Systems and methods for spinal cord magnetostimulation

Abstract

Systems and methods directed to controlling a magnetic spinal cord stimulation system and model-based stimulation plan are provided. A magnetic spinal cord stimulation system includes at least one magnetic coil and a power source. The power source is operatively connected to the at least one magnetic coil to deliver an electric current to the at least one magnetic coil. A controller receives a plurality of input parameters. The controller utilizes an electromagnetic and neurophysiological prediction model to create a stimulation plan based on the plurality of input parameters. The control controls the power source and the model-based stimulation plan to deliver the electrical current through the at least one magnetic coil. Time-varying magnetic fields are created that induce stimulating electric fields within spinal cord tissue at a depth to directly stimulate motor pathways in the spinal cord.

Description

125141.04927. MGH2024-128-02SYSTEMS AND METHODS FOR SPINAL CORD MAGNETOSTIMULATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 722,750, filed on November 20th, 2024, and entitled “System for and Method of Spinal Cord Magnetostimulation.”STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under award numbers 5R01EB028250-04 and 5R01EB033853-02 (agreements 2019A010010 and 2022A015383) from the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] Spinal cord stimulation (SCS) is a widespread clinical therapy for chronic pain and other disorders that uses surgically implanted electrode cuffs or arrays to stimulate neurons in the spinal cord or branching roots. In some examples, SCS can be used to improve the speed and quality of motor neurorehabilitation in patients with spinal cord injury (SCI) when used as an adjuvant therapy to conventional task-specific training or physical therapy (PT).

[0004] Variants of SCS for motor rehabilitation include SCS-epidural and SCS- transcutaneous. SCS-epidural utilizes the implantation of an electrode array in the epidural space of the spinal column, which is a space containing lymphatic fluid, fat and the nerve roots located on the dorsal side of the cord. SCS-epidural is highly invasive and, because of its location on the dorsal side of the cord, tends to mainly activate afferent dorsal nerves pathways in the cord that transport sensory information from the body to the brain. This can be problematic because motor rehabilitation requires stimulation of the efferent motor nerves in the cord, which are predominantly located in the ventral area.

[0005] Surgical implantation of SCS electrodes is difficult to justify for rehabilitation because this process typically lasts a couple of months and electrodes would have to be removed at the end, thus increasing cost as well as risk to the patient. Because of that, a variant of SCS called SCS-transcutaneous may be used, which uses surface electrodes placed on the skin of the patient to attempt stimulating nerves in the cord during PT. This technique is non-invasive, cheap and easy to deploy; however, its clinical efficacy is unclear. First, the electric fields (E-fields) created by the transcutaneous electrodes decrease very rapidly with125141.04927. MGH2024-128-02 the distance to the electrode, thus resulting (like SCS-epidural) in predominantly dorsal stimulation of the nerves in the cord.

[0006] Moreover, since electrodes are placed in the skin of the patient, they can activate the pain receptors in the skin as well as the back muscle, which, at the high voltages required to reach the motor nerves located deep in the cord, can be very painful. Finally, SCS- transcutaneous is performed ‘blind’ to the specifics of the patient because there is no characterization of the stimulating E-fields and currents, which are needed to optimize the treatment and obtain consistent outcomes for a specific patient. At best, a clinician selects stimulation levels and electrode placements based on general assumptions about the broad patient population.

[0007] Magnetic spinal cord stimulation is an emerging alternative technology that uses external coils placed close to the skin to create time-varying magnetic fields that in turn induce stimulating E-fields deep in the body (Faraday induction). This method may be configured for direct and non-invasive stimulation of motor pathways in the ventral spinal column while avoiding painful stimulation of muscle and skin pain receptors. Some have used transcranial magnetic stimulation (TMS) hardware to apply magnetically induced E-fields to the spinal cord. However, most studies have reported difficulties in directly stimulating the spinal cord because of its deeper position than the stimulation target for which TMS hardware was developed and optimized (the cortex). Magnetic spinal cord stimulation may use greater power levels and larger coils with deeper penetration than those used in TMS, which raises questions about cardiac stimulation, secondary stimulation of critical peripheral nerves such as the vagus or paravertebral sympathetic nerves, and patient tolerability, pain, and muscle contractions.

[0008] Therefore, there is a need for improved systems and methods for magnetostimulation of the spinal cord that safely increase efficacy.SUMMARY OF THE DISCLOSURE

[0009] The present disclosure provides systems and methods that overcome the aforementioned drawbacks by providing systems and methods for a magnetic spinal cord stimulator using a model-based stimulation plan. In one non-limiting example, a model-based stimulation plan can be created using an electromagnetic and neurophysiological prediction model. Generic, detailed body models can be used as well as patient-specific model to guide the treatment.

