The present invention relates to a system and method for providing a user interface in which a representation of stimulation parameters of an electrode leadwire, for example that provides electrical stimulation to an anatomical region, e.g., a leadwire of a Deep Brain Stimulation (DBS) device or a Spinal Cord Stimulation (SCS) device, is provided. The present invention further relates additionally or alternatively to a system and method, using software which provides a visual point-and-click interface that allows a user to optimize a subject's (e.g., patient's) stimulation parameters, without the user having to keep track of the precise settings for each electrode.
Stimulation of anatomical regions of a patient is a clinical technique for the treatment of disorders. Such stimulation can include deep brain stimulation (DBS), spinal cord stimulation (SCS), Occipital NS therapy, Trigemenal NS therapy, peripheral field stimulation therapy, sacral root stimulation therapy, or other such therapies. For example, DBS may include stimulation of the thalamus or basal ganglia and may be used to treat disorders such as essential tremor, Parkinson's disease (PD), and other physiological disorders. DBS may also be useful for traumatic brain injury and stroke. Pilot studies have also begun to examine the utility of DBS for treating dystonia, epilepsy, and obsessive-compulsive disorder.
However, understanding of the therapeutic mechanisms of action remains elusive. The stimulation parameters, electrode geometries, or electrode locations that are best suited for existing or future uses of DBS also are unclear.
For conducting a therapeutic stimulation, a neurosurgeon can select a target region within the patient anatomy, e.g., within the brain for DBS, an entry point, e.g., on the patient's skull, and a desired trajectory between the entry point and the target region. The entry point and trajectory are typically carefully selected to avoid intersecting or otherwise damaging certain nearby critical structures or vasculature. A stimulation electrode leadwire used to provide the stimulation to the relevant anatomical region is inserted along the trajectory from the entry point toward the target region. The stimulation electrode leadwire typically includes multiple closely-spaced electrically independent stimulation electrode contacts.
The target anatomical region can include tissue that exhibit high electrical conductivity. For a given stimulation parameter setting, a respective subset of the fibers are responsively activated. A stimulation parameter can include a current amplitude or voltage amplitude, which may be the same for all of the electrodes of the leadwire, or which may vary between different electrodes of the leadwire. The applied amplitude setting results in a corresponding current in the surrounding fibers, and therefore a corresponding voltage distribution in the surrounding tissue. The complexity of the inhomogeneous and anisotropic fibers makes it difficult to predict the particular volume of tissue influenced by the applied stimulation.
A treating physician typically would like to tailor the stimulation parameters (such as which one or more of the stimulating electrode contacts to use, the stimulation pulse amplitude, e.g., current or voltage depending on the stimulator being used, the stimulation pulse width, and/or the stimulation frequency) for a particular patient to improve the effectiveness of the therapy. Parameter selections for the stimulation can be achieved via tedious and variable trial-and-error, without visual aids of the electrode location in the tissue medium or computational models of the volume of tissue influenced by the stimulation. Such a method of parameter selection is difficult and time-consuming and, therefore, expensive. Moreover, it may not necessarily result in the best possible therapy.
Systems have been proposed that provide an interface that facilitates parameter selections. See, for example, U.S. patent application Ser. No. 12/454,330, filed May 15, 2009 (“the '330 application”), U.S. patent application Ser. No. 12/454,312, filed May 15, 2009 (“the '312 application”), U.S. patent application Ser. No. 12/454,340, filed May 15, 2009 (“the '340 application”), U.S. patent application Ser. No. 12/454,343, filed May 15, 2009 (“the '343 application”), and U.S. patent application Ser. No. 12/454,314, filed May 15, 2009 (“the '314 application”), the content of each of which is hereby incorporated herein by reference in its entirety.
Such systems display a graphical representation of an area within which it is estimated that there is tissue activation or volume of activation (VOA) that results from input stimulation parameters. The VOA can be displayed relative to an image or model of a portion of the patient's anatomy. Generation of the VOA may be based on a model of fibers, e.g., axons, and a voltage distribution about the leadwire and on detailed processing thereof. Performing such processing to provide a VOA preview in real-time response to a clinician's input of parameters is not practical because of the significant required processing time. Therefore, conventional systems pre-process various stimulation parameter settings to determine which axons are activated by the respective settings.
Those systems also provide interfaces via which to input selections of the stimulation parameters and notes concerning therapeutic and/or side effects of stimulations associated with graphically represented VOAs.
The leadwire can include cylindrically symmetrical electrodes, which, when operational, produce approximately the same electric values in all positions at a same distance from the electrode in any plain that cuts through the electrode. Alternatively, the leadwire can include directional electrodes that produce different electrical values depending on the direction from the electrode. For example, the leadwire can include multiple separately controllable electrodes arranged cylindrically about the leadwire at each of a plurality of levels of the leadwire.
Each electrode may be set as an anode or cathode in a bipolar configuration or as a cathode, with, for example, the leadwire casing being used as ground, in a monopolar arrangement. When programming a leadwire for tissue stimulation, e.g., DBS, the clinical standard of care is often to perform a monopolar review (MPR) upon activation of the leadwire in order to determine the efficacy and side-effect thresholds for all electrodes on the leadwire, on an electrode by electrode basis. Monopolar review, rather than bipolar review, is performed because monopolar stimulation often requires a lower stimulation intensity than bipolar stimulation to achieve the same clinical benefit. The MPR can inform the selection of a first clinical program (parameters for stimulation) for treating a patient. For example, in a single current source, voltage-controlled DBS device, a time-consuming review is performed involving sequentially measuring efficacy and side-effect thresholds for all electrodes of a leadwire and recording these threshold values. Such a tedious review is described in Volkmann et al., Introduction to the Programming of Deep Brain Stimulators, Movement Disorders Vol. 17, Suppl. 3, pp. S181-S187 (2002) (“Volkmann”). See, for example, FIG. 3 of Volkmann and the corresponding text, which describes gradually increasing amplitude separately for each of a plurality of electrodes, and recording the amplitude at which a minimum threshold of therapeutic efficacy is observed, and the maximum amplitude that does not exceed a permitted adverse side-effect threshold.
According to example embodiments, a leadwire includes multiple electrodes, for each of which a respective independent current source is provided, by which current can be “steered” longitudinally and/or rotationally about the leadwire for localization of stimulation at points between electrodes (such a point hereinafter referred to as a “virtual electrode”). The electrical variation about a leadwire produced by virtual electrodes creates an added layer of complexity concerning stimulation parameters and their effects. Example embodiments of the present invention provide a visual point-and-click interface that includes a graphical representation of a stimulation parameter for virtual electrodes, via which to input settings therefor, and/or via which to obtain and/or output annotations concerning stimulation parameters thereof. According to example embodiments of the present invention, the interface includes controls for gradual directional steering of current about the leadwire, without the requirement for separately setting individual electrical amplitude settings of the individual electrodes, where the steering occurs between actual and virtual electrodes, the interface further providing for the system to receive input of efficacy and adverse side effect information. According to an example embodiment, the obtained input is recorded in association with the settings for which the input was provided, the system thereby generating longitudinal and/or rotational maps of efficacy and side effect information arranged about the leadwire according to the actual and/or virtual electrode positions with which the efficacy and side effect information are associated.
Thus, according to an example embodiment of the present invention, the system performs an iterative process, where each iteration corresponds to a single selected stimulation parameter value, e.g., amplitude, to which value a respective electric field corresponds. For each iteration, the electric field is shifted to various actual and/or virtual electrode locations about the leadwire, and for each of a plurality of the locations to which the respective field of the iteration has been shifted, clinical information regarding therapeutic effect and/or adverse side effect for a stimulation produced by the electric field is recorded. After completion of an iteration, the value of the parameter is changed for a new iteration, in which the shifting and information recording is repeated for the new value. The information obtained during a plurality of the iterations is then usable for construction of a graph on which basis optimal settings are selectable. Such settings include, in an example embodiment, a combination of respective values of the parameter for a selected subset of electrodes of the leadwire.
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.
Alternatively, different combinations of amplitudes of the electrodes can be set, where each combination can be characterized as having an amplitude setting at a respective longitudinal position of the leadwire, producing a cylindrically symmetric stimulation about the leadwire at that respective longitudinal leadwire position. Positions along the abscissa can represent discrete locations from a first position of the leadwire towards another position of the leadwire, where some of the locations can be those of respective ones of the cylindrically symmetrical electrodes, and others can be other locations corresponding to the combination of stimulation settings of a plurality of the electrodes.
The therapy onset curve 101 indicates amplitude thresholds at which a therapeutic result is expected, depending on the electrode or longitudinal leadwire position at which the respective stimulation amplitude is set. The side effect onset curve 102 indicates a maximum stimulation amplitude at respective electrode or longitudinal leadwire positions, above which the stimulation is expected to cause an adverse side effect. Information on which the curves 101 and 102 are based can include empirically obtained data and/or model-based data. The graphs 101 and 102 can be specific to an indicated desired therapy and/or to an indicated adverse side effect. For example, the graphical user interface, e.g., in a target settings section, can include an input field for inputting a desired therapeutic effect and/or side effect to be avoided, and output a graph such that shown in
Such graphs can be useful for a clinician to eyeball a target range of possible target settings for one or more of the electrodes. For example, the clinician likely would choose to try an amplitude settings that falls at about the center of the shaded area 105 between the curves 101 and 102 since it is that region that is expected to produce a therapeutic effect and to avoid production of an adverse side effect.
