阅读说明：本技术 治疗情绪障碍的系统和方法 (Systems and methods for treating mood disorders ) 是由 埃里克·克劳德·鲁塔德 丹尼尔·W·莫兰 梅龙·格里贝茨 于 2019-06-20 设计创作，主要内容包括：本发明公开一种用于治疗一患者的情绪障碍的系统,包括可植入装置,其包括一个或多个电极,用于感测皮质信号并刺激一个或多个大脑区域。处理器/控制器,与电极通信,用于接收和处理来自电极的皮质信号,并控制对大脑区域的刺激。所述系统包括由患者操作的便携式通信装置,其具有一软件,用于获取代表所述患者的瞬时情绪的生态情绪评估(EMA)数据,并将EMA数据传送到处理器/控制器及/或至少一个远程处理器。多个传感器也用于记录患者数据。数据由处理器/控制器及/或由便携式通信装置的处理器及/或远程处理器处理,用于调节及/或控制对大脑区域的刺激以治疗情绪障碍。可植入装置包括电源。可植入装置被植入颅内及/或颅骨内。(A system for treating an emotional disorder in a patient includes an implantable device including one or more electrodes for sensing cortical signals and stimulating one or more brain regions. A processor/controller in communication with the electrodes for receiving and processing cortical signals from the electrodes and controlling stimulation of the brain region. The system includes a portable communication device operated by a patient having software for acquiring ecological emotion assessment (EMA) data representative of the patient's instantaneous emotion and communicating the EMA data to a processor/controller and/or at least one remote processor. Multiple sensors are also used to record patient data. The data is processed by the processor/controller and/or by the processor of the portable communication device and/or a remote processor for adjusting and/or controlling the stimulation of the brain region to treat the mood disorder. The implantable device includes a power source. Implantable devices are implanted intracranial and/or intracranial.)

1. A system for treating a mood disorder in a patient, comprising: the system comprises: one or more implantable devices, each of said devices comprising one or more electrodes for sensing cortical signals in one or more cortical regions of the brain and for stimulating one or more regions of the brain; one or more processor/controllers in communication with the one or more electrodes for receiving and processing sensed cortical signals and for controlling stimulation of one or more brain regions by the one or more electrodes;

at least one portable communication device operable by the patient and having an application software running on the portable communication device for obtaining Ecological Mood Assessment (EMA) data representative of the patient's instantaneous mood and for communicating the data to the at least one processor/controller and/or at least one remote processor, wherein the data is processed by the one or more processors/controllers and/or by a processor included in the portable communication device and/or by the at least one remote processor for adjusting and/or controlling stimulation of one or more brain regions to treat the mood disorder; and

at least one power source is suitably electrically connected to the one or more implantable devices to provide power to the one or more implantable devices.

2. The system of claim 1, wherein: the one or more implantable devices are selected from the group consisting of one or more intracranial implantable devices, one or more implantable intracranial devices, and any combination thereof.

3. The system of any of claims 1 to 2, wherein: the one or more electrodes are selected from the group consisting of one or more intracranial electrodes, one or more intracranial electrode arrays, and any combination thereof.

4. A system according to any one of claims 1 to 3, wherein: at least one of the one or more implantable devices is an intracranial device having a plurality of intracranial electrodes disposed between an outer plate and an inner plate of the patient's skull without completely penetrating the inner plate of the skull.

5. The system of claim 4, wherein: at least some electrodes of an intracranial implant are in contact with an outer surface of the inner plate of the skull.

6. A system according to any preceding claim, wherein: the system includes one or more implantable Frequency Interference (FI) devices configured to stimulate one or more brain regions by using a frequency interference stimulation method.

7. The system of claim 6, wherein: the one or more brain regions stimulated by the implantable Frequency Interference (FI) device are selected from the group consisting of at least one cortical region, at least one deep brain structure, and any combination thereof.

8. The system of claim 7, wherein: the at least one cortical region is selected from the group consisting of right dorsolateral prefrontal cortex (RDLPFC), left dorsolateral prefrontal cortex (LDLPFC), one or more regions of cingulate cortex, one or more regions of prefrontal cortex (PFC), and any combination thereof.

9. The system of claim 7, wherein: the at least one deep brain structure is selected from the group consisting of a Ventral Striatum (VS), one or more parts of the limbic system of the brain, a subtotal cingulate region (BA 25), a ventral sac (VC), a nucleus accumbens, a lateral reins, a ventral caudate nucleus, a subthalamic pedicle, an island lobe, and any combination thereof.

10. The system of claim 1, wherein: the one or more cortical regions are selected from a region of the right dorsolateral prefrontal cortex (RDLPFC), the left dorsolateral prefrontal cortex (LDLPFC), the prefrontal cortex (PFC), and any combination thereof.

11. A system according to any preceding claim, wherein: the system further comprises one or more sensor units for sensing one or more additional biomarkers indicative of an emotion of the patient.

12. The system of claim 11, wherein: the one or more sensor units are selected from the group consisting of a heart rate sensor, a sweat sensor, a pupillometry sensor, an AR headset, an eye tracking sensor, a microphone, a serotonin sensor, a blood dopamine sensor, and any combination thereof.

13. The system according to any one of claims 11 to 12, wherein: the one or more biomarkers are selected from the group consisting of heart rate, heart rate variability, blood pressure, changes in sweat rate, changes in pupil size in response to the occurrence of a negative word, eye movement parameters, changes in vowel space speech by the patient, changes in blood serotonin levels, changes in blood dopamine levels, and any combination thereof.

14. The system of any one of claims 1 to 11, wherein: the mood disorder is selected from the group consisting of Major Depressive Disorder (MDD), post traumatic stress syndrome (PTSD), anxiety disorder, and any combination thereof.

15. A system according to any preceding claim, wherein: the system also includes one or more effector devices controlled by the one or more processors/controllers and/or the one or more communication devices, the one or more effector devices selected from a device for delivering serotonin to the brain of the patient, a device for delivering dopamine to the brain of the patient, and any combination thereof.

16. A system according to any preceding claim, wherein: the one or more processors/controllers are programmed to process the cortical signal and the EMA data to determine a value of an emotional index MX, and deliver stimulation to one or more brain regions if the value of the emotional index MX is less than or equal to a threshold level.

17. The system of claim 16, wherein: the numerical value of the mood index MX is calculated from the cortical signal and the EMA data, or from the cortical signal, the EMA data, and one or more patient biomarker data sensed by one or more sensors.

18. The system of any one of claims 1 to 17, wherein: the one or more processors/controllers are programmed to process the cortical signal and the EMA data to determine a value of an emotional index MX, and to deliver a graded stimulus to one or more brain regions in response to the value of the emotional index MX.

19. The system of claim 18, wherein: the mood index MX includes a modulation index MI calculated from the cortical signal and the EMA data.

20.A method for treating a mood disorder in a patient, comprising: the method comprises the following steps: receiving cortical signals sensed from one or more cortical regions of the patient;

automatically receiving ecological emotion assessment (EMA) data for the patient from at least one portable communication device operated by the patient, the at least one communication device having an application software running on the portable communication device for automatically obtaining data representative of parameters of the patient using the at least one communication device to calculate the EMA data on-site and/or to receive calculated EMA data from a remote processor; and

processing the cortical signal and the EMA data to detect an indication that the patient is at a depressed mood requiring therapeutic stimulation; and

stimulating at least one brain region of the patient in response to detecting the indication.

21. The method of claim 20, wherein: the signal of the receiving step is recorded by one or more implants selected from the group consisting of a plurality of intracranial implants, a plurality of calvarial endosseous implants, and any combination thereof.

22. The method of claim 20, wherein: the signals of the receiving step are recorded by one or more intracranial electrodes, at least some of which are disposed between an outer plate and an inner plate of a skull of the patient without completely penetrating the inner plate of the skull.

23. The method of claim 22, wherein: the one or more intracranial electrodes are disposed in contact with or adjacent an outer surface of the inner plate of the skull.

24. The method of any one of claims 20 to 23, wherein: the EMA data comprises data selected from: data representing parameters automatically obtained by the patient using at least one portable communication device, and data representing a subjective mood assessment provided by the patient in response to automatically making a mood assessment request to the patient.

25. The method of any one of claims 20 to 24, wherein: the EMA data comprises data selected from: data representing an application used by the patient, data representing a number of calls made by the patient, acceleration data resulting from movement of the patient, communication data, ambient light data, ambient sound data, location data of the patient, a call log of the patient, voice content of the patient, text content of the patient, sleep data of the patient, social network data of the patient, and any combination thereof.

26. The method of any one of claims 20 to 25, wherein: the step of automatically receiving further comprises the step of automatically receiving biomarker data from one or more sensors, and wherein the step of processing comprises processing the cortical signal, the EMA data, and the biomarker data to detect an indication that the patient is in a mood depression requiring therapeutic stimulation.

27. The method of any one of claims 20 to 26, wherein: the processing step includes processing the sensed cortical signals and the EMA data to calculate a value of a modulation index parameter MI and/or to calculate a mood index MX of a patient.

28. The method of claim 26, wherein: the processing step includes processing the sensed cortical signals and the EMA data and biomarker data obtained from one or more sensors to calculate a value of a modulation index parameter MI and/or to calculate a patient's mood index MX.

29. The method of any one of claims 20 to 28, wherein: the processing step comprises processing the sensed cortical signal by calculating spectral power in one or more spectral bands, calculating a modulation index MI and/or calculating an emotion index MX.

30. The method of any one of claims 27 to 29, wherein: the processing step comprises a comparison of the value of modulation index MI with a threshold value, and wherein the stimulating step comprises stimulating one or more brain regions if the value of modulation index MI is equal to or greater than the threshold value.

31. The method of any one of claims 28 to 29, wherein: said processing step comprises comparing a value of an emotion index MX with a threshold value, and wherein said stimulating step comprises stimulating one or more brain areas if said value of emotion index MX is equal to or greater than said threshold value.

32. The method of any one of claims 20 to 31, wherein: the stimulating step comprises stimulating one or more brain regions selected from the group consisting of one or more cortical brain regions, one or more deep brain structures, and any combination thereof.

33. The method of claim 32, wherein: the one or more cortical brain regions in the stimulating step are selected from the group consisting of right dorsolateral prefrontal cortex (DLPFC), left dorsolateral prefrontal cortex (DLPFC), prefrontal cortex (PFC) region, subtotal cingulate cortex, and any combination thereof, wherein the one or more deep brain structures in the stimulating step are selected from the group consisting of Ventral Striatum (VS), one or more parts of the limbic system of the brain, subtotal cingulate region (BA 25), ventral bursa (VC), nucleus accumbens, lateral hamulus, ventral caudate nucleus, subthalamic pedicle, insular lobe, and any combination thereof.

34. The method of any one of claims 20 to 33, wherein: the receiving step comprises receiving a cortical signal from one or more cortical regions selected from the group consisting of right dorsolateral prefrontal cortex (DLPFC), left dorsolateral prefrontal cortex (DLPFC), prefrontal cortex (PFC) regions, and any combination thereof.

35. The method of any one of claims 20 to 34, wherein: the mood disorder is selected from the group consisting of Major Depressive Disorder (MDD), post traumatic stress syndrome (PTSD), anxiety disorder, and any combination thereof.

