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Title:
METHODS AND SYSTEMS FOR SENSING BALANCE BETWEEN NEURONAL ACTIVITY INPUTS AND PATHWAYS AND NEUROMODULATION
Document Type and Number:
WIPO Patent Application WO/2020/095111
Kind Code:
A1
Abstract:
Methods are provided for treating a neurological disorder in a patient. Systems are provided for applying electrical stimulation to the brain. The methods and systems measure first and second neuronal activity signals of a brain at first and second cortex areas of interest, respectively, utilizing one or more sensors. The methods and systems determine a quantitative relation between the first and second neuronal activity signals and obtains a balanced relation that is indicative of a select level of balance between neuronal activity at the first and second cortex areas of interest. The methods and systems compare the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition and applies electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition.

Inventors:
RIDDER DIRK (NZ)
Application Number:
PCT/IB2019/001207
Publication Date:
May 14, 2020
Filing Date:
November 06, 2019
Export Citation:
Click for automatic bibliography generation   Help
Assignee:
RIDDER DIRK DE (NZ)
International Classes:
A61N1/05; A61B5/04; A61N1/36
Domestic Patent References:
WO2001093953A12001-12-13
WO2001093953A12001-12-13
Foreign References:
US20170001016A12017-01-05
US20120116475A12012-05-10
US20180020972A12018-01-25
US20160279380A12016-09-29
US8401655B22013-03-19
USPP60685036P
US8682441B22014-03-25
US7571007B22009-08-04
US7212110B12007-05-01
US20060170486A12006-08-03
US7228179B22007-06-05
US20090326608A12009-12-31
US8897870B22014-11-25
US5938690A1999-08-17
US89509610A2010-09-30
Other References:
ENGEL ET AL.: "Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity", NEURON, vol. 80, no. 4, 20 November 2013 (2013-11-20), pages 867 - 886, XP028780883, DOI: 10.1016/j.neuron.2013.09.038
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Claims:
WHAT IS CLAIMED IS:

1. A method of treating a neurological disorder in a patient, comprising:

measuring first and second neuronal activity signals of a brain at first and second cortex areas of interest, respectively, utilizing one or more sensors;

determining a quantitative relation between the first and second neuronal activity signals;

obtaining a balanced relation that is indicative of a select level of balance between neuronal activity at the first and second cortex areas of interest;

comparing the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition; and

applying electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition.

2. The method of claim 1 , wherein the first and second cortex areas of interest comprise one or more of: a right dorsal anterior cingulate cortex (rdACC), a somatosensory cortex (SSC) and a pregenual anterior cingulate cortex (pgACC).

3. The method of claim 2, wherein the first cortex area of interest represents at least one of the rdACC or SSC, while the second cortex area of interest represents the pgACC.

4. The method of claim 1 , wherein the one or more sensors include first and second electrodes implanted in intradural or extradural regions.

5. The method of claim 1 , wherein the determining the quantitative relation comprises determining at least one of a quantitative activity relation or a quantitative connectivity relation, the quantitative connectivity relation including at least one of functional connectivity relation or an effective connectivity relation.

6. The method of claim 1 , wherein the balanced relation includes a balanced activity relation defined at least in part based on a ratio of current density between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition.

7. The method of claim 6, wherein the balanced activity relation is defined by the ratio of a mean of the current densities between the first and second baseline neuronal activity signals exhibited by multiple individuals in a patient population, where the baseline neuronal activity signals are measured at first and second cortex areas of interest from the patient population while exhibiting a non-pathological neurological condition.

8. The method of claim 1 , wherein the balanced relation includes a balanced connectivity relation defined at least in part based on functional synchronization between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition.

9. The method of claim 1 , further comprising filtering the first and second neuronal activity signals to obtain first and second frequency components of interest, respectively, the determining comprising determining the present relation between the first and second frequency components of interest.

10. The method of claim 1 , further comprising automatically programming stimulation parameters for an implantable pulse generator to deliver the electrical stimulation to the one or more sites from one or more implanted electrodes.

11. The method of claim 1 , wherein the one or more sites where the electrical stimulation is applied corresponds to at least one of the first and second cortex areas of interest.

12. A system for applying electrical stimulation to one or more locations within a brain of a patient, the system comprising:

sensors configured to measure first and second neuronal activity signals of a brain at first and second cortex areas of interest, respectively;

memory to store program instructions;

one or more processors configured to execute the program instructions for:

determining a quantitative relation between the first and second neuronal activity signals;

obtaining a balanced relation that is indicative of a select level of balance between neuronal activity at the first and second cortex areas of interest;

comparing the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition; and

applying electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition.

13. The system of claim 12, wherein the first and second cortex areas of interest comprise one or more of: a right dorsal anterior cingulate cortex (rdACC), a somatosensory cortex (SSC) and a pregenual anterior cingulate cortex (pgACC).

14. The system of claim 12, wherein the first cortex area of interest represents at least one of the rdACC or SSC, while the second cortex area of interest represents the pgACC.

15. The system of claim 12, wherein the one or more sensors include first and second electrodes implanted in intradural or extradural regions.

16. The system of claim 12, wherein the one or more processors are configured to determine the determining the quantitative relation by determining at least one of a quantitative activity relation or a quantitative connectivity relation, the quantitative connectivity relation including at least one of functional connectivity relation or an effective connectivity relation.

17. The system of claim 12, wherein the balanced relation includes a balanced activity relation defined at least in part based on a ratio of current density between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition.

18. The system of claim 17, wherein the balanced activity relation is defined by the ratio of a mean of the current densities between the first and second baseline neuronal activity signals exhibited by multiple individuals in a patient population, where the baseline neuronal activity signals are measured at first and second cortex areas of interest from the patient population while exhibiting a non-pathological neurological condition.

19. The system of claim 12, wherein the balanced relation includes a balanced connectivity relation defined at least in part based on functional synchronization between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition.

20. The system of claim 12, further comprising an implantable pulse generator that houses the memory, the implantable pulse generator connected to a lead that includes electrodes representing the sensors.

Description:
METHODS AND SYSTEMS FOR SENSING BALANCE BETWEEN NEURONAL ACTIVITY INPUTS AND PATHWAYS AND NEUROMODULATION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of United States Provisional Application No. 62/756,862, which was filed on November 7, 2018 and which is incorporated herein by reference in its entirety.

BACKGROUND

[0002] Neurostimulation (NS) systems are devices that generate electrical pulses and deliver the pulses to nervous tissue to treat a variety of disorders. For example, spinal cord stimulation (SCS) has been used to treat chronic and intractable pain. Another example is deep brain stimulation (DBS), which has been used to treat movement disorders such as Parkinson’s disease and affective disorders such as depression. SCS therapy, delivered via epidurally implanted electrodes, is a widely used treatment for chronic intractable neuropathic pain of different origins. Traditional tonic therapy evokes paresthesia covering painful areas of a patient. During SCS therapy calibration, the paresthesia is identified and localized to the painful areas by the patient in connection with determining correct electrode placement.

[0003] Although some neurological disorders have been treated through known neurostimulation methods, many other neurological disorders exhibit physiological complexity, functional complexity, or other complexity and have not been adequately treated through known neurostimulation methods.

[0004] In particular, chronic pain is one of the most important medical problems facing society; there has been limited progress in the development of novel therapies for this condition. Over one-third of the world's population suffers from persistent or chronic pain, resulting in tremendous burden for the individual as well as society. This creates a huge cost to society that is double that of heart disease, cancer, or diabetes. Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage and it is often accompanied by other health issues such as fatigue, sleep disturbance, decreased appetite, and mood changes. It has led to an opioid epidemic, with four percent of the US population having or misusing prescription opioids. Many of the currently available pain therapies are either inadequate or cause uncomfortable to deleterious side effects.

[0005] An important aspect to more successful pain treatment is to understand the mechanisms that generate and maintain chronic pain.

[0006] However, the mechanisms that generate and maintain chronic pain are not well understood and thus far, conventional neurostimulation systems have experiences limitations in treating chronic pain.

SUMMARY

[0007] In accordance with embodiments herein, a method of treating a neurological disorder in a patient is provided. The method measures first and second neuronal activity signals of a brain at first and second cortex areas of interest, respectively, utilizing one or more sensors. The method determines a quantitative relation between the first and second neuronal activity signals and obtains a balanced relation that is indicative of a select level of balance between neuronal activity at the first and second cortex areas of interest. The method compares the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition and applies electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition.

[0008] Optionally, the first and second cortex areas of interest may comprise one or more of: a right dorsal anterior cingulate cortex (rdACC), a somatosensory cortex (SSC) and a pregenual anterior cingulate cortex (pgACC). The first cortex area of interest may represent at least one of the rdACC or SSC, while the second cortex area of interest may represent the pgACC. The one or more sensors may include first and second electrodes implanted in intradural or extradural regions. The determining the quantitative relation may comprise determining at least one of a quantitative activity relation or a quantitative connectivity relation. The quantitative connectivity relation may include at least one of functional connectivity relation or an effective connectivity relation.

[0009] Optionally, the balanced relation may include a balanced activity relation defined at least in part based on a ratio of current density between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition. The balanced activity relation may be defined by the ratio of a mean of the current densities between the first and second baseline neuronal activity signals exhibited by multiple individuals in a patient population. The baseline neuronal activity signals may be measured at first and second cortex areas of interest from the patient population while exhibiting a non- pathological neurological condition. The balanced relation may include a balanced connectivity relation defined at least in part based on functional synchronization between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition.

[0010] Optionally, the method may filter the first and second neuronal activity signals to obtain first and second frequency components of interest, respectively. The determining may comprise determining the present relation between the first and second frequency components of interest. The method may automatically program stimulation parameters for an implantable pulse generator to deliver the electrical stimulation to the one or more sites from one or more implanted electrodes. The one or more sites where the electrical stimulation may be applied may correspond to at least one of the first and second cortex areas of interest. [0011] In accordance with embodiments herein, a system for applying electrical stimulation to one or more locations with a brain of a patient is provided. Sensors are configured to measure first and second neuronal activity signals of a brain at first and second cortex areas of interest, respectively. Memory stores program instructions. One or more processors are configured to execute the program instructions for determining a quantitative relation between the first and second neuronal activity signals, obtaining a balanced relation that is indicative of a select level of balance between neuronal activity at the first and second cortex areas of interest, comparing the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition and applying electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition.

[0012] Optionally, the first and second cortex areas of interest may comprise one or more of: a right dorsal anterior cingulate cortex (rdACC), a somatosensory cortex (SSC) and a pregenual anterior cingulate cortex (pgACC). The first cortex area of interest may represent at least one of the rdACC or SSC, while the second cortex area of interest may represent the pgACC. The one or more sensors may include first and second electrodes implanted in intradural or extradural regions. The one or more processors may be configured to determine the determining the quantitative relation by determining at least one of a quantitative activity relation or a quantitative connectivity relation. The quantitative connectivity relation may include at least one of functional connectivity relation or an effective connectivity relation.

[0013] Optionally, the balanced relation may include a balanced activity relation defined at least in part based on a ratio of current density between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition. The balanced activity relation may be defined by the ratio of a mean of the current densities between the first and second baseline neuronal activity signals exhibited by multiple individuals in a patient population. The baseline neuronal activity signals may be measured at first and second cortex areas of interest from the patient population while exhibiting a non- pathological neurological condition.

[0014] Optionally, the balanced relation may include a balanced connectivity relation defined at least in part based on functional synchronization between first and second baseline neuronal activity signals of a brain as measured at first and second cortex areas of interest when the brain is exhibiting a non-pathologic neurological condition. The system may comprise an implantable pulse generator that may house the memory. The implantable pulse generator may be connected to a lead that may include electrodes representing the sensors.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1A illustrates an example neurological stimulation (NS) system for electrically stimulating a predetermined site area to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0016] Figure 1 B illustrates an example neurological stimulation (NS) system for electrically stimulating a predetermined site area to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0017] Figure 1 C depicts an NS system that delivers stimulation therapies in accordance with embodiments herein.

[0018] Figure 2A illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0019] Figure 2B illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein. [0020] Figure 2C illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0021] Figure 2D illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0022] Figure 2E illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0023] Figure 2F illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0024] Figure 2G illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0025] Figure 2H illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0026] Figure 2I illustrates example stimulation leads that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions in accordance with embodiments herein.

[0027] Figure 3A illustrates an example of the various brainwave frequency bands in accordance with embodiments herein.

[0028] Figure 3B illustrates models that provide an overview of three pain pathways of interest in accordance with embodiments herein.

[0029] Figure 3C illustrates MRI images collected in connection with a meta-analysis via neurosynthesis of various subject populations in accordance with embodiments herein. [0030] Figure 3D illustrates a model of example cortex areas of interest in accordance with embodiments herein.

[0031] Figure 3E illustrates a table of example neuronal activity signals that may be measured at various cortex areas of interest, as well as quantitative relations that may be derived therefrom in accordance with embodiments herein.

[0032] Figure 4A illustrates functional connectivity models denoting levels of functional communication between different cortex areas, associated with the medial, lateral and descending pain pathways that are utilized in accordance with embodiments herein.

[0033] Figure 4B illustrates effective connectivity models denoting levels of effective communication between different cortex areas, associated with the medial, lateral and descending pain pathways that are utilized in accordance with embodiments herein.