[0010] In accordance with one aspect of the disclosure, a magnetic spinal cord stimulation system can include at least one magnetic coil configured to be positioned on skin125141.04927. MGH2024-128-02 proximate to a spinal cord of a patient and a power source. The power source can be operatively connected to the at least one magnetic coil to deliver an electric current to the at least one magnetic coil. A controller can receive a plurality of input parameters including at least one of: a Faraday induction value, one or more magnetic coil dimensions, a stimulation amplitude, or a stimulation phase. The controller can utilize a electromagnetic and neurophysiological prediction model to create a stimulation plan based on the plurality of input parameters. The control can control the power source and the model-based stimulation plan to deliver the electrical current through the at least one magnetic coil. Time-varying magnetic fields can be created that induce stimulating electric fields within spinal cord tissue at a depth to directly stimulate motor pathways in the spinal cord.

[0011] In accordance with another aspect of the disclosure, a method for magnetic spinal cord stimulation can include receiving a plurality of input parameters including at least one of: a Faraday induction value, one or more magnetic coil dimensions, a stimulation amplitude, or a stimulation phase. An electromagnetic and neurophysiological prediction model can be utilized to create a stimulation plan based on the plurality of input parameters. A power source of a magnetic spinal cord stimulator and the model-based stimulation plan can be controlled to deliver an electrical current through the at least one magnetic coil. Timevarying magnetic fields can be created that induce stimulating electric fields within spinal cord tissue at a depth to directly stimulate motor pathways in a spinal cord.

[0012] The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a preferred embodiment. This embodiment does not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The following drawings are provided to help illustrate various features of nonlimiting examples of the disclosure, and are not intended to limit the scope of the disclosure or exclude alternative implementations.

[0014] FIG. l is a flowchart setting forth steps for a process of controlling a magnetic spinal cord stimulation system, in accordance with the present disclosure.

[0015] FIG. 2 is a schematic block diagram of an example system for magnetic spinal cord stimulation.125141.04927. MGH2024-128-02

[0016] FIG. 3 is a set of correlated graphics illustrating example simulation results generated by the electromagnetic and neurophysiological prediction model for a stimulation in accordance with the present disclosure.

[0017] FIG. 4 is a further set of correlated graphics illustrating example simulation results generated by the electromagnetic and neurophysiological prediction model for stimulation in accordance with the present disclosure.

[0018] FIG. 5 is a block diagram of an example system for creating a model-based stimulation plan for magnetic spinal cord stimulation.DETAILED DESCRIPTION

[0019] In magnetic resonance imaging, peripheral nerve stimulation (PNS) is a substantial problem that can undermine the ability to acquire clinically-useful images. As such, many efforts have been made to create systems and methods that stop magnetic nerve stimulation in the MRI system environment.

[0020] In one non-limiting example, systems and methods have been developed for designing MRI coils, which may be gradient coils for use in an MRI system or electrodes or electrode arrays or coil winding patterns for use with electromagnetic stimulation systems, with an intrinsic capability to control PNS, either to reduce PNS in MRI or create it in pain and other electromagnetic stimulation treatments. As described in US Patent No. 12,066,511, a model is provided that can be used to design coils and otherwise control against magnetic nerve stimulation.

[0021] Contrary to the uses of such models such as described above to control against nerve stimulation, the present disclosure, counterintuitively, can use such models for developing and controlling magnetic stimulation of patients. That is, while such models were used to minimize magnetic stimulation, the present disclosure provides systems and methods to utilize a model to determine, provide, and / or control magnetic stimulation in a clinically- desirable application, such as spinal stimulation, for example. In accordance with one nonlimiting aspect, systems and methods are provided for modeling responses of nerve fibers to select, control, and / or optimize dose metrics of magnetic spinal cord stimulation (mSCS).

[0022] As will be described, the systems and methods of the present disclosure can be used with or for magnetic stimulation. As one non-limiting example of magnetic stimulation, the systems and methods of the present disclosure may be used with mSCS. In particular, mSCS uses external coils placed close to the skin to create time-varying magnetic fields that in turn induce stimulating E-fields deep in the body (Faraday induction). The human body is125141.04927. MGH2024-128-02 transparent to magnetic fields, therefore, mSCS creates higher E-fields at depth than transcutaneous current conduction and thus can stimulate motor pathways in the ventral spinal column more efficiently. In addition, mSCS largely avoids stimulation of skin pain receptors. Moreover, while TMS has been used to evaluate spinal cord injuries (SCI), this is significantly different from the invention disclosed herein. In some examples, TMS applied to SCI involves stimulation of the brain using TMS and taking measurements of the speed and amplitude of neural signals transmitted via the spinal cord to the body. In contrast, the systems and methods described herein use direct stimulation of the spinal cord, such as, for example, the ventral portion of the spinal cord, which may be used not only for evaluation of SCI but also for therapeutic purposes.

[0023] Described herein is an adapted electromagnetic and neurophysiological prediction model for use in mSCS. In some examples, the model may be used to predict quantitative stimulation thresholds. Moreover, the model is further adapted for optimization of mSCS coils beyond conventional TMS coil designs, including single and double loops, H-coils, multi-coil arrays, and more complex, fully optimized coil wire patterns designed using the boundary element method-stream function approach (BEM-SF). However, it should be noted that the systems and methods described herein may be applicable to all mSCS devices, including, but not limited to, TMS coils, loops, and loop arrays. Any device creating an electricfield in the body by time-varying magnetic induction can be modeled using the approach described herein.