36. A system for treating a mood disorder in a patient, comprising: the system comprises: one or more intracranial implants, each implant comprising a power source; a plurality of intracranial electrodes for sensing cortical signals of the brain and for stimulating one or more regions of the brain; and a telemetry module for transmitting sensed cortical signals and/or data and for wirelessly receiving data and/or control signals; at least some of the plurality of intracranial electrodes disposed between an outer plate and an inner plate of the patient's skull without completely penetrating the inner plate of the skull, each of the one or more implantable intracranial implants including one or more processors/controllers in communication with the plurality of intracranial electrodes for processing sensed cortical signals and for controlling stimulation of one or more regions of the brain;

at least one portable communication device operable by the patient and having an application software running on the portable communication device for obtaining ecological emotion assessment (EMA) data representative of the patient's instantaneous emotion and for communicating the EMA data to the one or more implantable intracranial implants and/or at least one remote processor, wherein the data is processed by the one or more processors/controllers of the one or more intracranial implants and/or by a processor included in the portable communication device and/or by the at least one remote processor for regulating and/or controlling stimulation of one or more regions of the brain to treat the emotional disorder.

37. A method for treating a mood disorder in a patient, comprising: the method comprises the following steps:

receiving electrical signals recorded from a cortical region of the patient using an intracranial implant comprising one or more intracranial electrodes, at least a portion of the intracranial electrodes being disposed between an outer plate and an inner plate of the patient's skull without completely penetrating the inner plate of the skull;

processing the signals to determine a stimulation pattern of the patient; and

stimulating at least one brain region of the patient that responds to the determined stimulation pattern.

38. The method of claim 37, wherein: the method further comprises the steps of: automatically receiving instantaneous mood assessment data for the patient from at least one portable communication device operated by the patient, the at least one communication device having an application software running on the portable communication device for automatically processing data representative of parameters of the patient's use of the at least one communication device without the need for patient intervention and calculating an instantaneous mood assessment, wherein the processing step comprises processing the instantaneous mood assessment and the electrical signal to determine a stimulation pattern for the patient.

39. The method of claim 38, wherein: the method further comprises the step of interacting with the patient by means of at least one portable communication device to receive voluntary patient input representative of a subjective emotional assessment of the patient, wherein the processing step comprises processing the subjective emotional assessment of the patient and the electrical signal to determine and/or modify a stimulation pattern for the patient.

40. The method of claim 38, wherein: the method further comprises the step of interacting with the patient by at least one portable communication device to receive voluntary patient input representative of a subjective emotional assessment of the patient, wherein the processing step comprises processing the subjective emotional assessment of the patient, the EMA data, and the electrical signal to determine and/or modify a stimulation pattern for the patient.

41. The system of any one of claims 1 to 19 and 36, wherein: the at least one portable communication device is selected from the group consisting of a mobile phone, a smartphone, a laptop, a mobile computer, a tablet, a laptop, a tablet, an Augmented Reality (AR) headset, and any combination thereof.

42. The method of any one of claims 20 to 35, wherein: the at least one portable communication device is selected from the group consisting of a mobile phone, a smartphone, a laptop, a mobile computer, a tablet, a laptop, a tablet, an Augmented Reality (AR) headset, and any combination thereof.

43. The method of any one of claims 37 to 40, wherein: the method further comprises the step of receiving ecological emotion assessment (EMA) data representative of the instantaneous emotion of the patient from at least one portable communication device, wherein the processing step comprises processing the signal and the EMA data to determine a stimulation pattern for the patient.

44. The method of claim 43, wherein: the receiving step further comprises receiving voluntary emotion assessment data from the patient in response to a system query, wherein the processing step comprises processing the signal and the EMA data and the patient voluntary emotion assessment data to determine a stimulation pattern for the patient.

45. The method of any one of claims 42 to 44, wherein: the at least one portable communication device is selected from the group consisting of a mobile phone, a smartphone, a laptop, a mobile computer, a tablet, a laptop, a tablet, an Augmented Reality (AR) headset, and any combination thereof.

Technical field and background

In some embodiments of the invention, the present invention relates to the field of systems and methods for treating mood disorders, and more particularly, to a system and method for a human-machine interface (BCI) for treating depression.

Existing antidepressant therapies have significant limitations in effectively controlling the symptoms associated with depression. Four million americans are diagnosed with recurrent or severe treatment-resistant depression, referred to as treatment-resistant major depression. Subjective diagnosis, various manifestations of the disease, and treatment with antidepressants with limited theoretical bases all result in limited therapeutic efficacy in refractory populations, with varying levels of therapeutic drug resistance. For these drug resistant patients, stimulus-based therapies have the trouble of inconsistent efficacy and variability of side effects. Many of these problems stem from unknown depression pathogenesis, which hinders the development of therapeutic approaches to specific underlying causes of depression. Other problems may also arise due to non-specific stimulation of various edge and edge-side structures in the open-loop configuration. The design of closed-loop neurostimulation devices has been proposed, but the lack of effective and validated biomarkers has hindered the ability of these systems to deliver appropriate and timely stimulation patterns.

In north america, depression is one of the leading causes of mortality and substandard daily function (Wells et al, 1989). The term "depression" as used herein is currently used to describe a series of different pathologies having common symptoms, manifested by emotional and emotional aberrant control and expression (Davidson et al, 2002). Patients with depression have a variety of clinical symptoms. This may include thoughts of coldness, diminished enjoyment of daily work, distorted sleep schedules, altered behavior/appetite/weight, altered motor dynamics, diminished energy, inattention, feelings of worthlessness or guilt, and thoughts of death or suicide over a long period of time (First and Ross, 2000; Kroenke et al, 2001). Current treatment regimens are not always effective in controlling the symptoms of many depression patients, especially those suffering from treatment-resistant major depression (treatment-resistant MDD) (Kessler et al, 2005; Cyberonics, 2007). Refractory MDD is characterized by recurrent episodes, long-lasting, severe, often suicidal, depressive episodes that cannot be treated for remission using a variety of antidepressants. Depressive episodes last for up to one year (Judd et al, 1998), severely impairing the patient's health, activity, work and well-being (Manji et al, 2001). Even with FDA-approved optimal antidepressant drug treatment, a significant proportion of MDD patients have recurrent episodes (Mueller et al, 1999; Kessler et al, 2003). Clearly, there is a need for more effective, reliable, personalized, and durable treatments.

Currently, 50% to 60% of all depression patients remain partially or completely unresponsive to the first-phase appropriately prescribed treatment (Fava, 2003). Up to 20% of these patients require more extreme treatment, with multiple anti-depressant drugs and/or electroshock therapy (ECT), but with varying success rates (Fava, 2003; Mayberg et al, 2005). A meta-analysis of 74 published and unpublished tests for antidepressant efficacy involving 12 antidepressants and 12,564 participants indicated that only 51% of the data submitted to the FDA were positive (Turner et al, 2008). An independent meta-analysis of the 47 published and unpublished clinical trial data sets of the FDA from a selective 5-hydroxytryptamine reuptake inhibitor (SSRI) efficacy trial showed that six of the seven most commonly used SSRI antidepressants over the last 25 years had clinically significant clinical significance only over placebo, indicating a "high end of the very severe depression category" (Kirsch et al, 2008). The latter study indicates that SSRIs are generally the first prescribed drug for depression, with a greater risk for health than benefit for symptom relief for most patients (Kirsch et al, 2008; Turner et al, 2008).

More recently, stimulation-based techniques designed to electrically modulate aberrant neural activity are becoming a potential approach to treating refractory MDD patients. However, the incomplete understanding of the pathophysiological mechanisms of depression and the lack of reproducible and quantifiable biomarkers (i.e., biomarkers) for depression status have hampered the effectiveness of these techniques (patient-reported symptom relief is still used to subjectively assess the therapeutic response of antidepressants, effectively ignoring the prospect of quantifying antidepressant response and optimizing treatment using objectively quantified levels of biomarkers associated with depression). To date, many structural, functional and genetic abnormalities associated with depression have been discovered. Findings in the field of epilepsy research have led to interest in closed-loop neuroprostheses, in which biological indicators of an impending seizure are used to determine when electrical or chemical stimulation must be taken to arrest the seizure (Dumitriu et al, 2008). This process, known as reactive nerve stimulation, is unique to closed loop devices. It is intended to replace continuous or periodic open-loop stimulation design in order to provide a tailored treatment based on quantifiable symptom-related biomarker abnormalities only when necessary in a dose-dependent manner (Sun et al, 2008; Goodman and Insel, 2009). It is speculated that a similar approach may also be used for depression. However, to date, despite new advances in the study of depression, there is no closed-loop prosthesis available to treat refractory MDD. This is largely due to the lack of candidate quantifiable biomarkers of depression that can meaningfully inform the brain stimulator over a range of time. Although seizures can reasonably be detected by implantable recording systems, there is limited evidence that similar signals can be identified for depressed activity states. This is due in part to the lack of scientific insight into the underlying mechanisms of depression, as well as the high degree of individual variability in the pathological causes of depression.

Existing diagnostic and therapeutic protocols.

Currently, a diagnosis of depression can be made by evaluating the patient's reported symptoms, clinical history and comprehensive physical examination. Patients are initially usually evaluated using a standardized assessment of depression specificity, such as the nine patients health questionnaire (PHQ-9), the Hamilton Depression rating Scale (HAM-D or HDRS) or the Montgomery-Aberger Depression rating Scale (MADRS) (Kearns et al, 1982; Kroenke et al, 2001). Each survey was used to assess the severity of symptoms diagnosed with depression according to DSM-IV criteria. Other obvious and treatable explanations of symptoms were then excluded based on the patient's clinical history and physical examination (depression guideline, 1994). Diagnosis of refractory MDD is a lengthy process that is often not in compliance with the patient's health due to potential life-threatening antidepressant side effects (e.g., violence, cardiovascular disease, and/or recurrent deaths/suicides) (Peretti et al, 2000; Mann, 2005). The most common first-line treatment for MDD patients is psychotherapeutic and/or small dose SSRI antidepressant therapy. During the course of psychotherapy, patients were instructed to alter thinking and behavioral patterns in an effort to modulate the limbic cortical pathway of the prefrontal cortex, hippocampus, and cingulate cortex regions associated with normal mood and behavior (Goldapple et al, 2004). After 6 to 12 weeks of recommended administration of a particular antidepressant (Quitkin et al, 1986; Mann, 2005), efficacy can be assessed using the HAM-D or MADRS questionnaire (efficacy is typically assessed 4 to 6 weeks after treatment, although there is a proposed assessment time frame). If the patient exhibits some asymptomatic or problem-free symptomatic benefit, a high dose of the same drug or a second antidepressant should be prescribed. If a patient does not receive significant benefit from at least two appropriate antidepressants (i.e., correct dose and adequate evaluation time period), then it will be diagnosed with refractory MDD (Dumitriu et al, 2008). One of several non-standardized algorithms was then used to estimate the level of therapeutic resistance, most notably the five-stage model proposed by Thase and Rush (1997) (Dumitriu et al, 2008). Objective diagnostic tests based on quantifiable depression-specific biomarkers are needed to improve diagnostic accuracy and classification of different manifestations of the disease. In summary, the main reason for the failure of treatment of depression is the lack of objective diagnostic criteria, which prevents a more accurate differentiation between depression patients who all have the same common symptoms but who progress to depression in different situations (lacase and Leo, 2005). It is not surprising that there are no clear targets, proven mechanisms of action, and consistent reports of clinical efficacy for antidepressant therapy, that varying degrees of therapeutic resistance are constantly reported (Thase and Rush, 1997; Fava, 2003; Mann, 2005; Belaker and Agam, 2008; Kirsch et al, 2008). If increased therapeutic efficacy is desired in refractory populations, more personalized antidepressant therapy on the pathology and improvement timescale is desired.