[0034] Figure 5 illustrates an overview of a process for building a control database of balanced relations from individuals in a control population in accordance with embodiments herein.

[0035] Figure 6A illustrates a process for building a control database of balanced relations from individuals in a control population in accordance with embodiments herein.

[0036] Figure 6B illustrates a process for verifying or removing candidate balanced relations for candidate cortex combinations in accordance with embodiments herein.

[0037] Figure 7 illustrates a method of treating a neurological disorder in a patient in accordance with embodiments herein.

[0038] Figure 8 illustrates models of the brain with cortex areas of interest highlighted that may be monitored for neuronal activity signals in accordance with embodiments herein.

[0039] Figure 9 illustrates a model related to reward deficiency syndromes in accordance with embodiments herein. [0040] Figure 10 illustrates a model of the brain with different regions of interest identified.

[0041] Figure 11 illustrates a conjunction analysis that demonstrates what disorders of interest have in common.

[0042] Figure 12 illustrates that the methods and systems herein are not only applicable to pain but also to the other pathologies.

DETAILED DESCRIPTION

[0043] While multiple embodiments are described, still other embodiments of the described subject matter will become apparent to those skilled in the art from the following detailed description and drawings, which show and describe illustrative embodiments of disclosed inventive subject matter. As will be realized, the inventive subject matter is capable of modifications in various aspects, all without departing from the spirit and scope of the described subject matter. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.

I. Definitions

[0044] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. For purposes of the description, the following terms are defined below. Further, additional terms are used herein that shall have definitions consistent with the definitions set forth in U.S. Patent 8,401 ,655, which is expressly incorporated herein by reference in its entirety.

[0045] As used herein, the use of the word "a" or "an" when used in conjunction with the term "comprising" in the claims and/or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one." Still further, the terms "having", "including", "containing" and "comprising" are interchangeable and one of skill in the art is cognizant that these terms are open ended terms.

[0046] The term“F” is used to refer to the F- statistic which is a value obtained when an ANOVA test or a regression analysis is performed to determine whether means between two populations are significantly different. The ANOVA test or regression analysis may be utilized to determine whether group of variables are jointly significant. The F statistic is utilized when deciding to support or reject a null hypothesis. In F test results, an F value and an F critical value are considered. The F critical value is also called the F statistic. The value calculates from the data is called the F value. In general, when a calculated F value in a test is larger than an F statistic, the null hypothesis can be rejected.

[0047] The term “p” or“p value” is determined by the F statistic and represents the probability that results could have happened by chance. As explained herein, the F statistic is used in combination with the p value when deciding whether overall results are significant. For example, an alpha level should be defined such that, when the p value is greater than an alpha level, results may not be significant and the null hypothesis can be rejected. By way of example, the alpha level may be set at 0.05 or some other value. The individual p values are analyzed to determine which of the individual variables are statistically significant.

[0048] The“Anterior cingulate cortex” or“ACC” is the frontal part of the cingulate cortex that resembles a "collar" surrounding the frontal part of the corpus callosum. The ACC includes the Brodmann areas 24, 32, and 33. The anterior cingulate cortex can be divided anatomically based on cognitive (dorsal), and emotional (ventral) components. The dorsal part of the ACC is connected with the prefrontal cortex and parietal cortex, as well as the motor system and the frontal eye fields, making it a central station for processing top-down and bottom-up stimuli and assigning appropriate control to other areas in the brain. By contrast, the ventral part of the ACC is connected with the amygdala, nucleus accumbens, hypothalamus, hippocampus, and anterior insula, and is involved in assessing the salience of emotion and motivational information. On a cellular level, the ACC is unique in its abundance of specialized neurons called spindle cells, or von Economo neurons. These cells are a relatively recent occurrence in evolutionary terms (found only in humans and other primates, cetaceans, and elephants) and contribute to this brain region's emphasis on addressing difficult problems, as well as the pathologies related to the ACC. The ACC registers physical pain as shown in functional MRI studies that showed an increase in signal intensity, typically in the posterior part of area 24 of the ACC that was correlated with pain intensity. The ACC is the cortical area that has been most frequently linked to the experience of pain and is involved in the emotional reaction to pain rather than to the perception of pain itself.

[0049] The pgACC is located between the subgenual and rostral ACC, and consists of part of BA24 and BA32 and BA33 (e.g., see Figure 10). Like all other parts of the cingulate cortex, the pgACC has a specific receptor profile, specific connectivity profile, and specific structure, differentiating it from other parts of the cingulate cortex. Figure 10 illustrates a model of the brain with different regions of interest identified.

[0050] The somatosensory cortex is a part of the cerebral cortex and is located in the middle of the brain. The somatosensory cortex receives all somatosensory input from the body, including touch, proprioceptive and pain stimuli.

[0051] The terms“neuronal activity component” and“NA component” refer to a frequency band associated with a type of brain wave. Non-limiting examples of NA components include infraslow waves or fluctuations, delta waves, theta waves, alpha-1 waves, alpha-2 waves, beta-1 waves, beta-2 waves, beta-3 waves and gamma waves. [0052] The term “common NA component” refers to two or more NA components, within a single frequency band, that were collected from two or more cortex areas. An example of common NA components includes theta waves from the pgACC and SSC cortex areas.

[0053] Activity between respective neural networks or modules occurs through intrinsic coupling modes (“ICMs”). The ICMs are reflected in cross- frequency coupling activity and are discussed in the article“Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity,” by Engel et al. , Neuron, Volume 80, Issue 4, 20 November 2013, pages 867-886, which is incorporated herein by reference. Through analysis of recordings of neural activity in respective networks in the brain, ICMs were shown to have relevance for the characterization of functional networks in ongoing activity. ICMs can be exhibited in phase-ICMs and envelope ICMs. Phase ICMs exhibit coupling in phase relationships (coherence or imaginary coherence). Envelope ICMs exhibit coupling in envelope correlation (amplitude or power correlation). Most commonly, power to phase cross-frequency coupling (e.g., gamma activity nested on theta activity) is exhibited in a number of physiological neural activities.

[0054] An example of the various brainwave frequency bands which include infraslow waves 302 (from 0 to 1 Hz); delta waves 304 (1-4Hz); theta waves 306 (4-8 Hz); alpha waves 308 (8-12 Hz), beta waves 310 (12-30 Hz), gamma waves 312 (greater than 30Hz), and sigma waves (not shown) (greater than 500Hz). Individual brainwave frequency bands and combinations of brainwave frequency bands are associated with various mental, physical and emotional characteristics. It should be recognized that the cutoff frequencies for the frequency bands for the various types of brain waves are approximations. Instead, the cutoff frequencies for each frequency band may be slightly higher or lower than the examples provided herein. [0055] Neural oscillations from various combinations of the brainwave frequency bands exhibit coupling with one another, wherein one or more characteristics of one type of brainwave effect (or are affected by) one or more characteristics of another type of brainwave. In general, the coupling phenomenon is referred to as cross-frequency coupling, various aspects of which are described in the papers referenced herein. Combinations of frequency bands couple with one another to different degrees, while the coupling of various types of brainwaves may occur in connection with physiologic behavior or pathologic behavior. For example, theta and gamma frequency coupling has been identified at the hippocampalcortical level in connection with physiologic behavior, but in thalamocortical activity this same theta-gamma coupling should be considered pathological, as normal activity consists of alpha-gamma coupling, except in sleep stages. Delta - gamma and delta-beta frequency coupling have been identified in connection with physiological reward system activity as well as in autonomic nervous system activity. As another example, alpha-gamma frequency coupling has been identified at the pulvinar region in connection with physiological processes mediating attention. Alpha-gamma coupling is physiological at the thalamocortical level in general.

[0056] In accordance with embodiments herein, methods and systems are described that objectively measure thalamocortical dysrhythmia (e.g., neuropathic pain, Parkinson Disease, Tinnitus, Depression) using electroencephalography (EEG) signals. Pain may be represented as an imbalance between pain input and pain suppression pathways. In general, there are two primary pain input pathways. One pain input pathway is associated with emotional/psychological pain via the medial pain pathway, which has a main hub of interest at the right rostro-dorsal anterior cingulate cortex (or rdACC). A second pain input pathway is associated with physical/sensory pain via the lateral pain pathway which has a main hub of interest at the somatosensory cortex (or SSC). In addition, pain suppression is mediated via a pain inhibitory pathway which has a main hub of interest at the pregenual anterior cingulate cortex (or pgACC).

[0057] In accordance with embodiments herein, current density is measured, and functional and effective communication is determined, within and between the hubs of interest (e.g., rdACC, pgACC and SSC). The current density measurements and functional and effective communication are analyzed to identify indicators for the presence of pain, as well as tinnitus, PD and depression as these diseases share a common pathophysiology. For example, objective electrophysiological measurements may be obtained in connection with the hubs of interest, and the relation there between analyzed to determine a therapy to be programmed into and delivered by an implantable pulse generator (IPG) or external device in connection with the treatment of pain, tinnitus, PD, depression, such as vertigo and other movement disorders, and the like. Additionally or alternatively, the electrophysiologic measurements may be analyzed in connection with determining therapies to be delivered by an IPG or external device for treatment of addiction, obsessive-compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD) and personality disorders. The EEG signals are analyzed to determine desired (e.g., optimal) stimulation parameters to afford a desired amount (e.g., optimal) of suppress for pain, tinnitus, PD and depression, as well as addiction, ADHD, OCD and personality disorders.

[0058] Additionally or alternatively, embodiments herein may utilize the analysis of the EEG signals as a feedback mechanism for a closed loop stimulation system. In a closed loop system, an IPG would periodically and automatically collect EEG signals, analyze quantitative relations of interest between the EEG signals, and automatically update stimulation parameters to afford a desired amount of suppression for the neurological disorder of interest. Embodiments herein analyze the quantitative relations of interest to determine, among other things, a balance of brain activity and connectivity, in contrast with classical closed loop systems that simply measure raw activity or connectivity at individual points or for individual brain waves.

[0059] According to some embodiments, electrodes using one or more deep brain or cortical leads are implanted in or adjacent to one or more cortex areas of interest. Respective electrodes may be employed to analyze neural activity to detect when pathological neural activity or connectivity is present. The electrodes may be implanted at intra-dural or extra-dural sites. As a further example, one or more electrodes may be implanted at hub/cortex areas of interest such as rdACC, pgACC and SSC areas of interest. The first and second cortex areas of interest may include a right dorsal anterior cingulate cortex (rdACC), a somatosensory cortex (SSC) and/or a pregenual anterior cingulate cortex (pgACC). As a further example, a first cortical area of interest may represent at least one of the rdACC or SSC, while the second cortex area of interest represents the pgACC. Optionally, first and second electrodes may be implanted in the intradural region proximate to the first and second cortex areas of interest, respectively. Respective electrodes may be employed to provide suitable electrical stimulation when pathological activity is detected.

[0060] Figure 3A illustrates an example of the various brainwave frequency bands which include infraslow waves 302 (from 0 to 1 Hz); delta waves 304 (1 -4Hz); theta waves 306 (4-8 Hz); alpha waves 308 (8-12 Hz), beta waves 310 (12-30 Hz), gamma waves 312 (greater than 30Hz), and sigma waves (not shown) (greater than 500Hz). Individual brainwave frequency bands and combinations of brainwave frequency bands are associated with various mental, physical and emotional characteristics. It should be recognized that the cutoff frequencies for the frequency bands for the various types of brain waves are approximations and different individuals may exhibit cutoff frequencies for each frequency band that are slightly higher or lower than the examples provided herein. It should also be recognized that the brainwave frequency bands indicated in Figure 3, correspond to non-pathological neuronal activity. In certain instances, patients exhibiting pathological neurological conditions may experience brainwave activity having a frequency that falls below or is greater than points at which normal cutoff frequencies are expected (e.g., a patient with a neurological disorder may experience alpha waves that slowed to a frequency in the theta wave range).

[0061] In connection with developing embodiments herein, inference maps from numerous MRI neuroimaging studies have been examined to identify inference maps that concerned the categories of pain (“pain,” “painful,” “noxious”). From the neuroimaging studies, a number of the studies were identified that related to suffering, sensory and inhibition. MRI images were analyzed to identify three cortex areas of interest, namely the cortex areas related to the different pain input and suppression pathways, namely the dorsal anterior cingulate cortex, the pregenual anterior cingulate cortex and the somatosensory cortex. For the sensory aspect of the pain, changes in the images were identified mainly in the somatosensory cortex. For the emotional/suffering aspect of pain, changes in the images were identified mainly in the dorsal anterior cingulate cortex. For the inhibition aspect of pain, changes in the images were identified mainly in the pregenual anterior cingulate cortex.

[0062] Figure 3B illustrates models that provide an overview of three pain pathways of interest in connection with embodiments herein. Models 330 and 332 illustrate ascending medial and lateral pain pathways which involve, respectively, the dorsal anterior cingulate cortex (dACC) that encodes the unpleasantness of the pain and the somatosensory cortex (SSC) processes the discriminatory/sensory components of the pain, such as the pain intensity, the pain localization, the pain character (burning, aching ... ). Model 334 illustrates a descending pain inhibitory pathway, involving the rostral and pregenual anterior cingulate cortex (pgACC), the reticular nucleus of the thalamus, the periaqueductal gray (PAG), the parahippocampal area, hypothalamus and rostral ventromedial brainstem, that is responsible for stress mediated pain inhibition, placebo analgesia, and is deficient in certain pain syndromes.

