SYSTEMS AND METHODS FOR MODIFYING PAIN SENSITIVITY

Described herein are systems and methods for modifying pain sensitivity in a subject. Example systems can include a plurality of sensors configured to detect electroencephalography (EEG) signals in the subject, and a processor communicably coupled to the plurality of sensors. The processor can be configured to receive first EEG signals from the sensors and determine, based on the first EEG signals, at least one of (i) a first value for a predicted pain sensitivity (PPS) associated with the subject or (ii) a second value for a peak alpha frequency (PAF) associated with the subject. The processor can be further configured to receive second EEG signals from the sensors and provide feedback to the subject when a characteristic of the second EEG-signals indicates a. reduced pain sensitivity.

TECHNICAL FIELD

The following disclosure is directed to methods and systems for modifying pain sensitivity in a subject and, more specifically, methods and systems for modifying pain sensitivity based on electroencephalography (EEG) signals of a subject.

BACKGROUND

Prolonged pain, including chronic pain, is conventionally managed by pharmaceuticals prescribed by physicians to patients. These pharmaceuticals can carry undesirable side effects with unknown impact on long-term patient health. In some cases, more invasive methods for mitigating chronic pain include surgeries or implants, which can be expensive and risky for patients. Examples of chronic pain include musculoskeletal pain (e.g., in a person's knees, hips, joints, etc.), neuropathic pain (e.g., diabetic neuropathy, pain associated with post-shingles, reflex sympathetic dystrophy, cancer pain, etc.), post-surgical pain, nociplastic pain (e.g. central sensitization) and pain associated with neurological disorders (e.g., anxiety, depression, attention deficit hyperactivity disorder (ADHD), etc.). For example, endometriosis is a condition affecting one in ten women of reproductive age and is characterized by chronic pelvic pain that is associated with abnormal sensitivity to pain, often unrelated to endometrial implant location. Typical surgical and hormonal treatments are found to be expensive and often ineffective.

SUMMARY

Described herein are systems and methods for modifying pain sensitivity in a subject based on the subject's EEG signals.

Disclosed herein, in one aspect, is a system for modifying pain sensitivity in a subject, the system comprising: a) a plurality of sensors configured to detect electroencephalography (EEG) signals in the subject; and b) a processor in operative communication with the plurality of sensors and configured to: 1) receive first EEG signals from the plurality of sensors; determine, based on the first EEG signals, (i) a first predicted pain sensitivity (PPS) associated with the subject, and/or (ii) a first peak alpha frequency (PAF) associated with the subject; 2) receive second EEG signals from the sensors; and 3) provide feedback to the subject when a characteristic of the second EEG signals correlates with a second predicted pain sensitivity that is lower than the first predicted pain sensitivity.

In some embodiments, the processor is configured to determine that the pain sensitivity of the subject is modified based on at least one of the characteristic, the first PPS, and the first PAF. In some embodiments, the first PAF is from about 8 Hz to about 12 Hz. In some embodiments, the characteristic of the second EEG signals comprises a second PAF associated with the subject, and wherein the second PPS is lower than the first PPS when the second PAF is greater than the first PAF.

In some embodiments, the processor is configured to: a) receive third EEG signals from the sensors subsequent to providing the feedback; b) determine, based on the third EEG signals, (i) a third PPS, and/or (ii) a third PAF; and c) compare (i) the third PPS to the first PPS, and/or (ii) the third PAF to the first PAF, to determine whether the pain sensitivity in the subject is modified. In some embodiments, the modified pain sensitivity correlates with (i) the third PPS being lower than first PPS by a first minimum threshold, or (ii) the third PAF being greater than the first PAF by a second minimum threshold. In some embodiments, the first minimum threshold and/or the second minimum threshold is from at least about 5% to at least about 40%. In some embodiments, the second minimum threshold is from about at least about 0.01 Hz to about 1.0 Hz, such as at least about 0.02 Hz, 0.04 Hz, 0.05 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz, 0.75 Hz, or 1.0 Hz.

In some embodiments, the processor determines the first PPS, the second PPS, and/or the third PPS based on one or more of age, gender, health history, past PPS and/or PAF data, etc. In some embodiments, the first PPS, the second PPS, and/or the third PPS is based on Fourier transforms of the received corresponding first EEG signals, the second EEG signals, and/or the third EEG signals, each in an alpha frequency range of about 8 Hz to about 12 Hz. In some embodiments, the first PPS is based on the first PAF. In some embodiments, the first PPS, the second PPS, and/or the third PPS is a number on a predetermined scale from 0 to 100.

In some embodiments, the feedback to the subject comprises an auditory stimulus, a visual stimulus, a haptic stimulus, an electrical stimulus, a magnetic stimulus, an ultrasonic stimulus, or a combination thereof.

In some embodiments, the system further comprises a computing device configured to provide at least one of the auditory stimulus and the visual stimulus. In some embodiments, the computing device comprises a laptop, desktop, and/or a mobile device such as a smart phone, tablet, smartwatch, etc. In some embodiments, the computing device comprises the processor. In some embodiments, the computing device is in operative communication with the processor. In some embodiments, the auditory stimulus comprises a sound or tone having a prescribed loudness and/or pitch. In some embodiments, the auditory stimulus comprises a change in a loudness and/or pitch of a sound or tone as compared with a previously provided sound or tone. In some embodiments, the visual stimulus comprises an image depicted on a display of the computing device. In some embodiments, the visual stimulus comprises a change in a depicted image, wherein the change comprises a change in one or more of color, shape, size, and/or pattern of the depicted image. In some embodiments, the visual stimulus comprises a change from a less pleasing image to a more pleasing image. In some embodiments, the system further comprises an apparatus configured to apply at least one of a magnetic stimulus and/or an ultrasonic stimulus to a brain or a peripheral nervous system of the subject.

In some embodiments, the feedback correlates only with a decreasing PPS.

In some embodiments, the feedback includes points based on the decreasing PPS that is optionally cumulative with each successive training session. In some embodiments, the plurality of sensors comprise electrodes configured to be positioned on a head of the subject. In some embodiments, the plurality of sensors are provided with a headwear assembly, such as a headband, hat, or helmet.

In some embodiments, the processor is part of a computing device. In some embodiments, the computing device comprises a mobile device, a desktop, a laptop, and/or a remote computing system. In some embodiments, the mobile device comprises a smartphone, a smart watch, a tablet, or any combination thereof. In some embodiments, the computing device comprises and/or is operatively coupled to a storage module coupled to the processor and configured to store the first EEG signals, the second EEG signals, the third EEG signals, the first PPS, the second PPS, the third PPS, the first PAF, the second PAF, the third PAF, or a combination thereof. In some embodiments, the computing device comprises a user interface configured to provide the feedback and/or receive input from the subject.

In some embodiments, the system further comprising: a) a first communication module coupled to the plurality of sensors and configured to transmit the first, second, and/or third EEG signals; and b) a second communication module configured to receive the first, second, and/or third EEG signals from the first communication module. In some embodiments, the second communication module is part of the computing device.

In some embodiments, the feedback has a type, and wherein the processor is further configured to provide additional feedback of any type to the subject after determining that the pain sensitivity of the subject is modified. In some embodiments, the system is configured to modify pain sensitivity (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof. In some embodiments, the system is configured to prevent or reduce a chronification of pain in the subject experiencing acute pain.

In some embodiments, the first EEG signals correspond to a lowest PPS score from a previous therapy session.

Disclosed herein, in another aspect, is a method for modifying pain sensitivity in a subject, the method comprising: a) receiving first EEG signals from a plurality of sensors coupled to the subject; b) determining, based on the first EEG signals, (i) a first predicted pain sensitivity (PPS) associated with the subject, and/or (ii) a first peak alpha frequency (PAF) associated with the subject; c) receiving second EEG signals from the plurality sensors; and d) providing feedback to the subject when a characteristic of the second EEG signals correlates with a second predicted pain sensitivity that is lower than the first predicted pain sensitivity.

In some embodiments, the first PAF is from about 8 Hz to about 12 Hz. In some embodiments, the characteristic of the second EEG signals comprises a second PAF associated with the subject, and wherein the second PPS is lower than the first PPS when the second PAF is greater than the first PAF.

In some embodiments, the method further comprises: a) receiving third EEG signals from the sensors subsequent to providing the feedback; b) determining, based on the third EEG signals, (i) a third PPS, and/or (ii) a third PAF; and c) comparing (i) the third PPS to the first PPS, and/or (ii) the third PAF to the first PAF, to determine whether the pain sensitivity in the subject is modified. In some embodiments, the modified pain sensitivity correlates with (i) the third PPS being lower than first PPS by a first minimum threshold, or (ii) the third PAF being greater than the first PAF by a second minimum threshold. In some embodiments, the first minimum threshold and/or the second minimum threshold is from at least about 5% to at least about 40%. In some embodiments, the second minimum threshold is from about at least about 0.01 Hz to about 1.0 Hz, such as at least about 0.02 Hz, 0.04 Hz, 0.05 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz, 0.75 Hz, or 1.0 Hz.

In some embodiments, the first PPS, the second PPS, and/or the third PPS is based on one or more of age, gender, health history, past PPS and/or PAF data, etc. In some embodiments, the first PPS, the second PPS, and/or the third PPS is based on Fourier transforms of the received corresponding first EEG signals, the second EEG signals, and/or the third EEG signals, each in an alpha frequency range of about 8 Hz to about 12 Hz. In some embodiments, the first PPS is based on the first PAF. In some embodiments, the first PPS, the second PPS, and/or the third PPS is a number on a predetermined scale from 0 to 100.

In some embodiments, the feedback to the subject comprises an auditory stimulus, a visual stimulus, a haptic stimulus, an electrical stimulus, a magnetic stimulus, an ultrasonic stimulus, or a combination thereof. In some embodiments, a computing device is configured to provide at least one of the auditory stimulus and the visual stimulus. In some embodiments, the computing device comprises a laptop, desktop, and/or a mobile device such as a smart phone, tablet, smartwatch, etc. In some embodiments, the auditory stimulus comprises a sound or tone having a prescribed loudness and/or pitch. In some embodiments, the auditory stimulus comprises a change in a loudness and/or pitch of a sound or tone as compared with a previously provided sound or tone. In some embodiments, the visual stimulus comprises an image depicted on a display of the computing device. In some embodiments, the visual stimulus comprises a change in a depicted image, wherein the change comprises a change in one or more of color, shape, size, and/or pattern of the depicted image. In some embodiments, the visual stimulus comprises a change from a less pleasing image to a more pleasing image. In some embodiments, the method further comprises using an apparatus configured to apply at least one of a magnetic stimulus and/or an ultrasonic stimulus to a brain or a peripheral nervous system of the subject.

In some embodiments, the feedback correlates only with a decreasing PPS. In some embodiments, the feedback includes points based on the decreasing PPS that is optionally cumulative with each successive training session.

In some embodiments, the feedback has a type, and wherein the processor is further configured to provide additional feedback of any type to the subject after determining that the pain sensitivity of the subject is modified. In some embodiments, the method enables pain sensitivity modification (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof. In some embodiments, the method prevents or reduces chronification of pain in the subject experiencing acute pain. In some embodiments, the first EEG signals correspond to a lowest PPS score from a previous therapy session.

Disclosed herein, in another embodiment, is a non-transitory computer readable medium for modifying pain sensitivity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations including: a) receiving first EEG signals from a plurality of sensors coupled to the subject; b) determining, based on the first EEG signals, (i) a first predicted pain sensitivity (PPS) associated with the subject, and/or (ii) a first peak alpha frequency (PAF) associated with the subject; c) receiving second EEG signals from the plurality sensors; and d) providing feedback to the subject when a characteristic of the second EEG signals correlates with a second predicted pain sensitivity that is lower than the first predicted pain sensitivity.

In some embodiments, the processor is configured to determine that the pain sensitivity of the subject is modified based on at least one of the characteristic, the first PPS, and the first PAF. In some embodiments, the first PAF is from about 8 Hz to about 12 Hz. In some embodiments, the characteristic of the second EEG signals comprises a second PAF associated with the subject, and wherein the second PPS is lower than the first PPS when the second PAF is greater than the first PAF.

In some embodiments, the processor is configured to: a) receive third EEG signals from the sensors subsequent to providing the feedback; b) determine, based on the third EEG signals, (i) a third PPS, and/or (ii) a third PAF; and c) compare (i) the third PPS to the first PPS, and/or (ii) the third PAF to the first PAF, to determine whether the pain sensitivity in the subject is modified. In some embodiments, the modified pain sensitivity correlates with (i) the third PPS being lower than first PPS by a first minimum threshold, or (ii) the third PAF being greater than the first PAF by a second minimum threshold. In some embodiments, the first minimum threshold and/or the second minimum threshold is from at least about 5% to at least about 40%. In some embodiments, the second minimum threshold is from about at least about 0.01 Hz to about 1.0 Hz, such as at least about 0.02 Hz, 0.04 Hz, 0.05 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz, 0.75 Hz, or 1.0 Hz.

In some embodiments, the processor determines the first PPS, the second PPS, and/or the third PPS based on one or more of age, gender, health history, past PPS and/or PAF data, etc. In some embodiments, the first PPS, the second PPS, and/or the third PPS is based on Fourier transforms of the received corresponding first EEG signals, the second EEG signals, and/or the third EEG signals, each in an alpha frequency range of about 8 Hz to about 12 Hz. In some embodiments, the first PPS is based on the first PAF. In some embodiments, the first PPS, the second PPS, and/or the third PPS is a number on a predetermined scale from 0 to 100.

In some embodiments, the feedback to the subject comprises an auditory stimulus, a visual stimulus, a haptic stimulus, an electrical stimulus, a magnetic stimulus, an ultrasonic stimulus, or a combination thereof.

In some embodiments, the non-transitory computer readable medium further comprises a computing device configured to provide at least one of the auditory stimulus and the visual stimulus. In some embodiments, the computing device comprises a laptop, desktop, and/or a mobile device such as a smart phone, tablet, smartwatch, etc. In some embodiments, the computing device comprises the processor. In some embodiments, the computing device is in operative communication with the processor. In some embodiments, the auditory stimulus comprises a sound or tone having a prescribed loudness and/or pitch. In some embodiments, the auditory stimulus comprises a change in a loudness and/or pitch of a sound or tone as compared with a previously provided sound or tone. In some embodiments, the visual stimulus comprises an image depicted on a display of the computing device. In some embodiments, the visual stimulus comprises a change in a depicted image, wherein the change comprises a change in one or more of color, shape, size, and/or pattern of the depicted image. In some embodiments, the visual stimulus comprises a change from a less pleasing image to a more pleasing image. In some embodiments, the non-transitory computer readable medium further comprises an apparatus configured to apply at least one of a magnetic stimulus and/or an ultrasonic stimulus to a brain or a peripheral nervous system of the subject.

In some embodiments, the feedback correlates only with a decreasing PPS.

In some embodiments, wherein the feedback includes points based on the decreasing PPS that is optionally cumulative with each successive training session. In some embodiments, the plurality of sensors comprise electrodes configured to be positioned on a head of the subject. In some embodiments, the plurality of sensors are provided with a headwear assembly, such as a headband, hat, or helmet.

In some embodiments, the processor is part of a computing device. In some embodiments, the computing device comprises a mobile device, a desktop, a laptop, and/or a remote computing system. In some embodiments, the mobile device comprises a smartphone, a smart watch, a tablet, or any combination thereof. In some embodiments, the computing device comprises and/or is operatively coupled to a storage module coupled to the processor and configured to store the first EEG signals, the second EEG signals, the third EEG signals, the first PPS, the second PPS, the third PPS, the first PAF, the second PAF, the third PAF, or a combination thereof. In some embodiments, the computing device comprises a user interface configured to provide the feedback and/or receive input from the subject.

In some embodiments, the non-transitory computer readable medium further comprising: a) a first communication module coupled to the plurality of sensors and configured to transmit the first, second, and/or third EEG signals; and b) a second communication module configured to receive the first, second, and/or third EEG signals from the first communication module. In some embodiments, the second communication module is part of the computing device.

In some embodiments, the feedback has a type, and wherein the processor is further configured to provide additional feedback of any type to the subject after determining that the pain sensitivity of the subject is modified. In some embodiments, the non-transitory computer readable medium is configured to modify pain sensitivity (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof. In some embodiments, the non-transitory computer readable medium is configured to prevent or reduce a chronification of pain in the subject experiencing acute pain.

In some embodiments, the first EEG signals correspond to a lowest PPS score from a previous therapy session.

Disclosed herein, in another aspect, is a non-transitory computer readable medium for modifying pain sensitivity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations including: a) receiving first EEG signals from a plurality of sensors coupled to the subject; b) determining, based on the first EEG signals, (i) a first predicted pain sensitivity (PPS) associated with the subject, and/or (ii) a first peak alpha frequency (PAF) associated with the subject; c) correlating an entrainment regimen based on the first PPS and/or the first PAF; and d) providing the entrainment regiment to the subject.

In some embodiments, the operations further include: a) receiving second EEG signals from the plurality of sensors or a different plurality of sensors coupled to the subject; b) determining, based on the second EEG signals, (i) a second PPS associated with the subject, and/or (ii) a second PAF associated with the subject; and c) identifying an effectiveness of the entrainment regimen.

In some embodiments, the operations further includes modifying the entrainment regimen based on the identified effectiveness. In some embodiments, the identified effectiveness is based on i) the second PPS being lower than the first PPS by a first minimum threshold, and/or ii) the second PAF being greater than the first PAF by a second minimum threshold. In some embodiments, the first minimum threshold and/or the second minimum threshold is from at least about 5% to at least about 40%. In some embodiments, the second minimum threshold is from about at least about 0.01 Hz to about 1.0 Hz, such as at least about 0.02 Hz, 0.04 Hz, 0.05 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz, 0.75 Hz, or 1.0 Hz.

In some embodiments, the entrainment regimen comprises one or more audio stimuli and/or one or more video stimuli. In some embodiments, the one or more audio stimuli comprises a volume of a tone, musical track, and/or other sound at a prescribed frequency based on the first PAF and/or the first PPS. In some embodiments, the one or more audio stimuli comprises sub perceptible background tone at the prescribed frequency. In some embodiments, the one or more audio stimuli comprises a beat frequency wherein two tones have a difference in frequency of the prescribed frequency. In some embodiments, the one or more video stimuli comprises a flicker and/or oscillation provided on a display device, wherein the flicker and/or oscillation is provided at the prescribed frequency. In some embodiments, the one or more video stimuli comprises a flicker and/or oscillation provided on a display device, wherein the flicker and/or oscillation is provided at the prescribed frequency.

In some embodiments, the entrainment regimen comprises providing a vibrotactile stimulation at the prescribed frequency. In some embodiments, the prescribed frequency is based on a minimum PAF that correlates with a PPS greater than the first PPS. In some embodiments, the prescribed frequency is from about 10 Hz to about 12 Hz.

In some embodiments, the processor is part of a computing device. In some embodiments, the computing device comprises a mobile device, a desktop, a laptop, and/or a remote computing system. In some embodiments, the mobile device comprises a smartphone, a smart watch, a tablet, or any combination thereof. In some embodiments, the computing device comprises and/or is operatively coupled to a storage module coupled to the processor and configured to store the first EEG signals, the second EEG signals, the first PPS, the second PPS, the first PAF, the second PAF, or a combination thereof. In some embodiments, the computing device comprises a user interface configured to provide the feedback and/or receive input from the subject. In some embodiments, the entrainment regimen is provided by the computing device.

In some embodiments, the processor determines the first PPS and/or the second PPS based on one or more of age, gender, health history, past PPS and/or PAF data, etc. In some embodiments, wherein the first PPS and/or the second PPS is based on Fourier transforms of the received corresponding first EEG signals and/or the second EEG signals, each in an alpha frequency range of about 8 Hz to about 12 Hz.

In some embodiments, the first PPS is based on the first PAF. In some embodiments, the first PPS and/or the second PPS is a number on a predetermined scale from 0 to 100. In some embodiments, the plurality of sensors comprise electrodes configured to be positioned on a head of the subject. In some embodiments, the plurality of sensors are provided with a headwear assembly, such as a headband, hat, or helmet.

In some embodiments, the computing device comprises and/or is operatively coupled to a storage module coupled to the processor and configured to store the first EEG signals, the second EEG signals, the third EEG signals, the first PPS, the second PPS, the third PPS, the first PAF, the second PAF, the third PAF, or a combination thereof. In some embodiments, the computing device comprises a user interface configured to receive input from the subject.

In some embodiments, the non-transitory computer readable medium further comprising: a) a first communication module coupled to the plurality of sensors and configured to transmit the first and/or second EEG signals, and b) a second communication module configured to receive the first and/or second EEG signals from the first communication module. In some embodiments, the second communication module is part of the computing device.

In some embodiments the non-transitory computer readable medium is configured to modify pain sensitivity (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof. In some embodiments, the non-transitory computer readable medium is configured to prevent or reduce a chronification of pain in the subject experiencing acute pain. In some embodiments, the first EEG signals correspond to a lowest PPS score from a previous therapy session.

Disclosed herein, in another aspect, is a method for modifying pain sensitivity in a subject, the method comprising: a) receiving first EEG signals from a plurality of sensors coupled to the subject; b) determining, based on the first EEG signals, (i) a first predicted pain sensitivity (PPS) associated with the subject, and/or (ii) a first peak alpha frequency (PAF) associated with the subject; and c) correlating an entrainment regimen based on the first PPS and/or the first PAF; and d) providing the entrainment regiment to the subject.

In some embodiments, the method further comprises a) receiving second EEG signals from the plurality of sensors or a different plurality of sensors coupled to the subject; b) determining, based on the second EEG signals, (i) a second PPS associated with the subject, and/or (ii) a second PAF associated with the subject; and c) identifying an effectiveness of the entrainment regimen.

In some embodiments, the method further comprises modifying the entrainment regimen based on the identified effectiveness. In some embodiments, the identified effectiveness is based on i) the second PPS being lower than the first PPS by a first minimum threshold, and/or ii) the second PAF being greater than the first PAF by a second minimum threshold. In some embodiments, the first minimum threshold and/or the second minimum threshold is from at least about 5% to at least about 40%. In some embodiments, the second minimum threshold is from about at least about 0.01 Hz to about 1.0 Hz, such as at least about 0.02 Hz, 0.04 Hz, 0.05 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz, 0.75 Hz, or 1.0 Hz.

In some embodiments, the entrainment regimen comprises one or more audio stimuli and/or one or more video stimuli. In some embodiments, the one or more audio stimuli comprises a volume of a tone, musical track, and/or other sound at a prescribed frequency based on the first PAF and/or the first PPS. In some embodiments, the one or more audio stimuli comprises sub perceptible background tone at the prescribed frequency. In some embodiments, the one or more audio stimuli comprises a beat frequency wherein two tones have a difference in frequency of the prescribed frequency. In some embodiments, the one or more video stimuli comprises a flicker and/or oscillation provided on a display device, wherein the flicker and/or oscillation is provided at the prescribed frequency. In some embodiments, the one or more video stimuli comprises a flicker and/or oscillation provided on a display device, wherein the flicker and/or oscillation is provided at the prescribed frequency.

In some embodiments, the entrainment regimen comprises providing a vibrotactile stimulation at the prescribed frequency. In some embodiments, the prescribed frequency is based on a minimum PAF that correlates with a PPS greater than the first PPS. In some embodiments, the prescribed frequency is from about 10 Hz to about 12 Hz. In some embodiments, the first PPS and/or the second PPS is based on one or more of age, gender, health history, past PPS and/or PAF data, etc. In some embodiments, the first PPS and/or the second PPS is based on Fourier transforms of the received corresponding first EEG signals and/or the second EEG signals, each in an alpha frequency range of about 8 Hz to about 12 Hz. In some embodiments, the first PPS is based on the first PAF. In some embodiments, the first PPS and/or the second PPS is a number on a predetermined scale from 0 to 100.

In some embodiments, the method enables pain sensitivity modification (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof. In some embodiments, the method is configured to prevent or reduce a chronification of pain in the subject experiencing acute pain.

In some embodiments, the first EEG signals correspond to a lowest PPS score from a previous therapy session.

In some embodiments, the entrainment regiment is provided using a computing device. In some embodiments, the computing device comprises a mobile device, a desktop, a laptop, and/or a remote computing system. In some embodiments, the mobile device comprises a smartphone, a smart watch, a tablet, or any combination thereof.

DETAILED DESCRIPTION

Disclosed herein are exemplary embodiments of systems and methods for modifying pain sensitivity in a human subject. In particular, the systems and methods may use a plurality of sensors (e.g., superficial electrodes) to measure brain waves to modify pain sensitivity for treating acute or chronic pain and/or prevent chronic pain from developing in a patient.

In an example use case, a subject may be presented with a headband that includes electrodes for collecting EEG signals. The headband may be connected to a processor (e.g., as part of a handheld device, as part of a computing device, etc.) and may be used for therapy for modifying a subject's pain sensitivity. Information about the therapy (e.g., including instructions, feedback signals, visual cues, etc.) may be provided to the user interface of a mobile device such that the subject may access the information.

The example systems and methods described herein can provide a non-invasive, long-term solution for chronic pain, which affects millions of people nationwide. As described further below, patients can be trained (e.g., as part of a therapy) to reduce their pain sensitivity by receiving neurofeedback based on their EEG signals.

Predicted Pain Sensitivity

Pain sensitivity in a subject may be predicted by collecting data of certain oscillations in their resting EEG signals and analyzing the frequency of the oscillations in the EEG signals. The result may be referred to as the “predicted pain sensitivity” (PPS) of a subject. Examples of predicting pain sensitivity may be found in International Application Publication No. WO2019/090041 A1 published on May 9, 2019 and titled “Method for Predicting Pain Sensitivity.”

In some embodiments, PPS is determined based a database (e.g., a normative database) of pain sensitivity data. In some embodiments, the PPS is determined based on resting EEG measurements. In some embodiments, the PPS does not need a pain stimulus for determination. The example database may include EEG signals, age information, gender, health history, family history, therapy history, demographic information, medications, pain sensitivity data, and/or PAF associated with subjects. For instance, the EEG signals in the database may include EEG signals before and/or after a medical intervention (e.g., a medical treatment, surgery, medication, psychotherapy, etc.). The database may further include the outcomes of such medical intervention (e.g., improvement in well-being, physical function, etc.). For example, EEG signals from a given patient (or groups of patients) before and after surgical operation may be collected into the database. Further, data indicating the pain medication (e.g., opioids) consumption and/or pain ratings of these patient(s) may also be collected. Note that the example database may draw on data from public sources or specifically collected data for a group of patients. In some embodiments, longitudinal resting state EEG data is used as a feature (in addition to other features in the normative database) to train a machine learning model (e.g., logistic regression, support vector machines, deep learning, etc.) that can generate the PPS. The EEG data may be from multiple points in time to refine the predicted pain sensitivity (e.g., before and/or after a specific medical procedure). In some embodiments, PPS may be derived using a Fourier transform of one or more EEG signals of the subject. The Fourier transform of the EEG signal(s) may be in an alpha frequency range of approximately 8-12 Hz (e.g., +/−1 Hz).

In some embodiments, PPS is determined based on a peak alpha frequency (PAF) of a subject. In some embodiments, PPS can be calculated by determining power calculations in 0.1 Hz bins and evaluating the ratio of slow alpha (summed power in the 8-9 Hz range) to fast alpha (summed power in the 10-11 Hz range). In another embodiment, PPS is calculated by assigning a correlation coefficient to sum 0.1 Hz bins across the 8-12 Hz range where positive coefficients are assigned to the slow alpha range (8-9 Hz) and negative coefficients are assigned to the fast alpha range (10-11 Hz). In some embodiments, these correlation coefficients are determined by an age and gender matched normative database.FIG.1illustrates EEG signal power as a function of frequency for PAF biomarker calculations. For instance, a subject having a PAF of less than 9 Hz can be classified as a subject having high sensitivity to pain (plot line102) while a PAF of greater than 9 Hz indicates low sensitivity (plot line104). In past studies, the PAF biomarker can differentiate between high and low pain sensitivity individuals for capsaicin heat pain (21 participants, p=0.026) (Furman et al., “Cerebral Peak Alpha Frequency Predicts Individual Differences in Pain Sensitivity,”Neuro Image, volume 167, 203-210, doi.org/10.1016/j.neuroimage.2017.11.042, Nov. 21, 2017). A study with a clinically-relevant, human model of prolonged pain (persisting for weeks) using intramuscular nerve growth factor injections demonstrated that the speed of pain-free, sensorimotor peak alpha frequency recorded during resting-state EEG predicts pain sensitivity (31 participants, p<0.01) (Furman et al., “Cerebral peak alpha frequency reflects average pain severity in a human model of sustained, musculoskeletal pain.” Neurophysiology, 122(4):1784-93, pubmed.ncbi.nlm.nih.gov/31389754/, Oct. 1, 2019). It was further found that PAF predicts an individual's pain sensitivity to multiple pain paradigms and is reliable at multiple time points. The experiments were repeated collecting the same measurement weeks apart (participants 61, p<0.01) (Furman et al., “Sensorimotor Peak Alpha Frequency Is a Reliable Biomarker of Prolonged Pain Sensitivity,” Cerebral Cortex, 30(12):6069-82, pubmed.ncbi.nlm.nih.gov/32591813/, Nov. 3, 2020).

In various embodiments, PPS is presented on a scale for use by a subject and/or heath care provider. For example, predicted pain sensitivity can be provided on a numeric scale (e.g., 0 to 10) and/or alphabetical scale (e.g., A to E, A to J, etc.). In some embodiments, the PPS is provided as a scale of 1-100 wherein 1 represents low sensitivity, and 100 represents high sensitivity.

In some embodiments, a health care provider may collect PPS data from a patient at the point of care. For example, a physician may collect PPS data of a patient after a traumatic injury (e.g., in an emergency care setting). In another example, a specialist may collect PPS data of a patient as part of disease management. In another example, a primary care provider may collect PPS data of a patient as part of routine health care. In another example, a physician may send a device configured to collect PPS data to a patient (e.g., as part of a tele-health care or prescribed therapy). In a particular example, PPS data may be collected for patients experiencing chronic pelvic pain with suspected or confirmed endometriosis. Endometriosis symptoms can be caused by pain sensitization that is often unrelated to disease burden. Sensitivity to this condition, which affects nearly 7.5 million women in the United States, may be improved by the example systems and methods described herein. As described further below, PPS and/or PAF data can be used in improving a patient's pain sensitivity.

In some embodiments, systems and methods described herein are configured to modify pain sensitivity (i) associated with endometriosis in the subject, (ii) as an adjuvant therapy to endometriosis related central sensitization, (iii) associated with musculoskeletal pain in the subject, (iv) associated with diabetic neuropathy in the subject, (iv) associated with shingles in the subject, (v) associated with reflex sympathetic dystrophy syndrome in the subject, (vi) associated with cancer in the subject, (vii) associated with post-surgical pain in the subject, (viii) associated with a neurological disorder in the subject, (ix) associated with anxiety in the subject, (x) associated with depression in the subject, (xi) associated with attention deficit hyperactivity disorder (ADHD) in the subject, (xii) associated with post traumatic stress disorder (PTSD), (xiii) associated with nociplastic pain, (xiv) associated with chronic pelvis pain, (xv) associated with fibromyalgia, (xvi) associated with post traumatic pain, (xvii) associated with post surgical pain, or (xvii) any combination thereof.

Modifying Pain Sensitivity

In some embodiments, a provider may prescribe neurofeedback therapy to modify a patient's pain sensitivity. For example, such therapy may be especially helpful to patients who are highly sensitive to pain (e.g., refer to example curve102inFIG.1).FIG.2Ashows a high-level data flowchart for neuromodulation based on PPS and/or PAF. In process202, the pain sensitivity of a patient is determined based on EEG signals received from sensors on the subject. Initial pain sensitivity data may be referred to as “baseline” pain sensitivity for that subject. As described further below, additional pain sensitivity data may be collected to modify the subject's pain sensitivity. In some embodiments, the neurofeedback therapy may be provided in discrete sessions, wherein each session comprises providing the neurofeedback for a duration of time and/or until a target PPS is achieved (as described herein). In some embodiments, each session may be separated by a time period. For example, some therapy sessions may be provided daily, multiple times during a day (e.g., morning, afternoon), every other day, weekly, bi-weekly, monthly, or any non-periodic schedule. As used herein, a “training” may refer to “therapy”.

In some embodiments, the baseline PPS correlates to a first recorded EEG signals during a therapy session. In some embodiments, the baseline PPS correlates to a lowest PPS in a first plurality of EEG signals recorded during a therapy session. In some embodiments, the baseline PPS is based on a previously recorded EEG signals from a previous session of therapy. For example, a previous session of therapy may be a previous date when the therapy occurred (as compared to the current therapy), or a different time period (e.g., morning vs. afternoon). In some embodiments, the previously recorded EEG signals correlate to a lowest recorded PPS from the previous session.

In process204, the neuromodulation feedback (NFB) protocol is selected (e.g., as part of training or therapy). The NFB protocol may include determining amount, type, frequency, etc. of feedback based on the sensitivity data. As used herein, “training” may refer to a given session in which a subject is receiving feedback based on the subject's EEG signals (e.g., neurofeedback). In some cases, training may be part of therapy. Feedback signals (also referred to as “rewards”) are provided to the patient when received EEG signals indicate a brainwave state that has improved (e.g., above the patient's baseline PPS). Note that, in some cases, such feedback signals reinforce shifts (e.g., natural shifts, random shifts, intentional shifts, etc.) in PAF to “lock” the patient in a desired brainwave state. In some embodiments, feedback is provided when the received EEG signal reaches a target PPS and/or exceeds a reward threshold. Feedback may be provided when the received EEG signal causes the change desired in the PPS. The reward rate is the amount of feedback to provide when the patient improves their PPS and/or PAF. The reward rate may be varied. For instance, feedback may be provided to a subject upon the subject exceeding one or more predetermined thresholds and/or meeting certain targets in her/his PPS and/or PAF. In some embodiments, the reward rate can be determined (e.g., calculated, measured, etc.) to reward the subject such that the subject is motivated to stay in an improved state, e.g., a state with reduced pain sensitivity and/or increased PAF. In some embodiments, no reward is given for a period of time (e.g., from about 1 to about 30 seconds, such as about 1 second to about 15 seconds, or about 2 seconds to about 5 seconds) when the determined PPS is not higher than the baseline PPS (as described herein). In some embodiments, PAF is calculated using the first EEG signals (e.g., at approximately 8-12 Hz) from the sensors (refer to step402ofFIG.4). Feedback may be provided to the patient by increasing the volume of a tone when alpha power in the range higher than the PAF (e.g., 10 Hz) is spontaneously increased, ultimately reinforcing in a shift in the PAF.

In some embodiments, the patient the performs an action, for which a resulting positive feedback (e.g., reward due to decreased PPS and/or increased PAF) prompts the patient to perform the action again. As an exemplary analogy, the neurofeedback herein may be similar to a person learning how to walk, where positive reinforcements of an action prompt the person to continue doing such action in learning how to walk, where eventually such actions may occur naturally to the person. For example, in some cases, the patient may think of a pleasant memory, may perform deep breathing, may smile, or performs any type of action that previously resulted in a positive feedback. In some embodiments, the continual and/or periodic notification of a positive feedback (e.g., reward) for a given action may result in the patient subconsciously performing such action causing a shift in the pain sensitivity. In some embodiments, a plurality of actions, and/or permutations of such actions may result in positive feedback.

In some embodiments, an algorithm (e.g., executed by a processor) is configured to determine (e.g., select, calculate, etc.) the rewards and/or reward rate based on the PAF and/or PPS for the particular patient. The algorithm may include a statistical model, a predictive model, etc. In some embodiments, a machine learning (ML) model can be trained on subject data and the trained ML model can be used to determine the rewards and/or reward rate. For example, the subject data may include one or more characteristics of the subject including, e.g., health history, past PPS and/or PAF data, age, gender, etc. In some embodiments, the rewards and/or reward rate may be selected (e.g., via a user interface) by the subject and/or health care provider. In some embodiments, a patient may undergo a tuning exercise in which the reward, reward rate, and/or method of feedback (e.g., audio, visual, stimulation, etc.) are varied at set intervals. The variations in the reward and/or reward rate may be used as input features to an ML model. During this tuning evaluation, the change in PPS and/or PAF is used as an output to train an ML model using the input features. The input features can then be selected by the user, healthcare professional, or automatically to optimize the trained ML output of change in PPS.

A subject reaching a target change in the PPS and/or PAF can be used to determine whether the subject has modified pain sensitivity. In some embodiments, the target change in the PPS and/or PAF may be a percentage improvement in the subject's PPS and/or a percentage greater than the subject's PAF, respectively. The percentage may be at most 10%, at most 15%, at most 20%, at most 25%, at most 30%, or more. In another embodiment, the target change in the PPS and/or PAF may be an amount exceeding a threshold above the PAF and/or PPS. For example, the threshold may be predetermined based on the characteristics of the patient (e.g., type of chronic pain, underlying condition, past pain sensitivity data, etc.). In some embodiments, a signal from about 1-3 Hz, such as about 1-2 Hz, above the PAF correlates to an improvement in pain sensitivity correlating with a reward.

In process206, the sensitivity can be optimized based on training including collecting EEG signals and providing feedback signals. As used herein, the term “training” may refer to a therapy, such as providing neurofeedback and/or providing entrainment (as described herein). The “optimization” of a subject's pain sensitivity may refer to the improvement of pain sensitivity and/or reduction in pain sensitivity. In some embodiments, the alpha power in the high frequency range (e.g., 10-12 Hz, also referred to as the fast range) is upregulated. This may be done with or without decreasing alpha power in the low frequency range (e.g., 8-10 Hz, also referred to as the slow range). This can result in a shift of the subject's PAF (e.g., from a lower frequency to a higher frequency) and/or an improvement in the subject's PPS.

FIG.2Billustrates an example improvement in a subject's PPS (represented by line214). Marker216indicates a subject's current PPS value and marker218indicates a subject's target PPS value. A subject's PPS value can move from her current PPS value216to a target PPS value218by shifting her brainwaves (e.g., spontaneously or with effort). As the PPS value moves, the subject is provided with a reward (e.g., a visual cue, an increased volume of an audio signal, etc.). The reward threshold220can be predetermined and/or may be based on the difference between the current PPS value216and the target PPS value218. In some embodiments, the difference between the current PPS value216and the target PPS value218can be a percentage222or other points scale. In some embodiments, the reward is only provided based on positive changes in PPS (e.g., a decreasing PPS). In some embodiments, the reward includes points provided after a therapy session, wherein said points may be cumulative. The reward rate is the amount of feedback to provide to a subject based on how long the subject maintains her PPS above the reward threshold220.

In some embodiments, the therapy provides a ramp-up reward methodology for a subject at the beginning (of a therapy session, or at the beginning of the entire therapy (e.g., at the first session)) and for a duration thereafter (ramp-up period). For example, in some embodiments, the subject may receive a reward (e.g., visual cue, auditory signal) when only achieving about 5%-15% of the difference between the current and target PPS values. As the subject continues to improve the PPS values, the rewards will be presented at higher intervals of PPS increase, such as about 15%-30%, 25%-50%4, 40%-60%, 50%-75%, 60%-85%, or 75%-99% of the difference between the current and target PPS values. In some embodiments, after the ramp-up period, the subject may only receive rewards after attaining the reward threshold (e.g., about 85%-100% of the difference between the current PPS value and target PPS value).

FIG.2Cprovides example data of a subject's PAF before training (line224), during training (line226), and after training (line228). The example PAF after training228was recorded 20 minutes after training. As depicted, the subject's PAF226increases while training (e.g., receiving feedback based on the subject's EEG signals). In this example training, the tone delivered was selected to be 0.5 Hz greater than the baseline PAF of 10.2 Hz. After training, the subject's PAF228appears to decrease to a frequency between the peaks224and226.

In various embodiments, the signals associated with the feedback is provided to a computing system208(e.g., device, server, etc.), which may send the data to an interface accessible by a health care provider210and/or a patient212. For example, the feedback may be in the form of an auditory, visual, haptic, electrical, magnetic, magnetic, and/or ultrasonic stimuli to the brain and/or peripheral nervous system of the subject. For instance, the feedback can be provided via a user interface of a mobile device (e.g., a smartphone, a tablet computer, etc.). In some cases, immediate (or near-immediate) feedback may help the patient improve his or her PPS and/or PAF more quickly.

In some embodiments, visual stimuli can be provided by a display screen of the mobile device. The visual stimuli can include changing a feature (e.g., color, shape, size, pattern, etc.) of an image on the display screen. For instance, when the PPS and/or PAF of a patient improves, the display screen can show a color change from red to yellow to green. Alternatively, the display screen can change the image itself from a less-pleasing image to a more-pleasing image. For example, in some embodiments, the image may become more brighter, correlating a with a more-pleasing image. In some embodiments, auditory stimuli can be provided by a speaker of the mobile device. The auditory stimuli may include a change in the loudness and/or pitch of the sound. For instance, the sound from the speaker of the mobile device can get increasingly louder and/or have increasingly higher pitch when the patient improves his or her PPS and/or PAF. In some embodiments, the auditory stimuli includes a frequency of a series of beeps that may progressively provided at a faster rate.

By seeing, hearing, and/or feeling a change in the feedback, the patient may be prompted to try to change the feedback further. In an example embodiment, the feedback may be part of a game that the patient can partake in. For instance, the display of a mobile device may illustrate racing objects (e.g., cars, runners, etc.). This type of feedback loop can tap into the natural competitiveness of a human to improve his or her pain sensitivity.

In some embodiments, to avoid the nocebo effect, the baseline pain sensitivity may be displayed (e.g., via the user interface) to the patient as a zero (0) and improvements in the patient's pain sensitivity are displayed as positive numbers (e.g., 1, 2, 3 . . . 10). In some instances, the patient is not told (e.g., via the interface or by the provider) that he or she is highly sensitive to pain but that the patient will benefit from the therapy. In some instances, a provider210may further use the sensitivity data to make treatment decisions including, e.g., prescribing medications, advising surgery, etc. for the patient.

In some embodiments, a patient's pain sensitivity data is analyzed periodically, intermittently, or continuously over a duration. For example, in treating endometriosis patients, the patient's PAF and/or PPS can be collected throughout the patient's menstrual cycle. The timing of the described feedback may accordingly be determined based on the proximity in time to menses. For example, in treating neuropathic pain, the patient's PAF and/or PPS can be collected on a daily or bi-weekly basis. Feedback may be applied in the morning or at time associated with the patient's greatest symptoms. For example, for patients having post-surgical or post-traumatic pain, therapy may be delivered prior to next scheduled pain dose, as needed (PRN), and/or scheduled daily if a given patient falls into a high pain sensitivity group, which may be at risk for the development of chronic pain. For example, for patients having chronic musculoskeletal pain (e.g., chronic lower back pain) suffering from osteoarthritis, the timing of the feedback may be related to the time at which their symptoms are worse (e.g., usually in the mornings).

In some embodiments, feedback is given asynchronously in which a patient receives rewards in the form of a visual cue (e.g., image of a beach or a pleasant animal) and/or an audio file when PPS is in a target state during real-time neurofeedback. Subsequent visual cues and/or audio files may be sent to the patient's mobile device at one or more time points (e.g., as described in the above examples) throughout the day to stimulate a shift in PAF or PPS.

In some embodiments, in addition to or alternative to providing neurofeedback for modifying pain sensitivity, an entrainment method may be used to help modify pain sensitivity.FIG.6provides an exemplary embodiment of said entrainment method600. In some embodiments, a patient obtains a baseline PPS and/or PAF602through collection of EEG data (e.g., first EEG signals). In some embodiments, the patient obtains the EEG data from a healthcare provider (e.g., physician, healthcare professional/administrator, other healthcare facility). In some embodiments, as described herein, the PPS is calculated based on the collected EEG data. In some embodiments, the PPS is calculated using an algorithm as described herein, which may take into account other trained data from other PPS and/or EEG data, and/or patient characteristics (e.g., age, health, gender, race, health condition, etc.). In some embodiments, based on the calculated baseline PPS, the patient is provided604with a prescribed entrainment regimen to help modify the pain sensitivity. In some embodiments, the prescribed entrainment regimen is tailored based on the subject, subject's characteristics, and/or the subject's PPS and/or EEG data. In some embodiments, the prescribed entrainment regimen is applicable to a large population, which may or may not be based on any tailored factors for the subject. This prescribed entrainment may be determined automatically from the users PPS with dosing (e.g. duration of entrainment of 10 minutes daily, or 20 minutes 3 times a week, or 20 minutes 3 days prior to menses) adjusted by monitoring PPS.

In some embodiments, the prescribed entrainment regimen comprises the patient receiving one or more audio and/or visual stimuli. In some embodiments, the audio stimuli and/or visual stimuli includes providing a sound that resonates with a PPS higher than the patient's baseline PPS. For example, in some embodiments, the audio stimuli comprises the patient listening to a volume of a tone, musical track, and/or other sound, which may be provided at a frequency from about 8 Hz to about 14 Hz, such as from about 9 Hz to about 13 Hz, or from about 10 Hz to about 12 Hz (so as to correlate to a PAF having a calculated PPS less than the baseline PPS). In some embodiments, the sound comprises a sub perceptible background tone to music with a frequency from about 8 Hz to about 14 Hz, such as from about 9 Hz to about 13 Hz, or from about 10 Hz to about 12 Hz. In some embodiments, the sound comprises a beat frequency, wherein two tones have a difference in frequency from about 8 Hz to about 14 Hz, such as from about 9 Hz to about 13 Hz, or from about 10 Hz to about 12 Hz. In some embodiments, the visual stimuli comprises a flicker and/or oscillation (e.g., on a smart device, such as a phone, TV, computing device, a light emitting device, etc.) at a frequency from about 8 Hz to about 14 Hz, such as from about 9 Hz to about 13 Hz, or from about 10 Hz to about 12 Hz. In some embodiments, the entrainment regimen comprises providing a vibrotactile stimulation at a frequency from about 8 Hz to about 14 Hz, such as from about 9 Hz to about 13 Hz, or from about 10 Hz to about 12 Hz.

In some embodiments, the entrainment method helps force the pain sensitivity to be modified based on a shift in the PAF.

In some embodiments, the entrainment regimen is provided a computing device, as described herein. For example, in some embodiments, the one or more video stimuli and/or the one or more audio stimuli are provided by a mobile device as described herein.

In some embodiments, after providing the prescribed entrainment regiment, the patient collects EEG data again 606 (e.g., second EEG signals) to calculate the respective PPS and/or determine the PAF, so as to identify a change in the calculated PPS as compared with the baseline PPS. In some embodiments, the first EEG signal in602is recorded at one visit, the entrainment regimen is performed at home, and then the second EEG signal (e.g. 606) to establish a new PPS and/or PAF (e.g. 608) is recorded at a follow up visit. In some embodiments, an effectiveness of the prescribed entrainment regimen is associated608based on the identified change in PPS. In some embodiments, the prescribed entrainment regiment is modified if i) an insufficient amount of change from the baseline PPS and/or ii) no change from the baseline PPS, was identified. In some embodiments, an increase in the measured PAF by at least about 0.04 Hz, 0.1 Hz, 0.25 Hz, or 0.5 Hz correlates with an effective entrainment regimen.

In some embodiments, the entrainment method is administered in combination with other treatments, such as a pharmacologic treatment. In some embodiments, the pharmacologic treatment comprises administering one or more centrally or peripherally acting neuromodulators to the patient during treatment, thereby modifying the pain sensitivity. In some embodiments, the entrainment method is provided to help wean off one or more pharmacologic agents. In some embodiments, the entrainment method is continued once the one or more pharmacologic agents is no longer received by the patient.

FIG.3is a diagram of an example system configured to modify pain sensitivity. The example system300includes one or more sensors302configured to detect EEG signals304from the human brain. In some embodiments, the sensors302may be superficial electrodes configured to be applied to the head of a human subject. In some embodiments, the sensors302may be part of a headband, a hat, or other item configured to be worn on the subject's head.

The system300can include one or more processors306configured to receive the EEG signals304. For instance, the sensors302may be coupled to a communication module303configured to transmit the sensed EEG signals from to a communication module305coupled to the processor306. As described further below, the processor306can provide feedback to the subject based on the detected EEG signals304. The processor306may be communicatively coupled to a memory308, which can be configured to store data (e.g., including the EEG signals304). In some embodiments, the processor306and/or the memory308may be part of a computing device. The computing device may be a mobile phone, a smartphone, a tablet, a laptop computer, a notebook computer, a smartwatch, a set of smart glasses, a handheld computing device, a desktop computer, a server, a server system, etc.

The processor306and/or memory308may be communicatively coupled to the user interface310, which can be configured to present information to the subject. The user interface310may be the interface of a mobile phone, a smartphone, a tablet, a laptop computer, a notebook computer, a smartwatch, a set of smart glasses, etc. The user interface310may be part of the same computing device as the processor306and/or memory308. The processor306, memory308, and/or user interface310may be communicatively coupled to one or more remote computing systems312(e.g., a server system, a cloud, etc.). For example, data from a therapy session with the subject may be sent to and stored at a server. The data may then be available to physicians (e.g., remotely located physicians) for monitoring, determining treatment, etc. For instance, the data may be presented in a web application (e.g., accessible by the health care provider and/or subject). In another example, the data may be sent to a remote computing system312and become part of a patient's electronic medical record (EMR).

FIG.4is a flowchart of an example method400for modifying pain sensitivity. In step402of method400, the processor306can receive a first set of EEG signals304from the sensors302(e.g., via communication modules303and/or305). For example, the processor306may obtain 5 seconds or less, 8 seconds or less, 10 seconds or less, 15 seconds or less, or more of EEG signal duration from a given subject. In step404, the processor306may determine, based on the first EEG signals304, a first value for PPS and/or a second value for a PAF associated with the subject, as described above. This value for the PPS and/or value for the PAF may be used as a baseline for comparison to additionally determined values.

In step406, the processor306can receive second EEG signals from the sensors. In some embodiments, the duration of received second EEG signals can be the same or different from the duration of received first EEG signals. In some embodiments, the second EEG signals can be continuously received (e.g., until the end of the training and/or therapy). In some embodiments, the processor306can determine a characteristic of the second EEG signal. For example, the characteristic can be the alpha power at a particular frequency. The characteristic of the second EEG signals can indicate reduced pain sensitivity when the processor306detects increased alpha power in the frequency range above (e.g., 0.5 Hz or less, 1 Hz or less, 1.5 Hz or less, 2 Hz or less, etc. greater than) the subject's PAF (e.g., baseline PAF based on the first EEG signals). In some embodiments, the processor306can determine a PPS value and/or PAF value based on the second EEG signals.

In step408, the processor306can provide feedback (e.g., visual stimuli, auditory stimuli, etc.) to the subject when the characteristic of the second EEG signals indicates a reduced pain sensitivity. In some embodiments, as described, the characteristic can indicate reduced pain sensitivity when the alpha power in the frequency range above the subject's PAF has increased. In some embodiments, the processor306can provide feedback based on a comparison of the PPS and/or PAF associated with the first EEG signals to the PPS and/or PAF associated with the second EEG signals. When the comparison indicates reduced pain sensitivity, the processor306can provide the feedback signal.

In some embodiments, the processor306(or another processor) can determine whether the pain sensitivity of the subject is modified based on the characteristic, the first value, and/or the second value. For instance, the processor306can determine that the pain sensitivity is improved when the characteristic indicates reduced pain sensitivity (as described above). In another example, the processor306can determine that the pain sensitivity is improved based on a comparison of the PPS and/or PAF associated with the first EEG signals to the PPS and/or PAF associated with the second EEG signals. For example, when the value of the PPS associated with the second EEG signals is reduced (indicating a reduced pain sensitivity) in comparison to the value of the PPS associated with the first EEG signals, the processor306can determine that pain sensitivity is improved. For example, when the value of the PAF associated with the second EEG signals is increased (indicating a reduced pain sensitivity) in comparison to the value of the PAF associated with the first EEG signals, the processor306can determine that pain sensitivity is improved. In some embodiments, the processor306determines that the subject's pain sensitivity is modified when the subject maintains a reduced sensitivity state for a certain amount of time (e.g., up to 30 minutes, up to 1 hour, up to 3 hours, up to 6 hours, up to 12 hours, up to 1 day, up to 3 days, up to 1 week, etc.).

In some embodiments, the processor308can report to a user interface310whether the subject's pain sensitivity has improved. In some embodiments, further EEG signals (e.g., third EEG signals, fourth EEG signals, etc.) are received by the processor306. The processor may determine, based on these further EEG signals, the PPS value and/or PAF value for the subject. These further values may be compared to respective values of, for example, the first EEG signals or the second EEG signals.

In some embodiments, the processor306can send additional feedback of the same type that was used during training at some later time. For example, if the feedback used in method400includes a pleasant sound and/or image, the processor306may send the same or similar pleasant sound and/or image to the mobile device of the subject at a later time (e.g., up to 30 minutes, up to 1 hour, up to 3 hours, up to 6 hours, up to 12 hours, up to 1 day, up to 3 days, up to 1 week, etc.). In this way, the subject may reenter the reduced pain sensitivity state based on the feedback prompt.

Example 1: Effectiveness of Neurofeedback for Modifying Pain Sensitivity as Determined by Changes in Evoked Pain

Three subjects participated in 10 in-person neurofeedback therapy sessions performed in a crossover neurofeedback versus sham feasibility study. Sessions included quantitative sensory testing (QST), wherein warmth detection thresholds, heat pain thresholds, and heat pain tolerance are measured using a thermode (QST Lab Thermal Cutaneous Stimulator TCS II.1.b). A series of temperatures were applied to the skin on different dermatomes. EEG data (qEEG) was collected from 19 points of the 10-20 system using a Brainmaster Discovery neurological EEG. Data was recorded at 5 kHz with 0.016-250 Hz hardware filters and impedance at all recording electrodes will be maintained below 20 kW. Additional, qEEG measurements were taken with BrainBit 4 dry electrode headband.

The neurofeedback was performed using a Brainmaster EEG biofeedback equipment using sensor positions at 01, 02, T5, and T6. A baseline resting state EEG was recorded before and after neurofeedback sessions. The neurofeedback was targeting alpha power was increased in the frequency band 1-2 Hz above the peak alpha frequency determined by visual inspection of eyes closed resting state EEG.

The sham protocol was performed to provide a reward if the alpha power remained in the same state (e.g., no change).

Participants were blinded to sham or neurofeedback (they were not aware which feedback was increasing PAF or decreasing PPS).

QEEG was first performed with a 4 electrode dry system, which included one minute of eyes open and two minutes of eyes closed EEG data that will be recorded. Subsequent QEEG was performed with Brainmaster Discovery.

Quantitative Sensory Testing (QST) thermal threshold testing was performed in a testing room with simultaneous EEG recording. Testing was performed using a standard thermode. Each test was repeated three times with a break in between tests to familiarize participants with upcoming task and rating procedures.

A “levels” program determined the best temperature for phasic heat pain. This test involved 12 stimuli of various temperatures (ranging between 37-48° C.) with each trial lasting a total of 15 seconds. At the end of each stimulus, participants provided verbal pain ratings on a scale anchored from 0 (“no pain at all”) to 10 (“worst pain imaginable”).

Following the levels task, a single trial of phasic heat pain ratings task was performed to ensure the temperature is suitable for the participant. This test consisted of 40 seconds of stimulation using the temperature determined previously and 20 seconds of room temperature stimulation (˜32° C.). Data was collected using verbally reported ratings.

Prior to neurofeedback or sham, participants completed a single phasic heat pain protocol using the suitable temperature that was determined. Data was collected via a physical lab form completed by the experimenter.

Participants were randomized to either 5 sessions of sham or neurofeedback.

Participants were instructed that they will receive 10 sessions of neurofeedback that may affect their sensitivity to heat. Participants were asked at the end of the last session after QST “Did you believe you received the active condition or the sham condition in the last 5 sessions?”. During the study participants were told they are receiving neurofeedback during all sessions.

Neurofeedback or sham was performed at visits 1-10. QST were performed before neurofeedback/sham at Visits 1 and 6. QST were performed after neurofeedback/sham at Visits 5 and 10.

Results for 3 participants are provided inFIG.7. Participants 001 and 003 had sham then neurofeedback, while Participant 002 had neurofeedback then sham.FIGS.8-9provide representative results for Participant 2, as described herein. ForFIG.8, a change in predicted pain sensitivity is identified as the area of post therapy curve over the pre therapy curve (e.g., see arrows onFIG.8).

Participant 1: Participant 1 was with neuropathic pain, migraines, chronic depression, post partum depression, and PTSD. During the study there was a reduction in evoked pain of 40% during the neurofeedback session and increase in evoked pain of 8% during sham. Pain metrics were recorded via brief pain inventory. Pain severity changed from 8 to 4 where prior to the study the participant reported worst pain in the last 24 hours of 4/10 and 1/10 after the study. Pain interference in the week prior to the study was 3/10 for general activity, 7/10 for mood, walking ability 1/10 (did not interfere), normal work 4/10, relationships with people 8/10, sleep 8/10, and enjoyment of life 8/10. After the study was completed the participant reported no pain in the last 24 hours. Pain interference for the week during neurofeedback therapy was general activity 1/10, mood 1/10, walking ability 1/10, normal work 1/10, relationships with people 1/10, sleep 1/10, enjoyment of life 1/10. Composite pain interference score decreased from 5.6 to 1 consistent with improvements in quality of life.

Participant 2: Participant was with neuropathic pain, chronic lower back pain, headaches, knee pain, and anxiety. During the study there was a 13.2% reduction in evoked pain during neurofeedback and a 23.3% increase in evoked pain which happened during sham which followed the 5 neurofeedback sessions. At the end of the study pain severity changed from 29 to 28 and pain interference changed from 8.4 to 7.6, however this was after the participants sensitivity returned to baseline during sham. The week of neurofeedback therapy the participants' reported pain severity changed from 29 to 19 and pain interference improved from 8.4 to 4.4.

Participant 3: Participant was with depression, anxiety, chronic lower back pain, somatic symptoms and chronic fatigue disorder. During the study there was a 22.2% reduction in evoked pain with neurofeedback and a 3.6% increase in evoked pain with sham. Pain severity was 15 with worst pain in the last 24 hours of 8/10. Pain severity decreased to 6 with worst pain in the last 24 hours of 2/10 after 5 daily sessions of neurofeedback. Pain interference stayed the same from 1.7 to 1.7.

Hardware and Software Implementations

In some examples, some or all of the processing described above can be carried out on a personal computing device, on one or more centralized computing devices, or via cloud-based processing by one or more servers. In some examples, some types of processing occur on one device and other types of processing occur on another device. In some examples, some or all of the data described above can be stored on a personal computing device, in data storage hosted on one or more centralized computing devices, or via cloud-based storage. In some examples, some data are stored in one location and other data are stored in another location. In some examples, quantum computing can be used. In some examples, functional programming languages can be used. In some examples, electrical memory, such as flash-based memory, can be used.

FIG.5is a block diagram of an example computer system500that may be used in implementing the technology described in this document. General-purpose computers, network appliances, mobile devices, or other electronic systems may also include at least portions of the system500. The system500includes a processor510, a memory520, a storage device530, and an input/output device540. Each of the components510,520,530, and540may be interconnected, for example, using a system bus550. The processor510is capable of processing instructions for execution within the system500. In some implementations, the processor510is a single-threaded processor. In some implementations, the processor510is a multi-threaded processor. The processor510is capable of processing instructions stored in the memory520or on the storage device530.

The memory520stores information within the system500. In some implementations, the memory520is a non-transitory computer-readable medium. In some implementations, the memory520is a volatile memory unit. In some implementations, the memory520is a non-volatile memory unit.

The storage device530is capable of providing mass storage for the system500. In some implementations, the storage device530is a non-transitory computer-readable medium. In various different implementations, the storage device530may include, for example, a hard disk device, an optical disk device, a solid-date drive, a flash drive, or some other large capacity storage device. For example, the storage device may store long-term data (e.g., database data, file system data, etc.). The input/output device540provides input/output operations for the system500. In some implementations, the input/output device540may include one or more of a network interface devices, e.g., an Ethernet card, a serial communication device, e.g., an RS-232 port, and/or a wireless interface device, e.g., an 802.11 card, a 3G wireless modem, or a 4G wireless modem. In some implementations, the input/output device may include driver devices configured to receive input data and send output data to other input/output devices, e.g., keyboard, printer and display devices560. In some examples, mobile computing devices, mobile communication devices, and other devices may be used.

In some implementations, at least a portion of the approaches described above may be realized by instructions that upon execution cause one or more processing devices to carry out the processes and functions described above. Such instructions may include, for example, interpreted instructions such as script instructions, or executable code, or other instructions stored in a non-transitory computer readable medium. The storage device530may be implemented in a distributed way over a network, such as a server farm or a set of widely distributed servers, or may be implemented in a single computing device.

The term “system” may encompass all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A processing system may include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). A processing system may include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

ADDITIONAL EMBODIMENTS

In one aspect, the disclosure features systems for modifying pain sensitivity in a subject. An example system can include a plurality of sensors configured to detect electroencephalography (EEG) signals in the subject, and a processor communicably coupled to the plurality of sensors. The plurality of sensors can receive first EEG signals from the sensors and determine, based on the first EEG signals, at least one of (i) a first value for a predicted pain sensitivity (PPS) associated with the subject or (ii) a second value for a peak alpha frequency (PAF) associated with the subject. The processor can be further configured to receive second EEG signals from the sensors, and provide feedback to the subject when a characteristic of the second EEG signals indicates a reduced pain sensitivity.

Various embodiments of the systems can include one or more of the following features.

The processor can be configured to determine that the pain sensitivity of the subject is modified based on at least one of the characteristic, the first value, or the second value. The PAF can be in a range of 8-12 Hz. The characteristic of the second EEG signals can include an alpha power value at a frequency, and the characteristic can indicate the reduced pain sensitivity when the alpha power value has increased at a frequency greater than the PAF. The processor can be configured to: receive third EEG signals from the sensors subsequent to providing feedback; determine, based on the third EEG signals, at least one of (i) a third value of the predicted pain sensitivity or (ii) a fourth value of the peak alpha frequency; and compare (i) the third value to the first value and/or (ii) the fourth value to the second value to determine whether the pain sensitivity in the subject is modified. Determining that the pain sensitivity is modified can be when the third value is a percentage greater the first value or the fourth value is a percentage greater than the second value. Determining that the pain sensitivity is modified can be when the third value is greater than a threshold above the first value or the fourth value is greater than a threshold above the second value.

The processor can be configured to: determine, based on the second EEG signals, at least one of (i) a third value of the predicted pain sensitivity or (ii) a fourth value of the peak alpha frequency; and compare (i) the third value to the first value and/or (ii) the fourth value to the second value; and provide the feedback based on the comparison. The feedback to the subject can include at least one of an auditory, visual, haptic, electrical, magnetic, or ultrasonic stimuli.

The system can include a mobile device configured to provide at least one of auditory stimuli or the visual stimuli. The system can include an apparatus configured to apply at least one of a magnetic stimuli or an ultrasonic stimuli to a brain or a peripheral nervous system of the subject.

The plurality of sensors can include electrodes configured to be positioned on a head of the subject. The predicted pain sensitivity can be based on Fourier transforms of the received first EEG signals or the second EEG signals in an alpha frequency range of 8-12 Hz. The first value of the predicted pain sensitivity can be based on the second value of the peak alpha frequency.

The predicted pain sensitivity can be a number on a predetermined scale. The system can include a first communication module coupled to the plurality of sensors and configured to transmit the first and second EEG signals; and a second communication module configured to receive the first and second EEG signals from the first communication module. The second communication module can be part of a mobile device. The processor can be part of the mobile device. The mobile device can include a user interface configured to present information based on the feedback signal. The processor can be part of a remote computing system. The remote computing system can include a storage module coupled to the processor and configured to store at least one of: (i) the first EEG signals, (ii) the second EEG signals, (iii) the first value for the predicted pain sensitivity, or (iv) the second value for the peak alpha frequency. The feedback can have a type, and the processor can be further configured to provide additional feedback of the type to the subject after determining that the pain sensitivity of the subject is modified.

The system can be configured to modify pain sensitivity associated with endometriosis in the subject. The system can be configured to modify pain sensitivity as an adjuvant therapy to endometriosis related central sensitization. The system can be configured to modify pain sensitivity associated with musculoskeletal pain in the subject. The system can be configured to modify pain sensitivity associated with chronic pain in the subject. The system can be configured to modify pain sensitivity associated with diabetic neuropathy in the subject. The system can be configured to modify pain sensitivity associated with shingles in the subject. The system can be configured to modify pain sensitivity associated with reflex sympathetic dystrophy syndrome in the subject. The system can be configured to modify pain sensitivity associated with cancer in the subject. The system can be configured to modify pain sensitivity associated with post-surgical pain in the subject. The system can be configured to modify pain sensitivity associated with a neurological disorder in the subject. The system can be configured to modify pain sensitivity associated with anxiety in the subject. The system can be configured to modify pain sensitivity associated with depression in the subject. The system can be configured to modify pain sensitivity associated with attention deficit hyperactivity disorder (ADHD) in the subject. The system is configured to prevent chronification of pain in the subject experiencing acute pain.

In another aspect, the disclosure features methods for modifying pain sensitivity in a subject. An example method can include receiving, by a processor from a plurality of sensors, first EEG signals and determining, by the processor based on the first EEG signals, at least one of (i) a first value for a predicted pain sensitivity (PPS) associated with the subject or (ii) a second value for a peak alpha frequency (PAF) associated with the subject. The example method can further include receiving, by a processor from a plurality of sensors, second EEG signals; and providing feedback to the subject when a characteristic of the second EEG signals indicates a reduced pain sensitivity.

Various embodiments of the methods can include one or more of the following features.

The method can include determining that the pain sensitivity of the subject is modified based on at least one of the characteristic, the first value, or the second value. The PAF is in a range of 8-12 Hz. The characteristic of the second EEG signals can include an alpha power value at a frequency, and the characteristic can indicate the reduced pain sensitivity when the alpha power value has increased at a frequency greater than the PAF. The method can include receiving third EEG signals from the sensors subsequent to providing feedback; determining, based on the third EEG signals, at least one of (i) a third value of the predicted pain sensitivity or (ii) a fourth value of the peak alpha frequency; and comparing (i) the third value to the first value and/or (ii) the fourth value to the second value to determine whether the pain sensitivity in the subject is modified. Determining that the pain sensitivity is modified can be when the third value is a percentage greater the first value or the fourth value is a percentage greater than the second value. Determining that the pain sensitivity is modified can be when the third value is greater than a threshold above the first value or the fourth value is greater than a threshold above the second value.

The method can include determining, based on the second EEG signals, at least one of (i) a third value of the predicted pain sensitivity or (ii) a fourth value of the peak alpha frequency; comparing (i) the third value to the first value and/or (ii) the fourth value to the second value to determine; and providing the feedback based on the comparison. The feedback to the subject can include at least one of an auditory, visual, haptic, electrical, magnetic, or ultrasonic stimuli. The feedback can be at least one of an auditory stimuli or a visual stimuli, and providing the feedback to the subject can be by a mobile device. The feedback can be at least one of a magnetic stimuli or an ultrasonic stimuli, and providing the feedback to the subject can be by an apparatus configured to apply the feedback. The plurality of sensors can include electrodes configured to be positioned on a head of the subject. The predicted pain sensitivity can be based on Fourier transforms of the received first EEG signals or the second EEG signals in an alpha frequency range of 8-12 Hz. The first value of the predicted pain sensitivity can be based on the second value of the peak alpha frequency.

The predicted pain sensitivity can be a number on a predetermined scale. The method can include transmitting, by a first communication module coupled to plurality of sensors, the first and second EEG signals; and receiving, by a second communication module, the first and second EEG signals from the first communication module. The second communication module can be part of a mobile device. The processor can be part of the mobile device. The mobile device can include a user interface configured to present information based on the feedback signal. The processor can be part of a remote computing system. The remote computing system can include a storage module coupled to the processor and configured to store at least one of: (i) the first EEG signals, (ii) the second EEG signals, (iii) the first value for the predicted pain sensitivity, or (iv) the second value for the peak alpha frequency. The feedback can have a type, and the method can further include providing additional feedback of the type to the subject after determining that the pain sensitivity of the subject is modified.

The method can be used to modify pain sensitivity associated with endometriosis in the subject. The method can be used to modify pain sensitivity as an adjuvant therapy to endometriosis related central sensitization. The method can be used to modify pain sensitivity associated with musculoskeletal pain in the subject. The method can be used to modify pain sensitivity associated with diabetic neuropathy in the subject. The method can be used to modify pain sensitivity associated with shingles in the subject. The method can be used to modify pain sensitivity associated with reflex sympathetic dystrophy syndrome in the subject. The method can be used to modify pain sensitivity associated with cancer in the subject. The method can be used to modify pain sensitivity associated with post-surgical pain in the subject. The method can be used to modify pain sensitivity associated with a neurological disorder in the subject. The method can be used to modify pain sensitivity associated with anxiety in the subject. The method can be used to modify pain sensitivity associated with depression in the subject. The method can be used to modify pain sensitivity associated with attention deficit hyperactivity disorder (ADHD) in the subject. The method is used to prevent chronification of pain in the subject experiencing acute pain.

Terminology