raw brain data is processed and converted into cross-modal spatiotemporal graphs (element 400) corresponding to memory networks in the brain. These networks are subregions of the brain believed to be most responsible for the behavior of interest, which, for memory performance, includes regions of the hippocampus, the inferotemporal lobe, and the prefrontal cortex.
  • the nodes in the graph will be voltage traces from single neurons and local field potentials (LFPs) extracted from extracellular recordings with implanted microelectrode arrays in the brain areas of interest, using standard frequency filtering, spike sorting, and source separation techniques.
  • Frequency filtering is a standard technique that means taking the power spectral density of the EEG, which gives the power in the signal as a function of frequency.
  • spike sorting techniques refer to Literature Reference No. 23.
  • source separation techniques refer to Literature Reference No. 22.
  • the nodes For data recorded from non-invasive electrodes (tCS, for transcranial current stimulation), the nodes (elements 410 and 412) correspond to current sources from scalp EEG signals mapped into voxels in a standardized brain volume.
  • the links between the nodes, within and between modalities, are of different kinds related to the underlying statistical (element 406) and causal (element 404) relations.
  • Statistical links (element 406) are represented using the time-resolved coherence spectra, which capture the time-varying multi-scale correlational structure for pairs of time series.
  • Time-variant causal links are computed using a robust version of the transfer entropy measure, which essentially quantifies the conditional mutual information between two random processes across multiple time scales and invariant to their relative amplitudes.
  • EEG sources and stimulation-induced currents are mapped to brain voxels using a forward model, and labeled to provide qualitative data for use in building hierarchical clusters in the graph and also enable discoveries in terms of functional interactions among specific brain regions.
  • relationship graph is prepared (here, the topological relations fall into brain subregions prefrontal cortex 500 (PFC), infero-temporal cortex 502 (ITC), hippocampus 504 (HC) as assessed by EEG 506), autoencoders 508 are run on local regions of the graph-based representation 402, forming compact concept features 510 (or representations) that are arranged into a concept lattice 512, which is searched (i.e., by SOC search) to find the most relevant areas to be stimulated to produce a desired behavioral effect.
  • PFC prefrontal cortex 500
  • ITC infero-temporal cortex 502
  • HC hippocampus 504
  • autoencoders 508 are run on local regions of the graph-based representation 402, forming compact concept features 510 (or representations) that are arranged into a concept lattice 512, which is searched (i.e., by SOC search) to find the most relevant areas to be stimulated to produce a desired behavioral effect.
  • the statistical relationship graphs are created by using a wavelet transform to compute time-varying properties of identified entities as well as those of statistical relations (element 406) between them in the various neural and non-neural modalities.
  • the time-varying causal links are quantified in the multimodal graph/graphical representation (element 402) using the transfer entropy (TE) metric, which is essentially a directional measure of information flow between a pair of time series:
  • TE transfer entropy
  • ⁇ is the time delay of information transfer.
  • the above equation computes the transfer entropy from node j to node i (the information flow across the link from node xj to ⁇ .
  • p is a Bayesian operator that assesses the probability that the quantity in parentheses is true.
  • the TE at each time point t depends on the duration T over which the information transfer is considered, and also on the choice of ⁇ .
  • the duration T can be assumed to be 100 milliseconds (ms) given the typical frequency of the dominant ⁇ rhythm (namely, 10 Hertz (Hz)) in the LFPs within memory circuits in the brain. Therefore, TE is computed at a spectrum of time delays, spanning the assumed duration T of 100 ms.
  • ATE associative transfer entropy
  • TE and ATE are computed through a scale-invariant symbolization method to remove any temporal scale dependencies.
  • the formulations of TE and ATE are defined on continuous random variables, which are discretized by using a symbolization method to estimate their probability distributions.