[0063] The neuronal activity along the three pathways in the models 330, 332, 334 collectively defines global pain experienced by an individual in connection with certain types of neurological disorders, such as disorders associated with thalamocortical dysrhythmia and reward deficiency syndrome. Chronic pain is a result of paradoxical salience attachment to pain signals. Chronic pain is also manifested as a consequence of a persistent imbalance between increased neuronal activity alone pain input pathways and decreased neuronal activity alone descending pain suppression pathways. While embodiments herein are described in connection with pain, it is recognized that the neuronal activity along the three pathways in the models may be used to measure and treat with other pathologies, including but not limited to, tinnitus, depression, Parkinson, addiction, OCD and ADHD.

[0064] The subjective experience of pain results from how the brain processes sensory input, rather than from the physical insult or sensory input itself. Pain is processed in the brain and modulated by at least three pathways in models 330, 332 and 334. Ascending pathways include the anatomically and functionally separated medial and lateral pain pathways. The medial pain pathway, which involves the amygdala, dorsal anterior cingulate (dACC) and anterior insular cortex, encodes the unpleasantness or suffering of pain, in other words the emotional component of the above definition. The lateral pathway, which involves the somatosensory cortex (SSC), processes the discriminatory/sensory components of the pain, such as pain intensity, pain localization, and pain character (burning, aching, etc.), in other words the painfulness. The two ascending pain pathways are counteracted by a descending pain inhibitory pathway, involving the rostral and pregenual anterior cingulate cortex (pgACC), the periaqueductal gray, the parahippocampal area, hypothalamus, and rostral ventromedial brainstem. This descending pathway is responsible for stress-mediated pain inhibition, placebo analgesia, and is deficient in pain syndromes such as fibromyalgia.

[0065] Chronic pain can be described as an imbalance between large and small fibers at the level of the spinal cord, in which the descending large fibers were insufficiently suppressing small ascending pain transmitting fibers. This is known as the pain gate theory. However, the mechanism in how the descending pain suppressing fibers control the ascending pain input has remained largely unknown. To better understand the interaction between the descending pain suppression pathway and a sending pain input pathway, a study was conducted to test the hypothesis that a pain-free state represents a balance between the two ascending and one descending pathways at the level of the brain and that chronic pain is the result of a persisting measurable imbalance between pain input and pain suppression in the brain rather than the spinal cord. To this end three studies are integrated into one model.

[0066] As a first step, automated Neurosynth meta-analysis of functional MRI studies (n = 420 studies; http://neurosynth.org) demonstrates that chronic pain is associated with activity in areas implicated in the medial (dACC), lateral (SSC), and descending (pgACC) pain pathways. In order to further confirm these findings, a second study source was applied utilizing localized EEG. The localized EEG signals quantify spontaneous resting state neuronal (brain) activity (e.g., as designated by current density) in anatomically specified areas of the brain. In a balanced model, it would be assumed that a neurophysiological signature that is limited to only three areas— the dACC (medial pathway), the SSC (lateral pathway), and the pgACC (descending pathway)— can objectively demonstrate the presence of pain. A support vector machine learning approach can be utilized to objectively detect chronic pain in comparison to pain-free control subjects, solely based on brain activity of the dACC (including a and b band), the SSC (including Q and g band), and the pgACC (including Q and a band) with a desired level of accuracy (e.g., greater than 85%). [0067] A random model has an accuracy not higher than chance (TPR: 50.1 %)(F = 2593.085, p < .001 ). When the neuronal activity in the cortex areas of interest was compared between patients exhibiting chronic pain and the pain free control subjects, chronic pain was detected with a relatively medium level of accuracy (e.g., 67.4% when based solely on activity in the dACC area: 68.8% when based solely on activity in the SSC area: and 63.3% when based solely on activity in the pgACC area). However, when neuronal activity in combinations of the cortex areas were considered, the accuracy significantly improved (e.g., 79.3% when based on activity in the combined dACC and SSC areas). Based on this significant increase in accuracy for detecting chronic pain, the balanced models described proposed herein confirm that chronic pain can better be detected and treated based on analysis of quantitative relations between neuronal activity at cortex areas related to pain input and suppression. The analysis herein, concerning cortex areas of interest, further shows that chronic pain is the result of an increase in neuronal activity activity in select brainwave frequency bands at the cortex areas of interest. For example, in connection with the analysis of the 430 studies noted above, it was found that the neuronal activity in certain brainwave frequency bands, as measured at certain cortex combinations, yielded a significant increase in the accuracy of predicting chronic pain. For example, in the SSC cortex area, the theta (Q) and gamma (y) bands yielded statistical F and P values of: F = 5.78, p = .004 for the theta waves at the left SSC; and F = 6.51 , p = .002 four gamma waves at the left and right SSC. In the dACC cortex area, the alpha (a) and beta (b) waves yielded; F = 10.77, p < .001 , and in the pgACC cortex area, the theta (Q) and alpha (a) waves yielded; F = 5.58, p = .005. The foregoing neuronal activity correlates with the pain percept for SSC (y: R2 = .39, p < .001 ), dACC (b: R2 = .21 p = .007) and pgACC (a: R2 = .21 , p = .007), and the ratio of current densities for neuronal activity between these cortex areas is significantly different between chronic patient subjects and healthy pain-free subjects (F = 17.14, p < .001 ) and furthermore correlates with pain percept (R2 = .13, p = .012).

[0068] The underlying oscillatory abnormality in electrical brain activity recorded in pain patients is consistent with the thalamocortical dysrhythmia model of pain. Thalamocortical dysrhythmia states that, in chronic pain, the dominant resting state a rhythm (dominant frequency) in the SSC cortex area decreases to a level near or within the theta brainwave activity band because of thalamic GABAA-mediated lateral disinhibition due to deprived input, inducing gamma g band activity experiencing cross frequency coupling to theta Q band activity.

[0069] The descending pain inhibitory system is controlled by the pgACC cortex area exhibits neuronal activity oscillating at rest in the theta Q waveband, and an activated (dys)functioning inhibitory mechanism works in the theta Q and alpha a bands, while the dACC cortex area exhibits neuronal activity oscillating in the alpha a and beta b frequency bands distressed patients. The EEG results demonstrate that chronic pain is associated with an increase in activity in cortex areas implicated in emotional (dACC in a and b band) and sensory processing of pain (SSC in Q and g band), but a paradoxical increase in pain-suppressing activity (pgACC in Q and a band) is also noted. This suggests that the increased pain-evoking neuronal activity driven by b (beta) waves in the dACC cortex area and gamma g waves in the SSC cortex area is compensated for, albeit insufficiently, by the pain suppression mechanism, creating a net imbalance that still results in pain.

[0070] Next the discussion turns to how the relationship balance operates. Mechanistically, the balance between pain input and suppression requires some form of communication between neuronal activity in the dACC, SSC, and pgACC cortex areas. Functional connectivity, based on phase coherence between neuronal activity in these cortex areas of the brain, demonstrates that chronic pain patients experience less connection in the alpha a wave band and more connected in the theta Q wave band. The theta Q and alpha a oscillations are important for long-range communication. Thus, the lack of communication between pain input and pain suppression results in an imbalance of neuronal activity. However, this does not explain which cortex area drives the balance, which requires applying effective connectivity techniques that can determine the directionality of information transfer in the brain. In chronic pain, the pgACC cortex area sends less information to the SSC cortex area in the theta and alpha wave bands (F = 9.13, p < .001 ), which explains why there is a reduction in pain inhibition as demonstrated by the negative correlation with the pain percept. For the emotional component of pain, the information flow is reversed: the dACC cortex area sends pain increasing information to the pgACC cortex area (F = 17.23, p < .001 ), while the pgACC cortex area sends reduced information to the dACC cortex area (F = 17.23, p < .001 ), which correlates with the pain percept (R2 = .24, p < .001 ). Taken together, these results imply that directional information flow between areas for pain input and pain suppression is dysfunctional, resulting in an imbalance of activity and an increase in pain. In this study, physiological a-g coupling is present more in the pain-free control subjects (F = 11.73, p < .001 ) than in the pain patients, but this is reversed in pathological q-g cross frequency coupling in chronic pain subjects (F = 7.68, p = .001 ). This cross frequency coupling is consistent with the TCD model of pain that proposes that a-g nesting is a physiological mechanism transmitting sensory information and q-g nesting is pathological. For the dACC cortex area, a significant increase in a-b cross frequency coupling was identified (F = 8.45, p = 005)(Figure 3A). It has been proposed that the Q and a frequencies act as a long range carrier wave on which the pain information, encoded locally by high frequency oscillatory activity, can be coupled.

[0071] If the balance model is correct, then the activity and connectivity signatures of the imbalance between pain input and pain suppression should reverse and normalize in response to treatment. [0072] In addition, in accordance with aspects herein, changes in neuronal activity were evaluated for chronic pain patients who were treated with spinal cord stimulation (n = 20) (and who were not treated with pharmacological pain treatment that could potentially interfere with resting state brain activity and connectivity independently of the pain). It was found that spinal cord stimulation suppresses the pain percept by 49.86% on a visual analogue scale. A support vector machine learning was only able to distinguish the SCS treated patients from pain-free control patients with an intermediate level of accuracy (e.g., the pain was detected with an accuracy of 59.2%). In contrast, when the neuronal activity of the chronic pain patients, before treatment, was compared to the pain- free control subjects, the model could differentiate pain from pain free state with a much higher degree of accuracy (e.g., 87.4% accuracy). A decrease in neuronal activity is identified post-treatment for the pgACC (Q and a band; F = 11.90, p = .001 ), dACC (a and b band; F = 3.60, p = .05), and SSC (Q and g band; left: F = 11.90, p = .001 ; right: F = 7.90, p = .005). Functional connectivity post-treatment for the alpha waves is increased in comparison to pre-treatment (t = 3.95, p < .05), while for Q no effect was obtained (t = .96, p = .74). Effective connectivity for both the theta and alpha wave bands is reversed, with neuronal activity in the pgACC cortex area signaling to the dACC and SSC cortex areas in addition to the thalamocortical q-g dysrhythmia that is decreased (F = 6.91 , p = .007). These findings suggest that pain inhibition normalizes and thereby rebalances pain input and suppression.

[0073] The remaining question is to describe the mechanism underlying the observed reduction in pain. When one compares these post-treatment findings in chronic pain patients with control subjects, we see that neuronal activity in the pgACC cortex area is changed for alpha waves (F = 6.31 , p = .008). The effective connectivity between the different cortex areas is normalized (a: pgACC SSC and pgACC dACC), significantly reduced (Q and a: SSC pgACC), as well as significantly reduced for q-g and a-g cross frequency coupling for SSC and a-b cross frequency coupling for dACC. This effect leads to a normalization in the balance and information flow that could explain a suppression in the pain percept.

[0074] The multimodal imaging analysis provides converging evidence that chronic pain is indeed an imbalance between the ascending and descending pathways and that after treatment this balance normalizes, supporting the concept that pain is truly a balance disorder between pain input and pain suppression in the brain, and not merely the result of more pain input via the spinal cord. The analysis also indicates that there are multiple ways of perceiving pain, either because of too much pain input, or because of deficient pain suppression, or a combination of the two. In accordance with embodiments herein, methods and systems help with classification of different types of pain rather than the traditional etiology-based categorization of pain and develop a mechanism-based approach for selecting therapy. The imbalance mechanism may be more universal in view of the pathophysiological analogy between pain, tinnitus, Parkinson’s disease and major depression, as well as reward-deficiency disorders, such as obesity, addiction, hyperactivity and personality disorders, as the described mechanism involves the same brain areas involved in reward- deficiency disorders, a balance problem of input and input suppression.

[0075] Figure 11 illustrates a conjunction analysis that demonstrates a non-limiting list what some disorders of interest have in common. For example, Figure 11 illustrates that pain, tinnitus, Parkinson’s disease, depression, addiction, OCD and ADFID have a common pathological core in the ventrolateral prefrontal cortex (VLPFC) and dACC and medial temporal cortex (amygdala- hippocampus) in beta2 and gamma activity. Embodiments herein utilize the common pathological core to implement the processes and systems described herein with the various disorders of interest. Figure 12 illustrates that the methods and systems herein are not only applicable to pain but also to the other pathologies (e.g., tinnitus, depression, Parkinson, addiction, OCD and ADFID). Thus, the balance always involves dACC and pgACC, but additional areas depend on the pathology. The balance is controlled by the reward-dysreward system which consists of the nucleus accumbens and habenula and VTA. The nucleus accumbens is linked to the pgACC (reward), and the habenula to the dACC (dysreward). Thus the dACC and pgACC could be equally replaced by the balance between the nucleus accumbens and habenula.