Brain stimulation for treating depression

There are few methods of drug therapy for the treatment of depression. In severe cases, electroconvulsive therapy (ECT) is most often used to help control the symptoms of depression over several weeks. This traditional treatment modality for treatment-resistant patients involves non-specific but non-invasive stimulation of a large area of the cortex. Patients must be subjected to mild anesthesia and/or sedation and often experience severe side effects (e.g., amnesia retrograde, often not improving completely over time) (Marangell et al, 2007; Dumitriu et al, 2008). However, despite its inherent limitations, ECT has provided patients with refractory MDD with greater antidepressant benefit than any other FDA-approved treatment regimen. In addition to the inherent complications associated with treatment, this approach is problematic because it requires significant tertiary medical resources and therefore does not fully extend the large clinical population required. Transcranial Magnetic Stimulation (TMS) was proposed by Barker et al (1985) (Klein et al, 1999). TMS enables researchers to selectively study brain function in a simplified and relatively safe manner by non-invasively activating the target cortical region (Figiel et al, 1998; Klein et al, 1999). Over the past few decades, it has become an important tool for the treatment of a variety of neurological diseases due to its good spatial selectivity for ECT, non-invasiveness, and generally tolerable side effects (fig. et al, 1998; Klein et al, 1999; Janicak et al, 2008). Therefore, TMS is now used as an FDA-approved refractory MDD treatment regimen.

Transcranial magnetic stimulation is typically achieved by passing a current through a circular or figure-8 coil located above the cortical region of interest. The resulting directed magnetic field pulse generates an electric field in the cortex surface (maximum depth of 1 cm, Dumitriu et al, 2008) which depolarizes the neuron when sufficient electric field is generated (Fitzgerald et al, 2002). Due to the size limitations of the device, this technique cannot be used for a fully implantable closed-loop neural prosthesis. Current TMS devices are large and are typically only accessible through outpatient procedures (e.g., home office procedures)TMS Therapy, Neuronics, 2009). The size of the TMS device is proportional to the size of the stimulated cortical region, limited by the tradeoff between the coil size and the amount of current required to generate the same magnetic field in a smaller device (Cohen and Cuffin, 1991). Therefore, TMS is not suitable for use in fully implanted neuroprosthesesUnless fundamental changes are made to the design to significantly reduce device size without sacrificing performance. Here again, the need for a high-level infrastructure using TMS limits the feasibility of this technology to scale to the entire population.

TMS has many subtypes, and is classified according to stimulation parameters and application patterns. Two classical TMS subtypes are: rapid/repetitive transcranial magnetic stimulation (rTMS), including any stimulation pattern with a frequency greater than 1 hertz) and low frequency/slow transcranial magnetic stimulation (sTMS), including any stimulation pattern with a frequency less than 1 hertz. The TMS subtypes produce different cortical activation properties, which depend largely on stimulation parameters, coil shape and size, stimulation site and stimulation direction, and are relevant for studies reporting contradictory therapeutic effects. However, rTMS is believed to produce more antidepressant effects, as a study of cerebral blood flow showed a significant increase in blood supply to the cortex and limbic region of the post-rTMS prefrontal and a significant decrease in blood supply following rTMS administration (Speer et al, 2000). Indeed, such changes may reflect challenges with respect to the variability of the neuropathology being treated. Again, this type of treatment is open-loop, with no provision according to any biomarker or adjustment according to the patient's symptoms. In 1954, Deep Brain Stimulation (DBS) was first used to treat depression (Poole, 1954; Hardesty and Sackeim, 2007). However, DBS gained considerable research interest and motivation in 1987, when Benabid et al (1987) successfully alleviated parkinson's disease in patients by high frequency stimulation of one thalamic ventral internuclear nucleus and ablation of another thalamic ventral internuclear nucleus. The Benabid et al paper shows that high frequency electrical stimulation dysfunctional brain structures are as effective as surgically resected the same part of the brain, promoting DBS therapy as an atraumatic and extreme alternative to resection surgery (Benabid et al, 1987; Hardesty and Sackeim, 2007).

The role of DBS in the treatment of intractable psychiatric disorders has become increasingly evident over the last decades, mainly through the unexpected side effects observed in non-depressive DBS patients. For example: in an elderly woman without any known mental disorder (implanted deep brain stimulator for Parkinson's disease), application of high frequency DBS therapy to the left substantia nigra causes temporary suicidal depression that reverses once stimulation ceases (electrical stimulation is inadvertently applied 2 mm below the Parkinson's symptom relief optimal stimulation point) (Bejjani et al, 1999; Hardesty and Sacketim, 2007). However, it is also caveat that the therapeutic efficacy of any treatment method depends largely on the specificity of its delivery, since small targeting errors can induce potentially dangerous nonlinear side effects. Case studies have shown that in addition to site-dependent, therapeutic efficacy of DBS is largely dose dependent (Fontaine et al, 2004; Hardesty and Sackeim, 2007). To date, DBS has been clinically tested and its clinical endpoint has not been reached to date. This may be due in part to the open-loop nature of the treatment (failure to treat at the time depression symptoms appear), as well as to individual differences in disease pathology and the site of optimal treatment target. Moreover, this therapy is more invasive and may limit the number of potential candidates for future treatment.

Brain stimulation target

Few neurostimulation targets have been evaluated for therapeutic efficacy in refractory MDD populations. In general, proposed stimulation targets are associated with limbic structures, directly from the hypothesis of neurological dysfunction in depression, the unexpected mood improvement observed in imaging studies, stimulation studies to treat other diseases, and the areas accessible using a given stimulation technique. TMS studies are usually directed to the left dorsolateral and/or right dorsolateral prefrontal cortex (DLPFC) because it can be in contact with large stimulation coils and has a good history of antidepressant action. Slow TMS (sTMS) has antidepressant effect only when used for right side DLPFC (Klein et al, 1999; Fitzgerald et al, 2006), while repeat/fast TMS has antidepressant effect only when used for left side DLPFC (Speer et al, 2000; Avery et al, 2006; Fitzgerald et al, 2006). Without surprise, the goal of DBS studies was deep brain structures such as the cingulate gyrus (SCG) (Mayberg et al, 2000, 2005; Lozano et al, 2008), the ventral bursa/ventral striatum (VC/VS) (Malone et al, 2009), the globus pallidus medial nucleus (GPi) (Kosel et al, 2007) and the subthalamic pedicle (ITP) (Jimenez et al, 2005).

Each stimulation technique uses a different set of stimulation parameters, using either constant current-based or voltage-based monophasic or biphasic waveforms, varying in amplitude, pulse duration and stimulation frequency range (see Albert et al, 2009 for a comprehensive review of the stimulation parameters used in VNS, TMS and DBS). Each waveform stimulates the target structure continuously or intermittently (in an open-loop configuration), with the desire to directly or indirectly modulate abnormal activity to be more normal in the neural pathways and structures associated with the limbus (e.g., VNS technology stimulates intermittently for 30 seconds every 5 minutes, brain activity is modulated indirectly through the left cervical vagus nerve, (Marangell et al, 2007). DBS stimulation parameters are programmed wirelessly according to patient specifics after about 2 weeks of implantation by using patient-reported symptom relief and side effects, stimulation pulse duration and amplitude steadily increase over a period of weeks to months (at a constant pulse repetition frequency) to determine the range of parameters that produce the most significant therapeutic effect and minimal side effects (monophasic constant current stimulation is typically used for VNS, monophasic constant voltage stimulation is typically used for DBS) (hardest and sacke, 2007. before surgery begins, the TMS device first measures the motor threshold (i.e. the strength of the magnetic pulse that causes a motor potential when applied to the motor cortex) of the patient (Marangell et al, 2007). The percentage of the observed motion threshold was then used as the baseline intensity for treatment with the application of electromagnetic pulses (Albert et al, 2009).

Stimulation programming programs are often uncomfortable for the patient because serious side effects are often caused by unintended neural stimulation due to improper stimulation sensor location, improper parameter selection, and/or limited spatial resolution of a given stimulation technique. Increasing the specificity of stimulus delivery to more precisely target dysfunctional neurons or neural networks will reduce side effects. Further adjustment of stimulation time to match the fluctuating clinical needs of the patient will also improve overall efficacy.

Disclosure of Invention

Thus, according to some embodiments of the system of the present application, a system for treating an emotional disorder in a patient is provided. The system includes one or more implantable devices, each of which includes one or more electrodes for sensing cortical signals in one or more cortical areas of the brain and for stimulating one or more areas of the brain. The system also includes one or more processors/controllers in communication with the one or more electrodes for receiving and processing sensed cortical signals and for controlling stimulation of one or more brain regions by the one or more electrodes. The system also includes at least one portable communication device operable by the patient and having an application software running on the portable communication device for acquiring ecological emotion assessment (EMA) data representative of the patient's instantaneous emotion and for communicating the data to the at least one processor/controller and/or at least one remote processor. The data is processed by the one or more processors/controllers and/or by a processor included in the portable communication device and/or by the at least one remote processor for adjusting and/or controlling stimulation of one or more brain areas to treat the mood disorder. The system also includes at least one power source suitably electrically connected to the one or more implantable devices to provide power to the one or more implantable devices.

In some embodiments, the one or more implantable devices are selected from one or more intracranial implantable devices, one or more implantable intracranial devices, and any combination thereof.

In some embodiments, the one or more electrodes are selected from the group consisting of one or more intracranial electrodes, one or more intracranial electrode arrays, and any combination thereof.

In some embodiments, at least one of the one or more implantable devices is an intracranial device having a plurality of intracranial electrodes disposed between an outer plate and an inner plate of the skull of the patient without completely penetrating the inner plate of the skull.

In some embodiments, at least some electrodes of an intracranial implant are in contact with an outer surface of the inner plate of the skull.

In some embodiments, the system includes one or more implantable Frequency Interference (FI) devices configured to stimulate one or more brain regions by using a frequency interference stimulation method.

In some embodiments, the one or more brain regions stimulated by the implantable Frequency Interference (FI) device are selected from the group consisting of at least one cortical region, at least one deep brain structure, and any combination thereof.

In some embodiments, the at least one cortical region is selected from the group consisting of right dorsolateral prefrontal cortex (RDLPFC), left dorsolateral prefrontal cortex (LDLPFC), one or more regions of cingulate cortex, one or more regions of prefrontal cortex (PFC), and any combination thereof.

