Systems and methods for selecting stimulation parameters by targeting and steering

Information

  • Patent Grant
  • 11110280
  • Patent Number
    11,110,280
  • Date Filed
    Friday, August 9, 2019
    4 years ago
  • Date Issued
    Tuesday, September 7, 2021
    2 years ago
Abstract
Methods and systems for selecting stimulation parameters using targeting and steering techniques are presented. For example, a method or system (via actions performed by a processor) can include receiving a name of an anatomical or physiological target or a name of a disease or disorder; receiving a clinical goal; and using at least 1) the anatomical or physiological target or disease or disorder and 2) the clinical goal, selecting a set of stimulation parameters. Another method or system (via actions performed by its processor) can include receiving a first set of stimulation parameters; receiving a command to alter the first set of stimulation parameters that does not include, or is not composed exclusively of, a numerical value for any of the stimulation parameters; and modifying the first set of stimulation parameters to create a second set of stimulation parameters based on the command.
Description
FIELD

The present invention is directed to the area of implantable electrical stimulation systems and methods of making and using the systems. The present invention is also directed to systems and methods for selecting stimulation parameters using targeting and steering mechanisms.


BACKGROUND

Implantable electrical stimulation systems have proven therapeutic in a variety of diseases and disorders. For example, spinal cord stimulation systems have been used as a therapeutic modality for the treatment of chronic pain syndromes. Peripheral nerve stimulation has been used to treat chronic pain syndrome and incontinence, with a number of other applications under investigation. Functional electrical stimulation systems have been applied to restore some functionality to paralyzed extremities in spinal cord injury patients. Stimulation of the brain, such as deep brain stimulation, can be used to treat a variety of diseases or disorders.


Stimulators have been developed to provide therapy for a variety of treatments. A stimulator can include a control module (with a pulse generator), one or more leads, and an array of stimulator electrodes on each lead. The stimulator electrodes are in contact with or near the nerves, muscles, or other tissue to be stimulated. The pulse generator in the control module generates electrical pulses that are delivered by the electrodes to body tissue.


BRIEF SUMMARY

One embodiment is a system for identifying a set of stimulation parameters, the system includes a computer processor configured and arranged to perform the following actions: receiving a name of an anatomical or physiological target or a name of a disease or disorder; receiving a clinical goal; and using at least 1) the anatomical or physiological target or disease or disorder and 2) the clinical goal, selecting a set of stimulation parameters.


Another embodiment is a non-transitory computer-readable medium having processor-executable instructions for identifying a set of stimulation parameters, the processor-executable instructions when installed onto a device enable the device to perform actions, including: receiving a name of an anatomical or physiological target or a name of a disease or disorder; receiving a clinical goal; and using at least 1) the anatomical or physiological target or disease or disorder and 2) the clinical goal, selecting a set of stimulation parameters.


Yet another embodiment is a method for identifying a set of stimulation parameters. The method includes receiving a name of an anatomical or physiological target or a name of a disease or disorder; receiving a clinical goal; and using at least 1) the anatomical or physiological target or disease or disorder and 2) the clinical goal, selecting a set of stimulation parameters.


In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes receiving a system goal, wherein selecting the set of stimulation parameters includes using the system goal to select the set of stimulation parameters. In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, the system goal refers to one or more components of an electrical stimulation system or to one or more stimulation parameters and also refers to an objective related to the one or more components or stimulation parameters.


In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, the clinical goal refers to the disease or disorder or to a symptom of the disease or disorder or to an effect or side effect associated with the disease or disorder or with electrical stimulation of the anatomical or physiological target.


In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes receiving a user modification of at least one of the stimulation parameters. In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, selecting the set of stimulation parameters further includes using previous stimulation instances or estimated stimulation instances to select the set of stimulation parameters. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes displaying an estimated stimulation region based on the selected set of stimulation parameters.


A further embodiment is a system for identifying a set of stimulation parameters, the system includes a computer processor configured and arranged to perform the following actions: providing a model of a lead including a plurality of electrodes, wherein the plurality of electrodes includes a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes includes a plurality of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead; receiving a selection of a target point external to the lead; projecting the target point onto a surface of the lead to identify a virtual electrode; and selecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.


Another embodiment is a non-transitory computer-readable medium having processor-executable instructions for identifying a set of stimulation parameters, the processor-executable instructions when installed onto a device enable the device to perform actions, including: providing a model of a lead including a plurality of electrodes, wherein the plurality of electrodes includes a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes includes a plurality of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead; receiving a selection of a target point external to the lead; projecting the target point onto a surface of the lead to identify a virtual electrode; and selecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.


Yet another embodiment is a method for identifying a set of stimulation parameters. The method includes providing a model of a lead including a plurality of electrodes, wherein the plurality of electrodes includes a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes includes a plurality of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead; receiving a selection of a target point external to the lead; projecting the target point onto a surface of the lead to identify a virtual electrode; and selecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.


In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, projecting the target point includes projecting the target point onto a nearest point of the surface of the lead to identify the virtual electrode. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes calculating the electrical field in a region that does not overlap with the lead and is bounded by a plane tangent to the lead at the virtual electrode. In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, the electrical field is a scalar potential field, a vector field, or a field of Hamiltonians of a divergence of an electrical field.


In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes receiving a user modification of at least one of the stimulation parameters. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above, further includes defining at least one guarding electrode adjacent the virtual electrode. In at least some embodiments of the system, non-transitory computer-readable medium, or method described above, defining at least one guarding electrode includes defining two guarding electrodes circumferentially disposed on opposite sides of the virtual electrode.


A further embodiment is a system for identifying a set of stimulation parameters, the system includes a computer processor configured and arranged to perform the following actions: receiving a first set of stimulation parameters; receiving a command to alter the first set of stimulation parameters, wherein the command does not include, or is not composed exclusively of, a numerical value for any of the stimulation parameters; and modifying the first set of stimulation parameters to create a second set of stimulation parameters based on the command.


Another embodiment is a non-transitory computer-readable medium having processor-executable instructions for identifying a set of stimulation parameters, the processor-executable instructions when installed onto a device enable the device to perform actions, including: receiving a first set of stimulation parameters; receiving a command to alter the first set of stimulation parameters, wherein the command does not include, or is not composed exclusively of, a numerical value for any of the stimulation parameters; and modifying the first set of stimulation parameters to create a second set of stimulation parameters based on the command.


Yet another embodiment is a method for identifying a set of stimulation parameters. The method includes receiving a first set of stimulation parameters; receiving a command to alter the first set of stimulation parameters, wherein the command does not include, or is not composed exclusively of, a numerical value for any of the stimulation parameters; and modifying the first set of stimulation parameters to create a second set of stimulation parameters based on the command.


In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes displaying an estimated stimulation region based on the first set of stimulation parameters. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes, upon modification of the first set of stimulation parameters to create the second set of stimulation parameters, modifying the display to display an estimated stimulation region based on the second set of stimulation parameters.


In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes receiving a user modification of at least one of the stimulation parameters of the second set of stimulation parameters. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes repeating the actions with the second set of stimulation parameters becoming the first set of stimulation parameters for the repeated actions. In at least some embodiments, the system, non-transitory computer-readable medium, or method described above further includes sending the second set of stimulation parameters to an implantable pulse generator of an electrical stimulation system.





BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified.


For a better understanding of the present invention, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings, wherein:



FIG. 1 is a schematic view of one embodiment of an electrical stimulation system, according to the invention;



FIG. 2 is a schematic side view of one embodiment of an electrical stimulation lead, according to the invention;



FIG. 3 is a schematic block diagram of one embodiment of a system for determining stimulation parameters, according to the invention;



FIG. 4 is a flowchart of one embodiment of a method of determining stimulation parameters, according to the invention;



FIG. 5 is a flowchart of a second embodiment of a method of determining stimulation parameters, according to the invention;



FIG. 6A is a diagrammatic illustration of one embodiment of a method of selecting a stimulation target, according to the invention;



FIG. 6B is a diagrammatic illustration of the embodiment of FIG. 6A rotated to view along a plane including line 610, according to the invention;



FIG. 7 is a flowchart of a third embodiment of a method of determining stimulation parameters, according to the invention;



FIG. 8 is a flowchart of a fourth embodiment of a method of determining stimulation parameters, according to the invention;



FIG. 9A is a diagrammatic illustration of one embodiment of a method of selecting a point source, according to the invention;



FIG. 9B is a diagrammatic illustration of the embodiment of FIG. 9A taken from an orthogonal position, according to the invention;



FIG. 10 is a diagrammatic illustration of an electrical field calculated for the point source of FIG. 9A, according to the invention;



FIG. 11 is a diagrammatic illustration of a portion of a model of a lead and a virtual electrode defined for the lead, according to the invention; and



FIG. 12 is a diagrammatic illustration of a cross-section of the lead of FIG. 11 illustrating the virtual electrode and a guarding electrode, according to the invention1





DETAILED DESCRIPTION

The present invention is directed to the area of implantable electrical stimulation systems and methods of making and using the systems. The present invention is also directed to systems and methods for selecting stimulation parameters using targeting and steering mechanisms.


Suitable implantable electrical stimulation systems include, but are not limited to, a least one lead with one or more electrodes disposed on a distal end of the lead and one or more terminals disposed on one or more proximal ends of the lead. Leads include, for example, percutaneous leads, paddle leads, cuff leads, or any other arrangement of electrodes on a lead. Examples of electrical stimulation systems with leads are found in, for example, U.S. Pat. Nos. 6,181,969; 6,516,227; 6,609,029; 6,609,032; 6,741,892; 7,244,150; 7,450,997; 7,672,734; 7,761,165; 7,783,359; 7,792,590; 7,809,446; 7,949,395; 7,974,706; 8,175,710; 8,224,450; 8,271,094; 8,295,944; 8,364,278; 8,391,985; and 8,688,235; and U.S. Patent Applications Publication Nos. 2007/0150036; 2009/0187222; 2009/0276021; 2010/0076535; 2010/0268298; 2011/0005069; 2011/0004267; 2011/0078900; 2011/0130817; 2011/0130818; 2011/0238129; 2011/0313500; 2012/0016378; 2012/0046710; 2012/0071949; 2012/0165911; 2012/0197375; 2012/0203316; 2012/0203320; 2012/0203321; 2012/0316615; 2013/0105071; and 2013/0197602, all of which are incorporated by reference. In the discussion below, a percutaneous lead will be exemplified, but it will be understood that the methods and systems described herein are also applicable to paddle leads and other leads.


A percutaneous lead for electrical stimulation (for example, deep brain or spinal cord stimulation) includes stimulation electrodes that can be ring electrodes, segmented electrodes that extend only partially around the circumference of the lead, or any other type of electrode, or any combination thereof. The segmented electrodes can be provided in sets of electrodes, with each set having electrodes circumferentially distributed about the lead at a particular longitudinal position. For illustrative purposes, the leads are described herein relative to use for deep brain stimulation, but it will be understood that any of the leads can be used for applications other than deep brain stimulation, including spinal cord stimulation, peripheral nerve stimulation, or stimulation of other nerves, muscles, and tissues.


Turning to FIG. 1, one embodiment of an electrical stimulation system 10 includes one or more stimulation leads 12 and an implantable pulse generator (IPG) 14. The system 10 can also include one or more of an external remote control (RC) 16, a clinician's programmer (CP) 18, an external trial stimulator (ETS) 20, or an external charger 22.


The IPG 14 is physically connected, optionally via one or more lead extensions 24, to the stimulation lead(s) 12. Each lead carries multiple electrodes 26 arranged in an array. The IPG 14 includes pulse generation circuitry that delivers electrical stimulation energy in the form of, for example, a pulsed electrical waveform (i.e., a temporal series of electrical pulses) to the electrode array 26 in accordance with a set of stimulation parameters. The implantable pulse generator can be implanted into a patient's body, for example, below the patient's clavicle area or within the patient's buttocks or abdominal cavity. The implantable pulse generator can have eight stimulation channels which may be independently programmable to control the magnitude of the current stimulus from each channel. In some embodiments, the implantable pulse generator can have more or fewer than eight stimulation channels (e.g., 4-, 6-, 16-, 32-, or more stimulation channels). The implantable pulse generator can have one, two, three, four, or more connector ports, for receiving the terminals of the leads.


The ETS 20 may also be physically connected, optionally via the percutaneous lead extensions 28 and external cable 30, to the stimulation leads 12. The ETS 20, which may have similar pulse generation circuitry as the IPG 14, also delivers electrical stimulation energy in the form of, for example, a pulsed electrical waveform to the electrode array 26 in accordance with a set of stimulation parameters. One difference between the ETS 20 and the IPG 14 is that the ETS 20 is often a non-implantable device that is used on a trial basis after the neurostimulation leads 12 have been implanted and prior to implantation of the IPG 14, to test the responsiveness of the stimulation that is to be provided. Any functions described herein with respect to the IPG 14 can likewise be performed with respect to the ETS 20.


The RC 16 may be used to telemetrically communicate with or control the IPG 14 or ETS 20 via a uni- or bi-directional wireless communications link 32. Once the IPG 14 and neurostimulation leads 12 are implanted, the RC 16 may be used to telemetrically communicate with or control the IPG 14 via a uni- or bi-directional communications link 34. Such communication or control allows the IPG 14 to be turned on or off and to be programmed with different stimulation parameter sets. The IPG 14 may also be operated to modify the programmed stimulation parameters to actively control the characteristics of the electrical stimulation energy output by the IPG 14. The CP 18 allows a user, such as a clinician, the ability to program stimulation parameters for the IPG 14 and ETS 20 in the operating room and in follow-up sessions.


The CP 18 may perform this function by indirectly communicating with the IPG 14 or ETS 20, through the RC 16, via a wireless communications link 36. Alternatively, the CP 18 may directly communicate with the IPG 14 or ETS 20 via a wireless communications link (not shown). The stimulation parameters provided by the CP 18 are also used to program the RC 16, so that the stimulation parameters can be subsequently modified by operation of the RC 16 in a stand-alone mode (i.e., without the assistance of the CP 18).


For purposes of brevity, the details of the RC 16, CP 18, ETS 20, and external charger 22 will not be further described herein. Details of exemplary embodiments of these devices are disclosed in U.S. Pat. No. 6,895,280, which is expressly incorporated herein by reference. Other examples of electrical stimulation systems can be found at U.S. Pat. Nos. 6,181,969; 6,516,227; 6,609,029; 6,609,032; 6,741,892; 7,949,395; 7,244,150; 7,672,734; and 7,761,165; 7,974,706; 8,175,710; 8,224,450; and 8,364,278; and U.S. Patent Application Publication No. 2007/0150036, as well as the other references cited above, all of which are incorporated by reference.



FIG. 2 illustrates one embodiment of a lead 110 with electrodes 125 disposed at least partially about a circumference of the lead 110 along a distal end portion of the lead and terminals 135 disposed along a proximal end portion of the lead.


The lead 110 can be implanted near or within the desired portion of the body to be stimulated such as, for example, the brain, spinal cord, or other body organs or tissues. In one example of operation for deep brain stimulation, access to the desired position in the brain can be accomplished by drilling a hole in the patient's skull or cranium with a cranial drill (commonly referred to as a burr), and coagulating and incising the dura mater, or brain covering. The lead 110 can be inserted into the cranium and brain tissue with the assistance of a stylet (not shown). The lead 110 can be guided to the target location within the brain using, for example, a stereotactic frame and a microdrive motor system. In some embodiments, the microdrive motor system can be fully or partially automatic. The microdrive motor system may be configured to perform one or more the following actions (alone or in combination): insert the lead 110, advance the lead 110, retract the lead 110, or rotate the lead 110.