[0076] Figure 3C illustrates MRI images collected in connection with a meta-analysis via neuro-synthesis of various subject populations. For example, the meta-analysis is described in more detail at http://neurosynth.org, the complete subject matter from the web-pages of which is expressly incorporated herein by reference in its entirety. The images in column 336 were compiled as a composite representation of MRI images collected from 163 individuals experienced generally common types of chronic pain. Highlighted areas, such as at 339-341 , within the images denote pain input cortex areas that exhibit abnormal neuronal activity as compared to a control group of subjects that did not experience chronic pain. The images in column 337 were compiled as a composite representation of MRI images collected from 94 individuals experiencing generally common types of suffering. Highlighted areas, such as at 342-344, within the images in column 337 denote pain input cortex areas that exhibit abnormal neuronal activity as compared to a control group that did not experience suffering. The images in column 338 were compiled from a study of a group of individuals experiencing some type of pain related neurological disorder. The images in column 338 include highlighted areas, such as 345-347 that denote pain suppression cortex areas that exhibit abnormal neuronal activity as compared to a control group. The images in column 335 were taken from a group of 420 individuals which combines the information from columns 336-338 which show global pain to be exhibited as abnormal neuronal activity in the pain input and pain suppression cortex areas. [0077] Figure 3D illustrates a model of example cortex areas of interest in accordance with an embodiment herein. The pgACC cortex area is associated with pain suppression, as described herein, whereas the dACC cortex area is associated with pain input. The activity levels at the pgACC and dACC may be measured, separated into brainwave frequency bands and analyzed as described herein. In addition, the interconnectivity may be measured and analyzed for one or more brainwave frequency bands, between the pgACC and dACC. Optionally, neurological activity signals may be collected and analyzed at additional cortex areas. For example, the posterior cingulate cortex (PCC) may afford an additional cortex area of interest at which neurological activity signals are measured and compared to balanced activity relations and/or balanced connectivity relations. As a further example, the parahippocampal cortex (PFIC) may afford an additional cortex area of interest at which neurological activity signals are measured and compared to balanced activity relations and/or balanced connectivity relations.

[0078] The foregoing discussion generally relates to activity (e.g., as measured by current density) at the various cortex combinations. For example, embodiments herein build a control database that defines balanced activity relations, and embodiments herein compare new quantitative relations, at least in part as defined based on activity ratios, with balanced activity relations. In addition, the same or alternative embodiments may include within the control database balanced connectivity relations. The same or alternative embodiments may compare new quantitative relations, as in part as defined based on connectivity, with balanced connectivity relations.

[0079] Figure 3E illustrates a table of example neuronal activity signals that may be measured at various cortex areas of interest, as well as quantitative relations that may be derived therefrom in accordance with embodiments herein. In Figure 3E, table 350 illustrates neuronal activity signals measured in connection with different cortex areas of interest 351 -354, namely the pregenual anterior cingulate cortex, dorsal anterior cingulate cortex, left somatosensory cortex and right somatosensory cortex. For each cortex area of interest, the neuronal activity signals are separated into the various types of brain waves, including delta, theta, alpha-1 , alpha-2, beta-1 , beta-2, beta-3 and gamma waves.

[0080] A first column 355 presents mean current densities for the various types of brain waves as measured for a pool of healthy individuals (e.g., 82 individuals) at the different cortex areas of interest. For example, the healthy patient population exhibited a mean current density of 2.51 for Delta brainwave activity and a mean current density of 2.12 for theta waves in the pgACC area. A second column 356 presents mean current densities for various types of brain waves as measured for a pool of individuals experiencing fibromyalgia (e.g., 34 individuals) at the different cortex areas of interest. For example, the patient population experiencing fibromyalgia exhibited a mean current density of 1.98 for Delta brainwave activity and a mean current density of 2.02 for theta wave activity in the pgACC area. A third column 358 presents mean current densities for various types of brain waves as measured for a pool of individuals experiencing neuropathic pain (e.g., 34 individuals) at the different cortex areas of interest. For example, the patient population experiencing neuropathic pain exhibited a mean current density of 2.83 for Delta brainwave activity and a mean current density of 2.64 for theta wave activity in the pgACC area. The fourth and fifth columns (collectively 359) represent the statistical parameters F and P to indicate a statistical relation between the neuronal activity signals of the corresponding brainwave type as compared between the healthy patient population, fibromyalgia patient population and neuropathic pain patient population.

[0081] In the example of table 350, the neuronal activity signals for the various patient populations do not afford a strong statistical indicator that may otherwise differentiate fibromyalgia or neuropathic pain from healthy brainwave activity.

[0082] Figure 3E also illustrates a relational table that illustrates quantitative relations between neuronal activity signals for various combinations of cortex areas of interest. Table 360 illustrates quantitative relations between select cortex combinations 361 -363, namely a relation between the dACC and pgACC, pgACC/dACC, SSC/pgACC and SSC+dACC/pgACC. It should be noted that the quantitative relations are not simply ratios, but instead may include ratios combined (e.g., added or subtracted) with neuronal activity signals from a number cortex area of interest. For each cortex combination 361 -363, the neuronal activity signals are separated based on the various types of brain waves, including delta, theta, alpha-1 , alpha-2, beta-1 , beta-2, beta-3 and gamma waves.

[0083] A first column 365 presents a quantitative relation between the mean current densities from table 350 for a corresponding cortex combination for the various types of brain waves for the healthy population. For example, the healthy patient population exhibited a quantitative relation of 1.04 between the mean current density for beta-1 brainwave activity in the dACC and pgACC areas. The healthy patient population exhibited a quantitative relation of 1.02 between the mean current density for beta-3 brainwave activity in the dACC and pgACC areas. A second column 366 presents a quantitative relation between the mean current densities from table 350 for a corresponding cortex combination for the various types of brain waves for the fibromyalgia population. For example, the fibromyalgia patient population exhibited a quantitative relation of 1.23 between the mean current density for beta-1 brainwave activity in the dACC and pgACC areas. The fibromyalgia patient population exhibited a quantitative relation of 1.57 between the mean current density for beta-3 brainwave activity in the dACC and pgACC areas. The quantitative relation for beta-1 and beta-3 brainwave activities between the dACC and pgACC cortex areas exhibits a statistically significant difference from the same quantitative relations for the healthy patient population. The beta-1 brainwave activity exhibited a quantitative relation for fibromyalgia patients, as compared to the baseline healthy patient population, having an F-number of 3.17 and a P-value of .017, while the beta three brainwave activity exhibited a quantitative relation for fibromyalgia patients, as compared to the baseline healthy patient population, having an F-number of 3.64 and a P value of .011. The table 360 indicates that a strong statistical deviation, from balanced relations, can be identified when comparing certain quantitative relations of brainwave activity as measured in different cortex areas.

[0084] A third column 368 presents a quantitative relation between the mean current densities from table 350 for a corresponding cortex combination for the various types of brain waves for the neuropathic pain population. While not illustrated, a similar analysis may be applied when identifying quantitative relations between mean current densities for the neuropathic pain population (column 369) as compared to mean current densities for the healthy patient population (column 364). While not illustrated in table 360, a statistical comparison could indicate that the quantitative relations for Delta and theta brainwave activity between the SSC and PGA cc (e.g., cortex area combination 363) affords a strong indicator for the presence of neuropathic pain when the quantitative relations exhibit statistically significant variation from the same quantitative relations for healthy patients.

[0085] Next the discussion turns to the role of communication and connectivity between cortex areas in establishing and maintaining a balanced relation between cortex areas.

[0086] The quantitative connectivity (communication) between the cortex areas generally may be defined based on two measures of quantitative connectivity, namely functional connectivity and effective connectivity. Functional connectivity represents the amount of activity synchrony between brainwave frequency bands of interest along the lateral, medial and descending pain pathways. When the SSC, pgACC and dACC cortex areas (and associated lateral, medial and descending pain pathways) exhibit increases and decreases in activity in a synchronous manner, this is an indicator of good or high functional connectivity, one measure of communication. Alternatively, when the level of activity at the SSC, pgACC and dACC cortex areas increases and decreases in an unsynchronized manner, the unsynchronized activity levels is an indicator of poor or low functional connectivity and thus indicates a lack of communication. Effective connectivity, the other measure of quantitative connectivity (communication) is similar to functional connectivity, but further adds direction to the communications. Effective connectivity indicates how/whether activity in one cortex area determines/causes activity in a second cortex area. For example, effective connectivity indicates when a first cortex area is communicating with a second cortex area.

[0087] Embodiments herein may measure functional and effective connectivity in various manners. For example, the neuronal activity signals at the SSC, pgACC and dACC cortex areas may be measured and filtered to obtain NA components for one or more brain wave frequency bands of interest. The NA components are analyzed for one or more characteristics of interest over the collection window, such as mean or average current density, energy content, number of peaks, number of direction changes and the like. The characteristics of interest may be analyzed for a single collection window or for multiple collection windows, the results from which are then combined (e.g., a mean or other mathematical operator) to form a composite characteristic of interest for the NA components. In connection with embodiments that build a control database, the functional and effective connectivity are analyzed from NA signals for healthy control individuals and individuals experiencing a neurological disorder to build balanced connectivity relations for various cortex areas and various brain wave frequency bands in connection with different types of neurological disorder. In embodiments that manage an NS therapy (e.g., through an implanted or external NS system), the functional and effective connectivity information for an individual are compared to balanced connectivity relations to identify episodes of neurological disorder and to determine what therapy to apply for the neurological disorder.

[0088] In order to identify an unbalanced relation and to maintain a balanced relation between the pain cortex areas, embodiments herein analyze the activity relations (as discussed above) and in addition analyze connectivity relations. For example, the control database is built to provide both balanced activity relations and balanced connectivity relations for a healthy control group. In addition, when analyzing a new individual, the NA signals are analyzed in connection with quantitative activity relations (as compared to balanced activity relations) and in connection with quantitative connectivity relations (as compared to balanced connectivity relations).

[0089] Figure 4A illustrates functional connectivity models denoting levels of functional communication between different cortex areas, associated with the medial, lateral and descending pain pathways that are utilized in accordance with embodiments herein. In Figure 4A, model 402 corresponds to an individual experiencing pain. The model 402 illustrates left and right SSC cortex areas 403, 404, a pgACC cortex area 405 and the dACC cortex area 406. The lines interconnecting the cortex areas 403-406 correspond to lateral medial and descending pain pathways, and indicate that brain waves in the alpha frequency band are not communicating, or communicating at an undesirably low level, between the cortex areas 403-406.

[0090] The model at 412 corresponds to a healthy control individual experiencing no pain. The model 412 illustrates left and right SSC cortex areas 413, 414, a pgACC cortex area 415 and the dACC cortex area 416. The lines interconnecting the cortex areas 413-416 correspond to lateral medial and descending pain pathways, and indicate that brain waves in the theta frequency band are communicating at a normal desired level between the cortex areas 413-416. The communication may be quantified in various manners, such as based on the amount of correlation between activity at the cortex areas 413-416. The functional connectivity represents the amount of correlation of phase synchrony for brainwave frequency bands of interest at the cortex areas 413- 416. When the cortex areas 413-416 are active together and in synchrony, good communication is achieved. Alternatively, when the level of activity at each of the cortex areas 413-416 increases and decreases in an unsynchronized manner, the unsynchronized activity indicates a lack of communication.

[0091] The model at 422 corresponds to an individual experiencing pain, but after implant of the neural stimulation system. The model 422 illustrates left and right SSC cortex areas 423, 424, a pgACC cortex area 425 and the dACC cortex area 426. The lines interconnecting the cortex areas 423-426 correspond to lateral medial and descending pain pathways, and indicate that brain waves are communicating at a normal desired level between the cortex areas 423-426. Normal communication has been restored between the cortex areas 423-426 and thus a balanced relation is reestablished.

[0092] Figure 4B illustrates effective connectivity models denoting levels of effective communication between different cortex areas, associated with the medial, lateral and descending pain pathways that are utilized in accordance with embodiments herein. The model 432 corresponds to a healthy control individual experiencing no pain. The model 432 illustrates left and right SSC cortex areas 433, 434, pgACC cortex area 435 and dACC cortex area 436.

[0093] The lines interconnecting the cortex areas 433-436 correspond to effective communications links along the lateral, medial and descending pain pathways. The arrows on lines indicate the direction in which the communication is traveling. For example, effective communications link 443 and 444 correspond to communication from the left and right SSC cortex areas 433 and 434, respectively, to the pgACC cortex area 435. Effective communications links 441 and 442 correspond to communication from the pgACC cortex area 435 to the left and right SSC cortex areas 433 and 434, respectively. Effective communications link 445 corresponds to communication from the pgACC cortex area 435 to the dACC cortex areas 436. Effective communications link 445 corresponds to communication from the dACC cortex areas 436 to the pgACC cortex area 435. The effective communications links 441 -446, correspond to a healthy control individual or group and indicate that brain waves in one or more frequency bands (e.g., theta and alpha frequency bands) are communicating at a normal desired level.

[0094] The model 452 corresponds to an individual experiencing a neurological disorder. The model 452 illustrates the same cortex areas as the healthy control model 432, namely the left and right SSC cortex areas 433, 434, pgACC cortex area 435 and dACC cortex area 436. The neurological disorder is manifest in part through an undesired level (e.g., low level or no) of effective connectivity.