In some embodiments, the at least one deep brain structure is selected from the group consisting of a Ventral Striatum (VS), one or more parts of the limbic system of the brain, a subtotal cingulate region (BA 25), a ventral sac (VC), an nucleus accumbens, a lateral reins, a ventral caudate nucleus, a subthalamic pedicle, an island lobe, and any combination thereof.

In some embodiments, the one or more cortical regions are selected from a region of the right dorsolateral prefrontal cortex (RDLPFC), the left dorsolateral prefrontal cortex (LDLPFC), the prefrontal cortex (PFC), and any combination thereof.

In some embodiments, the system further comprises one or more sensor units for sensing one or more additional biomarkers indicative of the mood of the patient.

In some embodiments, the one or more sensor units are selected from the group consisting of a heart rate sensor, a sweat sensor, a pupillometry sensor, an AR headset, an eye tracking sensor, a microphone, a serotonin sensor, a blood dopamine sensor, and any combination thereof.

In some embodiments, the one or more biomarkers are selected from the group consisting of heart rate, heart rate variability, blood pressure, changes in sweat rate, changes in pupil size in response to the occurrence of a negative word, eye movement parameters, changes in vowel space where the patient speaks, changes in blood serotonin levels, changes in blood dopamine levels, and any combination thereof.

In some embodiments, the mood disorder is selected from Major Depressive Disorder (MDD), post traumatic stress syndrome (PTSD), anxiety disorder, and any combination thereof.

In some embodiments, the system further comprises one or more effector devices controlled by the one or more processors/controllers and/or the one or more communication devices, the one or more effector devices selected from the group consisting of a device for delivering serotonin to the brain of the patient, a device for delivering dopamine to the brain of the patient, and any combination thereof.

In some embodiments, the one or more processors/controllers are programmed to process the cortical signal and the EMA data to determine a value of an emotional index MX, and deliver stimulation to one or more brain regions if the value of the emotional index MX is less than or equal to a threshold level.

In some embodiments, the numerical value of the mood index MX is calculated from the cortical signal and the EMA data, or from the cortical signal, the EMA data, and one or more patient biomarker data sensed by one or more sensors.

In some embodiments, the one or more processors/controllers are programmed to process the cortical signal and the EMA data to determine a value of an emotional index MX, and to deliver a graded stimulus to one or more brain regions in response to the value of the emotional index MX.

In some embodiments, the mood index MX comprises a modulation index MI calculated from the cortical signal and the EMA data.

There is also provided, in accordance with some embodiments of the system of the present application, a system for treating a mood disorder in a patient. The system comprises: one or more intracranial implants, each implant comprising a power source; a plurality of intracranial electrodes for sensing cortical signals of the brain and for stimulating one or more regions of the brain; and a telemetry module for transmitting sensed cortical signals and/or data and for wirelessly receiving data and/or control signals. At least some of the plurality of intracranial electrodes are disposed between an outer plate and an inner plate of the patient's skull without completely penetrating the inner plate of the skull. Each of the one or more implantable intracranial implants includes one or more processors/controllers in communication with the plurality of intracranial electrodes for processing the sensed cortical signals and for controlling stimulation of one or more regions of the brain. The system also includes at least one portable communication device operable by the patient and having an application software running on the portable communication device for acquiring ecological emotion assessment (EMA) data representative of the instantaneous emotion of the patient and for communicating the EMA data to the one or more implantable intracranial implants and/or at least one remote processor. Said data is processed by said one or more processors/controllers of said one or more intracranial implants and/or by a processor comprised in said portable communication device and/or by said at least one remote processor for regulating and/or controlling stimulation of one or more regions of the brain to treat said emotional disorder.

In some embodiments of the system of the present application, the at least one portable communication device is selected from the group consisting of a mobile phone, a smartphone, a laptop, a mobile computer, a tablet, a laptop, a tablet, an Augmented Reality (AR) headset, and any combination thereof.

According to some embodiments of the methods of the present application, there is also provided a method for treating a mood disorder in a patient. The method comprises the following steps: receiving cortical signals sensed from one or more cortical regions of the patient; automatically receiving ecological emotion assessment (EMA) data for the patient from at least one portable communication device operated by the patient, the at least one communication device having an application software running on the portable communication device for automatically obtaining data representative of parameters of the patient using the at least one communication device to calculate the EMA data on-site and/or to receive calculated EMA data from a remote processor; and processing the cortical signal and the EMA data to detect an indication that the patient is at a mood depression requiring therapeutic stimulation; and stimulating at least one brain region of the patient in response to detecting the indication.

According to some embodiments of the method, the signals of the receiving step are recorded by one or more implants selected from the group consisting of a plurality of intracranial implants, a plurality of calvarial endosseous implants, and any combination thereof.

According to some embodiments of the method, the signals of the receiving step are recorded by one or more intracranial electrodes. At least some of the plurality of intracranial electrodes are disposed between an outer plate and an inner plate of a skull of the patient without completely penetrating the inner plate of the skull.

According to some embodiments of the method, the one or more intracranial electrodes are disposed in contact with or adjacent to an outer surface of the inner plate of the skull.

According to some embodiments of the method, the EMA data comprises data selected from: data representing parameters automatically obtained by the patient using at least one portable communication device, and data representing a subjective mood assessment provided by the patient in response to automatically making a mood assessment request to the patient.

According to some embodiments of the method, the EMA data comprises data selected from: data representing an application used by the patient, data representing a number of calls made by the patient, acceleration data resulting from movement of the patient, communication data, ambient light data, ambient sound data, location data of the patient, a call log of the patient, voice content of the patient, text content of the patient, sleep data of the patient, social network data of the patient, and any combination thereof.

According to some embodiments of the method, the step of automatically receiving further comprises the step of automatically receiving biomarker data from one or more sensors, and wherein the step of processing comprises processing the cortical signal, the EMA data, and the biomarker data to detect an indication that the patient is in a mood depression requiring therapeutic stimulation.

According to some embodiments of the method, the processing step comprises processing the sensed cortical signals and the EMA data to calculate a value of a modulation index parameter MI and/or to calculate a mood index MX of a patient.

According to some embodiments of the method, the processing step comprises processing the sensed cortical signals and the EMA data and biomarker data obtained from one or more sensors to calculate a value of a modulation index parameter MI and/or to calculate a patient mood index MX.

According to some embodiments of the method, the processing step comprises processing the sensed cortical signal by calculating spectral power in one or more spectral bands, calculating a modulation index MI, and/or calculating an emotion index MX.

According to some embodiments of the method, the processing step comprises a comparison of the value of modulation index MI with a threshold value, and wherein the stimulating step comprises stimulating one or more brain regions if the value of modulation index MI is equal to or greater than the threshold value.

According to some embodiments of the method, the processing step comprises comparing a value of a mood index MX with a threshold value, and wherein the stimulating step comprises stimulating one or more brain areas if the value of the mood index MX is equal to or greater than the threshold value.

According to some embodiments of the method, the stimulating step comprises stimulating one or more brain regions selected from the group consisting of one or more cortical brain regions, one or more deep brain structures, and any combination thereof.

According to some embodiments of the method, the one or more cortical brain regions in the stimulating step are selected from the group consisting of right dorsolateral prefrontal cortex (DLPFC), left dorsolateral prefrontal cortex (DLPFC), prefrontal cortex (PFC) regions, below knee cingulate cortex, and any combination thereof, wherein the one or more deep brain structures in the stimulating step are selected from the group consisting of Ventral Striatum (VS), one or more parts of the limbic system of the brain, below knee cingulate region (BA 25), ventral bursa (VC), nucleus accumbens, lateral caudate nucleus, ventral caudate nucleus, subthalal pedicle, islets, and any combination thereof.

According to some embodiments of the method, the receiving step comprises receiving cortical signals from one or more cortical regions selected from the group consisting of right dorsolateral prefrontal cortex (DLPFC), left dorsolateral prefrontal cortex (DLPFC), prefrontal cortex (PFC) regions, and any combination thereof.

According to some embodiments of the method, the mood disorder is selected from the group consisting of Major Depressive Disorder (MDD), post traumatic stress syndrome (PTSD), anxiety disorder, and any combination thereof.

There is also provided, in accordance with some embodiments of the methods of the present invention, a method for treating a mood disorder in a patient. The method comprises the following steps: receiving electrical signals recorded from a cortical region of the patient using an intracranial implant comprising one or more intracranial electrodes, at least a portion of the intracranial electrodes being disposed between an outer plate and an inner plate of the patient's skull without completely penetrating the inner plate of the skull; processing the signals to determine a stimulation pattern of the patient; and stimulating at least one brain region of the patient that responds to the determined stimulation pattern.

In some embodiments of the method, the method further comprises the steps of: automatically receiving instantaneous mood assessment data for the patient from at least one portable communication device operated by the patient, the at least one communication device having an application software running on the portable communication device for automatically processing data representative of parameters of the patient's use of the at least one communication device without the need for patient intervention and calculating an instantaneous mood assessment, wherein the processing step comprises processing the instantaneous mood assessment and the electrical signal to determine a stimulation pattern for the patient.

In some embodiments of the method, the method further comprises the step of interacting with the patient via at least one portable communication device to receive voluntary patient input representative of a subjective emotional assessment of the patient, wherein the processing step comprises processing the subjective emotional assessment of the patient and the electrical signal to determine and/or modify a stimulation pattern for the patient.

In some embodiments of the method, the method further comprises the step of interacting with the patient via at least one portable communication device to receive voluntary patient input representative of a subjective emotional assessment of the patient, wherein the processing step comprises processing the subjective emotional assessment of the patient, the EMA data, and the electrical signal to determine and/or modify a stimulation pattern for the patient.

In some embodiments of the method, the method further comprises the step of receiving ecological emotion assessment (EMA) data representative of the instantaneous emotion of the patient from at least one portable communication device, wherein the processing step comprises processing the signal and the EMA data to determine a stimulation pattern for the patient.

In some embodiments of the method, the receiving step further comprises receiving voluntary emotion assessment data from the patient in response to a system query, wherein the processing step comprises processing the signal and the EMA data and the patient voluntary emotion assessment data to determine a stimulation pattern for the patient.

Finally, in some embodiments of the methods of the present application, the at least one portable communication device is selected from the group consisting of a mobile phone, a smartphone, a laptop, a mobile computer, a tablet, a laptop, a tablet, an Augmented Reality (AR) headset, and any combination thereof.

Drawings

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

In the drawings:

fig. 1 is a block schematic diagram illustrating components of a system for treating a mood disorder according to some embodiments of the system of the present application.

Fig. 2 shows a schematic isometric view of an intracranial implant, which may be used in some embodiments of the system for treating an emotional disorder in the present application.

Fig. 3 is a schematic bottom view of the intracranial implant of fig. 2.

Fig. 4 is a side view schematic of the intracranial implant of fig. 2.

Fig. 5 is a schematic cross-sectional view of the intracranial implant of fig. 2, taken along line V-V, and also showing the position of the implant relative to the skull after implantation in a patient's skull.

Fig. 6 is a flow diagram illustrating steps of a method of brain stimulation therapy by processing sensed cortical activity and ecological transient mood assessment data of a patient, in accordance with some embodiments of the method of the present application.

