System for patient and adaptive therapy modeling and simulation

Abstract

Systems and methods are described to support simulating the delivery of a therapy, such as an electrical stimulation therapy, to a patient. In one example, a simulation system is described to include a first model representing a patient and a second model representing a stimulation device. The second model interacts with the first model by providing one or more simulated electrical signals generated in accordance with a simulation scenario as inputs for processing by the first model. The simulation scenario may be defined and adjusted by a user of the simulation system.

Description

SYSTEM FOR PATIENT AND ADAPTIVE THERAPY MODELING AND SIMULATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to U.S. Provisional Application No.63 / 712,261 filed October 25, 2024, and U.S. Provisional Application No. 63 / 728,600 filed December 5, 2024, each of which is incorporated herein by reference in its entirety.FIELD

[0002] The present disclosure is generally directed to simulation tools and relates more particularly to a tool for simulating therapeutic treatments, such as brain stimulation procedures and patient responses thereto.BACKGROUND

[0003] Implantable electrical stimulators may be used to deliver electrical stimulation therapy to patients to treat a variety of symptoms or conditions such as chronic pain, tremor, Parkinson's disease, epilepsy, urinary or fecal incontinence, sexual dysfunction, obesity, or gastroparesis. In general, an implantable stimulator delivers neurostimulation therapy in the form of electrical pulses. An implantable stimulator may deliver neurostimulation therapy via one or more leads that include electrodes located proximate to target tissues of the brain, the spinal cord, pelvic nerves, peripheral nerves, or the stomach of a patient. Hence, stimulation may be used in different therapeutic applications, such as deep brain stimulation (DBS), spinal cord stimulation (SCS),pelvic stimulation, gastric stimulation, or peripheral nerve stimulation. Stimulation also may be used for muscle stimulation, e.g., functional electrical stimulation (FES) to promote muscle movement or prevent atrophy.BRIEF SUMMARY

[0004] A clinician may select values for a number of programmable parameters to define the electrical stimulation therapy to be delivered by the implantable stimulator to a patient. For example, the clinician may select one or more electrodes for delivery of the stimulation, a polarity of each selected electrode, a voltage or current pulse amplitude, a pulse width, and a pulse frequency as stimulation parameters.

[0005] A set of parameters, such as a set including electrode combination, electrode polarity, amplitude, pulse width, and pulse rate, may be referred to as a program in the sense that they define the electrical stimulation therapy to be delivered to the patient. Clinicians develop a sense of the appropriate program to apply to a patient over years of experience and trial-and-error with the patient’s input. It would be desirable to lead the clinician to an appropriate program initially or to minimize the amount of back and forth required between the patient and the clinician.

[0006] As health data and in particular, dynamic bio-signals, continue to become more available and sophisticated, users of that data can benefit from a method by which an experience is built around those dynamics. As modern therapies are developed to function dynamically upon those biosignals, users of those therapies benefit from a method with which to build experience around the control of those dynamic systems. Furthermore, as those modern therapies are developed, there exists a desire to test the therapies performance in an appropriately dynamic environment. The building of experience and systems around dynamic bio-signals traditionally required live patients who exhibit those signals, presenting serious limitations.

[0007] For a clinician learning to treat a patient based on a bio-signal, the patient may incur less than idea treatment until the clinician’s intuition around the bio-signal can build. For a clinician learning to optimize a closed-loop therapy that is based on a bio-signal, they will similarly benefit from learning the novel dynamics of that system on a live patient. Finally, the development and validation of future therapies and algorithms is limited by the logistical, financial, and ethical barriers presented by the interaction with live patients.

[0008] Embodiments of the present disclosure take the understood variables surrounding the dynamics of the bio-signal and emulate its behavior as a digital simulation. A “digital patient” is thereby provided and its associated bio-signals, symptoms, activities, etc. can be used to build clinical experience prior to live therapy delivery, as well as to build and validate new therapies.

[0009] Embodiments of the present disclosure provide a simulation interface that enables the clinician to simulate various scenarios (e.g., different programs / parameter settings) with a patient or a virtual patient, thereby allowing the clinician to learn by trial-and-error without subjecting the patient to undue pain, stress, or in-person visits with their clinician. In some embodiments, the simulations conducted by the clinician on the simulation interface may help the clinician select the appropriate or optimal program for a patient without requiring any actual patient interactions. For instance, the clinician may have a patient model that is developed to represent a particular patient (e.g., a patient-specific model). The clinician may interact with the patient model in the proposed simulation interface to determine which types of parameters or settings are appropriate for aparticular patient. Thus, when it comes time to implant an actual stimulation device in a patient and define the initial settings for the patient’s stimulation device, the settings for the physical device may be defined based on the clinician’s interactions with the patient model.

[0010] In some embodiments, an individualized model (e.g., possibly fitted to a real patient) may be provided that is useable to predict both “high symptom” and “low symptom” reactions in patients, as well as the beta signal based on stimulation and / or medication. The model may be designed to incorporate effects of circadian and other rhythms, activity, and other stressors on a beta signal. The simulation provided to the clinician may include a simulation of in-office behavior (e.g., a simulation of an in-office treatment) and / or a simulation of at-home behavior (e.g., a simulation of at-home treatment), with a timeline, event reporting, and patient diary developed as part of an output provided by the simulation. In some embodiments, the simulation may be configured to simulate the dynamics of medication wash-in and medication wash-out.

[0011] The simulation interface may also enable the clinician to test whether open-loop or closed-loop therapy is better for a particular patient. In some embodiments, the simulation interface may be configured to enable switching of the various models therein between an open-loop configuration and a closed-loop configuration. The simulation interface may also be configured to provide medication therapies (e.g., a levodopa therapy) and / or stimulation therapies (e.g., DBS) to the patient model. As a non-limiting example, the simulation interface may support a clinician’s ability to test various treatments for diseases such as Parkinson Disease (PD) in which the substantia nigra degeneration causes a dopamine deficiency in the striatum (e.g., impacting motor symptoms of the patient).

[0012] It is known that the basal ganglia controls which intentions of the cortex are translated into actions. In an overactivation of the basal ganglia, intentions of the patient are overly suppressed, which can lead to “Parkinsonian gate” or Bradykinesia (slowness). According to at least some embodiments, the simulation interface can be used to supplement dopamine in a patient model (e.g., by providing a dopamine therapy or a levodopa therapy) in addition to providing DBS or adaptive DBS to the patient model.

[0013] In some embodiments, a patient model may be referred to as a virtual patient. The patient model or virtual patient may correspond to a heuristic model that captures known effectors of stimulation and medication on beta and symptoms, which incorporates documents beta variability (e.g., activity and sleep). By interacting with the patient model or virtual patient within a simulation environment, a clinician can visualize the directional effect of their changes as applied to the patient model or virtual patient. Parameters of the patient model or virtual patient can be adjusted tosymptoms of specific patients (e.g., an individualized patient model may be provided). Thus, a clinician can also plan out customizable patient scenarios. In each scenario, the clinician can customize and assess a number of scenarios: effects of DBS, therapeutic ranges, effects of medication, and the potential presence of other side effects.

[0014] Example aspects of the present disclosure include a simulation system having one or more processors, a memory, and one or more programs stored in the memory, where the one or more programs are executed by the processors, the one or more programs including instructions for: generating a first model representing a patient; generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.

[0015] In some embodiments, a simulation system is provided that includes: one or more processors, a memory, and one or more programs stored in the memory, where the one or more programs are executed by the processors, the one or more programs including instructions for: generating a first model representing a patient; generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.

[0016] In some aspects, the simulation input comprises one or more stimulation waveforms from the second model.

[0017] In some aspects, simulation outputs from the first model comprise one or more of: an indication of Dyskinesia; an indication of Bradykinesia; and a side effect.

[0018] In some aspects, the simulation inputs comprise a preprogrammed input, user input, or both.

[0019] In some aspects, the simulation outputs from the first model comprise an alpha-beta Local Field Potential (LFP).

[0020] In some aspects, the alpha-beta LFP is provided as a feedback to the second model.

[0021] In some aspects, an interaction between the first model and the second model is adjustable by switching between an open-loop simulation configuration and a closed-loop simulation configuration.

[0022] In some aspects, the one or more programs further include instructions for: simulating the first model responding to medication; and updating the one or more simulation outputs to reflect the first model responding to the medication.

[0023] In some aspects, the one or more programs further include instructions for: simulating the first model performing a particular activity; and updating the one or more simulation outputs to reflect the first model performing the particular activity.

[0024] In some aspects, the one or more programs further include instructions for: simulating the first model experiencing an amount of time under the stimulation therapy; and summarizing a response of the first model experiencing the amount of time under the stimulation therapy.

[0025] In some aspects, the first model comprises an Artificial Intelligence (Al) model that processes at least one input received at the first model.

[0026] In some aspects, the second model comprises one or more Artificial Intelligence (Al) models that process the one or more simulated electrical signals.

[0027] In some aspects, the one or more Al models comprises a plurality of models interconnected with one another.

[0028] In some aspects, the system further includes a user interface that enables a user to interact with at least one of the first model and the second model and to adjust an operating parameter thereof.

[0029] In some aspects, the user interface provides a display of the one or more simulated electrical signals.

[0030] In some aspects, the user interface further displays a reaction produced by the first model in response to processing the one or more simulated electrical signals.