[0095] The model 452 reflects a lack of effective connectivity (also referred to“ineffective” connectivity) by omitting the communications links 443- 445 where too low or no communication is occurring between the corresponding cortex areas. Communications links 441 , 442 and 446 are still present, indicating that the pgACC cortex area is receiving communications from the left and right SSC cortex areas 433 and 434, respectively, and the dACC cortex area 446. However, the communications links 443-445 are ineffective as the left and right SSC cortex areas 433 and 434, and the dACC cortex area 446 are not receiving communications in the return directions at a desired level from the pgACC cortex area 435. Thus, an effective connectivity imbalance is present, which may wholly or partially be the basis for the quantitative relation between the cortex areas 433-436 to vary from the balanced relation.

[0096] It is recognized that the example of model 452 is merely one example of a manner in which effective connectivity may become unbalanced. For example, one or more of the communications links 441 , 442 and 446 may have too low of activity level and/or be absent entirely. As another example, one of the communications links 443-445 may be present, but one or more other of the communications links 443-445 may be too low or absent.

[0097] Next, the discussion turns to various methods and systems implemented in accordance with embodiments herein.

[0098] Figure 5 illustrates an overview of a process for building a control database of balanced relations from individuals in a control population in accordance with embodiments herein. The information entered into the database is collected from individuals who are healthy and have no pathological neurological condition and/or individuals who, may have neurological disorders (e.g., tinnitus, depression, Parkinson, addiction, OCD and ADHD), but are not experiencing a pathological neurological condition at the time that the information is collected. Optionally, while the following example is describing in connection with building a control database, neurological disorder database may be built based on neuronal activity signals and patient pain perception inputs from individuals who are experiencing a pathological neurological condition at the time of the information is collected.

[0099] One or more processors apply a machine learning process at 502 to the control database to identify balanced relations that afford good differentiators between pathological neurological conditions and normal, healthy conditions. As part of the machine learning process, one or more cortex areas of interest are identified (manually or automatically), one or more brainwave frequency bands of interest are identified (manually or automatically). In addition, the machine learning process may be provided with training data indicating which individuals (and corresponding neurological activity signal collections) correspond to a healthy control patients and which individuals (and corresponding neurological activity signal collections) correspond to a particular type of thalamocortical dysrhythmia or reward deficiency syndrome (e.g., tinnitus, fibromyalgia, etc.). The machine learning process learns to distinguish NA signal collections associated with control individuals and individuals experiencing a known neurological disorder. The machine learning process may utilize one or more of various types of learning, such as designated at 504. A non-limiting example of the type of machine learning includes unsupervised learning, which may be utilized when training data is not available. The unsupervised learning may utilize various algorithms. One example of a class of algorithms includes clustering algorithms 506 that categorize and cluster the data to produce categorical outputs.

[00100] Another non-limiting example of the type of machine learning includes supervised learning which may be utilized when training data is available. An example of training data would be a collection of neurological activity signals for a group of individuals for which known neurological disorders exist and were being experienced at the time the NA signals were collected, as well as a collection of neurological activity signals for a group of individuals who are healthy and are not experiencing any neurological disorder. The supervised learning process may utilize classification algorithms and/or regression algorithms that output categorical outputs and/or numeric outputs, respectively.

[00101] While the examples herein generally describe the cortex areas dACC, pgACC and SSC, it is recognized that neurological activity signals may be collected from additional cortex areas as well, such as the PHC and sgACC areas. Further, additional classifiers may be applied, such as a Riemannian classifier (e.g., to distinguish pain intensity, pain unpleasantness, etc.).

[00102] Furthermore, in a further embodiment the brain activity (power, current density, BOLD signal... ) can be correlated to predictive clinical or electrophysiological signs of pain or pain worsening, such as fatigue, depression, stress etc. which allows a better closed loop adjustment of the system. Similarly predictive signs of improvement such as pleasure, attention, distraction can be used to reduce stimulation by updated, i.e. adjusted closed loop stimulation. These relationships will be extracted via machine learning algorithms, either through a cloud based or non-cloud based database.

[00103] Figure 5 also illustrates theoretical plots of a manner in which machine learning may be applied to separate data within a population database. The machine learning algorithm may apply a nonlinear separation function as illustrated in the data plot 508 and/or a linear separation function as illustrated in the data plot 509. The data points within the plots 508 and 509 may correspond to different individuals in the patient population, where the patient’s in the region 507, 510 represent healthy individuals, while the patients in the regions 511 , 513 represent individuals experiencing a known or unknown neurological disorder.

[00104] Figure 6A illustrates a process for building a control database of balanced relations from individuals in a control population in accordance with embodiments herein. While embodiments herein are described in connection with pain, it is recognized that the process of Figure 6A is also useful with other pathologies, including but not limited to, tinnitus, depression, Parkinson, addiction, OCD and ADFID. At 612, the one or more processors measure neuronal activity signals for one or more individuals in the patient population. The individuals in the patient population may be limited to healthy individuals that are not experiencing pain and/or may include a combination of individuals who are not experiencing pain and individuals who are experiencing pain. The neuronal activity signals are measured at cortex areas of interest, such as at pain input areas of interest (rdACC, SSC) and pain suppression areas of interest (e.g., pgACC) or inferred via inverse solution algorithms, which localize brain activity based on computation of activity and phases, such as LORETA or BESA etc. based on extracranial recordings of electrophysiological signals. The neuronal activity signals are measured for one or more predetermined periods of time. For example, a first collection of neuronal activity signals may be collected for a predetermined number of milliseconds, seconds, etc., at sensing electrodes located proximate to the pgACC, while a second collection of neuronal activity signals are collected for the same predetermined number of milliseconds, seconds, etc., at a different set of sensing electrodes located proximate to the dACC.

[00105] The NA signal measurements for a given cortex area may be collected over a single collection window, or over multiple collection windows. For example, NA signals may be measured during multiple successive or intermittent collection windows from a common cortex area to obtain an ensemble of NA signals. The ensemble of NA signals may be combined in various manners, such as by determining a mean (or other mathematical combination) for the ensemble of NA signals to form a composite NA signal for a corresponding cortex area.

[00106] At 614, the one or more processors receive pain perception information that is input based on patients who answer pain perception related questions concerning the patient’s pain state and pain level. Healthy individuals in the control population would enter corresponding low pain levels or no pain levels, whereas individuals in the control population that are experiencing a neurological disorder would enter appropriate pain levels, as well as descriptors of the nature of the pain perceived (e.g., fibromyalgia, tinnitus, Parkinson’s disease, depression, OCD, addiction, ADHD, personality disorders, etc.).

[00107] At 616, the neuronal activity signals are filtered to separate NA components, similar to the process described in connection with the operations at 703 (Figure 7). The one or more processors filter the NA signals to separate NA components associated with brainwave frequency bands of interest. For example, the filtering operation at 616 may be performed by hardware filter circuits, and/or by the one or more processors that implement software filters to separate the neuronal activity signals into the NA components of interest. For example, a first NA component may include neuronal activity in the alpha wave frequency band, while a second NA component includes neuronal activity in the theta wave frequency band, and a third NA component includes neuronal activity in the gamma wave frequency band. The filtering operation may produce multiple NA components from neuronal activity signals collected at a single cortex area. For example, the neuronal activity signals, collected from the pgACC cortex area, may be filtered to separate and isolate first and second NA components for delta and alpha waves, respectively. As a further example, the neuronal activity signals collected from the SSC cortex area may be filtered to separate and isolate first and second NA components for the delta and alpha waves, respectively. Additionally or alternatively, the neuronal activity signals associated with each cortex area may be filtered to separate and isolate a common collection of brainwave activity frequency bands.

[00108] At 618, the one or more processors analyze the NA components for one or more component characteristics of interest. For example, the characteristics of interest may represent current density for the filtered neuronal activity signal associated with a brainwave frequency band of interest as measured over a predetermined time window, although it is recognized that other characteristics of interest may be analyzed in place of or in addition to current density.

[00109] At 620, the one or more processors select a candidate cortex combination, for which various in a components are to be analyzed. For example, the candidate cortex combination makes correspond to one or more of the following combinations: dACC and pgACC, or SSC and pgACC or SSC, dACC and pgACC.

[00110] At 622, the one or more processors build one or more candidate balanced relations for NA components associated with the candidate cortex combination. For example, when the candidate cortex combination is the dACC and pgACC, the processors may build candidate balanced relations for all or a portion of the brainwave frequency bands (e.g., delta, theta, alpha one, alpha-2, beta-1 , beta-2, beta-3 and gamma) for NA signals measured at the dACC and pgACC. The balanced relation includes a balanced activity relation and a balanced connectivity relation. By way of example, a balanced activity relation may indicate a ratio between current densities associated with a particular brainwave frequency band and two or more cortex areas of interest (e.g., a ratio threshold of less than 0.95 for beta-2 brain waves between the pgACC and dACC cortex areas). The balanced connectivity relation indicates a balance in functional and effective connection.

[00111] At 624, the one or more processors determine whether more cortex combinations are to be analyzed. When more cortex combinations are to be analyzed, flow returns to 620 and a next candidate cortex combination is selected. Once all of the candidate cortex combinations of interest have been selected and candidate balanced relations are built at 622, flow moves from 624 to Figure 6B. The term“candidate” is utilized in connection with describing the cortex combination as the process has not yet confirmed that the cortex combination and balanced relation satisfy certain statistical characteristics sufficient to utilize the cortex combination and relation as part of a control database. For example, certain relations between components for certain cortex combinations may exhibit substantial variability in current densities and/or connectivity between different individuals in the control group, even though the individuals are not experiencing any pain. When an excessive amount of variability in current density and/or connectivity is exhibited between individuals in the control group, the particular relation and/or cortex combination may be deemed an unsatisfactory candidate and thus removed.

[00112] Additionally or alternatively, the basis for verifying or removing an NA component and cortex combination may be based on analysis of a combination of i) individuals in a control group that are healthy and not experiencing pain and ii) individuals in a control group that have a neurological disorder and are experiencing a known type of pain.

[00113] Figure 6B illustrates a process for verifying or removing candidate balanced relations for candidate cortex combinations in accordance with embodiments herein. While embodiments herein are described in connection with pain, it is recognized that the process of Figure 6B is also useful with other pathologies, including but not limited to, tinnitus, depression, Parkinson, addiction, OCD and ADHD. At 630, the one or more processors select a candidate balanced relation for analysis. At 632, the one or more processors apply the candidate balanced relation to all or a portion of the corresponding type of NA signals in the control database for the corresponding cortex combination and for a select type of pain (e.g., fibromyalgia, Parkinson’s disease, depression, etc.).

[00114] At 634, the one or more processors determine whether the candidate balanced relation, when applied to control database, yielded a statistically significant differentiation between normal and pathological neurological conditions. When the candidate balanced relation yields a statistically significant differentiation, flow moves to 636 where the candidate is relabeled and retained as a resultant balanced relation. Alternatively, when the candidate balanced relation does not yield a statistically significant differentiation, flow moves to 638 where the candidate is disregarded and the balanced relation is not used for the corresponding type of pain. It is recognized that different balanced relations will provide better indicators of different types of pain. For example, certain NA components for certain cortex combinations may yield good indicators for fibromyalgia, whereas the same in a components and cortex combination is not a good indicator of Parkinson’s disease.

[00115] Is recognized that the operations of Figures 6A and 6B may be combined in various combinations. For example, the verification of a candidate balanced relation may be determined at the same time the balanced relation is built. Further, it is recognized that the“candidate balanced relations” may merely refer to defining a ratio or other mathematical combination of a characteristic of interest from a select brainwave frequency band as measured at two or more cortex areas. For example, the “candidate balanced relation” may merely represent: [beta-1 current density (at dACC)]/ [beta-1 current density (at pgACC)]. Optionally, the candidate balanced relation may define actual thresholds for ratios of current densities.

[00116] The methods and systems herein build and utilize databases applicable to different types of neurological disorders. Each type of neurological disorder may have a corresponding homeostatic balanced relation that is defined by different types of brain waves and is to be measured at different cortex areas. For example, the homeostatic balanced activity and connectivity relation that distinguishes fibromyalgia from a control group, will differ from the homeostatic balanced activity and connectivity relation that distinguishes neuropathic pain from a control group and it is clear that the homeostatic balance between pain and tinnitus might be differentiated by SSC and auditory cortex activity, between PD and pain by differences in SSC and motor cortex activity.

[00117] Figure 7 illustrates a method of treating a neurological disorder in a patient in accordance with embodiments herein. The operations of Figure 7 may be implemented entirely or in part by an implantable pulse generator, a local external device and/or a remote server. The operations of Figure 7 may be performed in various orders, and less a particular operation is dependent upon a result of another operation. While embodiments herein are described in connection with pain, it is recognized that the process of Figure 7 is also useful with other pathologies, including but not limited to, tinnitus, depression, Parkinson, addiction, OCD and ADFID.

[00118] At 702, one or more processors measure first and second neuronal activity (NA) signals of a brain at first and second cortex areas of interest, respectively, utilizing one or more sensors. The neuronal activity signals are measured for one or more collection windows where each window has a predetermined duration. For example, a first collection of neuronal activity signals may be collected for a predetermined number of milliseconds, seconds, etc., at sensing electrodes located proximate to the pgACC, while a second collection of neuronal activity signals are collected for the same predetermined number of milliseconds, seconds, etc., at a different set of sensing electrodes located proximate to the dACC. The neuronal activity signals may be manifest in different manners, such as measurements of current densities. The measurements are taken utilizing one or more sensors located proximate to each of the corresponding cortex areas of interest. For example, the cortex areas of interest include pain input cortex areas of interest (rdACC, SSC) and pain suppression cortex areas of interest (e.g., pgACC). As a further example, the first and second cortex areas of interest are selected from the group comprising: dorsal anterior cingulate cortex (dACC), right dorsal anterior cingulate cortex (rdACC), left and/or write somatosensory cortex (SSC) and pregenual anterior cingulate cortex (pgACC). Optionally, the first cortex area of interest may represent at least one of the rdACC or SSC, while the second cortex area of interest represents the pgACC. While embodiments herein are described in connection with pain, it is recognized that the measurements may be taken in connection with other pathologies, including but not limited to, tinnitus, depression, Parkinson, addiction, OCD and ADHD.

