System for treatment of a brain disorder

Abstract

A system for treating a brain disorder includes a processor configured to process signals measured by at least one electrode implanted within a neural network in at least one brain region of a subject. The processor analyzes combined neurotraining (NT) and deep brain stimulation (DBS) records incorporating the processed signals. Based on this analysis, the processor generates at least one of: an adjusted NT parameter for improving NT clinical outcomes following a preceding DBS session, and an adjusted DBS parameter for improving DBS clinical outcomes following a preceding NT session. The processor further generates instructions for at least one of: applying DBS via the implanted electrode according to the adjusted DBS parameter, and conducting an NT session by operating a feedback device that generates non-invasive feedback to incentivize the subject's brain to modulate neural network activity from a baseline state toward a pre-defined activity state according to adjusted NT parameter.

Description

[0001] SYSTEM FOR TREATMENT OF A BRAIN DISORDER

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of priority of US Provisional Patent Application No.

[0004] 63 / 778,709 filed on 27 March 2025. This application is also related to PCT Patent Application No. PCT / IL2025 / 050153 having International filing date of February 12, 2025. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety.

[0005] BACKGROUND

[0006] The present invention, in some embodiments thereof, relates to neuromodulation and, more specifically, but not exclusively, to systems and methods for applying neuromodulation.

[0007] Neuromodulation is the process of altering brain activity through targeted delivery of electrical, chemical, or other stimuli. It is used to treat neurological and psychiatric disorders like chronic pain, epilepsy, and depression. Techniques include deep brain stimulation (DBS), spinal cord stimulation (SCS), and vagus nerve stimulation (VNS). It works by adjusting neural signaling to restore function or relieve symptoms.

[0008] SUMMARY

[0009] According to a first aspect, a system for treatment of a brain disorder, comprise: a processor executing a code for: processing signals measured by at least one electrode implanted within a neural network in at least one region of a brain of a subject, analyzing a combination of at least one neurotraining (NT) record and at least one deep brain stimulation (DBS) record, including the processed signals, generating based on the analysis , at least one of: an adjustment of at least one NT parameter for applying NT for obtaining an improved at least one NT clinical outcome following a preceding DBS session delivered using at least one DBS parameter, and an adjustment of at least one DBS parameter for applying DBS for obtaining an improved at least one DBS clinical outcome following a preceding NT session delivered using at least one NT parameter, and generating instructions for at least one of: applying DBS via the at least one electrode according to the adjustment of the at least one DBS parameter, and for applying a NT session by operating a feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity according to the adjustment of the at least one NT parameter. In a further implementation of the first aspect, wherein the at least one NT record is collected during at least one preceding NT session, the at least one NT record including at least one NT parameterof applied NT and at least one NT clinical outcome measured in response to at least one preceding NT session delivered using the at least one NT parameter and following at least one preceding DBS session delivered using at least one DBS parameter, wherein the at least one DBS record is collected during at least one preceding DBS session, the DBS record including at least one DBS parameter of applied DBS and at least one DBS clinical outcome measured during and / or after the at least one preceding DBS session delivered using the at least one DBS parameter and following at least one preceding NT session delivered using at least one NT parameter.

[0010] In a further implementation of the first aspect, the DBS session is implemented by monitoring the signals sensed by the at least one electrode, identifying a pathological state of activity of the neural network, and applying a predefined pattern of stimulation via the at least one electrode.

[0011] In a further implementation of the first aspect, the NT session is implemented by: computing a baseline state of activity of the neural network in at least one region of a brain of a subject that corresponds to the external non-invasively applied stimuli with valence according to signals sensed by at least one electrode when implanted in the at least one region of the brain, generating instructions for operating a feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity, monitoring the current activity of the neural network in response to the non-invasive feedback according to the signals sensed by the at least one electrode when implanted in the at least one region of the brain, and dynamically adapting the non-invasive feedback during each respective iteration according to a real-time or near-real time current valence state measurement of the neural network until the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

[0012] In a further implementation of the first aspect, further comprising dynamically iterating the analyzing, the generating based on the analysis, and the instructions.

[0013] In a further implementation of the first aspect, analyzing comprises feeding into a machine learning (ML) model, and wherein generating based on the analysis comprises generating based on the ML model.

[0014] In a further implementation of the first aspect, further comprising dynamically generating a record comprising at least one of: the adjustment of the at least one NT parameter and the at least one NT clinical outcome following the NT applied with the adjusted at least one NT parameter, and an adjustment of at least one DBS parameter for applying DBS and the at least one DBS clinicaloutcome following the DBS applied with the adjusted at least one DBS parameter, and dynamically updating the ML model using the record.

[0015] In a further implementation of the first aspect, the ML model is trained on a training dataset comprising a plurality of records, wherein a record is selected from: at least one NT parameter used during application of NT, at least one NT clinical outcome obtained following the NT applied using the at least one NT parameter, and at least one DBS parameter of a DBS session preceding the NT, and at least one DBS parameter used during application of DBS, at least one DBS clinical outcome obtained following the DBS applied using the at least one DBS parameter, and at least one NT parameter of a NT session preceding the DBS session.

[0016] In a further implementation of the first aspect, further comprising analyzing the processed signals for computation of a pathological pattern of activity of the neural network, wherein the pathological pattern is analyzed, and wherein a rate of application of NT sessions is generated based on the analysis.

[0017] In a further implementation of the first aspect, at least one NT record includes an indication of a provocation test using a personalized external stimuli conducted as part of the NT session in which specific pathological states are inflicted and specific regions are responding to the provocation test, and the based on the analysis a location of stimuli and / or direction of stimuli and / or pattern of stimuli including shape, frequency, and / or intensity, is generated.

[0018] In a further implementation of the first aspect, the at least one DBS record includes an indication of a current emotional state of the subject determined according to the processed signals, and a provocation test of the NT session is selected to inflict a specific emotional response according to the current emotional response, wherein in response to the current emotional state indicating a sensitive state an intensity of the provocation test is relatively reduced, and wherein in response to the current emotional state indicating a robust state the intensity of the provocation test is relatively increased.

[0019] In a further implementation of the first aspect, the at least one NT record includes at least one of: amount of time spent in each one of a plurality of different valence states, valence trajectory change, a classification of at least one valence state.

[0020] In a further implementation of the first aspect, the at least one NT record and corresponding generation based on the analysis include at least one of: (i) anxiety and / or resilience and / or adaptive capacity level and selection of a trigger intensity, (ii) most intrusive and / or painful thought and corresponding trigger that inflicts a corresponding valence state, (iii) neutral valence state and corresponding selection of a target and / or threshold to reach in the NT session.In a further implementation of the first aspect, the at least one DBS record includes at least one of: recording of activity in the neural network a short time interval after pausing DBS, recording of activity of the neural network over a time interval after DBS.

[0021] In a further implementation of the first aspect, the at least one DBS record includes monitoring of change in the activity of the neural network following DBS indicating a natural trajectory shift and the an adjustment to at least one parameter of a transfer function for reinforcing neuromodulation in a similar trajectory during a subsequent DBS is generated based on the ML model.

[0022] In a further implementation of the first aspect, the at least one NT record includes at least one of: trigger of the NT session, valence state range, different between valence states, time interval to reach a NT target, success rate in achieving the NT target, and wherein the corresponding generation of at least one DBS parameter based on the analysis includes at least one of: stimulation parameter, pulse pattern, intensity, frequency, closed loop time.

[0023] In a further implementation of the first aspect, the combination that is analyzed further includes at least one of the following associated with the at least one NT record and / or the at least one DBS record: measurements by at least one extracorporeal physiological sensor, reports from at least one of: the subject, a caregiver, a therapist, and event recording of general feeling and / or specific episodes.

[0024] In a further implementation of the first aspect, the generation by the ML is selected for at least one of: minimization of severe episodes as subjectively reported by at least one user, reduction of episodic events as defined mathematically by analyzing the neuronal activity and applying classification processes, and maximal extension of a learning effect over time.

[0025] In a further implementation of the first aspect, the at least one NT record and / or the at least one DBS record and / or the at least one NT parameter and / or the at least one DBS parameter each include at least one of: parameters of an external non-invasively applied stimuli with valence, a baseline state of activity in response to the parameters of the external non-invasively applied stimuli with valence, parameters of a non-invasive feedback generated by a feedback device designed to incentivize the brain of the subject, activity of a neural network in response to the non-invasive feedback, valence state measurement of the neural network, pre-defined state within a first predefined threshold and / or a second pre-defined threshold indicating a significant change from a baseline state.

[0026] In a further implementation of the first aspect, the DBS comprises LFS.

[0027] According to a second aspect, a system for treatment of a brain disorder, comprises: a processor executing a code for: generating instructions for operating a feedback device forgenerating a non-invasive feedback designed as part of a neurotraining (NT) session delivered to an individual for improving response to stimuli provoking an aversive memory, and generating instructions for applying a low frequency stimulation (LFS) session to at least one electrode when the at least one electrode is implanted in a region of a brain of the individual, the LFS session applied preceding and / or in conjunction with and / or following the NT session, the LFS session selected for facilitating plasticity generated during the NT session.

[0028] In a further implementation of the second aspect, the LFS is selected to be applied to at least one electrode positioned in the dorsal anterior cingulate cortex (dACC).

[0029] In a further implementation of the second aspect, further comprising code for: processing signals measured by at least one electrode implanted within neural network in at least one region of a brain of a subject, analyzing a combination of at least one neurotraining (NT) record and at least one low frequency stimulation (LFS) record, including the processed signals, and generating based on the analysis, at least one of: an adjustment of at least one NT parameter for applying NT for obtaining an improved at least one NT clinical outcome following at least one preceding LFS session delivered using at least one LFS parameter, and an adjustment of at least one LFS parameter for applying LFS for improving plasticity for at least one subsequent NT session.

[0030] In a further implementation of the second aspect, further comprising monitoring of a plasticity effect of the NT session including at least one of: time rate in original and / or target valence state, and time of shift between valence states, and the at least one LFS parameter for applying LFS predicted to maximize the plasticity effect is generated based on the analysis.

[0031] In a further implementation of the second aspect, wherein the at least one NT record is collected during at least one preceding NT session, the at least one NT record including at least one NT parameter of applied NT and at least one plasticity outcome measured in response to the at least one preceding NT session delivered using the at least one NT parameter and following at least one preceding LFS session delivered using at least one LFS parameter, wherein the at least one LFS record is collected during at least one preceding LFS session, the LFS record including at least one LFS parameter of applied LFS.

[0032] In a further implementation of the second aspect, analyzing comprises feeding into a ML model, and generating based on the analysis comprises generated by the ML model.

[0033] In a further implementation of the second aspect, the ML model is trained on a training dataset comprising a plurality of records, wherein a record is selected from: at least one NT parameter used during application of NT, at least one NT clinical outcome obtained following the NT applied using the at least one NT parameter, and at least one LFS parameter of a LFS session performed in association with the NT session, and at least one LFS parameter used duringapplication of LFS, at least one LFS clinical outcome obtained following the LFS applied using the at least one LFS parameter, and at least one NT parameter of a NT session associated with the LFS.

[0034] According to a third aspect, a system for neuromodulation, comprises: at least one processor executing a code for: in a plurality of iterations: monitoring activity of a neural network in at least one region of a brain of a subject for detection of a baseline state that corresponds to an external non-invasively applied stimuli with valence, by processing signals sensed by at least one electrode when implanted in the at least one region of the brain, in response to the detected baseline state, generating instructions for operating a wearable feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity, monitoring the current activity of the neural network in response to the non-invasive feedback according to the signals sensed by the at least one electrode when implanted in the at least one region of the brain, dynamically adapting the non-invasive feedback generated by the wearable feedback device during each respective iteration according to a real-time or near-real time current valence state measurement of the neural network, and terminating the non-invasive feedback generated by the wearable feedback device of each respective iteration when the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

[0035] In a further implementation of the third aspect, the wearable feedback device is selected from: a bracelet that vibrates, smart glasses that present images, a smart ring that applies a tactile sensation, a watch that applies a tactile sensation, and headphones that play audio.