Fig. 7 is a flow diagram illustrating steps of a method for assessing a correlation between one or more parameters of a recorded cortical signal and an emotion index calculated from ecological instantaneous emotion assessment (EMA) data of a patient, according to some embodiments of the method of the present application.

Fig. 8A-8B are flow diagrams illustrating method steps for providing a graded brain stimulation therapy to a patient by processing the patient's perceived cortical activity and ecological transient mood assessment data, according to some embodiments of the methods of the present application.

Fig. 9A-9B are flow diagrams illustrating steps of a method of providing brain stimulation therapy to a patient by using power values at a gamma band (P γ) of a sensed cortical signal and ecological instantaneous emotion assessment (EMA) data of the patient, according to some embodiments of the method of the present application.

Fig. 10 is a flow diagram illustrating a method for providing fractionated stimulation therapy to a patient in response to processing cortical signals, EMA data, and other sensor data according to some embodiments of the methods of the present application.

Fig. 11 illustrates a flow diagram of a method of providing intermittent brain stimulation therapy to a patient in response to processing cortical signals, EMA data, and additional sensor data, in accordance with some embodiments of the methods of the present application.

Fig. 12 shows a block schematic diagram of a system for treating a mood disorder, including scalp electrodes for transcranial frequency interferential stimulation of cortical and/or brain deep structures, and an array of intracranial-implanted ECOG electrodes for sensing and/or stimulating one or more cortical areas, according to some embodiments of the system of the present application.

Fig. 13 shows a block schematic diagram of functional components of an intracranial portion of the system of fig. 12.

Fig. 14 shows a schematic diagram of a system for treating a mood disorder having multiple intracranial ECOG arrays for performing sensing in one or more cortical regions and for performing Transcranial Frequency Interference Stimulation (TFIS) on one or more deep brain structures and/or direct stimulation on one or more cortical regions, according to some embodiments of the system of the present application.

FIG. 15 shows a functional block diagram of a number of functional components included in the system of FIG. 14.

Fig. 16 shows a human skull with an implanted intracranial implant for delivering deeper brain stimulation to the brain of a patient implanted in the skull of the skull, according to some embodiments of the intracranial implant of the present application.

FIG. 17 is a top view of the skull shown in FIG. 16.

Detailed Description

Systems and methods disclosed herein disclose a multi-closed loop cortical neuromodulation system that performs electroencephalographic stimulation therapy based on sensed cortical signals of a patient and one or more related patient inputs based on ecological temporal assessment and/or other patient physiological form biomarkers. The "patient and sensor information closed loop cortical" (PASICC) neuromodulation system does not require prior identification of cortical signals or physiological biomarkers, but rather learns biomarkers through continued utilization by the patient. The system comprises an intracranial implant capable of stimulation and recording from a focal region of the cortex; a mobile communication device (e.g., a mobile phone with communication capability, a smartphone, a laptop, a tablet, an Augmented Reality (AR) headset) may interact with the patient to actively or passively provide a patient's mood assessment, such as an ecological instantaneous mood assessment (EMA), to the system. The system also includes software for correlating the sensed cortical electrical activity with the mood assessment to enable detection of a mood state requiring treatment and delivery of a selected stimulation regimen. The system can adapt to each individual patient by using appropriate training and/or testing periods, and can provide patient-specific cortical biomarkers that can be used for optimized cortical stimulation to address mood-related symptoms in patients with depression.

Unless defined otherwise, 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 this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present invention, the methods and/or materials described below are exemplary. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Implementation of the method and/or system of embodiments of the present invention may be in connection with performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, the actual instrumentation and equipment according to embodiments of the method and/or system of the invention may fulfill several selected tasks through the use of an operating system, either in hardware, through software, or through firmware, or a combination thereof.

For example, hardware performing selected tasks according to embodiments of the invention could be implemented as a chip or circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of the methods and/or systems described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes volatile memory for storing instructions and/or data and/or non-volatile memory for storing instructions and/or data, such as a magnetic hard disk and/or a removable media. Optionally, a network connection is also provided. A display and/or user input device, such as a keyboard or mouse, may further optionally be provided.

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. It is expected that during the life of a patent maturing from this application many relevant electrodes and electrode arrays will be developed and the scope of the terms "electrode" and "electrode array" is intended to include all such new technologies in preference. The term "about" as used herein means ± 10%. The term "exemplary" as used herein means "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 features from other embodiments.

The term "optionally" as used herein means "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.

As used herein, the terms "comprising," including, "" containing, "" having, "and variations thereof mean" including but not limited to.

The term includes the terms "consisting of and" consisting essentially of.

The term "consisting essentially of", as used herein, means that the composition, method, or 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.

As used herein, the singular forms "a", "an" and "the" include plural referents 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.

Throughout this application, various embodiments of the invention may exist in a range of forms. 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 described range descriptions should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, it is contemplated that the description of a range from 1 to 6 has specifically disclosed sub-ranges such as, for example, 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 a range such as, for example, 1, 2, 3, 4, 5, and 6, as applicable regardless of the range.

Whenever a numerical range is indicated herein, it is meant to include any number (fractional or integer) recited within the indicated range. The terms "interval/range" between a first indicated digit and a second indicated digit "and" interval/range "from a first indicated digit" to a second indicated digit are used interchangeably herein and are meant to include the first and second indicated digits and all fractions and integers therebetween.

Patient and sensor information closed loop cortical (pascc) neuromodulation system for depression

The PASICC neuromodulation system overcomes many of the existing disorders and provides a personalized treatment for depression. The system includes 1) a cranial implant (or in some embodiments, other types of cranial implants or intracranial implants) capable of stimulation and recording from a focal region in the cortex; 2) A mobile communication device, such as a mobile computer or another portable (and/or wearable communication device, such as a mobile phone or smartphone or AR headset with communication capability) that can be in contact with a patient to actively or passively (and without intrusively) provide an emotional assessment, such as an ecological instantaneous assessment (EMA); 3) one or more software programs or applications for integrating and linking cortical physiology with mood assessments to inform stimulation protocols.

By creating a multi-closed loop cortical neuromodulation device that combines both cortical signals and related patient inputs for neuromodulation in the form of an ecologically transient assessment, the system can derive patient-specific biomarkers that will define an optimal stimulation regimen to help improve the patient's mood. The system does not require the prior identification of cortical signals or physiological biomarkers, but rather learns biomarkers through the continued utilization of the patient. As the system operates, it can "learn" patient-specific cortical biomarkers that can inform optimal cortical stimulation to address mood-related symptoms in depressed patients.

The system may operate in the following manner. Intracranial implants can be implanted in the skull of a patient covering a cortical site, which would be useful for stimulating treatment of depression. The location of the implant can be determined by anatomical imaging and functional imaging. According to some embodiments of the system, the dorsolateral prefrontal cortex (DLPFC) may be anatomically selected. More specific regions can be selected using functional magnetic resonance imaging. There are many types of functional magnetic resonance imaging that can aid in localization. In particular, this includes resting state functional nuclear magnetic resonance imaging for identifying critical networks (e.g. dorsal attention network and default mode network), task-based functional nuclear magnetic resonance imaging for exciting cortical activation of the relevant region, and Diffusion Tensor Imaging (DTI) for identifying critical white matter tracts in the vicinity of the stimulated region. The intracranial implant may be wirelessly connected to the user's mobile phone. The mobile phone or other communication device will include a software application and may have computing power or access to such computing power (either through the use of a processor on the phone or through communication with a computer having the required processing power (e.g., a cloud server wirelessly accessed by the computer)) to process data and stimulation parameters recorded from the patient's brain and/or mood-related data provided by the patient and/or sensed by sensors connected to the patient or found on the mobile phone.

Referring now to fig. 1, fig. 1 is a block schematic diagram illustrating components of a system for treating a mood disorder in accordance with some embodiments of the system of the present application.

The system 10 may comprise an intracranial implant 20, one or more communication devices 100, and (optionally) auxiliary sensor(s) 15 for implantation in the patient's body 1, for connection to the patient's body 1, or for wearing on the patient's body 1. The system 10 may (optionally) also include one or more effector devices 13. The effector device(s) 13 may be connected to the processor (s)/controller 14 to receive control signals therefrom for controlling the operation thereof. For example, effector device(s) 13 may include one or more therapeutic devices (e.g., neurotransmitters or neuromodulator delivery devices) capable of delivering neurotransmitters and/or neuromodulators to the brain of a patient, such as serotonin delivery devices and/or dopamine delivery devices disclosed in more detail below.

The communication unit(s) 100 may comprise one or more devices having communication capabilities and may also have some processing capabilities. For example, the communication unit(s) 100 of fig. 1 may include a mobile phone 70 and/or a laptop 9 and an AR headset 11. Other options for the communication unit may include a tablet and/or tablet computer and/or laptop computer, which may have communication capabilities that enable it to wirelessly communicate with the telemetry module 133 of the implant 20 and/or with each other and/or with a cloud server.

The implant 20 may include one or more processor/controller units 14 suitably connected to a memory unit(s) 18. The memory unit(s) 18 may be any suitable type of memory known in the art. Non-limiting example memory and/or data storage devices that may be used in the system 10 may include one or more devices such as Read Only Memory (ROM), Random Access Memory (RAM), Electrically Programmable Read Only Memory (EPROM), Erasable Electrically Programmable Read Only Memory (EEPROM), flash memory devices, optical memory, and/or memory devices or any other type of memory known in the art and any combination thereof. Note that memory unit(s) 18 can also be memory unit(s) that are integrated into processor/controller(s) 14.

The processor (s)/controller(s) 14 may be any type of processor(s) or controller(s) known in the art, such as a CPU, microprocessor, microcontroller, Digital Signal Processor (DSP), Graphics Processing Unit (GPU), optical processor, quantum computing device, and any combination thereof.

Implant 20 may also include electrode unit(s) 120. Electrode unit(s) 120 may be any suitable type of electrode for sensing electrical activity in one or more regions of the patient's brain 8 and for stimulating one or more regions of the patient's brain 8. Some or all of the electrodes of the electrode unit(s) may be suitably coupled to a stimulation generation unit 170 included in the implant 20 for delivering electrical stimulation to the electrodes included in the electrode unit(s) to stimulate one or more regions of the brain 8. The stimulus generation unit 170 is suitably connected to the processor/controller 14 for receiving control signals therefrom. The processor/controller 14 may control the operation of the stimulus generation module 170. Some or all of the electrodes in the electrode unit(s) 120 may be suitably (optionally) electrically connected to a signal conditioning module 155, which signal conditioning module 155 may be suitably connected to the processor/controller(s) 14. The signal conditioning module 155 may include all electronics/circuitry (e.g., filtering circuitry, band limiting circuitry, multiplexing circuitry, and analog-to-digital conversion circuitry, a clock, or any other necessary electronics) required to filter and/or amplify and/or multiplex and/or digitize signals sensed by electrode unit(s) in the area of the brain 8. Alternatively, such circuitry, or some of it, may be included in the processor/controller(s) 14.

