Systems and methods for steering electrical stimulation of patient tissue and determining stimulation parameters

Description

FIELD

The invention is directed to the field of electrical stimulation systems. The present invention is also directed to systems and methods for steering electrical stimulation of patient tissue and determining stimulation parameters, as well as methods of making and using systems.

BACKGROUND

Electrical stimulation can be useful for treating a variety of conditions. Deep brain stimulation can be useful for treating, for example, Parkinson's disease, dystonia, essential tremor, chronic pain, Huntington's disease, levodopa-induced dyskinesias and rigidity, bradykinesia, epilepsy and seizures, eating disorders, and mood disorders. Typically, a lead with a stimulating electrode at or near a tip of the lead provides the stimulation to target neurons in the brain. Magnetic resonance imaging (“MM”) or computerized tomography (“CT”) scans can provide a starting point for determining where the stimulating electrode should be positioned to provide the desired stimulus to the target neurons.

After the lead is implanted into a patient's brain, electrical stimulus current can be delivered through selected electrodes on the lead to stimulate target neurons in the brain. The electrodes can be formed into rings or segments disposed on a distal portion of the lead. The stimulus current projects from the electrodes. Using segmented electrodes can provide directionality to the stimulus current and permit a clinician to steer the current to a desired direction and stimulation field.

BRIEF SUMMARY

One embodiment is a computer-implemented method for determining a set of stimulation parameters for an electrical stimulation lead, the method including: a) receiving, by a computer processor, a target geometrical parameter describing a stimulation field; b) receiving, by the computer processor, a first programming state; c) determining, by the computer processor, a first stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 10% of the target geometrical parameter; and d) outputting, by the computer processor, a set of stimulation parameters to be received by an electrical stimulation device for delivery of electrical stimulation to a patient via an electrical stimulation lead, wherein the set of stimulation parameters includes the first stimulation parameter and represents the first programming state.

In at least some embodiments, the target geometrical parameter is a target maximum radius, the method further including receiving, by the computer processor, an angle, wherein determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius at the angle within at least 10% of the target maximum radius.

In at least some embodiments, the target geometrical parameter is a target maximum radius, the method further including receiving, by the computer processor, a range of angles, wherein determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius within the range of angles within at least 10% of the target maximum radius.

In at least some embodiments, the target geometrical parameter is a target maximum radius, the method further including receiving, by the computer processor, an axial position or range of axial positions, wherein determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius at the axial position or within the range of axial positions within at least 10% of the target maximum radius.

In at least some embodiments, the method further includes repeating steps b)-d) for at least one additional programming state. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 5% of the target geometrical parameter. In at least some embodiments, the target geometrical parameter is a target volume. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter using a look-up table with previously determined first stimulation parameters for a plurality of programming states.

Another embodiment is a computer-implemented method for determining a set of stimulation parameters for an electrical stimulation lead, the method including: a) receiving, by a computer processor, either i) a first set of stimulation parameters or ii) a starting programming state and starting first stimulation parameter; b) determining, by the computer processor and from either i) the first set of stimulation parameters or ii) the starting programming state and starting first stimulation parameter, a target geometrical parameter describing a stimulation field; c) receiving, by the computer processor, a first programming state; d) determining, by the computer processor, a first stimulation parameter for the first programming state that achieves the target geometrical parameter; and e) outputting, by the computer processor, a second set of stimulation parameters to be received by an electrical stimulation device for delivery of electrical stimulation to a patient via an electrical stimulation lead, wherein the set of stimulation parameters includes the first stimulation parameter for the first programming state and represents the first programming state.

In at least some embodiments, the method further includes receiving, by the computer processor, an angle, wherein determining the target geometrical parameter includes determining, by the computer processor, a target maximum radius at the angle. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius at the angle within at least 10% of the target maximum radius.

In at least some embodiments, the method further includes receiving, by the computer processor, a range of angles, wherein determining the target geometrical parameter includes determining, by the computer processor, a target maximum radius within the range of angles. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius within the range of angles within at least 10% of the target maximum radius.

In at least some embodiments, the method further includes receiving, by the computer processor, an axial position or a range of axial positions, wherein determining the target geometrical parameter includes determining, by the computer processor, the target maximum radius at the angle. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target maximum radius at the axial position or within the axial position within at least 10% of the target maximum radius.

In at least some embodiments, the method further includes repeating steps c)-e) for at least one additional programming state. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 5% of the target maximum radius or target volume. In at least some embodiments, the target geometrical parameter is a target volume. In at least some embodiments, determining the first stimulation parameter includes determining, by the computer processor, the first stimulation parameter using a look-up table with previously determined first stimulation parameters for a plurality of programming states.

Yet another embodiment is a system for determining a set of stimulation parameters for an electrical stimulation lead, the system including: a display; and a computer processor coupled to the display and configured and arranged to perform any of the methods describe above.

A further embodiment is a non-transitory computer-readable medium having processor-executable instructions for determining a set of stimulation parameters, the processor-executable instructions when installed onto a device enable the device to perform any of the methods describe above.

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 side view of one embodiment of a device for brain stimulation, according to the invention;

FIG. 2 is a schematic diagram of radial current steering along various electrode levels along the length of a lead, according to the invention;

FIG. 3A is a perspective view of an embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3B is a perspective view of a second embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3C is a perspective view of a third embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3D is a perspective view of a fourth embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3E is a perspective view of a fifth embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3F is a perspective view of a sixth embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3G is a perspective view of a seventh embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 3H is a perspective view of an eighth embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 4 is a perspective view of a ninth embodiment of a portion of a lead having a plurality of segmented electrodes, according to the invention;

FIG. 5 is a schematic illustration of one embodiment of a system for practicing the invention;

FIG. 6 is a schematic flowchart of one embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target maximum radius, according to the invention;

FIG. 7 is a schematic flowchart of another embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target maximum radius, according to the invention;

FIG. 8 is a schematic flowchart of a third embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target maximum radius, according to the invention;

FIG. 9 is a schematic flowchart of a fourth embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target maximum radius, according to the invention;

FIG. 10 is a schematic flowchart of one embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target volume, according to the invention; and

FIG. 11 is a schematic flowchart of another embodiment of a method of determining a set of stimulation parameters or steering stimulation with a target volume, according to the invention.

DETAILED DESCRIPTION

The invention is directed to the field of electrical stimulation systems. The present invention is also directed to systems and methods for visualizing and directing electrical stimulation of neural elements, as well as methods of making and using systems.

A lead for electrical stimulation can include one or more stimulation electrodes. In at least some embodiments, one or more of the stimulation electrodes are provided in the form of segmented electrodes that extend only partially around the circumference of the lead. These segmented electrodes can be provided in sets of electrodes, with each set having electrodes radially 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, dorsal root ganglia stimulation, vagal nerve stimulation, basoreceptor stimulation, or stimulation of other nerves, organs, or tissues.