In some embodiments, measurement devices coupled to the muscles or other tissues stimulated by the target neurons, or a unit responsive to the patient or clinician, can be coupled to the implantable pulse generator or microdrive motor system. The measurement device, user, or clinician can indicate a response by the target muscles or other tissues to the stimulation or recording electrode(s) to further identify the target neurons and facilitate positioning of the stimulation electrode(s). For example, if the target neurons are directed to a muscle experiencing tremors, a measurement device can be used to observe the muscle and indicate changes in, for example, tremor frequency or amplitude in response to stimulation of neurons. Alternatively, the patient or clinician can observe the muscle and provide feedback.


The lead 110 for deep brain stimulation can include stimulation electrodes, recording electrodes, or both. In at least some embodiments, the lead 110 is rotatable so that the stimulation electrodes can be aligned with the target neurons after the neurons have been located using the recording electrodes.


Stimulation electrodes may be disposed on the circumference of the lead 110 to stimulate the target neurons. Stimulation electrodes may be ring-shaped so that current projects from each electrode equally in every direction from the position of the electrode along a length of the lead 110. In the embodiment of FIG. 2, two of the electrodes 120 are ring electrodes 120. Ring electrodes typically do not enable stimulus current to be directed from only a limited angular range around of the lead. Segmented electrodes 130, however, can be used to direct stimulus current to a selected angular range around the lead. When segmented electrodes are used in conjunction with an implantable pulse generator that delivers constant current stimulus, current steering can be achieved to more precisely deliver the stimulus to a position around an axis of the lead (i.e., radial positioning around the axis of the lead). To achieve current steering, segmented electrodes can be utilized in addition to, or as an alternative to, ring electrodes.


The lead 100 includes a lead body 110, terminals 135, and one or more ring electrodes 120 and one or more sets of segmented electrodes 130 (or any other combination of electrodes). The lead body 110 can be formed of a biocompatible, non-conducting material such as, for example, a polymeric material. Suitable polymeric materials include, but are not limited to, silicone, polyurethane, polyurea, polyurethane-urea, polyethylene, or the like. Once implanted in the body, the lead 100 may be in contact with body tissue for extended periods of time. In at least some embodiments, the lead 100 has a cross-sectional diameter of no more than 1.5 mm and may be in the range of 0.5 to 1.5 mm. In at least some embodiments, the lead 100 has a length of at least 10 cm and the length of the lead 100 may be in the range of 10 to 70 cm.


The electrodes 125 can be made using a metal, alloy, conductive oxide, or any other suitable conductive biocompatible material. Examples of suitable materials include, but are not limited to, platinum, platinum iridium alloy, iridium, titanium, tungsten, palladium, palladium rhodium, or the like. Preferably, the electrodes are made of a material that is biocompatible and does not substantially corrode under expected operating conditions in the operating environment for the expected duration of use.


Each of the electrodes can either be used or unused (OFF). When the electrode is used, the electrode can be used as an anode or cathode and carry anodic or cathodic current. In some instances, an electrode might be an anode for a period of time and a cathode for a period of time.


Deep brain stimulation leads may include one or more sets of segmented electrodes. Segmented electrodes may provide for superior current steering than ring electrodes because target structures in deep brain stimulation are not typically symmetric about the axis of the distal electrode array. Instead, a target may be located on one side of a plane running through the axis of the lead. Through the use of a radially segmented electrode array (“RSEA”), current steering can be performed not only along a length of the lead but also around a circumference of the lead. This provides precise three-dimensional targeting and delivery of the current stimulus to neural target tissue, while potentially avoiding stimulation of other tissue. Examples of leads with segmented electrodes include U.S. Pat. Nos. 8,473,061; 8,571,665; and 8,792,993; U.S. Patent Application Publications Nos. 2010/0268298; 2011/0005069; 2011/0130803; 2011/0130816; 2011/0130817; 2011/0130818; 2011/0078900; 2011/0238129; 2012/0016378; 2012/0046710; 2012/0071949; 2012/0165911; 2012/197375; 2012/0203316; 2012/0203320; 2012/0203321; 2013/0197424; 2013/0197602; 2014/0039587; 2014/0353001; 2014/0358208; 2014/0358209; 2014/0358210; 2015/0045864; 2015/0066120; 2015/0018915; 2015/0051681; U.S. patent application Ser. Nos. 14/557,211 and 14/286,797; and U.S. Provisional Patent Application Ser. No. 62/113,291, all of which are incorporated herein by reference.


An electrical stimulation lead can be implanted in the body of a patient (for example, in the brain or spinal cord of the patient) and used to stimulate surrounding tissue. In at least some embodiments, it is useful to estimate the effective region of stimulation (often called a volume of activation (VOA) or stimulation field model (SFM)) given the position of the lead and its electrodes in the patient's body and the stimulation parameters used to generate the stimulation. Any suitable method for determining the VOA/SFM and for graphically displaying the VOA/SFM relative to patient anatomy can be used including those described in, for example, U.S. Pat. Nos. 8,326,433; 8,675,945; 8,831,731; 8,849,632; and 8,958,615; U.S. Patent Application Publications Nos. 2009/0287272; 2009/0287273; 2012/0314924; 2013/0116744; 2014/0122379; and 2015/0066111; and U.S. Provisional Patent Application Ser. No. 62/030,655, all of which are incorporated herein by reference. Several of these references also discloses methods and systems for registering an atlas of body structures to imaged patient physiology.


In conventional systems, a VOA is determined based on a set of stimulation parameters input into the system. The VOA can then be modified by the user by modifying the stimulation parameters and determining the new VOA from the modified stimulation parameters. This allows the user to tailor the stimulation volume.


In contrast to these conventional systems which determine the VOA from user-selected stimulation parameters, in at least some embodiments, the present systems or methods allow the user to define the target that is desired for stimulation and then the systems or methods determine a set of stimulation parameters based on that target. There are a number of different methods for selecting a desired stimulation target described below. In some embodiments, the user selects the volume directly. In other embodiments, the user selects the target indirectly by indicating an anatomical structure, disease or disorder, clinical goal, or system goal, or any combination thereof. #



FIG. 3 illustrates one embodiment of a system for practicing the invention. The system can include a computing device 300 or any other similar device that includes a processor 302 and a memory 304, a display 306, an input device 308, and, optionally, the electrical stimulation system 312. The system 300 may also optionally include one or more imaging systems 310.


The computing device 300 can be a computer, tablet, mobile device, or any other suitable device for processing information. The computing device 300 can be local to the user or can include components that are non-local to the computer including one or both of the processor 302 or memory 304 (or portions thereof). For example, in some embodiments, the user may operate a terminal that is connected to a non-local computing device. In other embodiments, the memory can be non-local to the user.


The computing device 300 can utilize any suitable processor 302 including one or more hardware processors that may be local to the user or non-local to the user or other components of the computing device. The processor 302 is configured to execute instructions provided to the processor, as described below.


Any suitable memory 304 can be used for the computing device 302. The memory 304 illustrates a type of computer-readable media, namely computer-readable storage media. Computer-readable storage media may include, but is not limited to, nonvolatile, non-transitory, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (“DVD”) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device.


Communication methods provide another type of computer readable media; namely communication media. Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, data signal, or other transport mechanism and include any information delivery media. The terms “modulated data signal,” and “carrier-wave signal” includes a signal that has one or more of its characteristics set or changed in such a manner as to encode information, instructions, data, and the like, in the signal. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media.


The display 306 can be any suitable display device, such as a monitor, screen, display, or the like, and can include a printer. The input device 308 can be, for example, a keyboard, mouse, touch screen, track ball, joystick, voice recognition system, or any combination thereof, or the like.


One or more imaging systems 310 can be used including, but not limited to, MRI, CT, ultrasound, or other imaging systems. The imaging system 310 may communicate through a wired or wireless connection with the computing device 300 or, alternatively or additionally, a user can provide images from the imaging system 310 using a computer-readable medium or by some other mechanism.


The electrical stimulation system 312 can include, for example, any of the components illustrated in FIG. 1. The electrical stimulation system 312 may communicate with the computing device 300 through a wired or wireless connection or, alternatively or additionally, a user can provide information between the electrical stimulation system 312 and the computing device 300 using a computer-readable medium or by some other mechanism. In some embodiments, the computing device 300 may include part of the electrical stimulation system, such as, for example, the IPG, CP, RC, ETS, or any combination thereof.


The methods and systems described herein may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Accordingly, the methods and systems described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Systems referenced herein typically include memory and typically include methods for communication with other devices including mobile devices. Methods of communication can include both wired and wireless (e.g., RF, optical, or infrared) communications methods and such methods provide another type of computer readable media; namely communication media. Wired communication can include communication over a twisted pair, coaxial cable, fiber optics, wave guides, or the like, or any combination thereof. Wireless communication can include RF, infrared, acoustic, near field communication, Bluetooth™, or the like, or any combination thereof.


Programming an electrical stimulation (for example, a deep brain stimulation (DBS) or spinal cord stimulation (SC S)) system has conventionally involved significant time spent by a variety of programming experts, having varying levels of training and experience, with those programmers subscribing to various schools of thought regarding the choice of stimulation parameters. A preferred programming solution would offer tools to users or programmers to program more quickly, to more often arrive at efficacious sets of stimulation parameters, and to increase the efficacy of the patient's therapy.


In at least some embodiments, two different tasks can be identified: Targeting and Steering. Targeting can refer to, for example, an automated or semi-automated selection of a set of stimulation parameters based on any number or type of data or criteria. Steering (for example, current steering) can refer to the automated or semi-automated selection of sets of stimulation parameters resulting in stepwise changes to stimulation, again based on any number of type of data or criteria. In at least some embodiments, targeting can be the selection of a final set of stimulation parameters which achieve a desired stimulation goal, and steering can be a method of exploring the stimulation parameter space using, for example, abstracted controls.


In one embodiment of a method of selecting stimulation parameters (e.g., targeting), the user employs alphanumeric descriptors, instead of visual or graphical views, to describe the objective of the electrical stimulation therapy. The system can use these alphanumeric descriptors, as well as data from actual or estimated stimulation instances, to select at least one set of stimulation parameters to achieve the described objective. In at least some embodiments, no graphical views are presented to the user. In at least some embodiments, no imaging data is required. In some other embodiments, imaging data related to surgical placement of the lead or imaging data of the anatomical structures in which the lead is (or is to be) placed, or graphical data relating to physiological structures or responses to stimulation mapped to 3D or anatomical space or a common space, such as MNI space, or any combination thereof may optionally be provided.



FIG. 4 illustrates a flowchart of one method of selecting stimulation parameters using alphanumeric descriptors. In step 402, the user provides the name of an anatomical or physiological target. Examples of such targets include, but are not limited to, the subthalamic nucleus (STN), internal segment of the globus pallidus (GPi), external segment of the globus pallidus (GPe), and the like. In at least some embodiments, an anatomical structure is defined by its physical structure and a physiological target is defined by its functional attributes. The user may input the name of the anatomical or physiological target using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the anatomical or physiological target from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, there can be more than one anatomical or physiological target provided.


In step 404, the user provides the name of a disease or disorder (e.g. Parkinson's disease, depression, epilepsy) that is to be treated. In some instances, the disease or disorder may be a symptom or condition associated with another disease or disorder. The user may input the name of the disease or disorder using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the disease or disorder from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, the system may present a list for the disease or disorder based on the previously provided anatomical or physiological target. In some embodiments, there can be more than one disease or disorder provided. In some embodiments, step 402 is skipped and the user-provided name from step 404 is utilized by the system to select a target (which can be an anatomical or physiological target) or to present a list of targets to the user.


In step 406, the user provides a clinical goal. In at least some embodiments, the clinical goal may be input as a verb/noun combination, for example, ‘reduce’+‘tremor’ or ‘avoid’+‘cognitive impairment’. The clinical goal generally refers the disease or disorder or to a symptom of the disease or disorder or to an effect or side effect associated with the disease or disorder or with electrical stimulation of the anatomical or physiological target. The user may input the clinical goal using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the clinical goal from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, the system may present a list for the clinical goal based on the previously provided anatomical or physiological target or provided disease or disorder or both. In some embodiments, there can be more than one clinical goal provided. In some embodiments, one or more clinical goals can be suggested by the system based on selections made in steps 402, 404, or 408.


In optional step 408, the user can provide a system goal, such as, for example, ‘extended battery life’, ‘low frequency’, ‘short pulse width’, or the like. The system goal generally refers to one or more of the components of the stimulation system or to one or more of the stimulation parameters and indicates an objective related to the component or parameter. The user may input the system goal using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the system goal from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, there can be more than one system goal provided. In some embodiments, one or more system goals can be suggested by the system based on selections made in steps 402, 404, or 406.


It will be understood that steps 402, 404, 406, and 408 can be rearranged in any order. In step 410, the system uses the provided inputs from steps 402, 404, 406, and 408 to select a set of stimulation parameters. In at least some embodiments, the system can utilize previous actual or estimated stimulation instances to correlate with the selections or inputs made by the user. For example, the anatomical or physiological target and, optionally, the disease or disorder and clinical goal, may be used to identify a target region for stimulation. In some embodiments, the anatomical or physiological target, disease or disorder, clinical goal, or system goal (or any combination thereof) may provide limits (for example, ranges, maximum values, or minimum values, or sets of combinations of parameters) for one or more stimulation parameters. Examples of methods to determine stimulation parameters are described in more detail below.



FIG. 5 illustrates another method for selecting stimulation parameters. In step 502, a graphical user interface (GUI) is presented with one or more views containing a two-dimension (2D) or three-dimensional (3D) representation of the lead or leads present, as well as optional anatomical structure or structures. The views may also contain notional views of programming parameters space, including or excluding displays of responses to stimulation from the existing patient, prior patients, or populations of patients For a 3D representation, elements displayed may be displayed as a 2D view into a 3D space, or using 3D technology (e.g. active displays, active glasses, passive glasses, holograms, haptic feedback ‘display’). Patient-specific imaging data may optionally be provided (e.g. pre-operative MRI/CT, intra-operative MRI/CT, post-operative MRI/CT, or the like or any combination thereof).


In step 504, the user selects at least one target. In some embodiments, the user selects at least one anatomical target. For example, the user can select an anatomical target that is shown on a display using any suitable selection device, such as a touchscreen, mouse, touchpad, track ball, or the like, or the user can select an anatomical target from a drop-down menu or the like or the user can utilize any other suitable selection mechanism.


In other embodiments, the user can select at least one target volume, which is not a specific anatomical structure, but may include parts of one or more anatomical structures. The target volume may be displayed in the one or more views of the GUI. In at least some embodiments, the target volume can be drawn or otherwise indicated by the user. In at least some embodiments, a sample target volume can be displayed and the user can manipulate the sample target volume by, for example, changing boundaries of the sample target volume, changing a diameter of the sample target volume, or the like. In at least some embodiments, the target volume can be estimated based on, for example, a selection of disease state, surgical target, an auto-segmentation method, or the like and then, at least in some instances, optionally modified by the user.


Methods for selecting a target are illustrated using FIGS. 6A and 6B. FIG. 6A illustrates diagrammatically one embodiment of a view 600 into a 3D space. The lead 602 and its electrodes 634 (FIG. 6B) are illustrated in the center of the view. The ellipse signifies a target 603, such as an anatomical or physiological target. In this case, the lead 602 has been positioned at least partially within the target 603, but in other embodiments, the lead may be near, but not inside, the target. The target 603 may have been selected using patient imaging data (e.g. MRI) or an atlas (a patient specific, probabilistic, or general atlas), or the user may have drawn the target. The view also includes an axis indicator 605, which shows the orientation of different views into the space.


In some embodiments, a target point 607 can be selected. In some embodiments, the target 603 or target point 607 is automatically placed by the system (using, for example, the input described above with respect to FIG. 4) or the target 603 is automatically placed with some user input (for example, if the user can move or otherwise select a target point 607). In other embodiments, the target 603 is placed entirely by the user. In some embodiments, using the target point 607, a target volume can be determined. For example, the selection of a particular target point 607 can be used to result in the selection of a larger target 603 based on, for example, anatomical or physiological information. For example, a target point placed in a region of the STN may result in the automatic selection of the entire STN as the target.