[0024] In mSCS, an external magnetic coil may be placed above the skin of a patient above nerve roots exits, corresponding to the limb being restored (e.g., C5-T1 for the arms, LILS for the legs). Since there is no contact between the skin and the mSCS device, there is much less pronounced activation of the back muscles and skin pain receptors, compared to SCS- transcutaneous. As a result, mSCS may be less painful, better tolerated, and can be moved around to optimize the treatment in patients with unknown SCI location and extent. An additional benefit of mSCS is that it creates electric fields by the process of Faraday induction, which tends to reach deeper in the body than the conduction electric fields created in SCS- transcutaneous.

[0025] As will be described, mSCS may be used for various applications and treatments and this disclosure is not limited to one of them. For example, such applications may include nerve root palsy, cauda equina syndrome and the rehabilitation of bowel, respiratory and bladder function. Such applications may require stimulation of cord segments from the cerebral to the coccygeal cord, and this disclosure is not limited to a specific cord segment or125141.04927. MGH2024-128-02 application.

[0026] Turning to FIG. 1, a process 100 in accordance with the present disclosure, is illustrated. As described below, a particular implementation can omit some or all illustrated features / steps, may be implemented in some embodiments in a different order, and may not require some illustrated features to be implemented in all embodiments. In some examples, an apparatus (e.g., computing device, server, a magnetic stimulator system, etc.) can be used to perform example process 100. However, it should be appreciated that any suitable apparatus or system for carrying out the operations or features described below may perform process 100.

[0027] In some aspects, the process 100 may begin at process block 105, where one or more input parameters, in accordance with the present disclosure, may be received. For example, the input parameters may be acquired as input data from a computing device, a medical imaging device, a magnetic spinal stimulation device, one or more sensors, or the like. In some examples, the input parameters may correspond to settings of a stimulator of an mSCS system (such as system 200, as described below). For example, the input parameters may include a Faraday induction value, dimensions of one or more magnetic coils of a stimulation device, a stimulation amplitude, a stimulation phase, or the like. In some examples, the input parameters may be manually received from a user, or they may be automatically determined by a connected computer system using the systems and methods described herein. For example, the input parameters may include stimulation levels selected by a clinician based on general assumptions for a broad patient population. Moreover, the input parameters may further include model -based information, such as physical models or scans of the patient’s anatomy. Therefore, a stimulation plan may take into account both the initial stimulation levels and data specific to the patient to create a model-based stimulation plan, as described below with respect to process block 110. The stimulation plan can also be based on generic body models that include correct placement of the coil but a generic description of the anatomy.

[0028] At process block 110, the model -based stimulation plan may be created using a electromagnetic and neurophysiological model. In some examples, the electromagnetic and neurophysiological prediction model may predict stimulation sites and thresholds in mSCS based on the input parameters received at block 105. The model -based stimulation plan may define optimized and / or patient-specific parameters to use with a mSCS device during stimulation, such as a coil design, stimulus waveform characteristics, position, configuration as well as the stimulation parameters such as waveforms, frequencies, repetition rates, or the like. In some examples, the patient-specific stimulation plan may further be created using information regarding a condition of the patient. For example, a user may define that a patient125141.04927. MGH2024-128-02 has a spinal cord injury, and specify the location(s) of such injury.

[0029] Moreover, during creation of the patient-specific stimulation plan, the electromagnetic and neurophysiological prediction model can be used to create detailed electromagnetic field simulations using realistic human body models, as well as neurodynamic modeling of the response of the nerves in the spinal cord and branches of the patient to induced E-fields. The body models can be generic or individualized to a specific patient. In some examples, the electromagnetic and neurophysiological prediction model may rely on a physical model of a patient, such as a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, or images obtained from other imaging modality techniques. In further examples, the model-based stimulation plan may further be created based on predicted nerve pathways inside a cord volume, as generated by the electromagnetic and neurophysiological prediction model. For example, the input data may further include information regarding the model-based organization of nerves inside the spinal cord, or mapped using diffusion MRI or other nerve imaging methods. In some examples, one or more tissue classes may be identified automatically or manually by a user. Accordingly, the electromagnetic and neurophysiological prediction may be used to predict electric fields using a number of algorithms, for example finite difference time-domain (FDTD), finite element modeling (FEM), boundary element modeling (BEM), or the like.

[0030] There are several metrics that can be derived using the electromagnetic and neurophysiological prediction model in order to create the model-based stimulation plan. In one example, the induced electric field magnitudes, which can be computed as a function of position (map) or averaged over a spatial region, may be derived. Another example of a computational metric determined by the electromagnetic and neurophysiological prediction model is the projection of electric-fields onto the nerve paths, which also can be reported as a map or averaged over a region. Another example metric derived using the electromagnetic and neurophysiological prediction model is the stimulation threshold for a certain nerve segment. For example, the stimulation threshold may be computed using a neurophysiological model of spinal nerves, for example the Mclntyre-Richardson-Grill model, and the projected E-field inputs. In further example, the electromagnetic and neurophysiological prediction model may further determine the inverse of the stimulation threshold (i.e., the stimulation propensity). Another computational metric of interest is the volume of tissue activated (VTA), which is computed as the number of nerve paths stimulated in a given region for a given coil current amplitude.