Suitable implantable electrical stimulation systems include, but are not limited to, at 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. 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 at least some embodiments, a practitioner may determine the position of the target neurons using recording electrode(s) and then position the stimulation electrode(s) accordingly. In some embodiments, the same electrodes can be used for both recording and stimulation. In some embodiments, separate leads can be used; one with recording electrodes which identify target neurons, and a second lead with stimulation electrodes that replaces the first after target neuron identification. In some embodiments, the same lead can include both recording electrodes and stimulation electrodes or electrodes can be used for both recording and stimulation.

FIG. 1 illustrates one embodiment of a device 100 for electrical stimulation (for example, brain or spinal cord stimulation). The device includes a lead 110, a plurality of electrodes 125 disposed at least partially about a circumference of the lead 110, a plurality of terminals 135, a connector 132 for connection of the electrodes to a control module, and a stylet 140 for assisting in insertion and positioning of the lead in the patient's brain. The stylet 140 can be made of a rigid material. Examples of suitable materials for the stylet include, but are not limited to, tungsten, stainless steel, and plastic. The stylet 140 may have a handle 150 to assist insertion into the lead 110, as well as rotation of the stylet 140 and lead 110. The connector 132 fits over a proximal end of the lead 110, preferably after removal of the stylet 140. The connector 132 can be part of a control module or can be part of an optional lead extension that is coupled to the control module.

The control module (for example, control module 514 of FIG. 5) can be an implantable pulse generator that can be implanted into a patient's body, for example, below the patient's clavicle area. The control module can have eight stimulation channels which may be independently programmable to control the magnitude of the current stimulus from each channel. In some cases, the control module can have more or fewer than eight stimulation channels (e.g., 4-, 6-, 16-, 32-, or more stimulation channels). The control module can have one, two, three, four, or more connector ports, for receiving the plurality of terminals 135 at the proximal end of the lead 110. Examples of control modules are described in the references cited above.

In one example of operation, 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 the stylet 140. 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, 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 control module 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 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. Ring electrodes typically do not enable stimulus current to be directed from only a limited angular range around of the lead. Segmented electrodes, however, can be used to direct stimulation energy to a selected angular range around the lead. When segmented electrodes are used in conjunction with an implantable control module 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. Though the following description discusses stimulation electrodes, it will be understood that all configurations of the stimulation electrodes discussed may be utilized in arranging recording electrodes as well. A lead that includes segmented electrodes can be referred to as a directional lead because the segmented electrodes can be used to direct stimulation along a particular direction or range of directions.

The lead 100 includes a lead body 110, one or more optional ring electrodes 120, and a plurality of sets of segmented electrodes 130. 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 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.

Stimulation electrodes in the form of ring electrodes 120 can be disposed on any part of the lead body 110, usually near a distal end of the lead 100. In FIG. 1, the lead 100 includes two ring electrodes 120. Any number of ring electrodes 120 can be disposed along the length of the lead body 110 including, for example, one, two three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen or more ring electrodes 120. It will be understood that any number of ring electrodes can be disposed along the length of the lead body 110. In some embodiments, the ring electrodes 120 are substantially cylindrical and wrap around the entire circumference of the lead body 110. In some embodiments, the outer diameters of the ring electrodes 120 are substantially equal to the outer diameter of the lead body 110. The length of the ring electrodes 120 may vary according to the desired treatment and the location of the target neurons. In some embodiments the length of the ring electrodes 120 are less than or equal to the diameters of the ring electrodes 120. In other embodiments, the lengths of the ring electrodes 120 are greater than the diameters of the ring electrodes 120. The distal-most ring electrode 120 may be a tip electrode (see, e.g., tip electrode 320a of FIG. 3E) which covers most, or all, of the distal tip of the lead.

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, 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. Patent Applications Publication Nos. 2010/0268298; 2011/0005069; 2011/0078900; 2011/0130803; 2011/0130816; 2011/0130817; 2011/0130818; 2011/0078900; 2011/0238129; 2011/0313500; 2012/0016378; 2012/0046710; 2012/0071949; 2012/0165911; 2012/197375; 2012/0203316; 2012/0203320; 2012/0203321; 2013/0197602; 2013/0261684; 2013/0325091; 2013/0317587; 2014/0039587; 2014/0353001; 2014/0358209; 2014/0358210; 2015/0018915; 2015/0021817; 2015/0045864; 2015/0021817; 2015/0066120; 2013/0197424; 2015/0151113; 2014/0358207; and U.S. Pat. No. 8,483,237, all of which are incorporated herein by reference in their entireties. Examples of leads with tip electrodes include at least some of the previously cited references, as well as U.S. Patent Applications Publication Nos. 2014/0296953 and 2014/0343647, all of which are incorporated herein by reference in their entireties.

The lead 100 is shown having a plurality of segmented electrodes 130. Any number of segmented electrodes 130 may be disposed on the lead body 110 including, for example, one, two three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen or more segmented electrodes 130. It will be understood that any number of segmented electrodes 130 may be disposed along the length of the lead body 110. A segmented electrode 130 typically extends only 75%, 67%, 60%, 50%, 40%, 33%, 25%, 20%, 17%, 15%, or less around the circumference of the lead.

The segmented electrodes 130 may be grouped into sets of segmented electrodes, where each set is disposed around a circumference of the lead 100 at a particular longitudinal portion of the lead 100. The lead 100 may have any number segmented electrodes 130 in a given set of segmented electrodes. The lead 100 may have one, two, three, four, five, six, seven, eight, or more segmented electrodes 130 in a given set. In at least some embodiments, each set of segmented electrodes 130 of the lead 100 contains the same number of segmented electrodes 130. The segmented electrodes 130 disposed on the lead 100 may include a different number of electrodes than at least one other set of segmented electrodes 130 disposed on the lead 100.

The segmented electrodes 130 may vary in size and shape. In some embodiments, the segmented electrodes 130 are all of the same size, shape, diameter, width or area or any combination thereof. In some embodiments, the segmented electrodes 130 of each circumferential set (or even all segmented electrodes disposed on the lead 100) may be identical in size and shape.

Each set of segmented electrodes 130 may be disposed around the circumference of the lead body 110 to form a substantially cylindrical shape around the lead body 110. The spacing between individual electrodes of a given set of the segmented electrodes may be the same, or different from, the spacing between individual electrodes of another set of segmented electrodes on the lead 100. In at least some embodiments, equal spaces, gaps or cutouts are disposed between each segmented electrode 130 around the circumference of the lead body 110. In other embodiments, the spaces, gaps or cutouts between the segmented electrodes 130 may differ in size or shape. In other embodiments, the spaces, gaps, or cutouts between segmented electrodes 130 may be uniform for a particular set of the segmented electrodes 130, or for all sets of the segmented electrodes 130. The sets of segmented electrodes 130 may be positioned in irregular or regular intervals along a length the lead body 110.

Conductor wires that attach to the ring electrodes 120 or segmented electrodes 130 extend along the lead body 110. These conductor wires may extend through the material of the lead 100 or along one or more lumens defined by the lead 100, or both. The conductor wires couple the electrodes 120, 130 to the terminals 135.