[0036] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0037] BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0038] Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, itis stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

[0039] In the drawings:

[0040] FIG. 1 is a block diagram of components of a system for treatment of a brain disorder by applying NT and DBS and / or LFS and / or automatically adapting NT, in accordance with some embodiments of the present invention;

[0041] FIG. 2 is a flowchart of a method of treatment of a brain disorder by applying NT and DBS, in accordance with some embodiments of the present invention;

[0042] FIG. 3 is a flowchart of a method of treatment of a brain disorder by applying NT and LFS, in accordance with some embodiments of the present invention;

[0043] FIG. 4 is a flowchart of a method of treatment of a brain disorder by automatically adapting NT, in accordance with some embodiments of the present invention;

[0044] FIG. 5 includes graphs depicting results of an experiment of applying DBS and NT, in accordance with some embodiments of the present invention; and

[0045] FIG. 6 includes graphs depicting results of another experiment of applying DBS and NT for clinical maintenance, in accordance with some embodiments of the present invention.

[0046] DETAILED DESCRIPTION

[0047] The present invention, in some embodiments thereof, relates to neuromodulation and, more specifically, but not exclusively, to systems and methods for applying neuromodulation.

[0048] As used herein, the term neurotraining (NT) session may sometimes be referred to in a shortened form as NT. The term DBS session may sometimes be referred to in a shortened form as DBS.

[0049] As used herein, the term real-time or near-real time current valence state measurement of the neural network may refer to a measurement made by electrodes and / or a computation of the current valence state based on the measurement made by the electrodes, which is provided within a time internal that is sufficiently small such that the provided (e.g., measured and / or computed) current valence state measurement accurately represents the actual physical current valence state of the neurons (e.g., within a tolerance range). A delay between the provided current valence state measurement and the actual physical current valence state is small enough such that the actual physical current valence state has not significantly changed (e.g., within the tolerance range) during the measurement and / or computation.An aspect of some embodiments of the present invention relates to systems, methods, computing devices, and / or code instructions (stored on a data storage device and executable by one or more processors) for treatment of a brain disorder in a subject by iterative applications of neurotraining (NT) and / or deep brain stimulation (DBS), optionally by a single device integrating NT capabilities and DBS capabilities, optionally using at least one common electrode. Each current session of the NT and / or the DBS is based on outcomes of previously applied NT and / or DBS sessions. The current session of the NT and / or DBS is applied based on a prediction of an improvement in the outcome of the current NT and / or DBS session. For example, NT is applied in dedicated sessions. Signals sensed by the electrode(s) are monitored for detecting pathological patterns in the brain which are treated by the DBS. The combined device that incorporates NT and DBS may be used for treatment of the subject for obtaining improved clinical outcomes. For example, in case of an increased rate of pathological patterns (which are detected and treated using DBS), the frequency of the NT session may be increased in an attempt to address the root cause of the increased rate of the pathological patterns, which is predicted to obtain the improved clinical outcome of reduced rate of pathological patterns which are predicted to be better than NT alone or DBS alone.

[0050] An aspect of some embodiments of the present invention relates to systems, methods, computing devices, and / or code instructions (stored on a data storage device and executable by one or more processors) for treatment of a brain disorder in a subject by iterative applications of NT and / or DBS, optionally integrate within a single device. Signals measured by one or more electrodes implanted within a neural network in at least one region of a brain of a subject are obtained. The signals may be processed as described herein, for example, for computing a baseline state of activity of the neural network, measuring a valence state, and the like. In some embodiments, valence refers to the intrinsic attractiveness (e.g., positive valence) or aversiveness (e.g., negative valence) of an externally applied stimulus, influencing for example, emotional and / or cognitive responses, for example, a picture of house triggering disturbing memories of a war. A combination of one or more NT records and one or more DBS records, including the processed signals (or computation based on the processed signals) are analyzed, for example, fed into a machine learning (ME) model. The outcome of the analysis, optionally the ML model, generates an adjustment of at least one NT parameter for applying NT for obtaining an improved NT outcome(s) following a preceding DBS session delivered using at least one DBS parameter. Alternatively or additionally, the ML model generates an adjustment of at least one DBS parameter for applying DBS for obtaining an improved DBS outcome(s) following a preceding NT session delivered using at least one NT parameter. Instructions are generated according to the adjustmentsgenerated by the ML model. The instructions are generated for applying DBS via the electrode(s) according to the adjustment of the DBS parameter(s). Alternatively or additionally, instructions are generated for applying a NT session by operating a feedback device for generating a non-invasive feedback. The non-invasive feedback may be designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity according to the adjustment of the at least one NT parameter.

[0051] Optionally, the features are iterated for alternatively applying DBS and NT. The parameters for treating the current DBS session are based on the parameters of previously applied NT session(s) and outcomes of the previously applied NT session(s), and optionally previously applied DBS session(s) and outcomes of the previously applied DBS session(s). The parameters for treating the current NT session are based on the parameters of previously applied DBS session(s) and outcomes of the previously applied DBS session(s), and optionally previously applied NT session(s) and outcomes of the previously applied NT session(s). It is noted that several sequential NT sessions may be applied followed by a DBS session, or several sequential DBS sessions may be applied followed by a NT session.

[0052] An aspect of some embodiments of the present invention relates to systems, methods, computing devices, and / or code instructions (stored on a data storage device and executable by one or more processors) for treatment of a brain disorder in a subject by application of NT in association with low frequency stimulation (LFS). The LFS may be applied prior to the NT session and / or during the NT session and / or after the NT session.

[0053] An aspect of some embodiments of the present invention relates to systems, methods, computing devices, and / or code instructions (stored on a data storage device and executable by one or more processors) for treatment of a brain disorder in a subject by application of NT in association with LFS. Instructions are generated for operating a feedback device for generating a non-invasive feedback designed as part of a NT session delivered to an individual, for example, for improving response to stimuli provoking an aversive memory. Instructions are generated for applying an LFS session to at least one electrode when the at least one electrode is implanted in a region of a brain of the individual. The LFS session is applied preceding and / or in conjunction with and / or following the NT session. The LFS session is selected for facilitating plasticity generated during the NT session. Parameters of the LFS session for applying LFS for improving plasticity for a subsequent NT session may be generated by a ML model fed a NT and / or a LFS record(s) of preceding NT and / or LFS sessions. Parameters for applying NT by the feedback device for improving NT clinical outcomes may be generated by the ML model fed the NT and / or LFS record(s) of preceding NT and / or LFS sessions.An aspect of some embodiments of the present invention relates to systems, methods, computing devices, and / or code instructions (stored on a data storage device and executable by one or more processors) for automatic neuromodulation in response to external stimuli, for example, stimuli that are independent of the subject and / or are not designed for the subject. The following features are implemented in multiple iterations: activity of a neural network in at least one region of a brain of a subject is monitored for detection of a baseline state that corresponds to an external non-invasively applied stimuli with valence. The external non-invasively applied stimuli with valence may be independent of the subject and / or are not designed for the subject. For example, the subject is walking in a park and hears explosions made by children lighting firecrackers, which trigger disturbing images of friends of the subject getting shot and / or being blow up by explosives during a war. The baseline state is computed by processing signals sensed by at least one electrode when implanted in the at least one region of the brain. In response to the detected baseline state, instructions are generated for operating a wearable feedback device for generating a non-invasive feedback. The wearable feedback device may be implemented as, for example, a bracelet that vibrates, a watch that applies a tactile sensation, smart glasses that present images, a smart ring that applies a tactile sensation, and headphones that play audio. The non-invasive feedback is designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity. The current activity of the neural network in response to the non-invasive feedback is monitored according to the signals sensed by the at least one electrode when implanted in the at least one region of the brain. The non-invasive feedback generated by the wearable feedback device is dynamically adapted during each respective iteration according to a real-time or near-real time current valence state measurement of the neural network. The non-invasive feedback generated by the wearable feedback device (of the current iteration, i.e., of each respective iteration) when the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

[0054] At least one embodiment described herein addresses the technical problem of improving application of NT and / or improving application of DBS to obtain improved clinical outcomes. At least one embodiment described herein improves the technology of application of NT and / or DBS, by providing improved clinical outcomes. At least one embodiment described herein improves upon existing approaches of application of NT and / or DBS. At least one embodiment described herein provides the practical application of improved clinical outcomes by application of NT and DBS.NT and DBS represent fundamentally different treatment modalities, based on distinct technologies and therapeutic approaches.

[0055] Neurotraining as used herein, maybe performed as described with reference to methods and systems for neuromodulation described in PCT Patent Application No. IL2025 / 050153.

[0056] Neurotraining (NT) is a non-invasive, closed-loop system designed to identify and modulate pathological lower-level brain states. It leverages deep electrophysiological recordings to compute and differentiate between distinct brain states in response to personalized, non-invasive stimuli with defined emotional valence. Based on this real-time analysis, NT provides targeted sensory feedback that encourages the brain to transition from a baseline (pathological) state toward a desired, pre-identified target state of activity. A core component of NT is its ability to map and decode neural responses to specific emotional triggers. The modulation of pathological activity is achieved through the brain’s intrinsic learning mechanisms, using direct, real-time feedback to retrain dysfunctional patterns. NT shares some similarities with neurofeedback (NF) in that it operates as a closed-loop system, delivering non-invasive feedback based on real-time neural signals. However, NT differs substantially in several key aspects:

[0057] • It identifies and computes distinct, personalized brain states in response to patient tailored stimuli with emotional valence

[0058] • It actively guides optimal transitions between states, rather than simply promoting general self-regulation

[0059] • It is designed to recode pathological neural patterns at their source, rather than reinforce standardized high-level brain patterns.

[0060] While traditional NF is often used to enhance focus, reduce anxiety, or support general self-regulation, NT is aimed at addressing patient- specific pathological patterns, targeting the root cause of dysfunction. Over repeated sessions, guided or self-administered, NT is expected to remediate pathological patterns, build lasting resilience and restore adaptive emotional responses.

[0061] In contrast, Deep Brain Stimulation (DBS) is an invasive approach in which electrodes are surgically implanted into specific brain regions to deliver electrical stimulation. DBS is widely used for movement disorders such as Parkinson’s disease, essential tremor, and dystonia, and has also been applied to psychiatric conditions such as depression and obsessive-compulsive disorder (OCD). Advanced DBS systems incorporate sensing capabilities, enabling recording and analysis of neural signals to support adaptive and responsive (closed-loop) stimulation. These systems can adjust stimulation parameters in real time and apply stimulation only when needed to optimize therapeutic outcomes, reduce habituation, and improve safety.However, current DBS solutions do not incorporate patient-specific, non-invasive stimuli with emotional valence to identify precise emotional states, nor do they leverage these states to drive personalized adaptive and responsive intervention strategies. DBS combined with NT uniquely combines state- specific decoding, personalized tailored stimuli, and feedback-driven modulation, offering a differentiated pathway to both understanding and treating pathological emotion brain activity.