  • the key step is to transform the continuous-valued time series x ⁇ ] into a symbolic time series with a suitable length n. For each time point t, the n consecutive values Xt > %t+l > ⁇ %t+n-l ⁇ are ordered in the ascending order. The sequence of corresponding permutation indices is then the t th element for the discrete-valued symbolic time series x ⁇ ). [00075] (3.2.3) Global Silencing of Indirect Links
  • EEG is recorded from the same electrodes used for transcranial current stimulation (tCS), so their locations further change between trials to optimize a specific stimulation pattern.
  • tCS transcranial current stimulation
  • EEG signals time series are converted into volumetric and temporal dynamics of current density sources within gray matter voxels in the brain, segmented from Tl -weighted MRI for each individual subject, using state-of-the-art methods for source localization.
  • a subject-specific forward model is built using finite element modeling (see Literature Reference No. 20, which describes how to build a forward model using the technique of forward element modeling) and an inverse model using multi-scale geodesic Sparse Bayesian learning (SBL) with a Laplacian prior (see Literature Reference No. 16, which describes how to build an inverse model using SBL with a Laplacian prior), which is widely regarded as being appropriate for localizing distributed sources in the domain of noninvasive imaging.
  • SBL multi-scale geodesic Sparse Bayesian learning
  • Laplacian prior see Literature Reference No. 16 which describes how to build an inverse model using SBL with a Laplacian prior
  • Tl MRI of each individual subject into different tissue categories (e.g., brain, skull, cerebrospinal fluid (CSF), electrode), based on pertinent state-of-the-art methods (see Literature Reference No. 7). Then, the aforementioned ART package will register the induced currents within the brain volume between subjects. In this way, the present system's solution can deal with arbitrary locations of scalp electrodes for non-invasive stimulation.
  • tissue categories e.g., brain, skull, cerebrospinal fluid (CSF), electrode
  • CSF cerebrospinal fluid
  • a unique dimensionality reduction method is employed for the challenging datafication of EEG signals obtained from different scalp electrode placements across subjects.
  • Datafication refers to converting phenomena or data into a computable format that aids in the extraction of knowledge. Given that the number of voxels in the transformed EEG data is much greater than the number of actual electrode channels, it is likely that EEG-derived current density source images are noisy. Additionally, the number of voxels is large enough to be prohibitive for instantiating a graph for the EEG modality.
  • SVM sparse support vector machine
  • SCCA sparse canonical correlational analysis
  • FIG. 9 depicts that a sparse SVM sub- selects discriminative voxels (element 900) from the EEG-derived current density volumes (element 902) based on the outcome of each behavioral trial (element 904), and then Sparse Canonical Correlation Analysis (SCCA) determines a further subset of voxels (element 906) that are maximally correlated with the known voxel locations of invasive LFP recordings (element 908).
  • SCCA Sparse Canonical Correlation Analysis
  • the SCCA will project the current density estimates for the discriminative set of voxels and the spatially localized LFPs (element 908) onto a common feature space in order to determine the semantics of the underlying neuronal activity patterns. From these semantics, SCCA will learn the subset of voxels for the EEG sources (Y) whose time series are maximally correlated with the time series from the low-dimensional ground truth of the invasive LFP measurements (X).
  • the addition of sparsity constraints to the SCCA mitigates the influence of outlier EEG source estimates in the neuronal data and is appropriate when many voxels are likely to be uninformative for neural decoding (see Literature Reference No. 2). Sparse SVMs successfully reduce feature dimensionality 50x to lOOx while maintaining high levels of classifier precision.
  • SCCA is a robust scale-invariant method that can readily be extended to use kernel methods to accommodate non-linear relationships between LFPs (element 908) and EEG source estimates (element 902) (see Literature Reference No. 9), feature learning over multiple subjects for increased statistical power (see Literature Reference No. 6), and Independent Component Analysis (ICA) if it is determined the model for multiple subject analysis requires feature independence within subjects but feature correlation between them (see Literature Reference No. 9).