When the lead 100 includes both ring electrodes 120 and segmented electrodes 130, the ring electrodes 120 and the segmented electrodes 130 may be arranged in any suitable configuration. For example, when the lead 100 includes two ring electrodes 120 and two sets of segmented electrodes 130, the ring electrodes 120 can flank the two sets of segmented electrodes 130 (see e.g., FIGS. 1, 3A, and 3E-3H—ring electrodes 320 and segmented electrode 330). Alternately, the two sets of ring electrodes 120 can be disposed proximal to the two sets of segmented electrodes 130 (see e.g., FIG. 3C—ring electrodes 320 and segmented electrode 330), or the two sets of ring electrodes 120 can be disposed distal to the two sets of segmented electrodes 130 (see e.g., FIG. 3D—ring electrodes 320 and segmented electrode 330). One of the ring electrodes can be a tip electrode (see, tip electrode 320a of FIGS. 3E and 3G). It will be understood that other configurations are possible as well (e.g., alternating ring and segmented electrodes, or the like).

By varying the location of the segmented electrodes 130, different coverage of the target neurons may be selected. For example, the electrode arrangement of FIG. 3C may be useful if the physician anticipates that the neural target will be closer to a distal tip of the lead body 110, while the electrode arrangement of FIG. 3D may be useful if the physician anticipates that the neural target will be closer to a proximal end of the lead body 110.

Any combination of ring electrodes 120 and segmented electrodes 130 may be disposed on the lead 100. For example, the lead may include a first ring electrode 120, two sets of segmented electrodes; each set formed of four segmented electrodes 130, and a final ring electrode 120 at the end of the lead. This configuration may simply be referred to as a 1-4-4-1 configuration (FIGS. 3A and 3E—ring electrodes 320 and segmented electrode 330). It may be useful to refer to the electrodes with this shorthand notation. Thus, the embodiment of FIG. 3C may be referred to as a 1-1-4-4 configuration, while the embodiment of FIG. 3D may be referred to as a 4-4-1-1 configuration. The embodiments of FIGS. 3F, 3G, and 3H can be referred to as a 1-3-3-1 configuration. Other electrode configurations include, for example, a 2-2-2-2 configuration, where four sets of segmented electrodes are disposed on the lead, and a 4-4 configuration, where two sets of segmented electrodes, each having four segmented electrodes 130 are disposed on the lead. The 1-3-3-1 electrode configuration of FIGS. 3F, 3G, and 3H has two sets of segmented electrodes, each set containing three electrodes disposed around the circumference of the lead, flanked by two ring electrodes (FIGS. 3F and 3H) or a ring electrode and a tip electrode (FIG. 3G). In some embodiments, the lead includes 16 electrodes. Possible configurations for a 16-electrode lead include, but are not limited to 4-4-4-4; 8-8; 3-3-3-3-3-1 (and all rearrangements of this configuration); and 2-2-2-2-2-2-2-2.

FIG. 2 is a schematic diagram to illustrate radial current steering along various electrode levels along the length of the lead 200. While conventional lead configurations with ring electrodes are only able to steer current along the length of the lead (the z-axis), the segmented electrode configuration is capable of steering current in the x-axis, y-axis as well as the z-axis. Thus, the centroid of stimulation may be steered in any direction in the three-dimensional space surrounding the lead 200. In some embodiments, the radial distance, r, and the angle θ around the circumference of the lead 200 may be dictated by the percentage of anodic current (recognizing that stimulation predominantly occurs near the cathode, although strong anodes may cause stimulation as well) introduced to each electrode. In at least some embodiments, the configuration of anodes and cathodes along the segmented electrodes allows the centroid of stimulation to be shifted to a variety of different locations along the lead 200.

As can be appreciated from FIG. 2, the stimulation can be shifted at each level along the length L of the lead 200. The use of multiple sets of segmented electrodes at different levels along the length of the lead allows for three-dimensional current steering. In some embodiments, the sets of segmented electrodes are shifted collectively (i.e., the centroid of simulation is similar at each level along the length of the lead). In at least some other embodiments, each set of segmented electrodes is controlled independently. Each set of segmented electrodes may contain two, three, four, five, six, seven, eight or more segmented electrodes. It will be understood that different stimulation profiles may be produced by varying the number of segmented electrodes at each level. For example, when each set of segmented electrodes includes only two segmented electrodes, uniformly distributed gaps (inability to stimulate selectively) may be formed in the stimulation profile. In some embodiments, at least three segmented electrodes in a set are utilized to allow for true 360° selectivity.

Turning to FIGS. 3A-3H, when the lead 300 includes a plurality of sets of segmented electrodes 330, it may be desirable to form the lead 300 such that corresponding electrodes of different sets of segmented electrodes 330 are radially aligned with one another along the length of the lead 300 (see e.g., the segmented electrodes 330 shown in FIGS. 3A and 3C-3G). Radial alignment between corresponding electrodes of different sets of segmented electrodes 330 along the length of the lead 300 may reduce uncertainty as to the location or orientation between corresponding segmented electrodes of different sets of segmented electrodes. Accordingly, it may be beneficial to form electrode arrays such that corresponding electrodes of different sets of segmented electrodes along the length of the lead 300 are radially aligned with one another and do not radially shift in relation to one another during manufacturing of the lead 300.

In other embodiments, individual electrodes in the two sets of segmented electrodes 330 are staggered (see, FIG. 3H) relative to one another along the length of the lead body 310. In some cases, the staggered positioning of corresponding electrodes of different sets of segmented electrodes along the length of the lead 300 may be designed for a specific application.

Segmented electrodes can be used to tailor the stimulation region so that, instead of stimulating tissue around the circumference of the lead as would be achieved using a ring electrode, the stimulation region can be directionally targeted. In some instances, it is desirable to target a parallelepiped (or slab) region 250 that contains the electrodes of the lead 200, as illustrated in FIG. 2. One arrangement for directing a stimulation field into a parallelepiped region uses segmented electrodes disposed on opposite sides of a lead.

FIGS. 3A-3H illustrate leads 300 with segmented electrodes 330, optional ring electrodes 320 or tip electrodes 320a, and a lead body 310. The sets of segmented electrodes 330 each include either two (FIG. 3B), three (FIGS. 3E-3H), or four (FIGS. 3A, 3C, and 3D) or any other number of segmented electrodes including, for example, three, five, six, or more. The sets of segmented electrodes 330 can be aligned with each other (FIGS. 3A-3G) or staggered (FIG. 3H)

Any other suitable arrangements of segmented electrodes can be used. As an example, arrangements in which segmented electrodes are arranged helically with respect to each other. One embodiment includes a double helix.

FIG. 5 illustrates one embodiment of a system for practicing the invention. The system can include a computer 500 or any other similar device that includes a processor 502 and a memory 504, a display 506, an input device 508, and, optionally, the electrical stimulation system 512.

The computer 500 can be a laptop computer, desktop computer, tablet, mobile device, smartphone or other devices that can run applications or programs, or any other suitable device for processing information and for presenting a user interface (such as the user interfaces of FIGS. 5A, 5B, 6A-6C, 9, and 5). The computer can be, for example, a clinician programmer, patient programmer, or remote programmer for the electrical stimulation system 512. The computer 500 can be local to the user or can include components that are non-local to the user including one or both of the processor 502 or memory 504 (or portions thereof). For example, in some embodiments, the user may operate a terminal that is connected to a non-local computer. In other embodiments, the memory can be non-local to the user.