However, such a representation does not reflect variations in amplitude at different directions cylindrically about the leadwire using directional electrodes. According to an example embodiment of the present invention, the system and method outputs stimulation amplitude information in a coordinate system in which each plotted data point is identified by a longitudinal position ‘z’, angle of rotation ‘θ’, and radius from center ‘r’, where the longitudinal position is the longitudinal position along the central axis of the leadwire, e.g., a distance from one of the ends, the angle of rotation is an angle between a selected direction extending outward from the leadwire, perpendicularly to the central axis thereof, and the direction in which stimulation is characterized as being produced by an electrode (or combination of electrodes), and radius is a distance from the leadwire along the direction in which the stimulation is characterized as being produced. The radius coordinate corresponds to the stimulation amplitude value, whereas the longitudinal position and angle of rotation information indicates the location of that stimulation. In an example embodiment of the present invention, a computer system provides a graphical user interface in which amplitude settings for a directional electrode leadwire are plotted in curves at planes that are perpendicular to the central axis of the leadwire according to the described coordinate system including longitudinal, angular, and radii values.
Stimulation using a combination of electrodes at an one longitudinal level can produce stimulation values characterized by a stimulation at a direction which can be between the electrodes. Similarly, stimulation using a combination of electrodes at a plurality of longitudinal levels can produce stimulation values characterized by a stimulation at a level between electrodes above and below. Therefore, the displayed graphs need not be a longitudinal positions at which there are electrodes (although an alternative example embodiment can be provided in which the graphs are displayed only at longitudinal positions at which at least one electrode is located). In an example embodiment, using graphs plotting stimulations values characterized as occurring between electrodes by combinations of stimulations of those electrodes, the system plots a plurality of two dimensional graphs of stimulation values in a plurality of continuous layers to form a three dimensional graph volume.
In an example embodiment of the present invention, the system displays a model of the leadwire 200, e.g., as shown in
For example,
Alternatively (or additionally), as shown in
Alternatively (or additionally), as shown in
In an example embodiment of the present invention, representations of respective electrodes in the model of the leadwire 200 or in the leadwire rotation control 410 are selectable, in response to which input, the system is configured to obtain user input of one or more settings to be set for the selected electrode. In an example embodiment, the system is configured to display one or more data fields in which to input parameter values for the selected electrode. In an example embodiment, as shown in
In an example embodiment of the present invention, the user interface display including the model of the leadwire 200 further includes a ray, like described ray 415, that extends from the model of the leadwire 200, and the ray is selectable and draggable towards the right and towards the left to modify a directionality of a stimulation, and inwards and outwards with respect to the model of the leadwire 200 to modify an amplitude of the stimulation in the selected direction.
The user interface further includes an up button 504 and a down button 506, for selection by the user of the longitudinal location along the leadwire at which the stimulation is to occur.
In an example embodiment, as shown in
In an example embodiment of the present invention, the user interface shown in
According to a variant of this embodiment, the buttons 504 and 506 are omitted since current steering is not supported. Alternatively, buttons 504 and 506 are provided, but, according to this embodiment, their selections do not cause the above-described current steering, but rather are used for traversing between settings of different electrode levels of the leadwire. For example, the user can use the controls shown in
In an example embodiment of the present invention, the stimulation controls and the settings map 515 are displayed in an interface in which a three-dimensional perspective of a model of the leadwire 200, e.g., as shown in
As described above with respect to
A therapy can cause both a therapeutic effect and an adverse side effect. Therefore, according to an example embodiment of the present invention, the system allows for input indicating both the therapeutic effect and the side effect.
According to an alternative example embodiment of the present invention, the annotation control interface includes a list of symptoms with an associated one or more input fields or selectable controls (e.g., discrete or by slider bar) by which to indicate a degree of therapeutic effect for that respective symptom and/or a list of adverse side effects with an associated one or more input fields or selectable controls (e.g., discrete or by slider bar) by which to indicate a degree to which the respective side effect is caused by the stimulation at the presently indicated settings. For example, as shown in
In an example embodiment of the present invention, the controls for inputting specific therapeutic and side effect information, including identification of particular symptoms for which therapeutic effect is provided and/or identification of particular adverse side effects produced by the therapy, such as controls of sections 606 and 626, and the controls for inputting the more generalized information as to whether a therapeutic effect has been provided and/or a side effect has been produced, such as controls 600-604 are all provided by the system. For example, in an example embodiment of the present invention, the system initially displays controls 600-604, and, responsive to selection of a “details” button or tab 605, the system displays the controls for inputting the information in detailed form. For example, the system updates the interface to simultaneously display all of the controls 600-604 and 606a-626e. Alternatively, the system responsively replaces the generalized controls with the more specific controls. According to either embodiment, the system, in an example embodiment, toggles between the two types of displays responsive to repeated selection of the details button 605.
According to an example embodiment of the present invention, the system is configured to output different graphs as described with respect to
Similarly, in an example embodiment of the present invention, the user can filter by adverse side effect, e.g., by dysarthia, in response to which filter the system is configured to output graphs like those shown in
Similarly, instead of or in addition to filtering by type of therapeutic effect and/or side effect, the system provides for filtering based on degree. For example, referring to
According to an example embodiment, if information is entered indicating the occurrence of a therapeutic effect or side effect, without additional details, e.g., by operation of one or more of the buttons 600-604, without providing additional details concerning degree or type, the system uses such information for the generation of a graph unconstrained by the above-described input criterion of degree and/or type, but does not consider such information for graphs provided in response to a user request constrained by such input criteria.
In an example embodiment of the present invention, the system is configured to output a combination of discrete graphs corresponding to respective types and/or degree. For example, in a plane drawn at a particular longitudinal position of the leadwire, the system outputs one or more graphs corresponding to therapeutic effect for tremor (at one or more degrees of effect) and one or more graphs corresponding to therapeutic effect for bradykinesia (at one or more degrees of effect). The system outputs indicia that identify the effect (and/or degree thereof) to which the different graphs correspond. For example, different colors (and/or hue, saturation, and/or transparency) can be used to represent different effects, and/or different labels can be displayed, e.g., perpetually or when selected or when a pointer is moved over or in close proximity to the graph. The system can similarly generate a plane of overlapping graphs corresponding to different side effects (and/or side effect severities).
In an example embodiment of the present invention, instead of or in addition to a user interface display in which a plurality of graphs for different therapeutic effects, and/or side effects, and/or degrees thereof are included in a single plane, the graphs indicating directional dependency of the amplitude about the leadwire, the system is configured to indicate a variation of stimulation effect (e.g., adverse side effect or therapeutic effect) along a single selected direction from the leadwire as amplitude is increased. For example,
While
As shown in
For example,
It is noted that that there may be certain adverse side effects that are tolerable and there may be certain therapeutic effects that are insignificant. The system is programmed to produce the graphical information for certain predetermined side effects and/or therapeutic effects. Additionally, in an example embodiment, the system includes a user interface via which a user can select one or more side effects and/or one or more therapeutic effects on which basis to generate the graphs.
When the graphs are provided in a three-dimensional perspective about the model of the leadwire 200, the leadwire model can partially obscure portions of the graphs. Although, as discussed above, example embodiments provide a control for rotating the model, so that the graphs can be rotated and viewed at the different angles, a user may desire to view entire graphs at a time for the respective longitudinal positions at which they are generated. Additionally, when the graphs are provided in a three-dimensional perspective, precise dimensions of the graph shape are distorted to account for depth in a two-dimensional display screen, for example, as can be seen by a comparison of the graphs in
In an example embodiment of the present invention, the system displays a model of the leadwire 200, e.g., as shown in
As explained above,
According to an example embodiment, information concerning therapeutic effect and/or adverse side effect is additionally or alternatively obtained using sensors. For example, a sensor can be used to sense patient tremor, speed, stability, heart rate, etc., based on which sensed information conclusions concerning therapeutic effect and/or side effect are automatically made and recorded.
Thus, according to an example embodiment of the present invention, a user interface facilitates gradual steering of a current, e.g., at a certain amplitude, frequency, and/or pulse width, about the leadwire, and user annotation of the steered current at various actual and/or virtual electrodes at which the current has been steered, as being in an “efficacy range” or a “side-effects range,” by clicking a button or menu item for those electrode locations. According to an example embodiment, the determination of whether the steered current, at an actual and/or virtual electrode at which the current has been steered, is in an “efficacy range” or a “side-effects range” is performed by a processor based on information concerning therapeutic effect and/or adverse side effect additionally or alternatively obtained using sensors. According to an example embodiment, the system records the input (and/or sensor) information in association with the electrode locations to which they correspond, and, based on the recorded information regarding a respective plurality of actual and/or virtual electrodes traversed via e-trolling, generates a curve that connects the annotated values for each such respective identified and annotated actual and/or virtual electrode, thereby graphically identifying the totality of the results (for a given stimulation parameter setting) for a set of electrodes of the leadwire 200 as shown in
Similar to that shown in
According to an alternative example embodiment, a three dimensional graph is used to plot variations in another, e.g., electrical, settings in addition to amplitude. A non-exhaustive list of examples of such parameters include pulse width and frequency. For example, an ‘x’ axis can correspond to electrode location, a ‘y’ axis can correspond to amplitude, and a ‘z’ axis can correspond to the other parameter, so that, for example, different amplitude values are plotted for different values of the other parameter at a same electrode position.
