Closed-loop neuromodulation for the treatment of affective neuropsychiatric disorders

Abstract

The present disclosure provides methods and systems for treating emotional disorders such as depression and / or anxiety and / or related disorders. Closed-loop therapy is performed using a neurostimulator that records electroencephalography signals from neural activity associated with symptoms of a neuropsychiatric disorder and delivers electrical stimulation when a pre-specified pattern of neural activity associated with the symptoms is detected. Methods are also disclosed for mapping a patient's brain to optimize electrode placement for detecting neural activity associated with symptoms of a neuropsychiatric disorder and optimize electrode placement for delivery of electrical stimulation. Methods are also provided for monitoring and evaluating the effects of electrical stimulation on clinical symptoms and conditions.

Description

[Technical field]

[0001] cross reference This application claims the benefit of U.S. Provisional Patent Application No. 63 / 183,833, filed May 4, 2021, and U.S. Provisional Patent Application No. 63 / 232,857, filed August 13, 2021, which applications are incorporated by reference herein in their entireties.

[0002] Introduction The development of new effective treatments for neuropsychiatric disorders is a major goal of 21st century medicine. Millions of individuals worldwide suffer from major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), bipolar disorder (BD), chronic pain, and related conditions. A vast number of patients remain effectively untreated. Neuropsychiatric disorders therefore constitute the largest global burden of disease of all clinical categories, accounting for 22.7% of all years lived with disability.

[0003] Established treatment modalities, including pharmacological treatment, cognitive behavioral therapy (CBT), surgical lesions, and electroconvulsive therapy (ECT), provide lasting benefits for a percentage of individuals but prove insufficient for many. For example, in the case of MDD, it is estimated that approximately 40-50% of adult patients treated with selective serotonin reuptake inhibitors do not respond. While CBT, for example, has shown some effectiveness in preventing MDD relapse after pharmacological treatment, relapse rates remain significant (approximately 35-50% in long-term studies). Surgical lesion approaches to neuropsychiatric disorders are highly invasive and produce suboptimal outcomes (e.g., less than 60% complete response rate in the case of anterior capsulotomy for OCD). Completely reliable treatment methods do not exist for any of the major affective disorders.

[0004] Gross-level electrophysiological interventions in the form of ECT have long been used for several neuropsychiatric disorders, but many severely affected patients do not experience lasting relief from ECT and endure significant undesirable cognitive side effects. Transcranial stimulation, while non-invasive, has not provided conclusive evidence of its efficacy, and findings are perhaps strongest for MDD, where short-term response rates are still typically below 60%. More advanced methods, involving targeted direct stimulation of specific subregions of the brain such as the subgenual cingulate, have provided promising early results in recent years but have failed in clinical trials due to variability in patient response. Summary of the Invention

[0005] Methods and systems are provided for the treatment of neuropsychiatric disorders in a subject. Closed-loop therapy is performed using a neurostimulator that records electroencephalography signals from neural activity associated with symptoms of the neuropsychiatric disorder and delivers electrical stimulation when a pre-specified pattern of neural activity associated with symptoms of the neuropsychiatric disorder is detected. Methods are also disclosed for mapping a patient's brain to optimize electrode placement for detecting neural activity associated with symptoms of the neuropsychiatric disorder and optimize electrode placement for delivery of electrical stimulation. Methods are also provided for monitoring and evaluating the effect of electrical stimulation on clinical symptoms and conditions. The methods and systems can be used for the treatment of emotional disorders associated with anxiety and / or depression and / or related disorders, including but not limited to major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), and bipolar disorder (BD), and chronic pain.

[0006] In certain cases, the neuropsychiatric disorder can be depression, and applying the electrical stimulation treats the depression. In certain cases, the neuropsychiatric disorder can be anxiety, and applying the electrical stimulation treats the anxiety. The neuropsychiatric disorder can be one or more of major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), bipolar disorder (BD), or chronic pain.

[0007] In one aspect, a closed loop method for treating a neuropsychiatric disorder in a subject is provided, the method including: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivery of electrical stimulation to treat the neuropsychiatric disorder; mapping the subject's brain to identify an optimal location in a second brain region for positioning a second electrode for detection of an electroencephalography signal from neural activity associated with a symptom of the neuropsychiatric disorder; positioning the first electrode at an optimal location in the first brain region for delivering electrical stimulation to the first brain region of the subject; positioning the second electrode at an optimal location in the second brain region for detecting the electroencephalography signal from neural activity associated with a symptom of the neuropsychiatric disorder; detecting the electroencephalography signal at the second region of the subject's brain using the second electrode; and applying electrical stimulation to the first brain region using the first electrode in a manner effective to treat the neuropsychiatric disorder in the subject when the electroencephalography signal detected using the second electrode exceeds a threshold level. In some embodiments, the first brain region and the second brain region are the same. In other embodiments, the first brain region and the second brain region are different.

[0008] In certain embodiments, the method further includes using a control algorithm to automate the application of the electrical stimulation when the electroencephalography signal exceeds a threshold level. In some embodiments, the control algorithm includes a machine learning algorithm, such as a supervised machine learning algorithm. In some embodiments, the control algorithm further adjusts one or more programmed stimulation parameters based on the level of the electroencephalography signal.

[0009] In certain embodiments, the method further includes positioning a plurality of electrodes in a second brain region for detection of electroencephalographic signals by stereotactic electroencephalography (sEEG).

[0010] In certain embodiments, the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region, or other regions of the brain. The site selected for stimulation may be different for different subjects, and will depend on the mapping of the brain of each individual subject to identify the optimal location for positioning electrodes for delivering electrical stimulation to treat neuropsychiatric disorders.

[0011] In certain embodiments, the second brain region comprises the right amygdala region, the left amygdala region, the right orbitofrontal cortex region, the left subgenual cingulate region, or the right hippocampus region, or other regions of the brain. The sites selected for detection may be different for different subjects and will depend on the mapping of the brain of each individual subject to identify the optimal location for positioning electrodes to detect electroencephalography signals from neural activity associated with symptoms of neuropsychiatric disorders.

[0012] In certain embodiments, the electroencephalography signal is gamma frequency power.

[0013] The method may further comprise evaluating the effectiveness of the treatment in the subject.For example, the subject may be monitored for a period of time, and an increase in the number of electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates an increase in the severity of the symptoms of the neuropsychiatric disorder, and a decrease in the number of electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates a decrease in the severity of the symptoms of the neuropsychiatric disorder.In some embodiments, the subject is monitored continuously or intermittently.

[0014] Aspects of the method include applying electrical stimulation unilaterally or bilaterally, in some embodiments, the electrical stimulation is applied at least twice and the sites to which the electrical stimulation is applied are alternating or otherwise spatially or temporally patterned.

[0015] Also provided is a method for ameliorating a symptom of a neuropsychiatric disorder in a subject. In one aspect, a closed loop method for alleviating a symptom of a neuropsychiatric disorder in a subject is provided, the method comprising: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivery of electrical stimulation to alleviate the symptom of the neuropsychiatric disorder; mapping the subject's brain to identify an optimal location in a second brain region for positioning a second electrode for detection of an electroencephalography signal from neural activity associated with the symptom of the neuropsychiatric disorder; positioning the first electrode at an optimal location in the first brain region for delivery of electrical stimulation to the first brain region of the subject; positioning the second electrode at an optimal location in the second brain region for detection of an electroencephalography signal from neural activity associated with the symptom of the neuropsychiatric disorder; detecting the electroencephalography signal at the second region of the subject's brain using the second electrode; and applying the electrical stimulation to the first brain region using the first electrode in a manner effective to alleviate the symptom of the neuropsychiatric disorder in the subject when the electroencephalography signal detected using the second electrode exceeds a threshold level. In some embodiments, the first brain region and the second brain region are the same. In other embodiments, the first brain region and the second brain region are different.

[0016] The symptom can be depression, and applying the electrical stimulation ameliorates the depression. In some cases, the depression can be measured by using a Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), or Beck Depression Inventory (BDI) score, and applying the electrical stimulation is effective in reducing the severity of the depression according to the VAS, HAM-D, MADRS, or BDI score.

[0017] In some cases, the symptom can be anxiety and applying the electrical stimulation improves the anxiety. In some cases, the anxiety can be measured by a Visual Analog Scale for Anxiety (VAS-A) or Beck Anxiety Inventory (BAI) score and applying the electrical stimulation is effective in reducing the anxiety according to the VAS-A or BAI score.

[0018] In some cases, the symptom can be chronic pain-related distress and applying the electrical stimulation ameliorates the chronic pain-related distress. In some cases, the chronic pain-related distress can be measured by a visual analog scale and applying the electrical stimulation can be effective in reducing the visual analog scale score.

[0019] The stimulation electrodes may be brain penetrating or non-brain penetrating electrodes. The detection electrodes may be brain penetrating or non-brain penetrating electrodes. Electrical stimulation may be applied as summarized above.

[0020] In certain embodiments, electrical signals are measured from the brain by intracranial electroencephalography (iEEG) or similar techniques to determine the severity of symptoms of a neuropsychiatric disorder in a subject. In some cases, electrical signals can be measured continuously, intermittently, or both. For continuous measurements, electroencephalography can be performed for an uninterrupted period of 10 seconds to 24 hours or more, such as 30 seconds to 18 hours, 1 minute to 12 hours, 10 minutes to 8 hours, 30 seconds to 10 minutes, 1 minute to 10 minutes, 3 minutes to 10 minutes, or 3 minutes to 6 minutes. In some cases, symptoms of a neuropsychiatric disorder can be detected by the presence of increased power of gamma (30Hz to 200Hz) frequency electrical activity in the brain. In some cases, treatment of a neuropsychiatric disorder can suppress symptoms and concomitantly increase the power of gamma (30Hz to 200Hz) frequency electrical activity in the brain. The reduction in gamma frequency power measured from the brain may be a reduction of 5%-20% or more, such as at least 5%, at least 10%, at least 15%, at least 20%, at least 40%, or more, compared to before treatment. Electrical activity may be recorded from any region of the brain, such as the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, and / or right hippocampal region, and / or regions of the brain with neural connections to the amygdala. In some cases, electrical activity may be recorded from multiple locations in the brain and activity in different frequency bands may be averaged. For example, frequencies in the range of 30 Hz-200 Hz recorded from multiple locations in the brain may be averaged. In some cases, the multiple locations may include multiple locations in the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, and / or right hippocampus. Electrical activity may be measured before, during, and / or after brain stimulation as disclosed herein. In some cases, increases in cortical excitability may indicate effective treatment. In some cases, other neural characteristics from single brain regions or combinations of brain regions may be associated with symptoms of a neuropsychiatric disorder or may indicate effective treatment.

[0021] In some cases, brain activity (e.g., gamma frequency electroencephalography signals) measured from the brain after a subject with a neuropsychiatric disorder is treated according to the methods and systems of the present disclosure may be below a threshold brain activity (a threshold level of gamma frequency electroencephalography signals). In certain cases, the threshold brain activity may be brain activity known to be associated with a neuropsychiatric disorder. In some cases, gamma frequency electroencephalography signals measured from the brain after a subject with a neuropsychiatric disorder is treated according to the methods and systems of the present disclosure may be below a threshold level.

[0022] In some cases, the measurement of electrical signals from the brain can be combined with alternative methods to assess neuropsychiatric disorders. For example, neuropsychiatric disorders can be assessed by mood assessment tools. Mood assessment tools can include verbal mood reports or instant mood scalers.

[0023] In certain embodiments, a closed loop method for treating major depressive disorder (MDD) in a subject is provided, the method comprising: positioning a first electrode to deliver electrical stimulation to a first region of the subject's brain; positioning a second electrode to detect gamma frequency electroencephalography signals from neural activity associated with MDD symptoms in a second region of the subject's brain; detecting the gamma frequency electroencephalography signals using the second electrode; and applying electrical stimulation to the first region of the subject's brain using the first electrode in a manner effective to treat the MDD symptoms in the subject when the gamma frequency electroencephalography signals detected using the second electrode exceed a threshold level. In some embodiments, bipolar electrical stimulation is applied to the first region of the subject's brain. In some embodiments, the electrical stimulation is applied at a current range of 1 mA to 6 mA, including any current within this range, such as 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, or 6 mA. In some embodiments, the electrical stimulation is applied for a time range of 5 seconds to 10 minutes per day, including any amount of time therebetween, such as 5 seconds, 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, or 10 minutes per day. In other embodiments, the electrical stimulation is applied continuously for a time range of 1 hour to 24 hours per day, including any amount of time within this range, such as 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, or 24 hours per day.

[0024] In certain embodiments, the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region, or other regions of the brain. The sites selected for stimulation may be different for different subjects and will depend on the mapping of the brain of each individual subject to identify the optimal location for positioning electrodes for delivering electrical stimulation to treat MDD.

[0025] In certain embodiments, the second brain region includes the right amygdala region, the left amygdala region, the right orbitofrontal cortex region, the left subgenual cingulate region, or the right hippocampus region, or other regions of the brain. The sites selected for detection may be different for different subjects and will depend on the mapping of the brain of each individual subject to identify optimal locations for positioning electrodes to detect electroencephalographic signals from neural activity associated with MDD symptoms.

[0026] In certain embodiments, the method further includes monitoring the subject for a period of time, where an increase in the number of gamma frequency electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates an increase in the severity of the MDD symptoms, and a decrease in the number of gamma frequency electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates a decrease in the severity of the MDD symptoms. For example, the subject can be monitored continuously or intermittently.

[0027] In another aspect, a system for treating a neuropsychiatric disorder in a subject is provided, the system comprising: a stimulating electrode adapted for positioning in a first region of the subject's brain; a detecting electrode adapted for positioning in a second region of the subject's brain and for recording an electroencephalography signal from the second region of the subject's brain; and a processor programmed to direct the stimulating electrode to apply electrical stimulation to the first region of the brain in a manner effective to treat the neuropsychiatric disorder in the subject when an electroencephalography signal exceeding a threshold level is detected using the second electrode.

[0028] In certain embodiments, the processor is further programmed to adjust one or more programmed stimulation parameters according to an algorithmic control law to apply the adjusted electrical stimulation to the first region of the brain via the stimulation electrodes in a manner effective to treat a neuropsychiatric disorder.

[0029] In certain embodiments, the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, the orbitofrontal cortex (OFC) region, or other regions of the brain.

[0030] In certain embodiments, the second brain region includes the right amygdala region, the left amygdala region, the right orbitofrontal cortex region, the left subgenual cingulate region, or the right hippocampus region, or other regions of the brain.

[0031] In certain embodiments, the processor is further programmed to set a maximum number of electrical stimulations per day.

[0032] In certain embodiments, the processor is further programmed to set a total amount of time of electrical stimulation per day. [Brief description of the drawings]