[0031] At process block 115, a stimulation and / or the model -based stimulation plan is125141.04927. MGH2024-128-02 controlled. In some examples, results of the electromagnetic and neurophysiological prediction model may cause stimulation to automatically be administered based on the model-based stimulation plan, corresponding to the input parameters received at block 105. Alternatively, or additionally, a user (e.g., a medical practitioner) may manually adjust or administer the magnetic stimulation to the patient based on an output of the model-based stimulation plan. Moreover, at block 115, controlling the model -based stimulation plan may include updating a pre-existing model-based stimulation plan, or stimulation levels initially entered by a clinician. For example, parameters specified in the plan may be changed based on the input parameters received at block 105.

[0032] In some examples, a report may further be output or generated. For example, the report can include one or more recommendations corresponding to the parameters used to operate the stimulator, such as a recommended electrode or magnetic loop design, and / or recommended stimulation parameters based on predicted electric fields in the body.

[0033] Referring to FIG. 2, an example magnetic spinal cord stimulation (mSCS) system 200 is illustrated. In the mSCS system 200, a medical patient 230 may be monitored and administered magnetic stimulation using a magnetic stimulation device 224, which may transmit and receive signals over a cable or via a communication network 235 to a computing device 210.

[0034] For clarity, a single block is used to illustrate the magnetic stimulation device 224 shown in FIG. 2. It should be understood that the magnetic stimulation device 224 shown is intended to represent one or more magnetic coils, power sources, sensors, or the like adapted to interact with the patient 230. In some examples, the magnetic stimulation device 224 may include any type of device configured for electrical and / or magnetic stimulation. For example, the magnetic stimulation device 224 may include any number of electrodes, magnetic coils, loop coils, or the like, in various configurations and combinations. Moreover, various combinations of numbers and types of sensors, as mentioned, are suitable for use with the mSCS system 200.

[0035] In some examples, the stimulation device 224 may include one or more magnetic coils, configured to deliver electrical current to a patient by creating time-varying magnetic fields that induce stimulation within the spinal cord tissue at depths that directly stimulate motor pathways in the spinal cord. In one example, the stimulation device 224 includes loop coils, the diameter of which can be tailored based on a target depth. For example, the loop coils may be made larger for reaching deep targets, such as the lumbar spine region. Alternatively, the loop coils may be made smaller for reaching shallower targets, such as those125141.04927. MGH2024-128-02 in the cerebral spine region. In further examples, multiple loop coils can be combined into an array that can be driven so as to selectively stimulate a certain location and depth of the spinal cord. In some examples, the electromagnetic and neurophysiological prediction model 222 and model-based stimulation plan described herein may further be utilized for optimization of such array drive amplitudes and phases requirements, as well as electrode or magnetic loop design, based on predicted electric fields in the body.

[0036] As shown in FIG. 2, a computing device 210 can be configured to control the stimulation device 224. For example, as described above with respect to process 100 of FIG. 1, the computing device 210 may be configured to receive input parameters, which may be processed by processor 214 and stored in memory 220. Moreover, the input parameters may further be provided as inputs to the electromagnetic and neurophysiological prediction model 222, and used to create the model-based stimulation plan. Accordingly, the computing device 210 may control the magnetic stimulation device based on the model -based stimulation plan.

[0037] In some embodiments, the computing device 210 can be any suitable computing device or combination or devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, or the like.

[0038] As shown in FIG. 2, in some embodiments, computing device 210 can include a display 212, a processor 214, one or more inputs 216, one or more communication systems 218, a memory 220, and the electromagnetic and neurophysiological prediction model 222. In some embodiments, processor 214 can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, display 212 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 216 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0039] In some embodiments, communications system(s) 218 can include any suitable hardware, firmware, and / or software for communicating information over communication network 235 and / or any other suitable communication networks. For example, communications system(s) 218 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications system(s) 218 can include hardware, firmware and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0040] In some embodiments, memory 220 can include any suitable storage device or125141.04927. MGH2024-128-02 devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 214 to present content using display 212, to communicate with the magnetic stimulation device 224 via communications system(s) 218, the communication network 235, and so on. Memory 220 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 220 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 220 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 210. In such embodiments, processor 214 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content created by the electromagnetic and neurophysiological prediction model 222.

[0041] In some embodiments, communication network 235 can be any suitable communication network or combination of communication networks. For example, communication network 235 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, communication network 235 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 2 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0042] In some embodiments of the mSCS system shown in FIG. 2, all of the hardware used to create a model-based stimulation plan via the electromagnetic and neurophysiological prediction model 222 and control the magnetic stimulation device 224 are housed within the same housing. In other embodiments, some of the hardware used to create the model-based stimulation plan is housed within a separate housing. In addition, the magnetic stimulation device 224 of certain embodiments includes hardware, software, or both hardware and software, whether in one housing or multiple housings, used to apply the stimulation plan created by the electromagnetic and neurophysiological prediction model 222.