[0031] In some aspects, the first model is stored on one or more servers and made available to a user device via a communication network.

[0032] In some aspects, the second model is stored on the one or more servers and made available to the user device via the communication network.

[0033] In some aspects, at least one of the first model and the second model are provided on a user device.

[0034] In accordance with at least some embodiments, a system is provided that includes: a processor; and memory storing a first model representing a patient, wherein the first model enables the processor to: receive an input from a second model representing a stimulation device, wherein the input comprises one or more simulated signals generated in accordance with a simulation scenario; pass the input through one or more processing components; and produce one or more outputs representing a simulated patient response to the input.

[0035] In some aspects, the first model comprises one of a plurality of patient models and wherein the first model is selected based on a user input.

[0036] In some aspects, the first model comprises a patient-specific model.

[0037] In some aspects, the one or more processing components comprises a simulated basal ganglia component that models a basal ganglia response to the one or more simulated signals.

[0038] In some aspects, the simulation scenario comprises an in-office scenario.

[0039] In some aspects, the simulation scenario comprises an at-home scenario.

[0040] In some aspects, the one or more processing components comprises a pathologic activation engine.

[0041] In some aspects, the processor is separated from the memory by a communication network.

[0042] In some aspects, the processor and memory are provided on a common device.

[0043] In some aspects, the one or more outputs comprises an alpha-beta Local Field Potential (LFP).

[0044] In some aspects, the one or more outputs comprises a side effect.

[0045] In some aspects, the one or more outputs comprises a response waveform.

[0046] In some aspects, the first model is configurable to operate in an open-loop simulation state where the one or more outputs are provided to a user interface and wherein the first model is further configurable to operate in a closed-loop simulation state where the one or more outputs are also provided back to the second model.

[0047] In accordance with at least some embodiments, a method is provided that includes: receiving, at a patient model, an input comprising one or more simulated signals generated in accordance with a simulation scenario; passing the input through one or more processing components of the patient model that simulate a patient response to the input; and producing one or more outputs with the patient model based on the one or more processing components processing the input.

[0048] In some aspects, the one or more outputs are provided as feedback from the patient model to a second model representing a stimulation device.

[0049] In some aspects, the input is received from a physical stimulation device.

[0050] In some aspects, the input is received from a model representing a stimulation device.

[0051] In some aspects, the method further includes: enabling the patient model to switch between an open-loop simulation configuration where the one or more outputs are provided to a user interface and a closed-loop simulation configuration where the one or more outputs are also provided back to a second model representing a stimulation device.

[0052] Example aspects of the present disclosure include any of the above aspects in combination with any one or more other aspects.

[0053] Example aspects of the present disclosure include any one or more of the features disclosed herein.

[0054] Example aspects of the present disclosure include any one or more of the features as substantially disclosed herein.

[0055] Example aspects of the present disclosure include any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.

[0056] Example aspects of the present disclosure include any one of the aspects / features / embodiments in combination with any one or more otheraspects / features / embodiments .

[0057] Example aspects of the present disclosure include the use of any one or more of the aspects or features as disclosed herein.

[0058] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described embodiment.

[0059] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0060] The phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. When each one of A, B, and C in the above expressions refers to an element, such as X, Y, and Z, or class of elements, such as XI -Xn, Yl-Ym, and Zl-Zo, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., XI and X2) as well as a combination of elements selected from two or more classes (e.g., Y1 and Zo).

[0061] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably.

[0062] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of the disclosure and its various aspects, embodiments, and configurations. It is intended neither to identifykey or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, embodiments, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.

[0063] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the embodiment descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0064] The accompanying drawings are incorporated into and form a part of the specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the disclosure. The drawings simply illustrate preferred and alternative examples of how the disclosure can be made and used and are not to be construed as limiting the disclosure to only the illustrated and described examples. Further features and advantages will become apparent from the following, more detailed, description of the various aspects, embodiments, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0065] Fig. 1 A is a diagram of a system in a first arrangement according to at least one embodiment of the present disclosure;

[0066] Fig. IB is a diagram of a system in a second arrangement according to at least one embodiment of the present disclosure;

[0067] Fig. 1C is a diagram of a system in a third arrangement according to at least one embodiment of the present disclosure;

[0068] Fig. ID is a diagram of a system in a fourth arrangement according to at least one embodiment of the present disclosure;

[0069] Fig. IE is a diagram of a system in a fifth arrangement according to at least one embodiment of the present disclosure;

[0070] Fig. 2 is a block diagram of system components according to at least one embodiment of the present disclosure;

[0071] Fig. 3 is a block diagram of components of a User Interface (UI) layer and model layer in a system according to at least one embodiment of the present disclosure;

[0072] Fig. 4 is a block diagram illustrating components of a simulation system according to at least one embodiment of the present disclosure;

[0073] Fig. 5 is a flow diagram illustrating a first method according to at least one embodiment of the present disclosure;

[0074] Fig. 6 is a flow diagram illustrating a second method according to at least one embodiment of the present disclosure;

[0075] Fig. 7 is a flow diagram illustrating a third method according to at least one embodiment of the present disclosure;

[0076] Fig. 8 is a flow diagram illustrating a fourth method according to at least one embodiment of the present disclosure;

[0077] Fig. 9 is a chart illustrating virtual patient responses to simulated therapies applied thereto according to at least one embodiment of the present disclosure; and

[0078] Figs. 10A-AG illustrate a number of views of a Graphical User Interface (GUI) presented as part of a simulation system according to at least one embodiment of the present disclosure.DETAILED DESCRIPTION

[0079] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example or embodiment, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, and / or may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the disclosed techniques according to different embodiments of the present disclosure). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0080] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively, or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). Computer-readable media mayinclude non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0081] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple All, A12, A12X, A12Z, or Al 3 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000-series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0082] Before any embodiments of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Further, the present disclosure may use examples to illustrate one or more aspects thereof. Unless explicitly stated otherwise, the use or listing of one or more examples (which may be denoted by “for example,” “by way of example,” “e.g.,” “such as,” or similar language) is not intended to and does not limit the scope of the present disclosure.

[0083] Measuring Evoked Compound Action Potentials (ECAPs) involves stimulating a group of neurons and then recording a response to the stimulation from a distance away. Described herein are systems and methods of simulation measuring ECAPs by generating a simulated electrical signal and a patient model and applying the simulated electrical signal to the patient model to generate a simulated body signal. In at least one embodiment of the present disclosure, a simulated electricalsignal applied to a patient model simulates an SCS procedure. The patient model outputs a simulated body signal and simulated patient feedback. In some implementations, the simulation may be in the form of a training tool which may be used by a user. The user may be enabled by interacting with the tool to select a patient model from a list and to select a particular scenario of arrangement of virtual contacts to apply the simulated electrical signal. These and other implementation details are described in greater detail below.

[0084] Figs. 1A-1E depict various arrangements of a system 100 according to at least some embodiments of the present disclosure. While the various arrangements of the system 100 will be described with respect to five particular examples, it should be appreciated that additional or different arrangements or configurations of the system 100 may be implemented without departing from the scope of the present disclosure. For instance, aspects of one arrangement (e.g., an arrangement as illustrated in Fig. 1A) may be combined with aspects of another arrangement (e.g., an arrangement as illustrated in Fig. IE). The examples depicted and described herein are not intended to limit the claims and should not be construed as such.

[0085] The system 100, in various arrangements, may be configured to provide a simulation interface 126 in which a user, such as a clinician, doctor, care provider, physician’s assistant, etc. is able to simulate, test, and / or modify simulation settings associated with delivering a simulated therapy. Illustratively and without limitation, the simulation interface 126 may be used by a clinician to deliver a simulated therapy, such as a stimulation therapy, a medical therapy, or a combination thereof, to a virtual patient or patient model 112, as will be described in further detail herein. Within the environment provided by the simulation interface, the user may be allowed to define parameters of a stimulation program and / or medication, then test the therapy within the simulation environment, using a patient model 112 and / or a stimulation device model 114. The system 100 may provide a simulation interface 126 that enables simulation of various types of stimulation, such as DBS, SCS, pelvic stimulation, gastric stimulation, peripheral nerve stimulation, or FES. While examples of the present disclosure will be primarily described in connection with DBS therapies and similar therapies used to treat certain disorders and diseases, such as Parkinson Disease, it should be appreciated that embodiments of the present disclosure are not so limited.

[0086] Referring initially to Fig. 1A, a first arrangement of the system 100 is illustrated and will be described in accordance with at least some embodiments of the present disclosure. The system 100 is shown to include a server 102, a user device 124, and a communication network 122. The server 102 is configured to communicate with the user device 124 via the communication network 122. The server 102, in some embodiments, may correspond to one or multiple servers. Alternativelyor additionally, the server 102 may be provided as cloud-based memory and / or processing resources. The illustration of a server 102 is intended to show a computing resource or set of computing resources that are configured to provide a simulation service to the user device 124.

[0087] The server 102 is illustrated to include a processor 104, a memory 106, and a communication interface 108. The user device 124 is shown to include a simulation interface 126, which may correspond to a set of Graphical User Interface (GUI) elements that are presented via a user interface of the user device 124. Specifically, but without limitation, the simulation interface 126 may correspond to a simulation environment (e.g., a web-based environment, an applicationbased environment, or the like) that interacts with the server 102 and presents a user of the user device 124 with the ability to define simulation settings, test simulation settings, adjust simulation settings, and control various aspects of a simulation scenario such that different configurations of a stimulation device are tested before being applied to a patient. In other words, the user device 124 may enable a user to access some or all of the components of the server 102 to facilitate a simulation scenario.