[0033] [Figure 1A] Neural biomarkers of severe depressive states. Figure 1A. A holistic approach for biomarker detection. Ten intracranial electrodes were implanted for 10 days across corticolimbic circuits. Resting-state neural activity was recorded for 15 min and paired with clinical reports of depression. Figure 1B. Clustering of clinical reports on anxiety and depression dimensions resulted in two symptom states. Shading is added to aid visualization. Figure 1C-D. Top neural features (as defined by F-score, ANOVA) that distinguish high vs. low symptom severity states. AUC curves show the predictive test performance of a logistic regression classifier fitted to these six features compared to a null model. Models with two levels of spatial resolution are shown (brain region level model: c, electrode contact level model: d). [Figure 1B]Neural biomarkers of severe depressive states. Figure 1A. A holistic approach for biomarker detection. Ten intracranial electrodes were implanted for 10 days across corticolimbic circuits. Resting-state neural activity was recorded for 15 min and paired with clinical reports of depression. Figure 1B. Clustering of clinical reports on anxiety and depression dimensions resulted in two symptom states. Shading is added to aid visualization. Figure 1C-D. Top neural features (as defined by F-score, ANOVA) that distinguish high vs. low symptom severity states. AUC curves show the predictive test performance of a logistic regression classifier fitted to these six features compared to a null model. Models with two levels of spatial resolution are shown (brain region level model: c, electrode contact level model: d). [Figure 1C] Neural biomarkers of severe depressive states. Figure 1A. A holistic approach for biomarker detection. Ten intracranial electrodes were implanted for 10 days across corticolimbic circuits. Resting-state neural activity was recorded for 15 min and paired with clinical reports of depression. Figure 1B. Clustering of clinical reports on anxiety and depression dimensions resulted in two symptom states. Shading is added to aid visualization. Figure 1C-D. Top neural features (as defined by F-score, ANOVA) that distinguish high vs. low symptom severity states. AUC curves show the predictive test performance of a logistic regression classifier fitted to these six features compared to a null model. Models with two levels of spatial resolution are shown (brain region level model: c, electrode contact level model: d). [Figure 1D]Neural biomarkers of severe depressive states. Figure 1A. A holistic approach for biomarker detection. Ten intracranial electrodes were implanted for 10 days across corticolimbic circuits. Resting-state neural activity was recorded for 15 min and paired with clinical reports of depression. Figure 1B. Clustering of clinical reports on anxiety and depression dimensions resulted in two symptom states. Shading is added to aid visualization. Figure 1C-D. Top neural features (as defined by F-score, ANOVA) that distinguish high vs. low symptom severity states. AUC curves show the predictive test performance of a logistic regression classifier fitted to these six features compared to a null model. Models with two levels of spatial resolution are shown (brain region level model: c, electrode contact level model: d). [Figure 2A] Identification of limbic sub-networks. Figure 2A. Directed network graph of the right hemisphere, where the percent circumference indicates the strength of the connections between any two brain regions. Color indicates the starting location for each set of connections. Figure 2B. Connection strength from the network graph is quantified by calculating weighted in-degree and out-degree, which indicate brain regions that are influential hubs in the corticolimbic network. The VC / VS has the highest weighted out-degree suggesting that it has strong connections to other brain regions, as observed in the AMY, and the HPC has the highest weighted in-degree suggesting that it receives connections from many other brain regions. Figure 2C. Example of dose-dependent mean EP (left). A summary of EPs (z-scores relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS is shown overlaid on the brain as a heatmap (right). Warmer colors indicate larger N1 amplitudes, suggesting that the VC / VS-AMY is a densely connected sub-network. Figure 2D. Right iEEG lead locations with fiber bundles (color-coded by orientation) showing structural connectivity between the AMY and clinically responsive VC / VS electrodes from deterministic tractography. Lead locations indicated by color as in a-b. Figure 2E. Gamma biomarker changes following periods of continuous stimulation for trials that led to a reduction in symptom severity (red) and trials that did not reduce symptom severity (blue). [Figure 2B] Identification of limbic sub-networks. Figure 2A. Directed network graph of the right hemisphere, where the percent circumference indicates the strength of the connections between any two brain regions. Color indicates the starting location for each set of connections. Figure 2B. Connection strength from the network graph is quantified by calculating weighted in-degree and out-degree, which indicate brain regions that are influential hubs in the corticolimbic network. The VC / VS has the highest weighted out-degree suggesting that it has strong connections to other brain regions, as observed in the AMY, and the HPC has the highest weighted in-degree suggesting that it receives connections from many other brain regions. Figure 2C. Example of dose-dependent mean EP (left). A summary of EPs (z-scores relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS is shown overlaid on the brain as a heatmap (right). Warmer colors indicate larger N1 amplitudes, suggesting that the VC / VS-AMY is a densely connected sub-network. Figure 2D. Right iEEG lead locations with fiber bundles (color-coded by orientation) showing structural connectivity between the AMY and clinically responsive VC / VS electrodes from deterministic tractography. Lead locations indicated by color as in a-b. Figure 2E. Gamma biomarker changes following periods of continuous stimulation for trials that led to a reduction in symptom severity (red) and trials that did not reduce symptom severity (blue). [Figure 2C]Identification of limbic sub-networks. Figure 2A. Directed network graph of the right hemisphere, where the percent circumference indicates the strength of the connections between any two brain regions. Color indicates the starting location for each set of connections. Figure 2B. Connection strength from the network graph is quantified by calculating weighted in-degree and out-degree, which indicate brain regions that are influential hubs in the corticolimbic network. The VC / VS has the highest weighted out-degree suggesting that it has strong connections to other brain regions, as observed in the AMY, and the HPC has the highest weighted in-degree suggesting that it receives connections from many other brain regions. Figure 2C. Example of dose-dependent mean EP (left). A summary of EPs (z-scores relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS is shown overlaid on the brain as a heatmap (right). Warmer colors indicate larger N1 amplitudes, suggesting that the VC / VS-AMY is a densely connected sub-network. Figure 2D. Right iEEG lead locations with fiber bundles (color-coded by orientation) showing structural connectivity between the AMY and clinically responsive VC / VS electrodes from deterministic tractography. Lead locations indicated by color as in a-b. Figure 2E. Gamma biomarker changes following periods of continuous stimulation for trials that led to a reduction in symptom severity (red) and trials that did not reduce symptom severity (blue). [Figure 2D]Identification of limbic sub-networks. Figure 2A. Directed network graph of the right hemisphere, where the percent circumference indicates the strength of the connections between any two brain regions. Color indicates the starting location for each set of connections. Figure 2B. Connection strength from the network graph is quantified by calculating weighted in-degree and out-degree, which indicate brain regions that are influential hubs in the corticolimbic network. The VC / VS has the highest weighted out-degree suggesting that it has strong connections to other brain regions, as observed in the AMY, and the HPC has the highest weighted in-degree suggesting that it receives connections from many other brain regions. Figure 2C. Example of dose-dependent mean EP (left). A summary of EPs (z-scores relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS is shown overlaid on the brain as a heatmap (right). Warmer colors indicate larger N1 amplitudes, suggesting that the VC / VS-AMY is a densely connected sub-network. Figure 2D. Right iEEG lead locations with fiber bundles (color-coded by orientation) showing structural connectivity between the AMY and clinically responsive VC / VS electrodes from deterministic tractography. Lead locations indicated by color as in a-b. Figure 2E. Gamma biomarker changes following periods of continuous stimulation for trials that led to a reduction in symptom severity (red) and trials that did not reduce symptom severity (blue). [Figure 2E]Identification of limbic sub-networks. Figure 2A. Directed network graph of the right hemisphere, where the percent circumference indicates the strength of the connections between any two brain regions. Color indicates the starting location for each set of connections. Figure 2B. Connection strength from the network graph is quantified by calculating weighted in-degree and out-degree, which indicate brain regions that are influential hubs in the corticolimbic network. The VC / VS has the highest weighted out-degree suggesting that it has strong connections to other brain regions, as observed in the AMY, and the HPC has the highest weighted in-degree suggesting that it receives connections from many other brain regions. Figure 2C. Example of dose-dependent mean EP (left). A summary of EPs (z-scores relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS is shown overlaid on the brain as a heatmap (right). Warmer colors indicate larger N1 amplitudes, suggesting that the VC / VS-AMY is a densely connected sub-network. Figure 2D. Right iEEG lead locations with fiber bundles (color-coded by orientation) showing structural connectivity between the AMY and clinically responsive VC / VS electrodes from deterministic tractography. Lead locations indicated by color as in a-b. Figure 2E. Gamma biomarker changes following periods of continuous stimulation for trials that led to a reduction in symptom severity (red) and trials that did not reduce symptom severity (blue). [Figure 3A]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3B]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3C]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3D]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3E]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3F]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 3J]Implementation of closed-loop neuromodulation. Figure 3A. An illustration of a fully implantable deep brain stimulation and recording device. Figure 3B. Reproducibility of targeting showing robust engagement of the stria terminalis and leg traps. VC / VS leads: yellow, AMY leads: pink. Figure 3C. Distribution of symptom severity scores associated with clusters identified in a mapping study for trials with 10 min resting-state continuous neurorecordings collected for the purpose of identifying biomarkers. Figure 3D. Positive correlation between gamma power in AMY contacts 1 / 2, 3 / 4 within a 10 min trial and HAMD-6 score. Figure 3E. Reproducibility of clinical effects. Each point represents stimulation trials with different bipolar configurations across right VC / VS and AMY (n=6, 2, 4, 5 for VAS-D, n=6, 2, 3, 4 for HAMD). Figure 3F. Left: Effect of burst duration on clinical measures. Stimulation parameters: contacts 3+ / 4-, 1 mA, 36 s total stimulation over 15.6 min with 6-36 s intervals. Highlighted conditions selected for the implementation of closed-loop therapy (burst duration of 6 seconds). Right: Effect of increasing dose (1-2 mA) and changing contact (3+ / 4- to 2+ / 3-) on clinical measures. Faded colors indicate that on / off states were detected by the patient (one trial per condition). Figure 3G. Schematic of closed-loop control for a deep brain stimulation device. Signals are recorded continuously. If neural biomarkers are detected and indicate a highly depressed state, a pulse of stimulation is delivered. Positive correlation between the number of gamma detections by the NeuroPace RNS system and the HAMD-6 score within the same 10-minute trial as in Figure 3H.f). Figure 3I. Symptom severity scores in the week before the start vs. after the end of closed-loop stimulation (n=3, 31 for VAS-D, n=2, 30 for HAMD6). Figure 3J. Relationship between daily mood ratings (purple) and the number of daily biomarker detections (grey) over two months of treatment with the RNS system. Dotted lines indicate dynamic time warping (DTW)-calculated distances between VAS-D scores and daily biomarker detection counts normalized to their mean (left). Significance was assessed by comparing DTW-distances with distances calculated from 10,000 randomly scrambled biomarker time series (right). [Figure 4A]Network connectivity. Figure 4A. Evoked potentials (z-scored relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS, OFC, and SGC are shown overlaid on the brain as a heat map. Warmer colors indicate larger N1 amplitudes. Figure 4B. Location of right sEEG leads targeting the AMY (pink), VC / VS (orange), SGC (green), and OFC (blue). Fiber bundles (colored by orientation) show inferred structural connectivity between candidate pairs of stimulating and sensing contacts (VC / VS-AMY, VC / VS-OFC, SGC-AMY, OFC-AMY) from deterministic tractography using 3 mm ROIs centered on each contact. Tractography parameters were the same for all pairs: minimum FA = 0.1, minimum fiber length = 80 mm, maximum angle = 20 degrees. [Figure 4B] Network connectivity. Figure 4A. Evoked potentials (z-scored relative to baseline) across the corticolimbic network resulting from single pulse stimulation in the right VC / VS, OFC, and SGC are shown overlaid on the brain as a heat map. Warmer colors indicate larger N1 amplitudes. Figure 4B. Location of right sEEG leads targeting the AMY (pink), VC / VS (orange), SGC (green), and OFC (blue). Fiber bundles (colored by orientation) show inferred structural connectivity between candidate pairs of stimulating and sensing contacts (VC / VS-AMY, VC / VS-OFC, SGC-AMY, OFC-AMY) from deterministic tractography using 3 mm ROIs centered on each contact. Tractography parameters were the same for all pairs: minimum FA = 0.1, minimum fiber length = 80 mm, maximum angle = 20 degrees. [Diagram 5]A holistic approach to stimulation and sensing target selection. The multimodal method for personalized responsive stimulation multilead targeting began with clinical mapping to identify candidate sites where stimulation reliably resulted in symptom improvement across a range of doses and symptom severity conditions. Candidate sensing locations were identified by pairing resting-state neural activity with symptom severity ratings to identify spectral power biomarkers that correlate with depression. The relationship between candidate stimulation and sensing targets was then tested using three approaches. First, effective network connectivity was assessed by examining evoked responses at nodes across the network resulting from single pulse stimulation at the candidate targets. Second, structural connectivity between candidate contact pairs was assessed using tractography. Influential bundles were identified to aid in retargeting during implantation of chronic stimulation devices. Finally, the feasibility of closed-loop control was evaluated by examining the impact of stimulation at the candidate stimulation site on putative biomarkers identified at the candidate sensing locations. This personalized approach was then enabled to identify the one best stimulation and sensing target pair that was leveraged for implantation of the RNS system and delivery of closed-loop neurostimulation therapy. [Figure 6] Flowchart of discovery strategy for development of personalized closed-loop neurostimulation therapy. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] Methods and systems are provided for the treatment of a neuropsychiatric disorder in a subject, wherein closed-loop therapy is performed using a neurostimulator that records electroencephalographic signals from neural activity associated with symptoms of the neuropsychiatric disorder and delivers electrical stimulation when a pre-specified pattern of neural activity associated with symptoms of the neuropsychiatric disorder is detected.

[0035] Neuropsychiatric disorders that can be treated with the systems and methods disclosed herein include, but are not limited to, MDD, GAD, PTSD, addiction, OCD, anorexia nervosa, bipolar disorder, chronic pain, and related conditions. A patient may suffer from one or more of these neuropsychiatric disorders. The methods disclosed herein may treat two or more neuropsychiatric disorders in the same patient.

[0036] Before describing exemplary embodiments of the invention, it is to be understood that the invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0037] Where a range of values ​​is provided, it is understood that each intervening value between the upper and lower limits of that range is also specifically disclosed to the tenth of the unit of the lower limit, unless the context clearly dictates otherwise. Each smaller range between any stated value or intervening value within a stated range and any other stated value or intervening value within that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the range, and each range in which either, neither, or both limits are included in the smaller range is also encompassed within the invention, subject to any specifically excluded limits in the stated range. Where a stated range includes one or both of the limits, ranges excluding either or both of those included limits are likewise included in the invention.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and exemplary methods and materials can be described here. Any and all publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials for which the publications are cited. In case of conflict, it should be understood that the present disclosure supersedes any disclosure of the incorporated publication.

[0039] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, a reference to "an electrode" or "the electrode" includes a plurality of such electrodes, a reference to "a symptom" or "the symptom" includes a reference to one or more symptoms, and so forth.

[0040] It is further noted that the claims may be drafted to exclude any element that may be optional. Accordingly, this specification is intended to serve as a predicate basis for using exclusive terminology, such as "solely," "only," or "negative" limitations in connection with the recitation of claim elements.

[0041] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present invention is not entitled to antedate such publications by virtue of prior invention. Further, the dates of publications provided may be different from the actual publication dates which may need to be independently confirmed. To the extent that such publications may provide definitions of terms that contradict the express or implied definitions of the present disclosure, the definitions of the present disclosure shall control.

[0042] As will be apparent to those skilled in the art upon reading this disclosure, each of the separate embodiments described and illustrated herein has separate components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method may be carried out in the order of events recited or in any other order which is logically possible.

[0043] definition The term "neuropsychiatric disorder" is used herein to refer to a group of conditions that affect the mood and / or behavior of a person suffering from the disorder. Neuropsychiatric disorders include major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), bipolar disorder (BD), and chronic pain.

[0044] The term "affective disorder" is used herein to refer to a group of neuropsychiatric disorders that typically affect the mood of the person suffering from such disorder. Neuropsychiatric disorders that affect mood are also called mood disorders and are generally associated with depression and / or anxiety. The main types of affective disorders are depression, bipolar disorder, and anxiety disorder. Symptoms vary from person to person, but they typically affect mood. They can range from mild to severe.

[0045] As used herein, the term "depression" refers to a mental state of morbid sadness, despondency, or melancholy.

[0046] As used herein, the term "anxiety" refers to an unpleasant and unjustified feeling of unease that is diffuse, unfocused, and often accompanied by physiological symptoms.

[0047] As used herein, the term "bipolar disorder" refers to a type of emotional disorder in which a person with the disorder goes through periods of depression and periods of mania (feeling very positive and active).

[0048] As used herein, the term "anxiety disorder" refers to a neuropsychiatric disorder characterized by feelings of tension, anxiety, and even fear. Anxiety disorders include social anxiety (anxiety caused by social situations), post-traumatic stress disorder (anxiety, fear, flashbacks caused by traumatic events), generalized anxiety disorder (generalized anxiety and fear without specific cause), panic disorder (anxiety causing panic attacks), and obsessive-compulsive disorder (obsessions causing anxiety and compulsive behaviors).

[0049] "Treatment" or "treating" means that one or more symptoms associated with a condition afflicting a subject are at least improved, such that the patient has a desired or beneficial clinical outcome, where improvement refers to at least a reduction in the parameters associated with the condition being treated, e.g., the magnitude of the symptoms. Thus, treatment includes a wide range of situations, including a reduction in the intensity, duration or extent of the disorder caused by and / or correlated with the condition, along with any associated symptoms, to the complete elimination of the condition. Thus, treatment includes situations in which a condition, or at least the symptoms associated with it, are completely inhibited, e.g., prevented from occurring or stopped, e.g., terminated, such that the subject no longer suffers from the condition, or at least the symptoms that characterize the condition. Treatment also includes situations in which the progression of a condition, or at least the progression of the symptoms associated with it, is slowed, delayed or stopped. In such cases, the subject may still have residual symptoms associated with the pathological condition, but any increase in the severity or magnitude of the symptoms is slowed, delayed or prevented.

[0050] As used herein, the term "orbitofrontal cortex" or "OFC" refers to a defined region of the brain known by those skilled in the art, as well as the surrounding or adjacent white matter tracts leading to and from the OFC. The OFC is a prefrontal cortical region in the frontal lobe of the brain. In humans, it consists of Brodmann areas 10, 11, and 47.

[0051] The term "subject" as used herein refers to a patient in need of the treatment disclosed herein. The patient may be a mammal, such as a rodent, cat, dog, primate, or human, for example, an adult, such as a child, adolescent, young, middle-aged, or elderly human. The patient may be diagnosed with a neuropsychiatric disorder, may be suspected of suffering from a neuropsychiatric disorder, or may be at risk of developing a neuropsychiatric disorder.

[0052] The term "user" as used herein refers to a person who interacts with the devices and / or systems disclosed herein to perform one or more steps of the methods disclosed herein. The user may be a patient receiving treatment. The user may be a medical professional, such as the patient's physician.

[0053] The term "symptoms" as used in the context of a neuropsychiatric disorder, such as symptoms of a neuropsychiatric disorder, refers to symptoms such as anxiety, depression, fear, mania, and / or uncontrollable behavior.

[0054] method The present disclosure provides methods for treating a neuropsychiatric disorder in a subject. Also provided herein are methods for ameliorating symptoms of a neuropsychiatric disorder in a subject, where various steps and aspects of the method will be described in more detail below.

[0055] The methods of the present disclosure utilize closed-loop therapy performed with a neurostimulator that records electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder and delivers electrical stimulation when pre-specified patterns of neural activity associated with symptoms of a neuropsychiatric disorder are detected.

[0056] The method includes positioning a first electrode (i.e., a stimulating electrode) to deliver electrical stimulation to a first brain region of a subject, and positioning a second electrode (i.e., a detecting electrode) to detect electroencephalography signals from neural activity associated with symptoms of a neuropsychiatric disorder in a second brain region of the subject. One or more stimulating or detecting electrodes may be positioned in the first brain region or the second brain region, respectively. In some embodiments, the first brain region and the second brain region are the same. In other embodiments, the first brain region and the second brain region are different. The stimulating and detecting electrodes may be non-brain penetrating surface electrodes or brain penetrating deep electrodes. Electrical stimulation may be applied to the first brain region using the stimulating electrode in a manner effective to treat the neuropsychiatric disorder and / or ameliorate symptoms of the neuropsychiatric disorder when an electroencephalography signal is detected from the second brain region using the detecting electrode having a signal above a threshold level indicating that the patient has a level of symptom severity requiring treatment. Such symptoms may include one or more of anxiety, depression, obsessive-compulsive behavior, and the like.

[0057] The electrical stimulation may be applied using a single electrode, an electrode pair, or an electrode array. The electrical stimulation may be applied unilaterally or bilaterally to the first brain region. Bilateral electrical stimulation may be simultaneous or sequential. In some embodiments, the electrical stimulation is applied to two or more sites. The sites to which the electrical stimulation is applied may be alternated or otherwise patterned spatially or temporally.

[0058] Positioning of electrodes to apply electrical stimulation to specific regions of the brain can be performed using standard surgical procedures for placement of intracranial electrodes. As used herein, the phrase "an electrode" or "the electrode" refers to a single electrode or multiple electrodes, such as an electrode array. As used herein, the term "contact" used in the context of an electrode in contact with a region of the brain refers to a physical association between the electrode and the region. In other words, an electrode in contact with a region of the brain is physically touching the region of the brain. An electrode in contact with a region of the brain can conduct electricity to the brain. Electrodes used in the methods disclosed herein can be unipolar (cathode or anode) or bipolar (e.g., having an anode and a cathode). An electrode used to apply electrical stimulation to a specific region of the brain is also referred to as a stimulating electrode.

[0059] In certain embodiments, the brain region to which electrical stimulation is applied includes the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region, or other regions of the brain suitable for stimulation. The sites selected for stimulation may be different for different subjects and will depend on mapping of the brain of each individual subject to identify optimal locations for positioning electrodes for delivering electrical stimulation to treat neuropsychiatric disorders, as discussed further below.

[0060] In certain cases, placing electrodes in the VC / VS, SGC, or OFC, or other regions may include positioning the electrodes on the surface of a designated region of the brain. The electrodes may be placed on the surface of the brain in the VC / VS, for example, in the nucleus accumbens (NA) or anterior limbus of the internal capsule (ALIC), or both. The electrodes may contact at least a portion of the surface of the brain in the VC / VS, SGC, or OFC, or other regions. In some embodiments, the electrodes may contact substantially the entire surface area in the VC / VS, SGC, or OFC, or other regions. In some embodiments, the electrodes may additionally contact regions adjacent to the VC / VS, SGC, or OFC. In some embodiments, an electrode array disposed on a planar support substrate may be used to electrically stimulate the VC / VS, SGC, or OFC regions, or other regions of the brain, as specified herein. The surface area of ​​the electrode array may be determined by the desired contact area between the electrode array and the brain. Electrodes for implantation on the brain surface, such as surface electrodes or surface electrode arrays, may be obtained from commercial suppliers. Commercially obtained electrodes / electrode arrays can be modified to achieve the desired contact area. In some cases, non-penetrating brain electrodes (also referred to as surface electrodes) that can be used in the methods disclosed herein can be electrocorticography (ECoG) electrodes or electroencephalography (EEG) electrodes.

[0061] In certain cases, placing an electrode (e.g., a stimulating electrode) in a target region or site may include positioning a penetrating electrode (also called a deep electrode) in a particular region of the brain. For example, the stimulating electrode may be placed in the VC / VS, SGC, or OFC region, or another region. In some embodiments, the stimulating electrode may additionally contact a region adjacent to the VC / VS, SGC, or OFC. In some embodiments, an electrode array may be used to electrically stimulate the VC / VS, SGC, or OFC region, or another region of the brain, as specified herein.