The computer 500 can utilize any suitable processor 502 including one or more hardware processors that may be local to the user or non-local to the user or other components of the computer. The processor 502 is configured to execute instructions provided to the processor, as described below.

Any suitable memory 504 can be used for the computer 502. The memory 504 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 computer.

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 506 can be any suitable display device, such as a monitor, screen, display, or the like, and can include a printer. The input device 508 can be, for example, a keyboard, mouse, touch screen, track ball, joystick, voice recognition system, or any combination thereof, or the like and can be used by the user to interact with a user interface or clinical effects map.

The electrical stimulation system 512 can include, for example, a control module 514 (for example, an implantable pulse generator) and a lead 516 (for example, the lead illustrated in FIG. 1.) The electrical stimulation system 512 may communicate with the computer 500 through a wired or wireless connection or, alternatively or additionally, a user can provide information between the electrical stimulation system 512 and the computer 500 using a computer-readable medium or by some other mechanism. In some embodiments, the computer 500 may include part of the electrical stimulation system.

In at least some instances, a treating physician may wish to tailor the stimulation parameters (such as which one or more of the stimulating electrode contacts to use, the stimulation pulse amplitude (such as current or voltage amplitude depending on the stimulator being used,) the stimulation pulse width, the stimulation frequency, or the like or any combination thereof) for a particular patient to improve the effectiveness of the therapy. Electrical stimulation systems can provide an interface that facilitates parameter selections. Examples of such systems and interfaces can be found in, for example, U.S. patent application Ser. Nos. 12/454,330; 12/454,312; 12/454,340; 12/454,343; and 12/454,314 and U.S. Patent Application Publication No. 2014/0277284, all of which are incorporated herein by reference in their entireties.

Stimulation region visualization systems and methods can be used to predict or estimate a region of stimulation for a given set of stimulation parameters. In at least some embodiments, the systems and methods further permit a user to modify stimulation parameters and visually observe how such modifications can change the predicted or estimated stimulation region. Such algorithms and systems may provide greater ease of use and flexibility and may enable or enhance specific targeting of stimulation therapy. The terms “stimulation field map” (SFM) and “volume of activation” (VOA) are often used to designate an estimated region of tissue that will be stimulated for a particular set of stimulation parameters. Any suitable method for determining the VOA/SFM 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.

For purposes of illustration of the methods and systems described below, one embodiment of a distal end of a lead 400 is presented in FIG. 4. The lead 400 includes a ring electrode 450, a first set of three segmented electrodes 452a, 452b, 452c, a second set of three segmented electrodes 454a, 454b, 454c, and a tip electrode 456. A number of state variables can be used to describe the electrical stimulation field. First, a “position” variable can be used to estimate or represent the central axial position of the field relative to the longitudinal axis of the lead. For example, if the stimulation is provided solely by ring electrode 450, then the position of the field is centered on ring electrode 450. However, combinations of electrodes can also be used. For example, if the stimulation is provided with 50% of the amplitude on ring electrode 450 and 50% of the amplitude on segmented electrode 452a, then the position of the field can be described as between electrodes 450, 452a (although it will be recognized that the field also extends in both axial directions from this position.)

Another state variable is “rotation” which represents the radial direction of the field. In the case of stimulation provided solely by ring electrode 450, the rotation variable is arbitrary because the stimulation is provided in all directions. On the other hand, if the stimulation is provided by segmented electrode 452a, the rotation can be described as directed outward from segmented electrode 452a. Again, combinations of electrodes can be used so that the rotation may be described as between electrodes 452a, 452b if 50% of the stimulation amplitude is provided to both electrodes.

Yet another state variable is “spread” which relates to the spread of the field around the circumference of the lead. In the case of stimulation provided solely by ring electrode 450, the spread variable is at a maximum because the stimulation is provided in all directions. On the other hand, if the stimulation is provided by segmented electrode 452a, the spread variable is at its minimum because the field is generated by only one segmented electrode 452a. Again, combinations of electrodes can be used so that the spread may be described as larger when 50% of the stimulation amplitude is provided on both electrodes 452a, 452b.

The stimulation (e.g., stimulation current) can be steered by changing these state variables. For example, the stimulation can be moved up or down the longitudinal axis of the lead by changing the position variable. As an example, the stimulation can be initially provided 100% through electrode 450. The stimulation can then be steered distally by directing a portion of the stimulation to the electrodes 452a, 452b, 452c. For example, in a first step, 90% of the stimulation remains on electrode 450 and 10% is divided equally among electrodes 452a, 452b, 452c. The second step can have 80% on electrodes 450 and 20% divided equally among electrodes 452a, 452b, 452c. This can continue until there is no stimulation on electrode 450 and 100% of the stimulation is divided among electrodes 452a, 452b, 452c. The process can proceed to incrementally transfer stimulation from electrodes 452a, 452b, 452c to electrodes 454a, 454b, 454c. Similarly, the stimulation then be incrementally transferred from electrodes 454a, 454b, 454c to electrode 456. The stimulation can also be rotated. For example, stimulation from electrode 452a and be rotated to electrode 452b. The stimulation field can also be spread. For example, stimulation field from electrode 452a can be spread so that the stimulation is from both electrodes 452a, 452b. That stimulation field can then be contracted so that the stimulation is only from electrode 452b.

It has been found, however, that the SFMs determined using these incremental steering steps can vary substantially in maximum radius (e.g., the maximum extent of the SFM measured orthogonal to the lead) despite having the same stimulation amplitude. In at least some instances, the maximum radius of the SFM can vary by 20%, 30%, 40% or more as the stimulation is steered along the lead with constant stimulation amplitude.

In at least some instances, it is desirable to steer stimulation along or around a lead and maintain a constant or nearly constant (e.g., within 1, 2, 3, 4, 5, 10, or 15%) maximum radius of the stimulation field. This can be accomplished by changing one or more stimulation parameters, such as stimulation amplitude, pulse width, or the like, to maintain the stimulation within the specified constraint, such as a specified maximum radius. Methods and systems for performing such steering are provided below.

In general, the methods and systems described herein include selection of one or more target geometrical parameters, such as a target maximum radius or a target volume, and maintenance of that target geometrical parameter with changes in programming state by determining one or more stimulation parameters that maintain the target geometrical parameter for the new programming state. In the examples below, target maximum radius and target volume are used as examples of the target geometrical parameter. The methods and systems described below can be used with any other suitable geometrical parameter including, but not limited to, a target minimum radius or other target radius, a target diameter, a target axial length (at the lead or at any distance from the lead such as at the edge of the encapsulation layer), a target cross-sectional area at any plane or plane sections relative to the lead (including planes orthogonal to, including, parallel to, or at any angle relative to the longitudinal axis of the lead) or the like. In addition, the methods and systems described below can be used with more than one target geometrical parameter including, for example, multiple target radii at different angles distributed around the lead.