As shown in
The efficacy curve 101 indicates amplitude thresholds at (or above) which a therapeutic result is expected, depending on the electrode or other longitudinal leadwire position (virtual electrode) at which the respective stimulation amplitude is set. The side effect curve 102 indicates a maximum stimulation amplitude at respective electrode or virtual electrode positions, above which the stimulation is expected to cause an adverse side effect (e.g., above a maximum threshold for such an adverse side effect). Information on which the curves 101 and 102 are based can include empirically obtained data and/or model-based data. The curves 101 and 102 can be specific to an indicated desired therapy and/or to an indicated adverse side effect. For example, the graphical user interface, e.g., in a target settings section, can include an input field for inputting a desired therapeutic effect and/or side effect to be avoided, and output a graph such that shown in
In an example embodiment of the present invention, the graphs are continuously updated as more data points are added via the above-described method of e-trolling. The curve begins as a simple straight line fit between the identified and annotated locations and as more data are added other curve-fitting techniques can be used to better match the recorded values. Curve fitting is the process of constructing a curve, e.g., by use of a mathematical function, which is a best fit to a series of data points, possibly subject to constraints. Curve fitting can involve, e.g., interpolation, where an exact fit to the data is required, or smoothing, in which a “smooth” function is constructed that approximately fits the data. Any suitably appropriate curve fitting function may be used. Accordingly, the output graph, in an example, embodiment, plots information for electrode locations for which therapeutic and/or side effect data has not been obtained, by “filling in” such information based on the information obtained for surrounding electrode locations.
According to an example embodiment, the graphs are also and/or alternatively continuously updated to plot different amplitude values for the therapeutic and or side effect curves for those locations for which input had been previously received, and for which the plotted values had previously reflected such previously obtained input, as more data are added for the previously identified and annotated location. For example, different results may be observed for settings for an electrode location at different times. Because of the variability in measured effects for a subject at a given stimulation location it is beneficial to overwrite any previous side effect threshold values for the location with a lower side effect threshold value for that location so that the user may be more sure about selecting stimulation parameters that will not cause undesired side effects. Likewise, it is beneficial to overwrite any previous efficacy threshold values for the location with a higher efficacy threshold value for that location so that the user may be more sure about selecting stimulation parameters that will produce therapeutic results, e.g. lessen undesired side effects. (Alternatively, averages can be plotted and/or the values to be plotted can be calculated based on a score affected by values of neighboring electrode locations.) Alternatively, time can be used as a third dimension, so that a user is able to see a history of the values.
The two-dimensional graph of
According to an alternative example embodiment, a two-dimensional graph is output, where positions along the abscissa represent longitudinal locations along the leadwire 200, as described above with respect to
According to an alternative example embodiment of the present invention, the system generates and outputs a three-dimensional graph, with amplitude plotted as radii, as shown in
According to this embodiment, the system and method outputs stimulation amplitude information in a three dimensional coordinate system in which each plotted data point is identified by a longitudinal position ‘z’, angle of rotation ‘θ’, and radius from center ‘r’ as shown by the indicated coordinate system of
In an example embodiment of the present invention, the system includes a control selectable for toggling between a three dimensional view of the graphs and two dimensional views of the graphs.
As noted above, there may be certain adverse side effects that are tolerable for a certain subject and there may be certain therapeutic effects that are insignificant for said subject. Therefore, in an example embodiment, the system includes a user interface via which a user can select one or more side effects and/or one or more therapeutic effects on which basis to generate the graphs.
Such graphs can be useful for a clinician to eyeball a target range of possible target stimulation settings for one or more of the electrodes. For example, with respect to the graph shown in
According to an example embodiment of the present invention, the graph is output as a user-interactive display, where positions within the graph are user-selectable as an instruction to set electrode parameters. For example, as shown in
In an example embodiment of the present invention, a leadwire 200 utilizing a single current source for all of the electrodes of the leadwire 200 is used, and after the user has selected a point associated with stimulation parameters and clinical data on a graph provided according to the user interface described for virtual electrode steering, the system uses pulse interleaving to approximate the stimulation localized in an area of the corresponding virtual electrode. The pulse interleaving uses a single current source that alternates between different current settings at high speed for the different electrodes of the leadwire 200, to provide the different current amplitudes to different ones of the electrodes of leadwire 200 in an alternating manner. In this way, the separate electrodes (e.g., 210 and 211) can receive short-timed pulses from the same current source at different current values (e.g., 13% and 87%) so that stimulation is localized in an area of the corresponding virtual electrode.
As described in detail above, in an example embodiment of the present invention, for a set of stimulation parameters, the system outputs a graphical representation of the parameters in the form of a ray extending from a model of the leadwire, where the directionality and length of the ray represents, respectively, a directionality of the stimulation produced by the parameters and the electrical amplitude. In an example embodiment, the system additionally outputs information regarding tissue stimulation produced by the electrical stimulation parameters represented by the array. For example, in an example embodiment, the system displays a first user interface frame identifying one or more of the stimulation parameters and/or including a graphical representation thereof, e.g., in the form of the described graphical information, and further displays a second user interface section displaying an estimated VOA, e.g., as described in the '330, '312, '340, '343, and '314 applications, corresponding to the indicated and/or represented stimulation parameters.
An example embodiment of the present invention is directed to one or more processors, which can be implemented using any conventional processing circuit and device or combination thereof, e.g., a Central Processing Unit (CPU) of a Personal Computer (PC) or other workstation processor, to execute code provided, e.g., on a hardware computer-readable medium including any conventional memory device, to perform any of the methods described herein, alone or in combination, and to generate any of the user interface displays described herein, alone or in combination. The one or more processors can be embodied in a server or user terminal or combination thereof. The user terminal can be embodied, for example, as a desktop, laptop, hand-held device, Personal Digital Assistant (PDA), television set-top Internet appliance, mobile telephone, smart phone, etc., or as a combination of one or more thereof. Specifically, the terminal can be embodied as a clinician programmer terminal, e.g., as referred to in the '330, '312, '340, '343, and '314 applications. Additionally, as noted above, some of the described methods can be performed by a processor on one device or terminal and using a first memory, while other methods can be performed by a processor on another device and using, for example, a different memory.
The memory device can include any conventional permanent and/or temporary memory circuits or combination thereof, a non-exhaustive list of which includes Random Access Memory (RAM), Read Only Memory (ROM), Compact Disks (CD), Digital Versatile Disk (DVD), and magnetic tape.
An example embodiment of the present invention is directed to one or more hardware computer-readable media, e.g., as described above, having stored thereon instructions executable by a processor to perform the methods and/or provide the user interface features described herein.
An example embodiment of the present invention is directed to a method, e.g., of a hardware component or machine, of transmitting instructions executable by a processor to perform the methods and/or provide the user interface features described herein.
The above description is intended to be illustrative, and not restrictive. Those skilled in the art can appreciate from the foregoing description that the present invention can be implemented in a variety of forms, and that the various embodiments can be implemented alone or in combination. Therefore, while the embodiments of the present invention have been described in connection with particular examples thereof, the true scope of the embodiments and/or methods of the present invention should not be so limited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and the following listed features.
The present application is a continuation of U.S. patent application Ser. No. 15/991,807 filed May 29, 2018, which is a continuation of U.S. patent application Ser. No. 15/729,383 filed Oct. 10, 2017, which issued as U.S. Pat. No. 10,016,610 on Jul. 10, 2018, which is a continuation of U.S. patent application Ser. No. 15/420,842 filed Jan. 31, 2017, which issued as U.S. Pat. No. 9,821,167 on Nov. 21, 2017, which is a divisional of U.S. patent application Ser. No. 15/012,698 filed Feb. 1, 2016, which issued as U.S. Pat. No. 9,561,380 on Feb. 7, 2017, which is a divisional of U.S. patent application Ser. No. 14/011,817 filed Aug. 28, 2013, which issued as U.S. Pat. No. 9,248,296 on Feb. 2, 2016, which claims priority to U.S. Provisional Patent Application Ser. Nos. 61/693,866 filed on Aug. 28, 2012, 61/699,135 filed on Sep. 10, 2012, 61/699,115 filed on Sep. 10, 2012, and 61/753,232 filed on Jan. 16, 2013, the content of all of which is hereby incorporated by reference herein in their entireties.