[0062] Each treatment modality (i.e., NT and DBS) has a unique configuration and a distinct mechanism of action. Adaptive DBS may be designed for continuously monitoring brain activity, identifying pathological state and applying external stimuli for a predetermined period. DBS does not use a provocation test and is unrelated to application of a NT session. In at least one embodiment, preceding application of a NT session(s) may be used (e.g., learned by the machine learning model) for how to apply the DBS more optimally. For example, the adaptive mechanism of the DBS may be used to detect a pathological state of the brain of the subject. The accuracy of the detection (e.g., sensitivity and specificity) of the pathological state may be improved following the process of a provocation test, conducted as part of the NT session. The provocation state may be designed to inflict specific pathological states using personalized external stimuli, for example, by applying an external non-invasively applied stimuli with valence. The specific regions responding to the provocation test, and the response patterns may be analyzed by the ML model. The ML model may generate an optimization of the stimulation parameters, for example, location and / or direction of stimuli and / or pattern parameters (e.g., shape, frequency, and / or intensity). Similarly, the continuous monitoring of the adaptive DBS and the response to treatment may be fed into the ML model. The ML model may generate an optimization for the DBR session. For example, the DBR system uses a provocation test to inflict specific emotional response. However, the response to the trigger is dependent on the subject’s current emotional baseline - the subject being in a sensitive state requires a relatively lower intensity trigger, while the subject being in a more robust state requires relatively higher intensity triggers. Such indication of the subject’s sensitive state of the more robust state may be extracted from the adaptive DBS monitoring. Another example for the cross functional dynamic calibration, is monitoring.

[0063] At least one embodiment described herein relates to a single system, optionally a single device that is designed to optimize complementation treatment by DBS and NT. The single device provides a synergy between DBS and NT. Using the single device, treatment by NT and / or treatment by DBS may be optimized based on an analysis of previously applied DBS and / or NT, alone or in combination. The NT may be selected and applied, for example, for reducing episodic traumatic events such as for treatment of PTSD. The single device with implanted electrodesenables monitoring the current activity of a neural network of the brain, and dynamically adapting parameters of an applied DBS and / or applied NT, optionally in real-time or near real-time. One or more parameters of the DBS and / or NT may be computed and / or adjusted in real time based on outcomes of previously applied DBS and / or NT, for example, personalized provocation states, valence states, threshold for determining when the current activity of the neural network has significantly shifted from a baseline state of activity (e.g., measure valence) corresponding to an external non-invasively applied stimuli with valence (e.g., that triggers an aversive memory, such as strongly aversive in PTSD), the baseline state of activity may be shifted to a target valence state (e.g., neutral), and / or others as described herein.

[0064] At least one embodiment described herein addresses the technical problem of improving application of NT to obtain improved clinical outcomes. At least one embodiment described herein improves the technology of application of NT, to obtain improved clinical outcomes. At least one embodiment described herein improves upon existing approaches for application of NT, which are unrelated to LFS. At least one embodiment described herein provides the practical application of improved clinical outcomes by applying LFS in association with a NT session, prior to the NT session and / or during the NT session and / or after the NT session.

[0065] At least one embodiment described herein that applies a combination of LFS and NT (e.g., sequentially, iteratively) may provide a synergistic and / or complementary clinical effect, for example, real-time or near real-time alleviation of symptoms and treatment of the root cause of the symptoms, for improving the health baseline of the subject. The LFS is designed to enhance the plasticity effect of the NT session. The LFS stimulation described herein, which may be based on DBS, is different than standard uses of DBS. The use of the DBS may be originally selected to induce neuromodulation in specific regions in order to provide alleviation of symptoms and / or improvement (or reduction) of pathological patterns over time. In contrast, LFS described herein is selected and applied to enhance the change (e.g., learning and / or neuromodulation) induced by the NT session.

[0066] It is noted that LFS is different than standard DBS. For example, standard DBS is used to down regulate the hyperactivity of specific brain regions (e.g., suppressing Theta waves). The use of LFS is provided in other regions and using different stimulations patterns to improve long term plasticity.

[0067] Inventors discovered that combining LFS with NT approaches improves long term plasticity of the effects generated in the brain from treatment by NT. Applying LFS prior to, during, and / or after a NT session in which a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of a neural network of the brain, is predicted todepress the dACC which maintains an aversive memory together with and by strengthening the memory in the amygdala. Applying the LFS is predicted to prevent the dACC from providing inputs to the amygdala that would maintain the aversive memory and interfere with recoding of a state of activity of the neural network corresponding to the aversive memory. This is predicted to reduce the time for spontaneous recovery of the aversive memory.

[0068] It is noted that the combination of LFS using NT described herein is different than use of LFS in other approaches. A previous approach described in a previous study having at least one Inventor in common with the present disclosure incorporated herein by reference in its entirety ( Klavir, Oded, Rotem Genud-Gabai, and Rony Paz. "Low -frequency stimulation depresses the primate anterior-cingulate-cortex and prevents spontaneous recovery of aversive memories. " Journal of Neuroscience 32.25 (2012): 8589-8597) demonstrated that the use of LFS has prolonged the effect of extinction training. In the previous study it was hypothesized that the effect (reduction in spontaneous recovery) is because the dACC is depressed which maintains the aversive memory together with and by strengthening the memory in the amygdala. In contrast, in at least one embodiment described herein, the learning is achieved with a distinctly different mechanism. The effect of the NT for representation of value in the amygdala may be enhanced, as it is expected to prevent the dACC from providing inputs to the amygdala that would maintain the memory as achieved with the NT approaches described herein and interfere with recoding of values as desired.

[0069] At least one embodiment described herein addresses the technical problem of improving application of NT to obtain improved clinical outcomes, in particular where an external stimuli that is unintended for the subject triggers an undesired emotional response in the subject, such as an object viewed by the subject triggers a painful memory. At least one embodiment described herein improves the technology of application of NT, to obtain improved clinical outcomes. At least one embodiment described herein improves upon existing approaches for application of NT. At least one embodiment described herein provides the practical application of improved clinical outcomes by automatic adjustment of a wearable feedback device applying non-invasive feedback during a NT session. At least one embodiment described herein improves NT to obtain improved clinical outcomes by using electrodes implanted in a region of a brain of a subject for automatically monitoring activity of a neural network in response to external stimuli which may be unintended for the subject. A wearable feedback device is automatically operated to treat the subject and the operation is automatically terminated, in multiple iterations, according to the monitoring of the brain of the subject.Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and / or methods set forth in the following description and / or illustrated in the drawings and / or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

[0070] The present invention may be a system, a method, and / or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0071] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0072] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0073] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), programmable logic arrays (PLA), programmable array logic (PAL), or complex programmable logic device (CPLD) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0074] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0075] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0076] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps tobe performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0077] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0078] Reference is now made to FIG. 1, which is a block diagram of components of a system 100 for treatment of a brain disorder by applying NT and DBS and / or LFS and / or automatically adapting NT, in accordance with some embodiments of the present invention. Reference is also made to FIG. 2, which is a flowchart of a method of treatment of a brain disorder by applying NT and DBS, in accordance with some embodiments of the present invention. Reference is also made to FIG. 3, which is a flowchart of a method of treatment of a brain disorder by applying NT and LFS, in accordance with some embodiments of the present invention. Reference is also made to FIG. 4, which is a flowchart of a method of treatment of a brain disorder by automatically adapting NT, in accordance with some embodiments of the present invention. Reference is also made to FIG. 5, which includes graphs 502 and 512 depicting results of an experiment of applying DBS and NT, in accordance with some embodiments of the present invention. Reference is also made to FIG. 6, which includes graphs 602 and 612 depicting results of another experiment of applying DBS and NT for clinical maintenance, in accordance with some embodiments of the present invention.

[0079] System 100 described with reference to FIG. 1 may implement the features of the method described with reference to FIGs 2-4, by one or more hardware processors 102 of a computingdevice 104 executing code instructions stored in a memory (also referred to as a program store) 106.

[0080] System 100 may include one or more sensors 112 for sensing signals used for monitoring a brain of a subject, as described herein. Optionally, sensors 112 are implemented as one or more electrodes designed for surgical implantation in one or more regions of the brain, optionally the amygdala.

[0081] Sensors 112 may be used for stimulation. Optionally, the electrodes designed for surgical implantation in the brain may be used for application of electrical stimulation.

[0082] Sensors 112 may be in communication with a controller 108 designed to receive signals from sensors 112 and / or designed to operator sensors 112 to apply electrical stimulation. Controller 108 may be implemented as, for example, a separate controller designed for surgical implantation in the brain and / or to be worn on the body of the subject external to the brain. In other embodiments, controller 108 may be implemented by computing device 104 and / or integrated within computing device 104. Controller 108 may be implemented in hardware and / or firmware and / or software.

[0083] System 100 may include one or more feedback devices 150 designed to generate a non-invasive feedback and / or applied stimulus, as described herein. For example, triggering a memory for which the subject is being treated for (e.g., PTSD, depression) and / or triggering an addiction for which the subject is being treated for (e.g., drugs, alcohol, smoking) and / or as part of a NT treatment. Examples of feedback devices 150 include a display for presenting images and / or video, speakers for playing audio, a haptic device such as for generating a sensation, a decision-making task, and / or a motor skill task.

[0084] System 100 may include a computing device 104 that obtains measurements made by sensor(s) 112 and / or obtains data computed from the measurements (e.g., the data may be computed by controller 108). Computing device 104 may compute the parameters for adapting one or more treatments as described herein. Computing device 104 may generate instructions for operating feedback device 150 according to the parameters, as described herein.

[0085] Computing device 104 may be implemented as, for example, a client terminal, a server, a virtual machine, a virtual server, a computing cloud, a mobile device, a desktop computer, a thin client, a Smartphone, a Tablet computer, a laptop computer, a wearable computer, glasses computer, and a watch computer.

[0086] Multiple architectures of system 100 based on computing device 104 may be implemented: In an exemplary implementation of a localized architecture, computing device 104 may be a local device executing code 106 A, for example, a standalone computer, code running on asurgical workstation, code running on a mobile device, and / or integrated with controller 108. Computing device 104 is in communication with controller 108 and / or sensor(s) 112 and / or feedback device 150. Computing device 104 obtains measurements made by sensors 112, computes parameters for one or more treatments, and / or operates feedback device 150, as described herein. When computing device 104 is physically separate from controller 108 and / or sensor(s) 112, computing device 104 may communicate with controller 108 and / or sensor(s) 112 via a network 110 and / or other data interface.

[0087] In an exemplary implementation of a centralized architecture, computing device 104 storing code 106A may be implemented as one or more servers (e.g., network server, web server, a computing cloud, a virtual server, surgical workstation) that provides centralized services for computing parameters for one or more treatments of one or more subjects and / or for generating instructions for one or more controllers 108 and / or feedback devices 150 and / or sensor(s) 112 over network 110, for example, providing software as a service (SaaS), providing software services accessible using a software interface (e.g., Application Programming Interface (API), Software D evelopment Kit (SDK)), providing an application for local download, and / or providing functions using a remote access session. For example, each controller 108 sends measurements made by its associated sensor(s) 112. Computing device 104 may compute parameters for one or more treatments and / or may generate instructions for operating respective feedback devices 150 associated with respective controllers 108 and / or sensor(s) 112.

[0088] Sensor (s) 112 may transmit sensed signals to controller 108 and / or computing device 104, for example, via a direct connected (e.g., local bus and / or cable connection and / or short range wireless connection), and / or via network 110 and a data interface 122 of computing device 104 and / or controller 108 (e.g., where sensor(s) 112 and / or controller(s) 108 are connected via short range wired and / or wireless communication channel and / or are located remotely from the computing device).

[0089] One or more components described with reference to computing device 104 may be implemented with respect to controller 108.