  • ICA Independent Component Analysis
  • embodiments of the present disclosure ingest outputs from the datafication system described above in response to queries for brain attributes in a specific modality and also for stimulation-induced currents that are linked in high-level concept space to the behavioral outcome of strong memory encoding. This section elaborates how these tools can help to discover optimal stimulation montages to facilitate behavioral enhancement.
  • FIG. 5 depicts turning the processed data into a concept lattice 512, and then the lattice is searched to find the best behavioral results and identify the concepts that represent the brain state (element 314) desired.
  • concept features 510 consist of auto-encoded (element 508) portions of the input data graph (element 402), and, thus, identify the target brain state (element 508) of individual brain voxels.
  • the key functional inference challenge is solving the hard high- dimensional inverse mapping problem of estimating the stimulation pattern, either applied invasively or non-invasively, that induces a particular current density distribution within the brain volume.
  • SOC search is used to solve the inverse problem of estimating invasive and non-invasive neurostimulation montages that induce an arbitrary distribution of current densities through the brain volume.
  • SOC search refers to Literature Reference No. 21 and U.S. Application No. 14/747,407, which is hereby incorporated by reference as though fully set forth herein.
  • the KR systems 304 discover and extract signature templates of stimulation-induced currents based on queries (element 600) related to behavioral phenomena of interest (e.g., strong memory encoding) (element 602).
  • queries e.g., strong memory encoding
  • a query may be in the form: "Which currents lead to enhanced memory?”
  • the KR system 304 ingests experimental data related to memory retrieval outcomes and associated multimodal multi-scale brain measurements (i.e., spatiolateral graphs annotated with relationships 604), as well as computational data of estimated induced currents 606 in the voxel space that are registered between subjects.
  • Inputs (element 610) for datafication (element 300) and the forward model 608 include, but are not limited to, EEG, spikes, LFPs, tCS, and iCS data.
  • the signature template of currents is warped to the particular brain structure.
  • the optimization algorithm/tool (element 310) then operates on the individualized template of the desired currents (i.e., forward model 608) to estimate a compatible stimulation montage (i.e., neural stimulation patterns that will have desired effect 612), which specifies the scalp locations for the electrodes and stimulation current parameters (e.g., amplitude, frequency) at each electrode.
  • the constraints for optimization are the maximum number of electrodes, maximum current per electrode, and maximum cumulative current from all electrodes.
  • the system builds a knowledge representation (element 304) from a set of data (element 614) and behavior (element 602), identifies which currents are most responsible for desired behavioral effect (element 608), and then deduces which neural stimulation patterns will produce that effect (element 612).
  • an iterative optimization algorithm is used.
  • SOC search is used to improve the placement of electrodes and, second, gradient descent (see Literature Reference No. 24 for a description of gradient descent) is used to improve the parameters for a given placement.
  • a finite set of possible stimulation sites is used (e.g., 256 predefined locations on a head cap), and the goal is to find the optimal pattern of electrodes from these locations.
  • SOC search uses a self-organized critical (SOC) process (see Literature Reference No. 3) to generate search patterns. As shown in FIG. 10, the SOC process can be defined on an arbitrary graph 1000.
  • each avalanche e.g., element 1004 is mapped in the graph of the SOC process one-to-one onto the graph over which the optimization problem is defined (element 1006).
  • FIG. 10 illustrates the operation of SOC search to find the ground state of an Ising spin glass.
  • An Ising spin glass is an array of spins 1000 (e.g., electrons with an electromagnetic moment, up or down) and couplings between the spins so that the energy of a spin depends on the orientations (up or down) of the surrounding spins.
  • the ground state is an arrangement of spin orientations so that the total energy of all spins is at a minimum.
  • SOC search iterates patterns obtained from an SOC process (e.g., the sequence of avalanches 1002 in the Bak-Tang-Wiesenfeld model (Reference No. 3)).