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 |
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 | Schulmann | 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 et al. | Aug 2003 | B1 |
6622048 | Mann et al. | Sep 2003 | B1 |
6631297 | Mo | Oct 2003 | B1 |
6654642 | North et al. | Nov 2003 | B2 |
6662053 | Borkan | Dec 2003 | B2 |
6675046 | Holsheimer | Jan 2004 | B2 |
6684106 | Herbst | Jan 2004 | B2 |
6687392 | Touzawa et al. | Feb 2004 | B1 |
6690972 | Conley et al. | Feb 2004 | B2 |
6690974 | Archer et al. | Feb 2004 | B2 |
6692315 | 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 | Samuelsson et al. | May 2006 | B2 |
7043293 | Baura | May 2006 | B1 |
7047082 | Schrom et al. | May 2006 | B1 |
7047084 | Erickson et al. | May 2006 | B2 |
7054692 | Whitehurst et al. | May 2006 | B1 |
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 |
7136518 | Griffin et al. | Nov 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 |
7437193 | Parramon et al. | Oct 2008 | B2 |
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 |
7826902 | Stone et al. | Nov 2010 | B2 |
7848802 | Goetz et al. | Dec 2010 | B2 |
7860548 | McIntyre et al. | Dec 2010 | B2 |
7904134 | McIntyre et al. | Mar 2011 | B2 |
7945105 | Jaenisch | May 2011 | B1 |
7949395 | Kuzma | May 2011 | B2 |
7974706 | Moffitt et al. | Jul 2011 | B2 |
8019439 | Kuzma et al. | Sep 2011 | B2 |
8175710 | He | May 2012 | B2 |
8180601 | Butson et al. | May 2012 | B2 |
8195300 | Gliner et al. | Jun 2012 | B2 |
8224450 | Brase | Jul 2012 | B2 |
8257684 | Covalin et al. | Sep 2012 | B2 |
8262714 | Hulvershorn et al. | Sep 2012 | B2 |
8364278 | Pianca et al. | Jan 2013 | B2 |
8429174 | Ramani et al. | Apr 2013 | B2 |
8452415 | Goetz et al. | May 2013 | B2 |
8543189 | Paitel et al. | Sep 2013 | B2 |
8606360 | Butson et al. | Dec 2013 | B2 |
8620452 | King et al. | Dec 2013 | B2 |
8918184 | Torgerson et al. | Dec 2014 | B1 |
20010031071 | Nichols et al. | Oct 2001 | A1 |
20020032375 | Bauch et al. | Mar 2002 | A1 |
20020062143 | Baudino et al. | May 2002 | A1 |
20020087201 | Firlik et al. | Jul 2002 | A1 |
20020099295 | Gil et al. | Jul 2002 | A1 |
20020115603 | Whitehouse | Aug 2002 | A1 |
20020116030 | Rezei | Aug 2002 | A1 |
20020123780 | Grill et al. | Sep 2002 | A1 |
20020128694 | Holsheimer | Sep 2002 | A1 |
20020151939 | Rezai | Oct 2002 | A1 |
20020183607 | Bauch et al. | Dec 2002 | A1 |
20020183740 | Edwards et al. | Dec 2002 | A1 |
20020183817 | Van Venrooij et al. | Dec 2002 | A1 |
20030097159 | Schiff et al. | May 2003 | A1 |
20030149450 | Mayberg | Aug 2003 | A1 |
20030171791 | KenKnight et al. | Sep 2003 | A1 |
20030212439 | Schuler et al. | Nov 2003 | A1 |
20040034394 | Woods et al. | Feb 2004 | A1 |
20040044279 | Lewin et al. | Mar 2004 | A1 |
20040044378 | Holsheimer | Mar 2004 | A1 |
20040044379 | Holsheimer | Mar 2004 | A1 |
20040054297 | Wingeier et al. | Mar 2004 | A1 |
20040059395 | North et al. | Mar 2004 | A1 |
20040106916 | Quaid et al. | Jun 2004 | A1 |
20040133248 | Frei et al. | Jul 2004 | A1 |
20040152957 | Stivoric et al. | Aug 2004 | A1 |
20040181262 | Bauhahn | Sep 2004 | A1 |
20040186532 | Tadlock | Sep 2004 | A1 |
20040199216 | Lee et al. | Oct 2004 | A1 |
20040267330 | Lee et al. | Dec 2004 | A1 |
20050021090 | Schuler et al. | Jan 2005 | A1 |
20050033380 | Tanner et al. | Feb 2005 | A1 |
20050049649 | Luders et al. | Mar 2005 | A1 |
20050060001 | Singhal et al. | Mar 2005 | A1 |
20050060009 | Goetz | Mar 2005 | A1 |
20050070781 | Dawant et al. | Mar 2005 | A1 |
20050075689 | Toy et al. | Apr 2005 | A1 |
20050085714 | Foley et al. | Apr 2005 | A1 |
20050165294 | Weiss | Jul 2005 | A1 |
20050171587 | Daglow et al. | Aug 2005 | A1 |
20050228250 | Bitter et al. | Oct 2005 | A1 |
20050251061 | Schuler et al. | Nov 2005 | A1 |
20050261061 | Nguyen et al. | Nov 2005 | A1 |
20050261601 | Schuler et al. | Nov 2005 | A1 |
20050261747 | 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 Ridder | 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 | 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 | Fischer et al. | Aug 2007 | A1 |
20070197891 | Shachar et al. | Aug 2007 | A1 |
20070203450 | Berry | Aug 2007 | A1 |
20070203532 | Tass 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 |
20070203545 | Stone et al. | Aug 2007 | A1 |
20070203546 | Stone et al. | Aug 2007 | A1 |
20070213789 | Nolan et al. | Sep 2007 | A1 |
20070213790 | Nolan et al. | Sep 2007 | A1 |
20070244519 | Keacher et al. | Oct 2007 | A1 |
20070245318 | Goetz et al. | Oct 2007 | A1 |
20070255321 | Gerber et al. | Nov 2007 | A1 |
20070255322 | Gerber et al. | Nov 2007 | A1 |
20070265664 | Gerber et al. | Nov 2007 | A1 |
20070276441 | Goetz | Nov 2007 | A1 |
20070282189 | Dan et al. | Dec 2007 | A1 |
20070288064 | Butson et al. | Dec 2007 | A1 |
20080027514 | DeMulling et al. | Jan 2008 | A1 |
20080039895 | Fowler et al. | Feb 2008 | A1 |
20080071150 | Miesel et al. | Mar 2008 | A1 |
20080081982 | Simon et al. | Apr 2008 | A1 |
20080086451 | Torres et al. | Apr 2008 | A1 |
20080103533 | Patel et al. | May 2008 | A1 |
20080114233 | McIntyre et al. | May 2008 | A1 |
20080114579 | McIntyre et al. | May 2008 | A1 |
20080123922 | Gielen et al. | May 2008 | A1 |
20080123923 | Gielen et al. | May 2008 | A1 |
20080133141 | Frost | Jun 2008 | A1 |
20080141217 | Goetz et al. | Jun 2008 | A1 |
20080154340 | Goetz et al. | Jun 2008 | A1 |
20080154341 | McIntyre et al. | Jun 2008 | A1 |
20080163097 | Goetz et al. | Jul 2008 | A1 |
20080183256 | Keacher | Jul 2008 | A1 |
20080188734 | Suryanarayanan et al. | Aug 2008 | A1 |
20080215118 | Goetz et al. | Sep 2008 | A1 |
20080227139 | Deisseroth et al. | Sep 2008 | A1 |
20080242950 | Jung et al. | Oct 2008 | A1 |
20080261165 | Steingart et al. | Oct 2008 | A1 |
20080269588 | Csavoy et al. | Oct 2008 | A1 |
20080300654 | Lambert et al. | Dec 2008 | A1 |
20080300797 | Tabibiazar et al. | Dec 2008 | A1 |
20090016491 | Li | Jan 2009 | A1 |
20090054950 | Stephens | Feb 2009 | A1 |
20090082640 | Kovach et al. | Mar 2009 | A1 |
20090082829 | Panken et al. | Mar 2009 | A1 |
20090112289 | Lee et al. | Apr 2009 | A1 |
20090118635 | Lujan et al. | May 2009 | A1 |
20090118786 | Meadows et al. | May 2009 | A1 |
20090149917 | Whitehurst et al. | Jun 2009 | A1 |
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 |
20090281595 | King et al. | Nov 2009 | A1 |
20090281596 | King et al. | Nov 2009 | A1 |
20090287271 | Blum et al. | Nov 2009 | A1 |
20090287272 | Kokones et al. | Nov 2009 | A1 |
20090287273 | Carlton et al. | Nov 2009 | A1 |
20090287467 | Sparks et al. | Nov 2009 | A1 |
20090299164 | Singhal et al. | Dec 2009 | A1 |
20090299165 | Singhal et al. | Dec 2009 | A1 |
20090299380 | Singhal et al. | Dec 2009 | A1 |
20100010566 | Thacker et al. | Jan 2010 | A1 |
20100010646 | Drew et al. | Jan 2010 | A1 |
20100023103 | Elborno | Jan 2010 | A1 |
20100023130 | Henry et al. | Jan 2010 | A1 |
20100030312 | Shen | Feb 2010 | A1 |
20100049276 | Blum et al. | Feb 2010 | A1 |
20100049280 | Goetz | Feb 2010 | A1 |
20100064249 | Groetken | Mar 2010 | A1 |
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 |
20100324410 | Paek et al. | Dec 2010 | A1 |
20100331883 | Schmitz et al. | Dec 2010 | A1 |
20110040351 | Butson et al. | Feb 2011 | A1 |
20110066407 | Butson et al. | Mar 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 |
20110306845 | Osorio | Dec 2011 | A1 |
20110306846 | Osorio | Dec 2011 | A1 |
20110307032 | Goetz et al. | Dec 2011 | A1 |
20120027272 | Akinyemi et al. | Feb 2012 | A1 |
20120046715 | Moffitt et al. | Feb 2012 | A1 |
20120078106 | Dentinger et al. | Mar 2012 | A1 |
20120089205 | Boyden et al. | Apr 2012 | A1 |
20120116476 | Kothandaraman | May 2012 | A1 |
20120165898 | Moffitt | Jun 2012 | A1 |
20120165901 | Zhu et al. | Jun 2012 | A1 |
20120207378 | Gupta et al. | Aug 2012 | A1 |
20120226138 | DeSalles et al. | Sep 2012 | A1 |
20120229468 | Lee et al. | Sep 2012 | A1 |
20120265262 | Osorio | Oct 2012 | A1 |
20120265268 | Blum et al. | Oct 2012 | A1 |
20120302912 | Moffitt et al. | Nov 2012 | A1 |
20120303087 | Moffitt et al. | Nov 2012 | A1 |
20120314924 | Carlton et al. | Dec 2012 | A1 |
20120316619 | Goetz et al. | Dec 2012 | A1 |
20130039550 | Blum et al. | Feb 2013 | A1 |
20130060305 | Bokil | Mar 2013 | A1 |
20130116748 | Bokil et al. | May 2013 | A1 |
20130116749 | Carlton et al. | May 2013 | A1 |
20130116929 | Carlton et al. | May 2013 | A1 |
20140067018 | Carcieri et al. | Mar 2014 | A1 |
20140277284 | Chen et al. | Sep 2014 | A1 |