Implant 20 may also include a telemetry module 133 suitably connected to processor/controller(s) 14. The telemetry module may be any suitable module capable of wirelessly transmitting data and/or control instructions or instruction signals to the communication unit 100 and receiving data and/or control signals from the communication unit 100. Telemetry module 133 may communicate with communication unit 100 using any suitable type of communication protocol and frequency band. For example, the telemetry module may communicate with the mobile phone 70 using RF signals and a mobile communication protocol. Optionally or additionally, the telemetry module 133 may communicate with the mobile phone 70 and/or the laptop 9 and/or the AR headset 11 using a WiFi protocol and/or a bluetooth protocol.

Preferably, the laptop 9 (if included in the system 10) may be connected wirelessly (or in a wired manner) to the cloud 31 via WiFi and the internet. Preferably, the mobile phone 70 is also wirelessly connected to the cloud 31 (via WiFi and/or a mobile data network), and the AR headset 11 is wirelessly connected to the mobile phone 70 and/or the laptop 9 and/or the cloud 31 using any suitable communication protocol and method. Such wireless communication may enable the processor/controller 14 to wirelessly communicate with an external device, such as a remote computer, a server (on the cloud 31), a mobile phone (e.g., mobile phone 70), an AR headset (e.g., AR headset 11), or any other type of computer that may be reached through the cloud 31. This may be useful where the processing power of the processor/controller 14 of the implant 20 is limited, as it may allow some or all of the computational burden to be transferred from the processor/controller to other processing devices, such as a remote calculator, a remote server, a cluster of calculators, or any other suitable computing device. And may enable processing of the recorded/sensed data using cloud computing or parallel mode computing, thereby reducing the computational load on the processor/controller 14. The results of such a divested calculation may then be returned (or preferably wirelessly) or communicated to the processor/controller 14 and used to perform control of sensing and/or stimulation of appropriate brain structures, as disclosed below.

The implant 20 may also include a power source 35 for providing power to the components of the implant 20. The power source 35 may be any suitable type of power source, such as a suitable electrochemical cell, a rechargeable electrochemical cell, a fuel cell, a supercapacitor, or any other type of suitable power source. Preferably, however, the power source 3 may be a power collector. For example, the particular embodiment of power source 35 shown in FIG. 1 is implemented as a power scavenging device having an implantable inductive coil 16, which inductive coil 16 may be implanted in a patient's body 8 with an implant 20. The induction coil 16 may be energized by an external induction coil 19 connected to an external Alternating Current (AC) power source 27. In particular, the portion of the power source 35 included within the implant 20 may also include suitable electronics/circuitry (not shown in detail for clarity of illustration) and a charge storage unit (not shown in detail) for rectifying the AC induced in the induction coil 16 into a direct current power source (DC), for example, a suitable supercapacitor and/or rechargeable electrochemical cell.

It should be noted that for clarity of illustration, the leads or wires connecting the power source 35 to the other components of the implant 20 are not shown in detail.

The auxiliary sensor(s) 15 of the system 10 may be one or more sensors for sensing one or more characteristics of the patient's body 1. For example, the auxiliary sensors 15 may include one or more of the following sensors, a temperature sensor, a perspiration sensor, a heart rate sensor, an eye tracking sensor, a pupil size sensor, a blood pressure sensor, an accelerometer, a chemical sensor, or any other type of sensor known in the art. The sensors may be implanted in the patient's body 1 and/or attached to the patient's body 1 and/or worn by the patient or attached to clothing worn by the patient. Optionally or additionally, some sensors may be included in or integrated in one of the communication unit(s) 100. For example, modern smartphones may include a heart rate measurement application as well as a pupil size measurement application, which may be readily used to determine the heart rate and pupil size of a patient.

According to some embodiments of the system, some sensors may be included in the AR headset (e.g. in the AR headset 11) and may include eye tracking sensors, pupil size sensors, accelerometers, motion sensors, microphones, perspiration sensors, heart rate sensors or any other type of suitable sensor integrated in the AR headset. This has the advantage of making the system more compact. In some embodiments of the system, the AR headset may integrate all the functions and capabilities of the mobile phone 70, as well as the computing functions of the laptop 9, making the mobile phone 70 and laptop 9 redundant.

As AR headsets become less cumbersome, lighter weight, and more computationally powerful, some embodiments of the system disclosed in the present application may include one AR headset 11, one or more implants (such as implant 200 or implant 180 described in detail below). The AR headset 11 is capable of communicating with the cloud 31 and may be used to shunt data from the implant, the communication data device (including EMA data, sensor data, and all other types of data) to a remote calculator/server on the cloud 31, and may also process some data and send command signals to the implant to control stimulation and sensing of the implant. In such an embodiment, the power source may be a power source included in the AR headset 11 and the implant is powered by a suitable power cord connected from the AR headset 11 to the implant.

If the sensor is not included in the mobile phone 70 or the laptop 9 (e.g., the auxiliary sensor 15), the sensor may be a sensor implanted in or attached to or worn by the user's body. In this case, such sensors may include wireless communication circuitry (not shown in detail) that may enable the sensors to wirelessly transmit signals and/or data sensed by the sensors to telemetry module 133 and/or mobile phone 70 and/or laptop 9 for storage and/or processing. As such, the system 10 may sense one or more parameters, including physical parameters (e.g., body acceleration or motion) and/or physiological parameters (e.g., body temperature, pupil size and/or changes thereof, sweat rate, heart rate, or other physiological parameters).

One example of a patient worn sensor is a Tobii Pro 2 type wearable eye tracker, available from Tobii AB, Stockholm, Sweden. The eye tracker is a lightweight, spectacle-like device that may be worn by a user and that provides eye tracking data and pupil size data for a patient.

It should be noted that, according to some embodiments of system 10, one or more of the auxiliary sensors 15 may be implanted with chemical sensors (e.g., serotonin sensors and/or dopamine sensors) for determining the concentration of neurotransmitters in the blood. Such sensors may provide data indicative of the patient's blood serotonin and/or dopamine concentration to the processor/controller 14 and/or mobile phone 70 and/or laptop 9. The data may also be processed by the system 10 and may be used to calculate values for the mood index (MX) disclosed below with respect to these methods.

Concentration data for such neurotransmitters may also be used to automatically control the operation of one or more of the effector devices 13 of fig. 1. For example, one or more of the effector devices 13 may be a neurotransmitter delivery device capable of delivering serotonin and/or dopamine to the relevant region of the brain of the patient on demand. Such a neurotransmitter delivery device, or a component thereof (e.g. a suitable cannula) for neurotransmitter delivery only, may be implanted in the skull of a patient. If the blood transmission level falls below a preset or predetermined threshold, the processor/controller 14 may activate the neurotransmitter delivery device to deliver a therapeutic dose of serotonin and/or dopamine to the patient's brain or the patient's blood (this chemotherapy may be performed independently of or in conjunction with therapeutic brain stimulation).

Some methods of operation of a system such as system 10 are disclosed in more detail below.

The implant 20 may be implemented in a variety of different embodiments. According to some embodiments of the system, implant 20 may be a cranial implant.

Reference is now made to fig. 2 to 5. Fig. 2 shows a schematic isometric view of an intracranial implant, which may be used in some embodiments of the system for treating an emotional disorder in the present application. Fig. 3 is a schematic bottom view of the intracranial implant of fig. 2. Fig. 4 is a side view schematic of the intracranial implant of fig. 2. Fig. 5 is a schematic cross-sectional view of the intracranial implant of fig. 2, taken along line V-V, and also showing the position of the implant relative to the skull after implantation in a patient's skull.

The intracranial implant 200 may include a housing 202. The housing 202 may be a cylindrical or disk-shaped housing, although other housing shapes may be used. The housing 202 may be made of any suitable biocompatible material, such as titanium, stainless steel, polymer-based materials, carbon,or any other suitable strong biocompatible structural material. The intracranial implant 200 also includes four electrodes 206, 208, 210, and 212, a reference electrode 214, and a ground strip 204. If the housing is made of a conductive metal, the grounding strap 204 may be electrically isolated from the housing by a non-conductive material (not shown) disposed between the housing 202 and the grounding strap 204. If the housing 202 is made of a non-conductive material, the grounding strap 204 may be a thin layer of conductive material (e.g., gold or platinum) that covers the outward facing surface of the housing 202, or (as shown in FIG. 5), the grounding strap 204 is disposed in a recess 202A formed in the sidewall of the housing 202.

Returning to fig. 3-4, each of the electrodes 206, 208, 210, and 212 has an electrode tip 206A, 208A, 210A, and 212A, respectively, and an electrode shank 206B, 208B, 210B, and 212B, respectively. The electrode tips 206A, 208A, 210A, and 212A, the reference electrode 214, and the ground strip 204 may be made of a conductive material (e.g., gold, platinum, stainless steel coated with gold or platinum, or any other biocompatible conductive material). The electrode shanks 206B, 208B, 210B, and 212B may be formed from an electrically insulating material (e.g., a non-conductive polymer-based material, a metal alloy, a,Or any other suitable biocompatible polymer). The reference electrode 204 may be made of the same conductive material of the electrode tips 206A, 208A, 210A, and 212A.

Returning to fig. 5, the intracranial implant 200 is illustrated as being implanted in the skull of a patient's skull. The shell 202 of the implant 200 is implanted in the cavity 111 made surgically in the skull 13 (by drilling, deburring or any other suitable surgical method). The cavity 111 opens at the outer surface 5A of the outer plate 5 of the skull 13 and extends through the cancellous bone layer 7 to the outer surface 6B of the inner plate 6 of the skull 13.

Note that the shape and size of the cavity 111 as shown in fig. 5 are not limited. For example, in some embodiments, the cavity 111 may be shaped to accommodate the housing 202 and the reference electrode 214, and include four narrow channels (not shown in fig. 5) to the inner plate 6. The electrodes 206, 208, 210, and 212 may be inserted into four narrow channels formed in the cancellous bone 7 such that the electrode tips 206A, 208A, 210A, and 212A are in contact with or in close proximity to the outer surface 6B of the inner plate 6. An advantage of this cavity configuration is that it minimizes the amount of cancellous bone that needs to be drilled and removed.

It is noted that in some embodiments, the cavity 111 may extend partially into the inner surface 6 (not shown in the embodiment shown in fig. 5) by carefully penetrating the surface 6B to extend the cavity 111 to the inner plate 6 without damaging the inner plate 6 (i.e. not completely penetrating the inner plate 6). This may advantageously reduce the thickness of the bone material interposed between the electrode tips 206A, 208A, 210A and 212A, which may result in reduced signal attenuation of cortical signals recorded from cortical regions (not shown) beneath the inner plate 6. In addition, reducing the thickness of the inner plate 6 may advantageously save power by reducing the current intensity required for stimulation, thereby advantageously improving stimulation of the cortex by the electrodes 206, 208, 210 and 212.

The implant 200 may include a power source 35 (not shown in detail in the cross-sectional view of fig. 5) and an electronics module 215. Electronics module 215 may include processor/controller 14, memory unit 18, signal conditioning module 155, stimulation generation module 170, and telemetry module 133.

The power source 35 may be any suitable type of power source, such as a battery or electrochemical cell (primary or rechargeable), a supercapacitor, a fuel cell, or any other suitable type of power source. Alternatively or additionally, the power source 35 may be an energy harvesting device capable of receiving energy and storing the energy as a stored charge. For example, one possible embodiment of the power source 35 is coupled to an induction coil 16, as disclosed in detail below and shown in FIG. 5.