  • each avalanche 1004 is mapped onto the spin array 1006 and marks the spins to be flipped to test if a lower energy state can be obtained. If this change results in a lower energy, the resulting spin arrangement forms the new baseline for optimization, otherwise the new spin arrangement is discarded. This process repeats until a desired optimum is reached. Refer to Reference No. 21 for additional details.
  • FIG. 7 illustrates how knowledge representation, ultimately a concept lattice derived by steps 1-4 (described below), relates neuroscience data concept features (element 510) to behavior, and can be applied to find the neural correlates behind memory enhancement. If the experiments involve different neural stimulation protocols, the system may be used to optimize the protocol to produce the best behavioral result.
  • Brain signals (step 1, element 700) are processed by the datafication module which builds links of correlated brain signals (step 2, element 702).
  • a sub-graph is identified through link clustering (step 3, element 704), and the sub-graph is mapped onto concept features (element 510) through the autoencoder (step 4, element 706).
  • FIG. 8 illustrates a signal graph 800.
  • the adjacency matrix of the graph representation can capture intra-model and cross-model links.
  • intramodel links are correlations between EEG channels (element 506)
  • cross-models links are correlations between EEG and LFPs (element 610).
  • the whole adjacency matrix is an example of a statistical layer 404 in the knowledge graph 402.
  • the invention described herein is a method and system to affect a desired behavioral result by sensing and intelligently stimulating the brain of a subject 1100 (or group of subjects), encompassing a brain monitoring subsystem 1102 incorporating a set of monitoring electrodes 1104 that can sense brain state (element 314) either invasively (i.e., chronically implanted electrodes providing neural unit or LFP data), or non-invasively (i.e., via EEG).
  • invasively i.e., chronically implanted electrodes providing neural unit or LFP data
  • non-invasively i.e., via EEG.
  • a large number of monitoring electrodes 1104 will give a high resolution result.
  • the system additionally includes a brain stimulation subsystem 1104 (shown as incorporated with the brain monitoring subsystem) incorporating a set of stimulating electrodes 1106 that can apply electrical current stimulation either invasively (i.e., chronically implanted electrodes) or non-invasively (i.e., transcranial current stimulation).
  • a brain stimulation subsystem 1104 shown as incorporated with the brain monitoring subsystem
  • stimulating electrodes 1106 that can apply electrical current stimulation either invasively (i.e., chronically implanted electrodes) or non-invasively (i.e., transcranial current stimulation).
  • the brain monitoring and brain stimulation subsystems 1104 may also be components of separate devices.
  • monitoring electrodes and stimulating electrodes are used in the invention described herein, only one of each type is shown in FIG. 11 for illustrative purposes. In one embodiment, the monitoring electrodes and the stimulating electrodes are co-located.
  • a forward model predicts behavioral outcomes (element 904) for different stimulations, and a search method (FIG. 10) is used to identify the ideal stimulation pattern related to a desired behavioral outcome.
  • the forward model consists of the following: a graphical representation 402 whose nodes (e.g., elements 410 and 412) describe the data, and edges describe relationships between data items. These relations are topological (element 408), statistical (element 406), and causal (element 404) (see FIG. 4).
  • the data of the graphical representation 402 may include a stimulation montage 316 applied to the brain (if any), either non-invasively or via chronically implanted electrode.
  • the data may include EEG data recorded from the monitoring electrodes as a result of a stimulation montage 316 applied via the stimulating electrodes.
  • the data graph/graphical representation 402 is processed in a series of steps that include one or more of the following.
  • Statistical relationships (element 406) between graph nodes are computed using a wavelet transform, and causality (element 404) is quantified with transfer entropy (TE) and associated transfer entropy (ATE), which transforms continuous time values into a symbolic time series.
  • Spurious indirect links are removed using the correlation matrix technique described in Literature Reference No. 4.
  • Measured and induced data for the non -invasive modality is transformed through a forward model (element 608) into the resulting currents (element 606) in voxels of brain volume, and those currents (element 606) are integrated into the graph representation 402. Data from multiple trials and/or multiple subjects is registered by warping.