[0088] Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the server 102. As will be described in further detail herein, it should be appreciated that certain arrangements of the system 100 may provide a single device with the components of the server 102 and the components of the user device 124. Said another way, a single computing device (e.g., a user device 124) may be provided with some or all of the components of the server 102. Alternatively or additionally, a server 102 may be provided with a user interface and / or simulation interface 126 without departing from the scope of the present disclosure.

[0089] The processor 104 may correspond to any processor described herein or any similar processing unit or processing resource. The processor 104 may be configured to execute or process data (e.g., instructions, Artificial Intelligence (Al) models, neural networks, etc.) stored in the memory 106. Upon executing or processing the data, the processor 104 may carry out one or more computing steps utilizing the patient model 112, the stimulation device model 114, simulation data 116, scenario data 118, and / or based on data received from the communication interface 108, or the simulation interface 126. Specifically, but without limitation, the processor 104 may include or correspond to one or more Central Processing Units (CPUs), one or more Graphics Processing Units (GPUs), one or more Data Processing Units (DPUs), one or more ASICs, one or more FPGAs, one or more processing circuits, one or more Integrated Circuit (IC) chips, combinations thereof, and the like.

[0090] Memory 106 may store data used to perform systems and methods described herein. Such data may include, for example, and as described in greater detail below, a patient model 112, a stimulation device model 114, simulation data 116, scenario data 118, and display instructions 120. The memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer-readable data and / or instructions. The memory 106 may store information or data useful for completing, for example, any steps of the methods depicted and described herein, or of any other methods. The memory 106 may store, for example, instructions and / or machine learning models that support the creation, management, and / or delivery of a simulation interface 126 to a user. In some embodiments, the data stored in memory 106 may enable one or more functions of the simulation of electrical signals and the application of simulated electrical signals to patient / s and / or patient models 112. For instance, the memory 106 may store content (e.g., instructions and / or machine learning models) that, when executed by the processor 104, cause the processor 104 to simulate an electrical signal being applied to a target anatomical element such as a nerve, such as to block or regulate chronic pain.

[0091] Content stored in the memory 106, if provided as in instruction, may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines. Alternatively, or additionally, the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by the processor 104 to carry out the various method and features described herein. Thus, although various contents of memory 106 may be described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, algorithms, and / or machine learning models. The data, algorithms, and / or instructions may cause the processor 104 to manipulate data stored in the memory 106 and / or received from or via the communication interface 108 (e.g., via the communication network 122).

[0092] The communication interface 108 may be used for receiving data from an external source (such as the user device 124, the communication network 122, a database (not illustrated), and / or any other system or component not part of the system 100), and / or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device, the user device 124, the communication network 122, a database, and / or any other system or component not part of the system 100). The communication interface 108 may comprise one or more wired interfaces (e.g., a USB port, an Ethernet port, a Firewire port) and / or one or more wireless transceivers or interfaces (configured, for example, to transmit and / or receive information via one ormore wireless communication protocols such as 802.11a / b / g / n, Bluetooth, NFC, ZigBee, and so forth). In some embodiments, the communication interface 108 may be useful for enabling the server 102 to communicate with one or more other processors 104 or computing devices (e.g., the user device 124 or another server 102), whether to reduce the time needed to accomplish a computingintensive task or for any other reason. Although not depicted, the user device 124 may also include a communication interface (e.g., similar to communication interface 108) to facilitate connectivity between the user device 124 and communication network 122.

[0093] In some implementations, data such as the patient model 112, stimulation device model 114, simulation data 114, scenario data 116, and display instructions 120 described herein may be obtained in whole or in part by the server 102 from one or more databases and / or cloud computing resources via communications exchanged over the communication network 122.

[0094] The communication network 122 may be or represent the Internet or any other wide area network. The server 102 may be connected to the communication network 122 via the communication interface 108, using a wired connection, a wireless connection, or both. In some embodiments, the server 102 may communicate with a database and / or an external device (e.g., a user device 124) via the communication network 122.

[0095] The patient model 112 may correspond to an instruction set, Al model, neural network, or the like that is capable of receiving one or more inputs, processing the one or more inputs in a way that simulates a patient processing the one or more inputs, and then generating one or more outputs that represent or simulate a patient response. In other words, the patient model 112 may provide an engine for simulating a patient that is affected by therapies applied thereto (e.g., medical therapies and / or stimulation inputs provided by an actual stimulation device or by the stimulation device model 114). The patient model 112 may have varying degrees of complexity depending upon a simulation scenario being analyzed in the simulation interface 126. In some embodiments, the patient model 112 may correspond to one of multiple patient models that are available for testing within the simulation interface 126. In such a scenario, the patient model 112 used for a particular simulation may be selected by the user of the user device 124 when defining a simulation scenario. Different patient models 112 may be selected to test different impacts of a common simulation scenario of different patient types. Alternatively or additionally, the same patient model 112 may be subjected to multiple different simulation scenarios to determine how a single type of patient might respond to various stimulation programs. In some embodiments, the patient model 112 may correspond to a generic patient model (e.g., representing a common or typical patient) or the patientmodel 112 may correspond to a patient- specific model that is developed to approximate a specific patient’s response to a simulation scenario.

[0096] As mentioned above, the user of the user device 124 may employ the simulation interface 126 to select one or more patient models 112 to be tested under a simulation scenario. In testing a patient model 112, one or more different simulation scenarios may be selected. Examples of different stimulation parameters and / or stimulation algorithms that may be selected for a particular simulation scenario include, without limitation, a number of electrodes to deliver a stimulation signal, an electrode combination to deliver a stimulation signal, electrode polarity, stimulation signal amplitude, stimulation signal pulse width, stimulation signal pulse rate, therapy duration, implant location, electrode placement, closed-loop versus open-loop, etc. Adjusting one or more of theses different parameters or algorithms may relate to selecting a different simulation scenario, which ultimately may help the clinician to select one or multiple therapeutic programs to be used on the patient. In other words, simulation scenarios may be deployed to simulate or emulate a potential therapeutic program to be delivered to a patient. Therapeutic programs may include stimulation(s) to be applied to a patient over a course of time, medications to be given to the patient over a course of time, timings associated with delivering stimulations and / or medications, etc.

[0097] The stimulation device model 114 may be configured, based on user selection, to implement a control algorithm of a closed-loop stimulation device. Alternatively or additionally, the stimulation device model 114 may be configured, based on user selection, to implement an openloop stimulation device. In some embodiments, the stimulation device model 114 may generate one or more simulated electrical signals in accordance with a simulation scenario, then deliver the one or more simulated electrical signals to the patient model 112. As an example, the stimulation device model 114 may output a simulated electrical signal having a stimulation amplitude that varies over time. The simulated electrical signal may also be delivered by different electrodes to the patient model 112 (e.g., at different distances from a target anatomical element).

[0098] In some embodiments, the patient model 112 may correspond to a heuristic model or set of heuristic models that capture known effects of simulation and medication on beta and symptoms, and which incorporates documented beta variability (e.g., activity and sleep). Illustratively and without limitation, the patient model 112 may include one or multiple parameters that are adjustable to represent an actual patient (e.g., parameters may be adjustable to represent different patient symptoms). The pathologic activation N(t) of a patient model 112 may be represented according to the following:• BD(t) =B0 - D(t)*ME• D(t) represents current dose of medication•I(t) = MAX(BM,BD(t) - Bs* MAX(0, C(t) - Cthr))• C(t) is the current computed by the stimulator• N(t) = N(t-l) * (1-a) + I(t) * a

[0099] The beta drive M(t) of a patient model 112 may be represented according to the following:• Beta drive M(t)• M(t) = MAX(BM, N(t) + BO*(S(t)-l) )- S(t) is cumulative effect of cycadean cycle, weekly fluctuations, and activity.S(t)=l implies no effect on the beta signal.

[0100] In the above equations (N(t) and M(t)), the following scenario- specific parameters may be utilized:• Scenario 1 - BO is the level of activation without any stimulation or medication• Scenario 2 - BM is beta level at a maximum suppression through either medication or stimulation or both• Scenario 3 - ME is medication effect defined as to an amount B0 is decreased with a full dose of medication• Scenario 4 - Bs is the slope with which activation changes as a function of current• Scenario 5 - Cthr is the threshold at which activation is affected

[0101] According to at least some embodiments, the following parameter may be a fixed parameter within the simulation system 100: a is a time constant, which may be set to a predetermined time (e.g., 71 msec).

[0102] In some embodiments, motor processing of the patient model 112 may be represented by N(t). Motor processing of the patient model 112 can be determined by comparing N(t) to one or more thresholds to determine symptoms of the patient model 112. Such comparisons and thresholds may be individualized on a per-scenario basis. For a slowly changing algorithm or eDBS:• N’ (t) = LP[N(t)] where LP is a recursive low-pass filter (e.g., time constant = 2.5 sec).• N’(t) > Nhigh produces bradykinesia / rigidity / tremor (under-treatment) depending on simulated patient scenario.• N’(t) < Niow produces dyskinesia (over-treatment) symptoms depending on simulated patient scenario.Current’(t) = LP[Current(t)] where LP is a recursive low-pass filter (e.g., time constant = 4 sec).Current > Currentside produces a side effect.