20150134031 | Moffitt et al. | May 2015 | A1 |
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 |
2007097859 | Aug 2007 | WO |
2007097861 | Aug 2007 | WO |
2007100427 | Sep 2007 | WO |
2007100428 | Sep 2007 | WO |
2007112061 | Oct 2007 | WO |
2009097224 | Aug 2009 | WO |
2010 120823 | Oct 2010 | WO |
2011025865 | Mar 2011 | WO |
2011139779 | Nov 2011 | WO |
2011159688 | Dec 2011 | WO |
2012057951 | May 2012 | WO |
2012088482 | Jun 2012 | WO |
Entry |
---|
Volkmann et al., Introduction 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. 1, 2007, vol. 97, No. 2, pp. 561-567. |
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. |
Schmidt et al. “Sketching and Composing Widgets for 3D Manipulation,” Eurographics, Apr. 2008, vol. 27, No. 2, pp. 301-310. |
Butson et al. “Patient-Specific Analysis of the Volume of Tissue Activated During Deep Brain Stimulation.” Neuroimage 34, 2007, pp. 661-670. |
Ericsson, A. et al., “Construction of a patient-specific atlas of the brain: Application to normal aging,” Biomedical Imaging: From Nano to Macro, ISBI 2008, 5th IEEE International Symposium, May 14, 2008, pp. 480-483. |
Kaikai Shen et al., “Atlas selection strategy using least angle regression in multi-atlas segmentation propagation,” Biomedical Imaging: From Nano to Macro, 2011, 8th IEEE International Symposium, ISBI 2011, Mar. 30, 2011, pp. 1746-1749. |
Liliane Ramus et al., “Assessing selection methods in the cotnext of multi-atlas based segmentation,” Biomedical Imaging: From Nano to Macro, 2010, IEEE International Symposium, Apr. 14, 2010, pp. 1321-1324. |
Olivier Commowick et al., “Using Frankenstein's Creature Paradigm to Build a Patient Specific Atlas,” Sep. 20, 2009, Medical Image Computing and Computer-Assisted Intervention, pp. 993-1000. |
Lotjonen J.M.P. et al., “Fast and robust multi-atlas segmentation of brain magnetic resonance images,” NeuroImage, Academic Press, vol. 49, No. 3, Feb. 1, 2010, pp. 2352-2365. |
Izad, Oliver, “Computationally Efficient Method in Predicating Axonal Excitation,” Dissertation for Master Degree, Department of Biomedical Engineering, Case Western Reserve University, May 2009. |
Sanchez Castro et al., “A cross validation study of deep brain stimulation targeting: From experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms,” IEEE Transactions on Medical Imaging, vol. 25, No. 11, Nov. 1, 2006, pp. 1440-1450. |
International Search Report and Written Opinion in International Application No. PCT/US2013/056975, dated Feb. 20, 2014. |
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. |
Butson et al. “StimExplorer: Deep Brain Stimulation Parameter Selection Software System,” Acta Neurochirugica, Jan. 1, 2007, vol. 97, No. 2, pp. 569-574. |
U.S. Appl. No. 12/029,141, filed Feb. 11, 2008. |
Meila, Marina, “Comparing Clusterings by the Variation of Information,” Learning Theory and Kernel Machines (2003): 173-187. |
Cover, T.M. et al., “Elements of information theory,” (1991) John Wiley & Sons, New York, NY. |
Hubert, Lawrence et al., “Comparing partitions,” Journal of Classification 2(1) (1985): 193-218, doi:10.1007/BF01908075. |
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, 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. |
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. |
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. |
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. |
Official Communication for U.S. Appl. No. 14/011,817 dated Jun. 11, 2015, 11 pages. |
Official Communication for U.S. Appl. No. 15/012,698, dated May 5, 2016, 5 pages. |
Official Communication for U.S. Appl. No. 15/991,807, dated Aug. 30, 2018, 9 pages. |
Nowinski, W. L., et al., “Statistical analysis of 168 bilateral subthalamic nucleus implantations by means of the probabilistic functional atlas.”, Neurosurgery 57(4 Suppl) (Oct. 2005),319-30. |
Obeso, J. A., et al., “Deep-brain stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in Parkinson's disease.”, N Engl J Med., 345{13I. The Deep-Brain Stimulation for Parkinson's Disease Study Group, (Sep. 27, 2001 ),956-63. |
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. |
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. |
Plaha, P. , et al., “Stimulation of the caudal zona incerta is superior to stimulation of the subthalamic nucleus in improving contralateral parkinsonism.”, Brain 129{Pt 7) (Jul. 2006), 1732-4 7. |
Rattay, F, “Analysis of models for external stimulation of axons”, IEEE Trans. Biomed. Eng. vol. 33 (1986),974-977. |
Rattay, F., “Analysis of the electrical excitation of CNS neurons”, IEEE Transactions on Biomedical Engineering 45(6). (Jun. 1998),766-772. |
Rose, T. L., et al., “Electrical stimulation with PT electrodes. VIII. Electrochemically safe charge injection limits with 0.2 ms pulses [neuronal application]”, IEEE Transactions on Biomedical Engineering, 37(11 ), (Nov. 1990), 1118-1120. |
Rubinstein, J. T., et al., “Signal coding in cochlear implants: exploiting stochastic effects of electrical stimulation”, Ann Otol Rhinol Laryngol Suppl.. 191, (Sep. 2003), 14-9. |
Schwan, H.P., et al., “The conductivity of living tissues.”, Ann NY Acad Sci., 65(6). (AUQ., 1957),1007-13. |
Taylor, R. S., et al., “Spinal cord stimulation for chronic back and leg pain and failed back surgery syndrome: a systematic review and analysis of prognostic factors”, Spine 30(1 ). (Jan. 1, 2005), 152-60. |
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. |
Viola, P., et al., “Alignment by maximization of mutual information”, International Journal of Com outer Vision 24(2), ( 1997), 137-154. |
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 Neural., 62(3) (Sep. 2004),216-25. |
Da Silva et al (A primer on diffusion tensor imaging of anatomical substructures. Neurosurg Focus 15(1): p. 1-4, Article 4, 2003.) |
Micheli-Tzanakou, E., et al., “Computational Intelligence for target assesment in Parkinson's disease”, Proceedings of SPIE vol. 4479. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV,(2001),54-69. |
Grill, W. M., “Stimulus waveforms for selective neural stimulation”, IEEE Engineering in Medicine and Biology Magazine, 14(4}, (Jul.-Aug. 1995), 375-385. |
Miocinovic. S., et al., “Sensitivity of temporal excitation properties to the neuronal element activated by extracellular stimulation”, J Neurosci Methods. 132(1). (Jan. 15, 2004), 91-9. |
Hunka, K. et al., Nursing Time to Program and Assess Deep Brain Stimulators in Movement Disorder Patients, J. Neursci Nurs., 37: 204-10 (Aug. 2005). |
Moss, J. , et al., “Electron microscopy of tissue adherent to explanted electrodes in dystonia and Parkinson's disease”, Brain, 127{Pt 12). (Dec. 2004 ),2755-63. |
Montgomery, E. B., et al., “Mechanisms of deep brain stimulation and future technical developments.”, Neurol Res. 22(3). (Apr. 2000),259-66. |
Merrill, D. R., et al., “Electrical stimulation of excitable tissue: design of efficacious and safe protocols”, J Neurosci Methods. 141(2), (Feb. 15, 2005), 171-98. |
Fisekovic et al., “New Controller for Functional Electrical Stimulation Systems”, Med. Eng. Phys. 2001; 23:391-399. |
Grill, W. M., et al., “Deep brain stimulation creates an informational lesion of the stimulated nucleus”, Neuroreport. 15I7t (May 19, 2004 ), 1137-40. |
Grill, WM., et al., “Electrical properties of implant encapsulation tissue”, Ann Biomed Eng. vol. 22. (1994),23-33. |
McNaughtan et al., “Electrochemical Issues in Impedance Tomography”, 1st World Congress on Industrial Process Tomography, Buxton, Greater Manchester, Apr. 14-17, 1999. |
Hardman, C. D., et al., “Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: volume and neuronal number for the output, internal relay, and striatal modulating nuclei”, J Comp Neurol., 445(3), (Apr. 8, 2002),238-55. |
Hashimoto, T. , et al., “Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons”, J Neurosci. 23(5). (Mar. 1, 2003),1916-23. |
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. |
Haueisen, J, et al., “The influence of brain tissue anisotropy on human EEG and MEG”, Neuroimage 15(1) (Jan. 2002),159-166. |
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. |
Hemm, S. , et al., “Evolution of Brain Impedance in Dystonic Patients Treated by GPi Electrical Stimulation”, Neuromodulation 7(2) (Apr. 2004),67-75. |
Hershey, T., et al., “Cortical and subcortical blood flow effects of subthalamic nucleus stimulation in PD.”, Neurology 61(6). (Sep. 23, 2003),816-21. |
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. |