[0090] Data interface 122 may be implemented as, for example, a network interface, a wire connection (e.g., physical port), a wireless connection (e.g., antenna), a network interface card, a wireless interface to connect to a wireless network, a physical interface for connecting to a cable for network connectivity, and / or virtual interfaces (e.g., software interface, Application Programming Interface (API), Software Development Kit (SDK), virtual network connection, a virtual interface implemented in software, and / or network communication software providing higher layers of network connectivity).Data communication 110 may be implemented as, for example, a network such as the internet, a local area network, a virtual network, a wireless network, and / or a cellular network. Alternatively or additionally, data communication 110 may be implemented using other architectures, for example, a local bus, a point to point link (e.g., wired or via BlueTooth), and / or combinations of the aforementioned.

[0091] Data interface 122 and / or data communication 110 may be implemented based on a Brain Communication Interface (BCI) architecture and / or based on BCI components. For example, in an architecture based on a BCI chip, a network implementation of data communication 110 may not be required.

[0092] Processor(s) 102 of computing environment 104 may be hardware processors, which may be implemented, for example, as a central processing unit(s) (CPU), a graphics processing unit(s) (GPU), field programmable gate array(s) (FPGA), digital signal processor(s) (DSP), application specific integrated circuit(s) (ASIC), and / or complex programmable logic device (CPLD). Processor(s) 102 may include a single processor, or multiple processors (homogenous or heterogeneous) arranged for parallel processing, as clusters and / or as one or more multi core processing devices.

[0093] Memory 106 stores code instructions executable by hardware processor(s) 102. Exemplary memories 106 include a random access memory (RAM), read-only memory (ROM), a storage device, non-volatile memory, magnetic media, semiconductor memory devices, hard drive, removable storage, and optical media (e.g., DVD, CD-ROM). For example, memory 106 may code 106A that execute one or more acts of the method described with reference to FIGs. 2-4.

[0094] Computing device 104 may include data storage device 120 for storing data, for example, a repository of parameters 120A for applying one or more treatments to one or more subjects, for example, NT, DBS, LFS, and the like. Data storage device 120 may be implemented as, for example, a memory, a local hard-drive, a removable storage unit, an optical disk, a storage device, a virtual memory and / or as a remote server 118 and / or computing cloud (e.g., accessed over network 110).

[0095] It is noted that server 118 is optional. Sever 118 may be used, for example, for central storage of data collected from one or more subjects, for downloading of updates, for central monitoring, and the like.

[0096] Data storage device 120 may host one or more ML models 120B described herein. Exemplary architectures of the ML model(s) include, for example, statistical classifiers and / or other statistical models, neural networks of various architectures (e.g., convolutional, fully connected, deep, encoder-decoder, recurrent, transformer, graph), support vector machines(SVM), logistic regression, k-nearest neighbor, decision trees, boosting, random forest, a regressor, and / or any other commercial or open source package allowing regression, classification, dimensional reduction, supervised, unsupervised, semi-supervised, and / or reinforcement learning. Machine learning models may be trained using supervised approaches and / or unsupervised approaches.

[0097] Computing device 104 and / or controller 108 may include and / or may be in communication with one or more physical user interfaces 124 that include a mechanism for inputting data (e.g., enter name of subject, select which disorder is being treated) and / or for viewing data, for example, one or more computed parameters. Exemplary user interfaces 124 include, for example, one or more of, a touchscreen, a display, a keyboard, a mouse, and voice activated software using speakers and microphone.

[0098] Referring now back to FIG. 2, at 202, a DBS treatment system for delivery of DBS (also referred to herein as DBS session) and / or a NT treatment system for delivery of NT (also referred to herein as NT session) may be provided and / or selected.

[0099] Embodiments described herein may be implemented using any DBS treatment system, such as standard DBS treatment systems with one or more electrodes designed to be implanted in a brain of a subject. A DBS session is implemented by the DBS treatment system monitoring signals sensed by the electrode(s), identifying a pathological state of activity of a neural network in the brain, and applying a predefined pattern of stimulation via the electrode. The pattern of stimulation is dynamically adapted as described herein. The DBS may include at least one electrode implanted for stimulating, for example, the Subcallosal Cingulate Cortex (SCC) for treating mental disorders (e.g., depression and PTSD)

[0100] Embodiments described herein may be implemented using any NT treatment system, such as standard NT treatment system.

[0101] At least one embodiment described herein may be implemented using the exemplary NT treatment system described with reference to International Patent Application No. IL2025 / 050153, entitled “SYSTEMS AND METHODS FOR BRAIN NEUROMODULATION”, filed on February 12, 2025, having at least one inventor in common with the present disclosure, incorporated herein by reference in its entirety. Using the exemplary NT treatment system the NT session is implemented by computing a baseline state of activity of the neural network in at least one region of a brain of a subject that corresponds to an external non-invasively applied stimuli with valence according to signals sensed by electrode(s) when implanted in the region(s) of the brain. Instructions are generated for operating a feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural networkto change from the baseline state towards a pre-defined state of activity. The current activity of the neural network in response to the non-invasive feedback is monitored according to the signals sensed by the electrode(s) when implanted in the region(s) of the brain. The non-invasive feedback is dynamically adapted during each respective iteration according to a real-time or near-real time current valence state measurement of the neural network. The non-invasive feedback may be dynamically adapted until the current activity of the neural network meets a pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold. Additional exemplary details of the exemplary NT treatment system described with reference to International Patent Application No. IL2025 / 050153 are described below.

[0102] The exemplary NT treatment system is for modulating a current activity of a neural network of a brain of a subject to change from a baseline state towards a pre-defined state of activity, for example, for treatment of mental disorders such as post-traumatic stress disorder (PTSD) and / or treatment of addictions. A baseline state of activity of a neural network in one or more regions of a brain of a subject that corresponds to the external non-invasively applied stimuli with valence, is computed. The baseline state may represent the state of the subject that to be treated, for example, a disturbing memory and / or a distressing emotion and / or invasive thoughts, such as in a patient suffering from PTSD, that is triggered by the external non-invasively applied stimuli with valence. In one or multiple iterations, a non-invasive feedback is generated, for example, an audio tone, a visual presentation, and / or a tactile sensation. The non-invasive feedback is designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity. The non-invasive feedback may be dynamically adapted for incentivizing the brain, for example, a task is increased in difficulty, and / or a tone where different frequencies of the tone are associated with varying appetitive / aversive reward levels is changed. The pre-defined state of activity may represent the state at which the patient experiences a certain way, for example, relief from the disturbing memory and / or the distressing emotion. Other examples of the pre-defined state of activity are described herein. The current activity of the neural network in response to the non-invasive feedback is monitored. The monitoring is performed while the non-invasive feedback is dynamically adapted. The iterations are terminated when the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

[0103] An example of the goal of treatment, which is not necessarily part of the criteria for success in a specific session of treatment, is now described: When the subject has reached the pre-definedstate, the initial external non-invasively applied stimuli with valence that triggered the disturbing memory and / or distressing emotion and / or invasive thoughts has shifted to triggering a more positive and / or less negative emotional and / or less intense response (e.g., positive valence) and / or has shifted to triggering a tolerable memory and / or tolerable emotion and / or tolerable thoughts (e.g., less negative and / or less intense valence) and / or has shifted to triggering a non-consequential memory and / or emotion and / or thought (e.g., neutral valence).

[0104] As used herein, the phrase stimulus (or stimuli) with valence may refer to a stimulus with negative valence, or a stimulus with a positive valence. A stimulus with negative valence may be a stimulus that a subject would prefer to avoid and / or a stimulus causing negative subjective sensation, for example, a picture that elicits a bad memory, a picture that elicits anxiety, and / or a sound that elicits fear. A stimulus with positive valence may be a stimulus that the subject would want / desire, and / or a stimulus causing positive subjective sensation for example, a picture of a tasty food or of a drug. It is noted that the stimulus may be combination of positive and negative valence, in which case the positive or negative valence may refer to the overall and / or higher effect. For example, the stimulus may result in a greater positive sensation but also associated with a smaller effect of an undesirable sensation such as stress. As used herein, the term positive or negative refers to the dominant effect, without necessarily excluding a smaller opposite effect.

[0105] As used herein, the change from the baseline state to the pre-defined state may be based on change on valence and / or change in intensity. A change from a baseline state that is negative may be to a state that is less-negative or more-positive or negative with less intensity. A change from a baseline state that is positive may be to a state that is less-positive or more-negative or positive with less intensity.

[0106] Other examples of definitions of the pre-defined state of activity are now described. Where the pre-defined state of activity was identified earlier (i.e., while presenting the stimuli to identify the baseline state) stimuli with a neutral / less-negative / positive value are also presented for defining the pre-defined state). This pre-defined state is then determined as being reached, optionally within a pre-defined threshold. In another example, a new state that is significantly different (e.g., statistically significantly different) than the baseline state is selected. Every time the neural state gets close enough to this pre-defined state (e.g., within a threshold), the new state is reinforced with a positive outcome, hence enforcing this new state to be positive / neutral, and / or vice versa.

[0107] As used herein, the activity of the neural network may refer to a neural representation of the neural network.A baseline state of activity of the neural network that corresponds to the external non-invasively applied stimuli with valence is computed.

[0108] The baseline state of activity may represent the state of the subject while the subject is experiencing (e.g., suffering from) a situation that is to be treated, for example, the state of the subject remembering a traumatic event, the state of the subject feeling a disturbing emotion, the state of the subject experiencing a disturbing thought, and / or unpleasant physiological phenomena (e.g., chest pain, rapid breathing, increased heart rate).

[0109] The baseline state of activity may represent a pre-existing memory, thought, experience, and / or emotion, which is re-triggered by the external non-invasively applied stimuli with valence. Alternatively, the baseline state of activity may represent a new association of the external non-invasively applied stimuli with valence with an appetitive and / or aversive reinforcement.

[0110] Examples of baseline states of activity that correspond to the external non-invasively applied stimuli with valence include:

[0111] • A disturbing memory, for example, associated with PTSD.

[0112] • A distressing emotion, for example, associated with a phobia.

[0113] • Symptoms associated with the valence representation may include physiological changes in the body such as increased heart rate, chest pain, and rapid breathing, for example, associated with anxiety.

[0114] • Urge to perform repetitive tasks, for example, associated with OCD.

[0115] • Feeling of euphoria, for example, associated with an addiction to drugs and / or alcohol.

[0116] Examples of the generated external non-invasively applied stimuli with valence include:

[0117] • As a visual presentation on a display, such as a still image and / or video. The image and / or video may be, for example, ocean waves crashing against a coast, rain falling into a puddle, written text, a non-realistic pattern such as a mix of colors, a single color, and the like.

[0118] • Audio played over speakers, for example, a tone at a selected frequency, music, speech, and the like.

[0119] • A tactile sensation generated by a haptic device, for example, a surface with a soft texture, a complex surface with elevations and depressions, and non-painful sharp ends.

[0120] • A decision-making task, for example, presenting four differently randomly selected images, and asking the user to pick their favorite. In another example, having the user play a quest video game.• A motor skill task, for example, tracing a finger over curves, building a tower of blocks, and threading a thread through an eye of a needle.

[0121] A non-invasive feedback is generated. The non-invasive feedback is designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity. The non-invasive feedback may be dynamically adapted, for example, according to a current state of activity for reaching the predefined state of activity. For example, adapting a difficulty of a game being played, adapting a frequency of a tone associated with aversive / appetitive reward, and the like.