[0103] For a rapidly changing algorithm (e.g., a single threshold), the over-treatment and side effect algorithms for the patient model 112 may remain unchanged. For undertreatment, however, the patient model 112 may be represented according to the following:• Nmax(t) < Nhigh and M(t)*BurstFactor > BetaHighThr• Where Nmax(t) = N (t) @ MaxAmp.• And M(t) is the beta drive incorporating effects of sleep and activity.• Peak amplitude is high enough to suppress beta, and there is a good likelihood that large beta burst will cross threshold.

[0104] The patient model 112 may also be configured to implement side-effect processing. As part of implementing side-effect processing, the patient model 112 may behave according to the following:• Patient is in other side effect if:• Current’ (t) = LP[Current(t)] where LP is a recursive low-pass filter (time constant = 4 sec).• Current’ (t) > Currentside produces side effect.• For rapidly changing algorithm (e.g., single- threshold), patient is considered to be in side effect due to rapid stimulation (tingling) if:• Tremor is controlled (current is therapeutic).• IncRate > SideEffectIncThrMaPer50mS

[0105] According to at least some embodiments, the beta signal B(t) implemented by the patient model 112 may be driven by M(t), but may have temporal characteristics similar to the observed beta signal: EQI: MEANt(B(t)) = MEANt(M(t)). The beta signal B(t) temporal dynamics may be constructed based on the literature and known items that impact the same. According to at least some embodiments, the beta signal B(t) may be a signal composed by bursts, with a distribution prepared according to known documentation of beta signals. Variables that may impact a beta signal distribution include, without limitation:• Different burst distributions for on / off levodopa• Burst Amplitude (BA): selected as BA = 0.1 + BD*0.8*R[I]• Where R[I] is a uniform distribution described below and where BD is burst duration.• Amplitude: trapezoidal, with raise and fall time equal to % of the burst.• For example, every 50 msec: amplitude decreased by D~U[t]*BA.• Inter- burst times: which can be drawn from a distribution depending on burst length• For example, Inter-burst time ~ U[0,max[200 ms, BD*0.7]]+l 50ms where BD is duration of the previous burst.• Baseline signal BBL[t]: which may be defined by the equation:• BBL[t] ~ (M[t] + (U[0,l]- 0.5) M[t]) 0.5+0.5 BBL[t-l]• Total beta signal: which may be computed as B(t) = G (BBL[t]+3.0 Bburst[t])• G may be adjustable to match EQI .

[0106] In some embodiments, the patient model 112 may be configured to generate the beta signal with desired or defined qualities. Examples of desired qualities for a beta signal generated by the patient model 112 include, without limitation, min-to-max variation (e.g., approximately 5x to lOx), non-stationary distribution of bursts (e.g., periods of time with larger amplitude bursts may be followed by periods of time with shortened bursts, such as 2-3 seconds per period), and weak dependency between amplitude and burst length. The generation of the beta signal may be performed according to a recursive equation for a random variable. For instance, the beta signal may be generated according to the following:• R[I] = (U[I]-0.5)*(l-rgLPFact)+R[I-l]*rgLPFact• Convert R[I] to a uniform variable by lookup table.• Set rgLPFact = 0.5

[0107] Simulations may be executed using scenario data 118 in the simulation interface 126. The scenario data 118 may correspond to the various parameters and algorithms selected for applying a simulation scenario within the simulation interface. As scenario data 118 is used to execute a simulation scenario with the patient model 112 and / or stimulation device model 114, simulation data 116 may be generated. The simulation data 116 may include a description of inputs being provided to the patient model 112 and / or stimulation device model 114. The simulation data 116 may also include a description of outputs being generated at the patient model 112 and / or stimulation device model 114. The inputs and / or outputs generated by the various models 112, 114 during execution of a simulation scenario may be displayed to a user via the simulation interface 126. In someembodiments, the display instructions 120 may be executed by the processor 104 to determine how to present the simulation data 116 and / or scenario data 118 to the user via the simulation interface 126. To the extent that the display size of a user interface may be limited, the amount of data and the manner in which the data is presented via the simulation interface 126 may be controlled by the display instructions 120. The display instructions 120 may also be responsible for changing a display of the simulation interface 126 based on user inputs and / or based on changes in the simulation scenario.

[0108] Referring now to Fig. IB, an alternative arrangement of the system 100 will be described in accordance with at least some embodiments of the present disclosure. The system 100 of Fig. IB is shown to provide a user device 124 with the components needed to facilitate execution of a simulation scenario. Specifically, but without limitation, the user device 124 is shown to include many of the components previously illustrated as being included in the server 102. It should be appreciated, however, that the user device 124 of Fig. IB may be similar or identical to the user device 124 of Fig. 1A. The user device 124 may correspond to a personal computing device or collection of computing devices. Non-limiting examples of a suitable user device 124 include a Personal Computer (PC), a laptop, a tablet, a smartphone, a Personal Digital Assistant (PDA), or the like.

[0109] The arrangement of Fig. IB is not shown to include a communication network 122, but it should be appreciated that the user device 124 is connectable to a communication network 122 via the communication interface 108. In some embodiments, the user device 124 may be capable of interacting with a server 102 that also has components as illustrated in Fig. 1A.

[0110] As mentioned above, the simulation interface 126 may be rendered / displayed to a user via the user interface 110 of the user device 124. The user interface 110 may be or comprise a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and / or any other device for receiving information from a user and / or for providing information to a user. The user interface 110 may be used, for example, to receive a user selection or other user input regarding any step of any method described herein. In some embodiments, the user interface 110 may be used to select one or more parameters for the virtual electrodes used in scenarios including, but not limited to, selecting a size and / or location of an electrode. The user interface 110 may receive input prior to a simulation, such as to select a patient model and / or scenario, and may receive input during a simulation, such as to adjust settings. The user interface 110 may also include user output capabilities, as mentioned above, thereby facilitating a visual display and / or audible presentation of the simulation interface 126. Notwithstanding the foregoing, any required input for any step of any method described herein maybe generated automatically by the system 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100. In some embodiments, the user interface 110 may be useful to allow a user or other user to modify instructions to be executed by the processor 110 according to one or more embodiments of the present disclosure, and / or to modify or adjust a setting of other information displayed on the user interface 110 or corresponding thereto.

[0111] Although the user interface 110 is shown as part of the user device 124, in some embodiments, the user device 124 may utilize a user interface 110 that is housed separately from one or more other components of the user device 124. In some embodiments, the user interface 110 may be located proximate one or more other components of the user device 124, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the user device 124.

[0112] Referring now to Fig. 1C, another possible arrangement of system 100 components will be described in accordance with at least some embodiments of the present disclosure. The system 100 shown in Fig. 1C provides a first computing device 128 and a second computing device 130. The first computing device 128 and / or second computing device 130 may be configured similarly or identically to the server 102 and / or to the user device 124. In other words, components described as being included in the server 102 and / or the user device 124 may be provided in the first computing device 128 and / or second computing device 130.

[0113] The first computing device 128 is shown to include the processor 104, the memory 106, the communication interface 108, and the user interface 110. The memory 106 of the first computing device is shown to include the patient model 112, simulation data 116, and scenario data 118. The second computing device 130 is shown to also include the processor 104, the memory 106, the communication interface 108, and the user interface 110. The memory 106 of the second computing device 130 is shown to include the stimulation device model 114, simulation data 116, scenario data 118, and display instructions 120.

[0114] The arrangement of Fig. 1C deploys one computing device (e.g., the first computing device 128) to operate a patient side of a simulation and another computing device (e.g., the second computing device 130) to operate a device side of a simulation. The first computing device 128 may communicate with the second communication device 130 directly (e.g., via a wired connection or wireless connection established between the communication interfaces 108 of the devices 128, 130). Alternatively or additionally, the first computing device 128 may communication with the second communication device 130 through the communication network 122.

[0115] The processor 104 of the first computing device 128 may be configured to execute the patient model 112. The processor 104 of the second computing device 128 may be configured to execute the stimulation device model 114. When a particular simulation scenario is being tested, the second computing device 130 may generate one or more simulated electrical signals in accordance with the simulation scenario, then the second computing device may transmit the one or more simulated electrical signals to the first computing device 128. Upon receiving the one or more simulated electrical signals from the second computing device 128, the processor 104 of the first computing device 128 may execute the patient model 112, which takes the inputs from the stimulation device model 114 and produces one or more outputs representing a simulated patient response to the one or more simulated electrical signals.

[0116] The output(s) of the patient model 112 may be displayed in a simulation interface 126 via the user interface 110 of the first computing device 128 or via the user interface 110 of the second computing device 130. When the simulation interface 126 is provided on the user interface 110 of the second computing device 130, the output(s) of the patient model 112 may be provided back to the second computing device 130 for processing by the display instructions 120. The display instruction 120 may cause the simulation interface 126 to display the simulation data 116, the scenario data 118, the one or more simulated electrical signals, and the one or more outputs via the user interface 110. It should be appreciated that the display instructions 120 may alternatively or additionally be provided in the first computing device 128 without departing from the scope of the present disclosure.