[0043] As mentioned above, in some examples, the electromagnetic and neurophysiological prediction model 222 described herein can be used to create the model-125141.04927. MGH2024-128-02 based stimulation plan, recommend an electrode or magnetic loop design, and / or recommend or change stimulation parameters based on predicted electric fields in the body. FIGS. 3 and 4 illustrate example simulation results generated by the electromagnetic and neurophysiological prediction model 222 for a surface electrode stimulation 305, two commercial TMS coils (an MRi-B91 coil stimulation 310 and Cool D-B80 coil stimulation 315), and a single loop coil stimulation 320. In some examples, the electromagnetic and neurophysiological prediction model 222 may be used to predict electric fields. Column 325 in FIG. 3 illustrates example results of the electric fields prediction. Moreover, in some examples, the electromagnetic and neurophysiological prediction model 222 may further be used to predict normalized electricalfield depth profiles. Column 330 in FIG. 3 illustrates example results of axial depth profiles of the electric fields, plotted on graphs. The electromagnetic and neurophysiological prediction model 222 may further be used to illustrate electric field projections onto models of nerve paths of a spinal cord of a patient. For example, column 335 in FIG. 3 illustrates electric fields plotted along peripheral nerve atlases, including the vagus and spinal nerves.

[0044] FIG. 4 illustrates further example simulation results generated by the electromagnetic and neurophysiological prediction model 222, which may be used for creating the model-based stimulation plan, recommending an electrode or magnetic loop design, and / or recommending or changing stimulation parameters. In particular, FIG. 4 illustrates predicted threshold values when 280 ps pulses are applied, as illustrated in column 405. The electromagnetic and neurophysiological prediction model 222 may further be used to determine a volume of tissue activated (VTA) as a function of stimulus intensity. As illustrated in column 410 of FIG. 4, the VTA versus stimulus intensity may be presented as a recruitment curve. Moreover, the electromagnetic and neurophysiological prediction model 222 may be used to identify recruited nerves in the spinal cord. For example, column 415 of FIG. 4 illustrates example indications of activated nerves corresponding to a VTA value of 50.

[0045] In some examples, the various simulation results and corresponding plots, as described above with respect to FIGS. 3 and 4, may be output on the computing device 210 via the display 212. For example, the display 212 may be used to output a physical rendering of the electric field projections. In some examples, the model -based stimulation plan may automatically be updated based on results of the electromagnetic and neurophysiological prediction model 222. Alternatively, or additionally, a user of the computing device 210 and / or the magnetic stimulation device 224 may manually adjust the parameters administered by the magnetic stimulation device 224 and / or the electrode or magnetic loop design based on the results presented on the display 212.125141.04927. MGH2024-128-02

[0046] Referring now to FIG. 5, an example of a system 500 for creating a model-based stimulation plan for magnetic spinal cord stimulation in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in FIG.5, the computing device 210 can receive one or more types of data (e.g., input parameters) from data source 502. In some embodiments, computing device 210 can run the electromagnetic and neurophysiological prediction model 222, stored either locally on the computing device 210, or remotely on server 504, based on the received input parameters to update the model-based stimulation plan.

[0047] Additionally, or alternatively, in some embodiments, the computing device 210 can communicate information about data received from the data source 502 to a server 504 over a communication network 254, which can execute at least a portion of the electromagnetic and neurophysiological prediction model 222. In such embodiments, the server 504 can return information to the computing device 210 (and / or any other suitable computing device) indicative of an output of the electromagnetic and neurophysiological prediction model 222.

[0048] In some embodiments, computing device 210 and / or server 504 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 210 and / or server 504 can also reconstruct images from the data.

[0049] In some embodiments, data source 502 can be any suitable source of data (e.g., measurement data, reference or simulation magnetic stimulation data, etc.), another computing device (e.g., a server storing image data), and so on. In some embodiments, data source 502 can be local to computing device 210. For example, data source 502 can be incorporated with computing device 210 (e.g., computing device 210 can be configured as part of a device for capturing, scanning, and / or storing images). As another example, data source 502 can be connected to computing device 210 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 502 can be located locally and / or remotely from computing device 210, and can communicate data to computing device 210 (and / or server 504) via a communication network (e.g., communication network 254).

[0050] In some examples, the electromagnetic and neurophysiological prediction model may utilize a current density pattern. The current density pattern over a two-dimensional surface can be represented in an indirect manner in the form of a scalar stream function. The stream function can be represented as a piece-wise linear (or higher order) function over the surface geometry on which the magnetic coils are to be placed. The stream function can include125141.04927. MGH2024-128-02 a single scalar value for each node in a mesh, and when all of the nodes are considered together, the stream function can be transformed to find the direction and magnitude of the current density in each element.

[0051] The current density representation, or the stream functions, can be used to produce a pattern of current density that achieves the set requirements for the magnetic stimulation based on the input parameters and / or the model-based stimulation plan. As one non-limiting example, the current density representation, or the stream functions, can be used to produce a pattern of current density that balances achieving a target magnetic or electric field, while at the same time satisfying specified requirements for net torque, net force, power dissipation, or combinations thereof.