A dashed line 610 in FIG. 6A shows the location of a cutaway plane for the view illustrated in FIG. 6B. FIGS. 6A and 6B may show two views into a single viewport at different times, or two views shown in two different viewports to the user at the same time. In some embodiments, the user can graphically or otherwise manipulate the views, such as, for example, sliding or otherwise moving the cut plane in FIG. 6A to alter the image shown in FIG. 6B.


In at least some embodiments, one or more targets 603 can be identified by the user. These targets 603 may be constructed using the GUI. A target 603 may be constructed entirely from scratch, or by using a set of primitives (for example, lines, spheres, cubes, rectangles, circles, triangles, or the like or any combination thereof), or in a stepwise fashion by modifying an initial target. For example, a user may place a sphere primitive in the space, and then modulate its radius or transition it to an ellipsoid with two axes. Targets may be combined as a union or an intersection to form final targets. Targets may be constructed out of a series of outlines in various planes. For example, the user may draw an outline on each of three orthogonal planes (e.g. first the xy plane, then the yz plane, then the xz plane.)


Returning to FIG. 5, in optional step 506, a type of neurostimulation intervention is selected for one or more of the targets. Types of neurostimulation intervention can include, for example, stimulation (application of a stimulating field or drug), activation (actuating neurological tissue), depression (reducing the likelihood of activation of the neurological tissue), silencing (preventing the activation of the neurological tissue), or other forms of neuromodulation. Multiple forms of neuromodulation as described previously may be used in various combinations, with varying temporal order or relation, on the same target or targets. The intervention can be accomplished using electrical stimulation, optical stimulation, drug stimulation, or the like or any combination thereof. The one or more targets may be selected with respect to the desired type of intervention, for example, one or more targets can be selected for activation, one or more targets for depression, and one or more targets for silencing, or any combination thereof. When multiple targets are identified, the targets may be selected for the same type of intervention or different targets may be selected for different types of intervention.


In at least some embodiments, the user indicates a type of neurological intervention for each target. In some embodiments, a default type of neurological intervention (for example, stimulation or activation) is assumed unless the user selects a different type of neurological intervention. In some embodiments, the user can select the type of neurological intervention from a pull-down menu, optionally associated or displayed on the target, or by any other selection technique.


In optional step 508, the user provides the name of a disease or disorder (e.g. Parkinson's disease, depression, epilepsy) that is to be treated. In some instances, the disease or disorder may be a symptom or condition associated with another disease or disorder. The user may input the name of the disease or disorder using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the disease or disorder from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, the system may present a list for the disease or disorder based on the previously provided target. In some embodiments, there can be more than one disease or disorder provided.


In optional step 510, the user provides a clinical goal or a system goal or any combination thereof. In at least some embodiments, the clinical goal may be input as a verb/noun combination, for example, ‘reduce’+‘tremor’ or ‘avoid’+‘cognitive impairment’. The clinical goal generally refers to a disease or disorder or to a symptom of the disease or disorder or to a side-effect associated with the disease, disorder, or stimulation. The system goal generally refers to one or more of the components of the stimulation system or to one or more of the stimulation parameters and indicates an objection related to the component or parameter, such as, for example, ‘extended battery life’, ‘low frequency’, ‘short pulse width’, or the like. The user may input the clinical or system goal using a keyboard, touchscreen, voice-recognition system, or any other suitable input device. Additionally or alternatively, the user may select the clinical or system goal from a list using, for example, a drop-down menu or any other suitable selection mechanism. In some embodiments, the system may present a list for the clinical or system goal based on the previously provided target or provided disease or disorder or both. In some embodiments, there can be more than one clinical or system goal provided.


It will be understood that steps 504, 506, 508, or 510 can be rearranged in any order. In step 512, the system uses the provided inputs from steps 504, 506, 508, or 510 to select a set of stimulation parameters. In at least some embodiments, the system can utilize previous actual or estimated stimulation instances to correlate with the selections or inputs made by the user.


When selecting stimulation parameters, for example, according to step 410 in FIG. 4 of step 512 in FIG. 5, individual parameters may be limited based on one or more considerations, such as, for example, the anatomical structures that are targeted or on the disease or disorder being treated (or any combination thereof). For example, if the chosen target volume includes the STN, optionally with Parkinson's disease (PD) or essential tremor (ET) as an indication, the system may identify a range of pulse rates (e.g. 60-10,000 Hz), or a range of pulse widths (e.g. 30-90 μs). These limits may be a range limit, a maximum limit, a minimum limit, a set of allowed parameter combinations, or the like. In an alternative or combined manner, the user may prescribe or modify one or more settings, for example, pulse width, rate, pulse regularity, waveform shape, pulse train information, spatio-temporal patterns, or the like.


Any suitable method can be used to select stimulation parameters including methods disclosed in copending U.S. Provisional Patent Application Ser. No. 62/186,184, entitled “Systems and Methods for Analyzing Electrical Stimulation and Selecting or Manipulating Volumes of Activation”, filed on even date herewith. The anatomical region that is stimulated for a particular set of stimulation parameters can be estimated using any suitable estimation technique. These estimates can include, for example, estimates of axonal activation, estimates of cell bodies that are activated, estimates of fiber pathways that are activated, and the like or any combination thereof. In at least some instances, the estimate is called a value of activation (VOA) or stimulation field model (SFM) Examples of suitable methods for making these estimations include, but are not limited to, those described in U.S. Pat. Nos. 8,326,433; 8,675,945; 8,831,731; 8,849,632; and 8,958,615; U.S. Patent Application Publications Nos. 2009/0287272; 2009/0287273; 2012/0314924; 2013/0116744; 2014/0122379; and 2015/0066111; and U.S. Provisional Patent Application Ser. No. 62/030,655, all of which are incorporated herein by reference. It will be understood that other methods of estimating the stimulation region that do not use the stimulation parameters can also be employed.


The estimated stimulation region can be compared to the target to determine correspondence to the target. In at least some embodiments, the system or user can set a threshold (for example, a percentage overlap between the estimated stimulation region and the target or a percentage difference between the target and the estimate stimulation region) which when met by the comparison indicates that a set of stimulation parameters can be accepted as a final set of stimulation parameters. In some embodiments, a set of stimulation parameters can be arrived at iteratively based on the comparison and subsequent modification of the stimulation parameters. Such an iterative process can be performed automatically by a processor or performed semi-automatically with occasional input from the user or can be performed with user input to direct which parameters to modify or the amount of the modifications. In other embodiments, a system may compare a target with previously determined estimations (for example, a set of previously calculated VOAs or SFMs) and select a set of parameters based on these comparisons.


In at least some embodiments, the set of stimulation parameters may include one or more subsets of stimulation parameters to provide a neuromodulation intervention for more than one (overlapping or non-overlapping) targets or to provide a neuromodulation intervention for a particular target at different times. The neuromodulation intervention in the multiple targets can be performed in time-dependent manner (e.g. target 1 occurs first, target 2 next, and so forth). Neuromodulation intervention in the multiple targets may or may not overlap spatially or temporally.


Targets, or estimated stimulation regions based on stimulation parameters, may be displayed to the user as a static image or as an animation (e.g. when multiple target volumes are present at multiple times). The display of targets, or estimated stimulation regions based on stimulation parameters, may be based on any type of construct, such as, for example, an electrical potential distribution, electric field, divergence of electric field (activating function), the Hermitian of the activating function, a Volume of Activation (VOA) model, or a Stimulation Field Model (SFM) or the like which considers the effects of the neuromodulation intervention on active neural elements in the target and deterministically quantifies the effect, allowing for the display of continuous (e.g. threshold) or discrete (e.g. binary fire/no-fire) data. Targets, or estimated stimulation regions based on stimulation parameters, may be displayed as 2D contours or 3D surfaces at a variety of thresholds (for example, electrical potential, electrical field, or the like which could be chosen by the user or method) or planar cutaways with overlayed false color maps. Different targets, or estimated stimulation regions based on stimulation parameters, may be distinguished by, for example, color, texture, luminosity, or the like.


In at least some embodiments, the user can modify the previously identified set of stimulation parameters. In at least some embodiments, an estimated stimulation region can be displayed based on the modified stimulation parameters.


In an alternative to the methods illustrated in FIGS. 4 and 5, after the user or system selects one or more targets, the system or the user can engage in a steering technique to select the stimulation parameters. FIG. 7 is a flowchart of one method of steering a set of initial stimulation parameters in order to obtain a set of final stimulation parameters. In step 702 an initial set of stimulation parameters is provided. The set can be provided in any manner including using any of the methods described above. Alternatively, the set of initial stimulation parameters can selected using any other technique. Optionally, a GUI can display a representation of the field generated using these parameters, a volume of activation or stimulation generated using these parameters, or a lead graphically or alphanumerically presenting the parameters, or any other representation, or any combination of these representations. In other embodiments, there is no graphical representation of the lead or region of stimulation.


In step 704, the user provides an alphanumeric command to alter one or more of the stimulation parameters. In at least some embodiments, this command does not include, or is not composed exclusively of, a numerical value for any of the stimulation parameters. This command can include one or more words that direct modification of the stimulation parameters to obtain the goal recited in the command. Examples of such words include, but are not limited to, ‘up’, ‘down’, ‘left’, ‘right’, ‘clockwise’, ‘counter-clockwise’, ‘spread’, ‘focus’, ‘faster’, ‘slower’, ‘longer’, ‘shorter’, and so forth. These commands are abstract steering controls that can affect, for example, the number or selection of active electrodes, the polarity and amplitude of the electrodes, pulse width, frequency, amplitude, inter-pulse interval, regularity, and the like of the stimulation.


In optional step 706, the modification of the stimulation parameters is observed. In some embodiments, the modification can be observed, for example, by a change in a display of the representation of the field generated using these parameters or a volume of activation or a volume of stimulation generated using these parameters. In some embodiments, the modification can be observed as a stimulation effect or side effect that is noted by the user or patient. The user or patient may provide a rating of the stimulation parameters.


In step 708, the modification and observation steps (steps 704 and 706) can be repeated to perform further modifications of the stimulation parameters. This process can continue until a final set of stimulation parameters is obtained.



FIG. 8 is a flowchart of one method of steering a set of initial stimulation parameters in order to obtain a set of final stimulation parameters. In step 802 an initial set of stimulation parameters is provided. The set can be provided in any manner including using any of the methods described above. Alternatively, the set of initial stimulation parameters can selected using any other technique. A GUI displays a representation of the field generated using these parameters, a volume of activation or stimulation generated using these parameters, or a lead graphically or alphanumerically presenting the parameters, or any other representation, or any combination of these representations.


Using a graphical interface, in step 802 the user modifies the stimulation parameters. For example, the interface can include components for selecting which electrodes to activate; components for raising or lowering parameters such as amplitude, frequency, pulse width, or the length; and so forth. In other embodiments, the interface may permit the user to modify the field or volume of activation generated using the parameters and then the system can calculate the new set of stimulation parameters based on the modified field or volume.


In optional step 806, the modification of the stimulation parameters is observed. In some embodiments, the modification can be observed, for example, a change in a display of the representation of the field generated using these parameters or a volume of activation or a volume of stimulation generated using these parameters. In some embodiments, the modification can be observed as a stimulation effect or side effect that is noted by the user or patient. The user or patient may provide a rating of the stimulation parameters.


In step 808, the modification and observation steps (steps 804 and 806) can be repeated to perform further modifications of the stimulation parameters. This process can continue until a final set of stimulation parameters is obtained.


In at least some embodiments of a method of selecting stimulation parameters, a coarse exploration of the parameter space is made with abstracted steering controls (such as the methods in FIG. 7 or 8), and then a targeting method (such as the methods of FIG. 4 or 5) predicts a set of stimulation parameters for the target, and then a subsequent pass with steering controls is made. This process can be iterated. It has also been found that in some steering methods, if the current or field is steered from one combination of electrodes to a different combination, or from a combination of electrodes to a single electrode (or vice versa), certain descriptive parameters may change, for example, the radius of the volume of tissue activated, the cumulative rate experienced, the total power injected, or the like. For example, steering a current or field from two electrodes A and B to a single electrode B may increase the radius of the area of stimulation. The system can be arranged to account for this and, instead, maintain the radius of the area of stimulation constant by modifying the stimulation parameters accordingly. The system can be configured to maintain one or more characteristics constant, or to adjust them as parameters are adjusted.



FIGS. 9A and 9B illustrate another method of identifying a target using a lead 902 and target point 907 from two orthogonal views. (FIGS. 9A and 9B also illustrate an axis indicator 905.) The user or system places the target point 907. The system projects back a line 909 from the target point 907 to the surface of the lead 902. In some embodiments, the line 909 can be a minimum distance line from the target point 907 to the surface of the lead 902. At the intersection of the lead and this projected line, a point source 911 is identified. This point source 911 can be used to model a monopolar point source (for example, cathodic) field. Other virtual electrical elements may be employed instead of a point source, such as a combination of point sources or a virtual electrode having some other form than the real electrodes on the lead. Alternatively, closed form solutions to approximating electrodes may be used. The virtual electrode or electrodes may, or may at times, take the form and position of one or more electrodes in the real electrode array. The user may select the boundaries of the virtual electrode or the system can determine boundaries which are optionally user-adjustable. Although a point source will be used below for illustrative purposes, it will be understood that a combination of point sources or a virtual electrode can be used instead of the point source.



FIG. 10 illustrates one embodiment of a point source 911 on a lead 902. In this embodiment, when calculating the potential field 913 for each point source 911, only the region not overlapping the lead, bounded by the plane tangent to the lead at the point is used. In this calculation, the current only contributing to ½ of the volume is considered so double the current I used to calculate the current at each point V(r)=2I/4πrσ where r is the distance from the point source and σ is the conductivity. In other embodiments, the solution for a point source on the exterior of an insulating rod may be used.


The system then uses the extant electrodes 934 of the lead 902 to determine a best-fit (for example, using a least squares calculation) to this point source field by activating the electrodes using a set of stimulation parameters to approximate the point source field. The point source field may be a scalar potential field, a vector field (for example, an electric field, or divergence of the electric field), or a field of Hamiltonians of the divergence of the electric field, or the like. In some embodiments, the user can modify the resulting stimulation parameters to modify the field. For example, the user may ‘focus’ or ‘spread’ the field radially or longitudinally. In at least some embodiments, radial focus can involve the addition of anodes antipodal to the target point.


In another embodiment, the target electric field may be computed from combinations of pre-computed basis fields, or by the combination of solutions of analytic fits to pre-computed data.


Instead of designating a target point, a user can alternatively designate a virtual electrode and use this electrode to generate stimulation. The system can then select stimulation parameters using the actual electrodes of the lead to approximate the virtual electrode. FIG. 11 illustrates a lead 1102 with electrodes 1134. The user can place and edit a virtual electrode 1135 on the surface of the lead 1132. The virtual electrode 1135 can have any desired shape (for example, circle, rectangle, triangle, or the like) which can be curved, if desired, or even form a ring around the lead. Typically, the virtual electrode is limited to the span of the electrode array.


In some embodiments, edges or segments of edges of the virtual electrode can be set to ‘guard’ to reduce or prevent extending the field generated by the virtual electrode beyond the edges. For example, if the virtual electrode is a cathode, the guarded edges can provide anodic guarding. In at least some embodiments, the strength of guarding can be set, independently for each edge or segment. FIG. 12 illustrates anodic guarding handled by creating one or more virtual guarding electrodes 1137, one for each edge of the virtual electrode 1135 of the lead 1102 which is guarded, and moving them closer or strengthening them as the user increases the level of guarding.


In any of the systems and methods described above, when a set of stimulation parameters is determined, the set of stimulation parameters can be communicated to an IPG, ETS, or other device.


It will be understood that the system can include one or more of the methods described hereinabove with respect to FIGS. 4-12 in any combination. The methods, systems, and units described herein may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Accordingly, the methods, systems, and units described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The methods described herein can be performed using any type of processor or any combination of processors where each processor performs at least part of the process.