[0062] The depth to which the stimulation electrodes are inserted into the brain may be determined by the desired level of contact between the electrode array and the brain. Penetrating brain electrode arrays may be obtained from commercial suppliers. Commercially obtained electrode arrays may be modified to achieve the desired insertion depth into brain tissue.

[0063] In certain embodiments of the methods of the present invention, electrical stimulation is applied only to the VC / VS region of the brain, while in other embodiments, electrical stimulation is applied to at least the VC / VS region of the brain and one or more additional regions of the brain.

[0064] In certain embodiments, one or more detection electrodes are used to record electroencephalography signals for neural activity associated with symptoms of neuropsychiatric disorders in one or more brain regions.Detection electrodes can be placed, for example, in the right amygdala, the left amygdala, the right orbitofrontal cortex, the left subgenual cingulate, or the right hippocampal region of the brain, or other regions of the brain suitable for detection.The site selected for detection can be different for different subjects, and will depend on the mapping of the brain of each individual subject to identify the optimal location for positioning electrodes to detect electroencephalography signals from neural activity associated with symptoms of neuropsychiatric disorders, as will be further discussed below.

[0065] In certain cases, placing the detection electrodes may include positioning the electrodes on the surface of a specified region of the brain. For example, the electrodes may be placed on the surface of the brain at the right amygdala, the left amygdala, the right orbitofrontal cortex, the left genu cingulate, or the right hippocampal region, or any combination thereof. The electrodes may contact at least a portion of the surface of the brain at the right amygdala, the left amygdala, the right orbitofrontal cortex, the left genu cingulate, or the right hippocampal region. In some embodiments, the electrodes may contact substantially the entire surface area at the right amygdala, the left amygdala, the right orbitofrontal cortex, the left genu cingulate, or the right hippocampal region. In some embodiments, the electrodes may additionally contact an area adjacent to the right amygdala, the left amygdala, the right orbitofrontal cortex, the left genu cingulate, or the right hippocampal region. In some embodiments, the electrode array disposed on the planar support substrate may be used to detect electroencephalographic signals for neural activity from one or more of the brain regions specified herein. The surface area of ​​the electrode array may be determined by the desired contact area between the electrode array and the brain. Electrodes for implantation on the brain surface, such as surface electrodes or surface electrode arrays, may be obtained from commercial suppliers. Commercially obtained electrodes / electrode arrays may be modified to achieve the desired contact area. In some cases, the non-brain penetrating electrodes (also referred to as surface electrodes) that may be used in the methods disclosed herein may be electrocorticography (ECoG) electrodes or electroencephalography (EEG) electrodes. In certain embodiments, multiple electrodes may be positioned in one or more of the brain regions specified herein for detection of electroencephalography signals by stereotactic electroencephalography (sEEG).

[0066] In certain cases, placing a detection electrode in a target region or site (e.g., right amygdala, left amygdala, right orbitofrontal cortex, left genual cingulate, or right hippocampus) may involve positioning a penetrating electrode (also referred to as a depth electrode) in a particular region of the brain. For example, the detection electrode may be placed in the right amygdala, left amygdala, right orbitofrontal cortex, left genual cingulate, or right hippocampus region. In some embodiments, the detection electrode may additionally contact an area adjacent to the right amygdala, left amygdala, right orbitofrontal cortex, left genual cingulate, or right hippocampus. In some embodiments, the electrode array may be used to detect neural activity from the right amygdala, left amygdala, right orbitofrontal cortex, left genual cingulate, or right hippocampus, as specified herein.

[0067] The depth to which the detection electrodes are inserted into the brain may be determined by the desired level of contact between the electrode array and the brain. Penetrating brain electrode arrays may be obtained from commercial suppliers. Commercially obtained electrode arrays may be modified to achieve the desired insertion depth into brain tissue.

[0068] The exact number of stimulating or detecting electrodes included in an electrode array (e.g., for electrical stimulation or detection of neural activity) can vary. In certain embodiments, an electrode array can include two or more electrodes, such as three or more, including four or more, e.g., about 3-6 electrodes, about 6-12 electrodes, about 12-18 electrodes, about 18-24 electrodes, about 24-30 electrodes, about 30-48 electrodes, about 48-72 electrodes, about 72-96 electrodes, or about 96 or more electrodes. The electrodes can be arranged in a regular repeating pattern (e.g., a grid, such as a grid with about 1 cm spacing between electrodes), or without a pattern. Electrodes that fit the target site for optimal delivery of electrical stimulation can be used. One such example is a single multi-contact electrode with eight contacts separated by 2 1 / 2 mm. Each contact will have a span of approximately 2 mm. Another example is an electrode with two 1 cm contacts with a 2 mm intermediate gap. Yet another example of an electrode that can be used in the present method is a two or three pronged electrode to cover the target site. Each of these three pronged electrodes has two center-to-center separations of 2.5 mm, and four 1-2 mm contacts with a span of 1.5 mm.

[0069] The size of each electrode may also vary depending on factors such as the number of electrodes in the array, the location of the electrodes, the material, the age of the patient, and other factors. In certain embodiments, the electrode array has a size (e.g., diameter) of about 5 mm or less, such as about 4 mm or less, including 4 mm to 0.25 mm, 3 mm to 0.25 mm, 2 mm to 0.25 mm, 1 mm to 0.25 mm, or about 3 mm, about 2 mm, about 1 mm, about 0.5 mm, or about 0.25 mm.

[0070] In certain embodiments, the method further comprises mapping the subject's brain to optimize the positioning of electrodes for applying electrical stimulation. The positioning of the stimulation electrodes is optimized to maximize clinical response to electrical stimulation to alleviate symptoms of neuropsychiatric disorders such as depression and / or anxiety. In some embodiments, the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, the orbitofrontal cortex (OFC) region, or other regions of the brain are mapped to determine the optimal positioning of the stimulation electrodes.

[0071] Assessment of the effectiveness of electrical stimulation at a particular site may be performed by neurological and / or neuropsychological tests (e.g., Minnesota Multiphasic Personality Test, Beck Depression Questionnaire, Mini-Mental State Examination (MMSE), Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), Wisconsin Card Sorting Test (WCST), Tower of London, Stroop task, Montgomery-Asberg Depression Scale (MADRS), Yale-Brown Obsessions and Compulsions Score (Y-BOCS)), motor tests, visual analog scales of pain symptoms, and / or cranial nerve tests.

[0072] In certain cases, the symptom that can be improved by electrical stimulation is anxiety. The anxiety of the subject can be evaluated using any standardized evaluation method. In certain cases, anxiety can be measured by self-report, such as the Beck Anxiety Inventory (BAI) score, the visual analog scale for anxiety (VAS-A), or the Hamilton Anxiety Rating Scale (HAM-A). Additional measures of anxiety include the State-Trait Anxiety Inventory (STAI), which consists of the State Anxiety Scale (S-Anxiety) and the Trait Anxiety Scale (T-Anxiety), and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). The improvement of anxiety can include a reduction in anxiety level compared to the anxiety level before electrical stimulation.

[0073] In certain cases, the symptom that can be improved by electrical stimulation is depression. The subject's depression can be assessed using any standardized assessment method. In certain cases, depression can be measured by self-report, such as the Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), Beck Depression Inventory (BDI) score, Center for Epidemiological Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), or Zung Self-Rating Depression Scale (Zung SDS). In certain cases, interviewer-administered depression assessments can be used alone or in combination with self-report tools. Interviewer-administered depression assessments include the Cornell Scale for Depression in Dementia (CSDD) and the RAND Corporation Self-Administration Depression Screener. Improvement of depression can include a decrease in depression level compared to the depression level before electrical stimulation.

[0074] In certain embodiments, the method further includes mapping the subject's brain to optimize positioning of the detection electrodes. The positioning of the detection electrodes is optimized to detect brain activity features that distinguish between symptom states of the neuropsychiatric disorder severe enough to require treatment and asymptomatic (baseline) or mild symptom states that do not require treatment. For example, the level of overall power, or power in a particular frequency range (e.g., alpha, delta, beta, gamma, and / or high gamma) may be correlated with symptom severity and the need for treatment with electrical stimulation. In some embodiments, the level of gamma frequency power (e.g., 30 Hz to 200 Hz) is correlated with symptom severity to determine whether the patient requires treatment with electrical stimulation. Thus, the detection electrodes may be positioned to optimize detection of brain activity in a particular frequency range that correlates with symptom severity of the neuropsychiatric disorder. Alternatively or additionally, other features of coherence or network connectivity within a particular spectral frequency band may be correlated with symptom severity and the need for treatment with electrical stimulation.

[0075] Detection of brain activity can be performed by any method known in the art. For example, functional brain imaging of neural activity can be performed by electrical methods such as electroencephalography (EEG), stereotactic electroencephalography (sEEG), magnetoencephalography (MEG), single photon computed tomography (SPECT), and metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In some embodiments, the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus, or other regions, are mapped to determine optimal positioning for the detection electrodes. One or more of these regions can be implanted with detection electrodes to measure electrical signals from neural activity associated with symptoms of neuropsychiatric disorders.

[0076] As described herein, the method includes applying electrical stimulation in a manner effective to treat a neuropsychiatric disorder and / or symptoms of the neuropsychiatric disorder in a subject when neural activity associated with a symptom of the neuropsychiatric disorder is detected. The closed-loop therapy may be performed with a neurostimulator that records electroencephalography signals from neural activity associated with a symptom of the neuropsychiatric disorder and delivers electrical stimulation when the level of the detected electroencephalography signal exceeds a set threshold level indicating a level of symptom severity requiring treatment. The parameters for applying the electrical stimulation may be empirically determined during treatment or may be predefined from clinical trials, etc. The parameters of the electrical stimulation may include one or more of frequency, pulse width / duration, duty cycle, intensity / amplitude, pulse pattern, program duration, program frequency, etc.

[0077] Frequency refers to the pulses generated per second during stimulation and is described in units of Hertz (Hz, e.g., 60 Hz = 60 pulses per second). The frequency of electrical stimulation used in the present method can vary widely depending on a number of factors and can be empirically determined or predefined during treatment of the subject. In certain embodiments, the present method includes a frequency of 10 Hz to 300 Hz, 10 Hz to 200 Hz, 10 Hz to 150 Hz, 10 Hz to 125 Hz, 10 Hz to 100 Hz, 15 Hz to 200 Hz, 15 Hz to 300 Hz, 20 Hz to 200 Hz, 25 Hz to 400 Hz, 25 Hz to 300 Hz, 25 Hz to 200 Hz, 25 Hz to 150 Hz, 25 Hz to 100 Hz, 50 Hz to 500 Hz, 50 Hz to It may include applying electrical stimulation at a frequency of 10 Hz to 500 Hz, such as 400 Hz, 50 Hz to 300 Hz, 50 Hz to 200 Hz, 50 Hz to 150 Hz, 50 Hz to 100 Hz, 75 Hz to 300 Hz, 75 Hz to 200 Hz, 75 Hz to 150 Hz, 75 Hz to 125 Hz, 75 Hz to 120 Hz, 75 Hz to 115 Hz, 75 Hz to 110 Hz, or 75 Hz to 100 Hz. The amplitude of the current may be 0.1 mA to 30 mA, such as 0.1 mA to 25 mA, 0.1 mA to 20 mA, 0.1 mA to 15 mA, 0.1 mA to 10 mA, 1 mA to 20 mA, 1 mA-10 mA, 2 mA to 30 mA, 2 mA to 15 mA, or 2 mA to 10 mA.

[0078] The electrical stimulation may be applied in pulses, such as monophasic or biphasic pulses. The time span of a single pulse is referred to as the pulse width or pulse duration. The pulse widths used in the present methods may vary widely depending on many factors (e.g., severity of disease, patient condition, etc.) and may be empirically determined or predefined. In certain embodiments, the present methods use pulse widths of about 10 μs to 990 μs, e.g., 30 μs to 990 μs, 50 μs to 990 μs, 75 μs to 990 μs, 100 μs to 990 μs, 200 μs to 990 μs, 300 μs to 990 μs, 500 μs to 990 μs, 500 μs to 900 μs, 30 μs to 900 μs, 5 ... This may include applying the electrical stimulation with a pulse width of 0 μsec, 75 μsec to 900 μsec, 100 μsec to 900 μsec, 200 μsec to 900 μsec, 300 μsec to 900 μsec, 500 μsec to 900 μsec, 30 μsec to 500 μsec, 50 μsec to 450 μsec, 75 μsec to 300 μsec, 100 μsec to 200 μsec, or 100 μsec to 550 μsec. In some embodiments, the electrical stimulation is applied with a pulse width of about 100 μsec to about 140 μsec, including any pulse width within this range, such as 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, or 140 μsec.

[0079] The electrical stimulation may be applied for stimulation periods of 0.1 seconds to 1 month, during which periods of rest (i.e., no electrical stimulation) are possible. In certain cases, the period of electrical stimulation may be 0.1 seconds to 1 week, 1 second to 1 day, 10 seconds to 12 hours, 1 minute to 6 hours, 10 minutes to 1 hour, etc. In certain cases, the period of electrical stimulation may be 1 second to 1 minute, 1 second to 30 seconds, 1 second to 15 seconds, 1 second to 10 seconds, 1 second to 6 seconds, 1 second to 3 seconds, 1 second to 2 seconds, or 6 seconds to 10 seconds. The period of rest between each stimulation period may be 60 seconds or less, 30 seconds or less, 20 seconds or less, or 10 seconds.

[0080] The electrical stimulation may be applied with a current amplitude of 0.1 mA to 30 mA, such as 0.1 mA to 25 mA, 0.1 mA to 20 mA, 0.1 mA to 15 mA, 0.1 mA to 10 mA, 0.1 mA to 2 mA, 0.1 mA to 1 mA, 1 mA to 20 mA, 1 mA to 10 mA, 2 mA to 30 mA, 2 mA to 15 mA, 2 mA to 10 mA, or 1 mA to 2 mA. In some embodiments, the current amplitude is 0.1 mA to 1.5 mA, or any amplitude of current in this range, such as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, or 1.5 mA.

[0081] Electrical stimulation with the parameters described above may be applied for a program duration of about one day or less, such as 18 hours, 6 hours, 3 hours, 1 hour, 45 minutes, 30 minutes, 20 minutes, 10 minutes, or 5 minutes, or less, e.g., 1-5 minutes, 2-10 minutes, 2-20 minutes, 2-30 minutes, 5-10 minutes, 5-30 minutes, or 5-15 minutes, which period will include application of pulses and intermediate rest periods. The program may be repeated at a desired program frequency until treatment of the subject is achieved. Thus, a treatment plan may include a program for electrical stimulation at a desired program frequency and program duration. In some embodiments, the treatment plan is controlled by a control unit in communication with a pulse generator connected to one or more stimulation electrodes in a closed-loop treatment plan.

[0082] In some embodiments, an upper limit can be set for the maximum number of electrical stimulations per day. For example, the maximum number of electrical stimulations per day can range from 50 therapy sessions per day to 500 therapy sessions per day, including any number of therapy sessions per day in this range, such as 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, or 500 therapy sessions per day. Alternatively or additionally, an upper limit can be set for the total amount of time of electrical stimulation per day. For example, the total amount of time of electrical stimulation per day can range from 10 minutes to 100 minutes of total time per day, including any amount of time within this range, such as 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 minutes of total stimulation time per day. Such upper limits on the maximum number of electrical stimulations per day and / or the total amount of time of electrical stimulation can be used to avoid disturbing the patient's sleep from nighttime therapy. In addition, quiet periods without electrical stimulation when the patient wishes to sleep can also be used to avoid sleep disturbances.

[0083] As mentioned above, the treatment may improve one or more symptoms of the neuropsychiatric disorder that the subject suffers from. Such symptoms include anxiety, depression, frequency of episodes of obsessive behavior, frequency of episodes of self-starvation, mania, panic attacks, social anxiety, or distress related to chronic pain. The evaluation of the effectiveness of the treatment may be performed by neurological and / or neuropsychological tests (e.g., Minnesota Multiphasic Personality Test, Beck Depression Questionnaire, Mini-Mental State Examination (MMSE), Visual Analog Scale for Depression (VAS-D), Hamilton Depression Rating Scale (HAM-D), Montgomery-Asberg Depression Rating Scale (MADRS), Wisconsin Card Sorting Test (WCST), Tower of London, Stroop task, Montgomery-Asberg Depression Rating Scale (MADRS), Yale-Brown Obsession-Compulsion Score (Y-BOCS)), motor tests, visual analog scale of pain symptoms, and / or cranial nerve tests.

[0084] In certain cases, the symptom that can be improved by the disclosed method can be anxiety. The subject's anxiety can be assessed using any standardized assessment method. In certain cases, anxiety can be measured by self-report, such as the Beck Anxiety Inventory (BAI) score, the Visual Analog Scale for Anxiety (VAS-A), or the Hamilton Anxiety Rating Scale (HAM-A). Additional measures of anxiety include the State-Trait Anxiety Inventory (STAI), which consists of the State Anxiety Scale (S-Anxiety) and the Trait Anxiety Scale (T-Anxiety), and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). An improvement in anxiety can include a reduction in anxiety levels compared to pre-treatment anxiety levels. A 5% or greater reduction in anxiety levels (e.g., as measured by the BAI or HAM-A) can indicate that the treatment was effective.

[0085] In certain cases, the symptom that can be improved by the disclosed method can be depression. The depression of the subject can be assessed using any standardized assessment method. In certain cases, depression can be measured by self-report, such as Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), Beck Depression Inventory (BDI) score, Center for Epidemiological Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), or Zung Self-Rating Depression Scale (Zung SDS). In certain cases, interviewer-administered depression assessments can be used alone or in combination with self-report tools. Interviewer-administered depression assessments include the Cornell Scale for Depression in Dementia (CSDD) and the RAND Corporation Self-Administration Depression Screener. Improvement of depression can include a decrease in depression level compared to pre-treatment depression level. A 5% or greater reduction in depression levels (eg, as measured by BDI score) can indicate that treatment has been effective.

[0086] In certain cases, the effectiveness of the treatment can be evaluated by detecting activity (e.g., electrical signals) from a secondary brain region, which may be in the amygdala, or another region. For example, the secondary region can be one or more of the amygdala, orbital precortex, hippocampus, septum, cingulate gyrus, cingulate cortex, genu cingulate, hypothalamus, epithalamus, anterior thalamus, mammillary bodies, and fornix. In certain embodiments, the secondary region can be the right amygdala region, the left amygdala region, the right orbital frontal cortex region, the left genu cingulate region, or the right hippocampal region of the subject's brain, or any combination thereof. The detection of brain activity in the secondary brain region can be performed by functional brain imaging. Functional brain imaging can be performed by electrical methods such as electroencephalography (EEG), magnetoencephalography (MEG), single photon computed tomography (SPECT), as well as metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). In some embodiments, electrical methods for assessing the effectiveness of a treatment may involve the use of detection electrodes as described herein, or the placement of additional electrodes to measure electrical signals in secondary regions of the brain. One or more regions of the brain may be implanted with electrodes, and electrical signals measured for evaluation of the effectiveness of a treatment. Any suitable electrodes may be used for the measurements, and may include one or more surface electrodes (non-brain penetrating electrodes), or one or more deep electrodes (brain penetrating electrodes), as described herein.