[00119] The NA signal measurements for a given cortex area may be collected over a single collection window, or over multiple collection windows. For example, NA signals may be measured during multiple successive or intermittent collection windows from a common cortex area to obtain an ensemble of NA signals. The ensemble of NA signals may be combined in various manners, such as by determining a mean (or other mathematical combination) for the ensemble of NA signals to form a composite NA signal for a corresponding cortex area.

[00120] At 703, the one or more processors filter the NA signals to separate NA components associated with brainwave frequency bands of interest. For example, the processors may direct hardware or software filtering of the neuronal activity signals to obtain the NA components of interest, respectively. For example, a first NA component may include neuronal activity in the alpha wave frequency band, while a second NA component includes neuronal activity in the theta wave frequency band, and a third NA component includes neuronal activity in the gamma wave frequency band. The filtering operation may produce multiple NA components from neuronal activity signals collected at a single cortex area. For example, the neuronal activity signals collected from the pgACC cortex area may be filtered to separate and isolate first and second NA components for Delta and alpha waves, respectively. As a further example, the neuronal activity signals collected from the SSC cortex area may be filtered to separate and isolate first and second NA components for the Delta and alpha waves, respectively.

[00121] Additionally or alternatively, the neuronal activity signals associated with each cortex area may be filtered to separate and isolate a common collection of brainwave activity frequency bands. For example, the neuronal activity signals collected from the gpACC, SSC and dACC cortex areas may be separately filtered to isolate the NA components associated with each of or a subset of the following brainwave frequency bands: Delta waves, theta waves, alpha-1 waves, alpha-2 waves, beta-1 waves, beta-2 waves, beta-3 waves and gamma waves.

[00122] At 704, the one or more processors determine a quantitative relation between the first and second neuronal activity signals. The quantitative relation may include one or both of an activity quantitative relation and a connectivity quantitative relation. For example, in connection with determining activity quantitative relations, the processors determine a ratio between a common NA component of NA signals collected from different cortex areas of interest. For example, the processors may determine a ratio of current densities for beta-1 waves collected at the dACC and pgACC cortex areas. Additionally or alternatively, the processors may determine a ratio of current densities for the theta waves collected at the DA cc and PG ACC cortex areas. Additionally or alternatively, the processors may determine separate ratios for current densities associated with each of the brainwave frequency bands of interest, including all or a subset of delta, theta, alpha-1 , alpha-2, beta-1 , beta-2, beta-3 and gamma waves.

[00123] The processors further determine activity quantitative relations between common NA components collected from other combinations of cortex areas, such as the pgACC/dACC cortex areas and SSC/pgACC cortex. In the present examples, the quantitative relations include a ratio between the NA components for two different cortex areas. Optionally, the quantitative relations may be defined by a more complex mathematical function, such as a combination of addition/subtraction and division/multiplication. As one nonlimiting example, the quantitative relation may be between common NA components that are combined as follows: SSC + dACC/pgACC cortex areas.

[00124] Additionally or alternatively, the processors determine a connectivity quantitative relation indicative of a level or degree of connectivity between two or more common or different NA components collected at a common cortex area or at a combination of cortex areas. For example, the connectivity quantitative relation may represent a level or degree of cross frequency coupling between two different types of brain waves, when collected at a common cortex area. For example, the connectivity quantitative relation may indicate a level of cross frequency coupling between alpha and gamma brain waves, theta and gamma brain waves, theta and alpha brain waves and the like.

[00125] At 706, the one or more processors obtain a balanced relation in connection with and a common NA component and cortex combination of interest. The balanced relation is indicative of a select level of balance between neuronal activity for at least first and second cortex areas of interest. By way of example, the balanced relation may correspond to the brainwave frequency ranges and cortex combinations identified in table 360 in Figure 3E. The balanced relation may develop based on baseline neuronal activity signals of the brain of a current patient as measured at the cortex areas of interest while the current patient exhibits a non-pathologic neurological condition. The normal healthy neurological condition is demonstrated by a select balanced relation between the first and second baseline neuronal activity signals.

[00126] Additionally or alternatively, the balanced relation may be derived from a larger control subject population, with the balanced relations defined based on means (or other mathematical combinations) of current density measurements of neuronal activity exhibited by the control subject population, where the baseline neuronal activity signals are measured while the individuals in the control subject population exhibit a non-pathological neurological condition. The balanced relations may be stored locally in an implantable neural stimulation system, locally and an external device and/or on a remote server. The balanced relations may be periodically updated at the remote server, through download over the Internet to a local external device, and/or through a wireless communication between a local external device and an implanted neurostimulation system.

[00127] At 708, the one or more processors compare the quantitative relation to the balanced relation to determine whether the quantitative relation is indicative of a presence of a pathological neurological condition. The comparison may be implemented in various manners. For example, the balanced relation may define a threshold, and when the quantitative relation has a value that exceeds the threshold, the processors interpret the quantitative relation to be indicative of a presence of a pathological neurological condition. When the quantitative relation falls below the threshold, the processors may interpret the quantitative relation to be indicative of a normal/healthy neurological condition. Additionally or alternatively, the balanced relation may define a range with upper and lower limits. When the quantitative relation falls within the range, the processors may interpret the quantitative relation to be indicative of a pathological neurological condition. When the balanced relation falls below the lower limit of the range, the processors may interpret the quantitative relation to be indicative of a normal healthy neurological condition. Optionally, when the quantitative relation exceeds an upper limit of the range, the processors may interpret the quantitative relation to be indeterminate, or vice versa.

[00128] Additionally or alternatively, the comparison between the present and balanced relations may include comparing multiple quantitative relations to multiple corresponding balanced relations. For example, with respect to Figure 4B, separate quantitative relations may be determined in connection with multiple different frequency bands and in connection with multiple different cortex combinations. The comparison at 708 may compare balanced relations for each of the respective different frequency bands and respective different cortex combinations. A mathematical or statistical combination of the comparisons may be developed (e.g., a weighted sum of the comparisons) to determine whether the collection of quantitative relations is indicative of a pathological neurological condition or a normal/healthy neurological condition.

[00129] At 710, the one or more processors determine stimulation parameters to be utilized in connection with a stimulation therapy, where the stimulation parameter based on the comparison. For example, the processors may determine to apply or suspend a stimulation therapy based on whether the quantitative relation exceeds a threshold defined by the balanced relation. Additionally or alternatively, the processors may select between different combinations of stimulation parameter levels based on a comparison. For example, when the quantitative relation slightly exceeds a threshold, a first set of stimulation parameters may be utilized having lower energy, shorter pulse widths, and the like. When the quantitative relation substantially exceeds the threshold, a second set of stimulation parameters may be utilized having higher energy, higher pulse width of the like.

[00130] As a further option, different sets of stimulation parameters may be chosen when different quantitative relations exceed corresponding balanced relations. For example, when the quantitative relation associated with Delta brain waves for the dACC/pgACC cortex combination exceeds a corresponding balanced relation, the processors may select a first set of stimulation parameters configured to deliver a desired therapy in connection with the frequency band associated with Delta waves. When the quantitative relation associated with beta waves for the dACC/pgACC cortex combination exceeds a corresponding balanced relation, the processors may select a second set of stimulation parameters configured to deliver a desired therapy in connection with the frequency band associated with beta waves.

[00131] Additionally or alternatively, different sets of stimulation parameters, as well as different combinations of electrodes and/or different leads, may be chosen when different quantitative relations exceed corresponding balanced relations. For example, when the quantitative relation associated with Delta brain waves for the SSC/pgACC cortex combination exceeds a corresponding balanced relation, the processors may select a first combination of stimulation parameters, electrodes and/or lead to deliver a desired therapy to one or both of the SSC and pgACC cortex areas. When the quantitative relation associated with delta waves for the pgACC/dACC cortex combination exceeds a corresponding balanced relation, the processors may select a second set of stimulation parameters, electrodes or lead configured to deliver a desired therapy in one or both of the pgACC and dACC cortex areas.

[00132] At 712, the one or more processors apply electrical stimulation to one or more sites in the brain in response to the quantitative relation being indicative of the presence of the pathological neurological condition. For example, the one or more sensors may include first and second electrodes implanted in intradural or extradural regions. Optionally, the one or more sensors may include first and second electrodes implanted in the intradural region proximate to the first and second cortex areas of interest, respectively. Optionally, the one or more processors automatically program stimulation parameters for an implantable pulse generator to deliver the electrical stimulation to the one or more sites from one or more implanted electrodes. Additionally or alternatively, an external programmer device or remote server to be utilized to assist the technician, physician or other clinician in guiding programming for a stimulation system. For example, the external programmer device or remote server may analyze the neurological activity signals measured, as well as the quantitative relation there between that is exhibited by a particular patient. The external programmer device and/or remote server may compare the quantitative relation to one or more balanced relations, and based thereon derive suggested adjustments to one or more stimulation parameters. For example, the stimulation parameter may correspond to a type of therapy (e.g., tonic, burst, etc.), pulse amplitude, pulse width, pulse duration, a number of pulses per burst, and enter burst interval, a pulse to pulse interval and the like. The external programmer device and/or remote server may select one or more of the stimulation parameters based on an extent to which the quantitative relation of the neuronal activity signals differs from the balanced relation. Additionally or alternatively, a level of adjustment in a particular stimulation parameter (e.g., an adjustment in pulse amplitude or pulse width) may be based on the extent to which the quantitative relation of the neuronal activity signals differ from the balanced relation.

[00133] In accordance with embodiments herein, neuronal activity (NA) signals may be recorded over time for various individuals and transfer to a common patient population database. The NA signals may be analyzed utilizing learning algorithms to develop a database for balanced relations indicative of normal/non-pathologic neurologic conditions and/or pathologic relationships indicative of pathologic neurological conditions.

[00134] The foregoing embodiments have been described in connection with disorders associated with thalamocortical dysrhythmias. Additionally or alternatively, embodiments may be implemented in connection disorders associated with reward deficiency syndrome.

[00135] Figure 8 illustrates models of the brain with cortex areas of interest highlighted that may be monitored for neuronal activity signals. The neuronal activity signals may be measured and analyzed to determine quantitative relations there between and for comparison with one or more balanced relations. Based on the comparison, electrical stimulation may be identified and delivered when a presence of a pathological neurological condition is indicated.

[00136] Figure 9 illustrates a model related to reward deficiency syndromes. Reward deficiency syndromes and fallible cortical dysrhythmia exhibit certain fundamental similarities in that both represent uncertainty disorders. Sensorimotor input deprivation (PC D) relates to P-channel activity. Autonomic or social input deprivation (RDS), in many instances, is related to CACNA1 C and CACNA1 D SNPs that encode for the L-type Ca channel receptor which function is decreased. Calcium T channel increases because of the loss in the L channel which modulates D2 receptor functioning.

[00137] Non-limiting examples of reward deficiency syndromes include addictive behaviors, impulsive behaviors, obsessive compulsive behaviors, and personality disorders. Examples of addictive behaviors include substance related behaviors, such as alcohol, cannabis, opioids, sedatives/hypnotics, stimulants, tobacco, glucose and food. Other examples of addictive behaviors that are non- substance related include thrill seeking, sexual sadism or masochism, hypersexual, gambling and Internet gaming. Examples of impulsive behaviors include spectrum disorders, such as attention deficit hyperactivity, Tourette’s and tie syndrome, and autism. Examples of impulsive behaviors also include disruptive impulse, such as antisocial, conduct, intermittent explosive, oppositional defiant, exhibitionistic. Examples of excessive compulsive behaviors include body dysmorphic, hoarding, trichotillomania (hair pulling), excoriation (skin picking), and non-suicidal self-injury. Examples of personality disorders include paranoid, schizoid, borderline, schizotypal, histrionic, narcissistic, avoidant and dependent. Embodiments herein may build population databases associated with one or more of the foregoing reward deficiency syndromes that are then utilized to develop therapies for treatment in connection there with.

Electrical Stimulation Devices

[00138] Figures 1A-1 B illustrate example neurological stimulation (NS) systems 10 for electrically stimulating a predetermined site area to treat one or more neurological disorders or conditions. NS system 10 may perform one, multiple, or all of the operations discussed herein related to cross-frequency coupling. In general terms, stimulation system 10 includes an implantable pulse generating source or electrical IMD 12 (generally referred to as an“implantable medical device” or“IMD”) and one or more implantable electrodes or electrical stimulation leads 14 for applying stimulation pulses to a predetermined site. In operation, both of these primary components are implanted in the person's body, as discussed below. In certain embodiments, IMD 12 is coupled directly to a connecting portion 16 of stimulation lead 14. In some embodiments, IMD 12 is incorporated into the stimulation lead 14 and IMD 12 instead is embedded within stimulation lead 14. Whether IMD 12 is coupled directly to or embedded within the stimulation lead 14, IMD 12 controls the stimulation pulses transmitted to one or more stimulation electrodes 18 located on a stimulating portion 20 of stimulation lead 14, positioned in communication with a predetermined site, according to suitable therapy parameters (e.g., duration, amplitude or intensity, frequency, pulse width, firing delay, etc.).