[0122] The pre-defined state may be defined as a state that corresponds to a second external non-invasively applied stimuli with neutral valence. The pre-defined state is a state that is significantly different from the baseline state, for example, a Euclidean distance above a threshold away from the baseline state. Alternatively or additionally, the pre-defined state may be a state that is defined independently of the baseline state, for example, the activity level falling within a range and / or above another threshold. Alternatively or additionally, the pre-defined state may be defined as a state having valence opposite to the valence used to compute the baseline state.

[0123] The pre-defined state may be conditioned with a neutral valence or with a valence opposite to the valence used to compute the baseline state.

[0124] The non-invasive feedback may be, for example, a visual presentation on a display, audio played over speakers, a tactile sensation generated by a haptic device, a decision-making task, and / or a motor skill task. Examples of the non-invasive feedback may be as described with reference to the examples of the external non-invasively applied stimuli.

[0125] The non-invasive feedback may be an adaptation of the external non-invasively applied stimuli. For example, the external non-invasively applied stimuli may be a sound, and the non-invasive feedback may be change in the frequency of one component of the sound. In another example, the external non-invasively applied stimuli may be a movie of a war at a certain field, while the non-invasive feedback may be another movie at the same field without the war, instead showing flowers, cows grazing, and children playing.

[0126] Alternatively, the non-invasive feedback may be different than the external non-invasively applied stimuli. For example, the external non-invasively applied stimuli is a movie showing a war scene that triggers traumatic thoughts of war by a patient, while the non-invasive feedback is a tone that changes in frequency, or classical music playing over speakers which reduces the intensity and / or frequency of the traumatic thoughts of war by the patient.The neurons may be unable to “switch” their activity quickly and / or in a single session from the baseline towards the pre-defined state of activity. Rather, the non-invasive feedback may be designed to incrementally modulate the current activity from the baseline towards the predefined state of activity, for example, in multiple sessions, and / or over time during a single session.

[0127] The pre-defined state of activity may represent a treatment target, such as a state in which the subject does not experience what the subject experienced in response to the external non-invasively applied stimuli (e.g., neutral valence). Alternatively, the pre-defined state of activity may represent the state in which the subject experiences a significant dampening of what the subject experienced in response to the external non-invasively applied stimuli, such that the quality of life and / or ability of the subject to function is not significantly impacted as it was in response to the external non-invasively applied stimuli (e.g. less negative valence, less positive valence, and / or less intense valence). Alternatively, the pre-defined state of activity may represent an “opposite” of what the subject experienced in response to the external non-invasively applied stimuli (e.g., change from negative valence to positive valence, change from positive valence to negative valence, and / or change in intensity of valence).

[0128] Different exemplary scenarios of the valence and corresponding non-invasive feedback are now described:

[0129] • When the valence of the applied stimuli used to identify the neural network represents a negative valence, the non-invasive feedback may be designed to shift the baseline state of activity to be closer to the pre-defined state identified and / or associated with a neutral valence, and / or to shift to a less negative valence and / or to shift to a more positive valence and / or less intense valence than the valence of the applied stimuli. • When the valence of the applied stimuli used to identify the neural network represents a positive valence, the non-invasive feedback may be designed to shift the baseline state of activity to be closer to the pre-defined state identified and / or associated with a neutral valence, and / or shift to a more negative valence, and / or shift to a less positive valence than the valence of the applied stimuli.

[0130] • When the subject has a mental condition (e.g., addiction) associated with a positive valence that is triggered by the external non-invasively applied stimuli, the subject may be treated for the addiction by generating the non-invasive feedback for changing from the baseline state to the pre-defined state of activity identified, and / or to the pre-defined state associated with the neutral and / or the more negative valence, and / or the predefined state associated with the valence that is less positive than the valence associated with the addiction.The non-invasive feedback may be determined according to a transfer function. Additional exemplary details of the transfer function are described herein.

[0131] The current activity of the neural network in response to the non-invasive feedback is monitored. The current activity of the neural network may be computed while the non-invasive feedback is applied. The current activity of the neural network may be computed from signals outputted by the electrode(s) implanted in the brain of the subject.

[0132] The current activity of the neural network may include the current valence state measurement of the neural network, which may represent a real-time or near-real time current valence state measurement of the neural network.

[0133] The current activity of the neural network (and / or the activity defining the baseline state) may be computed based on one or more of: single-neurons (1-many), local-field-potentials, synchronicity between activity in different cells within a region, synchronicity between different cells in different brain regions, as a multi-dimensional representation that is computed from the activity of many cells (2-many), an evoked local-field-potential including a spectral power and / or phase across several spectral frequencies, and synchronicity of power and / or phase across electrodes located in one or more brain regions

[0134] A transfer function may be computed (e.g., trained) and / or updated (e.g., adapted).

[0135] The transfer function may be dynamically computed and / or updated during treatment sessions, in response to application of the non-invasive feedback and monitoring of the current activity generated in response to the application of the non-invasive feedback. Alternatively or additionally, the transfer function may be a mathematical operator defined in advance of the treatment session, and dynamically used on data collected during the treatment session for generation of the non-invasive feedback, for example, as described herein.

[0136] The transfer function may by dynamically adapted, for example, over time, over different treatment sessions, and the like. The transfer function may be dynamically adapted, for example, due to changing conditions of the brain, when another transfer function may provide better results after a few sessions in comparison to an initial transfer function, and / or in response to changing conditions of the signal acquisition (e.g., quality of signals).

[0137] Multiple transfer functions may be used for treatment combining multiple neuromodulation effects. For example, one transfer function may be selected based on its predicted ability to lead to neuromodulation of one brain region (e.g., amigdala), while another transfer function may be selected based on a prediction of leading to correlated neuromodulation between different regions in the brain (e.g., amygdala and substanitia-innomata). The selection and / or use of the different transfer functions may follow an assessment of the treatment effect achieved with each transferfunction and / or proper selection of the most advantageous transfer function with regard to a desired effect. Additionally, it may be aligned with the treatment progress, for example, starting with evaluation of different transfer functions aiming to maximize the therapeutic effect and further along the treatment shifting / adding transfer functions which promote neuromodulation related to long term neuro-plasticity.

[0138] The transfer function maps between activity of the neural network and the non-invasive feedback predicted to shift the current activity of the neural network from the baseline state to the pre-defined state.

[0139] Examples of transfer functions:

[0140] • Where the non-invasive stimuli provided as feedback is represented as a linear or nonlinear monotonic function of the number of emitted spikes (e.g., firing rate) of 1...N neurons in a pre-defined time window.

[0141] • Where the non-invasive stimuli provided as feedback is represented as a monotonic function of the synchrony between emitted spikes of 1..N neurons and 1..N other neurons.

[0142] • Where the non-invasive stimuli provided as feedback is represented as a monotonic function of the distance between the current network state and a pre-defined network state, and the metrics is computed on the low-dimensional representation (manifold) of the combined activity in 2.. ,N neurons.

[0143] The non-invasive stimuli provided as feedback may either have a metric(s) imposed by its physical properties (examples: tone frequency, volume, picture contrast, color) and / or is a set of stimuli with pre-defined order.

[0144] The transfer function may be implemented as a machine learning model. The non-invasive feedback may be generated by the machine learning model in response being fed the current activity of the neural network, and optionally the baseline state and / or the pre-defined state.

[0145] The machine learning model may be trained on a training dataset of multiple records. A record may include at least a sample current activity of the neural network, and a target non-invasive feedback that when applied to a subject shifted the sample current activity towards the sample pre-defined state. The record may further include a sample baseline state of the subject. The record may further include a sample pre-defined state. The ground truth may indicate the target non-invasive feedback that when applied to the subject shifted the sample current activity from the sample baseline state towards the sample pre-defined state.

[0146] The records may be personalized records, which may be dynamically created for the subject during treatment sessions. For example, the non-invasive feedback is dynamically varied, and thecorresponding current activity is computed. Records including the non-invasive feedback and corresponding current activity may be dynamically generated, and used to train the customized ML model. The records may include the activity of the baseline state measured for the subject. The customized ML model may serve as the transfer function for generating and / or selecting the non-invasive feedback.

[0147] Alternatively or additionally, the records may be obtained from different subjects. The ML model may be a generic ML model trained on data from different subjects, and / or used for guiding the non-invasive feedback for different subjects. The generic ML model may be customized for the subject, by using a transfer learning approach to further train the generic ML model on the personalized records of the subject.

[0148] The current activity is evaluated relative to the pre-defined state.

[0149] The NT session may be terminated when the current activity of the neural network meets the pre-defined state within a first pre-defined threshold. Alternatively or additionally, the NT session may be terminated when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold. The pre-defined state may be implicitly defined as being statistically significantly different from the baseline by the second pre-defined threshold.

[0150] The first pre-defined threshold and / or the second pre-defined threshold may be selected for treatment of the subject for a mental disorder triggered by the external non-invasively applied stimulus. For example, when the subject is suffering from symptoms of PTSD experienced by an external stimulus, reaching and / or exceeding the first pre-defined threshold and / or the second predefined threshold may indicate that the subject is no longer experiencing PTSD symptoms triggered by an external stimulus.

[0151] At 204, a subject may be selected for treatment using approaches described herein. The treatment may be based on a combination of NT and DBS designed to generate synergistic clinical outcomes which are predicted to be better than clinical outcomes obtained from using NT alone or DBS alone, or without synergy described herein.

[0152] Subjects suffering from a mental disorder may be selected for treatment. The mental disorder may be triggered by one or more external stimuli, which may generate disturbing emotions and / or memories in the subject.

[0153] The subject may be suffering from a mental disorder in which the subject feels a negative valence, such as negative affect, distress, and / or otherwise disturbing emotions and / or thoughts. Such subjects may be treated by shifting from the negative valence to a neural, positive valence, or less negative valence, and / or less intense valence. Examples of such mental disorders include: PTSD, anxiety, phobia, obsessive compulsive disorder, and social disorder.Alternatively, the subject may be suffering from a mental disorder in which the subject feels a positive valence, such as positive affect, euphoria, and / or otherwise pleasant emotions and / or thoughts. The positive valence may be associated with harmful behavior. Such subjects may be treated by shifting from the positive valence to a neural, negative valence, or less positive valence, and / or less intense valence. Examples of such mental disorders include: additions such as to alcohol and / or drugs, and obsessive compulsive disorder (OCD).

[0154] Specific memories and / or stimuli that evoke substantial distress to the subject may be identified.

[0155] The subject may be suffering from other neurological indications, for example, epilepsy. Examples of one or more regions of the brain in which the electrode(s) is implanted includes: amygdala, insula, Anterior-cingulate-cortex (ACC), medial-prefrontal-cortex (mPFC), nucleus accumbens (NAc), hippocampus, para hippocampus, and temporal pole.

[0156] The electrodes may be designed for monitoring action potentials of individual neurons, and / or monitoring local field potentials of one or more brain regions that include multiple neurons.

[0157] In some embodiments, the electrodes may include one or multiple contacts. Each contact may be associated with about 1-4 nerve cells, or other number. The number of contacts on an electrode may range, for example, from 1 (or 2) to about 1000.

[0158] The electrode(s) may be designed to sense a neuronal population of less than about 5000, or about 1000, or about 500, or about 10 neurons, or other values, or a single neuron. The current activity of the neural network may be computed for the neuronal population as a whole.

[0159] At 206, one or more NT records are collected and / or accessed.

[0160] Each NT record may be collected during a preceding NT session. Multiple NT records may be collected for multiple preceding NT sessions.

[0161] A NT record of a preceding NT session may include one or more of:

[0162] • One or more NT parameter of NT applied to the subject in the preceding NT session.

[0163] • One or more NT clinical outcomes measured in response to preceding NT session delivered using the NT parameter(s).