[0052] Addition of an explicit PNS constraint requires construction of a matrix (similarly to the magnetic field or torque matrices) that links the basis weight vector x to the PNS propensity of each nerve segment in the body. This “P-matrix” is computed using a PNS model such as a PNS oracle, which is a linear approximation of the PNS phenomenon. Multiplication of the P-matrix by the stream function weight vector x superimposes the effects of all current stream function bases and generates a vector describing the PNS propensity for each peripheral nerve segment. This P-matrix is formed using the “PNS oracle” formulation (the inverse of the PNS threshold) rather than the threshold itself since the oracle is linear with respect to the coil current. This allows for expressing the optimization constraint as a simple inequality (Px < Pmax). In other examples, the PNS threshold can be calculated via the inverse PNS oracle.

[0053] The workflow to obtain the P-matrix starts with computation of the electromagnetic fields created by a winding pattern or current density basis element within a detailed male or female body model. In one example, two body positions can be used: headcentered or heart-centered (head-first supine). The electromagnetic fields can be simulated for a 1 kHz sinusoidal current waveform applied to the current basis element using a low-frequency magneto quasi-static solver. Given the E-fields induced by a subset of the stream function basis elements, the electric potential changes V(r) can be computed along each of the 1900 nerve segments in the body (each of which is labeled by an estimate of the local axon diameter). The response of each nerve fiber can then be predicted using either a full nonlinear neurodynamic model or the linear PNS oracle.

[0054] The PNS oracle metric (which is the reciprocal PNS threshold) follows a previous definition of the neural activation function and modified driving function. It estimates the threshold of action potential generation of a nerve segment based on the spatial pattern of125141.04927. MGH2024-128-02 the applied electric potential using only linear operations. Note that the PNS oracle does not explicitly model ion dynamics and action potential initiation or propagation and thus ignores the non-linear aspects of nerve stimulation. This provides the needed linear relationship between the electric potential V(r) and the propensity for stimulation. The PNS oracle (PNSO) is defined as:K(D) V(r - L) - 2V(r) + V(r + L)PNSO(r) = m(P) L(P)2where V(r) is the extracellular potential along the nerve position r , L(D) is the distance between consecutive nodes of Ranvier (which is a function of the axon diameter D), K(D) is a spatial kernel, and m(D) is a calibration factor. The PNS oracle is based on the second spatial difference of the electric potential V(r) across adjacent nodes of Ranvier. The result is convolved with a spatial kernel K(D) that describes cross-talk between adjacent nodes of Ranvier. Finally, the factor m(D) accounts for the varying excitability of myelinated axons with their diameter. Both the kernel K(D) and the myelination factor m(D) are calibrated for a specific temporal waveform of the driving fields (for example, a 1 kHz sinusoid). The PNS oracle may correlate with the inverse thresholds obtained from the full neurodynamic model (R2 > 0.995). The linear nature of the PNS oracle with respect to the electric potentials along the nerve and thus the local E-field and ultimately the coil currents or current density allows it to be assembled into a matrix form (the P-matrix). Note that a P-matrix can be pre-computed for each body model, coil former geometry (including the body position within the coil former) and each driving waveform. This process yields a matrix of size p x n where p is the number of nerve locations and n is the number of stream function bases. The resulting P-matrix is incorporated in the BEM-SF optimization as an additional linear constraint: |Px| <1Pmax with PmaxMin.PNS threshold

[0055] 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 accompanying description or illustrated in the accompanying drawings. The disclosure is capable of other configurations 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. Unless specified or limited otherwise, the terms “mounted,” “connected,” “supported,” and “coupled” and variations thereof are used broadly and125141.04927. MGH2024-128-02 encompass both direct and indirect mountings, connections, supports, and couplings. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings.

[0056] As used herein, unless otherwise limited or defined, discussion of particular directions is provided by example only, with regard to particular configurations or relevant illustrations. For example, discussion of “top,” “front,” or “back” features is generally intended as a description only of the orientation of such features relative to a reference frame of a particular example or illustration. Correspondingly, for example, a “top” feature may sometimes be disposed below a “bottom” feature (and so on), in some arrangements or configurations. Further, references to particular rotational or other movements (e.g., counterclockwise rotation) is generally intended as a description only of movement relative a reference frame of a particular example of illustration.

[0057] In some configurations, aspects of the disclosure, including computerized implementations of methods according to the disclosure, can be implemented as a system, method, apparatus, or article of manufacture using standard programming or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a processor device (e.g., a serial or parallel general purpose or specialized processor chip, a single- or multi-core chip, a microprocessor, a field programmable gate array, any variety of combinations of a control unit, arithmetic logic unit, and processor register, and so on), a computer (e.g., a processor device operatively coupled to a memory), or another electronically operated controller to implement aspects detailed herein. Accordingly, for example, configurations of the disclosure can be implemented as a set of instructions, tangibly embodied on a non-transitory computer-readable media, such that a processor device can implement the instructions based upon reading the instructions from the computer-readable media. Some configurations of the disclosure can include (or utilize) a control device such as an automation device, a special purpose or general purpose computer including various computer hardware, software, firmware, and so on, consistent with the discussion below. As specific examples, a control device can include a processor, a microcontroller, a field-programmable gate array, a programmable logic controller, logic gates etc., and other typical components that are known in the art for implementation of appropriate functionality (e.g., memory, communication systems, power sources, user interfaces and other inputs, etc.).