[0087] The evaluation of the effectiveness of the treatment and the evaluation of the improvement of the symptoms of the neuropsychiatric disorder can be performed at any suitable time point after the start of the treatment procedure, for example, during closed-loop therapy or after the treatment plan is completed. An embodiment of the subject method includes evaluating the effectiveness of the treatment or improvement of the symptoms of the neuropsychiatric disorder within seconds, minutes, hours, or days after the initial treatment plan is completed. In some cases, the evaluation can be performed at multiple time points. In some cases, two or more types of evaluation can be performed at different time points. In some embodiments, the subject's brain activity (e.g., in one or more secondary regions) can be measured before the application of electrical stimulation, and the evaluation can include comparing the subject's brain activity in one or more secondary regions after the treatment with that before the treatment, and a change in brain activity after the treatment can indicate successful treatment.

[0088] In some embodiments, the subject is monitored for a period of time during closed-loop therapy using the detection electrodes, and an increase in the number of electroencephalography signals above a threshold level detected by the detection electrodes in a later recording period compared to a previous recording period indicates an increase in the severity of the symptoms of the neuropsychiatric disorder, and a decrease in the number of electroencephalography signals above the threshold level detected by the detection electrodes in a later recording period compared to a previous recording period indicates a decrease in the severity of the symptoms of the neuropsychiatric disorder.

[0089] The methods and systems provided herein can be used to ameliorate one or more symptoms of a neuropsychiatric disorder in a patient. One skilled in the art will appreciate that amelioration of symptoms can provide relief to a patient suffering from two separate neuropsychiatric conditions that share symptoms.

[0090] Upon completion of the treatment plan, the patient may be evaluated for the effectiveness of the treatment, and if necessary, the treatment plan may be repeated. In certain cases, the treatment plan may be modified before repeating. For example, one or more of the frequency, pulse width, current amplitude, period of electrical stimulation, programmed duration, programmed frequency, and / or placement of the stimulation or sensing electrodes may be modified before commencing a second treatment plan.

[0091] In certain cases, treatment plan can be adjusted to meet desired outcome.For example, for patients suffering from acute type of neuropsychiatric disorder, treatment plan can be selected to provide acute relief of one or more symptoms of the disorder.In contrast, treatment plan for patients suffering from chronic neuropsychiatric disorder can be adjusted for chronic relief of one or more symptoms of the disorder.The treatment plan for chronic relief of one or more symptoms of the disorder can also delay the reappearance of symptoms of chronic relief of one or more symptoms of the disorder, and even prevent the appearance of symptoms.In certain cases, a treatment plan that is a combination of treatment plan for acute and chronic relief of one or more symptoms of the disorder can be used.

[0092] The application of the method may include a prior step of selecting a patient for treatment based on the need determined by clinical evaluation, which may include cognitive, anatomical, behavioral, and / or neurophysiological evaluation. In certain cases, a subject may be selected for treatment if the subject is at risk of suffering from a neuropsychiatric disorder. Patients who have a family history of a neuropsychiatric disorder or have previously suffered from a neuropsychiatric disorder may be at risk. Such patients may also be implanted with detection and stimulation electrodes.

[0093] In certain aspects, the methods and systems of the present disclosure may include measuring brain activity, for example electrical activity in the amygdala, and the level of gamma frequency power may be measured. In certain cases, electrical activity from multiple locations in the amygdala may be measured and averaged. In some embodiments, electrical activity in the gamma frequency range (such as 30Hz-200Hz) may be measured from the right amygdala, the left amygdala, the right orbitofrontal cortex, the left subgenual cingulate, or the right hippocampus, or any combination thereof. In some cases, electrical activity in one or more locations in the brain may be measured during a period spanning from pre-stimulus to a period during which stimulation is applied to the brain, or to a period after stimulation is applied to the brain, and monitored for a decrease in power of gamma frequency (such as 30Hz-200Hz) activity and / or an increase in cortical excitability. In some cases, when the reduced power and / or increased cortical excitability of gamma frequency (e.g., 30 Hz-200 Hz) activity is within a normal range (e.g., a range associated with a substantial absence of a disorder), the methods and systems do not apply further stimulation to the brain. Alternatively, when the reduced power and / or increased cortical excitability of gamma frequency (e.g., 30 Hz-200 Hz) activity is not within a normal range (e.g., a range associated with a substantial absence of a disorder), the methods and systems may apply further stimulation to the brain. In certain cases, application of electrical stimulation to the brain may suppress gamma frequency power across the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus. The reduction may be compared to the power before application of the stimulation. In certain cases, application of electrical stimulation to the brain may alter other neural characteristics from one or more regions of the brain. The alteration may be compared to the state of these characteristics before application of the stimulation.

[0094] The closed-loop method allows for the determination of parameters of the electrical stimulation based on real-time feedback signals from the subject's brain. The closed-loop method and system allows for the automation of the subject's treatment, including adjustments based on real-time needs of the treatment plan. Exemplary closed-loop methods and related systems for the treatment of neuropsychiatric disorders are further discussed in the Examples section and shown in Figures 3 and 5. Closed-loop methods and systems for the automated delivery of electrical stimulation are further described below.

[0095] A closed-loop method for the automated delivery of electrical stimulation - Patents.com In certain embodiments, the control algorithm is used to automate the delivery of electrical stimulation in response to detection of neural activity associated with symptoms of a neuropsychiatric disorder. According to certain embodiments, the method may include receiving electrical signals from a region of the subject's brain via the detection electrodes, applying electrical signal metrics to a control algorithm that is tuned to a clinically relevant target (e.g., a range of signals indicative of effective treatment and / or a range of signals indicative of severity of symptoms and need for treatment), and if the electrical signal metrics indicate that the patient is in need of treatment, automatically delivering electrical stimulation to the brain via the stimulation electrodes in a manner effective to treat the neuropsychiatric disorder or ameliorate symptoms of the neuropsychiatric disorder. For example, electrical activity in the gamma frequency range (e.g., 30 Hz-200 Hz) from the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus may be measured with the detection electrodes, and a control algorithm receives the electrical activity data from the detection electrodes and automates the delivery of electrical stimulation to the VC / VS regions via the stimulation electrodes when the level of gamma frequency power (e.g., 30 Hz-200 Hz) is within a specified range that indicates the patient has symptoms of a neuropsychiatric disorder severe enough to require treatment. In some embodiments, one or more programmed stimulation parameters are adjusted according to the control laws of the algorithm based on the recorded electrical activity data, and the adjusted electrical stimulation is delivered to the VC / VS regions via the stimulation electrodes in a manner effective to ameliorate symptoms of the neuropsychiatric disorder.

[0096] As described in the above section, the effectiveness of the treatment or amelioration of symptoms of a neuropsychiatric disorder can be evaluated by detecting electrical activity from a second region of the brain using a detection electrode. In an open-loop system, stimulation is delivered in a pre-programmed manner or manually by the user, but is not automatically controlled by real-time neurofeedback from the patient's brain. The electrical activity can be analyzed by a computing means that can output recommendations based on comparing the electrical activity to a predetermined range. The user can then implement the recommendations, such as changing parameters of the electrical stimulation program before starting another treatment plan. In a closed-loop system, in contrast, the computing means can automatically update the stimulation parameters and / or automatically deliver stimulation according to the electrical stimulation program based on an analysis of the recorded electrical signals. In some embodiments, either the open-loop or closed-loop system can be integrated with mechanisms for user intervention, for example, by allowing a user override of the open-loop or closed-loop stimulation program to perform or prevent stimulation that would normally occur, or to manually change parameters of such stimulation.

[0097] In some embodiments, the computing means for directing the closed-loop stimulation may be a hardware / software combination that may be connected wirelessly or by wires to the measurement electrodes. The computing means may communicate with a control unit (also referred to as a control module) that controls a pulse generator connected to the stimulation electrodes. In certain embodiments, the computing means may be connected to a recorder (e.g., a neurophysiological recorder) that records the brain activity measured by the detection electrodes. The computing means may include a control algorithm that determines the modification of the stimulation parameters based on the real-time output of the neurophysiological recorder. The algorithm may operate by a simple on / off control of the stimulation at set parameters, may modify only the on / off parameters at each evaluation cycle, or may determine a very advanced modification of the range of the stimulation parameters at each cycle. In some cases, the algorithm may be based on information related to the neuropsychiatric disorder, such as a range of electrical activity that indicates the presence of the neuropsychiatric disorder or the severity of the symptoms. The algorithm may also include additional information, such as brain activity profiles of normal subjects (not suffering from a neuropsychiatric disorder). Regardless of the particular control algorithm structure, the computing means can be tuned to a clinically relevant target (e.g., a range of signals indicative of effective treatment and / or a range of signals indicative of symptom severity and need for treatment) that directs adjustment of one or more programmed stimulation parameters in accordance with the control laws of the algorithm, and applies adjusted electrical stimulation via the stimulation electrodes (e.g., to the VC / VS, SGC, or OFC regions, or other regions).

[0098] In some cases, the computing means may determine, via a control algorithm, whether the received electrical signal is within or outside a predetermined range of neural signals indicative of the presence of a neuropsychiatric disorder. When the received electrical signal is outside this predetermined range, the computing means determines that the neuropsychiatric disorder is being treated and / or that symptoms of the neuropsychiatric disorder (e.g., depression, anxiety, compulsive / repetitive, self-starving behavior, and / or chronic pain-related distress) are ameliorated. The computing means may then communicate with the control unit to direct stimulation cutoff by the pulse generator. When the received electrical signal is within a predetermined range of neural signals indicative of the presence of a neuropsychiatric disorder, the computing means determines that the neuropsychiatric disorder requires treatment and / or that symptoms of the neuropsychiatric disorder (e.g., depression, anxiety, compulsive / repetitive, self-starving behavior, and / or chronic pain-related distress) are sufficiently severe to require treatment. The control algorithm in the computing means may then determine whether the initial step of applying electrical stimulation should be repeated and / or whether parameters of the electrical stimulation should be modified prior to the step of applying electrical stimulation. The computing means may then communicate with the control unit in order to provide appropriate instructions to the pulse generator via the control unit.

[0099] In some embodiments, the computing means may determine whether the received electrical signal is within or outside a second predetermined range, the second predetermined range indicating treatment of the neuropsychiatric disorder and / or improvement of the symptoms of the neuropsychiatric disorder. When the received electrical signal is within the second predetermined range, the computing means determines that the neuropsychiatric disorder is being treated and / or the symptoms of the neuropsychiatric disorder (e.g., depression, anxiety, compulsive / repetitive, self-starving behavior, and / or chronic pain-related distress) are being improved. The computing means may then communicate with the control unit to instruct the pulse generator to switch off the stimulation. When the received electrical signal is outside the second predetermined range, the computing means determines that the neuropsychiatric disorder is not being treated and / or the symptoms of the neuropsychiatric disorder (e.g., depression, anxiety, compulsive / repetitive, self-starving behavior, and / or chronic pain-related distress) are not being improved. A control algorithm within the computing means may then determine whether the initial step of applying the electrical stimulus should be repeated and / or whether parameters of the electrical stimulus have been modified prior to the step of applying the electrical stimulus. The processor may then communicate with the control unit to provide appropriate instructions to the pulse generator.

[0100] Thus, in certain aspects, the subject methods operate as a closed-loop control system that may automatically adjust one or more parameters in response to electrical activity from regions of the subject's brain and / or automatically deliver stimulation according to an electrical stimulation program. In some embodiments, the closed-loop control system automatically delivers stimulation according to set parameters when the received electrical signal is within a predetermined range indicating the need for treatment. Exemplary closed-loop methods and associated systems are described in the Examples section of this application and illustrated in Figures 3 and 5.

[0101] In some embodiments, a closed-loop system can be used to detect the need for treatment of a subject using the methods disclosed herein.For example, the closed-loop system can be programmed to monitor brain activity from one or more regions of the brain and compare the brain activity with the range that indicates a neuropsychiatric disorder or a symptom of a neuropsychiatric disorder.Upon detection of electrical activity that indicates a neuropsychiatric disorder or a symptom of a neuropsychiatric disorder, the closed-loop system can automatically initiate a treatment protocol that applies electrical stimulation.

[0102] In additional embodiments, the closed-loop system can be used as a system for monitoring brain activity and correlating brain activity with the mental state of a subject.For example, the closed-loop system is configured to record electrical signals from the brain of a subject, so that the mood and / or behavior of the subject can be monitored in real time and correlated to the measured electrical signals to provide biomarkers related to the mental state of the subject.For example, the electrical activity measured when a subject is anxious or depressed can be used to develop biomarkers, such as the range of electrical activity that indicates anxiety or depression.Therefore, the closed-loop system is useful for the prognosis and diagnosis of neuropsychiatric disorders.

[0103] It is understood that electrical signals indicative of the subject's mental state can be recorded from the subject's brain and used in aspects outside the closed-loop system. For example, electrical signals indicative of the subject's mental state can be recorded from the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus, or other regions, using electrodes or another device operably coupled to the patient's brain, which electrodes or devices may or may not be part of the closed-loop system. The patient can be treated as disclosed herein (e.g., by applying electrical stimulation to the VC / VS, SGC, or OFC regions, or other regions), and electrical signals are recorded from the same regions as the treatment is administered or in real time after the treatment is administered. The electrical signals recorded after the administration of electrical stimulation begins can then be compared to electrical signals recorded before the treatment to determine features in the recorded electrical signals that change after the treatment. These features provide a feedback signal that indicates whether the treatment is affecting the patient's mental state. These features can also serve as feedback signals to the closed-loop system. These features may include overall power, or power in particular frequency ranges (e.g., alpha, delta, beta, gamma, and / or high gamma). In some cases, these features may be patient-specific, or specific to a particular mental state, or both. For example, some of the features may be features found in multiple patients with a particular mental state (e.g., anxiety or depression), and some of the features may be features of a particular patient that may not be found in a significant number of other patients with the same mental state. In some embodiments, a combination of patient-specific and mental state-specific features may be monitored to assess the effectiveness of a treatment.

[0104] In certain aspects, the closed-loop systems and methods provided herein may involve recording of electrical signals from one or more regions of the brain (e.g., right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus) of a patient who has a neuropsychiatric disorder. The patient may then be treated by application of electrical stimulation to the VC / VS, SGC, or OFC regions, or other regions of the brain, and electrical signals may be recorded from the same regions of the brain (e.g., right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus) and compared to pre-treatment recordings. Characteristics of the recorded signals that change after treatment correspond to biomarkers that indicate whether the treatment has had an effect. The changes in the recorded signals can also be optionally correlated with mental states reported by the patient after treatment. The changes can be used to adjust the treatment in the closed-loop system. For example, if the changes in the recorded signals correlate with an improvement in the patient's mood, those characteristics will indicate to the computing means of the closed-loop system that no further treatment needs to be administered.

[0105] In certain aspects, the methods of the present disclosure, which may be embodied in an open or closed loop system, may include measuring brain activity from a subject having or suspected of having a neuropsychiatric disorder, such as anxiety and / or depression, and when the subject has a level of gamma range frequencies (e.g., 30 Hz to 200 Hz) that is higher than the normal range (e.g., a range reflecting a normal brain, such as a brain of a person not afflicted with the disorder) or higher than a threshold level (e.g., indicating symptoms severe enough that treatment is indicated), the subject is treated by applying electrical stimulation to the VC / VS, SGC, or OFC region, or other region, via electrodes placed on or adjacent to the VC / VS, SGC, or OFC region, or other region, in a manner effective to reduce the level of gamma range frequencies in the subject's brain. Brain activity may be measured in one or more brain regions (e.g., the right amygdala, the left amygdala, the right orbitofrontal cortex, the left subgenual cingulate, or the right hippocampus, or any combination thereof). In some embodiments, the system may monitor brain activity, such as the level of gamma frequency activity, and if the level of gamma frequency activity is higher than a reference range (e.g., a range reflecting a normal brain, such as the brain of an individual not afflicted with a disorder) and / or higher than a threshold level (e.g., indicative of symptoms severe enough that treatment is indicated), the closed loop system may apply electrical stimulation to the VC / VS, SGC, or OFC region, or other region, via electrodes placed in or adjacent to the VC / VS, SGC, or OFC region, or other region, in a manner effective to reduce the level of gamma frequency activity (e.g., 30 Hz to 200 Hz) in the subject's brain.

[0106] In some embodiments, one or more pattern recognition methods can be used in analyzing the recorded brain electrical activity data to automate the detection of brain activity features that distinguish between symptom states of a neuropsychiatric disorder severe enough to require treatment and asymptomatic (baseline) or mildly symptomatic states that do not require treatment. Models and / or algorithms can be provided in machine-readable form and used to correlate overall power or levels of power in specific frequency ranges (e.g., alpha, delta, beta, gamma, and / or high gamma) with symptom severity and the need for treatment with electrical stimulation. In some embodiments, levels of gamma frequency power (e.g., 30 Hz to 200 Hz) are correlated with symptom severity to determine whether the patient requires treatment with electrical stimulation. Alternatively or additionally, other features of coherence or network connectivity within specific spectral frequency bands can be correlated with symptom severity and the need for treatment with electrical stimulation.

[0107] Analyzing the recorded brain electrical activity may include the use of an algorithm or classifier. In some embodiments, a machine learning algorithm is used to classify the brain activity as indicative of whether symptoms of a neuropsychiatric disorder are severe enough that electrical stimulation should be delivered to the patient's brain. The machine learning algorithm may include a supervised learning algorithm. Examples of supervised learning algorithms may include average-order dependence estimators (AODEs), artificial neural networks (e.g., backpropagation), Bayesian statistics (e.g., naive Bayes classifiers, Bayesian networks, Bayesian knowledge bases), case-based reasoning, decision trees, inductive logic programming, Gaussian process regression, group methods of data processing (GMDH), learning automata, learning vector quantization, minimum message length (decision trees, decision graphs, etc.), lazy learning, instance-based learning nearest neighbor algorithms, analogical modeling, probabilistic approximately correct learning (PAC) learning, ripple-down rules, knowledge acquisition methodologies, symbolic machine learning algorithms, sub-symbolic machine learning algorithms, support vector machines, random forests, ensembles of classifiers, bootstrap bagging, and boosting. Supervised learning may include regression analysis and ordinal classification such as information fuzzy networks (IFNs). Alternatively, supervised learning methods may include statistical classification, such as AODE, linear classifiers (e.g., Fisher's Linear Discriminant, logistic regression, naive Bayes classifier, perceptron, and support vector machines), quadratic classifiers, k-nearest neighbors, boosting, decision trees (e.g., C4.5, random forests), Bayesian networks, and hidden Markov models.

[0108] Machine learning algorithms may also include unsupervised learning algorithms. Examples of unsupervised learning algorithms may include artificial neural networks, data clustering, expectation maximization algorithms, self-organizing maps, radial basis function networks, vector quantization, generative topographic maps, information bottleneck methods, and IBSEAD. Unsupervised learning may also include association rule learning algorithms, such as the Apriori algorithm, the Eclat algorithm, and the FP-growing algorithm. Hierarchical clustering, such as single-link clustering and concept clustering, may also be used. Alternatively, unsupervised learning may include partitional clustering, such as the K-means algorithm and fuzzy clustering.

[0109] In some cases, the machine learning algorithm includes a reinforcement learning algorithm. Examples of reinforcement learning algorithms include, but are not limited to, time difference learning, Q-learning, and learning automata. Alternatively, the machine learning algorithm may include data preprocessing.