Alternatively and/or additionally, the power source may include any type of suitable power harvesting device for receiving or generating power and storing the received or generated power. For example, the power source 35 may include a piezoelectric assembly for receiving acoustic energy from an external sound or ultrasound generator placed in proximity to the implant 200. In another embodiment, the power source 35 may include an electromechanical generator device that converts head or body movements of the patient into storable electrical charge. Such energy harvesting devices are not the subject of the present invention, are well known in the art, and are therefore not described in detail below.

It is noted that for implants that may require a large amount of power to operate, the power source 35 disposed inside the implant 200 (or inside any other implant disclosed in this application) may be replaced with a power source (not shown) implanted in or carried or worn by the patient. In some embodiments, a medical-surgical implantable power supply (not shown) may be implanted in the patient and suitably electrically coupled to an implant (e.g., implant 200) by a suitable lead (not shown) that may enter implant 200 through hollow channels 32A and 32B, as described below (see fig. 2). To this end, any implantable power source for energizing a pacemaker and/or defibrillator may be used, as is known in the pacemaker and defibrillator arts. For example, such a power source may be implanted in a suitable subcutaneous pocket made in the chest of the patient and connected to the implant by suitable leads. Any other suitable implantation method and implantation location for such medical power sources may also be used.

The electrode tips 206A, 210A, and 212A may be connected to the electronics module 215 by suitable leads 206C, 210C, and 212C (which may preferably be insulated leads). Note that the wires connecting the electrode tip 208A to the electronics module 215 are not shown in the cross-sectional view of fig. 5. The reference electrode 214 may be electrically connected to the electronic module 215 through an insulated wire 214C. The ground strap 204 may be connected to an electronics module 215 through an insulated wire 204. The electronics module 215 may be connected to the power source 35 by a pair of suitable electrically conductive insulated wires 27.

The power source 35 may be electrically coupled to the induction coil 16 by a pair of electrically conductive insulated wires 28, the pair of electrically conductive insulated wires 28 sealingly passing through two suitable hollow passages 32A and 32B (see fig. 2) formed in the housing 202. The induction coil 16 of fig. 5 is shown disposed between the patient's housing 202 and the scalp 109 after implantation. The patient may periodically charge the power source 35 by placing the induction coil 19 (not shown in fig. 5) on the scalp area covering the induction coil 16 and passing alternating current from the alternating current power source 27 through the induction coil 19.

Stimulation format

According to some embodiments, each of the four electrodes 206, 208, 210, and 212 may be capable of independent and simultaneous biphasic electrical supply. In general, an asymmetric, charge balanced biphasic waveform can be acquired/absorbed simultaneously from all four electrodes 206, 208, 210 and 212. The current magnitude (typically up to 6 milliamps (mA)) of each of the four (source) electrodes 206, 208, 210 and 212 is independent of each other and programmable. If all four electrodes 206, 208, 210, and 212 are in a maximum active state, the total current from the entire implant 200 may be 24 mA. The electrical return path for all four electrodes 206, 208, 210 and 212 may be a large ground strip 204 on the housing 202. Each individual electrode of the four electrodes 206, 208, 210 and 212 may have a constant current output voltage of up to 12 volts. Reference electrode 214 is typically not used for stimulation. In some embodiments of system 10, the stimulation parameters may be in a range of pulse widths in a range of 5 to 750 microseconds (μ S) and pulse frequencies in a range of 5 to 500 hertz (Hz). However, other parameter values outside (below or above) the above ranges may also be used.

Recording format

The four (source) electrodes 206, 208, 210 and 212 may also be capable of recording voltage-based field potentials. According to one embodiment of the implant 200, the implant 200 will not stimulate and record simultaneously, but rather may be rapidly interleaved between recording and stimulation modes (e.g., using interleaved stimulation and recording periods having durations less than 100 milliseconds; or alternating frequencies greater than 10 hertz). Each of the electrodes 206, 208, 210 and 212 may be differentially registered with respect to a slightly larger centrally placed reference electrode 214, which reference electrode 214 may be impedance matched to the four (source) electrodes 206, 208, 210 and 212. The ground band electrode 204 may be located near an outer plate of the skull (see fig. 5). The reference electrode 214 may be disposed in a cavity 111 within the central medulla of the cancellous layer 7 of the skull. The electrodes 206, 208, 210, and 212 may be positioned such that their electrode tips 206A, 208A, 210A, and 212A are positioned near or in contact with the outer surface 6B of the intracranial plate 6 (as shown in FIG. 5).

The frequency range for recording may be in the range of 3 to 200 hertz. The noise floor for the intermediate gamma band (75 to 105 hertz) may be less than 200 nanovolts (nV). The maximum differential field potential is 100 microvolts (μ V). However, an amplifier (not shown in detail) included in the electronics module 215 may have a single-ended input (e.g., electrode 206 to ground strap 204) of up to 5 millivolts (mV). After a unit gain differential record with a maximum input of +/-5 millivolts, the signal can be band pass filtered (3 to 200 hertz) and amplified with approximately 50 times the gain. A 12-bit analog-to-digital converter (a/D) with a maximum input of +/-5 millivolts may sample at a minimum rate of 2 kilohertz (10 times sampling). Within 50 gain and a maximum input range of +/-5 volts, the analog to digital converter sampled voltage resolution may be less than 50 nanovolts.

In operation of system 10, in the case of continuous use, communication unit 100 will "poll" (query or inquire) the patient (e.g., by using mobile phone 70) to receive information or data about the patient's current emotional state (or mood). The received information may be used to correlate an emotional state with a given cortical physiological parameter. These parameters may include band amplitude, frequency-phase interaction, band amplitude ratio, phase-amplitude coupling at a given electrode and between different recording electrodes. Using machine learning algorithms (e.g., support vector machines, deep learning, multi-level neural networks, etc.), a statistical model can then be created to predict emotional states from physiological signals.

As statistical models evolve using system 10, stimulation parameters can be constructed to stimulate the brain to evoke the physiological state that best predicts a positive emotional state. The basic stimulation parameters may be set initially, but will vary with the ongoing closed loop interaction. According to some system and method embodiments, such stimulation parameter modification may occur automatically. Optionally and/or additionally, the modification of the stimulation parameters may be performed by a caregiver of the patient, such as a psychiatrist or another medical caregiver monitoring the patient.

Such a multi-circuit system can be continuously optimized with continuous input from the patient. As the patient intermittently provides input to the application operating on the mobile phone 70, the system 10 operates continuously to increase the accuracy of the biomarkers to indicate the patient's positive or negative emotions.

Automated and voluntary EMA assessment method

To collect self-monitored mood data (the goal of the prediction task), the system 10 may use eMate, an EMA mobile phone application developed at amsterdam free university. The application prompts the participant to score his mood on the smartphone at five set time points per day (i.e., approximately 09:00, 12:00, 15:00, 18:00, and 21: 00). As shown in the Robert LiKamWa et al (2013) article cited in the list of references, emotion can be assessed by a cyclic model of emotion [ see Robert a Russel (1980) article cited in the list of references ], which conceptualizes emotion as a two-dimensional structure including different levels of coordination (positive/negative emotion) and arousal. The levels of both dimensions can be scored with a 5-point scale, with a score from-2 to 2 (low to high). Since recent studies have shown that a single mood measure may provide predictive information about the development of depressive symptoms (for detailed information, see the article 2012 by Gerard d. van Rijsbergen et al, in the reference list below), a one-dimensional mood question may be added that requires participants to score their current mood in 10 points, where 1 is negative and 10 is positive.

Ecological transient assessment of unobtrusive mood predictors

For non-significant evaluation, the system/method of the present application may use iYouVU, an anonymous handset application based on the Funf open sensing framework (Aharony, n., Gardner, a., Sumter, c., and Pentland, a. (2011). Funf: open sensing framework), where previous research on communication habits was based on handset data collection, but users were not fully aware of this. This application runs in the background and is not noticeable to the user to collect the specified sensor data and application records. The application records call events (e.g., time/date of call, duration, and contacts to access and place an outgoing call), Short Message Service (SMS) text message events (e.g., time/date and contacts), screen on/off events (e.g., time/date), application usage (i.e., which applications were launched, launch time and duration of launch time), and usage of the cell phone camera (i.e., time/date when the photograph was taken). During data collection by an application through the built-in cryptographic hash function of the Funf framework, all collected sensitive personal data, such as contact details (name, phone number), can be anonymized. At regular intervals of time each day, and only when the participant's handset is connected to Wi-Fi, the application will send the collected data to the remote central data server over the internet, with each data file being approximately 5 to 10 Megabytes (MB). The additional data may also include Global Positioning System (GPS) location data and accelerometer data.

According to some embodiments of the system 10, the data collected by the mobile phone 70 may be sent over WiFi to the internet (or by using mobile network data transfer protocols) to a remote central data server for cloud processing and/or data logging. The data resulting from such remote processing and recording may be accessed by the mobile phone 70 or the laptop 9 and may be used for calculating values such as the mood index MX and/or the modulation index MI, or for calculating other values required for the method disclosed in detail below. Optionally, the processing and/or computation may be offloaded to a cloud remote server that may transmit any computed values (e.g., MX and/or MI disclosed in detail below) over the internet (using WiFi or mobile data transfer protocols, or any other suitable communication protocol) to mobile phone 70 and/or laptop 9 for use and/or for telemetrically sending these values to telemetry module 133 for use by processor/controller 14.

Data preprocessing and feature engineering

As disclosed in detail in Joost Asselbergs et al (2016), cited in the list of references below, raw EMA and insignificant EMA data can be preprocessed into a data file that aggregates each day of each participant into a continuous 53 variables.

Prediction indexes are as follows: ecological transient assessment of mood

In LiKamWa et al, the EMA data (i.e., the one-dimensional emotion measurement and the two measurements of the cyclic model, the parity and arousal) are summarized as daily averages as indicators of the emotion prediction algorithm. Daily means were normalized among each participant (i.e., using mean and standard deviation calculated separately for each participant).

Emotion prediction feature set

The raw, insignificant EMA data is summarized into daily summaries from which feature sets can be derived, as disclosed in detail in table 1 of the Asselbergs et al article cited in the reference list below. For telephone and SMS messages, the system counts the number of interactions of the participant with the five most frequent contacts. According to the LiKamWa et al study, a histogram of this interaction frequency can be created over a 3-day history window, and the normalized frequency count can be used as a sample in the feature set. Similarly, a normalized 3-day histogram of the durations of calls with the first five contacts may be created. Most participants only accidentally interact with people other than the first five via telephone or SMS messages. In summary, the raw call/SMS short message data is summarized into three predictive features (frequency and duration of the first five calls and frequency of the first five contacts SMS short messages), including 15 variables.

The original mobile phone screen on/off event is converted into two characteristics: (1) total number of screen openings per day; (2) the total time of screen on per day (calculated as the time difference between screen on/off events between two screens). Both of these features translate into standard normal variables in each participant.