[0058] The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier (e.g., non-transitory signals), or media (e.g., non-transitory media). For example, computer-readable media can125141.04927. MGH2024-128-02 include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, and so on), optical disks (e.g., compact disk (CD), digital versatile disk (DVD), and so on), smart cards, and flash memory devices (e.g., card, stick, and so on). Additionally, it should be appreciated that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Those skilled in the art will recognize that many modifications may be made to these configurations without departing from the scope or spirit of the claimed subject matter.

[0059] Certain operations of methods according to the disclosure, or of systems executing those methods, may be represented schematically in the FIGS, or otherwise discussed herein. Unless otherwise specified or limited, representation in the FIGS, of particular operations in particular spatial order may not necessarily require those operations to be executed in a particular sequence corresponding to the particular spatial order. Correspondingly, certain operations represented in the FIGS., or otherwise disclosed herein, can be executed in different orders than are expressly illustrated or described, as appropriate for particular configurations of the disclosure. Further, in some configurations, certain operations can be executed in parallel, including by dedicated parallel processing devices, or separate computing devices configured to interoperate as part of a large system.

[0060] As used herein in the context of computer implementation, unless otherwise specified or limited, the terms “component,” “system,” “module,” and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0061] In some implementations, devices or systems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure. Correspondingly, description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing125141.04927. MGH2024-128-02 disclosed (or otherwise known) components to support these purposes or capabilities. Similarly, unless otherwise indicated or limited, discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system, is intended to inherently include disclosure, as configurations of the disclosure, of the utilized features and implemented capabilities of such device or system.

[0062] As used herein, unless otherwise defined or limited, ordinal numbers are used herein for convenience of reference based generally on the order in which particular components are presented for the relevant part of the disclosure. In this regard, for example, designations such as “first,” “second,” etc., generally indicate only the order in which the relevant component is introduced for discussion and generally do not indicate or require a particular spatial arrangement, functional or structural primacy or order.

[0063] As used herein, unless otherwise defined or limited, directional terms are used for convenience of reference for discussion of particular figures or examples. For example, references to downward (or other) directions or top (or other) positions may be used to discuss aspects of a particular example or figure, but do not necessarily require similar orientation or geometry in all installations or configurations.

[0064] This discussion is presented to enable a person skilled in the art to make and use configurations of the disclosure. Various modifications to the illustrated examples will be readily apparent to those skilled in the art, and the generic principles herein can be applied to other examples and applications without departing from the principles disclosed herein. Thus, configurations of the disclosure are not intended to be limited to configurations shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein and the claims below. The accompanying detailed description is to be read with reference to the figures, in which like elements in different figures have like reference numerals. The figures, which are not necessarily to scale, depict selected examples and are not intended to limit the scope of the disclosure. Skilled artisans will recognize the examples provided herein have many useful alternatives and fall within the scope of the disclosure.

[0065] Also as used herein, unless otherwise limited or defined, “or” indicates a nonexclusive list of components or operations that can be present in any variety of combinations, rather than an exclusive list of components that can be present only as alternatives to each other. For example, a list of “A, B, or C” indicates options of: A; B; C; A and B; A and C; B and C; and A, B, and C. Correspondingly, the term “or” as used herein is intended to indicate exclusive alternatives only when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” Further, a list preceded by “one or more” (and variations125141.04927. MGH2024-128-02 thereon) and including “or” to separate listed elements indicates options of one or more of any or all of the listed elements. For example, the phrases “one or more of A, B, or C” and “at least one of A, B, or C” indicate options of: one or more A; one or more B; one or more C; one or more A and one or more B; one or more B and one or more C; one or more A and one or more C; and one or more of each of A, B, and C. Similarly, a list preceded by “a plurality of’ (and variations thereon) and including “or” to separate listed elements indicates options of multiple instances of any or all of the listed elements. For example, the phrases “a plurality of A, B, or C” and “two or more of A, B, or C” indicate options of: A and B; B and C; A and C; and A, B, and C. In general, the term “or” as used herein only indicates exclusive alternatives (e.g. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”

[0066] Also as used herein, unless otherwise specified or limited, the terms “about” and “approximately,” as used herein with respect to a reference value, refer to variations from the reference value of ± 15% or less (e.g., ± 10%, ± 5%, etc.), inclusive of the endpoints of the range. Similarly, the term “substantially equal” (and the like) as used herein with respect to a reference value refers to variations from the reference value of less than ± 30% (e.g., ± 20%, ± 10%, ± 5%) inclusive. Where specified, “substantially” can indicate in particular a variation in one numerical direction relative to a reference value. For example, “substantially less” than a reference value (and the like) indicates a value that is reduced from the reference value by 30% or more, and “substantially more” than a reference value (and the like) indicates a value that is increased from the reference value by 30% or more.