[0117] Referring now to Fig. ID, yet another arrangement of the system 100 will be described in accordance with at least some embodiments of the present disclosure. The system 100 is shown to include the first computing device 128, which may be similar or identical to the first computing device 128 of Fig. 1C. The system 100 is also shown to include a stimulation device 132 in place of the second computing device 130.

[0118] The stimulation device 132 may correspond to any suitable type of physical device that generates an actual stimulation signal and delivers the stimulation signal to a patient. In some embodiments, the stimulation device 132 may correspond to an implantable stimulation device. In some embodiments, the stimulation device 132 may not be implantable, but rather may be configured to deliver a stimulation signal to a patient via one or more implanted electrodes. Nonlimiting examples of stimulation devices 132 that may be utilized include the devices depicted and described in U.S. PatentNos. 7,313,445; 7,761,985; 9,757,555; and 10,953,221, the disclosures of which are hereby incorporated herein by reference in their entirety.

[0119] The stimulation device 132 is shown to include a processor 104 and a communication interface 108. The communication interface 108 may facilitate communications with the first computing device 128 during a simulation involving the stimulation device 132. In this arrangement, the first computing device 128 may present the simulation interface 126 to a user via the user interface 110. Simulation data 116 and / or scenario data 118 may be shared between the first computing device 128 and the stimulation device 132 via the communication interfaces 108 of each device.

[0120] In addition to the processor 104 and communication interface 108, the stimulation device 132 is shown to include one or more signal generation circuits 134 and one or more signal delivery circuits 136. The signal generation circuit(s) 134 may be configured to generate a stimulation signal or series of stimulation signals based on scenario data 118 received from the first computing device 128. The signal generation circuit(s) 134 may cooperate with the signal delivery circuit(s) 136 to deliver the stimulation signal(s) to a patient. When interacting with the first computing device 128, the stimulation signal(s) may be delivered to the first computing device 128 as inputs to the patient model 112. This arrangement may allow the testing / simulation of an actual stimulation device 132 with a virtual patient (e.g., via the patient model 112).

[0121] Referring now to Fig. IE, another arrangement of the system 100 will be described in accordance with at least some embodiments of the present disclosure. The system 100 is shown to include a stimulation delivery device 138 and electrodes 140 in communication with the second computing device 130. In some embodiments, the stimulation delivery device 138 may be similar or identical to stimulation device 132, meaning that components of the stimulation device 132 may be provided in the stimulation delivery device 138. The stimulation delivery device 138 may be configured, in some embodiments, to deliver stimulation to a patient 142 via one or more electrodes 140 according to simulation data 116 and / or scenario data 118. In other words, the second computing device 130 may operate as a controller for the stimulation delivery device 138. The stimulation delivery device 138 may then be configured to deliver physical stimulation signals to a patient 142 via the electrode(s) 140. While delivering stimulation signals to a patient 142 may not be the same as implementing a simulation scenario as described herein, the second computing device 130 may still be configured to store simulation data 116 and scenario data 118 from previously-run simulation scenarios. Based on those scenarios, the second computing device 130 may then be used to control a stimulation delivery device 140, which delivers stimulation signals to the patient 142 according to a program developed with the help of the simulation scenarios run in the second computing device 130.

[0122] The stimulation delivery device 138 and / or electrode(s) 140 may be implantable in the patient 142. It should be appreciated, however, that the stimulation delivery device 138 and / or electrode(s) 140 do not necessarily need to be implantable or remain implanted in the patient 142. The communication between the second computing device 130 and the stimulation delivery device 138 may be achieved via a wired communication link (e.g., with a wired communication interface 108) or via a wireless communication link (e.g., with a wireless communication interface 108).

[0123] The various arrangements of the system 100 depicted and described herein may be configured to provide a simulation environment or testing tool that enables a clinician to simulate and test various scenarios. The outcomes of the simulations may eventually be stored as simulation data 116 and / or scenario data 118, which may be useable for determining a stimulation program to apply to a patient. Referring now to Fig. 2, additional details of a testing tool 200 will be described in accordance with at least some embodiments. It should be appreciated that the testing tool 200 may comprise components of the system 100 or that the system 100 may include components of the testing tool 200 without departing from the scope of the present disclosure. In other words, the testing tool 200 may be deployed within the context of the system 100 (e.g., within the simulation interface 126).

[0124] As illustrated in Fig. 2, a testing tool 200 may include applying an electrical signal simulation 204 (e.g., one or more simulated electrical signals) to a patient model 112 to output a patient model response 208. Before the stimulation 204 is applied to the patient model 112, user inputs 202, configuration settings, and other variables may affect one or both of the signal simulation 204 and the patient model 112. For example, a user may select a patient model 112, select a simulation scenario, select settings for a patient model 112, and / or adjust other settings associated with another device of the system 100. During the simulation, other user inputs 206, such as the adjustment of thresholds, scenarios, program variables, and settings may affect one or both of the signal simulation 204 and the patient model 112. As the testing tool 200 generates the patient model response 208 (e.g., Bradykinesia, Parkinsonian gate, Dyskinesia, heaviness in the legs, changes in movements, muscle activity, muscle tightness, headaches, dizziness, numbness, trouble with speech or balance, changes with gait, mood changes, fatigue, pain, discomfort, etc.), live feedback visualizations 210 may be generated and / or displayed on a user interface 110 and / or an assessment 212 may be generated and / or displayed as part of the simulation interface 126.

[0125] The simulation of a DBS signal may depend at least in part on user input and configuration settings. For example, a user may select or design a scenario as described in greater detail herein.

[0126] The signal simulation 204 may be performed by the system 100 to replicate realistic functionality of a stimulation device 132 and / or stimulation delivery device 138 in an open-loop configuration or a closed-loop configuration. In this way, the testing tool 200 helps test multiple device configurations (e.g., open-loop configuration and / or closed-loop configuration) prior to administering treatment to a patient. In switching between an open-loop configuration and closed-loop configuration, the patient model 112 may not necessarily change, but the amount of feedback provided from the patient model 112 to a stimulation device model 114 may change. The feedback may include patient feedback, which may include but is not limited to jolts / shocks, cramping, heaviness in the legs, changes in movements, muscle activity, muscle tightness, headaches, dizziness, numbness, trouble with speech or balance, changes with gait, mood changes, fatigue, pain, discomfort, etc.

[0127] The electrical signal simulation 204 may correspond to a process executed by the processor 104 to simulate an electrical signal as which may be used for electrical stimulation and / or nerve blocking. Generating the electrical signal may be achieved by adjusting one or more of a signal frequency, a signal type (e.g., square wave, sinusoidal wave, triangle wave, etc.), a duty cycle, etc. An electrical signal simulation 204 may be out from a virtual device or application process (e.g., the stimulation device model 114) which produces simulated electrical stimulation. A patient model 112 as described herein may be a simulation of a patient that is capable of producing response signals based on the electrical signal simulation 204.

[0128] Referring now to Fig. 3, additional details of components of a system 100 will be described in accordance with at least some embodiments of the present disclosure. The system 100 may include a UI layer 304 and a model layer 308 that cooperate with one another to provide the simulation capabilities depicted and described herein. The UI layer 304 may correspond to the components presented or made available to a user (e.g., a clinician) as part of providing a simulation tool. The UI layer 304 may include a scenario selector 312, a user interface 316, and a patient visualizer 320.

[0129] The model layer 308 may correspond to a layer of code or an application layer in which the models (e.g., the patient model 112 and / or stimulation device model 114) interact with one another. The model layer 308 may include a device simulator 324 (e.g., the stimulation device model 114) as well as a virtual patient simulator 328 (e.g., the patient model 112). The simulators 324, 328 may correspond to one or multiple different models. For instance, a virtual patient simulator 328 may correspond to a single patient model 112 or multiple patient models 112 without departing from the scope of the present disclosure.

[0130] In some embodiments, the UI layer 304 may interact with the model layer 308 via a browser-based communication protocol. For instance, the user interface 316 may interact with the device simulator 324 using an IP-based communication protocol. Outputs of the device simulator 324 may be provided to the virtual patient simulator 328. The virtual patient simulator 328, upon processing inputs from the device simulator 324, may be configured to provide outputs to the patient visualizer 320 via the same IP-based communication protocol used between the user interface 316 and device simulator 324. Outputs of the virtual patient simulator 328 may be transmitted to the patient visualizer 320, where they are displayed to the user via one or more GUI elements.

[0131] Referring now to Fig. 4, additional details of components of the patient model 112 and device model 114 will be described in accordance with at least some embodiments. In the example of Fig. 4, a device 404 is illustrated as interacting with a virtual patient 412. It should be appreciated that the device 404 may correspond to an example of the stimulation device model 114, an example of the second computing device 130, an example of a stimulation device 132, and / or an example of a device simulator 324. It should also be appreciated that the virtual patient 412 may correspond to an example of the patient model 112, an example of the first computing device 128, and / or an example of a virtual patient simulator 328. Interactions between the device 404 and virtual patient 412 may be controlled by an interface 408 (e.g., a tablet interface or another example of a simulation interface 126). Patient outputs 472 generated by the virtual patient 412 may also be viewed via the interface 408.