In at least some embodiments of the methods and systems described herein, the maintenance of the target geometrical parameter can be performed to maintain the target geometrical parameter at the same value. In other embodiments, the maintenance of the target geometrical parameter can be performed to maintain the target geometrical parameter within 1, 2, 3, 4, 5, 10%, or 15% of the original or target value. Unless otherwise indicated, the maintenance of the target geometrical parameter is performed to maintain the target geometrical parameter within 10% of the target value. In some embodiments, the percentage deviation from the original value that is allowed is fixed and not user-defined. In other embodiments, the percentage deviation from the original value that is allowed can be user-defined or user-modified from an initial value.

Instead of maintaining the target geometrical parameter within a particular percentage, in some embodiments, the one or more stimulation parameters are selected so that the stimulation field does not exceed (e.g., is less than or equal) the target geometrical parameter or at a minimum meets (e.g. is greater than or equal) the target geometrical parameter. In yet other embodiments, for example, when the stimulation parameter can only be changed in discreet steps (for example, stimulation amplitude may only be variable in steps of 0.1 mA), the target geometrical parameter is maintained using the largest (or smallest) value of the stimulation parameter for which the resulting stimulation field does not exceed (e.g., is less than or equal) the target geometrical parameter or at a minimum meets (e.g. is greater than or equal) the target geometrical parameter.

It will be recognized that the stimulation fields described herein are not necessarily the actual stimulation field generated upon application of a set of stimulation parameters. Instead, the stimulation field, and associated geometrical parameter, can be estimates or approximations calculated based on models, such as those discussed above for determining SFMs or VOAs.

In addition, in the examples of systems and methods described below, stimulation amplitude is altered to maintain the target geometrical parameter, but it will be recognized that other stimulation parameters can be used instead of stimulation amplitude including, but not limited to, pulse width, stimulation frequency, or the like. Moreover, in some embodiments, two or more stimulation parameters can be altered to maintain the target geometrical parameter.

In at least some embodiments of the systems and methods described herein, a table of maximum radii (or other geometrical parameter) can be determined for multiple programming states that are defined by multiple programming state variables, such as position, rotation, spread, pulse width, amplitude, and the like. Each of the programming states is defined using two or more of these programming state variables. The maximum radius can then be determined for each of these programming states. For example, a look-up table of maximum radii can be defined for a set of programming states using, as programming state variables, pulse width, spread, rotation, position, and amplitude. As one example, such a table can be generated for a set of programming states obtained using 44 different pulse widths, 11 different spreads, 12 different rotations, 31 different positions, and 16 different amplitudes. The number of possible values for each programming state variable can be varied from this example, as well as the selection of which programming state variable to use. Such a look-up table can then be used in the methods described below.

FIG. 6 illustrates one example of a method of maintaining a selected maximum radius during a change in programming state. In this method, the target maximum radius is received and the stimulation amplitude is then adjusted to obtain the target maximum radius at different programming states.

In step 602, a target maximum radius is received from a clinician, user, or other source. In step 604, a desired programming state is received.

In step 606, the stimulation amplitude is determined that produces the target maximum radius for the desired programming state. In at least some embodiments, the stimulation amplitude (or other stimulation parameter) can be determined from a look-up table, such as the one described above, which provides maximum radii for multiple programming states. If the target maximum radius or programming state are not provided in the look-up table, then interpolation between entries can be used to obtain a stimulation amplitude that corresponds to the target maximum radius and desired programming state. Any suitable interpolation technique can be used including linear or non-linear interpolation techniques.

Alternatively or additionally, a model of the neural region near the lead can be created, such as the models used for the calculation of SFMs or VOAs as discussed in the references cited above. The model can determine, for each volume element in the region, the threshold stimulation current needed to activate a neural element at that volume element. In at least some embodiments, the stimulation amplitude can be determined to be the minimum threshold stimulation current for the volume elements at the target maximum radius.

In optional step 608, stimulation parameters based on the stimulation amplitude and programming state are output to a stimulation device, for example, the control module of FIG. 5, that can produce stimulation signals for delivery to the patient via the lead electrodes. The stimulation parameters can be associated with the programming state including, for example, the pulse width and selection of electrodes corresponding to the spread, rotation, and position state variables, as well as the division of the stimulation amplitude between electrodes where there are more than one anode or cathode. For example, the processor performing the method of FIG. 6 can initiate a signal directed to the stimulation device in order to convey the stimulation parameters to the stimulation device. The stimulation device can receive the stimulation parameters and can then operate a stimulation program to deliver electrical stimulation to the patient using the stimulation parameters.

In at least some embodiments, after performing step 606 or step 608, the process can return to step 604 to receive a new programming state. Steps 606 and, optionally, step 608 can be then be performed based on the new programming state. This process can be repeated as many times as desired for any number of programming states.

FIG. 7 illustrates one example of a method of maintaining a selected maximum radius during a change in programming state. In this method, either a set of steering parameters or a starting programming state and starting stimulation amplitude is received. A maximum radius is determined from the initial information and then stimulation amplitude is adjusted to obtain the maximum radius at different programming states.

In step 702, either a) stimulation parameters (including a starting stimulation amplitude) or b) a starting programming state and starting stimulation amplitude is received from a clinician, user, or other source.

In step 704, the maximum radius is determined based on the information received in step 702. In some embodiments, the maximum radius is determined from a look-up table, such as the look-up table described above. In other embodiments, the maximum radius can be determined using the SFM/VOA calculation methods described above. In yet other embodiments, the maximum radius can be determined from a model of the neural region near the lead, such as the models used for the calculation of SFMs or VOAs as discussed in the references cited above. The model can determine, for each volume element in the region, the threshold stimulation current needed to activate a neural element at that volume element. In at least some embodiments, the maximum radius can correspond to the largest radius at which the starting stimulation amplitude is equal to the threshold stimulation current for one of the volume elements at that radius.

In step 706, a desired programming state is received. In step 708, the stimulation amplitude (or other stimulation parameter) is determined that produces the maximum radius for the desired programming state. In at least some embodiments, the stimulation amplitude can be determined from a look-up table, such as the one described above, which provides maximum radii for multiple programming states. If the maximum radius or programming state are not provided in the look-up table, then interpolation between entries can be used to obtain a stimulation amplitude that corresponds to the maximum radius and desired programming state. Any suitable interpolation technique can be used including linear or non-linear interpolation techniques.

In optional step 710, stimulation parameters based on the stimulation amplitude and programming state are output to a stimulation device, for example, the control module of FIG. 5, that can produce stimulation signals for delivery to the patient via the lead electrodes. The stimulation parameters can be associated with the programming state including, for example, the pulse width and selection of electrodes corresponding to the spread, rotation, and position state variables, as well as the division of the stimulation amplitude between electrodes where there are more than one anode or cathode. For example, the processor performing the method of FIG. 7 can initiate a signal directed to the stimulation device in order to convey the stimulation parameters to the stimulation device. The stimulation device can receive the stimulation parameters and can then operate a stimulation program to deliver electrical stimulation to the patient using the stimulation parameters.