It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations and methods disclosed herein, can be implemented by computer program instructions. These program instructions may be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart block or blocks disclosed herein. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer implemented process. The computer program instructions may also cause at least some of the operational steps to be performed in parallel. Moreover, some of the steps may also be performed across more than one processor, such as might arise in a multi-processor computer system. In addition, one or more processes may also be performed concurrently with other processes, or even in a different sequence than illustrated without departing from the scope or spirit of the invention.


The computer program instructions can be stored on any suitable computer-readable medium including, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (“DVD”) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device.


The above specification provides a description of the structure, manufacture, and use of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention also resides in the claims hereinafter appended.

Claims
  • 1. A system for identifying a set of stimulation parameters, the system comprising: a computer processor configured and arranged to perform the following actions: providing a model of a lead comprising a plurality of electrodes, wherein the plurality of electrodes comprises a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes comprises two or more of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead;receiving a selection of a target point external to the model of the lead;projecting a straight line from the target point onto a surface of the model of the lead to identify a virtual electrode on the model of the lead; andselecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.
  • 2. The system of claim 1, wherein projecting the target point comprises projecting the target point onto a nearest point of the surface of the model of the lead to identify the virtual electrode.
  • 3. The system of claim 1, wherein the actions further comprise calculating the electrical field in a region that does not overlap with the lead and is bounded by a plane tangent to the lead at the virtual electrode.
  • 4. The system of claim 1, wherein the electrical field is a scalar potential field, a vector field, or a field of Hamiltonians of a divergence of an electrical field.
  • 5. The system of claim 1, wherein the actions further comprise receiving a user modification of at least one of the stimulation parameters.
  • 6. The system of claim 1, wherein the actions further comprise defining at least one guarding electrode adjacent the virtual electrode.
  • 7. The system of claim 6, wherein defining at least one guarding electrode comprises defining two guarding electrodes circumferentially disposed on opposite sides of the virtual electrode.
  • 8. The system of claim 1, wherein the virtual electrode is modeled as a monopolar point source.
  • 9. The system of claim 1, wherein the actions further comprise receiving a user modification to modify the electrical field radially.
  • 10. The system of claim 1, wherein the actions further comprise receiving a user modification to modify the electrical field longitudinally.
  • 11. The system of claim 1, wherein selecting the set of stimulation parameters comprises selecting two or more of the electrodes to approximate the electrical field generated by the virtual electrode.
  • 12. The system of claim 9, wherein receiving a user modification comprises receiving the user modification to spread the electrical field radially.
  • 13. The system of claim 9, wherein receiving a user modification comprises receiving the user modification to focus the electrical field radially.
  • 14. The system of claim 10, wherein receiving a user modification comprises receiving the user modification to spread the electrical field longitudinally.
  • 15. The system of claim 10, wherein receiving a user modification comprises receiving the user modification to focus the electrical field longitudinally.
  • 16. A method for identifying a set of stimulation parameters, the method comprising: providing a model of a lead comprising a plurality of electrodes, wherein the plurality of electrodes comprises a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes comprises two or more of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead;receiving a selection of a target point external to the model of the lead;projecting a straight line from the target point onto a surface of the model of the lead to identify a virtual electrode on the model of the lead; andselecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.
  • 17. The method of claim 16, wherein the actions further comprise calculating the electrical field in a region that does not overlap with the lead and is bounded by a plane tangent to the lead at the virtual electrode.
  • 18. The method of claim 16, wherein the actions further comprise receiving a user modification of at least one of the stimulation parameters.
  • 19. The method of claim 16, wherein the actions further comprise defining at least one guarding electrode adjacent the virtual electrode.
  • 20. A non-transitory computer-readable medium having processor-executable instructions for identifying a set of stimulation parameters, the processor-executable instructions when installed onto a device enable the device to perform actions, including: providing a model of a lead comprising a plurality of electrodes, wherein the plurality of electrodes comprises a plurality of segmented electrodes forming at least one set of segmented electrodes, wherein each set of segmented electrodes comprises two or more of the segmented electrodes disposed around a circumference of the lead at a same longitudinal position along the lead;receiving a selection of a target point external to the model of the lead;projecting a straight line from the target point onto a surface of the model of the lead to identify a virtual electrode on the model of the lead; andselecting a set of stimulation parameters for at least one of the electrodes to approximate an electrical field generated from the virtual electrode.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No. 15/194,380, filed Jun. 27, 2016, which issued as U.S. Pat. No. 10,441,800, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/186,172, filed Jun. 29, 2015, both of which are incorporated herein by reference.