[0110] Administration of pharmacologic agents The embodiments of the methods and systems provided in the present disclosure may also include administration of an effective amount of at least one pharmacological agent. By "effective amount" is meant a dosage sufficient to prevent or treat a neuropsychiatric disorder in a subject as desired. The effective amount may vary somewhat from subject to subject and may depend on factors such as the age and physical condition of the subject, the severity of the neuropsychiatric disorder being treated, the duration of treatment, the nature of any concurrent treatment, the form of the agent, the pharmacologic carrier used, if any, the route and method of delivery, and similar factors within the knowledge and expertise of those skilled in the art. The appropriate dosage may be determined according to conventional pharmacological procedures known to those skilled in the art, as described in more detail below.

[0111] When using pharmacological approaches in the treatment of neuropsychiatric disorders, the specific nature of the agent and the administration schedule will vary depending on the specific nature of the disorder being treated. Exemplary pharmacological agents that may be utilized in certain embodiments of the subject invention include, but are not limited to, selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine (Prozac), paroxetine (Paxil, Pexeva), sertraline (Zoloft), citalopram (Celexa) and escitalopram (Lexapro), serotonin-norepinephrine reuptake inhibitors (SNRIs) (e.g., duloxetine (Cymbalta), venlafaxine (Effexor XR), desvenlafaxine (Pristiq, Khedezla) and levomilnacipran (Fetzima).

[0112] In certain aspects, administration of the pharmacological agent includes using a pharmacological delivery device, such as, but not limited to, a pump (implantable or external device), an epidural injector, a syringe or other injection device, a catheter and / or a reservoir operably associated with a catheter. For example, in certain embodiments, a delivery device used to deliver at least one pharmacological agent to a subject can be a pump, a syringe, a catheter or a reservoir operably associated with a connecting device, such as a catheter, a tube, etc. Containers suitable for delivery of at least one pharmacological agent to a pharmacological agent administration device include containment devices that can be used to deliver, position, attach, and / or insert at least one pharmacological agent into a delivery device to administer the pharmacological agent to a subject, including, but not limited to, vials, ampoules, tubes, capsules, bottles, syringes, and bags. Administration of the pharmacological agent can be performed by a user or by a closed loop system.

[0113] system The present disclosure also provides a system for use, for example, in implementing the subject method. The system may be a closed-loop system configured to execute the method provided herein. In some embodiments, the closed-loop system may include a stimulating electrode adapted for positioning in the VC / VS, SGC, or OFC region, or other region, of the subject's brain, and a detecting electrode adapted for positioning in a secondary region of the brain (e.g., the right amygdala, the left amygdala, the right preorbital cortex, the left subgenual cingulate, or the right hippocampus, or other region) and for recording electrical signals from the secondary region before, during, or after electrical stimulation is applied to the VC / VS, SGC, or OFC region, or other region. The system may also include a computing means and control unit programmed to instruct the stimulation electrodes to apply electrical stimulation to the VC / VS, SGC, or OFC regions, or other regions, in a manner effective to treat a neuropsychiatric disorder in the subject and / or ameliorate symptoms of the neuropsychiatric disorder (e.g., depression, anxiety, obsessive-compulsive behavior, etc.), receive electrical signals from secondary regions of the subject's brain via the detection electrodes, apply electrical signal metrics to a control algorithm that is tuned to clinically relevant targets (e.g., severity of symptoms and ranges of signals indicative of need for treatment), and if the electrical signal metrics indicate that the patient is in need of treatment, automatically deliver electrical stimulation to the brain via the control unit, the pulse generator, and the stimulation electrodes in a manner effective to treat the neuropsychiatric disorder or ameliorate symptoms of the neuropsychiatric disorder. For example, electrical activity in the gamma frequency range (e.g., 30 Hz-200 Hz) from the right amygdala, left amygdala, right orbitofrontal cortex, left subgenual cingulate, or right hippocampus, or other regions, can be measured with detection electrodes using the system, and a control algorithm receives the electrical activity data from the detection electrodes and automates the delivery of electrical stimulation to the VC / VS, SGC, or OFC regions, or other regions, via the control unit, pulse generator, and stimulation electrodes, when the level of gamma frequency power (e.g., 30 Hz-200 Hz) is within a specified range that indicates the patient has symptoms of a neuropsychiatric disorder severe enough to require treatment.In some embodiments, one or more programmed stimulation parameters are adjusted according to an algorithmic control law based on the recorded electrical activity data, and the adjusted electrical stimulation is delivered via the control unit, the pulse generator, and the stimulation electrodes to the VC / VS, SGC, or OFC regions, or other regions, in a manner effective to ameliorate symptoms of a neuropsychiatric disorder. A closed loop system may include an on-body pulse generator connected to the implanted stimulation electrodes, and thus can automatically apply electrical stimulation to the brain upon receiving communication from the control unit.

[0114] The processor of the closed-loop system may execute programming to evaluate the effectiveness of the treatment and / or the effectiveness of the improvement of the symptoms of the neuropsychiatric disorder, and may adjust the parameters of the treatment as necessary without user intervention. Thus, the closed-loop system may not necessarily include a user interface to instruct the stimulation electrodes to apply electrical stimulation to the VC / VS, SGC, or OFC regions, or other regions, to treat the neuropsychiatric disorder in the subject. However, in some embodiments, a user interface may be included in the closed-loop system that may be used to confirm or override the recommendations of the closed-loop system, or to change the recommendations.

[0115] In certain aspects, a control algorithm for the methods and systems of the present disclosure may include comparing an electrical signal from a region of the subject's brain to a normal or reference electrical signal, and when the electrical signal significantly differs from the normal or reference electrical signal, the control algorithm includes directing a device to apply an electrical stimulus to the subject's brain, and then measuring the electrical signal from the region of the brain and comparing it to the normal or reference electrical signal, and when the measured signal significantly differs from the normal or reference electrical signal, the algorithm includes applying another electrical stimulus to the brain.

[0116] In some embodiments, the control algorithm utilizes machine learning algorithms to analyze the input brain electrical activity data to automate detection of brain activity features that distinguish between symptom states of a neuropsychiatric disorder severe enough to require treatment and asymptomatic (baseline) or mild symptom states that do not require treatment. The control algorithm then instructs the device to apply electrical stimulation to the subject's brain if the brain activity features indicate a symptom state severe enough that the subject should be treated with electrical stimulation. For example, the machine learning algorithm may be used to correlate the level of overall power, or power in specific frequency ranges (e.g., alpha, delta, beta, gamma, and / or high gamma), with symptom severity and the need for treatment with electrical stimulation. In some embodiments, the level of gamma frequency power (e.g., 30 Hz to 200 Hz) is correlated with symptom severity to determine whether the patient requires treatment with electrical stimulation. In some embodiments, electrical stimulation is delivered when the level of the detected electrical signal exceeds a set threshold level.

[0117] Components of systems for carrying out the methods of the present disclosure are further described in the Examples below.

[0118] usefulness The disclosed methods and systems find use in a variety of different applications, including the treatment of neuropsychiatric conditions that affect the mood and / or behavior of a person suffering from the condition. Neuropsychiatric disorders that may be treated using the methods and systems presented herein include major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), bipolar disorder (BD), and chronic pain.

[0119] In certain embodiments, the methods and systems of the present disclosure are utilized to treat subjects suffering from a moderate to severe form of a neuropsychiatric condition that affects the mood and / or behavior of the person suffering from the condition, for example, the subject may suffer from moderate or severe depression (e.g., major depressive disorder (MDD) or treatment-resistant depression (TRD)), anxiety, addiction, anorexia, OCD, bipolar disorder (BD), chronic pain, or PTSD. In certain embodiments, the subject may suffer from a milder form of a neuropsychiatric disorder and may be treated according to the methods or using the systems of the present disclosure to treat the milder form of the disorder and prevent progression to a more severe form.

[0120] In certain embodiments, the disclosed methods and systems may be used to treat chronic pain, such as one or more symptoms of chronic pain, such as constant attention to the experience of pain, the emotional debilitation of the constant experience of pain, depression and / or anxiety. The effectiveness of treatment of patients suffering from chronic pain may be measured in art-recognized manners, such as visual analog scales.

[0121] Open-loop intracranial stimulation of other brain targets, such as the subcallosal cingulate cortex for MDD and the ventral capsule / ventral striatum or nucleus accumbens for OCD, has failed to demonstrate reliable efficacy in normalizing low or anxious mood in controlled clinical trials. Based on the controlled studies described herein, closed-loop therapy with electrical stimulation consistently improves mood and / or reduces symptoms of anxiety and / or depression.

[0122] Electrical stimulation methods involving the implantation of electrodes are much less invasive than targeted ablation approaches. Furthermore, study results indicate that in instances of acute stimulation, the effects on mood state are similarly acute, indicating that this method represents a reversible intervention. This attribute offers the added benefits of (a) precise tuning of therapeutic stimulation to fit the patient's needs, and (b) incorporation of stimulation into well-controlled clinical trials featuring on / off periods and / or treatment crossover designs.

[0123] Closed-loop stimulation can be finely targeted and tuned in an individualized manner to achieve more reliable and / or more effective acute symptom relief compared to transcranial stimulation techniques. Similarly, these same attributes of targeting and tuning, given the ability to deliver fairly precise stimulation paradigms through intracranial electrodes, are more likely to result in neuroplastic changes and lasting relief of disease symptoms than transcranial methods. The relatively high precision and tunability of closed-loop stimulation methods have the potential to minimize side effects in individual patients. In addition, unlike transcranial methods, closed-loop stimulation does not require a daily or near-daily intraclinical treatment plan.

[0124] Closed-loop stimulation methods have consistently demonstrated efficacy in reducing symptoms of anxiety and depression in affected patients. In addition, stimulation has the potential to be targeted and tuned in an individualized manner that may achieve more reliable and / or more effective symptom relief compared to less targeted peripheral interventions such as vagus nerve stimulation.

[0125] For example, cognitive behavioral therapy and pharmacological intervention using serotonin reuptake inhibitors are among the most established and widely implemented treatment approaches for neuropsychiatric disorders, but have not been demonstrated to produce reliable remission in all patients for any given indication.In particular, stimulation likely achieves its symptom-relieving effects through mechanisms different from those enacted by CBT or pharmacological intervention.Therefore, stimulation represents a potentially complementary treatment modality that can be combined with CBT and / or pharmacological treatment to optimize patient benefits.

[0126] Similarly, stimulation methods can be optimized through the incorporation of complementary assessment techniques, including physiological and behavioral measures, performance on functionally relevant cognitive tasks, and patient-directed neurofeedback. Furthermore, when integrated in real time, these assessment approaches can improve the targeting and / or tuning of the stimulation and its therapeutic effectiveness.

[0127] Examples of Non-Limiting Aspects of the Disclosure Aspects including embodiments of the subject matter described above may be useful alone or in combination with one or more other aspects or embodiments. Without limiting the above description, certain non-limiting aspects of the present disclosure, numbered 1 to 77, are provided below. As will be apparent to one of skill in the art upon reading this disclosure, each of the individually numbered aspects may be used or combined with any of the preceding or succeeding individually numbered aspects. This is intended to provide support for all such combinations of aspects, and is not limited to the combinations of aspects explicitly provided below. 1. A closed loop method for treating a neuropsychiatric disorder in a subject, the method comprising: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivery of electrical stimulation to treat a neuropsychiatric disorder; mapping the subject's brain to identify an optimal location in a second brain region for positioning a second electrode for detection of electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder; positioning a first electrode at an optimal location in a first brain region of a subject to deliver electrical stimulation to the first brain region; positioning a second electrode at an optimal location in a second brain region to detect electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder; Detecting an electroencephalography signal in a second region of the subject's brain using a second electrode. and applying electrical stimulation to a first brain region using a first electrode in a manner effective to treat a neuropsychiatric disorder in the subject when an electroencephalography signal detected using the second electrode exceeds a threshold level. 2. The method of embodiment 1, further comprising using a control algorithm to automate the application of electrical stimulation when the electroencephalography signal exceeds a threshold level. 3. The method of embodiment 2, wherein the control algorithm comprises a machine learning algorithm. 4. The method of aspect 3, wherein the machine learning algorithm is a supervised machine learning algorithm. 5. The method of any one of aspects 2-4, wherein the control algorithm further adjusts one or more programmed stimulation parameters based on the level of the electroencephalography signal. 6. The method of any one of aspects 1-5, further comprising positioning a plurality of electrodes in a second brain region for detection of electroencephalography signals by stereotactic electroencephalography (sEEG). 7. The method of any one of aspects 1-6, wherein the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain. 8. The method of any one of aspects 1-7, wherein the second brain region comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region. 9. The method of any one of aspects 1-8, wherein the electroencephalography signal is gamma frequency power. 10. The method of any one of aspects 1-9, wherein the first electrode is positioned on the surface of the first brain region or within the first brain region. 11. The method of any one of aspects 1-10, wherein the second electrode is positioned on the surface of the second brain region or within the second brain region. 12. The method of any one of aspects 1-11, wherein the first electrode or the second electrode is a non-penetrating brain surface electrode array. 13. The method of any one of aspects 1-12, wherein the first electrode or the second electrode is a brain penetrating electrode array. 14. The method of any one of aspects 1-13, wherein the neuropsychiatric disorder includes depression, and applying electrical stimulation treats the depression. 15. The method of any one of aspects 1-14, wherein the neuropsychiatric disorder comprises anxiety, and applying electrical stimulation treats the anxiety. 16. The method of any one of aspects 1-15, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain. 17. The method of any one of aspects 1-16, wherein the electrical stimulation is applied unilaterally or bilaterally. 18. The method of any one of aspects 1-17, wherein the method further comprises evaluating the efficacy of the treatment in the subject. 19. The method of any one of aspects 1-18, further comprising monitoring the subject for a period of time, wherein an increase in the number of electroencephalography signals above the threshold level detected by the second electrode in the later recording period compared to the earlier recording period indicates an increase in the severity of the symptoms of the neuropsychiatric disorder, and a decrease in the number of electroencephalography signals above the threshold level detected by the second electrode in the later recording period compared to the earlier recording period indicates a decrease in the severity of the symptoms of the neuropsychiatric disorder. 20. The method of embodiment 19, wherein the subject is monitored continuously or intermittently. 21. The method of any one of aspects 1-20, wherein the first brain region and the second brain region are the same or different. 22. A closed-loop method for treating major depressive disorder (MDD) in a subject, the method comprising: Positioning a first electrode to deliver electrical stimulation to a first region of a subject's brain; positioning a second electrode to detect gamma frequency electroencephalography signals from neural activity associated with MDD symptoms in a second region of the subject's brain; detecting a gamma frequency electroencephalography signal using a second electrode; and applying electrical stimulation to a first region of the subject's brain using a first electrode in a manner effective to treat MDD symptoms in the subject when a gamma frequency electroencephalography signal detected using the second electrode exceeds a threshold level. 23. The method of aspect 22, wherein the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain. 24. The method of aspect 22 or 23, wherein the second brain region comprises the right amygdala, the left amygdala region, the right orbitofrontal cortex region, the left subgenual cingulate region, or the right hippocampal region. 25. The method of aspect 23 or 24, wherein applying electrical stimulation comprises applying bipolar electrical stimulation to the VC / VS region of the subject's brain. 26. The method of any one of aspects 22-25, wherein the electrical stimulation is applied with a current in the range of 1 mA to 6 mA. 27. The method of any one of aspects 22-26, wherein the electrical stimulation is applied for a maximum of 10 minutes per day. 28. The method of any one of aspects 22-26, wherein the electrical stimulation is applied continuously for a period of time ranging from 1 hour per day to 24 hours per day. 29. The method of any one of aspects 22-28, further comprising monitoring the subject for a period of time, wherein an increase in the number of gamma frequency electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates an increase in the severity of the MDD symptoms, and a decrease in the number of gamma frequency electroencephalography signals above a threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates a decrease in the severity of the MDD symptoms. 30. The method of embodiment 29, wherein the subject is monitored continuously or intermittently. 31. A closed-loop method for ameliorating a symptom of a neuropsychiatric disorder in a subject, the method comprising: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivering electrical stimulation to ameliorate a symptom of a neuropsychiatric disorder; mapping the subject's brain to identify an optimal location in a second brain region for positioning a second electrode for detection of electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder; positioning a first electrode at an optimal location in a first brain region of a subject to deliver electrical stimulation to the first brain region; positioning a second electrode at an optimal location in a second brain region to detect electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder; Detecting an electroencephalography signal in a second region of the subject's brain using a second electrode. and applying electrical stimulation to a first brain region using a first electrode in a manner effective to ameliorate symptoms of a neuropsychiatric disorder in the subject when an electroencephalography signal detected using the second electrode exceeds a threshold level. 32. The method of embodiment 31, wherein the symptoms include depression, and applying the electrical stimulation ameliorates the depression. 33. The method of embodiment 32, wherein depression is measured by a Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), or Beck Depression Inventory (BDI) score, and applying electrical stimulation is effective in reducing symptom severity according to the VAS, HAM-D, MADRS, or BDI score. 34. The method of embodiment 31, wherein the symptom includes anxiety, and applying electrical stimulation ameliorates the anxiety. 35. The method of embodiment 34, wherein anxiety is measured by a Visual Analog Scale for Anxiety (VAS-A) or Beck Anxiety Inventory (BAI) score, and applying electrical stimulation is effective in reducing anxiety according to the VAS-A or BAI score. 36. The method of any one of aspects 31-35, wherein the first electrode is positioned on the surface of the first brain region or within the first brain region. 37. The method of any one of aspects 31-36, wherein the second electrode is positioned on the surface of the second brain region or within the second brain region. 38. The method of any one of aspects 31-37, wherein the first electrode or the second electrode is a non-penetrating brain surface electrode array. 39. The method of any one of aspects 31 to 38, wherein the first electrode or the second electrode is a brain penetrating electrode array. 40. The method of any one of aspects 31 to 39, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain. 41. The method of any one of aspects 31 to 40, wherein the method further comprises evaluating the efficacy of the treatment in the subject. 42. The method of any one of aspects 31-41, further comprising using a control algorithm to automate the application of electrical stimulation when the electroencephalography signal exceeds a threshold level. 43. The method of aspect 42, wherein the control algorithm comprises a machine learning algorithm. 44. The method of aspect 43, wherein the machine learning algorithm is a supervised machine learning algorithm. 45. A method according to any one of aspects 42-44, wherein the control algorithm further adjusts one or more programmed stimulation parameters based on the level of the electroencephalography signal. 46. ​​The method of any one of aspects 31-45, further comprising positioning a plurality of electrodes in a second brain region for detection of electroencephalography signals by stereotactic electroencephalography (sEEG). 47. The method of any one of aspects 31-46, wherein the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain. 48. The method of any one of aspects 31-47, wherein the second brain region comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region. 49. The method of any one of aspects 31 to 48, wherein the electroencephalography signal is gamma frequency power. 50. A system for treating a neuropsychiatric disorder in a subject, the system comprising: a stimulation electrode adapted for positioning in a first region of the subject's brain; a sensing electrode adapted to be positioned at a second region of the subject's brain and to record an electroencephalographic signal from the second region of the subject's brain; and a processor programmed to direct the stimulation electrode to apply electrical stimulation to a first region of the brain in a manner effective to treat a neuropsychiatric disorder in the subject when an electroencephalography signal above a threshold level is detected using the second electrode. 51. The system of aspect 50, wherein the first brain region includes the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain. 52. The system of aspect 50 or 51, wherein the second brain region includes a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region. 53. A system described in any one of aspects 50 to 52, wherein the electroencephalography signal is gamma frequency power. 54. A system described in any one of aspects 50 to 53, wherein the stimulating electrode or the detecting electrode is a non-brain penetrating electrode array. 55. A system described in any one of aspects 50 to 54, wherein the stimulating electrode or detecting electrode is a brain penetrating electrode array. 56. The system of any one of aspects 50 to 55, wherein the neuropsychiatric disorder includes depression, and applying electrical stimulation treats the depression. 57. The system of any one of aspects 50 to 56, wherein the neuropsychiatric disorder includes anxiety, and applying electrical stimulation treats the anxiety. 58. The system of any one of aspects 50 to 57, further comprising a user interface including an input electronically coupled to the processor for instructing the stimulation electrode to apply electrical stimulation to a first region of the brain to treat a neuropsychiatric disorder in a subject. 59. The system of aspect 58, wherein the user interface is password protected and operable by a medical professional. 60. The system of any one of aspects 50 to 59, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain. 61. The system of any one of aspects 50 to 60, wherein the processor is further programmed to adjust one or more programmed stimulation parameters according to an algorithmic control law and apply regulated electrical stimulation to a first region of the brain via the stimulation electrodes in a manner effective to treat a neuropsychiatric disorder. 62. The system of any one of aspects 50 to 61, wherein the processor is further programmed to set a maximum number of electrical stimuli per day. 63. The system of any one of aspects 50 to 62, wherein the processor is further programmed to set a total amount of time of electrical stimulation per day. 64. A system for improving a symptom of a neuropsychiatric disorder in a subject, the system comprising: a stimulation electrode adapted for positioning in a first region of the subject's brain; a sensing electrode adapted to be positioned at a second region of the subject's brain and to record an electroencephalographic signal from the second region of the subject's brain; and a processor programmed to direct the stimulation electrodes to apply electrical stimulation to a first region of the brain in a manner effective to ameliorate symptoms of a neuropsychiatric disorder in the subject when an electroencephalography signal above a threshold level is detected using the detection electrodes. 65. The system of aspect 64, wherein the first brain region includes the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain. 66. The system of aspect 64 or 65, wherein the second brain region includes a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region. 67. A system described in any one of aspects 64 to 66, wherein the electroencephalography signal is gamma frequency power. 68. A system described in any one of aspects 64 to 67, wherein the symptoms include depression and applying electrical stimulation ameliorates the depression. 69. A system described in any one of aspects 64 to 68, wherein the symptom includes anxiety and applying the electrical stimulation improves the anxiety. 70. A system described in any one of aspects 64 to 69, wherein the stimulating electrode or the detecting electrode is a non-brain penetrating electrode. 71. A system described in any one of aspects 64 to 70, wherein the stimulating electrode or the detecting electrode is a brain penetrating electrode. 72. The system of any one of aspects 64 to 71, further comprising a user interface including an input electronically coupled to the processor for instructing the stimulation electrodes to apply electrical stimulation to the first area to ameliorate symptoms of a neuropsychiatric disorder in a subject. 73. The system of aspect 72, wherein the user interface is password protected and operable by a medical professional. 74. The system of any one of aspects 64 to 73, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain. 75. The system of any one of aspects 64 to 74, wherein the processor is further programmed to adjust one or more programmed stimulation parameters according to an algorithmic control law and apply regulated electrical stimulation to a first region of the brain via the stimulation electrodes in a manner effective to ameliorate symptoms of a neuropsychiatric disorder. 76. The system of any one of aspects 64 to 75, wherein the processor is further programmed to set a maximum number of electrical stimuli per day. 77. The system of any one of aspects 64 to 76, wherein the processor is further programmed to set a total amount of time of electrical stimulation per day. EXAMPLES