Hines, M. L., et al., “The NEURON simulation environment”, Neural Comput. 9(6). (Aug. 15, 1997), 1179-209. |
Holsheimer, J. , et al., “Chronaxie calculated from current-duration and voltage-duration data”, J Neurosci Methods. 97(1). (Apr. 1, 2000),45-50. |
Johnson, M. D., et al., “Repeated voltage biasing improves unit recordings by reducing resistive tissue impedances”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, [see also IEEE Trans. on Rehabilitation Engineering (2005), 160-165. |
Kitagawa, M., et al., “Two-year follow-up of chronic stimulation of the posterior subthalamic white matter for tremor-dominant Parkinson's disease.”, Neurosurgery. 56(2). (Feb. 2005),281-9. |
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. |
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. |
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. |
Wakana, S., et al., “Reproducibility of quantitative tractography methods applied to cerebral white matter,” Neuroimage 36 (3) (2007), pp. 630-644. |
Viola, et al., “Importance-driven focus of attention,” IEEE Trans Vis Comput Graph 12 (5) (2006), pp. 933-940. |
Saxena, et al., “Cerebral glucose metabolism in obsessive-compulsive hoarding,” Am J Psychiatry. 161 (6) (2004), pp. 1038-1048. |
Zhang, Y., et al., “Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy,” Neuroimage 52(4) (2010), pp. 1289-1301. |
““BioPSE” The Biomedical Problem Solving Environment”, htt12://www.sci.utah.edu/cibc/software/index.html, MCRR Center for Integrative Biomedical Computing,(2004). |
Andrews, R. J., “Neuroprotection trek—the next generation: neuromodulation I. Techniques—deep brain stimulation, vagus nerve stimulation, and transcranial magnetic stimulation.”, Ann NY Acad Sci. 993. (May 2003),1-13. |
Carnevale, N.T. et al., “The Neuron Book,” Cambridge, UK: Cambridge University Press (2006), 480 pages. |
Chaturvedi: “Development of Accurate Computational Models for Patient-Specific Deep Brain Stimulation,” Electronic Thesis or Dissertation, Jan. 2012, 162 pages. |
Chaturvedi, A. et al.: “Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions.” Brain Stimulation, Elsevier, Amsterdam, NL, vol. 3, No. 2 Apr. 2010, pp. 65-77. |
Frankemolle, et al., “Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modeling approach to deep brain stimulation programming,” Brian 133 (2010), pp. 746-761. |
McIntyre, C.C., et al., “Modeling the excitablitity of mammalian nerve fibers: influence of afterpotentials on the recovery cycle,” J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006. |
Peterson, et al., “Predicting myelinated axon activation using spatial characteristics of the extracellular field,” Journal of Neural Engineering, 8 (2011), 12 pages. |
Warman, et al., “Modeling the Effects of Electric Fields on nerver Fibers; Dermination of Excitation Thresholds,”IEEE Transactions on Biomedical Engineering, vol. 39, No. 12 (Dec. 1992), pp. 1244-1254. |
Wesselink, et al., “Analysis of Current Density and Related Parameters in Spinal Cord Stimulation,” IEEE Transactions on Rehabilitation Engineering, vol. 6, No. 2 Jun. 1998, pp. 200-207. |
Andrews, R. J., “Neuroprotection trek—the next generation: neuromodulation II. Applications—epilepsy, nerve regeneration, neurotrophins.”, Ann NY Acad Sci. 993 (May 2003), 14-24. |
Astrom, M. , et al., “The effect of cystic cavities on deep brain stimulation in the basal ganglia: a simulation-based study”, J Neural Eng., 3(2), (Jun. 2006).132-8. |
Bazin et al., “Free Software Tools for Atlas-based Volumetric Neuroimage Analysis”, Proc. SPIE 5747, Medical Imaging 2005: Image Processing, 1824 May 5, 2005. |
Back, C. , et al., “Postoperative Monitoring of the Electrical Properties of Tissue and Electrodes in Deep Brain Stimulation”, Neuromodulation, 6(4), (Oct. 2003 ),248-253. |
Baker, K. B., et al., “Evaluation of specific absorption rate as a dosimeter of MRI-related implant heating”, J Magn Reson Imaging., 20(2), (Aug. 2004),315-20. |
Brown, J. “Motor Cortex Stimulation,” Neurosurgical Focus ( Sep. 15, 2001) 11(3):E5. |
Budai et al., “Endogenous Opioid Peptides Acting at m-Opioid Receptors in the Dorsal Horn Contribute to Midbrain Modulation of Spinal Nociceptive Neurons,” Journal of Neurophysiology (1998) 79(2): 677-687. |
Cesselin, F. “Opioid and anti-opioid peptides,” Fundamental and Clinical Pharmacology (1995) 9(5): 409-33 (Abstract only). |
Rezai et al., “Deep Brain Stimulation for Chronic Pain” Surgical Management of Pain, Chapter 44 pp. 565-576 (2002). |
Xu, MD., Shi-Ang, article entitled “Comparison of Half-Band and Full-Band Electrodes for Intracochlear Electrical Stimulation”, Annals of Otology, Rhinology & Laryngology (Annals of Head & Neck Medicine & Surgery), vol. 102 (5) pp. 363-367 May 1993. |
Bedard, C. , et al. “Modeling extracellular field potentials and the frequency-filtering properties of extracellular space”, Biophys J .. 86(3). (Mar. 2004),1829-42. |
Benabid, A. L., et al., “Future prospects of brain stimulation”, Neural Res.;22(3), (Apr. 2000),237-46. |
Brummer, S. B., et al., “Electrical Stimulation with Pt Electrodes: II—Estimation of Maximum Surface Redox (Theoretical Non-Gassing) Limits”, IEEE Transactions on Biomedical Engineering, vol. BME-24, Issue 5, (Sep. 1977),440-443. |
Butson, Christopher R., et al., “Deep Brain Stimulation of the Subthalamic Nucleus: Model-Based Analysis of the Effects of Electrode Capacitance on the Volume of Activation”, Proceedings of the 2nd International IEEE EMBS, (Mar. 16-19, 2005),196-197. |
Mcintyre, Cameron C., et al., “Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition,” J Neurophysiol, 91(4) (Apr. 2004), pp. 1457-1469. |
Chaturvedi, A., et al., “Subthalamic Nucleus Deep Brain Stimulation: Accurate Axonal Threshold Prediction with Diffusion Tensor Based Electric Field Models”, Engineering in Medicine and Biology Society, 2006. EMBS' 06 28th Annual International Conference of the IEEE, IEEE, Piscataway, NJ USA, Aug. 30, 2006. |
Butson, Christopher et al., “Predicting the Effects of Deep Brain Stimulation with Diffusion Tensor Based Electric Field Models” Jan. 1, 2001, Medical Image Computing and Computer-Assisted Intervention-Mic CAI 2006 Lecture Notes in Computer Science; LNCS, Springer, Berlin, DE. |
Butson, C, R., et al., “Deep brainstimulation interactive visualization system”, Society for Neuroscience 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 al., “Multigrid solution of the potential field in modeling electrical nerve stimulation,” Comput Biomed Res., 31(5) (Oct. 1998), pp. 348-362. |
Holsheimer, J., et al., “Identification of the target neuronal elements in electrical deep brain stimulation,” Eur J Neurosci., 12(12) (Dec. 2000), pp. 4573-4577. |
Jezernik, S., et al., “Neural network classification of nerve activity recorded in a mixed nerve,” Neural Res., 23(5) (Jul. 2001), pp. 429-434. |
Jones, DK., et al., “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging,” Magn. Reson. Med., 42(3) (Sep. 1999), pp. 515-525. |
Krack, P., et al., “Postoperative management of subthalamic nucleus stimulation for Parkinson's disease,” Mov. Disord., vol. 17(suppl 3) (2002), pp. 188-197. |
Le Bihan, D., et al., “Diffusion tensor imaging: concepts and applications,” J Magn Reson Imaging, 13(4) (Apr. 2001), pp. 534-546. |
Lee, D. C., et al., “Extracellular electrical stimulation of central neurons: quantitative studies,” In: Handbook of neuroprosthetic methods, WE Finn and PG Lopresti (eds) CRC Press (2003), pp. 95-125. |
Levy, AL., et al., “An Internet-connected, patient-specific, deformable brain atlas integrated into a surgical navigation system,” J Digit Imaging, 10(3 Suppl 1) (Aug. 1997), pp. 231-237. |
Liu, Haiying, et al., “Intra-operative MR-guided DBS implantation for treating PD and ET,” Proceedings of SPIE vol. 4319, Department of Radiology & Neurosurgery, University of Minnesota, Minneapolis, MN 55455 (2001), pp. 272-276. |
Mcintyre, C. C., et al., “Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output,” J. Neurophysiol., 88(4), (Oct. 2002), pp. 1592-1604. |
Mcintyre, C. C., et al., “Microstimulation of spinal motoneurons: a model study,” Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology society, vol. 5, (1997), pp. 2032-2034. |
Mcintyre, Cameron C., et al., “Model-based Analysis of deep brain stimulation of the thalamus,” Proceedings of the Second joint EMBS/BM ES Conference, vol. 3, Annual Fall Meeting of the Biomedical Engineering Society (Cal. No. 02CH37392) IEEEPiscataway, NJ (2002), pp. 2047-2048. |