US Referenced Citations (472)
Number Name Date Kind
3999555 Person Dec 1976 A
4144889 Tyers Mar 1979 A
4177818 De Pedro Dec 1979 A
4341221 Testerman Jul 1982 A
4378797 Osterholm Apr 1983 A
4445500 Osterholm May 1984 A
4735208 Wyler et al. Apr 1988 A
4765341 Mower et al. Aug 1988 A
4841973 Stecker Jun 1989 A
5067495 Brehm Nov 1991 A
5099846 Hardy Mar 1992 A
5222494 Baker, Jr. Jun 1993 A
5255693 Dutcher Oct 1993 A
5259387 dePinto Nov 1993 A
5304206 Baker, Jr. et al. Apr 1994 A
5344438 Testerman et al. Sep 1994 A
5361763 Kao et al. Nov 1994 A
5452407 Crook Sep 1995 A
5560360 Filler et al. Oct 1996 A
5565949 Kasha, Jr. Oct 1996 A
5593427 Gliner et al. Jan 1997 A
5601612 Gliner et al. Feb 1997 A
5607454 Cameron et al. Mar 1997 A
5620470 Gliner et al. Apr 1997 A
5651767 Schulman et al. Jul 1997 A
5711316 Elsberry et al. Jan 1998 A
5713922 King Feb 1998 A
5716377 Rise et al. Feb 1998 A
5724985 Snell et al. Mar 1998 A
5749904 Gliner et al. May 1998 A
5749905 Gliner et al. May 1998 A
5776170 MacDonald et al. Jul 1998 A
5782762 Vining Jul 1998 A
5843148 Gijsbers et al. Dec 1998 A
5859922 Hoffmann Jan 1999 A
5868740 Leveen et al. Feb 1999 A
5897583 Meyer et al. Apr 1999 A
5910804 Fortenbery et al. Jun 1999 A
5925070 King et al. Jul 1999 A
5938688 Schiff Aug 1999 A
5938690 Law et al. Aug 1999 A
5978713 Prutchi et al. Nov 1999 A
6016449 Fischell et al. Jan 2000 A
6029090 Herbst Feb 2000 A
6029091 de la Rama et al. Feb 2000 A
6050992 Nichols Apr 2000 A
6058331 King May 2000 A
6066163 John May 2000 A
6083162 Vining Jul 2000 A
6094598 Elsberry et al. Jul 2000 A
6096756 Crain et al. Aug 2000 A
6106460 Panescu et al. Aug 2000 A
6109269 Rise et al. Aug 2000 A
6128538 Aschell et al. Oct 2000 A
6129685 Howard, III Oct 2000 A
6146390 Heilbrun et al. Nov 2000 A
6161044 Silverstone Dec 2000 A
6167311 Rezai Dec 2000 A
6181969 Gord Jan 2001 B1
6192266 Dupree et al. Feb 2001 B1
6205361 Kuzma Mar 2001 B1
6208881 Champeau Mar 2001 B1
6240308 Hardy et al. May 2001 B1
6246912 Sluijter et al. Jun 2001 B1
6253109 Gielen Jun 2001 B1
6289239 Panescu et al. Sep 2001 B1
6301492 Zonenshayn Oct 2001 B1
6310619 Rice Oct 2001 B1
6319241 King Nov 2001 B1
6336899 Yamazaki Jan 2002 B1
6343226 Sunde et al. Jan 2002 B1
6351675 Tholen et al. Feb 2002 B1
6353762 Baudino et al. Mar 2002 B1
6366813 Dilorenzo Apr 2002 B1
6368331 Front et al. Apr 2002 B1
6389311 Whayne et al. May 2002 B1
6393325 Mann et al. May 2002 B1
6421566 Holsheimer Jul 2002 B1
6435878 Reynolds et al. Aug 2002 B1
6442432 Lee Aug 2002 B2
6463328 John Oct 2002 B1
6491699 Henderson et al. Dec 2002 B1
6494831 Koritzinsky Dec 2002 B1
6507759 Prutchi et al. Jan 2003 B1
6510347 Borkan Jan 2003 B2
6516227 Meadows et al. Feb 2003 B1
6517480 Krass Feb 2003 B1
6539263 Schiff Mar 2003 B1
6560490 Grill et al. May 2003 B2
6579280 Kovach et al. Jun 2003 B1
6600956 Maschino et al. Jul 2003 B2
6606523 Jenkins Aug 2003 B1
6609029 Mann et al. Aug 2003 B1
6609031 Law et al. Aug 2003 B1
6609032 Woods et al. Aug 2003 B1
6622048 Mann et al. Sep 2003 B1
6631297 Mo Oct 2003 B1
6654642 North et al. Nov 2003 B2
6662053 Borkan Dec 2003 B2
6675046 Holsheimer Jan 2004 B2
6684106 Herbst Jan 2004 B2
6687392 Touzawa et al. Feb 2004 B1
6690972 Conley et al. Feb 2004 B2
6690974 Archer et al. Feb 2004 B2
6692315 Soumillon et al. Feb 2004 B1
6694162 Hartlep Feb 2004 B2
6694163 Vining Feb 2004 B1
6708096 Frei et al. Mar 2004 B1
6741892 Meadows et al. May 2004 B1
6748098 Rosenfeld Jun 2004 B1
6748276 Daignault, Jr. et al. Jun 2004 B1
6778846 Martinez et al. Aug 2004 B1
6788969 Dupree et al. Sep 2004 B2
6795737 Gielen et al. Sep 2004 B2
6827681 Tanner et al. Dec 2004 B2
6830544 Tanner Dec 2004 B2
6845267 Harrison et al. Jan 2005 B2
6850802 Holsheimer Feb 2005 B2
6895280 Meadows et al. May 2005 B2
6909913 Vining Jun 2005 B2
6937891 Leinders et al. Aug 2005 B2
6937903 Schuler et al. Aug 2005 B2
6944497 Stypulkowski Sep 2005 B2
6944501 Pless Sep 2005 B1
6950707 Whitehurst Sep 2005 B2
6969388 Goldman et al. Nov 2005 B2
7003349 Andersson et al. Feb 2006 B1
7003352 Whitehurst Feb 2006 B1
7008370 Tanner et al. Mar 2006 B2
7008413 Kovach et al. Mar 2006 B2
7035690 Goetz Apr 2006 B2
7043293 Baura May 2006 B1
7047082 Schrom et al. May 2006 B1
7047084 Erickson et al. May 2006 B2
7050857 Samuelsson et al. May 2006 B2
7054692 Whitehurst et al. May 2006 B1
7136518 Griffin et al. May 2006 B2
7058446 Schuler et al. Jun 2006 B2
7082333 Bauhahn et al. Jul 2006 B1
7107102 Daignault et al. Sep 2006 B2
7126000 Ogawa et al. Oct 2006 B2
7127297 Law et al. Oct 2006 B2
7136695 Pless et al. Nov 2006 B2
7142923 North et al. Nov 2006 B2
7146219 Sieracki et al. Dec 2006 B2
7146223 King Dec 2006 B1
7151961 Whitehurst Dec 2006 B1
7155279 Whitehurst et al. Dec 2006 B2
7167760 Dawant et al. Jan 2007 B2
7177674 Echauz et al. Feb 2007 B2
7181286 Sieracki et al. Feb 2007 B2
7184837 Goetz Feb 2007 B2
7191014 Kobayashi et al. Mar 2007 B2
7209787 Dilorenzo Apr 2007 B2
7211050 Caplygin May 2007 B1
7216000 Sieracki et al. May 2007 B2
7217276 Henderson May 2007 B2
7218968 Condie et al. May 2007 B2
7228179 Dampen et al. Jun 2007 B2
7231254 DiLorenzo Jun 2007 B2
7236830 Gliner Jun 2007 B2
7239910 Tanner Jul 2007 B2
7239916 Thompson et al. Jul 2007 B2
7239926 Goetz Jul 2007 B2
7242984 DiLorenzo Jul 2007 B2
7244150 Brase et al. Jul 2007 B1
7252090 Goetz Aug 2007 B2
7254445 Law et al. Aug 2007 B2
7254446 Erickson Aug 2007 B1
7257447 Cates et al. Aug 2007 B2
7266412 Stypulkowski Sep 2007 B2
7294107 Simon et al. Nov 2007 B2
7295876 Erickson Nov 2007 B1
7299096 Balzer et al. Nov 2007 B2
7308302 Schuler et al. Dec 2007 B1
7313430 Urquhart Dec 2007 B2
7324851 DiLorenzo Jan 2008 B1
7346382 McIntyre et al. Mar 2008 B2
7388974 Yanagita Jun 2008 B2
7450997 Planea et al. Nov 2008 B1
7463928 Lee et al. Dec 2008 B2
7499048 Sieracki et al. Mar 2009 B2
7505815 Lee et al. Mar 2009 B2
7548786 Lee et al. Jun 2009 B2
7565199 Sheffield et al. Jul 2009 B2
7603177 Sieracki et al. Oct 2009 B2
7617002 Goetz Nov 2009 B2
7623918 Goetz Nov 2009 B2
7650184 Walter Jan 2010 B2
7657319 Goetz et al. Feb 2010 B2
7672734 Anderson et al. Mar 2010 B2
7676273 Goetz et al. Mar 2010 B2
7680526 McIntyre et al. Mar 2010 B2
7734340 De Ridder Jun 2010 B2
7761165 He et al. Jul 2010 B1
7783359 Meadows Aug 2010 B2
7792590 Bianca et al. Sep 2010 B1
7809446 Meadows Oct 2010 B2
7826902 Stone et al. Nov 2010 B2
7848802 Goetz et al. Dec 2010 B2
7860548 McIntyre et al. Dec 2010 B2
7904134 McIntyre et al. Mar 2011 B2
7945105 Jaenisch May 2011 B1
7949395 Kuzma May 2011 B2
7974706 Moffitt et al. Jul 2011 B2
8019439 Kuzma et al. Sep 2011 B2
8175710 He May 2012 B2
8180601 Butson et al. May 2012 B2
8195300 Gliner et al. Jun 2012 B2
8224450 Brase Jul 2012 B2
8257684 Covalin et al. Sep 2012 B2
8262714 Holvershorn et al. Sep 2012 B2
8271094 Moffitt et al. Sep 2012 B1
8295944 Howard et al. Oct 2012 B2
8326433 Blum et al. Dec 2012 B2
8364278 Pianca et al. Jan 2013 B2
8391985 McDonald Mar 2013 B2
8429174 Ramani et al. Apr 2013 B2
8452415 Goetz et al. May 2013 B2
8473061 Moffitt et al. Jun 2013 B2
8543189 Paitel et al. Sep 2013 B2
8571665 Moffitt et al. Oct 2013 B2
8606360 Butson et al. Dec 2013 B2
8620452 King et al. Dec 2013 B2
8675945 Barnhorst et al. Mar 2014 B2
8688235 Rance et al. Apr 2014 B1
8792993 Pianca et al. Jul 2014 B2
8831731 Blum et al. Sep 2014 B2
8849632 Sparks et al. Sep 2014 B2
8958615 Blum et al. Feb 2015 B2
9248272 Romero Feb 2016 B2
20010031071 Nichols et al. Oct 2001 A1
20020032375 Bauch et al. Mar 2002 A1
20020062143 Baudino et al. May 2002 A1
20020087201 Firlik et al. Jul 2002 A1
20020099295 Gil et al. Jul 2002 A1
20020115603 Whitehouse Aug 2002 A1
20020116030 Rezei Aug 2002 A1
20020123780 Grill et al. Sep 2002 A1
20020128694 Holsheimer Sep 2002 A1
20020151939 Rezai Oct 2002 A1
20020183607 Bauch et al. Dec 2002 A1
20020183740 Edwards et al. Dec 2002 A1
20020183817 Van Venrooij et al. Dec 2002 A1
20030097159 Schiff et al. May 2003 A1
20030149450 Mayberg Aug 2003 A1
20030171791 KenKnight et al. Sep 2003 A1
20030212439 Schuler et al. Nov 2003 A1
20040034394 Woods et al. Feb 2004 A1
20040044279 Lewin et al. Mar 2004 A1
20040044378 Holsheimer Mar 2004 A1
20040044379 Holsheimer Mar 2004 A1
20040054297 Wingeier et al. Mar 2004 A1
20040059395 North et al. Mar 2004 A1
20040106916 Quaid et al. Jun 2004 A1
20040133248 Frei et al. Jul 2004 A1
20040152957 Stivoric et al. Aug 2004 A1
20040181262 Bauhahn Sep 2004 A1
20040186532 Tadlock Sep 2004 A1
20040199216 Lee et al. Oct 2004 A1
20040267330 Lee et al. Dec 2004 A1
20050021090 Schuler et al. Jan 2005 A1
20050033380 Tanner et al. Feb 2005 A1
20050049649 Luders et al. Mar 2005 A1
20050060001 Singhal et al. Mar 2005 A1
20050060009 Goetz Mar 2005 A1
20050070781 Dawant et al. Mar 2005 A1
20050075689 Toy et al. Apr 2005 A1
20050085714 Foley et al. Apr 2005 A1
20050165294 Weiss Jul 2005 A1
20050171587 Daglow et al. Aug 2005 A1
20050228250 Bitter et al. Oct 2005 A1
20050251061 Schuler et al. Nov 2005 A1
20050261061 Nguyen et al. Nov 2005 A1
20050261601 Schuler et al. Nov 2005 A1
20050261747 Schiller et al. Nov 2005 A1
20050267347 Oster Dec 2005 A1
20050288732 Schuler et al. Dec 2005 A1
20060004422 De Ridder Jan 2006 A1
20060017749 McIntyre et al. Jan 2006 A1
20060020292 Goetz et al. Jan 2006 A1
20060069415 Cameron et al. Mar 2006 A1
20060094951 Dean et al. May 2006 A1
20060095088 De Riddler May 2006 A1
20060155340 Schuler et al. Jul 2006 A1
20060206169 Schuler Sep 2006 A1
20060218007 Bjorner et al. Sep 2006 A1
20060224189 Schuler et al. Oct 2006 A1
20060235472 Goetz et al. Oct 2006 A1
20060259079 King Nov 2006 A1
20060259099 Goetz et al. Nov 2006 A1
20070000372 Rezai et al. Jan 2007 A1
20070017749 Dold et al. Jan 2007 A1
20070027514 Gerber Feb 2007 A1
20070043268 Russell Feb 2007 A1
20070049817 Preiss et al. Mar 2007 A1
20070067003 Sanchez et al. Mar 2007 A1
20070078498 Rezai et al. Apr 2007 A1
20070083104 Butson et al. Apr 2007 A1
20070123953 Lee et al. May 2007 A1
20070129769 Bourget et al. Jun 2007 A1
20070135855 Foshee et al. Jun 2007 A1
20070150036 Anderson Jun 2007 A1
20070156186 Lee et al. Jul 2007 A1
20070162086 DiLorenzo Jul 2007 A1
20070162235 Zhan et al. Jul 2007 A1
20070168004 Walter Jul 2007 A1
20070168007 Kuzma et al. Jul 2007 A1
20070185544 Dawant et al. Aug 2007 A1
20070191887 Schuler et al. Aug 2007 A1
20070191912 Ficher et al. Aug 2007 A1
20070197891 Shachar et al. Aug 2007 A1
20070203450 Berry Aug 2007 A1
20070203532 Tess et al. Aug 2007 A1
20070203537 Goetz et al. Aug 2007 A1
20070203538 Stone et al. Aug 2007 A1
20070203539 Stone Aug 2007 A1
20070203540 Goetz et al. Aug 2007 A1
20070203541 Goetz et al. Aug 2007 A1
20070203543 Stone et al. Aug 2007 A1
20070203544 Goetz et al. Aug 2007 A1
20070203545 Stone et al. Aug 2007 A1
20070203546 Stone et al. Aug 2007 A1
20070213789 Nolan et al. Sep 2007 A1
20070213790 Nolan et al. Sep 2007 A1
20070244519 Keacher et al. Oct 2007 A1
20070245318 Goetz et al. Oct 2007 A1
20070255321 Gerber et al. Nov 2007 A1
20070255322 Gerber et al. Nov 2007 A1
20070265664 Gerber et al. Nov 2007 A1
20070276441 Goetz Nov 2007 A1
20070282189 Dan et al. Dec 2007 A1
20070288064 Butson et al. Dec 2007 A1
20080027514 DeMulling et al. Jan 2008 A1
20080039895 Fowler et al. Feb 2008 A1
20080071150 Miesel et al. Mar 2008 A1
20080081982 Simon et al. Apr 2008 A1
20080086451 Torres et al. Apr 2008 A1
20080103533 Patel et al. May 2008 A1
20080114233 McIntyre et al. May 2008 A1
20080114579 McIntyre et al. May 2008 A1
20080123922 Gielen et al. May 2008 A1
20080123923 Gielen et al. May 2008 A1
20080133141 Frost Jun 2008 A1
20080141217 Goetz et al. Jun 2008 A1
20080154340 Goetz et al. Jun 2008 A1
20080154341 McIntyre et al. Jun 2008 A1
20080163097 Goetz et al. Jul 2008 A1
20080183256 Keacher Jul 2008 A1
20080188734 Suryanarayanan et al. Aug 2008 A1
20080215118 Goetz et al. Sep 2008 A1
20080227139 Deisseroth et al. Sep 2008 A1
20080242950 Jung et al. Oct 2008 A1
20080261165 Steingart et al. Oct 2008 A1
20080269588 Csavoy et al. Oct 2008 A1
20080300654 Lambert et al. Dec 2008 A1
20080300797 Tabibiazar et al. Dec 2008 A1
20090016491 Li Jan 2009 A1
20090054950 Stephens Feb 2009 A1
20090082640 Kovach et al. Mar 2009 A1
20090082829 Panken et al. Mar 2009 A1
20090112289 Lee et al. Apr 2009 A1
20090118635 Lujan et al. May 2009 A1
20090118786 Meadows et al. May 2009 A1
20090149917 Whitehurst et al. Jun 2009 A1
20090187222 Barker Jul 2009 A1
20090196471 Goetz et al. Aug 2009 A1
20090196472 Goetz et al. Aug 2009 A1
20090198306 Goetz et al. Aug 2009 A1
20090198354 Wilson Aug 2009 A1
20090204192 Carlton et al. Aug 2009 A1
20090208073 McIntyre et al. Aug 2009 A1
20090210208 McIntyre et al. Aug 2009 A1
20090242399 Kamath et al. Oct 2009 A1
20090276008 Lee et al. Nov 2009 A1
20090276021 Meadows et al. Nov 2009 A1
20090281595 King et al. Nov 2009 A1
20090281596 King et al. Nov 2009 A1
20090287271 Blum et al. Nov 2009 A1
20090287272 Kokones et al. Nov 2009 A1
20090287273 Carlton et al. Nov 2009 A1
20090287467 Sparks et al. Nov 2009 A1
20090299164 Singhal et al. Dec 2009 A1
20090299165 Singhal et al. Dec 2009 A1
20090299380 Singhal et al. Dec 2009 A1
20100010566 Thacker et al. Jan 2010 A1
20100010646 Drew et al. Jan 2010 A1
20100023103 Elborno Jan 2010 A1
20100023130 Henry et al. Jan 2010 A1
20100030312 Shen Feb 2010 A1
20100049276 Blum et al. Feb 2010 A1
20100049280 Goetz Feb 2010 A1
20100064249 Groetken Mar 2010 A1
20100076535 Pianca et al. Mar 2010 A1
20100113959 Pascual-Leon et al. May 2010 A1
20100121409 Kothandaraman et al. May 2010 A1
20100135553 Joglekar Jun 2010 A1
20100137944 Zhu Jun 2010 A1
20100152604 Kuala et al. Jun 2010 A1
20100179562 Linker et al. Jul 2010 A1
20100268298 Moffitt Oct 2010 A1
20100324410 Paek et al. Dec 2010 A1
20100331883 Schmitz et al. Dec 2010 A1
20110004267 Meadows Jan 2011 A1
20110005069 Pianca Jan 2011 A1
20110040351 Buston et al. Feb 2011 A1
20110066407 Butson et al. Mar 2011 A1
20110078900 Pianca et al. Apr 2011 A1
20110130803 McDonald Jun 2011 A1
20110130816 Howard et al. Jun 2011 A1
20110130817 Chen Jun 2011 A1
20110130818 Chen Jun 2011 A1
20110172737 Davis et al. Jul 2011 A1
20110184487 Alberts et al. Jul 2011 A1
20110191275 Lujan et al. Aug 2011 A1
20110196253 McIntyre et al. Aug 2011 A1
20110213440 Fowler et al. Sep 2011 A1
20110238129 Moffitt Sep 2011 A1
20110306845 Osorio Dec 2011 A1
20110306846 Osorio Dec 2011 A1
20110307032 Goetz et al. Dec 2011 A1
20110313500 Barker et al. Dec 2011 A1
20120016378 Pianca et al. Jan 2012 A1
20120027272 Akinyemi et al. Feb 2012 A1
20120046710 Digiore et al. Feb 2012 A1
20120046715 Moffitt et al. Feb 2012 A1
20120071949 Pianca et al. Mar 2012 A1
20120078106 Dentinger et al. Mar 2012 A1
20120089205 Boyden et al. Apr 2012 A1
20120116476 Kothandaraman May 2012 A1
20120165898 Moffitt Jun 2012 A1
20120165901 Zhu et al. Jun 2012 A1
20120165911 Pianca Jun 2012 A1
20120197375 Pianca et al. Aug 2012 A1
20120203316 Moffitt et al. Aug 2012 A1
20120203320 Digiore et al. Aug 2012 A1
20120203321 Moffitt et al. Aug 2012 A1
20120207378 Gupta et al. Aug 2012 A1
20120226138 DeSalles et al. Sep 2012 A1
20120229468 Lee et al. Sep 2012 A1
20120265262 Osorio Oct 2012 A1
20120265268 Blum et al. Oct 2012 A1
20120302912 Moffitt et al. Nov 2012 A1
20120303087 Moffitt et al. Nov 2012 A1
20120314924 Carlton et al. Dec 2012 A1
20120316615 Digiore et al. Dec 2012 A1
20120316619 Goetz et al. Dec 2012 A1
20130039550 Blum et al. Feb 2013 A1
20130060305 Bokil Mar 2013 A1
20130105071 Digiore et al. May 2013 A1
20130116744 Blum et al. May 2013 A1
20130116748 Bokil et al. May 2013 A1
20130116749 Cariton et al. May 2013 A1
20130116929 Carlton et al. May 2013 A1
20130197424 Bedenbaugh Aug 2013 A1
20130197602 Pianca et al. Aug 2013 A1
20140039587 Romero Feb 2014 A1
20140067018 Carcieri et al. Mar 2014 A1
20140074180 Heldman et al. Mar 2014 A1
20140122379 Moffitt et al. May 2014 A1
20140277284 Chen Sep 2014 A1
20140353001 Romero et al. Dec 2014 A1