[0128] As can be understood from the disclosure provided above, the present disclosure has a wide variety of applications. Therefore, the following examples are presented to provide those skilled in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as the invention, nor are they intended to represent that the following experiments are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to the numbers used (e.g., amounts, dimensions, etc.), but some experimental error and deviation should be accounted for. Those skilled in the art will readily recognize a variety of non-critical parameters that can be changed or modified to obtain essentially similar results.

[0129] Example 1: Closed-loop neuromodulation for the treatment of resistant depression Major depressive disorder (MDD) is a common, disabling, and highly treatment-resistant disorder 1 Intracranial brain stimulation has shown promise as a treatment for refractory MDD, but heterogeneity in interindividual responses has contributed to inconsistent findings in definitive clinical trials of deep brain stimulation. 2~4 Previous approaches have largely used fixed, constant stimulation doses to target single structures in the brain in every subject. This open-loop approach has been shown to be effective in treating Parkinson's disease. 5 and epilepsy 6 However, multiple potential anatomical stimulation targets have been identified in MDD. 7 However, none have proven sufficiently promising for sustained therapeutic responses. Evidence that distinct neural circuits appear to underlie distinct subsets of symptoms experienced by MDD patients speaks to the need for individualized circuit standardization. 8 .

[0130] Individualized temporal control over therapy delivery may further complement individualized circuit targeting. In closed-loop neuromodulation, the patient's own physiological activity is used to selectively trigger stimulation only when a pathological condition is detected. 9 This approach may offer advantages over open-loop therapies because it addresses underlying circuit dysfunction and delivers therapy in a manner that changes in response to ongoing dynamics in the state of neural networks. Mood-related effects of neuromodulation have been shown to be state-dependent. 10、11 This specificity may be important for success in MDD patients with frequent state changes. Furthermore, closed-loop stimulation mitigates concerns about reduced efficacy of treatment due to neuroadaptation. 12 , preserving battery life by shortening intervention time and reducing side effects. However, symptom-specific biomarkers are a prerequisite for closed-loop therapy and have not been identified before in MDD. Here, we report our experience with a patient, in which, during 10 days of intracranial corticolimbic circuit mapping, we identify biomarkers of MDD symptoms. We then successfully implement the biomarkers in an implanted closed-loop neuromodulation therapy. This is the first demonstration of chronic closed-loop neuromodulation in a psychiatric disorder. Our approach can be broadly applied to any network disorder.

[0131] The patient was a 36-year-old woman with childhood-onset severe treatment-resistant MDD (Montgomery-Asberg Depression Rating Scale: 36 / 54) who had not responded to four antidepressants, augmentation strategies, electroconvulsive therapy, and transcranial magnetic stimulation; she participated in a clinical trial of personalized closed-loop neurostimulation (Presidio trial; for additional case history, see Scangos et al. 2021). 10 In the first phase of the study, we performed a randomized controlled trial of the orbitofrontal cortex (OFC), amygdala (AMY), hippocampus (HPC), ventral capsule / ventral striatum (VC / VS), and subgenual cingulate cortex (SGC) associated with different types of depression symptoms. 13~17We found a dimensionally restricted clinical response to stimulation across this corticolimbic stimulation-response mapping of the patient's emotional circuitry using 10 stereotactic electroencephalography (sEEG) electrodes implanted bilaterally into the circuitry. 10 Neural activity was measured when patients scored for depression (VAS-D) and anxiety (VAS-A). 18 Participants were recorded over 10 consecutive days while completing 335 visual analog scales and taking multiple Hamilton Depression Rating Scale assessments (6-item version [HAMD6]). 19 Scores were distributed across the full range of patients' natural emotions (VAS_A: mean 17.5, SD 21.7; VAS_D: mean 26.3, SD 12.9). We used these scales to define two symptom states (high symptom severity and low symptom severity, Figure 1A-1B).

[0132] We then identified 15 min of resting-state sEEG activity that matched clinical measures in which one of the two states was present (n=35). We identified sEEG spectral activity features that distinguished between high and low symptom states using six standard frequency bands and two cross-validated supervised machine learning models at different spatial resolutions. In the first model, the spectral power was averaged across all contacts in a brain region (60 features). We found that gamma power had high state discrimination potentials in the bilateral AMY, right OFC, left SGC, and right HPC (Accuracy: Mean 0.73, Std 0.08; AUC: Mean 0.76, Std 0.10) (Figure 1C). The second model, in which the feature set was defined by the spectral power of each electrode contact (246 features), allowed us to take into account intra-regional variability due to electrode positioning. We found that bilateral AMY gamma power alone was sufficient to detect high symptom severity states (precision: mean 0.77, standard 0.09; AUC: mean 0.82, standard 0.11, Figure 1D). Collectively, these findings suggest that AMY gamma power is a biomarker of high depression.

[0133] We identified the correct VC / VS as the brain region in this patient where electrical stimulation produced consistent improvement in depressive symptoms in a sustained and dose-dependent manner. 10 We then examined connectivity across the corticolimbic network to determine whether right hemisphere VC / VS and AMY nodes constitute a structurally and functionally connected subnetwork such that VC / VS stimulation directly affects AMY biomarker activity. To examine effective network connectivity, we delivered brief stimulation pulses across adjacent contacts in one brain region and examined evoked potentials (EPs) at distant contacts to generate an overall directional network graph. 20 (Figure 2A). We then quantified the relative importance of the nodes in this graph. 21We found that the AMY and HPC were sites integrating signals from remote brain regions (highest weighted in-degree, Fig. 2B left). The VC / VS was the site that strongly influenced many remote brain regions (highest weighted out-degree, Fig. 2B right). Furthermore, we observed a strong AMY-evoked response to VC / VS stimulation (Fig. 2c), which was relatively stronger than other network nodes (Fig. 4A). We used deterministic tractography with diffusion tensor imaging to examine putative axonal fiber bundles that may mediate effective connectivity and found that there was also dense structural connectivity between the right VC / VS and the AMY, which consists of two fiber bundles, the stria terminalis and the peduncula (Fig. 2D, Fig. 4B). Finally, VC / VS stimulation at contact 2+ / 3- led to clinical improvement of symptom severity in 11 / 13 trials. We examined the changes in AMY gamma power as a result of this stimulation in trials in which we had pre- and post-VAS scales as well as resting-state sEEG activity (n=5 trials). We observed a reduction in AMY gamma power in both studies where symptom severity improved (VAS-D pre-post: 29–20, 27–21; VAS-A pre-post: 4–0, 13–8; Fig. 2E), which was absent in studies where symptom severity worsened due to the alternative stimulation paradigm (VAS-D: 14–76, VAS-A: 4–77) or remained stable due to minimal symptoms at baseline (VAS-D: 16–17, VAS-A: 6–4) or a primary increase in energy relative to depression (VAS-D 30–31; VAS-A: 8–13; VAS-E 7–87). The validative and structural network mapping and the findings of spectral power biomarkers together suggest that the AMY-VC / VS may represent an important limbic sub-network in this patient that both reflects depression severity and can be modulated to reduce clinical symptoms.

[0134] We next performed a 3D neuroimaging study using two separate sensing / stimulating 4-contact depth leads placed in the AMY and VC / VS, respectively (Figure 3B), using the FDA-approved, commercially available NeuroPace RNS® system. 22The RNS system neurostimulators were implanted unilaterally in the right hemisphere (Figure 3A). The RNS system neurostimulator continuously senses neural activity, and detection of pre-specified activity patterns is coupled to the delivery of electrical stimulation [trains of charge-balanced biphasic square-wave pulses] to designated electrodes. We assessed whether, using correct AMY recordings, we could reproduce symptom-specific biomarkers for closed-loop control. Using the NeuroPace RNS system telemetry wand to stream wireless real-time electrocorticography recordings in the laboratory, we recorded consecutive 10-minute trials of AMY activity while patients engaged in naturalistic activities (e.g., social media, talking with family on the phone, watching a movie, virtual therapy appointment) and paired the neural activity with clinical severity scales (Figure 3C). Again, we assessed whether AMY gamma power correlated with the VAS-D (r 2 : 0.82, p: 3.4e-05), VAS-A (r 2 : 0.52, p: 0.01), and HAMD-6 (r 2 We found that the ANOVA correlated highly with both the high symptom severity condition and the low symptom severity condition (ANOVA, F: 8.33, p = 0.015), as well as significantly distinguishing high from low symptom severity conditions (ANOVA, F: 0.73, p: 4.3e-04, Figure 3D).

[0135] The clinical effect of right VC / VS stimulation was also replicated. Bipolar stimulation at VC / VS contacts engaged in the stria terminalis and leg trap (VC / VS contacts 2+ / 3-, 3+ / 4, 100 Hz, 120us, 1-2mA) produced acute dose-dependent symptom improvement over the majority of trials, with the effect being strongest in the predicted location (VC / VS contacts 3+ / 4-) and preferred by the patient (described as enjoyable and energizing) compared to other VC / VS and AMY contact pairs (Figure 3E). Furthermore, we found that 6 s intermittent stimulation at 1mA over 15 and 30 min was clinically effective and below the patient's perception threshold such that the on / off state did not affect normal daily function (Figure 3F, left). Increasing the dose to 2mA or changing the contact caused the patient to perceive the stimulation and worsened the clinical response in some trials (Figure 3F, right).

[0136] We then implemented closed-loop therapy such that 6 seconds of stimulation was delivered after the device's automatic detection of the biomarker (Figure 3G). The NeuroPace system uses a half-band detector to estimate power in specific frequency bands. We calibrated the device to continuously sense the AMY gamma biomarker. A "detection" was triggered when gamma power exceeded a threshold of 0.8% of the total amplitude scale assessed within a 2-second sliding window for AMY contacts 3 / 4 or 1 / 2. We found that the number of detections within a 10-minute recording period was 87% predictive of symptom severity status and that the number of detections was associated with a significant improvement in VAS-D (r 2 : 0.59, p: 1.2e-04), VAS-A (r 2 : 0.52, p: 4.6e-04), and HAMD-6 (r 2 We found that the mean stimulation frequency was highly correlated with the mean stimulation frequency (p = 0.05, p = 0.05, Fig. 3H). Over a 2-month period, we found an average of 468 detections per day (SD: 206), with a diurnal distribution, with minimal stimulation at night. We set a stimulation upper limit of 300 treatments / day (30 min total stimulation) to avoid disturbing sleep from nighttime therapy.

[0137] The implementation of closed-loop therapy resulted in rapid improvements in both symptom severity (measured daily with the HAMD-6, VAS scale) and depression (measured periodically with the MADRS scale). These findings indicate that closed-loop stimulation can achieve the intended outcomes, but adequately powered randomized controlled trials with blinding are needed to determine its efficacy. The subject's MADRS score dropped from 33 before turning on treatment to 14 (58% reduction) at the first mid-treatment assessment conducted after 12 days of stimulation, and to less than 10 (remission) several months later. Similarly, her HAMD-6 and VAS_D scores declined sharply the morning after stimulation began (HAMD6: 12.0–1.0; VAS_D: 77–23) and were lower the week after stimulation was turned on compared to the previous week (HAMD6: mean 16 (SD 2.82)–1.6 (SD 1.60), Welch's t-test, p=0.08; VAS-D: 77.33 (14.57)–10.48 (5.74), Welch's t-test, p=0.02, Figure 3I).

[0138] To further evaluate whether our defined closed-loop algorithm was linked to patients' symptoms and triggered the delivery of therapy in a non-random manner, we examined the relationship between the morning symptom severity scale and the number of daily detections over a two-month period. Because the morning symptom severity scale may reflect a symptom state over a variable length of time and therefore may mismatch the neural biomarkers detected by the device in either amplitude or phase, we used dynamic time warping (DTW) to first non-linearly align the daily symptom severity (VAS-D) and biomarker detection count time traces and then calculate their relative post-alignment distances. 23 We found that day-to-day symptom fluctuations were significantly associated with fluctuations in the number of biomarker events detected by the device (p=2.8e-04, Figure 3J). These results suggest that our biomarker detection algorithm detected changes in symptom severity significantly better than random chance, and provide initial evidence that the RNS system is functioning in a manner that informs treatment delivery in a closed-loop fashion, as dysfunctional circuit activity underlying MDD symptoms.

[0139] Consideration To our knowledge, this is the first successful development of personalized biomarkers of depression-specific symptoms and implementation of a closed-loop therapy for MDD. 24 This was predicated on clinical mapping studies prior to chronic device placement, a strategy that has been leveraged in epilepsy to map seizure foci in patients with encephalopathy but not previously implemented in other neuropsychiatric conditions. We developed a comprehensive multimodal framework to select sensing and stimulating brain targets during this multiday mapping period (Figure 5). Our approach included personalized stimulus-response mapping, pairing resting-state signals with clinical symptom measures, and identification of functionally and structurally connected subnetworks across the corticolimbic network.

[0140] We found that AMY gamma power alone was sufficient to predict pathologically high symptom severity states, and that VC / VS stimulation reduced AMY gamma power in conjunction with improvement of MDD symptoms. Previous studies have demonstrated that the AMY plays an important role in processing and mediating emotions. 25、26 VC / VS and AMY are important nodes in the mood regulation circuitry 27 , that bursts of high-frequency activity in the AMY correlate with emotional states. 15 This supports our findings that AMY activity is a potent neurotransmitter that is essential for depression. The mechanisms underlying the relationship between AMY activity and depressive states, and how VC / VS stimulation improves this state and normalizes AMY activity, remain unknown. However, the VC / VS region, a node in the reward pathway, is one of the most thoroughly studied DBS targets for depression. 13 , which may be particularly effective in treating anhedonia 28In the face of emotionally negative input, stimulation of the VC / VS may reduce AMY hyperactivity, both by driving less active direct striatal connections and through dopamine-mediated interactions within the reward pathway, which in turn may affect fronto-mediated evaluation of emotional input. Successful reduction in AMY gamma may be a prerequisite for improving symptoms. Studies are needed to determine the extent to which this AMY biomarker and AMY-VS / VS subcircuit are present within an individual over time, and whether they underlie depression in general or are specific to this patient or to a subset of patients with MDD. Our methodology is designed to address this variability, allowing us to identify stimulation targets and neural biomarkers that are individualized for each subject. Based on previous studies, we anticipate that SGCs will be another important brain stimulation target in a subset of subjects. 29、30 Group-level analyses may determine that specific symptom profiles help define symptom state thresholds that predict stimulation targets and enhance biomarker discrimination.

[0141] We also identified a set of detection algorithms using the NeuroPace RNS system that were able to detect AMY gamma power despite device features designed for seizure detection. Whether this capability is limited to high frequency activity remains unknown as device limitations limited our ability to explore other frequency bands. We could only determine that alpha frequency power may also correlate with symptom severity but is not useful for detecting mood state in this patient. New device capabilities with the ability to sense continuous neural activity and integrate information over time scales of minutes may improve our ability to incorporate more complex biomarkers into closed-loop control. 31 While conventional DBS was performed bilaterally, the NeuroPace device was implanted exclusively on the right side based on clinical mapping studies and safety considerations. The reasons for the hemispheric differences we observed are unknown, but we believe they may be related to the lateralization of emotion regulation. 32、33Alternatively, we speculate that this may be due to the bilateral placement of electrodes within the VC / VS.