[0164] • An indication of one or more DBS sessions delivered using one or more DBS parameters that were applied prior to the preceding NT session, i.e., the preceding NT session followed the DBS sessions.

[0165] • Data collected during or in association with the NT session, for example: amount of time spend in each one of different valence states, valence trajectory change, and a classification of at least one valence state.Examples of data included as part of the NT record includes:

[0166] • Monitored valence states, for example, amount of time spent in each of multiple different valence states (e.g., from strong negative to strong positive).

[0167] • Monitored valence trajectory changes.

[0168] • Tagging of valence states. The tagging may be done by dynamic classification of the valence state, for example, by feeding signals sensed by the electrodes and / or values computed based on the signals into a classified trained on a training dataset of records. A record may include sample signals of a sample individual sensed by the electrodes and a ground truth label indicating the classification state (e.g., tag).

[0169] • Medication uptake.

[0170] • Indication that the subject is sleeping.

[0171] At 208, one or more DBS records are collected and / or accessed.

[0172] Each DBS record may be collected during a preceding DBS session. Multiple DBS records may be collected for multiple preceding DBS sessions.

[0173] A DBS record of a preceding DBS session may include one or more of:

[0174] • One or more DBS parameters of DBS in the preceding DBS session.

[0175] • One or more DBS clinical outcomes measured in response to the preceding DBS session delivered using the DBS parameter(s).

[0176] • An indication of one or more NT sessions delivered using one or more NT parameters that were applied prior to the preceding DBS session, i.e., the preceding DBS session followed the NT sessions.

[0177] • Data collected in association with the DBS session, such as monitored response to stimulation. For example, recording of activity in the neural network (e.g., neuronal activity in related subnetworks) a short time interval after pausing the stimulation, for example, about 30 seconds, or 1 minute, or 5 minutes, or 10 minutes, or 15 minutes, or 30 minutes, or other values. In another example, a long term effect following the stimulation by recording the neuronal activity of the neural network and / or in related subnetworks over a time interval after DBS, for example, about 6 hours, or 12 hours, or 24 hours, or 3 days or other values.

[0178] It is noted that 206 and 208 may be performed sequentially and / or iteratively, for example, collecting a NT record for a NT session, and collecting a DBS record for a DBS session that follows the NT session.At 210, signals are measured by the electrode(s) implanted within the neural network in at least one region of the brain of the subject. The measured signals may be processed, for example, values may be computed using the measured signals.

[0179] The signals may processed (or the processed signals may be analyzed) for computation of a pathological pattern (and / or the rate of the pathological pattern) of activity of the neural network, wherein the pathological pattern (and / or the rate of the pathological pattern is fed into the ML model, and wherein the ML model generates a rate of application of NT sessions.

[0180] At 212, a combination of at least one NT record and / or at least one DBS record and / or the processed signals (or data computed from the processed signals and / or based on an analysis of the processed signals) is analyzed, optionally fed into a ML model. It is noted that other types of analysis may be applied, for example, deterministic models, such as a system of equations, heuristic based approaches, and the like.

[0181] The combination may include a sequence of multiple NT records from a sequence of preceding NT sessions and / or a sequence of multiple DBS records from a sequence of preceding DBS sessions.

[0182] Optionally, the combination fed into the ML model includes additional data associated with the NT record(s) and / or the DBS record(s):

[0183] • Measurements by at least one physiological sensor (which may be internal and / or external to the body), for example, heart rate (HR) and / or heart rate variability (HRV) by a heart rate sensor, blood pressure by a blood pressure sensor, body temperature by a temperature sensor, electrophysiological (EP) measurements made by an EP sensor, galvanic skin resistance by a galvanic skin resistance sensor, other vital signs, other sympathetic responses, and the like. The measurements made by the physiological sensor(s) may by synchronized with the signals sensed by the electrodes. For example, different valence states may be associated with different values measured by the physiological sensor(s).

[0184] • External reports, for example, self-reported by the subject (using a dedicated application such as a mobile app), vital sign monitoring, report from a caregiver of the subject, report by a therapist of the subject (e.g., via a questionnaire, during therapy session, via voice and / or text), indication of anxiety and / or other mental state monitoring and / or assessment devices, and medication use (e.g., self-reported directly and / or indirectly such as by an application).

[0185] • Event recoding of general feeling and / or specific episodes.

[0186] An exemplary approach of training the ML model is described with reference to 224.At 214, the ML model generates an adjustment of one or more NT parameters for applying NT. The NT parameters may be generated based on a prediction of obtaining an improvement in one or more NT clinical outcomes from the NT session applied using the generated NT parameters. The NT parameters generated by the ML model may be for application by the NT session that follows the preceding DBS session delivered using one or more DBS parameters, where the DBS record of the preceding DBS session was fed into the ML model.

[0187] The ML model may be designed to optimize one or more target functions. Examples of target functions include: minimization of severe episodic events (e.g., as subjectively reported by one of the users), reduction of episodic events (e.g., as defined mathematically by analyzing the neuronal activity and / or applying classification processes such as a classifier to the signals measured by the electrodes), and / or maximal extension of the learning effect (e.g., plasticity effect) over time.

[0188] Examples of improvement of NT clinical outcomes include: minimization of severe episodes (e.g., as subjectively reported by at least one user), reduction of episodic events (e.g., as defined mathematically by analyzing the neuronal activity and applying classification processes), and maximal extension of a learning effect over time.

[0189] In another example, the DBS record fed into the ML model includes an indication of a current emotional state of the subject determined according to the processed signals. The ML model may generate a provocation test (also referred to herein as provocation trigger, or trigger) of the NT session. The provocation trigger may be an external non-invasive stimulus (e.g., generated by a feedback device) selected to inflict a specific emotional response, optionally according to the current emotional response and / or to trigger a target valence. In response to the current emotional state indicating a sensitive state, the ML model generates instructions for relatively reducing an intensity of the provocation test. In response to the current emotional state indicating a robust state, the ML model generates instructions for relatively increasing the intensity of the provocation test.

[0190] In yet another example, the NT record fed into the ML model indicates anxiety and / or resilience and / or adaptive capacity level of the subject. The ML model may generate a suitable trigger (e.g., provocation trigger) intensity and / or may calibrate the trigger. For example, in response to an input indicting that the subject is in an extreme anxiety mode, the ML model may select a less aversive trigger. In response to an input indicating that the subject is in a restful mode, the ML model may select a more aversive trigger.

[0191] In yet another example, the NT record fed into the ML model indicates a most intrusive and / or painful thought. The ML model may generate a selection of a corresponding trigger designed to inflicts a corresponding valence state in the subject.In yet another example, the NT record fed into the ML model indicates a neutral valence state. The ML model may generate a corresponding selection of an alternative target of activity of the neural network to reach in the NT session and / or adjust a threshold of the activity of the neural network to reach in the NT session.

[0192] In yet another example, the DBS record fed into the ML model includes an indication of monitored change in the activity of the neural network following DBS indicating a natural trajectory shift. The ML model may generate an adjustment to at least one parameter of the transfer function described herein for reinforcing neuromodulation in a similar trajectory during the DBS and / or in a subsequent (e.g., deep NT) NT session.

[0193] At 216, instructions are generated according to the adjustment of the NT parameter generated by the ML model for operating a feedback device for generating a non-invasive feedback for application during the subsequent NT session. The NT session is applied to the subject by executing the instructions. The subject is treated accordingly by the NT session.

[0194] In embodiments that use the exemplary NT system, the non-invasive feedback may be designed according to the adjustment of the NT parameter generated by the ML model to incentivize the brain of the subject to modulate the current activity of the neural network to change from the baseline state towards a pre-defined state of activity.

[0195] At 218, the ML model generates an adjustment of one or more DBS parameters for applying DBS. The DBS parameters may be generated based on a prediction of obtaining an improvement in one or more DBS clinical outcomes from the DBS session applied using the generated DBS parameters. The DBS parameters generated by the ML model may be for application by the DBS session that follows the preceding NT session delivered using one or more NT parameters, where the NT record of the preceding NT session was fed into the ML model.

[0196] In an example, the NT record fed into the ML model includes an indication of a provocation test conducted as part of the NT session in which specific pathological states are inflicted using a personalized external stimuli, and specific regions responding to the provocation test. The ML model generates a location of stimuli and / or direction of stimuli and / or pattern of stimuli including shape, frequency, and / or intensity for application during the subsequent DBS session.

[0197] In another example, the NT record fed into the ML model includes at least one of: trigger of the NT session (e.g., trigger calibration which may be performed in advance of the NT session and / or as part of the NT session), valence state range, difference between valence states (e.g., negative versus neutral), time interval to reach a NT target, and success rate in achieving the NT target. The corresponding generation of the DBS parameter by the ML model may includes at leastone of: stimulation parameters setting (e.g., pulse pattern, intensity, frequency), adjustment of closed loop threshold and / or adjustment of closed loop time.

[0198] Alternatively or additionally to 216-218, at 220, instructions are generated according to the adjustment of the DBS parameter(s) generated by the ML model, for applying DBS via the electrode(s). The DBS session is applied to the subject by executing the instructions. The subject is treated accordingly by the DBS session.

[0199] At 222, one or more features described with reference to 206-220 are dynamically iterated. The iterations may be implemented for continuously (or step-wise, or event triggered) calibration of parameters for applying NT and / or DBS sessions.

[0200] During each iteration, one of 214 and 216, or 218 and 222 may be selected. At 214-216 the ML model generates adjustment of NT parameters in which case the subject is treated with a NT session, or at 218-220 the ML model generates adjustment of DBS parameters in which case the subject is treated with a DBS session.

[0201] The ML model may determine which treatment should be administered next, the NT session or the DBS session according to the generation of the corresponding parameters. Alternatively, the ML model may alternate between generating parameters for the NT session and for the DBS session.

[0202] During each iteration, one of 206-208 may be selected according to the most recent treatment administered to the subject. When 214-216 are implemented, 206 follows, i.e., a NT record is collected at 206 based on the NT session administered according to 214-216. When 218-220 are implemented, 208 follows, i.e., a DBS record is collected at 208 based on the DBS session administered according to 218-220.

[0203] Over multiple iterations, the subject may be alternatively treated with NT and DBS, such as a sequence of one or more NT sessions alternating with a sequence of one or more DBS sessions.

[0204] Optionally, a record is dynamically generated for an iteration. A NT record or DBS record may be dynamically generated for the iteration, according to whether the subject was treated using NT or DBS. The NT record and / or the DBS record may include one or more of: the adjustment of the NT parameter(s) generated by the ML model used to administer the NT session, the NT clinical outcome(s) in response to the NT session using the generated adjusted NT parameters, the adjustment of the DBS parameter(s) generated by the ML model used to administer the DBS session, the DBS clinical outcome(s) in response to the DBS session administered using the generated DBS parameters.

[0205] Exemplary data included in the NT record and / or the DBS record include: the external non-invasively applied stimuli with valence, the baseline state of activity in response to the parametersof the external non-invasively applied stimuli with valence, parameters of the non-invasive feedback generated by a feedback device designed to incentivize the brain of the subject, activity of the neural network in response to the non-invasive feedback, valence state measurement of the neural network, and pre-defined state within a first pre-defined threshold and / or a second predefined threshold indicating a significant change from the baseline state.

[0206] At 224, the ML model may be trained.

[0207] The ML model may be trained initially and / or dynamically trained. The ML model may be initially trained on a training dataset created from data of different individuals. The ML model may be dynamically trained (i.e., updated) using the dynamically generated record(s) of a specific subject. The dynamic updating of the ML model using the dynamically generated records may customize the ML model to each specific subject. The ML model may learn how to adapt the NT session and / or the DBS session to the specific subject for obtaining improved clinical outcomes.