20140358208 Howard et al. Dec 2014 A1
20140358209 Romero et al. Dec 2014 A1
20140358210 Howard et al. Dec 2014 A1
20150018915 Leven Jan 2015 A1
20150045864 Howard Feb 2015 A1
20150051681 Hershey Feb 2015 A1
20150066111 Blum et al. Mar 2015 A1
20150066120 Govea Mar 2015 A1
20150134031 Moffitt et al. May 2015 A1
20150151113 Govea et al. Jun 2015 A1
Foreign Referenced Citations (27)
Number Date Country
1048320 Nov 2000 EP
1166819 Jan 2002 EP
1372780 Jan 2004 EP
1559369 Aug 2005 EP
9739797 Oct 1997 WO
9848880 Nov 1998 WO
0190876 Nov 2001 WO
0226314 Apr 2002 WO
0228473 Apr 2002 WO
02065896 Aug 2002 WO
02072192 Sep 2002 WO
03086185 Oct 2003 WO
2004019799 Mar 2004 WO
2004041080 May 2005 WO
2006017053 Feb 2006 WO
2006113305 Oct 2006 WO
20071097859 Aug 2007 WO
20071097861 Aug 2007 WO
2007100427 Sep 2007 WO
2007100428 Sep 2007 WO
2007112061 Oct 2007 WO
2009097224 Aug 2009 WO
2010120823 Oct 2010 WO
2011025865 Mar 2011 WO
2011139779 Nov 2011 WO
2011159688 Dec 2011 WO
2012088482 Jun 2012 WO
Non-Patent Literature Citations (273)
Entry
Nowinski, W. L., et al., “Statistical analysis of 168 bilateral subthalamic nucleus implantations by means of the probabilistic functional atlas.”, Neurosurgery 57(4 Suppl) (Oct. 2005),319-30.
Obeso, J. A., et al., “Deep-brain stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in Parkinson's disease.”, N Engl J Med., 345{13I. The Deep-Brain Stimulation for Parkinson's Disease Study Group, (Sep. 27, 2001 ),956-63.
Butson et al.. “Current Steering to control the volume of tissue activated during deep brain stimulation,” vol. 1, No. 1, Dec. 3, 2007, pp. 7-15.
Patrick, S. K., et al., “Quantification of the UPDRS rigidity scale”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, [see also IEEE Trans. on Rehabilitation Engineering 9(1). (2001),31-41.
Phillips, M. D., et al., “Parkinson disease: pattern of functional MR imaging activation during deep brain stimulation of subthalamic nucleus—initial experience”, Radiology 239(1). (Apr. 2006),209-16.
Ericsson, A. et al., “Construction of a patient-specific atlas of the brain: Application to normal aging,” Biomedical Imaging: From Nano to Macro, ISBI 2008, 5th IEEE International Symposium, May 14, 2008, pp. 480-483.
Kaikai Shen et al., “Atlas selection strategy using least angle regression in multi-atlas segmentation propagation,” Biomedical Imaging: From Nano to Macro, 2011, 8th IEEE International Symposium, ISBI 2011, Mar. 30, 2011, pp. 1746-1749.
Liliane Ramus et al., “Assessing selection methods in the cotnext of multi-atlas based segmentation,” Biomedical Imaging: From Nano to Macro, 2010, IEEE International Symposium, Apr. 14, 2010, pp. 1321-1324.
Olivier Commowick et al., “Using Frankenstein's Creature Paradigm to Build a Patient Specific Atlas,” Sep. 20, 2009, Medical Image Computing and Computer-Assisted Intervention, pp. 993-1000.
Lotjonen J.M.P. et al., “Fast and robust multi-atlas segmentation of brain magnetic resonance images,” NeuroImage, Academic Press, vol. 49, No. 3, Feb. 1, 2010, pp. 2352-2365.
McIntyre, C. C., et al., “How does deep brain stimulation work? Present understanding and future questions.”, J Clin Neurophysiol. 21 (1 ). (Jan.-Feb. 2004 ),40-50.
Sanchez Castro et al., “A cross validation study of deep brain stimulation targeting: From experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms,” IEEE Transactions on Medical Imaging, vol. 25, No. 11, Nov. 1, 2006, pp. 1440-1450.
Plaha, P. , et al., “Stimulation of the caudal zona incerta is superior to stimulation of the subthalamic nucleus in improving contralateral parkinsonism.”, Brain 129{Pt 7) (Jul. 2006), 1732-4 7.
Rattay, F, “Analysis of models for external stimulation of axons”. IEEE Trans. Biomed. Eng. vol. 33 (1986),974-977.
Rattay, F., “Analysis of the electrical excitation of CNS neurons”, IEEE Transactions on Biomedical Engineering 45 (6). (Jun. 1998).766-772.
Rose, T. L., et al., “Electrical stimulation with Pt electrodes. VIII. Electrochemically safe charge injection limits with 0.2 ms pulses [neuronal application]”, IEEE Transactions on Biomedical Engineering, 37(11 ), (Nov. 1990), 1118-1120.
Rubinstein, J. T., et al., “Signal coding in cochlear implants: exploiting stochastic effects of electrical stimulation”, Ann Otol Rhinol Laryngol Suppl.. 191, (Sep. 2003), 14-9.
Schwan. H.P., et al., “The conductivity ofliving tissues.”, Ann NY Acad Sci., 65(6). (AUQ., 1957),1007-13.
Taylor, R. S., et al., “Spinal cord stimulation for chronic back and leg pain and failed back surgery syndrome: a systematic review and analysis of prognostic factors”, Spine 30(1 ). (Jan. 1, 2005), 152-60.
Siegel, Ralph M. et al., “Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey,” Cerebral Cortex, New York, NY, vol. 17, No. 2, Feb. 2007, pp. 378-390.
Klein, A. et al., “Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration,” NeuroImage, Academic Press, Orlando, FL, vol. 46, No. 3, Jul. 2009, pp. 786-802.
Geddes, L. A., et al., “The specific resistance of biological material—a compendium of data for the biomedical engineer and physiologist.”, Med Biol Ena. 5(3). (May 1967),271-93.
Gimsa, J., et al., “Choosing electrodes for deep brain stimulation experiments-electrochemical considerations.”, J Neurosci Methods, 142(2), (Mar. 30, 2005),251-65.
Vidailhet, M. , et al., “Bilateral deep-brain stimulation of the globus pallidus in primary generalized dystonia”, N Engl J Med. 352(5) (Feb. 3, 2005),459-67
Izad, Oliver, “Computationally Efficient Method in Predicating Axonal Excitation,” Dissertation for Master Degree, Department of Biomedical Engineering, Case Western Reserve University, May 2009.
Jaccard, Paul, “Elude comparative de la distribution florale dans une portion odes Aples et des Jura,” Bulletin de la Societe Vaudoise des Sciences Naturelles (1901), 37:547-579.
Dice, Lee R., “Measures of the Amount of Ecologic Association Between Species,” Ecology 26(3) (1945): 297-302. doi: 10.2307/ 1932409, http://jstor.org/stable/1932409.
Rand, WM., “Objective criteria for the evaluation of clustering methods,” Journal of the American Statistical Association (American Statistical Association) 66 (336) (1971 ): 846-850, doi:10.2307/2284239, http://jstor.org/stable/2284239.
Hubert, Lawrence et al., “Comparing partitions,” Journal of Classification 2(1) (1985): 193-218, doi:10.1007/ BF01908075.
Cover, T.M. et al., “Elements of information theory,” (1991) John Wiley & Sons, New York, NY.
Meila, Marina, “Comparing Clusterings by the Variation of Information,” Learning Theory and Kernel Machines (2003): 173-187.
Viola, P., et al., “Alignment by maximization of mutual information”, International Journal of Com outer Vision 24(2). ( 1997), 137-154.
Butson et al. “StimExplorer: Deep Brain Stimulation Parameter Selection Software System,” Acta Neurochirugica, Jan. 1, 2007, vol. 97, No. 2, pp. 569-574.
Butson et al. “Role of Electrode Design on the Volume of Tissue Activated During Deep Brain Stimulation,” Journal of Neural Engineering, Mar. 1, 2006, vol. 3, No. 1, pp. 1-8.
Volkmann et al., Indroduction to the Programming of Deep Brain Stimulators, Movement Disorders, vol. 17, Suppl. 3, pp. S181-S187 (2002).
Miocinovic et al. “Cicerone: Stereotactic Neurophysiological Recording and Deep Brain Stimulation Electrode Placement Software System,” Acta Neurochiruraica Suppl., Jan. 1, 2007, vol. 97, No. 2, pp. 561-567.
Schmidt et al. “Sketching and Composing Widgets for 3D Manipulation,” Eurographics, Apr. 2008, vol. 27, No. 2, pp. 301-310.
Volkmann, J. , et al., “Basic algorithms for the programming of deep brain stimulation in Parkinson's disease”, Mov Disord., 21 Suppl 14. (Jun. 2006),S284-9.
Walter, B. L., et al., “Surgical treatment for Parkinson's disease”, Lancet Neural. 3(12). (Dec. 2004),719-28.
Wei, X. F., et al., “Current density distributions, field distributions and impedance analysis of segmented deep brain stimulation electrodes”, J Neural Eng .. 2(4). (Dec. 2005), 139-47.
Zonenshayn, M. , et al., “Location of the active contact within the subthalamic nucleus (STN) in the treatment of idiopathic Parkinson's disease.”, Surg Neurol., 62(3) (Sep. 2004),216-25.
Da Silva et al (A primer on diffusion tensor imaging of anatomical substructures. Neurosurg Focus 15(1): p. 1-4, Article 4, 2003.).
Micheli-Tzanakou, E., et al., “Computational Intelligence for target assesment in Parkinson's disease”, Proceedings of SPIE vol. 4479. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV,(2001),54-69.
Grill, W. M., “Stimulus waveforms for selective neural stimulation”, IEEE Engineering in Medicine and Biology Magazine, 14(4), (Jul.-Aug. 1995), 375-385.
Miocinovic, S., et al., “Sensitivity of temporal excitation properties to the neuronal element activated by extracellular stimulation”, J Neurosci Methods. 132(1). (Jan. 15, 2004), 91-9.
Hunka, K. et al., Nursing Time to Program and Assess Deep Brain Stimulators in Movement Disorder Patients, J. Neursci Nurs., 37: 204-10 (Aug. 2005).
Moss, J. , et al., “Electron microscopy of tissue adherent to explanted electrodes in dystonia and Parkinson's disease”, Brain, 127{Pt 12). (Dec. 2004 ),2755-63.
Montgomery, E. B., et al., “Mechanisms of deep brain stimulation and future technical developments.”, Neurol Res. 22(3). (Apr. 2000),259-66.
Merrill, D. R., et al., “Electrical stimulation of excitable tissue: design of efficacious and safe protocols”, J Neurosci Methods. 141(2), (Feb. 15, 2005), 171-98.
Fisekovic et al., “New Controller for Functional Electrical Stimulation Systems”, Med. Eng. Phys. 2001; 23:391-399.
Zhang, Y., et al., “Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy,” Neuroimage 52(4) (2010), pp. 1289-1301.
““BioPSE” The Biomedical Problem Solving Environment”, htt12://www.sci.utah.edu/cibc/software/index.html, MCRR Center for Integrative Biomedical Computing,(2004).
Andrews, R. J., “Neuroprotection trek—the next generation: neuromodulation I. Techniques—deep brain stimulation, vagus nerve stimulation, and transcranial magnetic stimulation.”, Ann NY Acad Sci. 993. (May 2003),1-13.
Carnevale, N.T. et al., “The Neuron Book,” Cambridge, UK: Cambridge University Press (2006), 480 pages.
Chaturvedi: “Development of Accurate Computational Models for Patient-Specific Deep Brain Stimulation,” Electronic Thesis or Dissertation, Jan. 2012, 162 pages.
Chaturvedi, A. et al.: “Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions.” Brain Stimulation, Elsevier, Amsterdam, NL, vol. 3, No. 2 Apr. 2010, pp. 65-77.
Frankemolle, et al., “Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modeling approach to deep brain stimulation programming,” Brian 133 (2010), pp. 746-761.
McIntyre, C.C., et al., “Modeling the excitablitity of mammalian nerve fibers: influence of afterpotentials on the recovery cycle,” J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006.
Peterson, et al., “Predicting myelinated axon activation using spatial characteristics of the extracellular field,” Journal of Neural Engineering, 8 (2011), 12 pages.
Warman, et al., “Modeling the Effects of Electric Fields on nerver Fibers; Dermination of Excitation Thresholds,” IEEE Transactions on Biomedical Engineering, vol. 39, No. 12 (Dec. 1992), pp. 1244-1254
Wesselink, et al., “Analysis of Current Density and Related Parameters in Spinal Cord Stimulation,” IEEE Transactions on Rehabilitation Engineering, vol. 6, No. 2 Jun. 1998, pp. 200-207.
Andrews, R. J., “Neuroprotection trek—the next generation: neuromodulation II. Applications—epilepsy, nerve regeneration, neurotrophins.”, Ann NY Acad Sci. 993 (May 2003), 14-24.
Astrom, M. , et al., “The effect of cystic cavities on deep brain stimulation in the basal ganglia: a simulation-based study”, J Neural Eng., 3(2), (Jun. 2006).132-8.
Bazin et al., “Free Software Tools for Atlas-based Volumetric Neuroimage Analysis”, Proc. SPIE 5747, Medical Imaging 2005: Image Processing, 1824 May 5, 2005.
Back, C. , et al., “Postoperative Monitoring of the Electrical Properties of Tissue and Electrodes in Deep Brain Stimulation”, Neuromodulation, 6(4), (Oct. 2003 ),248-253.
Baker, K. B., et al., “Evaluation of specific absorption rate as a dosimeter of MRI-related implant heating”, J Magn Reson Imaging., 20(2), (Aug. 2004),315-20.
Brown, J. “Motor Cortex Stimulation,” Neurosurgical Focus ( Sep. 15, 2001) 11(3):E5.
Budai et al., “Endogenous Opioid Peptides Acting at m-Opioid Receptors in the Dorsal Horn Contribute to Midbrain Modulation of Spinal Nociceptive Neurons,” Journal of Neurophysiology (1998) 79(2): 677-687.
Cesselin, F. “Opioid and anti-opioid peptides,” Fundamental and Clinical Pharmacology (1995) 9(5): 409-33 (Abstract only).
Rezai et al., “Deep Brain Stimulation for Chronic Pain” Surgical Management of Pain, Chapter 44 pp. 565-576 (2002).
Xu, MD., Shi-Ang, article entitled “Comparison of Half-Band and Full-Band Electrodes for Intracochlear Electrical Stimulation”, Annals of Otology, Rhinology & Laryngology (Annals of Head & Neck Medicine & Surgery), vol. 102 (5) pp. 363-367 May 1993.
Bedard, C. , et al., “Modeling extracellular field potentials and the frequency-filtering properties of extracellular space”, Biophys J .. 86(3). (Mar. 2004),1829-42.
Benabid, A. L., et al., “Future prospects of brain stimulation”,Neurol Res.;22(3), (Apr. 2000),237-46.
Brummer, S. B., et al., “Electrical Stimulation with Pt Electrodes: II—Estimation of Maximum Surface Redox (Theoretical Non-Gassing) Limits”, IEEE Transactions on Biomedical Engineering, vol. BME-24, Issue 5, (Sep. 1977),440-443.
Butson, Christopher R., et al., “Deep Brain Stimulation of the Subthalamic Nucleus: Model-Based Analysis of the Effects of Electrode Capacitance on the Volume of Activation”, Proceedings of the 2nd International IEEE EMBS, (Mar. 16-19, 2005),196-197.
Mcintyre, Cameron C., et al., “Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition,” J Neurophysiol, 91(4) (Apr. 2004), pp. 1457-1469.
Chaturvedi, A., et al., “Subthalamic Nucleus Deep Brain Stimulation: Accurate Axonal Threshold Prediction with Diffusion Tensor Based Electric Field Models”, Engineering in Medicine and Biology Society, 2006. EMBS' 06 28th Annual International Conference of the IEEE, IEEE, Piscataway, NJ USA, Aug. 30, 2006.
Butson, Christopher et al., “Predicting the Effects of Deep Brain Stimulation with Diffusion Tensor Based Electric Field Models” Jan. 1, 2001, Medical Image Computing and Computer-Assisted Intervention-Mic CAI 2006 Lecture Notes in Computer Science; LNCS, Springer, Berlin, DE.
Butson, C. R., et al., “Deep brainstimulation interactive visualization system”, Society for Neuroscience vol. 898.7 (2005).
Hodaie. M., et al., “Chronic anterior thalamus stimulation for intractable epilepsy.” Epilepsia, 43(6) (Jun. 2002), pp. 603-608.
Hoekema, R., et al., “Multigrid solution of the potential field in modeling electrical nerve stimulation,” Comput Biomed Res., 31(5) (Oct. 1998), pp. 348-362.
Holsheimer, J., et al., “Identification of the target neuronal elements in electrical deep brain stimulation,” Eur J Neurosci., 12(12) (Dec. 2000), pp. 4573-4577.
Jezernik, S., et al., “Neural network classification of nerve activity recorded in a mixed nerve,” Neurol Res., 23(5) (Jul. 2001), pp. 429-434.
Jones. DK., et al., “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging,” Magn. Reson. Med., 42(3) (Sep. 1999), pp. 515-525.
Krack, P., et al., “Postoperative management of subthalamic nucleus stimulation for Parkinson's disease,” Mov. Disord., vol. 17 (suppl 3) (2002), pp. 188-197.
Le Bihan, D., et al., “Diffusion tensor imaging: concepts and applications,” J Magn Reson Imaging, 13(4) (Apr. 2001), pp. 534-546.
Lee, D. C., et al., “Extracellular electrical stimulation of central neurons: quantitative studies,” In: Handbook of neuroprosthetic methods, WE Finn and PG Lopresti (eds) CRC Press (2003), pp. 95-125.