In at least some embodiments, after performing step 708 or step 710, the process can return to step 706 to receive a new programming state. Steps 708 and, optionally, step 710 can be then be performed based on the new programming state. This process can be repeated as many times as desired for any number of programming states.

In the methods described with respect to FIGS. 6 and 7, there is no spatial limit on where the maximum radius can reside. The directional leads illustrated in, for example, 3A-3F and 4, can be used to generate stimulation that is not symmetric around the longitudinal axis of the lead, but rather can have directionality with respect to the longitudinal axis of the lead. For example, providing stimulation current using electrode 452a and not electrodes 452b, 452c will extend the stimulation from electrode 452a which much less stimulation near electrodes 425b, 452c.

FIGS. 8 and 9 illustrated embodiments of methods of maintaining a selected maximum radius at an angle or range of angles during a change in programming state. The steps of FIGS. 8 and 9 are the same as those in FIGS. 6 and 7, respectively, except as noted below.

In step 802, in addition to receiving the target maximum radius, an angle or range of angles where the target maximum radius is to reside is also received. In step 806, the stimulation amplitude (or other stimulation parameter) that achieves the target maximum radius at the specified angle or within the specified range of angles is determined.

Similarly, in step 902, an angle or range of angles is received in addition to the other information. In step 904, the maximum radius is determined at that angle or within that range of angles. In step 908, the stimulation amplitude that achieves the maximum radius at the specified angle or within the specified range of angles is determined.

The methods can be further modified. For example, in steps 804 and 906, in addition to receiving a new programming state, a new angle or range of angles can be received. The stimulation amplitude (or other stimulation parameter) determined in steps 806 and 908 will achieve the maximum radius for the new angle or within the new range of angles. This modification to the methods can be used to maintain a constant radius at the stimulation is rotated around a lead.

Yet another modification can include specifying a particular axial position or range of axial positions along the lead where the target maximum radius is to reside instead of, or in addition to, the angle or range of angles in the methods of FIGS. 8 and 9 or the modified methods described in the preceding paragraph.

Instead of maintaining a constant maximum radius, a constant volume can be maintained. The volume for a particular state can be determined by calculation of a SFM or VOA and then determining the volume of that SFM or VOA. In at least some embodiments, a table of maximum volumes can be determined for multiple states that are defined by multiple state variables, such as position, rotation, spread, pulse width, amplitude, and the like. Each of the states is defined using two or more of these state variables. The maximum volume can then be determined for each of these states. For example, a look-up table of maximum volumes can be defined for a set of states using, as state variables, pulse width, spread, rotation, position, and amplitude. As one example, such a table can be generated for a set of states obtained using 44 different pulse widths, 11 different spreads, 12 different rotations, 31 different positions, and 16 different amplitudes. The number of possible values for each state variable can be varied from this example, as well as the selection of which state variable to use. Such a look-up table can then be used in the methods described below.

FIG. 10 illustrates one example of a method of maintaining a selected volume during a change in programming state. In this method, the target volume is received and the stimulation amplitude is adjusted to obtain the target volume at different programming states.

In step 1002, a target volume is received from a clinician, user, or other source. In step 1004, a desired programming state is received.

In step 1006, the stimulation amplitude (or other stimulation parameter) is determined that produces the target volume for the desired programming state. In at least some embodiments, the stimulation amplitude can be determined from a look-up table, such as the one described above, which provides maximum radii for multiple programming states. If the target volume or programming state are not provided in the look-up table, then interpolation between entries can be used to obtain a stimulation amplitude that corresponds to the target volume and desired programming state. Any suitable interpolation technique can be used including linear or non-linear interpolation techniques.

Alternatively or additionally, a model of the neural region near the lead can be created, such as the models used for the calculation of SFMs or VOAs as discussed in the references above. The model can determine, for each volume element in the region, the threshold stimulation current needed to activate a neural element at that volume element. In at least some embodiments, a stimulation field can be created by sequentially increasing a current value and adding volume elements with a threshold stimulation current equal to the current value until the target volume is reached. Alternatively, a stimulation field can be created by sequentially decreasing a current value and subtracting volume elements with a threshold stimulation current greater than the current value until the target volume is reached. In either case, the final current value at which the target volume is reached is the stimulation current.

In optional step 1008, stimulation parameters based on the stimulation amplitude and programming state are output to a stimulation device, for example, the control module of FIG. 5, that can produce stimulation signals for delivery to the patient view the lead electrodes. The stimulation parameters can be associated with the programming state including, for example, the pulse width and selection of electrodes corresponding to the spread, rotation, and position state variables, as well as the division of the stimulation amplitude between electrodes where there are more than one anode or cathode. For example, the processor performing the method of FIG. 10 can initiate a signal directed to the stimulation device in order to convey the stimulation parameters to the stimulation device. The stimulation device can receive the stimulation parameters and can then operate a stimulation program to deliver electrical stimulation to the patient using the stimulation parameters.

In at least some embodiments, after performing step 1006 or step 1008, the process can return to step 1004 to receive a new programming state. Steps 1006 and, optionally, step 1008 can be then be performed based on the new programming state. This process can be repeated as many times as desired for any number of programming states.

FIG. 11 illustrates one example of a method of maintaining a selected target volume during a change in programming state. In this method, either a set of steering parameters or a starting programming state and starting stimulation amplitude is received. A target volume is determined from the initial information and then stimulation amplitude is adjusted to obtain the target volume at different programming states.

In step 1102, either a) stimulation parameters (including a starting stimulation amplitude) or b) a starting programming state and starting stimulation amplitude is received from a clinician, user, or other source. In step 1104, the target volume is determined based on the information provided in step 1102. In some embodiments, the target volume is determined from a look-up table, such as the look-up table described above. In other embodiments, the target volume can be determined using the SFM/VOA calculation methods described above.

In step 1106, a desired programming state is received. In step 1108, the stimulation amplitude (or other stimulation parameter) is determined that produces the target volume for the desired programming state. In at least some embodiments, the stimulation amplitude can be determined from a look-up table, such as the one described above, which provides maximum radii for multiple programming states. If the target volume or programming state are not provided in the look-up table, then interpolation between entries can be used to obtain a stimulation amplitude that corresponds to the target volume and desired programming state. Any suitable interpolation technique can be used including linear or non-linear interpolation techniques.

In optional step 1110, stimulation parameters based on the stimulation amplitude and programming state are output to a stimulation device, for example, the control module of FIG. 5, that can produce stimulation signals for delivery to the patient view the lead electrodes. The stimulation parameters can be associated with the programming state including, for example, the pulse width and selection of electrodes corresponding to the spread, rotation, and position state variables, as well as the division of the stimulation amplitude between electrodes where there are more than one anode or cathode. For example, the processor performing the method of FIG. 11 can initiate a signal directed to the stimulation device in order to convey the stimulation parameters to the stimulation device. The stimulation device can receive the stimulation parameters and can then operate a stimulation program to deliver electrical stimulation to the patient using the stimulation parameters.

In at least some embodiments, after performing step 1108 or step 1110, the process can return to step 1106 to receive a new programming state. Steps 1108 and, optionally, step 1110 can be then be performed based on the new programming state. This process can be repeated as many times as desired for any number of programming states.