[0208] The initial training of the ML model may be performed on a training dataset of multiple records. The records may include a NT record including at least one NT parameter used during application of NT, and at least one NT clinical outcome obtained following the NT applied using the at least one NT parameter. The NT record may further include an indication of at least one DBS parameter of a DBS session preceding the NT. The records may include a DBS record including at least one DBS parameter used during application of DBS, at least one DBS clinical outcome obtained following the DBS applied using the at least one DBS parameter. The DBS record may further include and at least one NT parameter of a NT session preceding the DBS session.

[0209] The records on which the ML model is trained on may include data from and / or may be based on one or more of the following: monitoring (e.g., continuous, period, event triggered) of signals sensed by the electrodes implanted in the brain of the subject, DBS sessions, monitored short and / or long term effects of the DBS sessions, NT sessions, measurements made by physiological sensors (e.g., vital signs extracted from electrophysiological (EP) measurements and / or measured externally), measurements by at least one physiological sensor as described herein, external reports as described herein, and definitions of safety limits (e.g., maximum intensity of DBS, maximum rate of external stimuli designed to trigger disturbing thoughts in NT sessions).

[0210] Referring now back to FIG. 3, at 302, a NT system and / or a LFS system is provided. The NT system may be implemented as described, for example, with reference to 202 of FIG. 2.

[0211] The LFS system may be selected to apply LFS to at least one electrode positioned in the dorsal anterior cingulate cortex (dACC) to prevent the dACC from providing input to the amygdalato prevent the amygdala from maintaining the aversive memory and reduce or prevent interference with the plasticity that occurs by the NT session.

[0212] At 304, a subject is selected for treatment. The subject may be selected as requiring treatment using NT, optionally enhanced NT with enhanced plasticity.

[0213] At 306, one or more NT records are collected and / or accessed.

[0214] Each NT record may be collected during a preceding NT session. Multiple NT records may be collected for multiple preceding NT sessions.

[0215] A NT record of a preceding NT session may include one or more of:

[0216] • One or more NT parameter of NT applied to the subject in the preceding NT session.

[0217] • One or more NT clinical outcomes measured in response to preceding NT session delivered using the NT parameter(s).

[0218] • An indication of one or more LFS sessions delivered using one or more LFS parameters in association with the preceding NT session.

[0219] • An indication of monitoring of a plasticity effect. For example, time rate in original and / or target valence state, and time of shift between valence states.

[0220] At 308, one or more EFS records are collected and / or accessed. Each EFS record may be collected during at least one preceding EFS session.

[0221] A LFS record of a preceding LFS session may include:

[0222] • At least one LFS parameter of the administered LFS.

[0223] • An indication of when LFS was applied, prior to the NT session, during the LFS session, and / or after the LFS session.

[0224] At 310, signals are measured by the electrode(s) implanted within the neural network in at least one region of the brain of the subject. The measured signals may be processed, for example, values may be computed using the measured signals.

[0225] The signals may processed (or the processed signals may be analyzed) for computation of a pathological pattern (and / or a rate of the pathological pattern) of activity of the neural network, wherein the pathological pattern (and / or a rate of the pathological pattern) is fed into the ML model, and wherein the ML model generates instructions for application of LFS in association with the NT session.

[0226] At 312, a combination of at least one NT record and / or at least one LFS record and / or the processed signals (or data computed from the processed signals and / or based on an analysis of the processed signals) is fed into a ML model. The combination may include a sequence of multiple NT records from a sequence of preceding NT sessions and / or a sequence of multiple LFS records from a sequence of preceding LFS sessions associated with the preceding NT sessions.In an example, the ML model may generate an adjustment of parameters of the LFS following a NT session for optimizing the plasticity effect over time, in response to an input of the plasticity effect following the NT session.

[0227] Optionally, the combination fed into the ML model includes additional data associated with the NT record(s) and / or the LFS record(s):

[0228] • Measurements by at least one extracorporeal physiological sensor, for example, blood pressure by a blood pressure sensor, body temperature by a temperature sensor, and the like.

[0229] • Reports from a user, for example, the subject, a caregiver of the subject, and a therapist of the subject.

[0230] • Event recoding of general feeling and / or specific episodes.

[0231] An exemplary approach of training the ML model is described with reference to 324. At 314, the ML model generates an adjustment of one or more NT parameters for applying NT. The NT parameters may be generated based on a prediction of obtaining an improvement in one or more NT clinical outcomes from the NT session applied using the generated NT parameters. The NT parameters generated by the ML model may be for application by the NT session in association with LFS delivered using one or more LFS parameters generated by the ML model as described with reference to 318.

[0232] At 316, instructions are generated for operating a feedback device for generating a non-invasive feedback designed as part of a NT session delivered to an individual for improving response to stimuli provoking an aversive memory.

[0233] The NT session is applied to the subject by executing the instructions. The subject is treated accordingly by the NT session.

[0234] In embodiments that use the exemplary NT system, the non-invasive feedback may be designed according to the adjustment of the NT parameter generated by the ML model to incentivize the brain of the subject to modulate the current activity of the neural network to change from the baseline state towards a pre-defined state of activity.

[0235] At 318, the ML model generates an adjustment of at least one LFS parameter for applying LFS for improving plasticity for the NT session to be applied using the NT parameters adjusted by the ML model as described with reference to 314.

[0236] At 320, instructions are generated for applying an LFS session via the electrode(s) implanted in the region of the brain of the subject. The LFS session is applied preceding and / or in conjunction with and / or following the NT session applied as described with reference to 316.

[0237] The LFS session is selected for facilitating plasticity generated during the NT session.At 322, one or more features described with reference to 306-322 may be iterated. In each iteration, NT may be applied in association with LFS according to the output of the ML model. Records of a current NT and LFS treatment session may be collected for feeding into the ML model for guiding the subsequent NT and LFS treatment session. LFS parameters may be iteratively adapted to obtain a maximal plasticity effect (e.g., using iterative optimization approaches).

[0238] A plasticity effect of the NT session may be monitored during the iterations. The monitoring of the plasticity effect may include time rate in original and / or target valence state, and time of shift between valence states.

[0239] The ML model may generate adjustments of at least one LFS parameter for applying LFS predicted to maximize the plasticity effect.

[0240] Optionally, a NT record and / or a LFS record may be dynamically generated during each iteration. The dynamically generated records may be used for updating the ML model. Additional details of the records are described with reference to 324.

[0241] At 324, the ML model may be trained.

[0242] The ML model may be trained initially and / or dynamically trained.

[0243] The ML model may be initially trained on a training dataset of records. The records may include a NT record and a LFS record.

[0244] The NT record may include at least one NT parameter used during application of NT, and at least one plasticity outcome obtained following the NT applied using the at least one NT parameter. The NT record may include an indication of and at least one LFS parameter of a LFS session performed in association with the NT session.

[0245] The LFS record may include at least one LFS parameter used during application of LFS, and at least one plasticity outcome obtained following the LFS applied using the at least one LFS parameter. The LFS record may include at least one NT parameter of a NT session associated with the LFS.

[0246] The ML model may be dynamically trained (i.e., updated) using the dynamically generated record(s). The dynamic updating the of the ML model using the dynamically generated records may customize the ML model to the subject. The ML model may learn how to adapt the LFS to the NT session for the specific subject for obtaining an improved plasticity effect.

[0247] Referring now back to FIG. 4, at 402, a subject is selected for treatment, for example, as described with reference to 202 of FIG. 2.

[0248] At 404, activity of a neural network in at least one region of a brain of a subject is monitored for detection of a baseline state that corresponds to an external non-invasively applied stimuli withvalence. The activity of the neural network is monitored by processing signals sensed by electrode(s) when implanted in the region of the brain.

[0249] The external non-invasively applied stimuli with valence may be incidental, not being applied in a control manner by a device. For example, sounds of firecrackers ignited by children in a park may trigger disturbing memories in a soldier that returned from a battle or in a civilian that was caught in a warzone. In another example, the sight of a person wearing a specific clothing item may trigger difficult emotions in a person who recently lost a loved one that used to wear the same clothing item.

[0250] At 406, in response to the detected baseline state, instructions may be generated for operating a wearable feedback device for generating a non-invasive feedback. The non-invasive feedback is designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity.

[0251] The wearable feedback device may be implemented as, for example, a bracelet that vibrates, a watch that applies a tactile sensation, smart glasses that present images, a smart ring that applies a tactile sensation, and headphones that play audio.

[0252] The wearable feedback device enables delivering real-time treatment to the subject in response to incidental triggers. The subject may be treated in real time while encountering triggers in the environment during daily activities, rather than having to wait until the next NT session in the clinic.

[0253] At 408, the current activity of the neural network in response to the non-invasive feedback is monitored according to the signals sensed by the implanted electrode(s). The current valence state of the neural network may be measured.

[0254] At 410, the non-invasive feedback generated by the wearable feedback device may be dynamically adapted in according to a real-time or near-real time current valence state measurement of the neural network.

[0255] At 412, the current activity of the neural network may be evaluated relative to pre-defined criteria for determining when to terminate the non-invasive feedback generated by the wearable feedback device.

[0256] Optionally, the non-invasive feedback generated by the wearable feedback device is terminated when the current activity of the neural network meets a pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

[0257] At 414, features described with reference to 404-412 may be dynamically iterated, for providing real-time treatment to the subject in response to incidental triggers as the subjectencounters the triggers in the environment during daily activities. The real-time treatment may be in contrast to having to wait until the next NT session in the clinic.

[0258] Various embodiments and aspects of the present disclosure as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.

[0259] EXAMPLES

[0260] Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the disclosure in a not necessarily limiting fashion.

[0261] Inventors performed experiments to evaluate the clinical effects of treatment using a combination of DBS and NT, and clinical maintenance using the combination of DBS and NT, according to at least one embodiment described herein. The NT was based on neuronal-level based NT using implanted electrodes, as described herein.

[0262] Referring now back to FIG. 5, graph 502 depicts expected mean effect of DBS and NT on clinical outcome and percent DBS active time. The x-axis of graph 502 indicates number of weeks of combined treatment with DBS and NT. Curve 504 indicates the percent of time with active closed loop DBS. Curve 506 indicates clinical improvement (CAPS-5 score).

[0263] Graph 512 depicts expected mean effect of DBS on clinical outcome and percent DBS active time. The x-axis indicates weeks of DBS. Curve 514 indicates the percent of time with active closed loop DBS. Curve 516 indicates clinical improvement (CAPS-5 score).

[0264] As can be seen by comparing graphs 502 and 512, applying NT in addition to DBS provides improved clinical outcomes in comparison to DBS alone.

[0265] Referring now back to FIG. 6, graph 602 depicts effects of DBS and NT on clinical outcome and percent DBS active time. The x-axis of graph 602 indicates number of weeks of combined treatment with DBS and NT. Curve 604 indicates the percent of time with active closed loop DBS. Curve 606 indicates clinical improvement (CAPS-5 score).

[0266] Graph 612 depicts long term maintenance using DBS and NT based on percent DBS active time. The x-axis indicates weeks of DBS and NT. Curve 614 indicates the percent of time with active closed loop DBS. Curve 616 indicates clinical improvement (CAPS-5 score). A NT reminder is generated at 618.

[0267] As can be seen graph 602 applying NT in addition to DBS provides improved clinical outcomes, which can be maintained as indicated by graph 612, by applying NT reminder 618 as needed.

[0268] The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

[0269] It is expected that during the life of a patent maturing from this application many relevant NT, DBS, and LFS approaches will be developed and the scope of the terms NT, DBS, and LFS are intended to include all such new technologies a priori.