Levy, AL., et al., “An Internet-connected, patient-specific, deformable brain atlas integrated into a surgical navigation system,” J Digit Imaging, 10(3 Suppl 1) (Aug. 1997), pp. 231-237.
Liu, Haiying, et al., “Intra-operative MR-guided DBS implantation for treating PD and ET,” Proceedings of SPIE vol. 4319, Department of Radiology & Neurosurgery, University of Minnesota, Minneapolis, MN 55455 (2001), pp. 272-276.
Mcintyre, C. C., et al., “Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output,” J. Neurophysiol., 88(4), (Oct. 2002), pp. 1592-1604.
Mcintyre, C. C., et al., “Microstimulation of spinal motoneurons: a model study,” Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology society, vol. 5, (1997), pp. 2032-2034.
Mcintyre, Cameron C., et al., “Model-based Analysis of deep brain stimulation of the thalamus,” Proceedings of the Second joint EMBS/BM ES Conference, vol. 3, Annual Fall Meeting of the Biomedical Engineering Society (Cal. No. 02CH37392) IEEEPiscataway, NJ (2002), pp. 2047-2048.
Mcintyre, C. C., et al., “Model-based design of stimulus trains for selective microstimulation of targeted neuronal populations,” Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1 (2001), pp. 806-809.
Mcintyre, C. C., et al., Model-based design of stimulus waveforms for selective microstimulation in the central nervous system,, Proceedings of the First Joint [Engineering in Medicine and Biology, 1999. 21st Annual Conf. and the 1999 Annual FallMeeting of the Biomedical Engineering Soc.] BM ES/EMBS Conference, vol. 1 (1999), p. 384.
Mcintyre, Cameron C., et al., “Modeling the excitability of mammalian nerve fibers: influence of aflerpotentials on the recovery cycle,” J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006.
Mcintyre, Cameron C., et al., “Selective microstimulation of central nervous system neurons,” Annals of biomedical engineering, 28(3) (Mar. 2000), pp. 219-233.
Mcintyre, C. C., et al., “Sensitivity analysis of a model of mammalian neural membrane,” Biol Cybern., 79(1) (Jul. 1998), pp. 29-37.
Mcintyre, Cameron C., et al., “Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both,” Clin Neurophysiol, 115(6) (Jun. 2004), pp. 1239-1248.
Mcintyre, Cameron C., et al., “Uncovering the mechanisms of deep brain stimulation for Parkinson's disease through functional imaging, neural recording, and neural modeling,” Crit Rev Biomed Eng., 30(4-6) (2002), pp. 249-281.
Mouine et al. “Multi-Strategy and Multi-Algorithm Cochlear Prostheses”, Biomed. Sci. Instrument, 2000; 36:233-238.
Mcintyre, Cameron C., et al., “Electric Field and Stimulating Influence generated by Deep Brain Stimulation of the Subthalamaic Nucleus,” Clinical Neurophysiology, 115(3) (Mar. 2004), pp. 589-595.
Mcintyre, Cameron C., et al., “Electric field generated by deep brain stimulation of the subthaiamic nucleus,” Biomedical Engineering Society Annual Meeting, Nashville TN (Oct. 2003), 16 pages.
Mcintyre, Cameron C., et al., “Excitation of central nervous system neurons by nonuniform electric fields,” Biophys. J., 76(2) (1999), pp. 878-888.
McNeal, DR., et al., “Analysis of a model for excitation of myelinated nerve,” IEEE Trans Biomed Eng., vol. 23 (1976), pp. 329-337.
Micheli-Tzanakou, E. , et al., “Computational Intelligence for target assesment in Parkinson's disease,” Proceedings of SPIE vol. 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV (2001 ), pp. 54-69.
Miocinovic, S., et al., “Computational analysis of subthalamic nucleus and lenticular fasciculus activation during therapeutic deep brain stimulation,” J Neurophysiol., 96(3) (Sep. 2006), pp. 1569-1580.
Miranda, P. C., et al., “The distribution of currents inducedin the brain by Magnetic Stimulation: a finite element analysis incorporating OT-MRI-derived conductivity data,” Proc. Intl. Soc. Mag. Reson. rvled. 9 (2001 ), p. 1540.
Miranda, P. C., et al., “The Electric Field Induced in the Brain by Magnetic Stimulation: A 3-D Finite-Element Analysis of the Effect of Tissue Heterogeneity and Anisotropy,” IEEE Transactions on Biomedical Enginering, 50(9) (Sep. 2003), pp. 1074-1085.
Moffitt, MA., et al., “Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models,” IEEE Transactions on Biomedical Engineering, 51 (2) (2003), pp. 229-236.
Moro, E, et al., “The impact on Parkinson's disease of electrical parameter settings in STN stimulation,” Neurology. 59(5) (Sep. 10, 2002), pp. 706-713.
Nowak, LG., et al., “Axons, but not cell bodies, are activated by electrical stimulation in cortical gray matter. I. Evidence from chronaxie measurements,” Exp. Brain Res., 118(4) (Feb. 1998), pp. 477-488.
Nowak, LG., et al., “Axons, but not cell bodies, are activated by electrical stimulation in cortical gray matter. II. Evidence from selective inactivation of cell bodies and axon initial segments,” Exp. Brain Res., 118(4) (Feb. 1998), pp. 489-500.
O'Suilleabhain, PE., et al., “Tremor response to polarity, voltage, pulsewidth and frequency of thalamic stimulation,” Neurology, 60(5) (Mar. 11, 2003), pp. 786-790.
Pierpaoli, C., et al., “Toward a quantitative assessment of diffusion anisotropy,” Magn Reson Med., 36(6) (Dec. 1996), pp. 893-906.
Plonsey, R., et al., “Considerations of quasi-stationarity in electrophysiological systems,” Bull Math Biophys., 29(4) (Dec. 1967), pp. 657-664.
Ranck, J B., “Specific impedance of rabbit cerebral cortex,” Exp. Neural., vol. 7 (Feb. 1963), pp. 144-152.
Ranck, J B., et al., “The Specific impedance of the dorsal columns of the cat: an anisotropic medium,” Exp. Neurol., 11 (Apr. 1965), pp. 451-463.
Ranck, J B., “Which elements are excited in electrical stimulation of mammalian central nervous system: a review,” Brain Res., 98(3) (Nov. 21, 1975), pp. 417-440.
Rattay, F., et al., “A model of the electrically excited human cochlear neuron. I. Contribution of neural substructures to the generation and propagation of spikes,” Hear Res., 153(1-2) (Mar. 2001), pp. 43-63.
Rattay, F., “A model of the electrically excited human cochlear neuron, II. Inftuence of the three-dimensional cochlear structure on neural excitability,” Hear Res., 153(1-2) (Mar. 2001), pp. 64-79.
Rattay, F., “Arrival at Functional Electrostimulation by modelling of fiber excitation,” Proceedings of the Ninth annual Conference of the IEEE Engineering in Medicine and Biology Society (1987), pp. 1459-1460.
Rattay, F., “The inftuence of intrinsic noise can preserve the temporal fine structure of speech signals in models of electrically stimulated human cochlear neurones,” Journal of Physiology, Scientific Meeting of the Physiological Society, London, England, UK Apr. 19-21, 1999 (Jul. 1999), p. 170P.
Rizzone, M., et al., “Deep brain stimulation of the subthalamic nucleus in Parkinson's disease: effects of variation in stimulation parameters,” J. Neural. Neurosurg. Psychiatry., 71(2) (Aug. 2001), pp. 215-219.
Saint-Cyr, J. A., et al., “Localization of clinically effective stimulating electrodes in the human subthalamic nucleus on magnetic resonance imaging,” J. Neurosurg., 87(5) (Nov. 2002), pp. 1152-1166.
Sances, A., et al., “In Electroanesthesia: Biomedical and Biophysical Studies,” A Sances and SJ Larson, Eds., Academic Press, NY (1975), pp. 114-124.
Si. Jean, P., et al., “Automated atlas integration and interactive three-dimensional visualization tools for planning and guidance in functional neurosurgery,” IEEE Transactions on Medical Imaging, 17(5) (1998), pp. 672-680.
Starr, P.A., et al., “Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and magnetic resonance imaging-verified lead locations,” J. Neurosurg., 97(2) (Aug. 2002), pp. 370-387.
Sterio, D., et al., “Neurophysiological refinement of subthalamic nucleus targeting,” Neurosurgery, 50(1) (Jan. 2002), pp. 58-69.
Struijk, J. J., et al., “Excitation of dorsal root fibers in spinal cord stimulation: a theoretical study,” IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 632-639.
Struijk, J J., et al., “Recruitment of dorsal column fibers in spinal cord stimulation: inftuence of collateral branching,” IEEE Transactions on Biomedical Engineering, 39(9) (Sep. 1992), pp. 903-912.
Tamma, F., et al., “Anatomo-clinical correlation of intraoperative stimulation-induced side-effects during HF-DBS of the subthalamic nucleus,” Neurol Sci., vol. 23 (Suppl 2) (2002), pp. 109-110.
Tarler, M., et al., “Comparison between monopolar and tripolar configurations in chronically implanted nerve cuff electrodes,” IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1093-1109.
Testerman, Roy L., “Coritical response to callosal stimulation: A model for determining safe and efficient stimulus parameters,” Annals of Biomedical Engineering, 6(4) (1978), pp. 438-452.
Tuch, D.S., et al, “Conductivity mapping of biological tissue using diffusion MRI,” Ann NY Acad Sci., 888 (Oct. 30, 1999), pp. 314-316.
Tuch, D.S., et al., “Conductivity tensor mapping of the human brain using diffusion tensor MRI,” Proc Nall Acad Sci USA, 98(20) (Sep. 25, 2001), pp. 11697-11701.
Veraart, C., et al., “Selective control of muscle activation with a multipolar nerve cuff electrode,” IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 640-653.
Vercueil, L., et al., “Deep brain stimulation in the treatment of severe dystonia.” J. Neurol., 248(8) (Aug. 2001 ), pp. 695-700.
Vilalte, “Circuit Design of the Power-on-Reset,” Apr. 2000, pp. 1-25.
Vitek, J. L., “Mechanisms of deep brain stimulation: excitation or inhibition,” Mov. Disord., vol. 17 (Suppl. 3) (2002), pp. 69-72.
Voges. J., et al., “Bilateral high-frequency stimulation in the subthalamic nucleus for the treatment of Parkinson disease: correlation of therapeutic effect with anatomical electrode position,” J. Neurosurg., 96(2) (Feb. 2002), pp. 269-279.
Wakana, S., et al., “Fiber tract-based atlas of human white matter anatomy,” Radiology, 230(1) (Jan. 2004), pp. 77-87.
Alexander, DC., et al., “Spatial transformations of diffusion tensor magnetic resonance images,” IEEE Transactions on Medical Imaging, 20 (11), (2001), pp. 1131-1139.
Wu, Y. R., et al., “Does Stimulation of the GPi control dyskinesia by activating inhibitory axons?,” Mov. Disord., vol. 16 (2001), pp. 208-216.
Yelnik, J., et al., “Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method,” J Neurosurg., 99(1) (Jul. 2003), pp. 89-99.
Ylanni, John, et al., “Globus pallidus internus deep brain stimulation for dystonic conditions: a prospective audit,” Mov. Disord., vol. 18 (2003), pp. 436-442.
Zonenshayn, M., et al., “Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting.” Neurosurgery, 47(2) (Aug. 2000), pp. 282-294.
Voghell et al., “Programmable Current Source Dedicated to Implantable Microstimulators” ICM '98 Proceedings of the Tenth International Conference, pp. 67-70.
Butson, Christopher R., et al., “Patient-specific analysis of the volume of tissue activated during deep brain stimulation”, NeuroImage. vol. 34. (2007),661-670.
Adler, DE., et al., “The tentorial notch: anatomical variation, morphometric analysis, and classification in 100 human autopsy cases,” J. Neurosurg., 96(6), (Jun. 2002), pp. 1103-1112.
Jones et al., “An Advanced Demultiplexing System for Physiological Stimulation”, IEEE Transactions on Biomedical Engineering, vol. 44 No. 12 Dec. 1997, pp. 1210-1220.
Alo, K. M., et al., “New trends in neuromodulation for the management of neuropathic pain,” Neurosurgery, 50(4), (Apr. 2002), pp. 690-703, discussion pp. 703-704.
Ashby, P., et al., “Neurophysiological effects of stimulation through electrodes in the human subthalamic nucleus,” Brain, 122 (Pi 10), (Oct. 1999), pp. 1919-1931.
Baker, K. B., et al., “Subthalamic nucleus deep brain stimulus evoked potentials: Physiological and therapeutic implications,” Movement Disorders, 17(5), (Sep./Ocl. 2002), pp. 969-983.
Bammer, R, et al., “Diffusion tensor imaging using single-shot SENSE-EPI”, Magn Reson Med., 48(1 ), (Jul. 2002), pp. 128-136.
Basser, P J., et al., “MR diffusion tensor spectroscopy and imaging,” Biophys J., 66(1 ), (Jan. 1994), pp. 259-267.
Basser, P J., et al., “New currents in electrical stimulation of excitable tissues,” Annu Rev Biomed Eng., 2, (2000), pp. 377-397.
Benabid, AL., et al., “Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders,” J. Neurosurg., 84(2), (Feb. 1996), pp. 203-214.
Benabid, AL., et al., “Combined (Ihalamotoy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson disease,” Appl Neurophysiol, vol. 50, (1987), pp. 344-346.
Benabid, A L., et al., “Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus,” Lancet, 337 (8738), (Feb. 16, 1991 ), pp. 403-406.
Butson, C. R., et al., “Predicting the effects of deep brain stimulation with diffusion tensor based electric field models.” Medical Image Computing and Computer-Assisted Intervention—Mic Cai 2006, Lecture Notes in Computer Science (LNCS), vol. 4191, pp. 429-437, LNCS, Springer, Berlin, DE.
Christensen, Gary E., et al., “Volumetric transformation of brain anatomy,” IEEE Transactions on Medical Imaging, 16(6), (Dec. 1997), pp. 864-877.
Cooper, S , et al., “Differential effects of thalamic stimulation parameters on tremor and paresthesias in essential tremor,” Movement Disorders, 17(Supp. 5), (2002), p. S193.
Coubes, P, et al., “Treatment of DYT1-generalised dystonia by stimulation of the internal globus pallidus,” Lancet, 355 (9222), (Jun. 24, 2000), pp. 2220-2221.
Dasilva, A.F. M., et a., “A Primer Diffusion Tensor Imaging of Anatomical Substructures,” Neurosurg. Focus; 15(1) (Jul. 2003), pp. 1-4.
Dawant, B. M., et al., “Compuerized atlas-guided positioning of deep brain stimulators: a feasibility study,” Biomedical Image registration, Second International Workshop, WBIR 2003, Revised Papers (Lecture notes in Comput. Sci. vol. 2717, Springer-Verlag Berlin, Germany(2003), pp. 142-150.
Finnis, K. W., et al., “3-D functional atalas of subcortical structures for image guided stereotactic neurosurgery,” Nueroimage, vol. 9, No. 6, Iss. 2 (1999), p. S206.
Finnis, K. W., et al., “3D Functional Database of Subcorticol Structures for Surgical Guidance in Image Guided Stereotactic Neurosurgery,” Medical Image Computing and Computer-Assisted Intervention—MICCAI'99, Second International Conference.Cambridge, UK, Sep. 19-22, 1999, Proceedings (1999), pp. 758-767.
Finnis, J. W., et al., “A 3-Dimensional Database of Deep Brain Functional Anatomy, and its Application to Image-Guided Neurosurgery,” Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention.Lecture Notes in Computer Science; vol. 1935 (2000), pp. 1-8.
Finnis, K. W., et al., “A functional database for guidance of surgical and therapeutic procedures in the deep brain,” Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3 (2000), pp. 1787-1789.
Finnis, K. W., et al., “Application of a Population Based Electrophysiological Database to the Planning and Guidance of Deep Brain Stereotactic Neurosurgery,” Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention—Part 11, Lecture Notes in Computer Science; vol. 2489 (2002), pp. 69-76.
Finnis, K. W., et al., “Subcortical physiology deformed into a patient-specific brain atlas for image-guided stereotaxy,” Proceedings of SPIE—vol. 4681 Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display (May 2002), pp. 184-195.
Finnis, Krik W., et al., “Three-Dimensional Database of Suncortical Electrophysiology of Image-Guided Stereotatic Functional Neurosurgery,” IEEE Transactions on Medical Imaging, 22(1) (Jan. 2003), pp. 93-104.
Gabriels, L , et al, “Deep brain stimulation for treatment-refractory obsessive-compulsive disorder: psychopathological and neuropsychological outcome in three cases,” Acta Psychiatr Scand., 107(4) (2003), pp. 275-282.
Gabriels, LA., et al., “Long-term electrical capsular stimulation in patients with obsessive-compulsive disorder,” Neurosurgery, 52(6) (Jun. 2003), pp. 1263-1276.