  • SCA Canonical Correlation Analysis
  • Local regions of the graphical representation 402 are compressed with autoencoders 508 and turned into concepts 510 in a concept lattice 512.
  • This concept lattice 512 is formed from a table whose rows are different experiments (i.e., different electrical stimulation montages), and whose columns are a set of concepts (the patterns produced by each local region autoencoder), and the behavioral result.
  • An inverse mapping technique is used to find a set of stimulus electrode placements and parameters that can recreate the desired brain state (element 314).
  • the inverse mapping technique is a self-organized criticality form of search (SOC search) (FIG.
  • the stimulating electrode placements and parameters found are applied to the brain via the brain stimulation subsystem, and the behavioral result is monitored. If the result is unsatisfactory, the brain monitoring subsystem is used to update the graph and create a new row in the concept lattice table, and the processes above are repeated. A result is determined to be satisfactory if it fails to achieve the desired behavioral effect. Depending on the desired effect, the determination of success may be subjective, or there may be a task-performance metric. For example, if the goal is to weaken selected memories that are causing posttraumatic stress disorder, the treatment is successful when the subject gets relief. If the goal is to improve certain memories, the ability to recall those memories after a certain amount of time may be the metric for success.
  • monitoring and stimulation are done by co-located
  • brain stimulation is accomplished by transcranial magnetic stimulation instead of electrical current.
  • brain stimulation and/or monitoring is another embodiment of the invention.
  • behavioral outcomes are promoted for a specific subject, versus a population subjects.
  • the invention described herein will have a significant scientific impact by facilitating discoveries related to the neural mechanisms underlying the complicated behavioral and psychological phenomena, and their external control, with either invasive or non-invasive current stimulation.
  • the knowledge representation framework, datafication, and discovery tools will enhance the understanding of the mapping between non-invasive and/or invasive stimulation to neural mechanisms, allowing more fine-tuned stimulation interventions to achieve a behavioral outcome.
  • the system according to embodiments of the present disclosure will not only advance the understanding of brain function, but it will also provide optimal interventions for behavioral deficits (e.g., memory impairment caused by traumatic brain injury) and psychological disorders (e.g., depression).
  • the invention will significantly enhance the long-term retention of memories in both normal and aging populations, and potentially restore memory function in subjects afflicted with neurodegenerative diseases and brain injuries.
  • the knowledge representation according to embodiments of the present disclosure can generate more nuanced hypotheses of stimulation-induced currents based on queries for desired behavior. Additionally, discovery tools can then ingest these outputs to better optimize the locations and parameters of stimulation electrodes, with the unique formulation and techniques described herein.
  • the system according to embodiments of the present disclosure is a process and apparatus for helping human subjects afflicted with behavioral deficits and psychological disorders. As such, it could be part of a service to help wounded soldiers and civilians. Moreover, the invention might be part of a product for memory restoration and enhancement that would have a huge potential commercial market.

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Abstract

La présente invention concerne un système d'induction d'un effet comportemental souhaité utilisant une stimulation de courant électrique. Le sous-système de surveillance cérébrale comprend des électrodes de surveillance pour détecter l'activité cérébrale, et un sous-système de stimulation cérébrale comprenant des électrodes de stimulation pour appliquer une stimulation de courant électrique. Des données distribuées à échelles multiples sont enregistrées dans une représentation graphique. Le système identifie un sous-graphe dans la représentation graphique et cartographie le sous-graphe sur des caractéristiques de concept, générant un réseau de concepts qui relie les caractéristiques de concept à un effet comportemental. Enfin, une stimulation de courant électrique à appliquer pour produire l'effet comportemental est déterminée.
PCT/US2018/028962 2017-05-03 2018-04-23 Procédé et appareil de détermination d'une stimulation cérébrale optimale pour induire un comportement souhaité Ceased WO2018204119A1 (fr)

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