[0132] The device 404 is shown to include settings 416, a stimulation engine 420, a sense and process unit 424, and one or more event logs 428. The virtual patient 412 is shown to include a simulated basal ganglia 436, simulated motor processing 464, and simulated nearby structures 468. The components of the device 404 and / or virtual patient 412 may be provided as Al models, neural networks, and / or instruction sets. It should be appreciated that some components of the device 404 and / or virtual patient 412 may be provided as Al models / neural networks whereas other components of the device 404 and / or virtual patient 412 may be provided as instruction sets.

[0133] Operations of the device 404 may be controlled according to settings 416, which are adjustable via the interface 408. In accordance with the settings 416, the stimulation engine 420 may generate one or multiple stimulation outputs, which are provided as inputs to the virtual patient 412 via the medium current spread unit 432. The medium current spread unit 432 may distribute stimulation inputs received from the device 404 between a pathologic activation engine 452 and the simulated nearby structures 468. The pathologic activation engine 452 may correspond to a simulation engine that generates a motor processing output N(t) according to the amount of currentspread 432 as well as an amount of levodopa simulated by a Levodopa Equivalent Daily Dose (LEDD) 440.

[0134] The output of the pathologic activation engine 452 may be provided directly to the simulated motor processing 464. The output of the pathologic activation engine 452 may also be combined with activity inputs (e.g., a sleep input 444 and / or an activity input 448), which define a simulated amount of activity for the virtual patient 412. A virtual alpha-beta 456 may receive the output of the pathologic activation engine 452 combined with the activity inputs and then produce a feedback that is delivered back to the device 404 through another medium current spread 460. The feedback provided from the simulated alpha-beta 456 may include a simulated local field potential, or alpha-beta Local Field Potential (LFP), which is a measure of brain activity produced by the virtual patient 412. Like other settings 416, the output of the LEDD 440, the amount of sleep 444, and the amount of activity 448 experienced by the virtual patient 412 may be adjustable by a user via the interface 408.

[0135] The simulated motor processing unit 464 and simulated nearby structures 468 may produce patient outputs 472 that are made visible via the interface 408. In some embodiments, the simulated motor processing unit 464 may be configured to output simulated motor outputs of the virtual patient 412 (e.g., degrees or amounts of Bradykinesia (tremors) and / or Dyskinesia). The simulated nearby structures 468 may be configured to output simulated side effects of the virtual patient 412. As can be appreciated, the simulated nearby structures 468 may produce outputs based directly on the simulated spread of stimulation current to the simulated nearby structures 468 whereas the simulated motor processing unit 464 may produce outputs based on the simulated basal ganglia 436. While patient outputs 472 will primarily be described as being presented in a visual manner via an interface 408, it should be appreciated that patient outputs 472 may also be stored in patient logs 428 or other historical records of the virtual patient 412 responses to particular inputs generated by the device 404.

[0136] In some embodiments, feedback produced by the virtual patient 412 (e.g., the simulated alpha-beta LFP) may be delivered to the device 404. Upon receiving the feedback from the virtual patient 412, the device 404 may utilize the sense and process unit 424 to adjust one or more outputs of the stimulation engine 420 in the event that the system is simulating a closed-loop feedback system between the device 404 and virtual patient 412.

[0137] As noted above, one or more logs 428 may be used to store historical information related to simulations generated with the device 404 as well as patient outputs 472 and / or patient feedback associated therewith. In other words, as simulations or scenarios are executed on the virtual patient412, the results of such simulations and / or scenarios may be stored in logs 428 for later reference and / or to provide training data for future models.

[0138] Referring now to Figs. 5-8, various methods will be described in accordance with at least some embodiments of the present disclosure. The various methods depicted and described herein may have steps or processes that are interchangeable with one another. Similarly, various methods may be combined and the order of steps or processes therein may be changed.

[0139] Referring initially to Fig. 5, a first method 500 will be described in accordance with at least some embodiments of the present disclosure. The method 500 begins by receiving, at a stimulation device model 114, one or more inputs from a user that define a simulation scenario to execute (step 504). The input(s) may be received from the user within the simulation interface 126 and may include inputs defining a type of patient model 112, a type of stimulation device model 114, parameters for a stimulation program to simulate, a selection of a particular scenario to simulate, and / or a selection of open-loop versus closed-loop configuration to implement during the simulation.

[0140] Based on the simulation scenario selected, the stimulation device model 114 may generate one or more simulated electrical signals (step 508). The simulated electrical signal(s) may be generated according to the type of stimulation device model 114 selected and according to stimulation parameters selected.

[0141] The stimulation device model 114 may then provide the simulated electrical signal(s) to a patient model 112 or virtual patient 412 as input thereto (step 512). The simulated electrical signal(s) may be delivered to the patient model 112 as one or more input functions, as code, or as instructions. Alternatively or additionally, the simulated electrical signal(s) may be delivered to the patient model 112 via a communication interface in the form of a signal having characteristics of an actual signal (e.g., with an amplitude, waveform, duty cycle, etc.).

[0142] The method 500 may further include receiving a feedback signal from the patient model 112 (step 516). The feedback signal may be generated by the patient model 112 in response to the patient model 112 processing the simulated electrical signal(s) provided thereto. The feedback may include an alpha-beta LFP or the like. The feedback may also include other outputs of the patient model 112, such as specific patient feedback reported by the patient model 112.

[0143] Information describing the feedback received from the patient model 112 may then be displayed along with information describing the simulated electrical signal(s) (step 520). In some embodiments, the simulated electrical signal(s) and the feedback from the patient model 112 may be displayed via a simulation interface 126.

[0144] The method 500 may include a step of enabling changes to be made to the patient model 112 and / or the stimulation device model 114 (step 524). The changes may be defined based on user input received at the simulation interface 126.

[0145] Referring now to Fig. 6, a second method 600 will be described in accordance with at least some embodiments of the present disclosure. The method 600 may include a method of operating a patient model 112 and / or virtual patient 412. According to at least some embodiments, the method 600 begins when an input is received at a patient model 112 (step 604). The input may comprise one or more stimulation signals, which may correspond to simulated stimulation signals generated by a stimulation device model 114 or to actual stimulation signals generated by a physical device (e.g., the stimulation device 132 and / or stimulation delivery device 138). In some embodiments, the input received at the patient model 112 and / or virtual patient 412 may define a simulation scenario to implement or simulated stimulation signals generated by the stimulation device model 114 according to a simulation scenario.

[0146] The input is then passed to one or more processing components of the patient model 112, which process the simulated stimulation signal(s) (step 608). As a non-limiting example, the input may be provided to one or more of a pathologic activation unit 452, a virtual basal ganglia 436, a simulated motor processing unit 464, and / or a simulated nearby structures 468.

[0147] As the components of the patient model 112 process the input, the patient model 112 may generate one or more outputs (step 612). The one or more outputs may include a Bradykinesia (tremor) output, a Dyskinesia output, a side effects output, an alpha-beta LFP output, or the like.

[0148] The method 400 may continue by generating feedback (e.g., the alpha-beta LFP) and delivering the feedback to a stimulation device model (step 616). The stimulation device model may utilize the feedback to adjust the simulated stimulation signal. The method may also include delivering one or more patient outputs 472 to a simulation interface 126 (step 620). The output(s) may be visually displayed in a GUI and the simulation interface 126 may change over time as the output(s) change.

[0149] As outputs of the patient model 112 are delivered to a user of the simulation interface 126, the method 600 may continue by enabling the user to adjust the simulation (step 624). Specifically, and without limitation, the simulation interface 126 presented to the user may enable the user to create changes to the patient model 112, the stimulation device 132, 138, and / or the stimulation device model 114. The changes may be used to change the simulation scenario (e.g., the scenario data 118), which may impact the inputs provided to the patient model 112 and / or behaviors of the various models.

[0150] Referring now to Fig. 7, a third method 700 will be described in accordance with at least some embodiments of the present disclosure. The method 700 may have multiple similar steps or processes as compared to other methods depicted and described herein. The method 700 begins with the implementation of an in-office simulation scenario (step 704). An in-office simulation may represent a real-time office visit in a more efficient manner than is required for an actual patient to go through an actual office visit. In some embodiments, a patient-specific patient model 112 or virtual patient 412 may be subjected to up to twenty different scenarios of different in-office simulations in less than a second. In this way, therapeutic treatments for a patient can be simulated without requiring the physical presence of the patient.

[0151] As different in-office scenarios are simulated, a user may adjust settings and observe different patient reactions to the various settings (step 708). In other words, the patient model 112 and / or virtual patient 412 may be subjected to different simulation scenarios as the settings are adjusted. Typical in-office activities that may be simulated include, without limitation, adjusting settings and observing patient reactions, streaming alpha-beta signal, patient walks around the office, and patient taking a medication and waiting for a wash-in.

[0152] As the different in-office simulations are implemented, the outcomes (e.g., patient outputs 472 and / or feedback) may be stored in connection with each simulation (step 712). The outcomes may also be analyzed by the user (e.g., clinician implementing the simulations) (step 716). Based on the analysis of the outcomes, the clinician may select or determine actual settings to be used for a therapeutic treatment of a patient (step 720). In some embodiments, the settings for the patient may be include stimulation therapies and / or medication therapies.