[0142] We conceptualize MDD as a dynamic process in which symptoms arise when dysfunctional activity emerges in one or more prefrontal / limbic brain networks that serve mood-related functions. In this model, stimulation has an immediate effect on symptom severity. Antidepressant effects are achieved by iteratively improving symptoms as they emerge over time. Acute responses to VC / VS stimulation have been observed in previous studies during the intraoperative or programming period, as we have repeatedly observed in this subject. 34、35 However, once continuous stimulation is initiated, it does not always persist. By delivering intermittent stimulation in a closed-loop fashion, we hypothesize that we can obtain these acute effects repeatedly and use them as a means to treat depression. Consistent with our model, this patient's depression improved acutely after closed-loop therapy was initiated, and the effects were sustained. Both the rapidity and intensity of the clinical response are highly unusual in treatment-resistant depression, with a 1-year remission rate of "treatment as usual" of approximately 3.5%. 36 and relief from DBS may take several months to appear. 2 It was not the intent of this report to address the efficacy of closed-loop neuromodulation for MDD, which requires double-blind, randomized controlled evaluations conducted in groups of patients large enough to provide sufficient power to detect clinically meaningful effects. Here, we establish proof-of-concept for a new, powerful approach for the treatment of neuropsychiatric disorders and demonstrate its feasibility in a single patient. Whether our methodology is capable of identifying subcircuits associated with biomarker / stimulation pairs that can be implemented as closed-loop therapy across individuals is an important area for further research to determine the promise of this approach.

[0143] Altogether, our findings suggest that quantitative circuit-based therapeutic development is feasible. The novel framework presented here can advance biomarker-based neurointerfaces to enhance the mechanistic understanding and treatment of a wide range of neuropsychiatric conditions.

[0144] Example 2: Method Surgical procedure The patient provided written informed consent to participate in a clinical trial of closed-loop neuromodulation for treatment-resistant major depressive disorder (MDD) approved by the University of California, San Francisco Institutional Review Board and by the U.S. Food and Drug Administration (FDA) (Presidio: clinicaltrials.gov / ct2 / show / NCT04004169). For further details, please refer to the Life Sciences Reports Abstract published with this case report. The patient underwent two implantation procedures. In the first, we report the results of a randomized controlled trial of closed-loop neuromodulation for treatment-resistant major depressive disorder (MDD) approved by the Institutional Review Board of the University of California, San Francisco and by the U.S. Food and Drug Administration (FDA). 10 Ten stereotactic electroencephalography (sEEG) electrodes (PMT Corporation, Chanhassen, MN) were surgically implanted in the orbitofrontal cortex (OFC), amygdala (AMY), hippocampus (HPC), ventral capsule / ventral striatum (VC / VS), and subgenual cingulate cortex (SGC) as described in. Exploratory intracranial stimulation and recordings were performed over a 10-day period (October 2019). After 10 days, the electrodes were explanted. In a second implantation procedure, we implanted a NeuroPace RNS system with two 4-contact depth leads (30 cm lead length, 3.5 mm electrode spacing) in the right VC / VS and AMY, guided by our findings in the 10-day recording and monitoring (mapping) period. Surgical targeting was performed using DTI according to published studies. 37 or coordinate-based targeting 3 Cranial plans were made in Brainlab iPlan Cranial Software using the MRI software. Computed tomography (CT) was used in the hands to confirm electrode placement. No surgical complications occurred.

[0145] clinical scale To assess moment-to-moment changes in MDD symptom severity, we used visual analog scales for depression (VAS-D) and anxiety (VAS-A). 38 We used the visual analog scale (VAS) and the HAMD6 subscale of the HAMD-17, which is thought to capture full-blown core symptoms and has been used to assess the rapid effect of antidepressants. 19、39 Our symptom assessment strategy included an a priori plan to consider dimensions of depression that may vary over the course of the day, as represented by the HAMD6 subscale of the HAMD-17, including Q1) sadness, Q2) guilt, Q3) apathy, Q4) fatigue, Q5) anxiety, and Q6) energy, but focused only on the minimum number of dimensions necessary to be meaningfully operationalized in the setting of repeated testing with a VAS (depression, anxiety) and to reflect patients' symptom profiles. Study data were collected and managed using REDCap electronic data capture tools hosted at UCSF. 40、41 REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture, 2) an audit trail for tracking data manipulation and export procedures, 3) an automated export procedure for seamless data download into common statistical packages, and 4) procedures for data integration and interoperability with external sources. Patients used the tool on their research iPAD to complete in-lab and home surveys. Once the NeuroPace device was in place, they were asked to complete the home survey 2-3 times per day.

[0146] Signal Processing: Intracranial EEG (iEG) recordings derived from sEEG electrodes were used to derive measures of activity and connectivity. This is a high-resolution intracranial recording technique. 42、43 , commonly used in the pre-surgical evaluation of drug-resistant epilepsy. Stimulation through these electrodes (single pulse 44 or continuous stimulation 14) was used to perform stimulus-response mapping, assess connectivity, and evaluate the effects of perturbations on the MDD network. Data acquisition of iEEG recordings and evoked potential (EP) mapping was performed on a 256-channel Nihon Kohden clinical system and secondary data streams at a sampling rate of 10 kHz. Standard sEEG pre-processing techniques were implemented in Python and included a band-pass filter from 2 to 250 Hz, a notch filter at line noise frequencies and harmonics, downsampling to 512 Hz, and application of a common average reference across all channels. Signal processing consisted of continuous wavelet transforms (Morlet, 6 cycles) at 30 s intervals to obtain power in six spectral frequency bands (delta = 1-4 Hz, theta = 5-8 Hz, alpha = 9-12 Hz, beta = 13-30 Hz, low gamma = 31-70 Hz, high gamma = 71-150 Hz). 45 Artifacts in the EP analysis were removed with an 8th order Butterworth filter.

[0147] electrode stimulation Our approach to clinical mapping is discussed elsewhere. 10Briefly, we tested the clinical efficacy of a set of stimulation parameters (1 or 100Hz, 100us, 1-6mA) used in the evaluation of epilepsy patients through a systematic bipolar stimulation study, followed by a blinded sham-control stimulation study over a 10-day period. Where indicated, brain stimulation configurations are represented by contact number and polarity (e.g., 2+ / 3- reflects that contact 2 is the anode and contact 3 is the cathode). Once the NeuroPace device was in place, we repeated the bipolar stimulation study and sham-control stimulation, using the same clinical measures as outcome measures. Patients were blinded to the stimulation location and parameters. If patients experienced non-emotional clinical sensations from the stimulation, such as facial pulling, stimulation intensity was reduced. Closed-loop neuromodulation introduces a new stimulation parameter, a burst duration that is delivered once an inhibitory state is detected. We tested the effect of burst duration in a systematic fashion by delivering increasing stimulation durations (6, 12, 18, 36 sec bursts, 100Hz, 120us, 1mA) while keeping the total stimulation time constant at 15 and 30 min time intervals. Our strategy was to identify a set of parameters where intermittent stimulation was clinically effective over a time interval but where each burst onset was not detected by the patient so as to prevent interference with normal activity, and to select shorter more frequent bursts over less frequent but longer stimulation periods to increase specificity.

[0148] Biomarker Discovery The development of symptom-specific biomarkers in depression has been challenged by the lack of objectively observable clinical symptoms, such as those present in movement disorders, and the multidimensionality of MDD. We addressed this challenge during the mapping study in several ways. We included a high trial count to address the inherent noise in self-ratings of internal states. We also included the presence of mood fluctuations as one of the inclusion criteria for the clinical trial and ensured that there was variation over time in the scales by having patients perform naturalistic activities in a laboratory environment that induced fluctuations in symptom states (e.g., recalling life events, watching movies, social media) during a 10-day period. Then, based on the anxiety and depression dimensions of the VAS scales, we attempted to objectively discriminate between high and low symptom severity states using semi-supervised k-means clustering. Specifically, to distinguish noisy ratings from clinically relevant ratings, we used cluster tuning and cluster aggregation to discriminate between two symptom severity states based on the VAS ratings that best correspond to the clinically established HAMD6 score.

[0149] To identify spectral biomarkers associated with symptom severity states, we asked patients to remain still for 15 min and then rated their MDD symptom severity (self-reported HAMD6, VAS scale) approximately five times daily. These 15-min intervals were required to be at least 1 h after stimulation to minimize potential effects of stimulation on neural activity. Spectral power was calculated at 30-s intervals and averaged over the 15-min recording period to obtain power in six frequency bands. We then modeled the spectral power differences between symptom states using cross-validated machine learning models to identify spectral power features that predicted high symptom severity. In the first model, the feature set was defined by the spectral power averaged across all contacts in a brain region (10 regions × 6 bands = 60 features). In the second model, the feature set was defined by the spectral power of each electrode contact (41 anatomically validated contacts × 6 bands = 246 features). The modeling pipeline consisted of feature selection based on ANOVA F-scores and classification using penalized logistic regression trained on 80% of the data and tested on the remaining 20%. We repeated the training / testing scheme 1000 times to ensure stability of the AUC scores and feature importance histograms. Significance was assessed by comparing the output of our logistic classifier 1000 times with a null model obtained from randomly permuted class labels.

[0150] In closed-loop mode, delivery of therapeutic stimulation is expected to normalize the biomarkers that trigger the stimulation. To test the effect of VC / VS stimulation on biomarker activity, we examined the changes in gamma power in two 30-second intervals before and after a period of continuous stimulation. We included all types of stimulation trials for which we had sufficient iEEG activity and VAS measures. All stimuli were 100Hz, 100us, and 1-3mA, bipolar contact 2+ / 3-. Stimuli were delivered continuously in 4 / 5 trials. Changes were examined in relation to changes in symptom severity due to stimulation.

[0151] We validated the biomarkers once the NeuroPace RNS system was in place. In a laboratory environment, we streamed continuous neural data (electrocorticography) using the RNS system wand held over an intracranially implanted neurostimulator with a flexible metal arm. Once the final RNS detection parameters were identified, we collected 18 trials with 10 minutes of resting-state neural activity followed by depression scales for 7 days. As with the 10-day mapping period, patients engaged in a series of naturalistic activities during the recordings. Logistic and linear regression models were then constructed to assess the relationship between AMY gamma power and symptom severity status or VAS-D, HAMD6 scores, respectively.

[0152] Effective connectivity: Effective connectivity is a functional connectivity measure that describes the directional influence of one brain region on another. 46 Intracranial EEG and intracortical stimulation offer the rare and unique capability to directly probe effective network connectivity by delivering brief stimulation pulses to adjacent contacts in one brain region and examining the electrical effects (evoked potentials (EPs)) of perturbations at distant contacts. EP mapping was performed on day 1. We delivered bipolar single-pulse stimulation (1, 3, 6 mA) at 0.5 Hz for 40 s across the central contact pair in each brain region to the adjacent contact pair. Standard methods 20、44、47was used to measure and quantify EPs, including calculating the mean over 20 trials, z-scoring the voltages against a 50 ms pre-stimulus baseline, identifying N1 and N2 by their peak deflection amplitudes in 10–50 ms and 50–500 ms time windows, respectively, constructing a connectivity matrix from the N1 response amplitudes of the full set of EPs, and filtering statistically significant EPs to generate a directed network graph. We determined which electrodes showed statistically significant EPs by comparing the distribution of N1 responses resulting from 20 single pulses to the distribution of pre-stimulus baseline fluctuations. The baseline score was calculated by quantifying the amount of spontaneous voltage fluctuations in the absence of stimulation, finding the magnitude of the peak deflection during the 50 ms pre-stimulus window, and z-scoring it against the mean voltage in the 50–100 ms pre-stimulus window. We used a Wilcoxon signed-rank one-tailed test with Benjamini-Hochberg false discovery rate correction to determine whether the N1 response was statistically greater than the baseline spontaneous fluctuations. Uniform z-score thresholds 21 Compared with, this approach took into account the variable noise between electrodes and allowed for identifying true EPs with higher sensitivity.

[0153] In graph theory, the graph represents an electrophysiological network where nodes are considered to be individual electrodes, and directed links or edges between nodes represent the effect of stimulation of a source node on the amplitude of the neural signal at a distal node. 48 We calculated two graph-theoretic metrics (in-degree and out-degree) from the EP connectivity maps to determine the direction and strength of functional connections between doses and to inform the relationship between stimulation and potential sensing targets. Nodes with high out-degree are hubs of high network influence, in that their stimulation will greatly affect other nodes in the network and may indicate good therapeutic targets. 49Nodes with high in-degree suggest areas affected by stimulation in other regions and may play a role in sensing modifiable neural signatures of MDD that are normalized with stimulation elsewhere in the network. We calculated in-degree by taking a weighted sum of significant connections (N1 amplitude) directed to each node, and out-degree by a weighted sum of significant connections projected from each node for the right and left hemispheres as well as the whole-brain network. We identified hubs that had the highest values ​​and were directionally connected.

[0154] Diffusion tensor imaging Diffusion tensor imaging (DTI) can be used to map structural connectivity by identifying putative axonal fiber bundles that may mediate effective connectivity, thereby constraining our models of functional integration across distant brain regions. 46 Engagement of specific white matter tracts or intersections of various bundles has been shown to improve outcomes of deep brain stimulation for depression. 50、51 Although the exact relationship between structural and functional connectivity remains unknown, it has been suggested that structural connectivity properties can be implemented as useful prior information in Bayesian models of effective connectivity. 52 Diffusion data were collected using a 32-channel head coil (B value: 2000 sec / mm 2 Diffusion-weighted imaging (HARDI) was acquired using axial DTI at 3 Tesla with 100 Hz to 55 axial directions. Tractography was performed using FiberTracking (FACT) software implemented within BrainLAB FiberTracking software. 53The tractography was performed using deterministic fiber assignment by. Tractography was based on spherical ROIs of 3 mm diameter centered on each contact of the candidate stimulus-sensing pair, and DTI parameters were the same for all pairs with minimum fractional anisotropy (FA) = 0.1, minimum fiber length = 80 mm, and maximum angle = 20 degrees. The resulting fiber bundles are color-coded by orientation (red: left-right, green: anterior-posterior, blue: up-down). Using these parameters, a number of streamlines were used to compare the strength of connectivity for each candidate stimulus-sensing pair (VC / VS stim-AMY sens, VC / VS stim-OFC sens, SGC stim-AMY sens, and OFC Stim-AMY sens).

[0155] Closed Loop Control The NeuroPace RNS system was first placed in a sense-only configuration where neural biomarkers could be detected but no stimulation was delivered (0 mA). Over the first 2.5 months, we modified various parameters including detector number, bandpass threshold, window size, and count criteria to find a detection algorithm capable of identifying the AMY gamma biomarker. Our final detector included the following parameters: Pattern A1 (AMY3 / 4): min-max frequency: 28.8-125 Hz, min amplification 0.8%, window size 160 ms, count criteria 10, bandpass threshold 3, detection analysis window size 2048; Pattern A2 (AMYl / 2): min-max frequency: 28.8-125 Hz, min amplification 0.8%, window size 160 ms, count criteria 10, bandpass threshold 10, detection analysis window size 2048. The RNS system does not implement a standard frequency decomposition algorithm, but the half-wave detector approximates the frequency components. In combination with the minimum amplitude setting, the detector was found to track gamma activity. With this detector on, we examined the number of detections in 10-minute resting-state intervals for the 18 trials utilized for biomarker validation. Logistic and linear regression models were constructed using the number of stimulations as the dependent variable and symptom status or HAMD6 / VAS-D scores as independent variables. On day 74, the RNS system was operated in closed-loop mode with stimulation on (1 mA, 120 us, 100 Hz). We capped the number of treatments to 300 per day (30 minutes total stimulation per day), which was reached on 81.1% of days. On days when the limit was reached, this occurred on average at 7:40 pm (SD 3.3 hours). This limit was chosen because stimulation delivered in the evening hours disrupted patients' sleep. The treatment limit reset time was 7:00 am.

[0156] To investigate the relationship between the time series of daily symptom severity scores (VAS-D) and daily biomarker detection counts, we used dynamic time warping (DTW), a method that has been demonstrated to be valid in the context of time series exhibiting naturalistic noise. 54After normalizing the symptom severity (VAS-D) and biomarker detection count time traces by their means, we calculated the DTW distortion function and distance 23 To prevent the warping function from skipping important symptom severity or biomarker features, consistent with standard practice, we incorporated a Sakoe-Chiba warp window of size 3. 55 We tested the significance of symptom severity and biomarker relationships by comparing our DTW distances to the DTW distances calculated between symptom severity time series and scrambled biomarker detection time series, where the biomarker time series were obtained by randomly shuffling the observed values ​​10,000 times.