[0270] As used herein the term “about” refers to ± 10 %.

[0271] The terms "comprises", "comprising", "includes", "including", “having” and their conjugates mean "including but not limited to". This term encompasses the terms "consisting of" and "consisting essentially of".

[0272] The phrase "consisting essentially of" means that the composition or method may include additional ingredients and / or steps, but only if the additional ingredients and / or steps do not materially alter the basic and novel characteristics of the claimed composition or method.

[0273] As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" may include a plurality of compounds, including mixtures thereof.

[0274] The word “exemplary” is used herein to mean “serving as an example, instance or illustration”. Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and / or to exclude the incorporation of features from other embodiments.

[0275] The word “optionally” is used herein to mean “is provided in some embodiments and not provided in other embodiments”. Any particular embodiment of the invention may include a plurality of “optional” features unless such features conflict.

[0276] Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging / ranges between” a first indicate number and a second indicate number and “ranging / ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

[0277] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0278] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

[0279] It is the intent of the applicant(s) that all publications, patents and patent applications referred to in this specification are to be incorporated in their entirety by reference into the specification, as if each individual publication, patent or patent application was specifically and individually noted when referenced that it is to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is / are hereby incorporated herein by reference in its / their entirety.

Claims

WHAT IS CLAIMED IS:

1. A system for treatment of a brain disorder, comprising:a processor executing a code for:processing signals measured by at least one electrode implanted within a neural network in at least one region of a brain of a subject;analyzing a combination of at least one neurotraining (NT) record and at least one deep brain stimulation (DBS) record, including the processed signals; generating based on the analysis , at least one of:an adjustment of at least one NT parameter for applying NT for obtaining an improved at least one NT clinical outcome following a preceding DBS session delivered using at least one DBS parameter, and an adjustment of at least one DBS parameter for applying DBS for obtaining an improved at least one DBS clinical outcome following a preceding NT session delivered using at least one NT parameter; and generating instructions for at least one of: applying DBS via the at least one electrode according to the adjustment of the at least one DBS parameter, and for applying a NT session by operating a feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity according to the adjustment of the at least one NT parameter.

2. The system of claim 1,wherein the at least one NT record is collected during at least one preceding NT session, the at least one NT record including at least one NT parameter of applied NT and at least one NT clinical outcome measured in response to at least one preceding NT session delivered using the at least one NT parameter and following at least one preceding DBS session delivered using at least one DBS parameter;wherein the at least one DBS record is collected during at least one preceding DBS session, the DBS record including at least one DBS parameter of applied DBS and at least one DBS clinical outcome measured during and / or after the at least one preceding DBS session delivered using the at least one DBSparameter and following at least one preceding NT session delivered using at least one NT parameter.

3. The system of claim 1, wherein the DBS session is implemented by monitoring the signals sensed by the at least one electrode, identifying a pathological state of activity of the neural network, and applying a predefined pattern of stimulation via the at least one electrode.

4. The system of claim 1, wherein the NT session is implemented by:computing a baseline state of activity of the neural network in at least one region of a brain of a subject that corresponds to the external non-invasively applied stimuli with valence according to signals sensed by at least one electrode when implanted in the at least one region of the brain;generating instructions for operating a feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity;monitoring the current activity of the neural network in response to the non- invasive feedback according to the signals sensed by the at least one electrode when implanted in the at least one region of the brain; anddynamically adapting the non-invasive feedback during each respective iteration according to a real-time or near-real time current valence state measurement of the neural network until the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

5. The system of claim 1, further comprising dynamically iterating the analyzing, the generating based on the analysis, and the instructions.

6. The system of claim 1, wherein analyzing comprises feeding into a machine learning (ML) model, and wherein generating based on the analysis comprises generating based on the ML model.

7. The system of claim 6, further comprising dynamically generating a record comprising at least one of:the adjustment of the at least one NT parameter and the at least one NT clinical outcome following the NT applied with the adjusted at least one NT parameter, andan adjustment of at least one DBS parameter for applying DBS and the at least one DBS clinical outcome following the DBS applied with the adjusted at least one DBS parameter; and dynamically updating the ML model using the record.

8. The system of claim 6, wherein the ML model is trained on a training dataset comprising a plurality of records, wherein a record is selected from:at least one NT parameter used during application of NT, at least one NT clinical outcome obtained following the NT applied using the at least one NT parameter, and at least one DBS parameter of a DBS session preceding the NT, andat least one DBS parameter used during application of DBS, at least one DBS clinical outcome obtained following the DBS applied using the at least one DBS parameter, and at least one NT parameter of a NT session preceding the DBS session.

9. The system of claim 6, further comprising analyzing the processed signals for computation of a pathological pattern of activity of the neural network, wherein the pathological pattern is analyzed, and wherein a rate of application of NT sessions is generated based on the analysis.

10. The system of claim 1, wherein at least one NT record includes an indication of a provocation test using a personalized external stimuli conducted as part of the NT session in which specific pathological states are inflicted and specific regions are responding to the provocation test, and the based on the analysis a location of stimuli and / or direction of stimuli and / or pattern of stimuli including shape, frequency, and / or intensity, is generated.

11. The system of claim 1, wherein the at least one DBS record includes an indication of a current emotional state of the subject determined according to the processed signals, and a provocation test of the NT session is selected to inflict a specific emotional response according to the current emotional response, wherein in response to the current emotional state indicating a sensitive state an intensity of the provocation test is relatively reduced, and wherein in response tothe current emotional state indicating a robust state the intensity of the provocation test is relatively increased.

12. The system of claim 1, wherein the at least one NT record includes at least one of: amount of time spent in each one of a plurality of different valence states, valence trajectory change, a classification of at least one valence state.

13. The system of claim 1, wherein the at least one NT record and corresponding generation based on the analysis include at least one of: (i) anxiety and / or resilience and / or adaptive capacity level and selection of a trigger intensity, (ii) most intrusive and / or painful thought and corresponding trigger that inflicts a corresponding valence state, (iii) neutral valence state and corresponding selection of a target and / or threshold to reach in the NT session.

14. The system of claim 1, wherein the at least one DBS record includes at least one of: recording of activity in the neural network a short time interval after pausing DBS, recording of activity of the neural network over a time interval after DBS.

15. The system of claim 1, wherein the at least one DBS record includes monitoring of change in the activity of the neural network following DBS indicating a natural trajectory shift and the an adjustment to at least one parameter of a transfer function for reinforcing neuromodulation in a similar trajectory during a subsequent DBS is generated based on the ML model.

16. The system of claim 1, wherein the at least one NT record includes at least one of: trigger of the NT session, valence state range, different between valence states, time interval to reach a NT target, success rate in achieving the NT target, and wherein the corresponding generation of at least one DBS parameter based on the analysis includes at least one of: stimulation parameter, pulse pattern, intensity, frequency, closed loop time.

17. The system of claim 1, wherein the combination that is analyzed further includes at least one of the following associated with the at least one NT record and / or the at least one DBS record: measurements by at least one extracorporeal physiological sensor, reports from at least one of: the subject, a caregiver, a therapist, and event recording of general feeling and / or specific episodes.

18. The system of claim 1, wherein the generation by the ML is selected for at least one of: minimization of severe episodes as subjectively reported by at least one user, reduction of episodic events as defined mathematically by analyzing the neuronal activity and applying classification processes, and maximal extension of a learning effect over time.

19. The system of claim 1, wherein the at least one NT record and / or the at least one DBS record and / or the at least one NT parameter and / or the at least one DBS parameter each include at least one of: parameters of an external non-invasively applied stimuli with valence, a baseline state of activity in response to the parameters of the external non-invasively applied stimuli with valence, parameters of a non-invasive feedback generated by a feedback device designed to incentivize the brain of the subject, activity of a neural network in response to the non-invasive feedback, valence state measurement of the neural network pre-defined state within a first pre-defined threshold and / or a second pre-defined threshold indicating a significant change from a baseline state.

20. The system of claim 1, wherein the DBS comprises LFS.

21. A system for treatment of a brain disorder, comprising:a processor executing a code for:generating instructions for operating a feedback device for generating a non-invasive feedback designed as part of a neurotraining (NT) session delivered to an individual for improving response to stimuli provoking an aversive memory; andgenerating instructions for applying a low frequency stimulation (LFS) session to at least one electrode when the at least one electrode is implanted in a region of a brain of the individual, the LFS session applied preceding and / or in conjunction with and / or following the NT session, the LFS session selected for facilitating plasticity generated during the NT session.

22. The system of claim 21, wherein the LFS is selected to be applied to at least one electrode positioned in the dorsal anterior cingulate cortex (dACC) .

23. The system of claim 21, further comprising code for:processing signals measured by at least one electrode implanted within neural network in at least one region of a brain of a subject;analyzing a combination of at least one neurotraining (NT) record and at least one low frequency stimulation (LFS) record, including the processed signals; andgenerating based on the analysis, at least one of:an adjustment of at least one NT parameter for applying NT for obtaining an improved at least one NT clinical outcome following at least one preceding LFS session delivered using at least one LFS parameter, and an adjustment of at least one LFS parameter for applying LFS for improving plasticity for at least one subsequent NT session.

24. The system of claim 23, further comprising monitoring of a plasticity effect of the NT session including at least one of: time rate in original and / or target valence state, and time of shift between valence states, and the at least one LFS parameter for applying LFS predicted to maximize the plasticity effect is generated based on the analysis.

25. The system of claim 23,wherein the at least one NT record is collected during at least one preceding NT session, the at least one NT record including at least one NT parameter of applied NT and at least one plasticity outcome measured in response to the at least one preceding NT session delivered using the at least one NT parameter and following at least one preceding LFS session delivered using at least one LFS parameter;wherein the at least one LFS record is collected during at least one preceding LFS session, the LFS record including at least one LFS parameter of applied LFS.

26. The system of claim 23, wherein analyzing comprises feeding into a ML model, and generating based on the analysis comprises generated by the ML model.

27. The system of claim 26, wherein the ML model is trained on a training dataset comprising a plurality of records, wherein a record is selected from:at least one NT parameter used during application of NT, at least one NT clinical outcome obtained following the NT applied using the at least one NT parameter, and at least one LFS parameter of a LFS session performed in association with the NT session, andat least one LFS parameter used during application of LFS, at least one LFS clinical outcome obtained following the LFS applied using the at least one LFS parameter, and at least one NT parameter of a NT session associated with the LFS.

28. A system for neuromodulation, comprising:at least one processor executing a code for:in a plurality of iterations:monitoring activity of a neural network in at least one region of a brain of a subject for detection of a baseline state that corresponds to an external non- invasively applied stimuli with valence, by processing signals sensed by at least one electrode when implanted in the at least one region of the brain;in response to the detected baseline state, generating instructions for operating a wearable feedback device for generating a non-invasive feedback designed to incentivize the brain of the subject to modulate a current activity of the neural network to change from the baseline state towards a pre-defined state of activity;monitoring the current activity of the neural network in response to the non- invasive feedback according to the signals sensed by the at least one electrode when implanted in the at least one region of the brain;dynamically adapting the non-invasive feedback generated by the wearable feedback device during each respective iteration according to a real-time or near- real time current vale nee state measurement of the neural network; and terminating the non-invasive feedback generated by the wearable feedback device of each respective iteration when the current activity of the neural network meets the pre-defined state within a first pre-defined threshold and / or when the current activity is statistically significantly different from the baseline state by a second pre-defined threshold.

29. The system of claim 28, wherein the wearable feedback device is selected from: a bracelet that vibrates, smart glasses that present images, a smart ring that applies a tactile sensation, a watch that applies a tactile sensation, and headphones that play audio.

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