Similar to the methods illustrated in FIGS. 8 and 9, the methods illustrated in FIGS. 10 and 11 can be modified so to maintain a target volume within a specified range of angles or within a specified axial range or both.

In other embodiments, the methods described herein may be modified to allow the user to select whether to maintain the maximum radius or maintain the target volume (or maintain another geometrical parameter). For example, the user may be permitted to specify whether to maintain the maximum radius or maintain the target volume at the start of the process. In other embodiments, the user may be permitted to specify whether to maintain the maximum radius or maintain the target volume each time a new programming state is received.

In yet other embodiments, the process may incorporate rules (which may or may not be user-modifiable or user-selectable) to determine whether to maintain the maximum radius or maintain the target volume (or other geometrical parameter). For example, the rules may specify that when decreasing a state variable, the maximum radius is maintained and when increasing that state variable, the target volume is maintained (or vice versa).

As one example, reducing the spread state variable often decreases the stimulation amplitude needed to maintain constant radius, while increasing the spread state variable at constant radius increases the volume of stimulation. Accordingly, one example of a rule is that when decreasing spread the maximum radius is maintained and when increasing spread the target volume is maintained (or vice versa).

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.

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 computer.

The above specification and examples provide a description of the invention 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 method for determining a set of electrical stimulation parameters for an electrical stimulation lead and delivering electrical stimulation, the method comprising: a) receiving, by a computer processor, a target geometrical parameter describing an electrical stimulation field, wherein the target geometrical parameter is a target maximum radius;b) receiving, by the computer processor, one of the following: an angle, a range of angles, an axial position, or a range of axial positions;c) receiving, by the computer processor, a first programming state;d) determining, by the computer processor, a first electrical stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 10% of the target geometrical parameter at the angle when the angle is received in step b), within the range of angles when the range of angles is received in step b), at the axial position when the axial position is received in step b) or within the range of axial positions when the range of axial positions is received in step b);e) outputting, by the computer processor, a set of electrical stimulation parameters to be received by an electrical stimulation device for delivery of electrical stimulation to a patient via an electrical stimulation lead, wherein the set of electrical stimulation parameters comprises the first electrical stimulation parameter and represents the first programming state; andf) delivering the electrical stimulation to the patient using the set of electrical stimulation parameters.
2. The method of claim 1, wherein the angle is received in step b).
3. The method of claim 1, wherein the range of angles is received in step b).
4. The method of claim 1, wherein the axial position or the range of axial positions is received in step b).
5. The method of claim 1, further comprising repeating steps c)-e) for at least one additional programming state.
6. A method for determining a set of electrical stimulation parameters for an electrical stimulation lead and delivering electrical stimulation, the method comprising: a) receiving, by a computer processor, a target geometrical parameter describing an electrical stimulation field;b) receiving, by the computer processor, a first programming state;c) determining, by the computer processor, a first electrical stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 10% of the target geometrical parameter, wherein determining the first electrical stimulation parameter comprises determining, by the computer processor, the first electrical stimulation parameter using a look-up table with previously determined first electrical stimulation parameters for a plurality of programming states;d) outputting, by the computer processor, a set of electrical stimulation parameters to be received by an electrical stimulation device for delivery of electrical stimulation to a patient via an electrical stimulation lead, wherein the set of electrical stimulation parameters comprises the first electrical stimulation parameter and represents the first programming state; ande) delivering the electrical stimulation to the patient using the set of electrical stimulation parameters.
7. The method of claim 6, wherein the target geometrical parameter is a target volume.
8. A method for determining a set of electrical stimulation parameters for an electrical stimulation lead, the method comprising: a) receiving, by a computer processor, either i) a first set of electrical stimulation parameters or ii) a starting programming state and starting first electrical stimulation parameter;b) the computer processor, one of the following: an angle, a range of angles, an axial position, or a range of axial positions;c) determining, by the computer processor and from either i) the first set of electrical stimulation parameters or ii) the starting programming state and starting first electrical stimulation parameter, a target geometrical parameter describing an electrical stimulation field, wherein the target geometrical parameter is a target maximum radius;d) receiving, by the computer processor, a first programming state;e) determining, by the computer processor, a first electrical stimulation parameter for the first programming state that achieves the target geometrical parameter within at least 10% of the target geometrical parameter at the angle when the angle is received in step b), within the range of angles when the range of angles is received in step b), at the axial position when the axial position is received in step b) or within the range of axial positions when the range of axial positions is received in step b);f) outputting, by the computer processor, a second set of electrical stimulation parameters to be received by an electrical stimulation device for delivery of electrical stimulation to a patient via an electrical stimulation lead, wherein the set of electrical stimulation parameters comprises the first electrical stimulation parameter for the first programming state and represents the first programming state; andg) delivering the electrical stimulation to the patient using the set of electrical stimulation parameters.
9. The method of claim 8, wherein the angle is received in step b), wherein determining the target geometrical parameter comprises determining, by the computer processor, the target maximum radius at the angle.
10. The of claim 8, wherein the range of angles is received in step b), wherein determining the target geometrical parameter comprises determining, by the computer processor, the target maximum radius within the range of angles.
11. The method of claim 8, wherein the axial position or a range of axial positions is received in step b), wherein determining the target geometrical parameter comprises determining, by the computer processor, the target maximum radius at the axial position or within the range of axial positions.
12. The method of claim 8, further comprising repeating steps d)-f) for at least one additional programming state.
13. The computer-implemented method of claim 8, wherein determining the first electrical stimulation parameter comprises determining, by the computer processor, the first electrical stimulation parameter using a look-up table with previously determined first electrical stimulation parameters for a plurality of programming states.
14. A system for determining a set of electrical stimulation parameters for an electrical stimulation lead and delivering electrical stimulation, the system comprising: a display;a computer processor coupled to the display; andan electrical stimulation device comprising an electrical stimulation lead and in communication with the computer processor;wherein the electrical stimulation system is configured and arranged to perform the method of claim 1.
15. A system for determining a set of electrical stimulation parameters for an electrical stimulation lead and delivering electrical stimulation, the system comprising: a display;a computer processor coupled to the display; andan electrical stimulation device comprising an electrical stimulation lead and in communication with the computer processor;wherein the electrical stimulation system is configured and arranged to perform the method of claim 8.
16. A non-transitory computer-readable medium having processor-executable instructions for determining a set of electrical stimulation parameters and delivering electrical stimulation, the processor-executable instructions when installed onto a device enable the device to perform the method of claim 1.
17. A system for determining a set of electrical stimulation parameters for an electrical stimulation lead and delivering electrical stimulation, the system comprising: a display;a computer processor coupled to the display; andan electrical stimulation device comprising an electrical stimulation lead and in communication with the computer processor;wherein the electrical stimulation system is configured and arranged to perform the method of claim 6.
18. A non-transitory computer-readable medium having processor-executable instructions for determining a set of electrical stimulation parameters and delivering electrical stimulation, the processor-executable instructions when installed onto a device enable the device to perform the method of claim 6.
19. A non-transitory computer-readable medium having processor-executable instructions for determining a set of electrical stimulation parameters and delivering electrical stimulation, the processor-executable instructions when installed onto a device enable the device to perform the method of claim 8.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/397,256, filed Sep. 20, 2016, which is incorporated herein by reference.