[0157] References 1.Howland,RHSequenced Treatment Alternatives to Relieve Depression(STAR*D).Part 2:Study outcomes.J Psychosoc Nurs Ment Health Serv 46,21-24(2008). 2.Holtzheimer, PE, et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4,839-849(2017). 3.Dougherty, DD, et al.A Randomized Sham-Controlled Trial of Deep Brain Stimulation of the Ventral Capsule / Ventral Striatum for Chronic Treatment-Resistant Depression.Biol Psychiatry 78,240-248(2015). 4.Bergfeld,I.O.,et al.Deep Brain Stimulation of the Ventral Anterior Limb of the Internal Capsule for Treatment-Resistant Depression:A Randomized Clinical Trial.JAMA Psychiatry 73,456-464(2016). 5.Weaver,F.M.,et al.Randomized trial of deep brain stimulation for Parkinson disease:thirty-six-month outcomes.Neurology 79,55-65(2012). 6.Fisher,R.,et al.Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy.Epilepsia 51,899-908(2010). 7.Drobisz,D.&Damborska,A.Deep brain stimulation targets for treating depression.Behav Brain Res 359,266-273(2019). 8.Williams,L.M.Precision psychiatry:a neural circuit taxonomy for depression and anxiety.Lancet Psychiatry 3,472-480(2016). 9.Bouthour,W.,et al.Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond.Nat Rev Neurol 15,343-352(2019). 10.Scangos,K.W.,Makhoul,G.S.,Sugrue,L.P.,Chang,E.F.&Krystal,A.D.State-dependent responses to intracranial brain stimulation in a patient with depression.Nat Med(2021). 11.Chiang,S.,et al.Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation.Brain Stimul 14,366-375(2021). 12.Fasano,A.&Helmich,R.C.Tremor habituation to deep brain stimulation:Underlying mechanisms and solutions.Mov Disord 34,1761-1773(2019). 13.Malone,D.A.,Jr.,et al.Deep brain stimulation of the ventral capsule / ventral striatum for treatment-resistant depression.Biol Psychiatry 65,267-275(2009). 14.Rao,V.R.,et al.Direct Electrical Stimulation of Lateral Orbitofrontal Cortex Acutely Improves Mood in Individuals with Symptoms of Depression.Curr Biol 28,3893-3902 e3894(2018). 15.Kirkby,L.A.,et al.An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood.Cell 175,1688-1700 e1614(2018). 16.Scangos,K.W.,et al.Pilot Study of An Intracranial Electroencephalography Biomarker of Depressive Symptoms in Epilepsy.J Neuropsychiatry Clin Neurosci 32,185-190(2020). 17.Mayberg,H.S.,et al.Deep brain stimulation for treatment-resistant depression.Neuron 45,651-660(2005). 18.Yeung,A.W.K.&Wong,N.S.M.The Historical Roots of Visual Analog Scale in Psychology as Revealed by Reference Publication Year Spectroscopy.Front Hum Neurosci 13,86(2019). 19.Timmerby,N.,Andersen,J.H.,Sondergaard,S.,Ostergaard,S.D.&Bech,P.A Systematic Review of the Clinimetric Properties of the 6-Item Version of the Hamilton Depression Rating Scale(HAM-D6).Psychother Psychosom 86,141-149(2017). 20.Keller,C.J.,et al.Mapping human brain networks with cortico-cortical evoked potentials.Philos Trans R Soc Lond B Biol Sci 369(2014). 21.Keller,C.J.,et al.Corticocortical evoked potentials reveal projectors and integrators in human brain networks.J Neurosci 34,9152-9163(2014). 22.Sun,F.T.&Morrell,M.J.The RNS System:responsive cortical stimulation for the treatment of refractory partial epilepsy.Expert Rev Med Devices 11,563-572(2014). 23.Toni,G.Computing and visualizing dynamic time warping alignments in R:The dtw package.Journal of Statistical Software 31,1-24(2009). 24.Garcia-Lorenzo,B.,et al.Stereoelectroencephalography for Refractory Epileptic Patients Considered for Surgery:Systematic Review,Meta-Analysis,and Economic Evaluation.Neurosurgery 84,326-338(2019). 25.Duval,E.R.,Javanbakht,A.&Liberzon,I.Neural circuits in anxiety and stress disorders:a focused review.Ther Clin Risk Manag 11,115-126(2015). 26.Zheng,J.,et al.Amygdala-hippocampal dynamics during salient information processing.Nat Commun 8,14413(2017). 27.Price,J.L.&Drevets,W.C.Neurocircuitry of mood disorders.Neuropsychopharmacology 35,192-216(2010). 28.Schlaepfer,T.E.,et al.Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression.Neuropsychopharmacology 33,368-377(2008). 29.Lozano,A.M.,et al.Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression.Biol Psychiatry 64,461-467(2008). 30.Mayberg,H.S.,et al.Cingulate function in depression:a potential predictor of treatment response.Neuroreport 8,1057-1061(1997). 31.Waltz,E.Green light for deep brain stimulator incorporating neurofeedback.Nat Biotechnol 38,1014-1015(2020). 32.Guinjoan,S.M.,et al.Asymmetrical contribution of brain structures to treatment-resistant depression as illustrated by effects of right subgenual cingulum stimulation.J Neuropsychiatry Clin Neurosci 22,265-277(2010). 33.Mayberg,H.S.,et al.Reciprocal limbic-cortical function and negative mood:converging PET findings in depression and normal sadness.Am J Psychiatry 156,675-682(1999). 34.Haq,I.U.,et al.Smile and laughter induction and intraoperative predictors of response to deep brain stimulation for obsessive-compulsive disorder.Neuroimage 54 Suppl 1,S247-255(2011). 35.Gibson,W.S.,et al.The Impact of Mirth-Inducing Ventral Striatal Deep Brain Stimulation on Functional and Effective Connectivity.Cereb Cortex 27,2183-2194(2017). 36.Dunner,D.L.,et al.Prospective,long-term,multicenter study of the naturalistic outcomes of patients with treatment-resistant depression.J Clin Psychiatry 67,688-695(2006). 37.Riva-Posse,P.,et al.Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression.Biol Psychiatry 76,963-969(2014). 38.Campbell,M.C.,et al.Mood response to deep brain stimulation of the subthalamic nucleus in Parkinson’s disease.J Neuropsychiatry Clin Neurosci 24,28-36(2012). 39.Salloum,N.C.,et al.Efficacy of intravenous ketamine treatment in anxious versus nonanxious unipolar treatment-resistant depression.Depress Anxiety 36,235-243(2019). 40.Harris,P.A.,et al.The REDCap consortium:Building an international community of software platform partners.J Biomed Inform 95,103208(2019). 41.Harris,P.A.,et al.Research electronic data capture(REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.J Biomed Inform 42,377-381(2009). 42.Mukamel,R.&Fried,I.Human intracranial recordings and cognitive neuroscience.Annu Rev Psychol 63,511-537(2012). 43.Chang,E.F.Towards large-scale,human-based,mesoscopic neurotechnologies.Neuron 86,68-78(2015). 44.Matsumoto,R.,et al.Functional connectivity in the human language system:a cortico-cortical evoked potential study.Brain 127,2316-2330(2004). 45.Schiff,S.J.,Aldroubi,A.,Unser,M.&Sato,S.Fast wavelet transformation of EEG.Electroencephalogr Clin Neurophysiol 91,442-455(1994). 46.Friston,K.J.Functional and effective connectivity:a review.Brain Connect 1,13-36(2011). 47.Keller,C.J.,et al.Induction and Quantification of Excitability Changes in Human Cortical Networks.J Neurosci 38,5384-5398(2018). 48.Bassett,D.S.&Sporns,O.Network neuroscience.Nat Neurosci 20,353-364(2017). 49.Khambhati,A.N.,et al.Functional control of electrophysiological network architecture using direct neurostimulation in humans.Netw Neurosci 3,848-877(2019). 50.Riva-Posse,P.,et al.A connectomic approach for subcallosal cingulate deep brain stimulation surgery:prospective targeting in treatment-resistant depression.Mol Psychiatry 23,843-849(2018). 51.Choi,K.S.,Riva-Posse,P.,Gross,R.E.&Mayberg,H.S.Mapping the “Depression Switch” During Intraoperative Testing of Subcallosal Cingulate Deep Brain Stimulation.JAMA Neurol 72,1252-1260(2015). 52.Sokolov,A.A.,et al.Linking structural and effective brain connectivity:structurally informed Parametric Empirical Bayes(si-PEB).Brain Struct Funct 224,205-217(2019). 53.Mori,S.&van Zijl,P.C.Fiber tracking:principles and strategies-a technical review.NMR Biomed 15,468-480(2002). 54.Rabiner,L.R.,Juang,B.H.Fundamentals of Speech Recognition,(PTR Prentice Hall,Englewood Cliffs,N.J.,1993). 55.Sakoe,H.,Chiba,S.Dynamic programming algorithm optimization for spoken word recognition.IEEE Transactions on Acoustics,Speech,and Signal Processing 26,43-49(1978).

[0158] Although the foregoing inventions have been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art, in light of the teachings of the present invention, that certain changes and modifications can be made thereto without departing from the spirit or scope of the appended claims. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0159] Thus, the foregoing merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various configurations that embody the principles of the present invention and are within the spirit and scope thereof, although not expressly described or shown herein. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts contributed by the inventor to further the art, and are not to be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. In addition, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Thus, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims.

Claims

1. 1. A closed loop method for treating a neuropsychiatric disorder in a subject, the method comprising: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivery of electrical stimulation to treat the neuropsychiatric disorder; mapping the brain of the subject to identify optimal locations in a second brain region for positioning a second electrode for detection of electroencephalographic signals from neural activity associated with symptoms of the neuropsychiatric disorder; positioning the first electrode at the optimal location in the first brain region to deliver electrical stimulation to the first brain region of the subject; positioning the second electrode at the optimal location in the second brain region to detect the electroencephalography signals from the neural activity associated with the symptoms of the neuropsychiatric disorder; detecting the electroencephalography signal at the second brain region of the subject's brain using the second electrode; and applying electrical stimulation to the first brain region using the first electrode in a manner effective to treat the neuropsychiatric disorder in the subject when the electroencephalography signal detected using the second electrode exceeds a threshold level.

2. 10. The method of claim 1, further comprising using a control algorithm to automate the application of the electrical stimulation when the electroencephalography signal exceeds the threshold level.

3. The method of claim 2 , wherein the control algorithm comprises a machine learning algorithm.

4. The method of claim 3 , wherein the machine learning algorithm is a supervised machine learning algorithm.

5. The method of any one of claims 2 to 4, wherein the control algorithm further adjusts one or more programmed stimulation parameters based on the threshold level of the electroencephalography signal.

6. 10. The method of claim 1, further comprising positioning a plurality of electrodes in the second brain region for detection of the electroencephalography signals by stereotactic electroencephalography (sEEG).

7. 2. The method of claim 1, wherein the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain.

8. 2. The method of claim 1, wherein the second brain region comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region.

9. 10. The method of claim 1, wherein the electroencephalography signal is gamma frequency power.

10. The method of claim 1 , wherein the first electrode is positioned on a surface of the first brain region or within the first brain region.

11. The method of claim 1 , wherein the second electrode is positioned on a surface of the second brain region or within the second brain region.

12. The method of claim 1 , wherein the first electrode or the second electrode is a non-penetrating brain surface electrode array.

13. The method of claim 1 , wherein the first electrode or the second electrode is a transbrain electrode array.

14. 10. The method of claim 1, wherein the neuropsychiatric disorder comprises depression, and applying the electrical stimulation treats depression.

15. 10. The method of claim 1, wherein the neuropsychiatric disorder comprises anxiety, and applying the electrical stimulation treats anxiety.

16. 2. The method of claim 1, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain.

17. The method of claim 1 , wherein the electrical stimulation is applied unilaterally or bilaterally.

18. The method of claim 1 , further comprising evaluating the effectiveness of a treatment in the subject.

19. 2. The method of claim 1, further comprising monitoring the subject for a period of time, wherein an increase in the number of electroencephalography signals above the threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates an increase in severity of the symptom of the neuropsychiatric disorder, and a decrease in the number of electroencephalography signals above the threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates a decrease in severity of the symptom of the neuropsychiatric disorder.

20. 20. The method of claim 19, wherein the subject is monitored continuously or intermittently.

21. The method of claim 1 , wherein the first brain region and the second brain region are the same or different.

22. 1. A closed-loop method for treating major depressive disorder (MDD) in a subject, the method comprising: positioning a first electrode to deliver electrical stimulation to a first region of the brain of the subject; positioning a second electrode to detect gamma frequency electroencephalography signals from neural activity associated with MDD symptoms in a second region of the subject's brain; detecting the gamma frequency electroencephalography signal using the second electrode; and applying electrical stimulation to the first region of the brain of the subject using the first electrode in a manner effective to treat the MDD symptoms in the subject when the gamma frequency electroencephalography signal detected using the second electrode exceeds a threshold level.

23. 23. The method of claim 22, wherein the first region of the brain comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate cortex (SGC) region, or the orbitofrontal cortex (OFC) region of the brain.

24. 24. The method of claim 22 or 23, wherein the second region of the brain comprises the right amygdala, the left amygdala region, the right orbitofrontal cortex region, the left subgenual cingulate region, or the right hippocampal region.

25. 24. The method of claim 23, wherein applying the electrical stimulation comprises applying bipolar electrical stimulation to the VC / VS region of the brain of the subject.

26. 23. The method of claim 22, wherein the electrical stimulus is applied with a current in the range of 1 mA to 6 mA.

27. 23. The method of claim 22, wherein the electrical stimulation is applied for a maximum of 10 minutes per day.

28. 23. The method of claim 22, wherein the electrical stimulation is applied continuously for a period of time ranging from 1 hour per day to 24 hours per day.

29. 23. The method of claim 22, further comprising monitoring the subject for a period of time, wherein an increase in the number of gamma frequency electroencephalography signals above the threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates an increase in severity of MDD symptoms, and a decrease in the number of gamma frequency electroencephalography signals above the threshold level detected by the second electrode in a later recording period compared to a previous recording period indicates a decrease in severity of MDD symptoms.

30. 30. The method of claim 29, wherein the subject is monitored continuously or intermittently.

31. 1. A closed loop method for ameliorating a symptom of a neuropsychiatric disorder in a subject, the method comprising: mapping the subject's brain to identify an optimal location in a first brain region for positioning a first electrode for delivering electrical stimulation to ameliorate the symptoms of the neuropsychiatric disorder; mapping the brain of the subject to identify optimal locations in a second brain region for positioning a second electrode for detection of electroencephalography signals from neural activity associated with the symptoms of the neuropsychiatric disorder; positioning the first electrode at the optimal location in the first brain region to deliver electrical stimulation to the first brain region of the subject; positioning the second electrode at the optimal location in the second brain region to detect the electroencephalography signals from the neural activity associated with the symptoms of the neuropsychiatric disorder; detecting the electroencephalography signal at the second brain region of the subject's brain using the second electrode; and when the electroencephalography signal detected using the second electrode exceeds a threshold level, applying electrical stimulation to the first brain region using the first electrode in a manner effective to ameliorate the symptoms of the neuropsychiatric disorder in the subject.

32. 32. The method of claim 31, wherein the symptom includes depression, and applying the electrical stimulation ameliorates the depression.

33. 33. The method of claim 32, wherein depression is measured by a Visual Analog Scale for Depression (VAS-D), Hamilton Rating Scale for Depression (HAM-D), Montgomery-Asberg Depression Scale (MADRS), or Beck Depression Inventory (BDI) score, and applying the electrical stimulation is effective in reducing symptom severity according to the VAS-D, HAM-D, MADRS, or BDI score.

34. 32. The method of claim 31, wherein the symptom comprises anxiety and applying the electrical stimulation ameliorates the anxiety.

35. 35. The method of claim 34, wherein anxiety is measured by a Visual Analog Scale for Anxiety (VAS-A) or Beck Anxiety Inventory (BAI) score, and applying the electrical stimulation is effective in reducing anxiety according to the VAS-A or the BAI score.

36. 32. The method of claim 31 , wherein the first electrode is positioned on a surface of the first brain region or within the first brain region.

37. 32. The method of claim 31 , wherein the second electrode is positioned on a surface of the second brain region or within the second brain region.

38. 32. The method of claim 31 , wherein the first electrode or the second electrode is a non-penetrating brain surface electrode array.

39. 32. The method of claim 31 , wherein the first electrode or the second electrode is a transbrain electrode array.

40. 32. The method of claim 31, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain.

41. 32. The method of claim 31, wherein the method further comprises evaluating the effectiveness of a treatment in the subject.

42. 32. The method of claim 31, further comprising using a control algorithm to automate the application of the electrical stimulation when the electroencephalography signal exceeds the threshold level.

43. The method of claim 42 , wherein the control algorithm comprises a machine learning algorithm.

44. 44. The method of claim 43, wherein the machine learning algorithm is a supervised machine learning algorithm.

45. 43. The method of claim 42, wherein the control algorithm further adjusts one or more programmed stimulation parameters based on the threshold level of the electroencephalography signal.

46. 32. The method of claim 31, further comprising positioning a plurality of electrodes in the second brain region for detection of the electroencephalography signals by stereotactic electroencephalography (sEEG).

47. 32. The method of claim 31, wherein the first brain region comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate (SGC) region, or the orbitofrontal cortex (OFC) region of the brain.

48. 32. The method of claim 31, wherein the second brain region comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region.

49. 32. The method of claim 31 , wherein the electroencephalography signal is gamma frequency power.

50. 1. A system for treating a neuropsychiatric disorder in a subject, the system comprising: a stimulation electrode adapted for positioning in a first region of the brain of the subject; a sensing electrode adapted to be positioned at a second region of the subject's brain and to record electroencephalographic signals from the second region of the subject's brain; and a processor programmed to direct the stimulation electrodes to apply electrical stimulation to the first region of the brain in a manner effective to treat the neuropsychiatric disorder in the subject when an electroencephalography signal exceeding a threshold level is detected using the detection electrodes.

51. 51. The system of claim 50, wherein the first region of the brain comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate cortex (SGC) region, or the orbitofrontal cortex (OFC) region of the brain.

52. 52. The system of claim 50 or 51, wherein the second region of the brain comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region.

53. 51. The system of claim 50, wherein the electroencephalography signal is gamma frequency power.

54. 51. The system of claim 50, wherein the stimulating electrodes or the sensing electrodes are a non-penetrating brain electrode array.

55. 51. The system of claim 50, wherein the stimulating electrodes or the sensing electrodes are a transbrain electrode array.

56. 51. The system of claim 50, wherein the neuropsychiatric disorder comprises depression, and applying electrical stimulation treats the depression.

57. 51. The system of claim 50, wherein the neuropsychiatric disorder comprises anxiety, and applying electrical stimulation treats the anxiety.

58. 51. The system of claim 50, further comprising a user interface including an input electronically coupled to the processor for instructing the stimulation electrodes to apply electrical stimulation to the first region of the brain to treat the neuropsychiatric disorder in the subject.

59. 60. The system of claim 58, wherein the user interface is password protected and operable by medical personnel.

60. 51. The system of claim 50, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain.

61. 51. The system of claim 50, wherein the processor is further programmed to adjust one or more programmed stimulation parameters according to an algorithmic control law to apply the adjusted electrical stimulation to the first region of the brain via the stimulation electrodes in a manner effective to treat the neuropsychiatric disorder.

62. 51. The system of claim 50, wherein the processor is further programmed to set a maximum number of electrical stimulations per day.

63. 51. The system of claim 50, wherein the processor is further programmed to set a total amount of time of electrical stimulation per day.

64. 1. A system for ameliorating a symptom of a neuropsychiatric disorder in a subject, the system comprising: a stimulation electrode adapted for positioning in a first region of the brain of the subject; a sensing electrode adapted to be positioned at a second region of the subject's brain and to record electroencephalographic signals from the second region of the subject's brain; and a processor programmed to direct the stimulation electrodes to apply electrical stimulation to the first region of the brain in a manner effective to ameliorate the symptoms of the neuropsychiatric disorder in the subject when an electroencephalography signal above a threshold level is detected using the detection electrodes.

65. 65. The system of claim 64, wherein the first region of the brain comprises the ventral capsule / ventral striatum (VC / VS) region, the subgenual cingulate cortex (SGC) region, or the orbitofrontal cortex (OFC) region of the brain.

66. 66. The system of claim 64 or 65, wherein the second region of the brain comprises a right amygdala region, a left amygdala region, a right orbitofrontal cortex region, a left subgenual cingulate region, or a right hippocampal region.

67. 65. The system of claim 64, wherein the electroencephalography signal is gamma frequency power.

68. 65. The system of claim 64, wherein the symptom includes depression, and applying the electrical stimulation ameliorates the depression.

69. 65. The system of claim 64, wherein the symptom includes anxiety and applying the electrical stimulation ameliorates the anxiety.

70. 65. The system of claim 64, wherein the stimulating electrode or the sensing electrode is a non-penetrating brain electrode.

71. 65. The system of claim 64, wherein the stimulating electrode or the sensing electrode is a penetrating brain electrode.

72. 65. The system of claim 64, further comprising a user interface including an input electronically coupled to the processor for instructing the stimulating electrodes to apply electrical stimulation to the first area to ameliorate the symptoms of the neuropsychiatric disorder in the subject.

73. 73. The system of claim 72, wherein the user interface is password protected and operable by medical personnel.

74. 65. The system of claim 64, wherein the neuropsychiatric disorder is major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, anorexia, obsessive-compulsive disorder (OCD), or bipolar disorder (BD), or chronic pain.

75. 65. The system of claim 64, wherein the processor is further programmed to adjust one or more programmed stimulation parameters according to an algorithmic control law to apply the adjusted electrical stimulation to the first region of the brain via the stimulation electrodes in a manner effective to ameliorate the symptoms of the neuropsychiatric disorder.

76. 65. The system of claim 64, wherein the processor is further programmed to set a maximum number of electrical stimulations per day.

77. 65. The system of claim 64, wherein the processor is further programmed to set a total amount of time of electrical stimulation per day.

Images