Goodall, E, V., et al., “Modeling study of activation and propagation delays during stimulation of peripheral nerve fibers with a tripolar cuff electrode,” IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 3(3) (Sep. 1995), pp. 272-282.
Goodall, E. V., et al., “Position-selective activation of peripheral nerve fibers with a cuff electrode,” IEEE Transactions on Biomedical Engineering, 43(8) (Aug. 1996), pp. 851-856.
Goodall, E. V., “Simulation of activation and propagation delay during tripolar neural stimulation,” Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (1993), pp. 1203-1204.
Grill, WM., “Modeling the effects of electric fields on nerve fibers: influence of tissue electrical properties,” IEEE Transactions on Biomedical Engineering, 46(8) (1999), pp. 918-928.
Grill, W. M., et al., “Neural and connective tissue response to long-term implantation of multiple contact nerve cuff electrodes,” J Biomed Mater Res., 50(2) (May 2000), pp. 215-226.
Grill, W. M., “Neural modeling in neuromuscular and rehabilitation research,” Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4 (2001 ), pp. 4065-4068.
Grill, W. M., et al., “Non-invasive measurement of the input-output properties of peripheral nerve stimulating electrodes,” Journal of Neuroscience Methods, 65(1) (Mar. 1996), pp. 43-50.
Grill, W. M., et al., “Quantification of recruitment properties of multiple contact cuff electrodes,” IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 4(2) (Jun. 1996), pp. 49-62.
Grill, W. M., “Spatially selective activation of peripheral nerve for neuroprosthetic applications,” Ph.D. Case Western Reserve University, (1995), pp. 245 pages.
Grill, W. M., “Stability of the input-output properties of chronically implanted multiple contact nerve cuff stimulating electrodes,” IEEE Transactions on Rehabilitation Engineering [see also IEEE Trans. on Neural Systems and Rehabilitation] (1998), pp. 364-373.
Grill, W. M., “Stimulus waveforms for selective neural stimulation,” IEEE Engineering in Medicine and Biology Magazine, 14(4) (Jul.-Aug. 1995), pp. 375-385.
Grill, W. M., et al., “Temporal stability of nerve cuff electrode recruitment properties,” IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1089-1090.
Gross, RE., et al., “Advances in neurostimulation for movement disorders,” Neurol Res., 22(3) (Apr. 2000), pp. 247-258.
Guridi et al., “The subthalamic nucleus, hemiballismus and Parkinson's disease: reappraisala neurological dogma,” Brain, vol. 124, 2001, pp. 5-19.
Haberler, C, et al., “No tissue damage by chronic deep brain stimulation in Parkinson's disease,” Ann Neurol., 48(3) (Sep. 2000), pp. 372-376.
Hamel, W, et al., “Deep brain stimulation of the subthalamic nucleus in Parkinson's disease: evaluation of active electrode contacts,” J Neural Neurosurg Psychiatry, 74(8) (Aug. 2003), pp. 1036-1046.
Hanekom, “Modelling encapsulation tissue around cochlear implant electrodes,” Med, Biol. Eng. Comput. vol. 43 (2005), pp. 47-55.
Haueisen, J , et al., “The influence of brain tissue anisotropy on human EEG and MEG,” Neuroimage, 15(1) (Jan. 2002), pp. 159-166.
D'Haese et al. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2005 Lecture Notes in Computer Science, 2005, vol. 3750, 2005, 427-434.
Rohde et al. IEEE Transactions on Medical Imaging, vol. 22 No. 11. 2003 p. 1470-1479.
Dawant et al., Biomedical Image Registration. Lecture Notes in Computer Science, 2003, vol. 2717, 2003, 142-150.
Miocinovic et al., “Stereotactiv Neurosurgical Planning, Recording, and Visualization for Deep Brain Stimulation in Non-Human Primates”, Journal of Neuroscience Methods, 162:32-41, Apr. 5, 2007, XP022021469.
Gemmar et al., “Advanced Methods for Target Navigation Using Microelectrode Recordings in Stereotactic Neurosurgery for Deep Brain Stimulation”, 21st IEEE International Symposium on Computer-Based Medical Systems, Jun. 17, 2008, pp. 99-104, XP031284774.
Acar et al., “Safety Anterior Commissure-Posterior Commissure-Based Target Calculation of the Subthalamic Nucleus in Functional Stereotactic Procedures”, Stereotactic Funct. Neurosura., 85:287-291, Aug. 2007.
Andrade-Souza, “Comparison of Three Methods of Targeting the Subthalamic Nucleus for Chronic Stimulation in Parkinson's Disease”, Neurosurgery, 56:360-368, Apr. 2005.
Anheim et al., “Improvement in Parkinson Disease by Subthalamic Nucleus Stimulation Based on Electrode Placement”, Arch Neural., 65:612-616, May 2008.
Butson et al., “Tissue and Electrode Capacitance Reduce Neural Activation Volumes During Deep Brain Stimulation”, Clinical Neurophysiology, 116:2490-2500, Oct. 2005.
Butson et al., “Sources and Effects of Electrode Impedance During Deep Brain Stimulation”, Clinical Neurophysiology, 117:44 7-454, Dec. 2005.
D'Haese et al., “Computer-Aided Placement of Deep Brain Stimulators: From Planning to Intraoperative Guidance” IEEE Transaction on Medical Imaging, 24:1469-1478, Nov. 2005.
Gross et al., “Electrophysiological Mapping for the Implantation of Deep Brain Stimulators for Parkinson's Disease and Tremor”, Movement Disorders, 21 :S259-S283, Jun. 2006.
Halpern et al., “Brain Shift During Deep Brain Stimulation Surgery for Parkinson's Disease”, Stereotact Funct. Neurosurg., 86:37-43, published online Sep. 2007.
Herzog et al., “Most Effective Stimulation Site in Subthalamic Deep Brain Stimulation for Parkinson's Disease”, Movement Disorders, 19:1050-1099, published on line Mar. 2004.
Jeon et al., A Feasibility Study of Optical Coherence Tomography for Guiding Deep Brain Probes, Journal of Neuroscience Methods, 154:96-101, Jun. 2006.
Khan et al., “Assessment of Brain Shift Related to Deep Brain Stimulation Surgery”, Sterreotact Fund, Neurosurg., 86:44-53, published online Sep. 2007.
Koop et al., “Improvement in a Quantitative Measure of Bradykinesia After Microelectrode Recording in Patients with Parkinson's Disease During Deep Brain Stimulation Surgery”, Movement Disorders, 21 :673-678, published on line Jan. 2006.
Lemaire et al., “Brain Mapping in Stereotactic Surgery: A Brief Overview from the Probabilistic Targeting to the Patient-Based Anatomic Mapping”, Neuroirnage, 37:S109-S115, available online Jun. 2007.
Machado et al., “Deep Brain Stimulation for Parkinson's Disease: Surgical Technique and Perioperative Management”, Movement Disorders, 21 :S247-S258, Jun. 2006.
Maks et al., “Deep Brain Stimulation Activation Volumes and Their Association with Neurophysiological Mapping and Thrapeutic Outcomes”, Downloaded from jnnp.bmj.com, pp. 1-21, published online Apr. 2008.
Moran et al., “Real-Time Refinment of Subthalamic Nucleous Targeting Using Bayesian Decision-Making on the Root Mean Square Measure”, Movement Disorders, 21: 1425-1431, published online Jun. 2006.
Sakamoto et al., “Homogeneous Fluorescence Assays for RNA Diagnosis by Pyrene-Conjugated 2′-0-Methyloligoribonucleotides”, Nucleosides, Nucleotides, and Nucleric Acids, 26:1659-1664, on line publication Oct. 2007.
Winkler et al., The First Evaluation of Brain Shift During Functional Neurosurgery by Deformation Field Analysis, J. Neural. Neurosurg. Psychiatry, 76:1161-1163, Aug. 2005.
Yelnik et al., “A Three-Dimensional, Histological and Deformable Atlas of the Human Basal J Ganglia. I. Atlas Construction Based on Immunohistochemical and MRI Data”, NeuroImage, 34:618,-638,Jan. 2007.
Ward, H. E., et al., “Update on deep brain stimulation for neuropsychiatric disorders,” Neurobiol Dis 38 (3) (2010), pp. 346-353.
Alberts et al. “Bilateral subthalamic stimulation impairs cognitive-motor performance in Parkinson's disease patients.” Brain (2008), 131, 3348-3360, Abstract.
Butson, Christopher R., et al., “Sources and effects of electrode impedance during deep brainstimulation”, Clinical Neurophysiology. vol. 117.(2006),447-454.
An, et al., “Prefronlal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys,” J Comp Neural 401 (4) (1998), pp. 455-479.
Bulson, C. R., et al., “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” Clinical Neurophysiology, vol. 116 (2005), pp. 2490-2500.
Carmichael, S. T., et al., “Connectional networks within the orbital and medial prefronlal cortex of macaque monkeys,” J Comp Neural 371 (2) (1996), pp. 179-207.
Croxson, et al., “Quantitative investigation of connections of the prefronlal cortex in the human and macaque using probabilistic diffusion tractoaraphy,” J Neurosci 25 (39) (2005), pp. 8854-8866.
Frankemolle, et al., “Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming,” Brain 133 (2010), pp. 746-761.
Freedman, et al., “Subcortical projections of area 25 (subgenual cortex) of the macaque monkey,” J Comp Neurol 421 (2) (2000), pp. 172-188.
Giacobbe, et al., “Treatment resistant depression as a failure of brain homeostatic mechanisms: implications for deep brain stimulation,” Exp Neural 219 (1) (2009), pp. 44-52.
Goodman, et al., “Deep brain stimulation for intractable obsessive compulsive disorder: pilot study using a blinded, staggered-onset design,” Biol Psychiatry 67 (6) (2010), pp. 535-542.
Greenberg, et al., “Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience,” Mol Psychiatry 15 (1) (2010), pp. 64-79.
Greenberg. et al., “Three-year outcomes in deep brain stimulation for highly resistant obsessive-compulsive disorder,” Neuropsychopharmacology 31 (11) (2006), pp. 2384-2393.
Gutman, et al., “A tractography analysis of two deep brain stimulation white matter targets for depression,” Biol Psychiatry 65 (4) (2009), pp. 276-282.
Haber, et al., “Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning,” J Neurosci 26 (32) (2006), pp. 8368-8376.
Haber, et al., “Cognitive and limbic circuits that are affected by deep brain stimulation,” Front Biosci 14 (2009), pp. 1823-1834.
Hines, M. L., et al., “The NEURON simulation environment,” Neural Comput., 9(6) (Aug. 15, 1997), pp. 1179-1209.
Hua, et al., “Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification,” Neuroimage 39 (1) (2008), pp. 336-347.
Johansen-Bern, et al., “Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression,” Cereb Cortex 18 (6) (2008), pp. 1374-1383.
Kopell, et al., “Deep brain stimulation for psychiatric disorders,” J Clin Neurophysiol 21 (1) (2004), pp. 51-67.
Lozano, et al., “Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression,” Biol Psychiatry 64 (6) (2008), pp. 461-467.
Lujan, et al., “Tracking the mechanisms of deep brain stimulation for neuropsychiatric disorders,” Front Biosci 13 (2008), pp. 5892-5904.
Lujan, J.L. et al., “Automated 3-Dimensional Brain Atlas Fitting to Microelectrode Recordings from Deep Brain Stimulation Surgeries,” Stereotact. Funel. Neurosurg. 87(2009), pp. 229-240.
Machado. et al., “Functional topography of the ventral striatum and anterior limb of the internal capsule determined by electrical stimulation of awake patients,” Clin Neurophysiol 120 (11) (2009), pp. 1941-1948.
Malone, et al., “Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression,” Biol Psychiatry 65 (4) (2009), pp. 267-275.
Mayberg, H. S., et al “Deep brain stimulation for treatment-resistant depression,” Neuron, 45(5) (Mar. 3, 2005), pp. 651-660.
Mayberg, H. S., et al., “Limbic-cortical dysregulation: a proposed model of depression,” J Neuropsychiatry Clin Neurosci. 9 (3) (1997), pp. 471-481.
McIntyre,C. C.. et al., “Network perspectives on the mechanisms of deep brain stimulation,” Neurobiol Dis 38 (3) (2010), pp. 329-337.
Miocinovic, S., et al., “Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation,” Exp Neurol 216 (i) (2009). pp, 166-176.
Nuttin. et al., “Electrical stimulation in anterior limbs of internal capsules in patients with obsessive-compulsive disorder,” Lancet 354 (9189) (1999), p. 1526.
Saxena, et al., “Cerebral glucose metabolism in obsessive-compulsive hoarding,” Am J Psychiatry. 161 (6) (2004), pp. 1038-1048.
Viola, et al., “Importance-driven focus of attention,” IEEE Trans Vis Comput Graph 12 (5) (2006), pp. 933-940.
Wakana, S., et al., “Reproducibility of quantitative tractography methods applied to cerebral white matter,” Neuroimage 36 (3) (2007), pp. 630-644.
Mayr et al., “Basic Design and Construction of the Vienna FES Implants: Existing Solutions and Prospects for New Generations of Implants”, Medical Engineering & Physics, 2001; 23:53-60.
McIntyre, Cameron , et al., “Finite element analysis of the current-density and electric field generated by metal microelectrodes”, Ann Biomed Eng . 29(3), (2001 ),227-235.
Foster, K. R., et al., “Dielectric properties of tissues and biological materials: a critical review.”, Grit Rev Biomed Ena. 17(1 ). (1989),25-10.
Limousin, P., et al., “Electrical stimulation of the subthalamic nucleus in advanced Parkinson's disease”, N Engl J Med .. 339(16), (Oct. 15, 1998), 1105-11.
Kitagawa, M., et al., “Two-year follow-up of chronic stimulation of the posterior subthalamic white matter for tremor-dominant Parkinson's disease.”, Neurosurgery. 56(2). (Feb. 2005),281-9.
Johnson, M. D., et al., “Repeated voltage biasing improves unit recordings by reducing resistive tissue impedances”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, [see also IEEE Trans. on Rehabilitation Engineering (2005), 160-165.
Holsheimer, J. , et al., “Chronaxie calculated from current-duration and voltage-duration data”, J Neurosci Methods. 97(1). (Apr. 1, 2000),45-50.
Hines, M. L., et al., “The NEURON simulation environment”, Neural Comput. 9(6). (Aug. 15, 1997), 1179-209.
Herzog, J., et al., “Most effective stimulation site in subthalamic deep brain stimulation for Parkinson's disease”, Mov Disord. 19(9). (Sep. 2004),1050-4.
Hershey, T., et al., “Cortical and subcortical blood flow effects of subthalamic nucleus stimulation in PD.”, Neurology 61(6). (Sep. 23, 2003),816-21.
Hemm, S. , et al., “Evolution of Brain Impedance in Dystonic Patients Treated by GPi Electrical Stimulation”, Neuromodulation 7(2) (Apr. 2004),67-75.
Hemm, S., et al., “Deep brain stimulation in movement disorders: stereotactic coregistration of two-dimensional electrical field modeling and magnetic resonance imaging.”, J Neurosurg. 103(6): (Dec. 2005),949-55.
Haueisen, J, et al., “The influence of brain tissue anisotropy on human EEG and MEG”, Neuroimage 15(1) (Jan. 2002),159-166.
Haslinger, B., et al., “Frequency-correlated decreases of motor cortex activity associated with subthalamic nucleus stimulation in Parkinson's disease.”, Neuroimage 28(3). (Nov. 15, 2005),598-606.
Hashimoto, T. , et al., “Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons”, J Neurosci. 23(5). (Mar. 1, 2003),1916-23.
Hardman, C. D., et al., “Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: volume and neuronal number for the output, internal relay, and striatal modulating nuclei”, J Comp Neurol., 445(3), (Apr. 8, 2002),238-55.
McNaughtan et al., “Electrochemical Issues in Impedance Tomography”, 1st World Congress on Industrial Process Tomography, Buxton, Greater Manchester, Apr. 14-17, 1999.
Grill, WM., et al., “Electrical properties of implant encapsulation tissue”, Ann Biomed Eng. vol. 22, (1994),23-33.
Grill, W. M., et al., “Deep brain stimulation creates an informational lesion of the stimulated nucleus”, Neuroreport. 15l7t (May 19, 2004 ), 1137-40.
Pulliam CL, Heldman DA, Orcutt TH, Mera TO, Giuffrida JP, Vitek JL. Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson's disease. Parkinsonism Relat Disord. Apr. 2015; 21(4):378-82.
International Search Report and Written Opinion for PCT/US2016/039644 dated Dec. 7, 2016.
Official Communication for U.S. Appl. No. 15/194,380 dated Jun. 4, 2018.
Official Communication for U.S. Appl. No. 15/194,380 dated Sep. 24, 2018.
Official Communication for U.S. Appl. No. 15/194,380 dated Feb. 6, 2019.
Related Publications (1)
Number Date Country
20190358461 A1 Nov 2019 US
Provisional Applications (1)
Number Date Country
62186172 Jun 2015 US
Divisions (1)
Number Date Country
Parent 15194380 Jun 2016 US
Child 16537210 US