Claims

125141.04927. MGH2024-128-02CLAIMS1. A magnetic spinal cord stimulation system comprising: at least one magnetic coil configured to be positioned proximate to a spinal cord of a patient; a power source operatively connected to the at least one magnetic coil to deliver an electric current to the at least one magnetic coil; and a controller configured to: receive a plurality of input parameters including at least one of: a Faraday induction value, one or more magnetic coil dimensions, a stimulation amplitude, or a stimulation phase; utilize an electromagnetic and neurophysiological prediction model to create a model-based stimulation plan based on the plurality of input parameters; and control the power source and the model-based stimulation plan to deliver the electrical current through the at least one magnetic coil to create time-varying magnetic fields that induce stimulating electric fields within spinal cord tissue at a depth to directly stimulate motor and sensory pathways in the spinal cord.

2. The magnetic spinal cord stimulation system of claim 1, further comprising: a display, wherein the controller is further configured to: determine, using the electromagnetic and neurophysiological prediction model, a plurality of electric field projections onto nerve paths of the spinal cord of the patient; and output, via the display, a simulation illustrating a physical rendering of the plurality of electric field projections.

3. The magnetic spinal cord stimulation system of claim 1, wherein the controller is further configured to: determine, using the electromagnetic and neurophysiological prediction model, a plurality of predicted electric field magnitudes induced within the spinal cord of the patient based on the plurality of input parameters.

4. The magnetic spinal cord stimulation system of claim 3, wherein the controller is further configured to output a report, the report comprising a plurality of recommended input125141.04927. MGH2024-128-02 parameters based on the plurality of predicted electric fields induced within spinal cord of the patient.

5. The magnetic spinal cord stimulation system of claim 1, wherein the controller is further configured to: receive a physical model of the spinal cord of the patient, wherein the model-based stimulation plan is further created by utilizing the physical model of the spinal cord of the patient.

6. The magnetic spinal cord stimulation system of claim 5, wherein the physical model is a magnetic resonance imaging (MRI) image or a computed tomography (CT) image.

7. The magnetic spinal cord stimulation system of claim 5, wherein the controller is further configured to: identify a plurality of tissue classes on the physical model of the spinal cord of the patient.

8. The magnetic spinal cord stimulation system of claim 1, wherein the controller is further configured to: receive an indication of a spinal cord injury location of the patient, wherein the model-based stimulation plan is further created based on the indication.

9. The magnetic spinal cord stimulation system of claim 1, wherein the controller is further configured to: determine, using the electromagnetic and neurophysiological prediction model, a plurality of predicted stimulation thresholds corresponding to a plurality of nerve segments based on the plurality of input parameters.

10. A method for magnetic spinal cord stimulation, the method comprising: receiving a plurality of input parameters including at least one of: a Faraday induction value, one or more magnetic coil dimensions, a stimulation amplitude, or a stimulation phase; utilizing an electromagnetic and neurophysiological prediction model to create a model -based stimulation plan based on the plurality of input parameters; and125141.04927. MGH2024-128-02 controlling a power source of a magnetic spinal cord stimulator and the model-based stimulation plan to deliver an electrical current through the at least one magnetic coil to create time-varying magnetic fields that induce stimulating electric fields within spinal cord tissue at a depth to directly stimulate motor pathways in a spinal cord.

11. The method of claim 10, further comprising: determining, using the electromagnetic and neurophysiological prediction model, a plurality of electric field projections onto nerve paths of the spinal cord of the patient; and outputting, via a display, a simulation illustrating a physical rendering of the plurality of electric field projections.

12. The method of claim 10, further comprising: determining, using the electromagnetic and neurophysiological prediction model, a plurality of predicted electric field magnitudes induced within the spinal cord of the patient based on the plurality of input parameters.

13. The method of claim 12, further comprising: outputting a report, the report comprising a plurality of recommended input parameters based on the plurality of predicted electric fields induced within spinal cord of the patient.

14. The method of claim 10, further comprising: receiving a physical model of the spinal cord of the patient, wherein the model-based stimulation plan is further created by utilizing the physical model of the spinal cord of the patient.

15. The method of claim 14, wherein the physical model is a magnetic resonance imaging (MRI) image or a computed tomography (CT) image.

16. The method of claim 14, further comprising: identifying a plurality of tissue classes on the physical model of the spinal cord of the patient.

17. The method of claim 10, further comprising:125141.04927. MGH2024-128-02 receiving an indication of a spinal cord injury location of the patient, wherein the model-based stimulation plan is further created based on the indication.

18. The method of claim 10, further comprising: determining, using the electromagnetic and neurophysiological prediction model, a plurality of predicted stimulation thresholds corresponding to a plurality of nerve segments based on the plurality of input parameters.

19. The method of claim 10, wherein the model -based stimulation plan created by the electromagnetic and neurophysiological predication model specifies a volume of tissue activated (VTA).

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