US Referenced Citations (514)

Number	Name	Date	Kind
3999555	Person	Dec 1976	A
4144889	Tyers et al.	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	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	Fischell 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	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	Soumillion 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	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	Campen 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	Pianca 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	Pianca 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
8000794	Lozano	Aug 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	Hulvershorn et al.	Sep 2012	B2
8271094	Moffitt et al.	Sep 2012	B1
8280514	Lozano et al.	Oct 2012	B2
8295944	Howard	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
8483237	Zimmermann et al.	Jul 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	Pianca 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	Slum 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	Rezai	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	Schuler 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	Tass et al.	Aug 2007	A1
20070203537	Goetz et al.	Aug 2007	A1
20070203538	Stone et al.	Aug 2007	A1
20070203539	Stone et al.	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
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	Nov 2009	A1
20090287272	Kokones	Nov 2009	A1
20090287273	Carlton	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
20100010488	Kassab	Jan 2010	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
20100049031	Fruland	Feb 2010	A1
20100049188	Nelson	Feb 2010	A1
20100049192	Holtz	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-Leone 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	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
20120101552	Lazarewicz 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
20120215106	Sverdlik	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
20120271376	Kokones	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
20120330622	Butson 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	Carlton et al.	May 2013	A1
20130116929	Carlton et al.	May 2013	A1
20130150922	Butson et al.	Jun 2013	A1
20130197424	Bedenbaugh	Aug 2013	A1
20130197602	Pianca et al.	Aug 2013	A1
20130218156	Kassab	Aug 2013	A1
20130226261	Sparks	Aug 2013	A1
20130245719	Zhu	Sep 2013	A1
20130261684	Howard	Oct 2013	A1
20130296995	Mahmood	Nov 2013	A1
20130317587	Barker	Nov 2013	A1
20130325091	Pianca et al.	Dec 2013	A1
20140039587	Romero	Feb 2014	A1
20140066999	Carcieri et al.	Mar 2014	A1
20140067018	Carcieri et al.	Mar 2014	A1
20140067022	Carcieri et al.	Mar 2014	A1
20140088672	Bedenbaugh	Mar 2014	A1
20140122379	Moffitt et al.	May 2014	A1
20140277284	Chen et al.	Sep 2014	A1
20140296953	Pianca et al.	Oct 2014	A1
20140343647	Romero et al.	Nov 2014	A1
20140353001	Romero et al.	Dec 2014	A1
20140358207	Romero	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
20150021817	Romero et al.	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
20150148869	Dorvall, II	May 2015	A1
20150151113	Govea et al.	Jun 2015	A1
20150335344	Aljuri	Nov 2015	A1
20160022995	Kothandaraman et al.	Jan 2016	A1
20160023008	Kothandaraman	Jan 2016	A1
20160030749	Carcieri et al.	Feb 2016	A1
20160096025	Moffitt et al.	Apr 2016	A1
20160136429	Massoumi et al.	May 2016	A1
20160136443	Kothandaraman	May 2016	A1
20160206370	Fruland	Jul 2016	A1
20160256693	Parramon	Sep 2016	A1
20160374710	Sinelnikov	Dec 2016	A1
20160375248	Carcieri et al.	Dec 2016	A1
20160375258	Steinke	Dec 2016	A1
20170100593	Zottola	Apr 2017	A1
20170231693	Nelson	Aug 2017	A1
20170252570	Serrano Carmona et al.	Sep 2017	A1
20180263647	Aljuri	Sep 2018	A1
20190069949	Vrba	Mar 2019	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
2001139779	Nov 2011	WO
2011159688	Dec 2011	WO
2012088482	Jun 2012	WO

Non-Patent Literature Citations (269)

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 paildus 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 International Symposium, May 14, 2008, pp. 480-483.
Kalkai 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 Pradigm 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 Regristration 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 subthalamic nucleaus 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. VII. 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 of living tissues.”, Ann NY Aced 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,” 173-187 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 Neurochirurgica Suppl., Jan. 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 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”, Proceeding 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. Apr. 2, 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 Opiod 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.
Casselin, F. “Opiod and anit-opiod 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”, Nuerol 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 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 brainstirnulation interactive visualization system”, Society for Neuroscience Volume 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 at, “Muitigrid 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 manaaement 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. Nuerophysiol., 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 waveform 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 nuerons,” Annals of biomedical engineering, 28(3) (Mar. 2000), pp. 219-233.
Mcintyre, C. C., et al., “Sensitivity analysis of a model of mammalian nueral 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 Subthalamic Nucleus,” Clinical Neurophysiology, 115(3) (Mar. 2004), pp. 589-595.
Mcintyre, Cameron C., et al., “Electric field generated by deep brain stimulation of the subthalamic 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 Neuraphysiol 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. Med. 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. Neurol., 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 influence 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. Neurol. 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 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: influence 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. 1093-109.
Testerrnan, 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,”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.
Yianni, 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., “Progammable 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 (P1 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./Oct. 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.
Bensbid, 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 (lhalamotoy 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 different tensor based electric field models,” Medical Image Computing and Computer-Assisted Intervention—Mic Cal 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-generalized dystonia by stimulation of the internal globus pallidus,” Lancet, 355 (9222), (Jun. 24, 2000), pp. 2220-2221.
Dasilva, A.F. M., et al., “A Primer Diffusion Tensor Imaging of Anatomical Substructuresm,” 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,” Neuroimage, 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, K. 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, Kirk W., et al., “Three-Dimensional Database of Subcortical Electrophysiology for Image-Guided Stereotatic Functional Neurosurgery,” IEEE Transactions on Medical Imaging, 22(1) (Jan. 2003), pp. 93-104.
Gabriels, L , et al., “Deep brain stimulation and neuropsychological outcome in 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 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 Society International Conference of the IEEE Engineering in Medicine and Biology, 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. K. “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 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: reappraisal of a 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 Neurol 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 Capactiance 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”, 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 Funct. Neurosurge., 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”, NeuroImage, 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 Volume and Their Association with Neurophysiological Mapping and Therapeutic Outcomes”, Downloaded from jnmp.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 lmmunohistochemical 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 brain stimulation”, Clinical Neurophysiology. vol. 117.(2006),447-454.
An, et al., “Prefrontal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys,” J Comp Neural 401 (4) (1998), pp. 455-479.
Butson, 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 prefrontal cortex of macaque monkeys,” J Comp Neural 371 (2) (1996), pp. 179-207.
Croxson, et al., “Quantitative investigation of connections of the prefontal cortex in the human and macaque using probabilistic diffusion tractography,” 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 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 enviroment,” 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-Berg, 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 Neural 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),25-104.
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.
Kitagavva, 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”, 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 Simulation”, 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 nuerons”, 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 striatel 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. 15I7t (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.

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	20180078776 A1	Mar 2018	US

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	62397256	Sep 2016	US

Systems and methods for steering electrical stimulation of patient tissue and determining stimulation parameters

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