Mcintyre, C. C., et al., “Model-based design of stimulus trains for selective microstimulation of targeted neuronal populations,” Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1 (2001), pp. 806-809. |
Mcintyre, C. C., et al., Model-based design of stimulus waveforms for selective microstimulation in the central nervous system,, Proceedings of the First Joint [Engineering in Medicine and Biology, 1999. 21st Annual Conf. and the 1999 Annual FallMeeting of the Biomedical Engineering Soc.] BM ES/EMBS Conference, vol. 1 (1999), p. 384. |
Mcintyre, Cameron C., et al., “Modeling the excitability of mammalian nerve fibers: influence of aflerpotentials on the recovery cycle,” J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006. |
Mcintyre, Cameron C., et al., “Selective microstimulation of central nervous system neurons,” Annals of biomedical engineering, 28(3) (Mar. 2000), pp. 219-233. |
Mcintyre, C. C., et al., “Sensitivity analysis of a model of mammalian neural membrane,” Biol Cybern., 79(1) (Jul. 1998), pp. 29-37. |
Mcintyre, Cameron C., et al., “Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both,” Clin Neurophysiol, 115(6) (Jun. 2004), pp. 1239-1248. |
Mcintyre, Cameron C., et al., “Uncovering the mechanisms of deep brain stimulation for Parkinson's disease through functional imaging, neural recording, and neural modeling,” Crit Rev Biomed Eng., 30(4-6) (2002), pp. 249-281. |
Mouine et al. “Multi-Strategy and Multi-Algorithm Cochlear Prostheses”, Biomed. Sci. Instrument, 2000; 36:233-238. |
Mcintyre, Cameron C., et al., “Electric Field and Stimulating Influence generated by Deep Brain Stimulation of the Subthalamaic Nucleus,” Clinical Neurophysiology, 115(3) (Mar. 2004), pp. 589-595. |
Mcintyre, Cameron C., et al., “Electric field generated by deep brain stimulation of the 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 Neurophysiol., 96(3) (Sep. 2006), pp. 1569-1580. |
Miranda, P. C., et al., “The distribution of currents inducedin the brain by Magnetic Stimulation: a finite element analysis incorporating OT-MRI-derived conductivity data,” Proc. Intl. Soc. Mag. Reson. 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 in functional neurosurgery,” IEEE Transactions on Medical Imaging, 17(5) (1998), pp. 672-680. |
Starr, P.A., et al., “Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and magnetic resonance imaging-verified lead locations,” J. Neurosurg., 97(2) (Aug. 2002), pp. 370-387. |
Sterio, D., et al., “Neurophysiological refinement of subthalamic nucleus targeting,” Neurosurgery, 50(1) (Jan. 2002), pp. 58-69. |
Struijk, J. J., et al., “Excitation of dorsal root fibers in spinal cord stimulation: a theoretical study,” IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 632-639. |
Struijk, J J., et al., “Recruitment of dorsal column fibers in spinal cord stimulation: inftuence of collateral branching,” IEEE Transactions on Biomedical Engineering, 39(9) (Sep. 1992), pp. 903-912. |
Tamma, F., et al., “Anatomo-clinical correlation of intraoperative stimulation-induced side-effects during HF-DBS of the subthalamic nucleus,” Neurol Sci., vol. 23 (Suppl 2) (2002), pp. 109-110. |
Tarler, M., et al., “Comparison between monopolar and tripolar configurations in chronically implanted nerve cuff electrodes,” IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1093-1109. |
Testerman, Roy L., “Coritical response to callosal stimulation: A model for determining safe and efficient stimulus parameters,” Annals of Biomedical Engineering, 6(4) (1978), pp. 438-452. |
Tuch, D.S., et al., “Conductivity mapping of biological tissue using diffusion MRI,” Ann NY Acad Sci., 888 (Oct. 30, 1999), pp. 314-316. |
Tuch, D.S., et al., “Conductivity tensor mapping of the human brain using diffusion tensor MRI,” Proc Nall Acad Sci USA, 98(20) (Sep. 25, 2001), pp. 11697-11701. |
Veraart, C., et al., “Selective control of muscle activation with a multipolar nerve cuff electrode,” IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 640-653. |
Vercueil, L., et al., “Deep brain stimulation in the treatment of severe dystonia,” J. Neurol., 248(8) (Aug. 2001 ), pp. 695-700. |
Vilalte, “Circuit Design of the Power-on-Reset,” Apr. 2000, pp. 1-25. |
Vitek, J. L., “Mechanisms of deep brain stimulation: excitation or inhibition,” Mov. Disord., vol. 17 (Suppl. 3) (2002), pp. 69-72. |
Voges, J., et al., “Bilateral high-frequency stimulation in the subthalamic nucleus for the treatment of Parkinson disease: correlation of therapeutic effect with anatomical electrode position,” J. Neurosurg., 96(2) (Feb. 2002), pp. 269-279. |
Wakana, S., et al., “Fiber tract-based atlas of human white matter anatomy,” Radioiogy, 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 Stimuiation 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 coregisiration 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., “Programmable Current Source Dedicated to Implantable Microstimulators” ICM '98 Proceedings of the Tenth International Conference, pp. 67-70. |
Butson, Christopher R. , et al., “Patient-specific analysis of the volume of tissue activated during deep brain stimulation”, NeuroImage. vol. 34 (2007), 661-670. |
Adler, DE., et al., “The tentorial notch: anatomical variation, morphometric analysis, and classification in 100 human autopsy cases,” J. Neurosurg., 96(6), (Jun. 2002), pp. 1103-1112. |
Jones et al., “An Advanced Demultiplexing System for Physiological Stimulation”, IEEE Transactions on Biomedical Engineering, vol. 44 No. 12 Dec. 1997, pp. 1210-1220. |
Alo, K. M., et al., “New trends in neuromodulation for the management of neuropathic pain,” Neurosurgery, 50(4), (Apr. 2002), pp. 690-703, discussion pp. 703-704. |
Ashby, P., et al., “Neurophysiological effects of stimulation through electrodes in the human subthalamic nucleus,” Brain, 122 (PI 10), (Oct. 1999), pp. 1919-1931. |
Baker, K. B., et al., “Subthalamic nucleus deep brain stimulus evoked potentials: Physiological and therapeutic implications,” Movement Disorders, 17(5), (Sep./Ocl. 2002), pp. 969-983. |
Bammer, R, et al., “Diffusion tensor imaging using single-shot SENSE-EPI”, Magn Reson Med., 48(1 ), (Jul. 2002), pp. 128-136. |
Basser, P J., et al., “MR diffusion tensor spectroscopy and imaging,” Biophys J., 66(1 ), (Jan. 1994), pp. 259-267. |
Basser, P J., et al., “New currents in electrical stimulation of excitable tissues,” Annu Rev Biomed Eng., 2, (2000), pp. 377-397. |
Benabid, AL., et al., “Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders,” J. Neurosurg., 84(2), (Feb. 1996), pp. 203-214. |
Benabid, AL., et al., “Combined (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 diffusion tensor based electric field models,” Medical Image Computing and Computer-Assisted Intervention—Mic Cai 2006, Lecture Notes in Computer Science (LNCS), vol. 4191, pp. 429-437, LNCS, Springer, Berlin, DE. |
Christensen, Gary E., et al., “Volumetric transformation of brain anatomy,” IEEE Transactions on Medical Imaging, 16 (6), (Dec. 1997), pp. 864-877. |
Cooper, S , et al., “Differential effects of thalamic stimulation parameters on tremor and paresthesias in essential tremor,” Movement Disorders, 17(Supp. 5), (2002), p. S193. |
Coubes, P, et al., “Treatment of DYT1-generalised dystonia by stimulation of the internal globus pallidus,” Lancet, 355 (9222), (Jun. 24, 2000), pp. 2220-2221. |
Dasilva, A.F. M., et al., “A Primer Diffusion Tensor Imaging of Anatomical Substructures,” Neurosurg. Focus; 15(1) (Jul. 2003), pp. 1-4. |
Dawant, B. M., et al., “Compuerized atlas-guided positioning of deep brain stimulators: a feasibility study,” Biomedical Image registration, Second International Workshop, WBIR 2003, Revised Papers (Lecture notes in Comput. Sci. vol. (2717), Springer-Verlag Berlin, Germany(2003), pp. 142-150. |
Finnis, K. W., et al., “3-D functional atalas of subcortical structures for image guided stereotactic neurosurgery,” Neuroimage, vol. 9, No, 6, Iss. 2 (1999), p. S206. |
Finnis, K. W., et al., “3D Functional Database of Subcortical 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, Krik 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 for treatment-refractory obsessive-compulsive disorder: psychopathological and neuropsychological outcome in three cases,” Acta Psychiatr Scand., 107(4) (2003), pp. 275-282. |
Gabriels, LA., et al., “Long-term electrical capsular stimulation in patients with obsessive-compulsive disorder,” Neurosurgery, 52(6) (Jun. 2003), pp. 1263-1276. |