[0153] Referring now to Fig. 8, a fourth method 800 will be described in accordance with at least some embodiments of the present disclosure. The method 800 may have multiple similar steps or processes as compared to other methods depicted and described herein. The method 800 begins with the implementation of an at-home simulation scenario (step 804). An at-home simulation may represent an accelerated span of time and activities experienced by a patient at home. In some embodiments, a patient-specific patient model 112 and / or virtual patient 412 may be subjected to a simulation in less than five seconds, where the simulation represents activities the patient would experience over the course of a week or a month. In this way, therapeutic treatments for a patient over an extended period of time can be simulated without requiring the entire amount of time from the patient. Examples of activities that may be simulated to represent an elapsed week or month include, without limitation, medication cycles, circadian cycles, and / or activities (e.g., walking, stretching, resting, etc.).

[0154] As different at-home scenarios are simulated, a user may adjust settings and observe different patient reactions to the various settings (step 808). In other words, the patient model 112 and / or virtual patient 412 may be subjected to different simulation scenarios as the settings are adjusted.

[0155] As the different at-home simulations are implemented, the outcomes (e.g., patient outputs 472 and / or feedback) may be stored in connection with each simulation (step 812). The outcomes may also be analyzed by the user (e.g., clinician implementing the simulations) (step 816). Based on the analysis of the outcomes, the clinician may select or determine actual settings to be used for a therapeutic treatment of a patient (step 820). In some embodiments, the settings for the patient may be include stimulation therapies and / or medication therapies.

[0156] As can be seen in Fig. 9, various patient scenarios can be customized and simulated to generate an extended graph showing various outcomes according to different simulation scenarios. In particular, Fig. 9 illustrates how each simulation scenario can be customized according to one or more of: an effect of stimulation, therapeutic ranges, effects of medication, and / or the presence of other side effects. In some embodiments, the various simulations can help identify the effects of medication on a therapeutic treatment. In some embodiments, a therapeutic range with medication 904 and without medication 908 can be simulated and compared to one another. The simulation of each scenario (e.g., with medication 904 and without medication 908) can help a clinician determine whether and to what extent medications should be given to a patient and / or how much electrical stimulation can be provided to the patient based on the medications provided thereto. Indeed, the area depicted between the tremor reaction, Dyskinesia reaction, and other side effects reaction (e.g., the area shown in white) may represent a therapeutic window available to a clinician to help a patient substantially avoid tremors, Dynkinesia, and other side effects. Stimulation device settings may differ based on whether or not the clinician also prescribes a medication for the patient. Such differences can be identified and visualized with the assistance of the simulation tool as depicted and described herein.

[0157] In some embodiments, the patient scenarios can be customized in an attempt to identify boundaries or limits associated with certain patient scenarios and therapies that should not be applied to an actual patient. For instance, as shown in Fig. 9, a beta source location [0,2.5,0] can be identified as the location where the pathology activation N(t) falls into Dyskinesia without the use of medications. As other examples, the testing of patient scenarios can help identify one or more of: a strength of beta at the source; beta decrease as a function of current; thresholds of onset and offset oftremor as well as other side effects; thresholds of Dyskinesia; drug effects on beta at the source (full dose); and a minimum beta (e.g., a threshold below which the beta drive is not able to fall).

[0158] Referring now to Figs. 10A through 10AG, a number of screen shots of a GUI that may be presented to a user (e.g., a clinician) as part of a simulation interface 126 will be depicted and described herein. Such GUI elements and the arrangement thereof are provided for purposes of explanation and should not be construed as being limited to the particular arrangement shown.Indeed other GUI layouts and configurations can be used without departing from the scope of the present disclosure. It should be appreciated that any of the methods depicted and described herein can be performed, in whole or in part, through the use of the simulation interface 126 and the GUI elements presented thereby.

[0159] A user may be presented, via the simulation interface 126, with a home screen (Fig. 10A), which provides the user with options for taking lessons to utilize the patient simulator, optimization tools, a stimulation sandbox (e.g., aDBS playground), as well as other resources such as simulation tests and support assets.

[0160] Upon selecting the optimization tool or stimulation sandbox, the user may be presented with a user interface (Fig. 10B) including instructions for use, a tab supporting acute symptoms, a tab supporting chronic symptoms, an assessment window, a patient visualization window (e.g., to view patient outputs 472 and a current medication state applied to the virtual patient), as well as a simulation window (e.g., to view outputs of the device 404 and / or to view other patient outputs 472 or feedback). The simulation window may also include a toggle switch to allow the user to switch between a sense only simulation and an adaptive therapy simulation.

[0161] The simulation window is shown to include one or more control boxes to control an amplitude of stimulation applied to the virtual patient as well as control boxes to set and / or adjust upper and lower UFP thresholds. When the user selects the start streaming icon in the simulation window (Fig. 10B), the simulation may begin and UFP power as well as stimulation amplitude (if any) may be generated according to the simulation settings 416 (Fig. 10C). The user may be allowed to adjust the amplitude of the simulated stimulation using a stimulation slider control element. As the simulated stimulation amplitude is adjusted (Fig. 10D), for example from 1.8mA to 2.3mA, the device 404 may provide the new simulated stimulation to the virtual patient. As the virtual patient receives the adjusted simulated stimulation, the virtual patient may exhibit discomfort (Fig. 10E). In addition to the animation of the patient, a status bar 1004 may be presented.

[0162] The status bar 1004 may provide a visual representation of the acute, cumulative effect of therapy for the patient. Programming changes of the stimulation amplitude (mA) are combined withthe effects of medication (On meds, Reduced meds, and Off meds) and are displayed in a bar format (e.g., the status bar 1004) to help the user understand at a glance if the patient is Under treated, Well treated, or Over treated in times of rest or activity. In addition to showing signs of discomfort, the reason for the discomfort (e.g., Dyskinesia in right leg) may be explained within the patient visualization window.

[0163] Further control of the simulated stimulation with the stimulation slider control element (Fig. 10F) may again adjust the simulated stimulation applied to the virtual patient, which may result in the virtual patient reverting back to a state of comfort (e.g., “All is well”). If the simulated stimulation is further decreased, however, then the virtual patient may enter a different state of discomfort (Fig. 10G). For example, the virtual patient may experience Bradykinesia or some other symptom associated with not receiving enough of a stimulation from the device 404.

[0164] Within the simulation environment, the user may select an option to give the virtual patient a medication (Fig. 10H), which may cause the virtual patient to simulate a medication wash-in over the course of 30 minutes. While the simulation may represent a period of 30 minutes following the patient taking a medication, the simulation may only require a couple of seconds to display the virtual patient’s response to receiving the medication (Fig. 101).

[0165] Within the simulation environment, the user may also select an option to ask the virtual patient to walk around the office for a minute (Fig. 10J). Selecting this option may cause the virtual patient to undergo physical activity 448, which may impact outputs of the simulated basal ganglia 436. Enabling a user to simulate different events or scenarios can help the user test different types of treatments on the virtual patient before applying the same treatment(s) to a physical patient.

[0166] In addition to facilitating the simulation of smaller events, such as having the virtual patient walk around the office or take medication, the simulation environment may also allow the virtual patient to experience a week of life using a prescribed therapy (e.g., stimulations and / or medications) (Fig. 10K). When a longer-term timeline is completed (Fig. 10L), the simulation environment may present the user with an output of the timeline (Fig. 10M). The output of the timeline may include a summary of simulated stimulations applied to the virtual patient over time as well as summary statistics associated with the therapy. In the example of Fig. 10M, the virtual patient spent no time in Dyskinesia and had no other side effects in response to receiving 2.5 hours / day of simulated treatment with stimulation amplitudes that were around 1.3 mA.

[0167] If the user selects to simulate an adaptive therapy (Fig. 10N), then the simulated therapy may be adjusted to present the adaptive therapy (e.g., a closed loop therapy) (Fig. 10O). The user may switch between the sense only therapy and the adaptive therapy by selecting the toggle switchin the simulation window. As the adaptive therapy is simulated, the user may select to test the settings by sending the virtual patient home for a virtual week (Fig. 10P). A timeline may be calculated to simulate the virtual patient’s week of life when treated with the adaptive therapy according to the settings (Fig. 10Q). The timeline output by the simulation environment may show patient responses to various therapies applied thereto (e.g., the simulated stimulation as well as the patient responses) in addition to a summary of the week for the virtual patient (Fig. 10R). A summary of the timeline may also be calculated (Fig. IOS) resulting in a further summary of the virtual patient’s response to the therapy applied over the week (Fig. 10T). The summary report may include a summary of therapy modifications, virtual patient reactions, and a summary of improvements, if any.

[0168] Referring back to the home screen (Fig. 10U), the user may also select an option to take lessons, which are designed to help the user learn the features provided by the simulation environment as well as identify approaches for constructing an effective therapy for a patient.Selection of the lesson icon may take the user to a menu of lessons (Fig. 10V). The menu of lessons may include a presentation of post-implant programming lessons, DBS activation lessons using a single threshold, DBS activation lessons using two thresholds, follow-ups for dual-threshold therapies, and follow-ups for single-threshold therapies. Selection of the post-implant programming lesson may present a patient demonstration video (Fig. 10W) in which a demo patient is shown to have a particular device implanted therein and in which other simulation settings are shown.