[00139] A doctor, the patient, or another user of IMD 12 may directly or in directly input therapy parameters to specify or modify the nature of the stimulation provided.

[00140] In Figure 1 B, the IMD 12 includes an implantable wireless receiver. In another embodiment, the IMD can be optimized for high frequency operation as described in U.S. Provisional Application Ser. No. 60/685,036, filed May 26, 2005, entitled "SYSTEMS AND METHODS FOR USE IN PULSE GENERATION," which is incorporated herein by reference. The wireless receiver is capable of receiving wireless signals from a wireless transmitter 22 located external to the person's body. The wireless signals are represented in Figure 1 B by wireless link symbol 24. A doctor, the patient, or another user of IMD 12 may use a controller 26 located external to the person's body to provide control signals for operation of IMD 12. Controller 26 provides the control signals to wireless transmitter 22, wireless transmitter 22 transmits the control signals and power to the wireless receiver of IMD 12, and IMD 12 uses the control signals to vary the signal parameters of electrical signals transmitted through electrical stimulation lead 14 to the stimulation site. Thus, the external controller 26 can be for example, a handheld programmer, to provide a means for programming the IMD.

[00141] The IMD 12 applies tonic, burst, nested, noise, and other suitable electrical stimulation to tissue of the nervous system of a patient. Specifically, the IMD includes a microprocessor and a pulse generation module. The pulse generation module generates the electrical pulses according to a defined pulse width and pulse amplitude and applies the electrical pulses to defined electrodes. The microprocessor controls the operations of the pulse generation module according to software instructions stored in the device.

[00142] For example, for burst stimulation, the IMD 12 can be adapted by programming the microprocessor to deliver a number of spikes (relatively short pulse width pulses) that are separated by an appropriate interspike interval. Thereafter, the programming of the microprocessor causes the pulse generation module to cease pulse generation operations for an interburst interval. The programming of the microprocessor also causes a repetition of the spike generation and cessation of operations for a predetermined number of times. After the predetermined number of repetitions has been completed within a stimulation waveform, the microprocessor can cause burst stimulation to cease for an amount of time (and resume thereafter). Also, in some embodiments, the microprocessor could be programmed to cause the pulse generation module to deliver a hyperpolarizing pulse before the first spike of each group of multiple spikes.

[00143] The microprocessor can be programmed to allow the various characteristics of the electrical stimulation to be set by a physician to allow the stimulation to be optimized for a particular pathology of a patient. For example, the spike amplitude, the interspike interval, the interburst interval, the number of bursts to be repeated in succession, the electrode combinations, the firing delay between stimulation waveforms delivered to different electrode combinations, the amplitude of the hyperpolarizing pulse, and other such characteristics could be controlled using respective parameters accessed by the microprocessor during burst stimulus operations. These parameters could be set to desired values by an external programming device via wireless communication with the implantable neuromodulation device.

[00144] In representative embodiments, IMD 12 applies electrical stimulation according to a suitable noise signal (white noise, pink noise, brown noise, etc.). Details regarding implementation of a suitable noise signal can be found in U.S. Patent No. 8,682,441 , which is incorporated herein by reference

[00145] In another embodiment, the IMD 12 can be implemented to apply burst stimulation using a digital signal processor and one or several digital-to- analog converters. The burst stimulus waveform could be defined in memory and applied to the digital-to-analog converter(s) for application through electrodes of the medical lead. The digital signal processor could scale the various portions of the waveform in amplitude and within the time domain (e.g., for the various intervals) according to the various burst parameters.

[00146] Figure 1 C depicts an NS system 100 that delivers stimulation therapies in accordance with embodiments herein. For example, the NS system 100 may be adapted to stimulate spinal cord tissue, peripheral nervous tissue, deep brain tissue, or any other suitable nervous/brain tissue of interest within a patient’s body.

[00147] The NS system 100 may be programmed or controlled to deliver various types of stimulation therapy, such as tonic stimulation, high frequency stimulation, burst stimulation, noise stimulation, and nested stimulation therapies and the like. High frequency neurostimulation includes a continuous series of monophasic or biphasic pulses that are delivered at a predetermined frequency. Burst neurostimulation includes short sequences of monophasic or biphasic pulses, where each sequence is separated by a quiescent period. In general, nested therapies include a continuous, repeating or intermittent pulse sequence delivered at a frequency and amplitude with multiple frequency components.

[00148] The NS system 100 may deliver stimulation therapy based on preprogrammed therapy parameters. The therapy parameters may include, among other things, pulse amplitude, pulse polarity, pulse width, pulse frequency, interpulse interval, inter burst interval, electrode combinations, firing delay and the like. Optionally, the NS system 100 may represent a closed loop neurostimulation device that is configured to provide real-time sensing functions from a lead. The configuration of the lead sensing electrodes may be varied depending on the neuronal anatomy of the sensing site(s) of interest. The size and shape of electrodes is varied based on the implant location. The electronic components within the NS system 100 are designed with both stimulation and sensing capabilities.

[00149] The NS system 100 includes an implantable medical device (IMD) 150 that is adapted to generate electrical pulses for application to tissue of a patient. The IMD 150 typically comprises a metallic housing or can 158 that encloses a controller 151 , pulse generating circuitry 152, a battery 154, a far-field and/or near field communication circuitry 155, battery charging circuitry 156, switching circuitry 157, memory 158 and the like. The switching circuitry 157 connects select combinations of the electrodes 121 a-d to the pulse generating circuitry 152 thereby directing the stimulation waveform to a desired electrode combination. As explained herein, the switching circuitry 157 successively connects the pulse generating circuitry 152 to successive electrode combinations 123 and 125. The components 151 -158 are also within the IMD 12 (FIGS. 1A and 1 B). IMD 150 may include sensing circuitry 153 (e.g., analog-to-digital converters) to sense neuronal signals of interest (e.g., local field potentials, neuronal spike activity, etc.).

[00150] The controller 151 typically includes one or more processors, such as a microcontroller, for controlling the various other components of the device. Software code is typically stored in memory of the IMD 150 for execution by the microcontroller or processor to control the various components of the device.

[00151] The IMD 150 may comprise a separate or an attached extension component 170. If the extension component 170 is a separate component, the extension component 170 may connect with the“header” portion of the IMD 150 as is known in the art. If the extension component 170 is integrated with the IMD 150, internal electrical connections may be made through respective conductive components. Within the IMD 150, electrical pulses are generated by the pulse generating circuitry 152 and are provided to the switching circuitry 157. The switching circuitry 157 connects to outputs of the IMD 150. Electrical connectors (e.g.,“Bal-Seal” connectors) within the connector portion 171 of the extension component 170 or within the IMD header may be employed to conduct various stimulation pulses. The terminals of one or more leads 110 are inserted within connector portion 171 or within the IMD header for electrical connection with respective connectors. Thereby, the pulses originating from the IMD 150 are provided to the lead 110. The pulses are then conducted through the conductors of the lead 110 and applied to tissue of a patient via stimulation electrodes 121 a- d that are coupled to blocking capacitors. Any suitable known or later developed design may be employed for connector portion 171. [00152] The stimulation electrodes 121 a-d may be positioned along a horizontal axis 102 of the lead 110, and are angularly positioned about the horizontal axis 102 so the stimulation electrodes 121a-d do not overlap. The stimulation electrodes 121 a-d may be in the shape of a ring such that each stimulation electrode 121a-d continuously covers the circumference of the exterior surface of the lead 110. Adjacent stimulation electrodes 121 a-d are separated from one another by non-conducting rings 112, which electrically isolate each stimulation electrode 121 a-d from an adjacent stimulation electrode 121a-d. The non-conducting rings 112 may include one or more insulative materials and/or biocompatible materials to allow the lead 110 to be implantable within the patient. Non-limiting examples of such materials include polyimide, polyetheretherketone (PEEK), polyethylene terephthalate (PET) film (also known as polyester or Mylar), polytetrafluoroethylene (PTFE) (e.g., Teflon), or parylene coating, polyether bloc amides, polyurethane. The stimulation electrodes 121 a-d may be configured to emit the pulses in an outward radial direction proximate to or within a stimulation target. Additionally or alternatively, the stimulation electrodes 121 a-d may be in the shape of a split or non-continuous ring such that the pulse may be directed in an outward radial direction adjacent to the stimulation electrodes 121a-d. The stimulation electrodes 121 a-d deliver tonic, high frequency and/or burst nested stimulation waveforms as described herein. Optionally, the electrodes 121 a-d may also sense neural oscillations and/or sensory action potential (neural oscillation signals) for a data collection window.

[00153] The lead 110 may comprise a lead body 172 of insulative material about a plurality of conductors within the material that extend from a proximal end of lead 110, proximate to the IMD 150, to its distal end. The conductors electrically couple a plurality of the stimulation electrodes 121 to a plurality of terminals (not shown) of the lead 110. The terminals are adapted to receive electrical pulses and the stimulation electrodes 121 a-d are adapted to apply the pulses to the stimulation target of the patient. Also, sensing of physiological signals may occur through the stimulation electrodes 121 a-d, the conductors, and the terminals. It should be noted that although the lead 110 is depicted with four stimulation electrodes 121a-d, the lead 110 may include any suitable number of stimulation electrodes 121 a-d (e.g., less than four, more than four) as well as terminals, and internal conductors. Additionally or alternatively, various sensors may be located near the distal end of the lead 110 and electrically coupled to terminals through conductors within the lead body 172.

[00154] Although not required for any embodiments, the lead body 172 of the lead 110 may be fabricated to flex and elongate upon implantation or advancing within the tissue (e.g., nervous tissue) of the patient towards the stimulation target and movements of the patient during or after implantation. By fabricating the lead body 172, according to some embodiments, the lead body 172 or a portion thereof is capable of elastic elongation under relatively low stretching forces. Also, after removal of the stretching force, the lead body 172 may be capable of resuming its original length and profile.

[00155] By way of example, the IMD 12, 150 may include a processor and associated charge control circuitry as described in U.S. Patent No. 7,571 ,007, entitled “SYSTEMS AND METHODS FOR USE IN PULSE GENERATION,” which is expressly incorporated herein by reference. Circuitry for recharging a rechargeable battery (e.g., battery charging circuitry 156) of an IMD using inductive coupling and external charging circuits are described in U.S. Patent No. 7,212,110, entitled“IMPLANTABLE DEVICE AND SYSTEM FOR WIRELESS COMMUNICATION,” which is expressly incorporated herein by reference. An example and discussion of “constant current” pulse generating circuitry (e.g., pulse generating circuitry 152) is provided in U.S. Patent Publication No. 2006/0170486 entitled “PULSE GENERATOR HAVING AN EFFICIENT FRACTIONAL VOLTAGE CONVERTER AND METHOD OF USE,” which is expressly incorporated herein by reference. One or multiple sets of such circuitry may be provided within the IMD 12, 150. Different burst and/or high frequency pulses on different stimulation electrodes may be generated using a single set of the pulse generating circuitry using consecutively generated pulses according to a“multi-stimset program” as is known in the art. Complex pulse parameters may be employed such as those described in U.S. Patent No. 7,228,179, entitled “Method and apparatus for providing complex tissue stimulation patterns,” and International Patent Publication Number WO 2001/093953 A1 , entitled“NEUROMODULATION THERAPY SYSTEM,” which are expressly incorporated herein by reference. Alternatively, multiple sets of such circuitry may be employed to provide pulse patterns (e.g., tonic stimulation waveform, burst stimulation waveform) that include generated and delivered stimulation pulses through various stimulation electrodes of one or more leads as is also known in the art. Various sets of parameters may define the pulse characteristics and pulse timing for the pulses applied to the various stimulation electrodes. Although constant current pulse generating circuitry is contemplated for some embodiments, any other suitable type of pulse generating circuitry may be employed such as constant voltage pulse generating circuitry.

[00156] The controller 151 delivers stimulation pulses to at least one electrode combination located proximate to nervous tissue of interest. The controller 151 may deliver the stimulation pulses based on preprogrammed therapy parameters. The preprogrammed therapy parameters may be set based on information collected from numerous past patients and/or test performed upon an individual patient during initial implant and/or during periodic checkups.

[00157] Optionally, the controller 151 senses intrinsic neural oscillations from at least one electrode on the lead. Optionally, the controller 151 analyzes the intrinsic neural oscillations signals to obtain brain activity data. The controller 151 determines whether the activity data satisfies criteria of interest. The controller 151 adjusts at least one of the therapy parameters to change the nested stimulation waveform when the activity data does not satisfy the criteria of interest. The controller 151 iteratively repeats the delivering operations for a group of TPS. The IMD selects a candidate TPS from the group of TPS based on a criteria of interest. The therapy parameters define at least one of a burst stimulation waveform or a high frequency stimulation waveform. The controller 151 may repeat the delivering, sensing and adjusting operations to optimize the nested stimulation waveform. The analyzing operation may include analyzing a feature of interest from a morphology of the neural oscillation signal over time, counting a number of occurrences of the feature of interest that occur within the signal over a predetermined duration, and generating the activity data based on the number of occurrences of the feature of interest.