Goodall, E. V., et al., “Modeling study of activation and propagation delays during stimulation of peripheral nerve fibers with a tripolar cuff electrode,” IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 3(3) (Sep. 1995), pp. 272-282. |
Goodall, E. V., et al., “Position-selective activation of peripheral nerve fibers with a cuff electrode,” IEEE Transactions on Biomedical Engineering, 43(8) (Aug. 1996), pp. 851-856. |
Goodall, E. V., “Simulation of activation and propagation delay during tripolar neural stimulation,” Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (1993), pp. 1203-1204. |
Grill, WM., “Modeling the effects of electric fields on nerve fibers: inftuence of tissue electrical properties,” IEEE Transactions on Biomedical Engineering, 46(8) (1999), pp. 918-928. |
Grill, W. M., et al., “Neural and connective tissue response to long-term implantation of multiple contact nerve cuff electrodes,” J Biomed Mater Res., 50(2) (May 2000), pp. 215-226. |
Grill, W. M., “Neural modeling in neuromuscular and rehabilitation research,” Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4 (2001 ), pp. 4065-4068. |
Grill, W. M., et al., “Non-invasive measurement of the input-output properties of peripheral nerve stimulating electrodes,” Journal of Neuroscience Methods, 65(1) (Mar. 1996), pp. 43-50. |
Grill, W. M., et al., “Quantification of recruitment properties of multiple contact cuff electrodes,” IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 4(2) (Jun. 1996), pp. 49-62. |
Grill, W. M., “Spatially selective activation of peripheral nerve for neuroprosthetic applications,” Ph.D. Case Western Reserve University, (1995), pp. 245 pages. |
Grill, W. M., “Stability of the input-output properties of chronically implanted multiple contact nerve cuff stimulating electrodes,” IEEE Transactions on Rehabilitation Engineering [see also IEEE Trans. on Neural Systems and Rehabilitation] (1998), pp. 364-373. |
Grill, W. M., “Stimulus waveforms for selective neural stimulation,” IEEE Engineering in Medicine and Biology Magazine, 14(4) (Jul.-Aug. 1995), pp. 375-385. |
Grill, W. M., et al., “Temporal stability of nerve cuff electrode recruitment properties,” IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1089-1090. |
Gross, RE., et al., “Advances in neurostimulation for movement disorders,” Neurol Res., 22(3) (Apr. 2000), pp. 247-258. |
Guridi et al., “The subthalamic nucleus, hemiballismus and Parkinson's disease: 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, Auqust 2007. |
Andrade-Souza, “Comparison of Three Methods of Targeting the Subthalamic Nucleus for Chronic Stimulation in Parkinson's Disease”, Neurosurgery, 56:360-368, Apr. 2005. |
Anheim et al., “Improvement in Parkinson Disease by Subthalamic Nucleus Stimulation Based on Electrode Placement”, Arch Neural., 65:612-616, May 2008. |
Butson et al., “Tissue and Electrode Capacitance Reduce Neural Activation Volumes During Deep Brain Stimulation”, Clinical Neurophysiology, 116:2490-2500, Oct. 2005. |
Butson et al., “Sources and Effects of Electrode Impedance During Deep Brain Stimulation”, Clinical Neurophysiology, 117:44 7-454, Dec. 2005. |
D'Haese et al., “Computer-Aided Placement of Deep Brain Stimulators: From Planning to Intraoperative Guidance”, IEEE Transaction on Medical Imaging, 24:1469-1478, Nov. 2005. |
Gross et al., “Electrophysiological Mapping for the Implantation of Deep Brain Stimulators for Parkinson's Disease and Tremor”, Movement Disorders, 21 :S259-S283, Jun. 2006. |
Halpern et al., “Brain Shift During Deep Brain Stimulation Surgery for Parkinson's Disease”, Stereotact Funct. Neurosurg., 86:37-43, published online Sep. 2007. |
Herzog et al., “Most Effective Stimulation Site in Subthalamic Deep Brain Stimulation for Parkinson's Disease”, Movement Disorders, 19:1050-1099, published on line Mar. 2004. |
Jeon et al., A Feasibility Study of Optical Coherence Tomography for Guiding Deep Brain Probes, Journal of Neuroscience Methods, 154:96-101, Jun. 2006. |
Khan et al., “Assessment of Brain Shift Related to Deep Brain Stimulation Surgery”, Sterreotact Funct. Neurosurg., 86:44-53, published online Sep. 2007. |
Koop et al., “Improvement in a Quantitative Measure of Bradykinesia After Microelectrode Recording in Patients with Parkinson's Disease During Deep Brain Stimulation Surgery”, Movement Disorders, 21 :673-678, published on line Jan. 2006. |
Lemaire et al., “Brain Mapping in Stereotactic Surgery: A Brief Overview from the Probabilistic Targeting to the Patient-Based Anatomic Mapping”, 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 Volumes and Their Association with Neurophysiological Mapping and Therapeutic Outcomes”, Downloaded from jnnp.bmj.com, pp. 1-21, published online Apr. 2008. |
Moran et al., “Real-Time Refinment of Subthalamic Nucleous Targeting Using Bayesian Decision-Making on the Root Mean Square Measure”, Movement Disorders, 21: 1425-1431, published online Jun. 2006. |
Sakamoto et al., “Homogeneous Fluorescence Assays for RNA Diagnosis by Pyrene-Conjugated 2′-0-Methyloligoribonucleotides”, Nucleosides, Nucleotides, and Nucleric Acids, 26:1659-1664, on line publication Oct. 2007. |
Winkler et al., The First Evaluation of Brain Shift During Functional Neurosurgery by Deformation Field Analysis, J. Neural. Neurosurg. Psychiatry, 76:1161-1163, Aug. 2005. |
Yelnik et al., “A Three-Dimensional, Histological and Deformable Atlas of the Human Basal J Ganglia. I. Atlas Construction Based on Immunohistochemical and MRI Data”,. NeuroImage, 34:618,-638,Jan. 2007. |
Ward, H. E., et al., “Update on deep brain stimulation for neuropsychiatric disorders,” Neurobiol Dis 38 (3) (2010), pp. 346-353. |
Alberts et al. “Bilateral subthalamic stimulation impairs cognitive-motor performance in Parkinson's disease patients.” Brain (2008), 131, 3348-3360, Abstract. |
Butson, Christopher R., et al., “Sources and effects of electrode impedance during deep brain stimulation”, Clinical Neurophysiology. vol. 117.(2006),447-454. |
An, et al., “Prefronlal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys,” J Comp Neural 401 (4) (1998), pp. 455-479. |
Bulson, C. R., et al., “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” Clinical Neurophysiology, vol. 116 (2005), pp. 2490-2500. |
Carmichael, S. T., et al., “Connectional networks within the orbital and medial prefronlal cortex of macaque monkeys,” J Comp Neural 371 (2) (1996), pp. 179-207. |
Croxson, et al., “Quantitative investigation of connections of the prefronlal cortex in the human and macaque using probabilistic diffusion 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 Neural 421 (2) (2000), pp. 172-188. |
Giacobbe, et al., “Treatment resistant depression as a failure of brain homeostatic mechanisms: implications for deep brain stimulation,” Exp Neural 219 (1) (2009), pp. 44-52. |
Goodman, et al., “Deep brain stimulation for intractable obsessive compulsive disorder: pilot study using a blinded, staggered-onset design,” Biol Psychiatry 67 (6) (2010), pp. 535-542. |
Greenberg, et al., “Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience,” Mol Psychiatry 15 (1) (2010), pp. 64-79. |
Greenberg. et al., “Three-year outcomes in deep brain stimulation for highly resistant obsessive-compulsive disorder,” Neuropsychopharmacology 31 (11) (2006), pp. 2384-2393. |
Gutman, et al., “A tractography analysis of two deep brain stimulation white matter targets for depression,” Biol Psychiatry 65 (4) (2009), pp. 276-282. |
Haber, et al., “Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning,” J Neurosci 26 (32) (2006), pp. 8368-8376. |
Haber, et al., “Cognitive and limbic circuits that are affected by deep brain stimulation,” Front Biosci 14 (2009), pp. 1823-1834. |
Hines, M. L., et al., “The NEURON simulation environment,” Neural Comput., 9(6) (Aug. 15, 1997), pp. 1179-1209. |
Hua, et al., “Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification,” Neuroimage 39 (1) (2008), pp. 336-347. |
Johansen-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 Neurol 216 (i) (2009), pp. 166-176. |
Nuttin, et al., “Electrical stimulation in anterior limbs of internal capsules in patients with obsessive-compulsive disorder,” Lancet 354 (9189) (1999), p. 1526. |
Official Communication for U.S. Appl. No. 15/420,842 dated Apr. 14, 2017. |
Official Communication for U.S. Appl. No. 15/420,842 dated Jul. 13, 2017. |
Official Communication for U.S. Appl. No. 15/729,383 dated Jan. 30, 2018. |
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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