[0169] The patient demonstration video may also include instructions for an electrode setup (Fig.10X) as well as a tutorial for configuring electrodes to track a patient’s LFP signals (Fig. 10Y). The demonstration video may further present the user with instructions for toggling between sense only and adaptive therapies (Fig. 10Z) and then instructions for increasing an amplitude of the simulated stimulation as part of finding a minimum amplitude that provides a therapeutic benefit (Fig. 10AA). The demonstration video may also present instructions and demonstrations for increasing the simulation amplitude to find the maximum amplitude with a therapeutic benefit, while avoid side effects (Fig. 10AB). In addition, the demonstration video may present a video to help the user capture thresholds for the electrode(s) of the device (Fig. 10AC). After an upper LFP threshold is identified, the demonstration video may present instructions and demonstrations for increasing the stimulation to a maximum amplitude of therapeutic benefit to capture a lower LFP threshold (Fig.10AD).

[0170] With thresholds captured, the demonstration video may present the user with instructions for capturing or adjusting one or both of the upper LFP threshold and lower LFP threshold (Fig.10AE). After upper and lower LFP thresholds are determined, the demonstration video may present the user with instructions for controlling or modifying the stimulation amplitude within the thresholds (Fig. 10AF).

[0171] As noted above, the menu of lessons may also present summaries for each of the lessons available within the simulation environment (Fig. 10AG). As the user reviews or interacts with a particular lesson, then the specific parts of the workflow may be identified as being completed (e.g., with a check mark).

[0172] The following examples are described herein.

[0173] Example 1. A simulation system, comprising one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs are executed by the processors, the one or more programs including instructions for: generating a first model representing a patient; generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.

[0174] Example 2. The simulation system of example 1, wherein the simulation input comprises one or more stimulation waveforms from the second model.

[0175] Example 3. The stimulation system of example 1 or 2, wherein simulation outputs from the first model comprise one or more of: an indication of Dyskinesia; an indication of Bradykinesia; and a side effect.

[0176] Example 4. The simulation system of any preceding example, wherein the simulation inputs comprise a preprogrammed input, user input, or both.

[0177] Example 5. The simulation system of any preceding example, wherein the simulation outputs from the first model comprise an alpha-beta Local Field Potential (LFP).

[0178] Example 6. The simulation system of example 5, wherein the alpha-beta LFP is provided as a feedback to the second model.

[0179] Example 7. The simulation system of any preceding example, wherein an interaction between the first model and the second model is adjustable by switching between an open-loop simulation configuration and a closed-loop simulation configuration.

[0180] Example 8. The simulation system of any preceding example, wherein the one or more programs further include instructions for: simulating the first model responding to medication; and updating the one or more simulation outputs to reflect the first model responding to the medication.

[0181] Example 9. The simulation system of any preceding example, wherein the one or more programs further include instructions for: simulating the first model performing a particular activity; and updating the one or more simulation outputs to reflect the first model performing the particular activity.

[0182] Example 10. The simulation system of any preceding example, wherein the one or more programs further include instructions for: simulating the first model experiencing an amount of time under the stimulation therapy; and summarizing a response of the first model experiencing the amount of time under the stimulation therapy.

[0183] Example 11. The simulation system of any preceding example, wherein the first model comprises an Artificial Intelligence (Al) model that processes at least one input received at the first model.

[0184] Example 12. The simulation system of any preceding example, wherein the second model comprises one or more Artificial Intelligence (Al) models that process the one or more simulated electrical signals.

[0185] Example 13. The simulation system of example 12, wherein the one or more Al models comprises a plurality of models interconnected with one another.

[0186] Example 14. The simulation system of any preceding example, further comprising: a user interface that enables a user to interact with at least one of the first model and the second model and to adjust an operating parameter thereof.

[0187] Example 15. The simulation system of example 14, wherein the user interface provides a display of the one or more simulated electrical signals.

[0188] Example 16. The simulation system of example 15, wherein the user interface further displays a reaction produced by the first model in response to processing the one or more simulated electrical signals.

[0189] Example 17. The simulation system of any preceding example, wherein the first model is stored on one or more servers and made available to a user device via a communication network.

[0190] Example 18. The simulation system of any preceding example, wherein the second model is stored on the one or more servers and made available to the user device via the communication network.

[0191] Example 19. The simulation system of any preceding example, wherein at least one of the first model and the second model are provided on a user device.

[0192] Example 20. A system, comprising: a processor; and memory storing a first model representing a patient, wherein the first model enables the processor to: receive an input from asecond model representing a stimulation device, wherein the input comprises one or more simulated signals generated in accordance with a simulation scenario; pass the input through one or more processing components; and produce one or more outputs representing a simulated patient response to the input.

[0193] Example 21. The system of example 20, wherein the first model comprises one of a plurality of patient models and wherein the first model is selected based on a user input.

[0194] Example 22. The system of examples 20 or 21, wherein the first model comprises a patientspecific model.

[0195] Example 23. The system of any of examples 20-22, wherein the one or more processing components comprises a simulated basal ganglia component that models a basal ganglia response to the one or more simulated signals.

[0196] Example 24. The system of any of examples 20-23, wherein the simulation scenario comprises an in-office scenario.

[0197] Example 25. The system of any of examples 20-24, wherein the simulation scenario comprises an at-home scenario.

[0198] Example 26. The system of any of examples 20-25, wherein the one or more processing components comprises a pathologic activation engine.

[0199] Example 27. The system of any of examples 20-26, wherein the processor is separated from the memory by a communication network.

[0200] Example 28. The system of any of examples 20-26, wherein the processor and memory are provided on a common device.

[0201] Example 29. The system of any of examples 20-28, wherein the one or more outputs comprises an alpha-beta Local Field Potential (LFP).

[0202] Example 30. The system of any of examples 20-29, wherein the one or more outputs comprises a side effect.

[0203] Example 31. The system of any of examples 20-30, wherein the one or more outputs comprises a response waveform.

[0204] Example 32. The system of any of the preceding examples 20-31, wherein the first model is configurable to operate in an open-loop simulation state where the one or more outputs are provided to a user interface and wherein the first model is further configurable to operate in a closed-loop simulation state where the one or more outputs are also provided back to the second model.

[0205] Example 33. A method, comprising: receiving, at a patient model, an input comprising one or more simulated signals generated in accordance with a simulation scenario; passing the inputthrough one or more processing components of the patient model that simulate a patient response to the input; and producing one or more outputs with the patient model based on the one or more processing components processing the input.

[0206] Example 34. The method of example 33, wherein the one or more outputs are provided as feedback from the patient model to a second model representing a stimulation device.

[0207] Example 35. The method of example 33, wherein the input is received from a physical stimulation device.

[0208] Example 36. The method of example 33, wherein the input is received from a model representing a stimulation device.

[0209] Example 37. The method of example 33, further comprising: enabling the patient model to switch between an open-loop simulation configuration where the one or more outputs are provided to a user interface and a closed-loop simulation configuration where the one or more outputs are also provided back to a second model representing a stimulation device.

[0210] The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the disclosure are grouped together in one or more aspects, embodiments, and / or configurations for the purpose of streamlining the disclosure. The features of the aspects, embodiments, and / or configurations of the disclosure may be combined in alternate aspects, embodiments, and / or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects he in less than all features of a single foregoing disclosed aspect, embodiment, and / or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0211] Moreover, though the foregoing has included description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, embodiments, and / or configurations to the extent permitted, including alternate, interchangeable and / or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and / or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

Claims

CLAIMSWhat is claimed is:

1. A simulation system, comprising one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs are executed by the processors, the one or more programs including instructions for:generating a first model representing a patient;generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.

2. The simulation system of claim 1 , wherein the simulation input comprises one or more stimulation waveforms from the second model.

3. The stimulation system of claims 1 or 2, wherein simulation outputs from the first model comprise one or more of:an indication of Dyskinesia;an indication of Bradykinesia; anda side effect.

4. The simulation system of any preceding claim, wherein the simulation inputs comprise a preprogrammed input, user input, or both.

5. The simulation system of any preceding claim, wherein the simulation outputs from the first model comprise an alpha-beta Local Field Potential (LFP).

6. The simulation system of claim 5, wherein the alpha-beta LFP is provided as a feedback to the second model.

7. The simulation system of any preceding claim, wherein an interaction between the first model and the second model is adjustable by switching between an open-loop simulation configuration and a closed-loop simulation configuration.

8. The simulation system of any preceding claim, wherein the one or more programs further include instructions for:simulating the first model responding to medication; andupdating the one or more simulation outputs to reflect the first model responding to the medication.

9. The simulation system of any preceding claim, wherein the one or more programs further include instructions for:simulating the first model performing a particular activity; andupdating the one or more simulation outputs to reflect the first model performing the particular activity.

10. The simulation system of any preceding claim, wherein the one or more programs further include instructions for:simulating the first model experiencing an amount of time under the stimulation therapy; and summarizing a response of the first model experiencing the amount of time under the stimulation therapy.

11. The simulation system of any preceding claim, further comprising:a user interface that enables a user to interact with at least one of the first model and the second model and to adjust an operating parameter thereof.

12. The simulation system of claim 11, wherein the user interface provides a display of the one or more simulated electrical signals.

13. The simulation system of claim 12, wherein the user interface further displays a reaction produced by the first model in response to processing the one or more simulated electrical signals.

14. The simulation system of any preceding claim, wherein the first model is stored on one or more servers and made available to a user device via a communication network.

15. The simulation system of any preceding claim, wherein the second model is stored on the one or more servers and made available to the user device via the communication network.

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