[00158] Memory 158 stores software to control operation of the controller 151 for nested stimulation therapy as explained herein. The memory 158 also stores neural oscillation signals, therapy parameters, neural oscillation activity level data, sensation scales and the like. For example, the memory 158 may save neural oscillation activity level data for various different therapies as applied over a short or extended period of time. A collection of neural oscillation activity level data is accumulated for different therapies and may be compared to identify high, low and acceptable amounts of sensory activity.

[00159] A controller device 160 may be implemented to charge/recharge the battery 154 of the IMD 150 (although a separate recharging device could alternatively be employed) and to program the IMD 150 on the pulse specifications while implanted within the patient. Although, in alternative embodiments separate programmer devices may be employed for charging and/or programming the NS system 100. The controller device 160 may be a processor-based system that possesses wireless communication capabilities. Software may be stored within a non-transitory memory of the controller device 160, which may be executed by the processor to control the various operations of the controller device 160. A“wand” 165 may be electrically connected to the controller device 160 through suitable electrical connectors (not shown). The electrical connectors may be electrically connected to a telemetry component 166 (e.g., inductor coil, RF transceiver) at the distal end of wand 165 through respective wires (not shown) allowing bi-directional communication with the IMD 150. Optionally, in some embodiments, the wand 165 may comprise one or more temperature sensors for use during charging operations.

[00160] The user may initiate communication with the IMD 150 by placing the wand 165 proximate to the NS system 100. Preferably, the placement of the wand 165 allows the telemetry system of the wand 165 to be aligned with the far- field and/or near field communication circuitry 155 of the IMD 150. The controller device 160 preferably provides one or more user interfaces 168 (e.g., touchscreen, keyboard, mouse, buttons, or the like) allowing the user to operate the IMD 150. The controller device 160 may be controlled by the user (e.g., doctor, clinician) through the user interface 168 allowing the user to interact with the IMD 150. The user interface 168 may permit the user to move electrical stimulation along and/or across one or more of the lead(s) 110 using different stimulation electrode 121 combinations, for example, as described in U.S. Patent Application Publication No. 2009/0326608, entitled “METHOD OF ELECTRICALLY STIMULATING TISSUE OF A PATIENT BY SHIFTING A LOCUS OF STIMULATION AND SYSTEM EMPLOYING THE SAME,” which is expressly incorporated herein by reference.

[00161] Also, the controller device 160 may permit operation of the IMD 12, 150 according to one or more therapies to treat the patient. Each therapy may include one or more sets of stimulation parameters of the pulse including pulse amplitude, pulse width, pulse frequency or inter-pulse period, firing delay, pulse repetition parameter (e.g., number of times for a given pulse to be repeated for respective stimset during execution of program), biphasic pulses, monophasic pulses, etc. The IMD 150 modifies its internal parameters in response to the control signals from the controller device 160 to vary the stimulation characteristics of the stimulation pulses transmitted through the lead 110 to the tissue of the patient. NS systems, stimsets, and multi-stimset programs are discussed in PCT Publication No. WO 01/93953, entitled “NEUROMODULATION THERAPY SYSTEM,” and US Patent No. 7,228,179, entitled “METHOD AND APPARATUS FOR PROVIDING COMPLEX TISSUE STIMULATION PATTERNS,” which are expressly incorporated herein by reference.

[00162] Figures 2A-2I illustrate example stimulation leads 14 that may be used for electrically stimulating the predetermined site to treat one or more neurological disorders or conditions. As described above, each of the one or more stimulation leads 14 incorporated in stimulation systems 10, 100 includes one or more stimulation electrodes 18 adapted to be positioned in communication with the predetermined site and used to deliver the stimulation pulses received from IMD 12 (or pulse generating circuitry 152 in Figure 1 C). A percutaneous stimulation lead 14 (corresponding to the lead 110 in Figure 1 C), such as example stimulation leads 14a-d, includes one or more circumferential electrodes 18 spaced apart from one another along the length of stimulating portion 20 of stimulation lead 14. Circumferential electrodes 18 emit electrical stimulation energy generally radially (e.g., generally perpendicular to the axis of stimulation lead 14) in all directions. A laminotomy, paddle, or surgical stimulation lead 14, such as example stimulation leads 14e-i, includes one or more directional stimulation electrodes 18 spaced apart from one another along one surface of stimulation lead 14. Directional stimulation electrodes 18 emit electrical stimulation energy in a direction generally perpendicular to the surface of stimulation lead 14 on which they are located. Although various types of stimulation leads 14 are shown as examples, embodiments herein contemplate stimulation system 10 including any suitable type of stimulation lead 14 in any suitable number. In addition, stimulation leads 14 may be used alone or in combination. For example, medial or unilateral stimulation of the predetermined site may be accomplished using a single electrical stimulation lead 14 implanted in communication with the predetermined site in one side of the head, while bilateral electrical stimulation of the predetermined site may be accomplished using two stimulation leads 14 implanted in communication with the predetermined site in opposite sides of the head.

[00163] The IMD 12, 150 allow each electrode of each lead to be defined as a positive, a negative, or a neutral polarity. For each electrode combination (e.g., the defined polarity of at least two electrodes having at least one cathode and at least one anode), an electrical signal can have at least a definable amplitude (e.g., current level or voltage), pulse width, and frequency, where these variables may be independently adjusted to finely select the sensory transmitting brain tissue required to inhibit transmission of neuronal signals. Generally, amplitudes, pulse widths, and frequencies are determinable by the capabilities of the neurostimulation systems, which are known by those of skill in the art.

[00164] In embodiments herein, the therapy parameter of signal frequency is varied to achieve a burst type rhythm, or burst mode stimulation. Generally, the burst stimulus frequency may be in the range of about 0.01 Hz to about 100 Hz, more particular, in the range of about 1 Hz to about 12 Hz, and more particularly, in the range of about 1 Hz to about 4 Hz, 4 Hz to about 7 Hz or about 8 Hz to about 12 Hz for each burst. Each burst stimulus comprises at least two spikes, for example, each burst stimulus can comprise about 2 to about 100 spikes, more particularly, about 2 to about 10 spikes. The respective spikes within a given burst may exhibit a pulse repetition rate or frequency in the range of about 50 Hz to about 1000 Hz, more particularly, in the range of about 200 Hz to about 500 Hz. The frequency of spike repetition within one or more burst can vary. The inter-spike interval can be also vary, for example, the inter-spike interval, can be about 0.1 milliseconds to about 100 milliseconds or any range there between.

[00165] The burst stimulus is followed by an inter-burst interval, during which substantially no stimulus is applied. The inter-burst interval has duration in the range of about 1 milliseconds to about 5 seconds, more preferably, 10 milliseconds to about 300 milliseconds. It is envisioned that the burst stimulus has a duration in the range of about 1 milliseconds to about 5 seconds, more particular, in the range of about 250 msec to 1000 msec (1 -4 Hz burst firing), 145 msec to about 250 msec (4-7 Hz), 145 msec to about 80 msec (8-12 Hz) or 1 to 5 seconds in plateau potential firing. The burst stimulus and the inter-burst interval can have a regular pattern or an irregular pattern (e.g., random or irregular harmonics). More specifically, the burst stimulus can have a physiological pattern or a pathological pattern. Additional details regarding burst stimulation may be found in U.S. Patent No. 8,897,870, which is incorporated herein by reference.

[00166] It is envisaged that the patient may require intermittent assessment with regard to patterns of stimulation. Different electrodes on the lead can be selected by suitable computer programming, such as that described in U.S. Pat. No. 5,938,690, which is incorporated by reference here in full. Utilizing such a program allows an optimal stimulation pattern to be obtained at minimal voltages. This ensures a longer battery life for the implanted systems.

[00167] Figures 2A-2I respectively depict stimulation portions for inclusion at the distal end of lead. Stimulation portion depicts a conventional stimulation portion of a “percutaneous” lead with multiple ring electrodes. Stimulation portion depicts a stimulation portion including several segmented electrodes. Example fabrication processes are disclosed in U.S. Patent Application Serial No. 12/895,096, entitled, “METHOD OF FABRICATING STIMULATION LEAD FOR APPLYING ELECTRICAL STIMULATION TO TISSUE OF A PATIENT,” which is incorporated herein by reference. Stimulation portion includes multiple planar electrodes on a paddle structure.

[00168] In certain embodiments, for example, patients may have an electrical stimulation lead or electrode implanted directly into the brain for deep brain stimulation or adjacent to the dura for cortical stimulation. The anatomical targets or predetermined site may be stimulated directly or affected through stimulation in another region of the brain.

[00169] Once electrical stimulation lead 14, 110 has been positioned adjacent to the dura or in the brain, lead 14, 110 is uncoupled from any stereotactic or other implant equipment present, and the equipment is removed. Where stereotactic equipment is used, the cannula may be removed before, during, or after removal of the stereotactic equipment. Connecting portion 16 of electrical stimulation lead 14, 110 is laid substantially flat along the skull. Where appropriate, any burr hole cover seated in the burr hole may be used to secure electrical stimulation lead 14, 110 in position and possibly to help prevent leakage from the burr hole and entry of contaminants into the burr hole.

[00170] Once electrical stimulation lead 14, 110 has been inserted and secured, connecting portion of lead 14, 110 extends from the lead insertion site to the implant site at which IMD 12, 150 is implanted. The implant site is typically a subcutaneous pocket formed to receive and house IMD 12, 150. The implant site is usually positioned a distance away from the insertion site, such as near the chest, below the clavicle or alternatively near the buttocks or another place in the torso area. Once all appropriate components of stimulation system 10, 100 are implanted, these components may be subject to mechanical forces and movement in response to movement of the person's body. A doctor, the patient, or another user of IMD 12, 150 may directly or in directly input signal parameters for controlling the nature of the electrical stimulation provided.

[00171] Although example steps are illustrated and described, embodiments herein contemplate two or more steps taking place substantially simultaneously or in a different order. In addition, embodiments herein contemplate using methods with additional steps, fewer steps, or different steps, so long as the steps remain appropriate for implanting an example stimulation system 10, 100 into a person for electrical stimulation of the person's brain. [00172] As described above, each of the one or more leads 14 incorporated in stimulation system 10 includes one or more electrodes 18 adapted to be positioned near the target brain tissue and used to deliver electrical stimulation energy to the target brain tissue in response to electrical signals received from IMD 12. A percutaneous lead 14 may include one or more circumferential electrodes 18 spaced apart from one another along the length of lead 14. Circumferential electrodes 18 emit electrical stimulation energy generally radially in all directions and may be inserted percutaneously or through a needle. The electrodes 18 of a percutaneous lead 14 may be arranged in configurations other than circumferentially, for example as in a "coated" lead 14. A laminotomy or paddle style lead 14, such as example leads 14e-i, includes one or more directional electrodes 18 spaced apart from one another along one surface of lead 14. Directional electrodes 18 emit electrical stimulation energy in a direction generally perpendicular to the surface of lead 14 on which they are located. Although various types of leads 14 are shown as examples, embodiments herein contemplate stimulation system 10 including any suitable type of lead 14 in any suitable number, including three-dimensional leads and matrix leads as described below. In addition, the leads may be used alone or in combination.

[00173] Although example steps are illustrated and described, embodiments herein contemplate two or more steps taking place substantially simultaneously or in a different order. In addition, embodiments herein contemplate using methods with additional steps, fewer steps, or different steps, so long as the steps remain appropriate for implanting stimulation system 10 into a person for electrical stimulation of the predetermined site.

[00174] One or more of the operations described above in connection with the methods may be performed using one or more processors. The different devices in the systems described herein may represent one or more processors, and two or more of these devices may include at least one of the same processors. In one embodiment, the operations described herein may represent actions performed when one or more processors (e.g., of the devices described herein) execute program instructions stored in memory (for example, software stored on a tangible and non-transitory computer readable storage medium, such as a computer hard drive, ROM, RAM, or the like).

[00175] The processor(s) may execute a set of instructions that are stored in one or more storage elements, in order to process data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within the controllers and the controller device. The set of instructions may include various commands that instruct the controllers and the controller device to perform specific operations such as the methods and processes of the various embodiments of the subject matter described herein. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software. Further, the software may be in the form of a collection of separate programs or modules, a program module within a larger program or a portion of a program module. The software also may include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to user commands, or in response to results of previous processing, or in response to a request made by another processing machine.

[00176] The controller may include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), logic circuits, and any other circuit or processor capable of executing the functions described herein. When processor- based, the controller executes program instructions stored in memory to perform the corresponding operations. Additionally or alternatively, the controllers and the controller device may represent circuits that may be implemented as hardware. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term“controller.”

[00177] It is to be understood that the subject matter described herein is not limited in its application to the details of construction and the arrangement of components set forth in the description herein or illustrated in the drawings hereof. The subject matter described herein is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or“having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[00178] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. While the dimensions, types of materials and coatings described herein are intended to define the parameters of the invention, they are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "including" and "in which" are used as the plain- English equivalents of the respective terms "comprising" and "wherein." Moreover, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 45 U.S.C. § 112(f), unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure.