Neural biomarker of food preoccupation

Abstract

The present disclosure provides systems and methods for optimizing a treatment regimen in a subject in need thereof. The methods may include detecting a biomarker and, in response to detecting the biomarker, altering one or more aspects of a treatment regimen for the subject. In an aspect, the methods include: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control, and altering a treatment regimen of the subject in response to detecting the biomarker.

Description

NEURAL BIOMARKER OF FOOD PREOCCUPATIONRELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of United States patent application no. 63 / 653,810, "Neural Biomarker Of Food Preoccupation" (filed May 30, 2024). All foregoing applications are incorporated herein by reference in their entireties for any and all purposes.GOVERNMENT RIGHTS

[0002] This invention was made with government support under 1 R01 MH 124760-01 Al awarded by the National Institutes of Health. The government has certain rights in the invention.TECHNICAL FIELD

[0003] The present invention relates to the field of electrophysiology associated with moments of food craving and disordered eating behavior.BACKGROUND

[0004] Incretins (Glucagon-like peptide-1, GLP-1, and glucose-dependent insulinotropic polypeptide, GIP) are hormones produced in the intestine regulating postprandial glucose and inhibiting appetite. Incretin-based therapies such as GLP-1 receptor agonists (GLP-lRAs) gained substantial attention for obesity treatment. Preclinical evidence suggests that GLP- lRAs impact eating behavior via modulating reward circuits (e.g., ventral tegmental area (VTA) and nucleus accumbens (NAc)) through GLP-1 receptors in these regions of the brain. Direct GLP-1RA injection to these regions decreased normal chow intake and even more so, palatable food intake in rats. A few studies explored GLP-lRAs’ impact on disordered eating behavior such as binge-eating, which entails eating an abnormally large amount of food with a subjective feeling of loss of control (LOC) and associated distress. These disordered eating behaviors involve extreme food preoccupation.

[0005] Direct evidence showing how GLP-lRAs modulate the human reward circuitry and impact eating behavior is still lacking.

[0006] There is a need for better analytics of food preoccupation and disordered eating behaviors. The present disclosure addresses this long-felt need.SUMMARY

[0007] In meeting the long-felt needs described above, the present disclosure provides systems and methods for detecting a biomarker in a subject.

[0008] In certain aspects, the present disclosure provides a method for optimizing a treatment regimen in a subject, comprising detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control, and altering a treatment regimen of the subject in response to detecting the biomarker. In certain aspects, the present disclosure provides a method for optimizing a treatment regimen in a subject, comprising detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-4 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control, and altering a treatment regimen of the subject in response to detecting the biomarker.

[0009] In certain aspects, the present disclosure provides a method for addressing Loss of Control (LOC) eating in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region, and altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power as compared to control. In certain aspects, the present disclosure provides a method for addressing Loss of Control (LOC) eating in a subject, comprising measuring a 1-4 Hz spectrum power in the ventral basal ganglia region, and altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power as compared to control.

[0010] In certain aspects, the present disclosure provides a method for identifying food preoccupation in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region, and measuring a number of LOC eating episodes per day.

[0011] In certain aspects, the present disclosure provides a method for identifying food preoccupation in a subject, comprising measuring a 1-4 Hz spectrum power in the ventral basal ganglia region, and measuring a number of LOC eating episodes per day.

[0012] Also provided is a method for determining a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-7 Hz spectrum power in the nucleus accumbens (NAc) in the brain of the subject when provoked with one or more food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

[0013] Further provided is a method for determining a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with an impulsivity state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with one or more food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

[0014] Additionally provided is a method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control, wherein the NAc signal is a correlate of an activation of a GLP-1 receptor, the detecting optionally being performed noninvasively.

[0015] Also disclosed is a method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control, wherein the NAc signal is a correlate of an activation of a GLP-1 receptor, the detecting optionally being performed noninvasively.

[0016] Further provided is a method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of a subject when provoked with food stimuli as compared to a control, wherein the first NAc signal is a correlate of an activation of a GLP-1 receptor, and wherein the subject has received a first GLP-1 receptor agonist, the detecting of the signal optionally being performed noninvasively.

[0017] Additionally disclosed is a method of determining a personalized therapeutic regimen, comprising: detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist, wherein the biomarker comprises an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of thesubject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of a GLP-1 receptor agonist and GIP receptor agonist.

[0018] Also provided is a method of determining a personalized therapeutic regimen, comprising: detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist; wherein the biomarker comprises an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of the GLP-1 receptor agonist and GIP receptor agonist.

[0019] Further provided is a computer-executable method comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.

[0020] Additionally disclosed is a computer-executable method, comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In the drawings, which are not necessarily drawn to scale, like numerals can describe similar components in different views. Like numerals having different letter suffixes can represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various aspects discussed in the present document. In the drawings:

[0022] FIG. 1 shows a schematic of the human reward circuitry of incretin-based therapies (e.g. GLP-lRAs).

[0023] FIG. 2 is a diagram showing two quadripolar depth electrodes that were placed bilaterally in the NAc. NAc shell - pink; NAc core - white; putamen - green; caudate - blue.

[0024] FIG. 3 shows a schematic of a timeline of events for the subject of Example 1.

[0025] FIGs. 4A-4C show spectrograms from magnet swipes of the subject of Example 1 for Control (FIG. 4 A), Severe Food Preoccupation (FIG. 4B), and Mild Food Preoccupation (FIG. 4C) conditions. These spectrograms show power values per frequency (1-10 Hz) when the patient was relaxing (Control; n=33), and when the patient was in a severe food preoccupation state (Severe Food Preoccupation; n=9), and when the patient was in a mild food preoccupation state (Mild Food Preoccupation; n=122). Each column corresponds to one magnet swipe episode. The red dashed line denotes the end of the nadir period.

[0026] FIGs. 5A-5C show a panel of graphs depicting electrophysiological data of a patient taking tirzepatide for her T2DM management. Exemplary raw traces (FIG. 5 A), power spectrum (FIG. 5B), and low frequency band (1-13 Hz) power (FIG. 5C) when the subject was relaxing (Control; blue), in a severe food preoccupation state (Severe Food Preoccupation; pink), and in a mild food preoccupation state (Mild Food Preoccupation; yellow) during Month 2-4 (Top) vs. Month 5-7 (Bottom). Power spectrums (FIG. 5B) and the averaged low frequency band (1-13 Hz) power bar plot (FIG. 5C) show suppression of the 1- 13 Hz low frequency band power during the nadir period (Month.2-Month.4) and the signal returned after the nadir period (Month.5 - Month.7).

[0027] FIGs. 6A-6I show absence of a biomarker during lab task in the nadir period and biomarker-correlated behavioral symptoms over time. FIG. 6A shows a design of the food provocation task. FIG. 6B (Top) shows an example of interacting stage and (Bottom) the first bite stage. FIG. 6C shows a craving rating per each stage. FIGs. 6D-6E show change of (“Low Frequency” 1-13 Hz) band power over time during the interacting stage (FIG. 6D) and the first bite stage (FIG. 6E). Here, t = 0 corresponds to when the subject started interacting with the food stimulus and to when the subject had the first bite. FIGs. 6F-6G show low frequency band (1-13 Hz) power during the interacting stage (FIG. 6F; ~30 seconds) and the first bite stage (FIG. 6G; 3 seconds). FIGs. 6H-6I show the change in patient-reported number of LOC eating episodes per month (FIG. 6H) and LOC eating severity captured by Eating Loss of Control Scale (ELOCS) (FIG. 61). The red dashed line denotes the approximate date of surgery, and the cyan block denotes the nadir period.

[0028] FIG. 7 is a table of two-sided permutation-testing values, left nucleus accumbens,*p<0.05.

[0029] FIG. 8 is a table of two-sided permutation-testing values, right nucleus accumbens,*p<0.05.

[0030] FIG. 9 shows the trajectory and localization of each electrode contact done in the planning software. Putative anatomical localization of each contact: 1. NAc Shell; 2. NAc Shell / core; 3, 4. Ventral Part of the ALIC(ventral capsule).

[0031] FIG. 10 shows an outline representing the region of the streamline heatmap within the seed region after threshold. Yellow: streamline heatmap to vmPFC (on the left). Purple: to Lateral Hypothalamus. Yellow rectangle represents a possible localization of the electrode when comparing with the Allen Brain Atlas: The shell subregion of the nucleus accumbens.

[0032] FIG. 11 shows the trajectory and localization of each electrode contact done in the planning software. Putative anatomical localization of each contact: 1. NAc Shell; 2. Nac Core; 3. Upper border of the NAc Core / Ventral Capsule;4. Ventral Part of the ALIC(ventral capsule).

[0033] FIG. 12 shows an outline representing the region of the streamline heatmap within the seed region after threshold. Yellow: streamline heatmap to vmPFC (on the left). Purple: to Lateral Hypothalamus. Yellow rectangle represents a possible localization of the electrode when comparing with the Allen Brain Atlas: The shell subregion of the nucleus accumbens.

[0034] FIG. 13 is a schematic of an RNS device and a magnet swipe.

[0035] FIG. 14 is a panel of graphs showing a Power spectrum and low frequency band (1- 13 Hz) in the right NAc.

[0036] FIGs. 15A-15B are a panel of graphs showing a Power spectrum and low frequency band (1-13 Hz) in the right NAc during the interacting stage of food provocation task.

[0037] FIGs. 16A-16B are a panel of graphs showing a Power spectrum and low frequency band (1-13 Hz) in the right NAc during the first bite stage of food provocation task.

[0038] FIG. 17 is a panel of graphs showing a Power spectrum of magnet swipes over time in the right NAc.

[0039] FIG. 18A-18H. Background information of Participant 3 and association of increased delta-theta power in the ventral NAc with food preoccupation in Participants 1 and 2. FIG. 18 A: Two quadripolar depth electrodes were placed bilaterally in the ventral NAc of Participant 3 with a neurostimulator fully implanted subgalealy in the skull. FIG. 18B:Anatomical figure of Participant 3 (view from posterior to anterior): three-dimensional rendering of DBS electrodes and their position within basal forebrain structures in the participant’s native space, ventral NAc - magenta; dorsal NAc - white; putamen - green; caudate - blue; Anterior Limb of Internal Capsule (ALIC) - light pink. FIG. 18C: Anatomical MRI image of Participant 3 (see FIGs. 20 - 23): (left-sided panel) left hemisphere and (right-sided panel) right hemisphere. Prior participants in this ongoing trial had similar electrode placement. FIG. 18D: Timeline of crucial events for Participant 3. Larger illustration and specifics for data collection and use are described in the Method section and FIGs. 24-25. FIG. 18E: Participant 1 (right ventral NAc): power spectrum (mean ± s.t.d) before the therapeutic stimulation was turned on (left-sided panel; pre-therapeutic phase) and after the therapeutic stimulation was turned on (right-sided panel; therapeutic phase) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The bottom black lines indicate frequencies with statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two-sided permutation testing (p<0.05) with cluster correction. For more information and for the left NAc, see FIG. 26, 28, and 30-31 and Tables 1-2 and 5. FIG. 18F: Data from Participant 2 (left ventral NAc), same format as FIG. 18E. For more information and for the right NAc, see FIG. 27, 29, and 32-33 and Tables 3-4 and 6. FIG. 18G-18H: This bar graph shows the number of Mild / Severe Food Preoccupation (yellow / pink) swipes per month for Participant 1 (FIG. 18G) and 2 (FIG. 18H), which could serve as a surrogate measure for the number of LOC eating episodes that they led to. Note that there is discrepancy between the reported number of Mild Food Preoccupation swipes through participant’s diary shown in FIG. 18G and the number of actual iEEG data analyzed shown in FIG. 18E due to technical limitations (e.g., device storage or trigger failure) and stimulation-related confounders. For Participant 1, Early stim-Month # corresponds to the period of FIG. 18E (left-sided panel; pre-therapeutic phase) and Postmax-Month # corresponds to the period of FIG. 18E (right-sided panel; therapeutic phase). For Participant 2, Prestim-Month # and Postmax-Month # corresponds to the period of FIG. 18F (left and right sided panel respectively).

[0040] FIG. 19A-19G. Association of increased delta-theta power in the ventral NAc with food preoccupation in Participant 3. FIGs. 19A-19B: The change in participant- reported number of LOC eating episodes per month relative to food preoccupation FIG. 19Aand LOC eating severity captured by Eating Loss of Control Scale (ELOCS) FIG. 19B: The navy dashed line denotes the approximate date of surgery, and the green block denotes the Biomarker Absent period. FIGs. 19C-19E: Participant 3 (left ventral NAc): Spectrograms from magnet swipes for Control FIG. 19C, Mild Food Preoccupation FIG. 19D, and Severe Food Preoccupation FIG. 19E conditions, acquired from the left ventral NAc. These spectrograms show power values per frequency from 1 to 10 Hz when the participant was relaxing (Control; n=33), when the participant was in a mild food preoccupation state (Mild Food Preoccupation; n=121), and in a severe food preoccupation state (Severe Food Preoccupation; n=10). Each column corresponds to one magnet swipe episode, which is a participant-triggered iEEG recording (90 or 180 s). The red dashed line denotes the end of the Biomarker Absent period and the beginning of the Biomarker Present period (see green and pink bars above each panel). For results of the right ventral NAc, see FIG. 34. Note that the apparent similarity in the number of food preoccupation episodes across two periods arise from two key reasons: First, Mild Food Preoccupation episodes were primarily associated with regular meals. Second, data shown in c-e reflects the number of available electrophysiological recordings. During Months 2-4, the reported number of Severe Food Preoccupation episodes was five but only four iEEG data were available due to technical limitations (e.g., device storage or trigger failure) as shown in e (under green bar). Likewise, during Months 5-7, the reported number was eleven but only six iEEG recordings were collected as shown in FIG. 19E (under pink bar). FIG. 19F: Participant 3 (left ventral NAc): Example raw traces (left-sided panel; for example raw traces for Participants 1 and 2, see FIG. 35), power spectrum (middle panel; mean ± s.t.d), and delta-theta-band (<7 Hz) power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink) during Months 2-4 (Biomarker Absent). The bottom black line in the power spectrum (middle panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition (two-sided permutation testing (p<0.05) with cluster correction). There was no significant difference except at 7.2-8.8 Hz where the power value from Severe Food Preoccupation was lower than control. In the box-whisker plot (right-sided panel), the central line indicates the median, and the bottom and top edges of the box indicates 25th and 75th percentiles, respectively. The individual points outside of whiskers are considered as outliers. The top black line with an asterisk mark in the box-whisker plot shows statisticallysignificant differences among three conditions after permutation testing (*p<0.05). During the Biomarker Absent period, there was also a 7% decrease in body weight relative to presurgery baseline (138 kg to 128 kg; FIG. 36). FIG. 19G Data from Months 5-7, same format as in FIG. 19F. For more information of FIG. 19F and FIG. 19G and for the right NAc, see FIG. 37 and Table 8.

[0041] FIG. 20. Anatomical image of each contact in the left NAc in Participant 3. Trajectory and localization of each electrode contact done in the planning software. Putative anatomical localization of each contact: 1. ventral NAc; 2. dorsal NAc; 3, 4. ventral part of the ALIC (ventral capsule)

[0042] FIGs. 21 A-21C. Anatomical image of the electrode in the left NAc in Participant 3 with the streamline acquired using diffusion imaging. FIG. 21A: Coronal T1 slice. Red to yellow overlay: increasing streamline count for each voxel. Blue overlay: electrode's CT artifact. FIG. 2 IB: Outline represents the region of the streamline heatmap within the seed region after thresholding. Yellow: streamline heatmap to vmPFC. Purple: to Lateral Hypothalamus. FIG. 21C: Yellow rectangle represents a possible localization of the electrode when compared to the Allen Brain Atlas: the NAc shell (purple).

[0043] FIG. 22. Anatomical image of each contact in the right NAc in Participant 3. Trajectory and localization of each electrode contact done in the planning software. Putative anatomical localization of each contact: 1. ventral NAc; 2. dorsal NAc; 3. Upper border of dorsal NAc / ventral capsule; 4. ventral part of the ALIC (ventral capsule)

[0044] FIGs. 23 A-23C. Anatomical image of the electrode in the right NAc in Participant 3 with the streamline acquired using diffusion imaging. FIG. 23 A: Coronal T1 slice. Red to yellow overlay: increasing streamline count for each voxel. Blue overlay: electrode's CT artifact. FIG. 23B: Outline represents the region of the streamline heatmap within the seed region after thresholding. Yellow: streamline heatmap to vmPFC (on the left). Purple: to Lateral Hypothalamus. FIG. 23C: Yellow rectangle represents a possible localization of the electrode when compared with the Allen Brain Atlas: the NAc shell (purple).

[0045] FIG. 24. RNS device (left-sided panel) and a figure depicting participant- triggered recordings are done (right-sided panel).

[0046] FIG. 25. Data timeline for Participants 1, 2, and 3. This diagram shows which dataset has been used to generate the results used in which figure per participant.

[0047] FIG. 26. Delta-theta-band (<7 Hz) in the right ventral NAc in Participant 1. Delta-theta-band (<7 Hz) power when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The top black line with an asterisk mark in the box -whisker plot (right-sided panel) shows statistically significant differences among three conditions after permutation testing (*p<0.05; Table 1).

[0048] FIG. 27. Delta-theta-band (<7 Hz) in the left ventral NAc in Participant 2. Delta-theta-band (<7 Hz) power when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The top black line with an asterisk mark in the box -whisker plot (right-sided panel) shows statistically significant differences among three conditions after permutation testing (*p<0.05; Table 3).

[0049] FIG. 28. Power spectrum and delta-theta-band (<7 Hz) in the left ventral NAc in Participant 1. Power spectrum (left-sided panel), and delta-theta-band (<7 Hz) power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two- sided permutation testing (p<0.05) with cluster correction. The top black line with an asterisk mark shows statistically significant differences among three conditions after permutation testing (*p<0.05; Table 2).

[0050] FIG. 29. Power spectrum and delta-theta-band (<7 Hz) in the right ventral NAc in Participant 2. Power spectrum (left-sided panel), and delta-theta-band (<7 Hz) power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two- sided permutation testing (p<0.05) with cluster correction. The top black line with an asterisk mark shows statistically significant differences among three conditions after permutation testing (*p<0.05; Table 4).

[0051] FIG. 30. Delta-theta-band (<7 Hz) power in the right ventral NAc in Participant 1 after the therapeutic stimulation was turned on. Delta-theta-band (<7 Hz) power when the participant was relaxing (Control; blue) or in a Severe Food Preoccupation state (pink). For p-value after permutation testing, see Table 5.

[0052] FIG. 31. Power spectrum and delta-theta-band (<7 Hz) power in the left ventral NAc in Participant 1 after the therapeutic stimulation was turned on. Power spectrum (left-sided panel), and delta-theta-band (<7 Hz)power (right-sided panel) when the participant was relaxing (Control; blue) or in a Severe Food Preoccupation state (pink). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two-sided permutation testing (p<0.05) with cluster correction. For p-value after permutation testing, see Table 5.

[0053] FIG. 32. Delta-theta-band (<7 Hz) power in the left ventral NAc in Participant 2 after the therapeutic stimulation was turned on. Delta-theta-band (<7 Hz)power when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). For p-values after permutation testing, see Table 6.

[0054] FIG. 33. Power spectrum and delta-theta-band (<7 Hz) power in the right ventral NAc in Participant 2 after the therapeutic stimulation was turned on. Power spectrum (left-sided panel), and delta-theta-band (<7 Hz) power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two-sided permutation testing (p<0.05) with cluster correction. For p-values after permutation testing, see Table 6.

[0055] FIG. 34. Power spectrum of magnet swipes over time in the right ventral NAc in Participant 3. Participant 3 (right ventral NAc): Spectrograms from magnet swipes for Control, Mild Food Preoccupation, and Severe Food Preoccupation conditions, acquired from the right ventral NAc. These spectrograms show power values per frequency (1-10 Hz) when the participant was relaxing (Control; n=33), when the participant was in a mild food preoccupation state (Mild Food Preoccupation; n=121), and in a severe food preoccupation state (Severe Food Preoccupation; n=10). Each column corresponds to one magnet swipe episode. The red dashed line denotes the end of the Biomarker Absent period and the beginning of the Biomarker Present period (see green and pink bars above each panel).

[0056] FIGs. 35A-35B. Exemplary raw traces in Participant 1 (right ventral NAc) and Participant 2 (left ventral NAc). Exemplary raw traces for FIG. 35 A: Participant 1 and FIG. 35B: Participant 2 when the participant was relaxing (Control; blue), in a severe food preoccupation state (Severe Food Preoccupation; pink), and in a mild food preoccupation state (Mild Food Preoccupation; yellow)

[0057] FIG. 36. The weight and BMI changes of Participant 3 over time up until Month 7 with her hemoglobin Ale level.

[0058] FIGs. 37A-37B. Power spectrum and delta-theta-band (<7 Hz)in the right ventral NAc in Participant 3. FIG. 37A: Participant 3: power spectrum (left-sided panel), and delta-theta-band (<7 Hz)power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink) during Month 2-4 (Biomarker Absent). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two-sided permutation testing (p<0.05) with cluster correction. The top black line with an asterisk mark shows statistically significant differences among three conditions after permutation testing (*p<0.05; see Table 8). FIG. 37B: Participant 3 : power spectrum (left-sided panel), and delta-theta-band (<7 Hz)power (right-sided panel) when the participant was relaxing (Control; blue), in a Mild Food Preoccupation state (yellow), or in a Severe Food Preoccupation state (pink) during Month 5-7 (Biomarker Present). The bottom black line in the power spectrum (left-sided panel) shows statistically significant differences in power values between the Control and the Severe Food Preoccupation condition after two-sided permutation testing (p<0.05) with cluster correction. The top black line with an asterisk mark shows statistically significant differences among three conditions after permutation testing (*p<0.05; see Table 8).

[0059] FIG. 38. Spectrograms from food preoccupation magnet swipes in Participant 3 and change point identification using various features. The top row shows spectrograms from food preoccupation magnet swipes of Participant 3 over time (corresponding to merged FIGs. 19D-19E). Note that the biomarker begins to appear on Month 5 Day 22, which we used to define the transition between the “Biomarker Present” and “Biomarker Absent” periods. However, as eating assessments are done monthly, we divided two periods on a monthly basis (Months 2-4 and 5-7). The second row shows thatfive self-reported ratings (craving, hunger, thirst, loss of control, and driven to eat) alone do not yield a transition point. Note that for the visualization we averaged over rating scores but for the actual analysis we used five feature dimensions as inputs for the model. The third row confirms that using only a delta-theta band power (<7 Hz) as an input feature, the model identifies the same transition point (Months Day22). The bottom row shows when we combined the delta-theta band power and ratings as input features (total of six feature dimensions), we obtained the same transition point (Months Day22). These results show that the ratings do not have a significant effect on identifying the transition point and confirm that the delta-theta band power (<7 Hz) is the most informative.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0060] The present disclosure may be understood more readily by reference to the following detailed description of desired embodiments and the examples included therein.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

[0062] The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0063] As used in the specification and in the claims, the term "comprising" can include the embodiments "consisting of' and "consisting essentially of.” The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that require the presence of the named ingredients / steps and permit the presence of other ingredients / steps. However, such description should be construed as also describing compositions or processes as "consisting of' and "consisting essentially of' the enumerated ingredients / steps, which allows the presence of only the named ingredients / steps, along with any impurities that might result therefrom, and excludes other ingredients / steps.

[0064] As used herein, the terms “about” and “at or about” mean that the amount or value in question can be the value designated some other value approximately or about the same. It is generally understood, as used herein, that it is the nominal value indicated ±10% variation unless otherwise indicated or inferred. The term is intended to convey that similar values promote equivalent results or effects recited in the claims. That is, it is understood that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but can be approximate and / or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. In general, an amount, size, formulation, parameter or other quantity or characteristic is “about” or “approximate” whether or not expressly stated to be such. It is understood that where “about” is used before a quantitative value, the parameter also includes the specific quantitative value itself, unless specifically stated otherwise.

[0065] Unless indicated to the contrary, the numerical values should be understood to include numerical values which are the same when reduced to the same number of significant figures and numerical values which differ from the stated value by less than the experimental error of conventional measurement technique of the type described in the present application to determine the value.

[0066] All ranges disclosed herein are inclusive of the recited endpoint and independently of the endpoints. The endpoints of the ranges and any values disclosed herein are not limited to the precise range or value; they are sufficiently imprecise to include values approximating these ranges and / or values.

[0067] As used herein, approximating language can be applied to modify any quantitative representation that can vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about” and “substantially,” may not be limited to the precise value specified, in some cases. In at least some instances, the approximating language can correspond to the precision of an instrument for measuring the value. The modifier “about” should also be considered as disclosing the range defined by the absolute values of the two endpoints. For example, the expression “from about 2 to about 4” also discloses the range “from 2 to 4.” The term “about” can refer to plus or minus 10% of the indicated number. For example, “about 10%” can indicate a range of 9% to 11%, and “about 1” can mean from 0.9-1.1. Other meanings of “about” can be apparent from the context, such as rounding off, so, for example “about 1” can also meanfrom 0.5 to 1.4. Further, the term “comprising” should be understood as having its open- ended meaning of “including,” but the term also includes the closed meaning of the term “consisting.” For example, a composition that comprises components A and B can be a composition that includes A, B, and other components, but can also be a composition made of A and B only. Any documents cited herein are incorporated by reference in their entireties for any and all purposes.

[0068] Food Preoccupation

[0069] Tirzepetide is a Glucagon-Like Peptide 1 (GLP-1) / Gastric Inhibitory Peptide (GIP) receptor dual -agonist. GLP-1 is a peptide secreted both centrally and peripherally; the former from neurons in the nucleus tractus solitarius (NTS) of the hindbrain and the latter from the L cells in the small and large intestine. Due to its effect on reducing appetite and body weight in addition to its incretin effect, there has been tremendous attention in GLP-1 receptor agonism for obesity treatment alongside Type 2 Diabetes Mellitus (T2DM) treatment. Moreover, GIP receptor agonism is often combined with GLP-1 receptor agonism to maximize its therapeutic effect by ameliorating GLP-1 -induced side effects through GIP receptor agonism's potent antiemetic properties.

[0070] The GLP-1 receptor is expressed in both ventral tegmental area (VTA) and the nucleus accumbens (NAc), which are two regions in the mesolimbic dopaminergic reward system. Both areas receive projections from GLP-1 producing neurons in the NTS. Activation of GLP-1 receptors or injection of GLP-1 receptor agonist both reduce reward-motivated behaviors. The former decreased palatable food-related motivated behavior, and the latter decreased intake of regular chow, high-fat diet, and body weight in animal models.Conversely, injection of GLP-1 receptor antagonist increased intake of regular chow and high-fat diet in animal models. Reduction of food intake and body weight via activation of GLP-1 receptor in VTA and NAc does not entail nausea responses, which is valuable for targeting these regions for development of obesity treatments.

[0071] Loss of control eating in obesity is an unwanted urge that is compulsive and represents similar sense of being out of control as reported in OCD, bulimia nervosa, binge eating disorder, and substance use disorder.

[0072] GLP-1 receptor agonism is receiving attention for its therapeutic potential on obesity and related food preoccupations, but how it interacts with mesolimbic reward circuitry expressing GLP-1 receptors is yet unknown. The inventors of the present disclosureobserved the first-in-human NAc electrophysiological data examining GLP-l / GIP receptor dual-agonism on human NAc electrophysiology. The inventors of the present disclosure made the unexpected discovery that amelioration of food preoccupations involves attenuation of low frequency oscillations. In some embodiments, the term “low frequency” comprises a range of frequencies within 1-13 Hz.

[0073] The present disclosure provides an electrophysiological signature to guide titration of GLP-1 receptor related agonists for managing food-preoccupation and related-binge-like eating behaviors. Without wishing to be bound by theory, if an administered dose of GLP-1 receptor-related agonists successfully attenuate this low-frequency signal, especially at low frequency band, during at-risk-to-binge period for the patient, that dose could be the effective dose for a patient. However, if this attenuation is not reached even with the maximum dose, the medicine may not be effective for that patient, or a higher dose may be tested. It has been reported that these novel incretin-based therapies have temporary blockade effects on urges to binge and LOC eating that go along with the significant body weight reduction. Thus, the electrophysiological signature of the present disclosure serves as an NAc monitor to better dose these agents to control unwanted urges to binge and / or LOC eating associated with distress.

[0074] For example, regression in LOC eating paired with a return in low frequency oscillations suggests dose titration is needed. This objective approach to medication titration would facilitate personalized care. Given obesity and associated eating disorders are extremely common, and LOC eating is pervasive, a medical device implant could provide data on dose titration for personalized medication dosing strategies.

[0075] In response to this unmet need, the electrophysiological signature of the present disclosure serves to guide pharmacologic treatment.

[0076] Embodiments

[0077] The present disclosure provides a method for optimizing a treatment regimen in a subject, the method comprising detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state.

[0078] In some embodiments, the biomarker comprises an elevation in the 1-4 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-6 Hz spectrum power in the ventral basal ganglia region in the brain of thesubject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-8 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-10 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-15 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control. In some embodiments, the biomarker comprises an elevation in the 1-20 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control.

[0079] In some embodiments, the method comprises altering a treatment regimen of a subject in response to detecting the biomarker.

[0080] In some embodiments, the biomarker is compared to a control 1-4 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when not provoked with food stimuli. In some embodiments, the biomarker is compared to a control 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when not provoked with food stimuli.

[0081] In some embodiments, the biomarker comprises harmonics at any one or more of about 1.4 Hz, about 2.8 Hz, and about 4.2 Hz. In some embodiments, the biomarker comprises a sawtooth-like waveform.

[0082] In some embodiments, the biomarker 1-4 Hz power spectrum is detected in one or more regions of the subject’s ventral striatum and / or amygdala. In some embodiments, the biomarker 1-13 Hz power spectrum is detected in one or more regions of the subject’s ventral striatum.

[0083] In some embodiments, the one or more regions comprise any one or more of the nucleus accumbens core, nucleus accumbens shell, ventral striatum, ventral pallidum, ventral caudate, olfactory tubercule, and the subthalamic nucleus. In some embodiments, the biomarker is detected in the nucleus accumbens.

[0084] In some embodiments, the biomarker 1-13 Hz power spectrum is detected using electroencephalogram (EEG) of the scalp, optionally with one or more scalp sensors. In some embodiments, the biomarker 1-4 Hz power spectrum is detected using electroencephalogram (EEG) of the scalp, optionally with one or more scalp sensors. In this regard, the disclosure of WO 2023 / 278784 Al (the “784 app”) is fully incorporated by reference herein in its entirety for all purposes. For example, the ‘784 app describes examples of non-invasive sensors configured to sense or record an electrophysiology signal at the scalp of the user that is a correlate of a user’s NAc signal, the method of which may be incorporated herein to sense or record a correlate of any of the NAc signals and / or ventral basal ganglia signals herein.

[0085] In some embodiments, the treatment regimen comprises administration of a therapeutic. In some embodiments, altering the treatment regimen comprises any one or more of (i) changing a dosage of the therapeutic, (ii) terminating the therapeutic and administering a different therapeutic, and (iii) administering an additional therapeutic.

[0086] In some embodiments, the therapeutic comprises any one or more of a GLP-1 receptor agonist and a GIP receptor agonist. In some embodiments, the therapeutic is tirzepatide, dulaglutide, or semaglutide.

[0087] In some embodiments, the subject failed bariatric surgery, behavioral therapy, and / or pharmacological therapy for weight loss. In some embodiments, the pharmacological therapy is dulaglutide or semaglutide.

[0088] The present disclosure provides, among other things, a method of addressing Loss of Control (LOC) eating in a subject, comprising measuring a 1-4 Hz spectrum power in the ventral basal ganglia region. The present disclosure provides a method of addressing Loss of Control (LOC) eating in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region.

[0089] In some embodiments, the method comprises altering a treatment regimen of the subject in response to elevation in the 1-4 Hz spectrum power as compared to control. The present disclosure provides a method of identifying food preoccupation in a subject, comprising measuring a 1-4 Hz spectrum power in the ventral basal ganglia region; and measuring a number of LOC eating episodes per day. In some embodiments, a subject experiences at least 5, at least 10, at least 12, at least 15, at least 17, at least 19, at least 20, at least 22, at least 24, or at least LOC eating episodes per day prior to weight loss therapy.

[0090] In some embodiments, the method comprises altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power as compared to control. The present disclosure provides a method of identifying food preoccupation in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region; and measuring a number of LOC eating episodes per day. In some embodiments, a subject experiences at least 5, at least 10, at least 12, at least 15, at least 17, at least 19, at least 20, at least 22, at least 24, or at least LOC eating episodes per day prior to weight loss therapy.EXAMPLES

[0091] Example 1 : GLP-1 receptor agonist ameliorates food preoccupation and suppresses low frequency band signal in the human nucleus accumbens.

[0092] In this example, intracranial electroencephalography recordings in the nucleus accumbens were acquired from a single subject with treatment-resistant obesity and extreme food preoccupation who was on GLP-l / GIP dual-receptor agonism (tirzepatide) for type 2 diabetes management. It was shown that GLP-l / GIP dual-receptor agonism alters an electrophysiological biomarker in low frequency (1-13 Hz) in the human nucleus accumbens whose change was correlated with food preoccupation and related behavioral symptoms.

[0093] Incretins (Glucagon-like peptide-1, GLP-1, and glucose-dependent insulinotropic polypeptide, GIP) are hormones produced in the intestine regulating postprandial glucose and inhibiting appetite. Due to their efficacy, incretin-based therapies such as GLP-1 receptor agonists (GLP-lRAs) gained substantial attention for obesity treatment. Preclinical evidence suggests that GLP-lRAs impact eating behavior via modulating reward circuits (e.g., ventral tegmental area (VTA) and nucleus accumbens (NAc)) through GLP-1 receptors in these regions. These disordered eating behaviors involve extreme food preoccupation. For example, binge-eating, which entails eating an abnormally large amount of food with a subjective feeling of loss of control (LOC) and associated distress.

[0094] However, direct evidence showing how GLP-lRAs modulate the human reward circuitry and impact eating behavior is still lacking. In this study, a target engagement biomarker provides an objective measure to assess on-target effects of GLP-lRAs in the reward circuit and a link to behavioral changes. A patient with in situ intracranial electrodes intracranial electroencephalography (iEEG) to be performed. The patient had an implantationof a neurostimulator with two depth electrodes in bilateral nucleus accumbens (NAc) (FIG. 2).

[0095] The subject was a 59-year-old woman with severe treatment-resistant obesity and food preoccupation. She weighed 340 lb prior to laparoscopic Roux-en-Y gastric bypass surgery and her lowest documented weight following surgery was 254 lb (BMI of 38.7 kg / m2). However, near the time of enrollment into the presently described study, her weight was 303 lb (BMI of 46.1 kg / m2). She was often preoccupied with thoughts of food, which led to unwanted eating behaviors. In the month prior to study entry, she reported 19 LOC eating episodes, which are similar to binge-eating but without consumption of objectively large amount of food. She endorsed eating until uncomfortably full, eating large amounts when she was not hungry, and feeling guilty after these episodes with high levels of distress. The subject fulfilled all eligibility criteria including the failure of a GLP-1 receptor agonist (Dulaglutide), which was discontinued. Subject was switched to a tirzepatide for T2D management and there was no reported impact on weight and LOC eating related symptoms. Due to this lack of efficacy on 7.5 mg / week dose, the subject was enrolled in the presently described study and was implanted the neurostimulator (Neuropace, Inc) bilaterally in the nucleus accumbens (FIG. 2). The dosage of tirzepatide was up-titrated by her primary care doctor immediately before the implantation surgery to 12.5 mg / week (FIG. 3).

[0096] With the implanted neurostimulator, patients can swipe a magnet over their device under the scalp to trigger a subject-initiated recording in their daily life. After the implantation, the iEEG recording (90 or 180 sec) from magnet swipes were acquired when the subject was in a food-preoccupied state (Severe / Mild Food Preoccupation swipes) or when the subject was relaxing (Control swipes). Severe and Mild Food Preoccupation swipes were classified based on the extent of craving the subject was feeling. A food provocation task was also conducted for two days, one day each in Month 3 and 4 (FIG. 3). This task (FIG. 6 A) involved providing palatable food and neutral food stimuli and asking the subject to interact with the stimulus as if she was going to eat it (interacting) and to take a first bite (first bite) while obtaining a continuous iEEG recording. The subject was also asked to rate their craving level in a 5 -Likert scale prior to each stage.

[0097] It was hypothesized that GLP-lRAs may alter disordered eating behavior in addition to normal food intake via modulating this the low frequency band (1-13 Hz) power biomarker in the NAc, a prime region in the reward circuit. In FIGs. 4A-4C, each verticalcolumn corresponds to spectral power density up to 10 Hz from a single magnet swipe and the low frequency band power increase was denoted as yellow. From Month 2 to 4, low frequency band power from Severe Food Preoccupation swipes was indistinguishable to a value from Control swipes (FIGs. 4A-4C), and thus, this Month 2-4 period was defined as the nadir period. During the nadir period, the raw iEEG trace for Severe Food Preoccupation condition (Pink) was similar to the Control condition (Blue) (FIG. 5 A, top panel). The low frequency band power acquired from 5-second-epochs of Severe Food Preoccupation swipes was indistinguishable to the one from Control swipes (FIGs. 5A-5B, top panel; permutation testing, *p<0.05, FIG. 7). To test whether the biomarker was also absent in the controlled setting, the data from the food provocation task, conducted during the nadir period was analyzed. The signal was again absent (FIGs. 6A-6G). Although the subject was feeling more craving for palatable food based on the ratings (FIG. 6C), which is similar to Severe Food Preoccupation swipes, there was no significant low frequency band power difference between the palatable and neutral food conditions during interacting and 1st bite stages over time (FIGs. 6D-6E; cluster-based permutation testing) or on average (FIGs. 6F-6G; permutation testing). During this period, there was a precipitous drop in LOC eating frequency and severity (FIGs. 6H-6I). The subject-reported LOC eating frequency dropped from 19 / month to 0-3 / month. This suggests that GLP-lRAs ameliorated food preoccupation and related symptoms via suppressing the low frequency band biomarker in the reward circuitry. Moreover, the low frequency band power from Mild Food Preoccupation swipes were slightly but significantly higher than other two swipes, which may suggest that GLP-lRAs alter severe food preoccupation more effectively than mild food preoccupation.

[0098] After the nadir period (Month 5-7), the suppressed low frequency band biomarker reappeared as denoted as yellow (FIGs. 4A-4C). In Severe Food Preoccupation swipes, a prominent sawtooth-like delta oscillatory waveform was observed (FIG. 5A, bottom panel). Sawtooth waves were characterized by a voltage that is rapidly rising on one end and more slowly falling off on the other end, and harmonics in the frequency spectrum, bumps around 1.4, 2.8, and 4.2 Hz were observed. Sawtooth waveforms have been reported including the theta wave in the hippocampus and previous literature suggests that the difference in the waveforms may represent different underlying generation mechanisms such as synchronous local spiking. In addition, a low frequency band power from 5-second-epochs of Severe Food Preoccupation swipes was significantly higher than the one from Control / Mild FoodPreoccupation swipes (FIGs. 5B-5C, bottom panel; permutation testing, *p<0.05, FIG. 7). The low frequency band power was the highest for Severe Food Preoccupation swipes, and then for Mild Food Preoccupation swipes, and the lowest for Control swipes. This observation suggests that the low frequency band biomarker is commensurate with the extent of food preoccupation. This electrophysiological change was correlated with the behavioral change (FIGs. 6H-6I). Exacerbated food preoccupation often leads to LOC eating episodes, and during Month 5-7, the number of LOC eating frequency returned to 7 in Month 7. Similarly, the LOC eating severity captured by Eating Loss of Control Scale (ELOCS) also increased to 4.17 in the early Month 8. These results suggest that as GLP-lRAs-mediated suppression of low frequency band biomarker reduces, the biomarker returns with the following aggravation of food preoccupation and related symptoms.

[0099] In this case report, a case was described that investigated the mechanism of incretinbased pharmacological agents in the human reward circuitry. A low frequency band power increase in the NAc during the moment of intense food preoccupation was demonstrated and this biomarker was suppressed for more than 3 months by GLP-l / GIP dual-receptor agonist (tirzepatide), which correlated with reduced food preoccupation during this time. However, the biomarker subsequently re-emerged with a corresponding return of food preoccupation. The results suggest that GLP-lRAs may regulate food preoccupation via suppressing the low frequency band biomarker in the human NAc.

[0100] This low frequency band biomarker had a sawtooth-like waveform with harmonics around 1.4, 2.8, and 4.2 Hz. This particular waveform suggested a synchronous local spiking in the NAc. In addition, the brain areas communicate via slower oscillations in the “receiver” side and via faster oscillations in the “sender” side. Ventral NAc gets input from various brain regions including prefrontal cortices (e.g., ventromedial prefrontal cortex (vmPFC)) and mesolimbic reward area (e.g., ventral tegmental area (VTA)). Thus, synchronous spiking activities may create sawtooth-like waveforms which makes the NAc be strongly driven by the other brain areas, such as VTA, resulting in malfunctioning compulsive behavior. Via modifying their receptors in the NAc, VTA and related regions, incretin-based agents may interfere with this synchronous activity.

[0101] There were observations that the biomarker was also present during Mild Food Preoccupation / Control swipes. It is reflected as a longer upward tail in box -whisker plots (FIG. 5C) and some swipes with higher low frequency band power in the spectrum graphs(FIGs. 4A-4C). A prominent low frequency signal is present, for instance, in some Mild Food Preoccupation swipes in Month 5 (FIG. 4C). For those swipes, the subject reported that she had family meals during holidays and was very stressed. Considering this, the low frequency band oscillation in the NAc may reflect not only the intensity of food preoccupation, but also other aspects of food preoccupation such as emotional eating.

[0102] This patient already failed to lose weight using a GLP-1RA (dulaglutide). This may suggest that the result could be due to a combinatorial effect of GLP-l / GIP dual -receptor agonist as there is evidence that this combinatorial therapy may yield superior efficacy. The return of food preoccupation despite the use of a GLP-1RA might suggest that an additional form of intervention may be needed for treatment-resistant obese patients with compulsive eating behavior.

[0103] GLP-lRAs’ direct action on the human reward circuit is shown in the present data through changes in both behavior and a neurophysiological signal. Here, GLP-l / GIP dualreceptor agonism (tirzepatide) suppressed a low frequency band (1-13 Hz) signal and food preoccupation for more than three months, and this biomarker reemerged preceding the return of food preoccupation showing a correlation between the electrophysical and behavioral data. The present findings show that this low frequency band biomarker may act as a target engagement biomarker to guide a therapeutically effective dose on incretin-based therapies. These data demonstrate that GLP-1RA engages the human reward circuit and supports the use of this biomarker as a target engagement biomarker of GLP-lRAs.

[0104] Methods

[0105] Participant. The participant was a 59-year-old woman with severe treatmentresistant LOC eating and obesity. Prior to laparoscopic Roux-en-Y gastric bypass surgery, she weighed 340 lb and she had cravings for calorically dense food choices. After her surgery, she was 254 lb (BMI of 38.7 kg / m2), but, near the time of enrollment, her weight was 303 lb (BMI of 46.1 kg / m2). She stated that she was often preoccupied with thoughts of food, which led to ordering a meal out or to continual snacking even though she wanted to resist. Her preoccupation focused on both sweet and salty foods such as pre-packaged cupcakes and roast beef sandwiches with fries. She reported 19 LOC episodes in the previous month upon study entry. These episodes were not objectively large and did not meet the criteria for objective binge episodes, likely given her restricted gastric volume following surgery. She endorsed eating until uncomfortably full, eating large amounts when she was nothungry, and feeling guilty after these episodes, with high levels of distress associated with them.

[0106] The participant fulfilled all eligibility criteria of the trial which are mainly; (1) BMI 40-60 kg / m2, (2) Failure of bariatric surgery, behavioral therapy, and pharmacological therapy for weight loss, (3) Shows LOC eating behaviors at least 4 per week. Importantly, these included the failure of a GLP-1 receptor agonist (Dulaglutide), which had not decreased her weight and LOC episodes, and thus, it was discontinued. She was switched to a GLP- 1 / GIP dual-receptor agonist (tirzepatide) for its FDA-approved indication treating type 2 diabetes. On a 7.5 mg / week dose, she reported temporary weight loss followed by subsequent return to her initial weight with no reported impact to her LOC episodes. This was interpreted as a failure for weight loss effect, and the subject was enrolled into the study and underwent implantation of the rDBS system (Neuropace, Inc) bilaterally in the NAc. The dosage of tirzepatide was uptitrated by her primary care doctor right before the implantation surgery to 12 mg / week and again to a maximum of 15 mg / week after about 4 months.

[0107] Surgical procedure. The surgical procedure has been previously reported6. In brief, probabilistic tractography was utilized to guide surgical targeting of the NAc shell as previously described. On the day of surgery, implantation of bilateral deep brain stimulation (DBS) electrodes in the nucleus accumbens was performed under awake conditions as per our standard institutional practice. Using a personalized appetitive provocation task, microelectrode recording was performed intraoperatively to identify single-unit or multi-unit appetitive neural activity. After confirmation of the target with electrophysiology and imaging, a quadripolar depth electrode was placed and macroelectrode monopolar stimulation mapping was conducted to confirm positive effects and no adverse effects. After securing the DBS electrodes, the patient was put under anesthesia and the electrodes were connected to the neurostimulator pulse generator, which was placed in the right parietal skull region.

[0108] Data acquisition. Intracranial electroencephalogram (iEEG) recordings were acquired from the FDA-approved responsive Deep Brain Stimulation (rDBS) device (NeuroPace, Inc). Responsive DBS device is different from regular DBS device in terms of that it stimulates only when it detects a predefined biomarker rather than stimulating continuously.

[0109] Neural recordings acquired from the rDBS device were used to identify biomarkers differentiating Severe Food Preoccupation swipes from Mild Food Preoccupation / Controlswipes. iEEG data were recorded at 250 Hz sampling rate and bipolar re-referenced online. Four electrode contacts were located in the following order: 1 - NAc shell (the most ventral contact), 2 - NAc core, 3 and 4 - Anterior limb of internal capsule (ALIC). For the subject, an electrode in the left side, one channel (Ch.l) was bipolar re-referenced between contact 1 and 3, and the other channel (Ch.2) was between contact 2 and 4. Likewise, an electrode in the right side, one channel (Ch.3) was bipolar re-referenced between contact 1 and 3, and the other channel (Ch.4) was between contact 2 and 4. Stimulation was not delivered throughout the data acquisition process. To reduce confounders due to stimulation, magnet swipes acquired at the beginning of stimulation period without any prominent eating behavior change and without any stimulation within the magnet swipe time window were used. Lastly, data from up to a month from the surgery date (Month 1) was excluded due to confounding with an implantation effect, which is a standard in the field.

[0110] Magnet swipe. Subjects can initiate intracranial EEG (iEEG) recordings via swiping their magnet over the surgically implanted device under the scalp. Magnet swipe triggers iEEG recordings which records a preset length of time before and after magnet swipe with a 2: 1 ratio. For instance, if the preset length is 90 seconds, it records 60 seconds before magnet swipe and 30 seconds after. For this study, the length was set to 90 seconds or 180 seconds. The Control condition was magnet swipes collected when the subject was relaxing and not feeling craving. For the Food Preoccupation condition, subject was asked to swipe the magnet when they are feeling craving for food and before eating. Conventionally, food preoccupation is a more general term including food cravings and thoughts about when / where / what to eat. As magnet swipes can only capture a short period of neural recordings, to clarify and to capture the most relevant moment of food preoccupation, subjects were asked to swipe when they were feeling craving. For all magnet swipes, subjects were asked to keep a magnet swipe diary, recording their craving / hunger / thirst level, the extent they felt a loss of control, and the extent they felt compelled to eat in a 5-Likert Scale (1 - none / not at all vs. 5 - extreme / extremely). Specifically, for craving 1 meant no craving, 3 meant moderate craving (e.g. can control / resist with effort), and 5 meant extreme craving (e.g. cannot control / resist). Severe and Mild Food Preoccupation episodes were classified based on these craving ratings per subject.

[0111] Food provocation task. A food provocation task which involves providing palatable food and neutral food stimuli in a progressive stage was conducted (FIG. 6A).Palatable food stimuli are food items which the subject often binges on (e.g., potato chips), and neutral food stimuli are food items that the subject does not binge on (e.g., plain yogurt). In the task, the subject was seated in front of a table and a research staff entered the room with a stimulus covered with a lid. The subject was asked to just look at it (Watching) and then to look and smell it after the staff removed the lid (Smelling). Then, the subject was asked to interact with it as if she was going to eat it (Interacting). After interacting, the subject was asked to taste it (Tasting). There was ~ 3 seconds of a brief moment immediately before the subject to have the first bite of food stimulus (1st Bite). Watching, Smelling, and Interacting stages last for 30 seconds and Tasting stage lasts for 15 seconds. In addition, the subject was asked to rate (FIG. 6C) how much they were craving before the stimulus was presented (Baseline), after seeing the stimulus and before smelling (After Watching), after smelling and seeing the stimulus and before interacting (After Smelling), after interacting the stimulus and before tasting (After Interacting), and after tasting (After Tasting). Rating was a 5-Likert scale, where 1 is no craving, 3 is a moderate craving that they can resist if they try and 5 is an extreme craving that they cannot resist. During the task, electrophysiological data were acquired using the research tool from NeuroPace including a wand tool, a wand case tool, a research tool and a NeuroPace programmer tablet. Continuous iEEG data were live- streamed using a wand tool and the programmer tablet and they were time-stamped using a research tool and a wand case tool.

[0112] Behavioral Data. The number of LOC eating episodes per month were collected through her magnet swipe diary, Ecological Momentary Assessment (EMA), and her verbal / written reports if she forgot to keep them in the diary ZEMA. Her LOC eating severity using the Eating Loss of Control Scale (ELOCS) was also assessed.

[0113] Clinical measures. The LOC eating severity was captured by Eating Loss of Control Scale (ELOCS). ELOCS measures LOC eating frequency and severity using 18-item assessment.

[0114] Signal processing. In the offline analysis, standard preprocessing techniques were conducted in MATLAB (R2022b) using FieldTrip Toolbox (Donders Institute for Brain, Cognition and Behavior, Radboud University, the Netherlands) which involved application of a 1-124 Hz band-pass filter. For food provocation task data, additional notch-filter was applied to remove line-noise (60 Hz) and its harmonics. Static spectral analysis was performed using multitaper method with 4 slepian multip-tapers per 5 seconds non-overlapping epochs. A full power spectrum (1-124 Hz) was acquired with 0.5 Hz frequency resolution, and 6 spectral frequency band powers were acquired (delta = 1-4 Hz, theta = 5-8 Hz, low-frequency = 1-13 Hz, alpha = 8-13 Hz, beta = 13-32 Hz, high-frequency = 32-120 Hz). 5-s epochs containing artifacts, mainly consisted of magnet swipe, were removed if the artifact was larger than 6x(standard deviation of the epoch data points). For power spectrums in FIGs. 6E-6G, power values per frequency were averaged per a magnet swipe.

[0115] References:

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[0104] Additional Disclosure

[0105] Obesity and related conditions are associated with distressing food preoccupation which often culminates in dysregulated eating behaviors including loss-of- control (LOC) eating. Incretin-based therapies can reduce excessive weight in obesity, but their impact on dysregulated eating behaviors remains largely unexamined. Understanding how these pharmacologies engage the brain’s mesolimbic circuitry could guide how to extend these therapies to this major unmet need. However, this mechanism of action remains unexplored in humans. We report a rare, first-in-human opportunity to investigate this physiological action by examining the electrophysiology directly within the human nucleus accumbens (NAc). Following a short-term course of tirzepatide, the patient-participant exhibited increased LOC eating episodes, which were preceded by an increased delta-theta- frequency (<7 Hz) power in the NAc region. We propose that the mechanism by which incretin-based therapies regulate food preoccupation and dysregulated eating behaviors may involve modulation of aberrant activity within a key hub of human mesolimbic circuitry.

[0106] Eating behaviors are regulated by homeostatic (e.g., eating based on energy needs) and hedonic (e.g., eating based on pleasure) processes, involving the hypothalamic and brainstem circuits as a hub for the former, and a mesolimbic circuit (including the nucleus accumbens, NAc) for the latter. These systems are highly interactive, and are further influenced by other intermediate brain regions to comprise complex motivational processes of ingestion. As such, the distinction between homeostatic and hedonic eating as entirely separate entities is increasingly viewed as a conceptual oversimplification. There is a preponderance of receptors of incretin-based therapies (e.g., glucose-dependent insulinotropic polypeptide, GIP; Glucagon-like peptide- 1, GLP-1 based receptor agonists) in central nervous system nuclei including the hypothalamus and NAc that regulate energy balance and reward processing underlying their therapeutic potential for obesity and type 2 diabetes.

[0107] However, the mechanism by which incretin-based therapies specifically influence the mesolimbic circuitry to alter human eating behaviors remains unclear. In concordance with homeostatic processes, the mesolimbic system contributes to food-related motivation and its dysregulation underlies disturbances in food hedonics including food preoccupation (i.e., heightened and / or persistent reactivity to food cues) which could lead to dysregulated eating behaviors, ranging from loss of control eating (LOC eating, eating with subjective feeling of loss of control and associated distress) to binge eating (i.e., the most extreme bout of LOC eating). These debilitating symptoms affect up to 60 percent of patients with obesity and related eating disorders. Although aberrations in the mesocorticolimbic system, hypothalamus, and brainstem are implicated in both obesity and binge eating disorder (BED) patients with BED may be more prone to these symptoms than those with obesity in the absence of BED, due to reward hypersensitivity and food impulsivity involving mesocorticolimbic dysregulation. While incretin-based therapies have exhibited some promise in ameliorating food preoccupation and dysregulated eating behaviors, early data suggest a tolerance effect for food preoccupation. Direct measures of neural activity could reveal how incretin-based therapies engage the mesolimbic circuitry and may help broaden their therapeutic scope to related eating disorders, possibly by identifying a target engagement biomarker.

[0108] Intracranial electroencephalography (iEEG), acquired using implanted depth electrodes, provides a rare opportunity to directly measure neural activity within human brain circuitry. iEEG has recently been used for investigation to identify electrographic biomarkers of neuropsychiatric disorders. An ongoing early feasibility trial (NCT03868670) has recruited participants with treatment-refractory obesity and LOC eating to identify related iEEG activity. Participants’ dysregulated eating episodes were classified as LOC eating rather than binge eating, as they did not consistently meet criteria for eating an objectively large amount - likely due to their restricted gastric volume following bariatric surgery. We previously reported iEEG activity within a low-frequency band (2-8 Hz) that ramped up during periods of LOC eating from previous participants. Here, we used a first-of-its-kind opportunity to report a case study provided by Participant 3 (FIGs. 18A-18D) to investigate an electrographic biomarker associated with the frequency of LOC eating episodes resulting from food preoccupation while taking tirzepatide, using preliminary findings from Participants 1 and 2 as a reference.Results

[0109] We analyzed ambulatory iEEG recordings from the NAc in Participants 1 and 2 acquired before the therapeutic stimulation was turned on. In this pre-therapeutic, biomarker discovery phase with high number of food preoccupation episodes, we found that the delta-theta band (<7 Hz) power in the ventral NAc was significantly higher (FIGs. 26-27; permutation testing, p= 2.1236e-05 and 2.4844e-l 1, Participant 1 and 2, respectively) during severe food preoccupation states (FIGs. 18E-18F left-sided panel, FIGs. 26-29, and Tables 5-6). Delta-theta band power (<7 Hz) could serve as a biomarker for the frequency of LOC eating episodes resulting from food preoccupation. The effects of tirzepatide on food preoccupation and LOC eating may be related to modulation of this delta-theta-band biomarker in the NAc, a hub of the mesolimbic reward circuitry where incretin receptors are also expressed.Table 1. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the right ventral NAc in Participant 1 in FIG. 18E left-sided panel and FIG. 26 (during Early stim - Month 1) in each condition

[0110] Unlike Participants 1 and 2, Participant 3 exhibited a lengthy absence of a food preoccupation and LOC eating for four months post-surgery (FIGs. 19A-19B), coinciding with uptitration of tirzepatide prior to surgical implantation. During this period, power values in <7 Hz from Severe Food Preoccupation swipes (participant-triggered iEEG recordings) were not significantly elevated compared to Control and there were no differences in other higher frequencies (FIGs. 19C-19E, under green bar, and FIG. 19F); a finding markedly different from prior reports. We therefore refer to this period (Months 2-4) as the ‘Biomarker Absent’ period (excluding Month 1 after surgery due to potential implantation effect). The length of this period was validated by using a method that identifiesthe transition point corresponding to the most pronounced change in power values within the delta-theta frequency band (<7 Hz, FIG. 38). Specifically, during this Biomarker Absent period, delta-theta band power values acquired from 5-second epochs from Severe Food Preoccupation swipes (n = 120) were indistinguishable from the ones from Control swipes (n = 790) (FIG. 19F, right-sided panel, and Table 7 for the left ventral NAc, permutation testing, p = 0.8105; for the right ventral NAc, see FIG. 37 and Table 8; p = 0.1011).[OHl] We refer to the period after the Biomarker Absent as the Biomarker Present period (Months 5-7), during which the delta-theta band biomarker appeared to emerge, and the participant began to report a return or breakthrough of food preoccupation despite the maximum dose of tirzepatide (see FIGs. 19C-19E, under pink bar, increased power values in low-frequency are noted as yellow, and FIGs. 19A-19B). For Severe Food Preoccupation swipes, a prominent low-frequency oscillatory waveform and a significant increase of power values in low-frequency were observed compared to Control (FIG. 19G, left-sided panel, see raw iEEG trace in pink, and middle panel; permutation testing with cluster correction). Specifically, delta-theta band (<7 Hz) power values from 5-second epochs from Severe Food Preoccupation swipes (n = 202) were significantly higher than Control (n = 306; FIG. 19G, right-sided panel, and Table 7 for the left ventral NAc, permutation testing, p = 1.5310e-22; for the right ventral NAc, see FIG. 37 and Table 8, p=1.0887e-06). Following the change in the biomarker, the number of LOC eating episodes and severity score increased in parallel and reached 7 per month and 4.2, respectively (FIGs. 19A-19B).Discussion

[0112] We present a case that provided an opportunity to investigate the associated electrophysiology of an incretin-based pharmacologic in the human NAc. A prominent reduction of the number of LOC eating behaviors and body -weight during the Biomarker Absent period was consistent with concomitant uptitration of tirzepatide for diabetes management. During this period, Participant 3 showed an absence of the expected delta-theta band (<7 Hz) biomarker in the ventral NAc, which suggests that this biomarker could be suppressed initially due to tirzepatide. This is further supported by emergence of the deltatheta band biomarker during the Biomarker Present period, which preceded a breakthrough of food preoccupation despite tirzepatide.

[0113] These results suggest that tirzepatide’ s mechanism of action for regulating food preoccupation and dysregulated eating behavior is associated with the modulation of thedelta-theta band biomarker in the human NAc. This signal is associated with the frequency of LOC eating episodes resulting from food preoccupation and was able to predict symptom exacerbation. Our findings suggest that this signal could serve as a target-engagement biomarker. Moreover, a biomarker alteration preceding actual behavioral change could provide for developing a biomarker-based approach to managing dysregulated eating with tirzepatide. The results reported here could inform preclinical studies given the conserved homologous neural signatures in mice. The findings may inspire non-invasive strategies as well to capture similar brain dynamics. Thus, the effects of incretin-based therapies’ suppression of food preoccupation in dysregulated eating behaviors address an unmet need not limited to obesity.Methods

[0114] Written informed consent was obtained from the participant to participate in an early feasibility trial of “responsive deep brain stimulation (rDBS) for patients with treatment-refractory obesity and loss of control (LOC) eating” (NCT03868670).

[0115] Participants 1 and 2. Participant 1 is a 50-y ear-old woman with severe treatment-resistant obesity (body-mass index, BMI 46.5 kg / m2) despite bariatric surgery and food preoccupation. After Roux-en-Y gastric bypass surgery, she lost 52.16 kg but she gradually regained weight, returning to her pre-surgery weight at the time of enrollment. She reported at least five LOC eating bouts per week. She had the comorbidities of neoplasm, lower back pain, kyphoscoliosis / scoliosis, hypertension, esophageal reflux, dyslipidemia, complicated migraine, and anxiety at the time of enrollment.

[0116] Participant 2 is a 60-y ear-old woman with severe treatment-resistant obesity (body-mass index, BMI 47.1 kg / m2) despite bariatric surgery and food preoccupation. After Roux-en-Y gastric bypass surgery, she lost 68.95 kg, but she regained weight and was back to within 9 % of her pre-surgery weight at the time of enrollment. She reported LOC eating about four times a week. She had the comorbidities of migraine at the time of enrollment. After the bariatric surgery and before the enrollment, both participants tried numerous other weight-loss strategies including behavior therapy, support groups, and medication, which were unsuccessful.

[0117] Neither participant reported previous testing or diagnosis regarding monogenic obesity, which is not part of our clinical standard. Both participants reported severe food preoccupations particularly related to emotional / stress-related triggers that oftenled to LOC eating episodes (approximately five episodes per week and four episodes per week, as measured by Eating Disorder Examination (EDE), respectively). Informed consent was obtained from both participants. Please refer to Shivacharan et al. for more details.

[0118] Participant 3. The participant (Participant 3) is a 59-y ear-old woman with severe treatment-resistant obesity (body-mass index, BMI 46.1kg / m2) despite bariatric surgery and comorbid Type 2 Diabetes Mellitus. She did not report any previous testing or diagnosis regarding monogenic obesity. She presented to us reporting significant distress from food preoccupation. Her frequent food preoccupations led to unwanted eating behaviors, including numerous LOC eating episodes. Prior to laparoscopic Roux-en-Y gastric bypass surgery, she weighed 154 kg and she had cravings for calorically dense food choices. After her bariatric surgery, she reached a nadir weight of 115 kg (BMI of 38.7 kg / m2), but near the time of enrollment, her weight had increased to 137 kg (BMI of 46.1 kg / m2). She stated that she was often preoccupied with thoughts of food, which led to ordering a meal out or to continual snacking even though she wanted to resist. Her preoccupation focused on both sweet and salty foods such as pre-packaged cupcakes and roast beef sandwiches with fries. She reported 19 LOC episodes in the previous month upon study entry. LOC eating is reported even in the absence of objective binges, particularly in patients who have undergone bariatric surgery. The participant endorsed eating until uncomfortably full, eating large amounts when she was not hungry, and feeling guilty after these episodes, with high levels of distress associated with them.

[0119] The participant fulfilled all eligibility criteria of the trial which are mainly; (1) BMI 40-60 kg / m2, (2) Unsuccessful intervention with of bariatric surgery, behavioral therapy, and pharmacological therapy for LOC episodes and weight loss, (3) Shows LOC eating episodes at least 4 times per week. Importantly, these included unsuccessful use of a GLP-1 receptor agonist (dulaglutide), which resulted in no significant relief on her weight or food preoccupation. She was switched to a GLP-l / GIP dual-receptor agonist (tirzepatide) for its FDA-approved indication treating type 2 diabetes. There was no reported impact on weight and food preoccupation at 7.5 mg / week of tirzepatide at the time of baseline assessment. She reported temporary weight loss followed by subsequent return to her initial weight with no reported impact to her LOC episodes. As this agent was intended for treating her type 2 diabetes, the participant was enrolled into the study and underwent implantation of the rDBS system (Neuropace, Inc) bilaterally in the NAc. The patient uptitrated thetirzepatide dose to optimize her diabetes management as suggested by the clinical team given the known risk of this medical comorbidity on surgical outcomes particularly infection given the medical device implantation. At the time of implantation, the patient was receiving 12.5 mg / week and it was further titrated up to 15 mg / week after approximately 4 months for continued optimization of glucose control. She had the comorbidities of hypertension, hyperlipidemia, coronary artery disease, nonalcoholic steatohepatitis, irritable bowel, migraine, asthma at the time of enrollment.

[0120] Surgical procedure. The surgical procedure has been previously reported. In brief, probabilistic tractography was utilized to guide surgical targeting of the NAc as previously described. On the day of surgery, implantation of bilateral deep brain stimulation (DBS) electrodes in the NAc was performed under awake conditions as per our standard institutional practice. Using a personalized appetitive provocation task, microelectrode recording was performed intraoperatively to identify single-unit or multi-unit appetitive neural activity. After confirmation of the target with electrophysiology and imaging, a quadripolar depth electrode was placed and macro-electrode monopolar stimulation mapping was conducted to confirm positive effects and no adverse effects. After securing the DBS electrodes, the electrodes were connected to the neurostimulator pulse generator, which was placed in the right parietal skull region of the patient.

[0121] Data acquisition. Intracranial electroencephalogram (iEEG) recordings were acquired from the FDA-approved rDBS device (NeuroPace, Inc) as previously reported. A rDBS device is different from regular DBS device in terms of that it stimulates only when it detects a predefined biomarker rather than stimulating continuously. Neural recordings acquired from the rDBS device were used to identify biomarkers differentiating Severe Food Preoccupation swipes from Mild Food Preoccupation / Control swipes. iEEG data were recorded at 250 Hz sampling rate and bipolar re-referenced online. We used data from Ch.l and Ch.3, which were referred to as the data from the left and right ventral NAc in this article. Four electrode contacts were located in the following order: 1 - ventral NAc (the most ventral contact; presumed as NAc shell), 2 - dorsal NAc (presumed as NAc core), 3 and 4 - anterior limb of internal capsule (ALIC). For all participants, an electrode in the left hemisphere, one channel (Ch. l) was bipolar re-referenced between contacts 1 and 3, and the other channel (Ch.2) was between contacts 2 and 4. Likewise, an electrode in the righthemisphere, one channel (Ch.3) was bipolar re-referenced between contacts 1 and 3, and the other channel (Ch.4) was between contacts 2 and 4.

[0122] FIG. 25 specifies the dataset used in each figure. Pre-therapeutic stimulation data of Participant 1 and Participant 2 overlaps with ambulatory data used in our previous report. However, the focus and analytic approach in the previous report were fundamentally different from the current study. In the previous report, the analysis was centered around pure “craving” in the absence of hunger, aiming to dissociate hedonic and homeostatic eating by stratifying data based on “craving” and “hunger” ratings. This allowed us to explore the NAc electrophysiology in states dominated by hedonic vs. homeostatic derives, given we were performing biomarker discovery to guide a responsive DBS. In this way, we evaluated stimulation that would be more behaviorally specific to hedonic states.

[0123] However, in this current report, we focused on a distinct construct - food preoccupation, as defined by “heightened and / or persistent reactivity to food cues”. This encompasses a broader range of influences including hunger and other sensorial, environmental, and social cues. Excessive food preoccupation observed in our participants seems to reflect combined alterations in both homeostatic and hedonic eating rather than hedonic eating alone. To better align with this conceptual shift, the data was stratified to “Severe Food Preoccupation” and “Mild Food Preoccupation” only using the craving rating and applied a more stringent cutoff for craving ratings.

[0124] For Participant 1, the bipolar-reference montage was altered to be identical with Participants 2 and 3 during the stimulation safety testing period. Thus, we used data that were collected for a month including 3 weeks of without stimulation period and 1 week of stimulation period for safety testing (pulse width 80 ps, current amplitude 3 mA, frequency 125 Hz) to show the delta-theta-band biomarker in FIG. 18E left-sided panel, FIG. 27, and FIG. 28 and named this period as ‘Earlystim-Month 1’. We checked that there was no prominent eating behavior change during this period compared to the period when the stimulation was not turned on, considering the number of food preoccupation magnet swipes per month. To show the delta-theta-band biomarker has been attenuated after a few months of stimulation, we used a dataset collected from the moment immediately after the stimulation parameter has been set to a maximum dose to the end of active clinical trial period (end of stimulation phase). Analysis results from this dataset are shown in FIG. 18E right-sided panel and FIGs. 30-31.Table 2. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the left ventral NAc in Participant 1 in FIG. 28 (during Early stim-Month 1) in each condition

[0125] For Participant 2, the bipolar-reference montage was adjusted to match that of Participants 1 and 3, two months after the implantation surgery. Thus, we used data that were collected from Month 3 to Month 6 without the stimulation being turned on to show the delta-theta-band biomarker in FIG. 18F left-sided panel, FIG. 27, and FIG. 29 and named this period as ‘Prestim-Month 3-6’. To show the delta-theta-band biomarker has been attenuated after a few months of stimulation, we used a dataset collected from the moment immediately after the stimulation parameter has been set to a maximum dose to the end of active clinical trial period (end of stimulation phase). Analysis results from this dataset are shown in FIG. 18F right-sided panel and FIGs. 32-33.Table 3. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the left ventral NAc in Participant 2 in FIG. 18F left-sided panel and FIG. 27 (during Prestim-Month 3-6) in each conditionTable 4. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the right ventral NAc in Participant 2 in FIG. 29 (during Prestim-Month 3-6 ) in each conditionTable 5. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the ventral NAc in Participant 1 in FIG. 30-31 (during Postmax-Month 1-3) in each conditionTable 6. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the ventral NAc in Participant 2 in FIGs. 32-33 (during Postmax-Month 1-4) in each conditionTable 7. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the left ventral NAc in Participant 3 in FIGs. 19F-19G (during the BiomarkerAbsent (Blue) and the Biomarker Present (Orange) period) in each conditionTable 8. Permutation tested p-values acquired by comparing delta-theta-band (^7 Hz) powers from the right ventral NAc in Participant 3 in FIG. 37 (during the Biomarker Absent (Blue) and the Biomarker Present (Orange) period) in each condition

[0126] For Participant 3, the bipolar-reference montage was adjusted to match that of Participants 1 and 2 at the time of surgery. Stimulation was not delivered throughout the data acquisition process for Participants 3. Moreover, we excluded data from up to a month from the surgery date (Month 1) due to confounding with an implantation effect, although implantation effects typically last less than a month in Parkinson’s patients. Thus, we used data that were collected from Month 2 to Month 4 for ‘Biomarker Absent period’ and from Month 5 to Month 7 for ‘Biomarker Present period’ . The results for the Biomarker Absent period are shown in FIGs. 19C-19F, and FIG. 34 and FIG. 37. The results for the Biomarker Present period are shown in FIGs. 19C-19E, FIG. 19G, and FIG. 34 and FIG. 37. We limit this case study’s interim analysis to the 6-month recording phase planned by the investigational device exemption trial to avoid further trial-related confounds.

[0127] For data that were collected while the stimulation was turned on, to reduce confounders due to stimulation, we used magnet swipes that had no stimulation within the magnet swipe time window.

[0128] Magnet swipe . With the rDBS system, participants can initiate iEEG recordings via swiping their magnet over the surgically implanted device under the scalp (FIG. 24). Magnet swipe triggers iEEG recordings which records a preset length of time before and after magnet swipe with a 2:1 ratio. For instance, if the preset length is 90 seconds, it records 60 seconds before magnet swipe and 30 seconds after. For this study, the length was set to 90 seconds (Participant 2) or 180 seconds (Participants 1 and 3).

[0129] For the Control condition of Participants 1 and 2, data were automatically recorded at a preset time (12 p.m. for Participant 1 and 5 p.m. for Participant 2), the timeparticipants answered that they were most likely to be at rest. This was to reduce their study burdens. For Participant 3, the Control condition consisted of magnet swipes collected when she was relaxing and not feeling craving. For Control episodes were from scheduled recordings, we removed them if craving swipes were present ± 3 hours (Participant 1) or ± 6 hours (Participant 2) from the scheduled recording time. Because Participant 1 had a shorter time window for swipe analysis due to bipolar-reference montage, to ensure enough swipe number, we applied relatively liberal criteria.

[0130] For the Food Preoccupation condition, we asked participants to swipe the magnet when they felt craving for food and before eating. Considering food preoccupation as “heightened and / or persistent reactivity to food cues”, we focused on the extent of food preoccupation regardless of hunger level unlike our prior report, because cues that could elicit food preoccupation include not only hunger but also other sensorial, environmental, and social aspects. Taken above, we defined food craving as an acute heightened food cue reactivity which could lead to distressful and unwanted eating behavior for patient populations with dysregulated eating. We asked participants to swipe when they were feeling a sense of craving to capture the most relevant moment of food preoccupation. These magnet-swipes were classified based on the extent of craving the participant was feeling (i.e., mild vs. severe). For all magnet swipes, participants were asked to keep a magnet swipe diary, recording their craving / hunger / thirst level, the extent they felt a loss of control, and the extent they felt compelled to eat in a five-point-Likert Scale (1 - none / not at all vs. 5 - extreme / extremely). Specifically, for craving 1 meant no craving, 3 meant moderate craving (e.g., can control / resist with effort), and 5 meant extreme craving (e.g., cannot control / resist). We classified Severe and Mild Food Preoccupation episodes based on these craving ratings per participant.

[0131] Behavioral Data. We collected the participant’s number of LOC eating episodes per month through her magnet swipe diary, ecological momentary assessment (EMA), and her verbal / written reports if she forgot to keep them in the diary ZEMA. We also assessed her LOC eating severity using the Eating Loss of Control Scale (ELOCS).

[0132] Clinical measures. The participant’s LOC eating severity was captured by ELOCS. ELOCS measures LOC eating frequency and severity using 18-item assessment. We used 18 b-items (all b-items except 6b and 20b) for LOC severity rating (0 - not at all severe to 10 - extremely severe) and averaged over to acquire LOC severity score.

[0133] Signal processing. In the offline analysis, standard preprocessing techniques were conducted in MATLAB (R2022b) using FieldTrip Toolbox which involved application of a 1-124 Hz band-pass filter. Static spectral analysis was performed using a multi-taper method with 4 slepian multi-tapers per epoch. These epochs were acquired by chunking magnet swipes every 5 seconds without overlaps. A full power spectrum (1-124 Hz) was acquired with 0.5 Hz frequency resolution, and delta-theta-band powers were acquired using a <7 Hz window. Epochs containing artifacts were removed if the artifact was larger than 6*standard deviation of a corresponding magnet swipe data points. For power spectrums in FIGs. 19C-19E, power values per frequency were averaged per a magnet swipe.

[0134] Statistical testing. Power spectral density values from the Control and Severe Food Preoccupation swipes were tested using two-sided permutation testing with 1000 permutations and p-value of 0.05. Then, it underwent cluster correction using a cluster size threshold of top 2.5 %. Delta-theta-band power values from three swipe conditions were tested pair-wise using two-sided permutation testing with 10,000 permutations and p-value of 0.05.

[0135] Transition point detection differentiating the Biomarker Absent and Biomarker Present period. The transition point, which was predefined based on the emergence of biomarker, was further validated by using a model that identifies the transition point corresponding to the most pronounced change in power values within the delta-theta frequency band (<7 Hz). More specifically, the transition point between two periods was determined by minimizing total residual error, calculated from deviations of each timepoint from the root-mean-square estimate of the period to which it belongs.References

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[0174] Aspects

[0175] The following Aspects are illustrative only and do not limit the scope of the present disclosure or the appended claims. Any part or parts of any one or more Aspects can be combined with any part or parts of any one or more other Aspects. The disclosed methods can be effected, for example, by a computer executable method that is stored in a non- transitory computer readable medium and is executable by a processor.

[0176] Aspect 1. A method for optimizing a treatment regimen in a subject, comprising detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker.

[0177] Aspect 2. The method of Aspect 1, wherein the control comprises a 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when not provoked with food stimuli.

[0178] Aspect 3. The method of Aspect 1, wherein elevation of the 1-13 Hz spectrum power in the ventral basal ganglia region is associated with a severity of food preoccupation in the subject.

[0179] Aspect 4. The method of Aspect 1, wherein elevation of the 1-13 Hz spectrum power in the ventral basal ganglia region is associated with a number of Loss of Control eating episodes.

[0180] Aspect 5. The method of Aspect 1, wherein the treatment regimen comprises administration of a therapeutic.

[0181] Aspect 6. The method of Aspect 5, wherein the altering the treatment regimen comprises any one or more of (i) changing a dosage of the therapeutic, (ii) terminating the therapeutic and administering a different therapeutic, and (iii) administering an additional therapeutic.

[0182] Aspect 7. The method of Aspect 5 or Aspect 6, wherein the therapeutic comprises any one or more of a GLP-1 receptor agonist and / or a GIP receptor agonist.

[0183] Aspect 8. The method of Aspect 7, wherein the therapeutic is tirzepatide.

[0184] Aspect 9. The method of Aspect 1, wherein the subject failed bariatric surgery, behavioral therapy, and / or pharmacological therapy for weight loss.

[0185] Aspect 10. The system of Aspect 1, wherein the altering effects a reduction in the 1-13 Hz spectrum power of the biomarker.

[0186] Aspect 11. The system of Aspect 1, wherein the altering reduces the food preoccupation state of the subject.

[0187] Aspect 12. The system of Aspect 1, wherein the altering reduces a number of Loss of Control eating episodes in the subject.

[0188] Aspect 13. The system of Aspect 1, wherein the biomarker comprises harmonics at any one or more of about 1.4 Hz, about 2.8 Hz, and about 4.2 Hz.

[0189] Aspect 14. A method for addressing Loss of Control (LOC) eating in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region, and altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power as compared to control.

[0190] Aspect 15. A method for identifying food preoccupation in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region, and measuring a number of LOC eating episodes per day.

[0191] Aspect 16. The system of Aspect 15, further comprising altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power and LOC episodes as compared to control.

[0192] Aspect 17. A method for identifying an impulsivity state in a subject, comprising measuring a 1-13 Hz spectrum power in the ventral basal ganglia region.

[0193] Aspect 18. The method of Aspect 17, wherein the 1-13 Hz spectrum power is measured in the nucleus accumbens (NAc) of the brain of a subject.

[0194] Aspect 19. A method for determining a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-7 Hz spectrum power in the nucleus accumbens (NAc) in the brain of the subject when provoked with one or more food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

[0195] Aspect 20. The method of Aspect 19, wherein elevation of the 1-7 Hz spectrum power in the nucleus accumbens (NAc) is associated with a severity of food preoccupation in the subject.

[0196] Aspect 21. A method for determining a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with an impulsivity state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with one or more food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

[0197] As described elsewhere herein, a biomarker can be detected by collecting one or more signals and detecting the biomarker from the signals, for example from among the signals.

[0198] Altering a treatment can include any one or more of increasing a dosage of a treatment, reducing a dosage of a treatment, or even eliminating a treatment. As an example, for a subject that has been receiving a GLP-1 receptor agonist, altering that patient’streatment can mean increasing the dose of the GLP-1 receptor agonist, reducing the dose of the GLP-1 receptor agonist, or even eliminating treatment with the GLP-1 receptor agonist.

[0199] Aspect 22. The method of Aspect 21, wherein the impulsivity state is determined by: obtaining a sensor signal corresponding to a detected electrophysiology signal from a sensor worn on the scalp of a subject; and determining an impulsivity state of the subject based on the sensor signal, the detecting optionally performed by a computing device receiving the sensor signal.

[0200] Aspect 23. The method of Aspect 21 or 22, wherein the electrophysiology signal is a correlate of a nucleus accumbens (NAc) signal.

[0201] Aspect 24. The method of any one of Aspects 21-23, wherein the sensor is a scalp sensor comprising an array of scalp sensors positioned to detect a dorsal -lateral prefrontal cortex (dlPFC) theta (4-8Hz) signal.

[0202] Aspect 25. The method of any one of Aspects 21-24, wherein the electrophysiological signal is a correlate of a nucleus accumbens (NAc) signal.

[0203] Aspect 26. The method of any one of Aspects 21-25, wherein the sensor is configured to detect a correlate of the delta band of a nucleus accumbens (NAc) signal.

[0204] Aspect 27. The method of any one of Aspects 21-26, wherein the impulsivity state is a loss of control eating behavior.

[0205] Aspect 28. The method of any one of Aspects 21-27, wherein the subject suffers from disordered eating behaviors comprising binge eating disorder.

[0206] Aspect 29. A method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control, wherein the NAc signal is a correlate of an activation of a GLP-1 receptor, the detecting optionally being performed noninvasively.

[0207] Aspect 30. The method of Aspect 29, further comprising altering a treatment regimen for the subject in response to the elevation in the 1-7 Hz spectrum power in the NAc in the brain of the subject, wherein physiological symptoms of GLP-1 receptor activation are not analyzed.

[0208] Aspect 31. The method of Aspect 30, wherein physiological symptoms of GLP-1 receptor activation comprise any one or more of nausea, malaise, and decreased appetite.

[0209] Aspect 32. The method of any one of Aspects 29-31, wherein the subject suffers from an impulse control disorder.

[0210] Aspect 33. A method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of a subject when provoked with food stimuli as compared to a control, wherein the first NAc signal is a correlate of an activation of a GLP-1 receptor, and wherein the subject has received a first GLP-1 receptor agonist, the detecting of the signal optionally being performed noninvasively. As described elsewhere herein, the detecting can be accomplished using one or more sensors on the subject’s scalp.

[0211] Aspect 34. The method of Aspect 33, further comprising detecting an elevation in the 1-7 Hz spectrum power in a second signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control, wherein the second NAc signal is a correlate of an activation of a GLP-1 receptor, wherein the subject has received a second GLP-1 receptor agonist; and determining a difference between the first NAc signal and the second NAc signal, the detecting of the second signal optionally being performed noninvasively.

[0212] Aspect 35. A method of determining a personalized therapeutic regimen, comprising: detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist, wherein the biomarker comprises an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of a GLP-1 receptor agonist and GIP receptor agonist.

[0213] The altering can comprise increasing the dose. The altering can also, however, comprise reducing the dose, which can in some instances comprise reducing the dose to zero.

[0214] Aspect 36. The method of Aspect 35, wherein the dose of the at least one of the GLP-1 receptor agonist and the GIP receptor agonist is maintained when the biomarker is not detected. Without being bound to any particular theory or embodiment, non-detection of the biomarker can be indicative of the dose of the at least one of the GLP-1 receptor agonist and the GIP receptor being effective for the subject in question.

[0215] Aspect 37. The method of any one of Aspects 35-36, wherein when the biomarker is not detected, (i) the dose of the at least one of the GLP-1 receptor agonist and GIP receptor agonist is reduced, (ii) the subject is provoked with food stimuli, and (iii) a presence of the biomarker is determined in the subject. Without being bound to any particular theory or embodiment, this can be useful in determining whether a diminished or even a zero dose of the at least one of the GLP-1 receptor agonist and GIP receptor agonist may be effective for the subject in question.

[0216] Aspect 38. The method of any one of Aspects 35-37, wherein the detecting of the biomarker is performed noninvasively, optionally with one or more sensors worn on the scalp of the subject.

[0217] It should be understood that signals - such as those indicative of cardiac activity - can also be used with the disclosed technology. Such signals can be collected with sensors placed at a distance from the user’s scalp. Thus, a first biomarker can be collected from the subject’s brain, and a second biomarker can be collected from another region of the subject.

[0218] Aspect 39. The method of any one of Aspects 35-38, wherein the biomarker is a correlate of a nucleus accumbens (NAc) signal.

[0219] Aspect 40. The method of Aspect 38 or Aspect 39, wherein the detecting comprises detection of a dorsal-lateral prefrontal cortex (dlPFC) theta (4-8Hz) signal.

[0220] Aspect 41. A method of determining a personalized therapeutic regimen, comprising: detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist; wherein the biomarker comprises an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of the GLP-1 receptor agonist and GIP receptor agonist. Changing the dose can comprise increasing the dose or reducing the dose; the dose can even be reduced to zero.

[0221] Aspect 42. A computer-executable method comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a 1-13 Hz spectrumpower in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist. Adjusting the dose can comprise increasing the dose or reducing the dose; the dose can even be reduced to zero. Adjusting can also comprise changing the frequency of the dose.

[0222] Aspect 43. A computer-executable method, comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist. Adjusting the dose can comprise increasing the dose or reducing the dose; the dose can even be reduced to zero. Adjusting can also comprise changing the frequency of the dose.

[0223] The present disclosure also provides a computer-readable medium, having stored thereon processor-executable instructions that cause one or more processors of a computing system to execute a method, the instructions comprising: instructions to detect brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; instructions to detect a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and instructions to, upon detecting the biomarker, provide output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.

[0224] The present disclosure also provides a computer-readable medium, having stored thereon processor-executable instructions that cause one or more processors of a computing system to execute a method, the instructions comprising: instructions to detect brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; instructions to detect a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and instructions to, upon detecting the biomarker, provide output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.

Claims

What is Claimed:

1. A method for optimizing a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the biomarker optionally being detected noninvasively.

2. The method of claim 1, wherein the control comprises a 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when not provoked with food stimuli.

3. The method of claim 1, wherein elevation of the 1-13 Hz spectrum power in the ventral basal ganglia region is associated with a severity of food preoccupation in the subject.

4. The method of claim 1, wherein elevation of the 1-13 Hz spectrum power in the ventral basal ganglia region is associated with a number of Loss of Control eating episodes.

5. The method of claim 1, wherein the treatment regimen comprises administration of a therapeutic.

6. The method of claim 5, wherein the altering the treatment regimen comprises any one or more of (i) changing a dosage of the therapeutic, (ii) terminating the therapeutic and administering a different therapeutic, and (iii) administering an additional therapeutic.

7. The method of claim 5 or 6, wherein the therapeutic comprises any one or more of a GLP-1 receptor agonist and / or a GIP receptor agonist.

8. The method of claim 7, wherein the therapeutic is tirzepatide.

9. The method of claim 1, wherein the subject failed bariatric surgery, behavioral therapy, and / or pharmacological therapy for weight loss.

10. The method of any one of claims 1-6, wherein the altering effects a reduction in the 1- 13 Hz spectrum power of the biomarker.

11. The method of any one of claims 1-6, wherein the altering reduces the food preoccupation state of the subject.

12. The method of any one of claims 1-6, wherein the altering reduces a number of Loss of Control eating episodes in the subject.

13. The method of any one of claims 1-6, wherein the biomarker comprises harmonics at any one or more of about 1.4 Hz, about 2.8 Hz, and about 4.2 Hz.

14. A method for addressing Loss of Control (LOC) eating in a subject, comprising: measuring a 1-13 Hz spectrum power in the ventral basal ganglia region; and altering a treatment regimen of the subject in response to elevation in the 1-13 Hz spectrum power as compared to control, the measuring optionally being performed noninvasively.

15. A method for identifying food preoccupation in a subject, comprising: measuring a 1-13 Hz spectrum power in the ventral basal ganglia region; and measuring a number of LOC eating episodes per day, the measuring optionally being performed noninvasively.

16. The method of claim 15, further comprising altering a treatment regimen of the subject in response to an elevation in the 1-13 Hz spectrum power and LOC episodes as compared to control.

17. A method for identifying an impulsivity state in a subject, comprising measuring a 1- 13 Hz spectrum power in the ventral basal ganglia region.

18. The method of claim 17, wherein the 1-13 Hz spectrum power is measured in the nucleus accumbens (NAc) of the brain of a subject.

19. A method for optimizing a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-7 Hz spectrum power in the nucleus accumbens (NAc) in the brain of the subject when provoked with one or more food stimuli as compared to a control; and altering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

20. The method of claim 19, wherein elevation of the 1-7 Hz spectrum power in the nucleus accumbens (NAc) is associated with a severity of food preoccupation in the subject.

21. A method for optimizing a treatment regimen in a subject, comprising: detecting a biomarker in the subject, the biomarker being associated with an impulsivity state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with one or more food stimuli as compared to a control; andaltering a treatment regimen of the subject in response to detecting the biomarker, the detecting optionally being performed noninvasively.

22. The method of claim 21, wherein the impulsivity state is determined by: obtaining a sensor signal corresponding to a detected electrophysiology signal from a sensor worn on the scalp of a subject; and determining an impulsivity state of the subject based on the sensor signal, the detecting optionally performed by a computing device receiving the sensor signal.

23. The method of any one of claims 21-22, wherein the electrophysiology signal is a correlate of a nucleus accumbens (NAc) signal.

24. The method of any one of claims 21-22, wherein the sensor is a scalp sensor comprising an array of scalp sensors positioned to detect a dorsal-lateral prefrontal cortex (dlPFC) theta (4-8Hz) signal.

25. The method of any one of claims 21-22, wherein the electrophysiological signal is a correlate of a nucleus accumbens (NAc) signal.

26. The method of any one of claims 21-22, wherein the sensor is configured to detect a correlate of the delta band of a nucleus accumbens (NAc) signal.

27. The method of any one of claims 21-22, wherein the impulsivity state is a loss of control eating behavior.

28. The method of any one of claims 21-22, wherein the subject suffers from disordered eating behaviors comprising binge eating disorder.

29. A method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control,wherein the NAc signal is a correlate of an activation of a GLP-1 receptor, the detecting optionally being performed noninvasively.

30. The method of claim 29, further comprising altering a treatment regimen for the subject in response to the elevation in the 1-7 Hz spectrum power in the NAc in the brain of the subject, wherein physiological symptoms of GLP-1 receptor activation are not analyzed.

31. The method of claim 30, wherein physiological symptoms of GLP-1 receptor activation comprise any one or more of nausea, malaise, and decreased appetite.

32. The method of any one of claims 29-31, wherein the subject suffers from an impulse control disorder.

33. A method for detecting a biomarker in a subject, comprising: detecting an elevation in the 1-7 Hz spectrum power in a signal of the nucleus accumbens (NAc) in the brain of a subject when provoked with food stimuli as compared to a control, wherein the first NAc signal is a correlate of an activation of a GLP-1 receptor, and wherein the subject has received a first GLP-1 receptor agonist, the detecting of the signal optionally being performed noninvasively.

34. The method of claim 33, further comprising detecting an elevation in the 1-7 Hz spectrum power in a second signal of the nucleus accumbens (NAc) in the brain of the subject when provoked with food stimuli as compared to a control, wherein the second NAc signal is a correlate of an activation of a GLP-1 receptor, wherein the subject has received a second GLP-1 receptor agonist; and determining a difference between the first NAc signal and the second NAc signal,the detecting of the second signal optionally being performed noninvasively.

35. A method of determining a personalized therapeutic regimen, comprising: detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist, wherein the biomarker comprises an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of a GLP-1 receptor agonist and GIP receptor agonist.

36. The method of claim 35, wherein the dose of the at least one of the GLP-1 receptor agonist and the GIP receptor agonist is maintained when the biomarker is not detected.

37. The method of any one of claims 35-36, wherein when the biomarker is not detected, (i) the dose of the at least one of the GLP-1 receptor agonist and GIP receptor agonist is reduced, (ii) the subject is provoked with food stimuli, and (iii) a presence of the biomarker is determined in the subject.

38. The method of any one of claims 35-37, wherein the detecting of the biomarker is performed noninvasively, optionally with one or more sensors worn on the scalp of the subject.

39. The method of any one of claims 35-36, wherein the biomarker is a correlate of a nucleus accumbens (NAc) signal.

40. The method of claim 38 or claim 39, wherein the detecting comprises detection of a dorsal -lateral prefrontal cortex (dlPFC) theta (4-8Hz) signal.

41. A method of determining a personalized therapeutic regimen, comprising:detecting a biomarker of a subject that has received at least one of a GLP-1 receptor agonist and a GIP receptor agonist; wherein the biomarker comprises an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject when provoked with food stimuli as compared to a control; and altering a treatment regimen for the subject upon detection of the biomarker, wherein the altering comprises changing a dose of the at least one of the GLP- 1 receptor agonist and GIP receptor agonist .

42. A computer-executable method comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals, the biomarker comprising an elevation in a 1-13 Hz spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.

43. A computer-executable method, comprising: causing, by one or more processors, performance of: detecting brain signals from a subject administered at least one of a GLP-1 receptor agonist and a GIP receptor agonist; detecting a biomarker or lack thereof from the brain signals,the biomarker comprising an elevation in a low frequency spectrum power in the ventral basal ganglia region of the brain of the subject in response to a food stimulus; and upon detecting the biomarker, providing output instructions to adjust a dose of the at least one of a GLP-1 receptor agonist and a GIP receptor agonist.

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