The accelerometer data represents the acceleration of the smartphone in the x, y, and z planes. The acceleration is sampled 5 seconds per minute (the sampling frequency is estimated to be 20 to 200 hz, depending on the hardware and software features of the participant's mobile phone). The raw data is summarized (by the activity probe of Funf on the phone) as a high activity variable by calculating the percentage of time that the sum variance of the acceleration of the device (in the x, y, z plane) exceeds a set "high activity" threshold (i.e., the sum variance exceeds 10 meters per second squared).

As a daily measure of mobile phone application usage, two 3-day normalized histograms were created for the day and duration of the five most common mobile phone applications. In addition, a normalized histogram of application class usage frequency and duration is created. According to LiKamWa et al, applications are classified as built-in, communication, entertainment, financial, gaming, public affairs, travel, utility, other, or unknown (total of 11 classes). The category of the recorded application is determined by script query of the Google Play store. Applications unknown to the Google Play store are manually categorized based on networked searches. In summary, the final data set is composed based on four features of the application usage record: the first five application frequencies, the first five application durations, the application category frequency (11 categories), and the application category duration (11 categories). These features yield 32 variables (5+5+11+ 11).

The mobile phone camera logs are summarized into the number of photos taken every day. Next, this summary is converted to a scale of 0 to 1 for each participant by dividing all values by the maximum number of pictures taken.

Finally, similar to the LiKamWa et al study, the set of predictive features that can simply represent an emotional history is expanded by adding record 1 and record 2 transformations for each emotional variable (normalized among each participant).

In general, a 53-dimensional set of variables comes from 13 different predictive features. Since the regression model is very sensitive to scale differences of the independent variables, the scale of the independent variables was transformed to a standard normal distribution (i.e., 99.7% of the values were between-3 and 3). According to the method of LiKamWa et al, interrelated variables (e.g., the call of the top 5 digits ranked and the application used by the top 5 digits ranked) are normalized to a range of 0 to 1.

The results of the therapeutic brain stimulation may also be reviewed periodically with an ecological instantaneous mood assessment (EMA) to determine the effect of the stimulation on the reported mood and resulting patient physiology as the stimulation is delivered. Stimulation parameters may also evolve and change based on mood, reporting, and physiological parameters. This may include variations in stimulation amplitude, stimulation pulse width, and pulse frequency. The end result is a dynamic recording and stimulation system that can continually self-assess its performance based on the patient's reports. This will therefore allow a patient's biomarkers to be not only specific, but also to be adjusted over time as the patient's baseline physiology is unstable or its basic brain state and physiology changes over time.

Methods and sensors for determining other depression biomarkers

It is noted that the (optional) auxiliary sensor 15 (of fig. 1) may optionally provide additional biomarkers that may be used as usable data. In some embodiments of the methods of the present application, a global sentiment index (e.g., sentiment index MX) is calculated. The sensor data may be sensed by the auxiliary sensor unit(s) 15 and may include Heart Rate (HR), perspiration data, pupil size (and/or time parameters of pupil size change when testing is performed on a patient), etc., as disclosed above.

For example, it has been shown in the following article that the values of the heart beat interval and the high frequency peak of the spectrum analysis of the patients with major depression are significantly lower than those of the normal group (control group).

Rechlin T, Weis M, Spitzer A, Kaschka W.P. "is mood disorder associated with altered heart rate variability? The journal of emotional disorders 32(I994) stage, pages 271-275.

This fluent article also shows that children with depression had a late reduction in pupil dilation 9-12 seconds after the appearance of the negative word compared to the control group of children. In the natural environment, the reduction of late stage pupil dilation to negative vocabulary presentation is associated with a higher level of negative emotions and a lower level of positive emotions.

Jennifer s.silk, Ronald e.dahl, Neal d.ryan, Erika e.forbes, David a.axelson, Boris biraher and Greg j.siegle, "pupils response to affective information in childhood and juvenile depression: connection to clinical and ecological measures ", IEEE affective computing bulletin, volume 7, phase 1, (2016).

It is well known that the reduction of the frequency range of vowel production is a speech feature in patients with psychological and neurological disorders, while mood disorders such as depression and post-traumatic stress syndrome (PTSD) affect motor control, particularly speech production.

For example, in the following article, authors use an automatic unsupervised machine learning based approach to evaluate the speaker's vowel space. Experiments based on 253 individual recordings showed a significant reduction in the vowel space for subjects scoring positive in the questionnaire. The decrease in vowel space in subjects with depressive symptoms can be explained by the common occurrence of psychomotor retardation affecting joint movement and motor control.

Steman Scherer, Gale m. lucas, Jonathan Gratch, Albert "Skip" Rizzo and Louis philippie Morency, "self-reported symptoms of depression and post-traumatic stress disorder are associated with reduced vowel space in screening interviews," IEEE symptomatology calculations, volume 7, phase 1, pages 59-72 (2016).

According to some embodiments of the systems and methods of the present application, such physiological parameters related to the effects of depression or other mood disorders may be used as additional (sensor-based) biomarkers for assessing a patient's mood.

For example, in some embodiments of the system 10, a Heart Rate (HR) sensor (included in the mobile phone 70, or a separate HR sensor that may be connected to the mobile phone 70 or to the patient's body) may be used to determine the patient's heart rate and provide heart rate data to the mobile phone 70.

In another example, in some embodiments of the system 10, an external microphone or microphone of the mobile phone 70 may be used for voice spectrum analysis of the patient's voice (recorded while the patient is talking on the mobile phone 70). The recorded data may then be processed (e.g., by the processor of the mobile phone 70 or in the cloud 31).

In another example, in some embodiments of the system 10, the size of the patient's pupil may be monitored and recorded by an appropriate application or separate device on the mobile phone 70, for example, the AR headset 11 or a patient-worn dedicated pupillometer with pupil size measurement capabilities may be used to acquire pupil size data (and optionally eye tracking data) periodically or in response to a test presented to the patient (negative/neutral/positive wording test as described by silk et al, supra). In short, a test session may be initiated using the mobile phone 70, where test words with different negative/neutral/positive emotional connotations are presented on the screen of the mobile phone 70, while measurements and recordings are made by the front-facing camera of the phone or a dedicated pupillometer device worn by the patient in response to temporal changes in pupil size stimulated by the presented words.

It should be noted that the method of acquiring EMA data may also include an unobtrusive method of monitoring patient pupil size changes as the patient browses web content, the changes being responsive to wording with negative emotional content. For example, if the patient is browsing web content using the AR headset 11, the eye tracking function of the AR headset 11 may enable the system to identify the word that the patient is currently watching, and the pupil size determination function of the AR headset 11 may monitor the change in pupil size due to reading a negative word to detect whether the patient is in a depressed mood. The words that the patient gazes may be identified as having normal (neutral) or negative emotional connotations based on a look-up table (LUT) stored in memory or another storage device (AR headset 11, laptop 9, mobile phone 70, or a remote server on cloud 31).

Such word lookup tables may include relatively few words (typically, in the range of tens to thousands of words) to speed up word recognition. If a word (using the LUT) is identified as having a negative emotional meaning, the system may store recorded pupil size data that ranges in time from a short time before the patient looks at the word to a few seconds (typically 10 to 15 seconds) after the patient begins to look at the word, ending. The stored data may then be processed to determine whether the parameters of pupillary response are indicative of a depressed mood, as disclosed in detail above and in the Silk et al (2016) article cited above. The advantage of this method of obtaining mood-related data by pupillometry is that it is completely unobtrusive and does not require an intrusive testing procedure to be performed on the patient.

Data representing parameters of the pupillary response may be processed to obtain parameters related to the patient's mood (e.g., magnitude of late pupillary dilation in response to presentation of a negative word, response latency and duration, or other pupil size characteristics). These parameters may be processed by the system 10 to assess the mood of the patient. It should be noted that each patient was evaluated individually during the test to determine the dynamics of the patient's pupil size changes, as the characteristics of the pupil's response to negative word presentation may vary with the patient's age, as children, adolescents and adults may also differ (as described by Silk et al). After obtaining the test results, statistical analysis can determine response parameters (assessed by EMA) associated with the severity of depressed mood. These parameters may then be included in the model.

An example of a pupillometer that can be used for such pupil sizing is the Tobii Pro 2 wearable eye tracker available from Tobii AB, stockholm, sweden.

Note that the above three examples (HR measurement, pupil size dynamics measurement, and vowel space measurement) are merely three non-limiting examples of biomarkers that may allow for "multimodal analysis" to build the "models" disclosed herein. Such biomarkers may include any other measurable physiological and/or behavioral characteristic of a patient that exhibits a correlation with the patient's mood, and any such biomarkers may be included in the data processing performed by the methods and algorithms for calculating a value of the mood index (MX) disclosed herein. For example, the pupillary dynamics variation test may be modified by: parameters of pupil size change are monitored in response to presentation of images having negative, neutral, or positive connotations to the patient instead of negative text presentation.

In some embodiments, the presentation of images (or text) and the monitoring of pupil size changes may be performed by the AR headset 11, which may be used for image (or word) presentation and for determining changes in pupil size by the AR headset 11. In other embodiments, the image (or text) may be presented on the screen of the mobile phone 70 or on the screen of the laptop 9, while the pupil size change may be monitored by a dedicated pupillometer (e.g., Tobii pro 2 as disclosed herein) or the AR headset 11.

The term "model" as used herein relates to recording a plurality of different biomarkers (brain activity, heart rate, pupil dilation, sound spectrogram, or any other relevant mood-indicating biomarker), user manual inputs (e.g., inputting their current feelings), and caregiver inputs, processing these multiple inputs using various algorithms to communicate information specific brain stimulation treatment patterns and/or provide visual/auditory feedback to the user or their caregiver.

Digital signal processing

The signals recorded by the system are processed in the following manner. Channels of amplitude anomalies (e.g., ± 1000 millivolts) or power spectra (e.g., harmonic noise) were flagged and removed from further analysis. The system performs spectral decomposition using Morlet wavelet convolution and estimates the phase and amplitude envelopes from the resulting complex signal. All signals are then down sampled to 300 hz. To avoid edge effects, all wavelet characteristics (i.e., phase, amplitude, and power) are generated from the entire signal before the test signal is extracted.

First method-Phase Amplitude Coupled (PAC) signals as emotional biomarkers

Two sets of wavelet banks are used for Phase Amplitude Coupling (PAC). These wave banks are created to satisfy the mathematical constraints of the phase-amplitude coupling measurements. Specifically, amplitude frequency (F)_a) Must be the relevant phase frequency (F)_p) Twice as much. The construction of the two wavelet banks is as follows.

Frequency of amplitude wavelet

The full width at half maximum (FWHM) of the Morlet wavelet was used as a lower limit estimate of the bandwidth. F_aThe wavelets were designed to have a FWHM of 20Hz and 21 wavelets were used, with a center frequency from 20Hz to 150 Hz in 5 Hz increments.

Frequency of phase wavelet: narrow band F_pWavelets are designed for phase specificity. The phase signal uses higher frequency resolution to distinguish the delta, theta and alpha rhythms. We used 20F_pWavelets, ranging from 1 Hz to 20Hz, at a pitch of 1 Hz, and a full width at half maximum of 0.8 Hz.

Quantizing Phase Amplitude Coupling (PAC) with modulation index

The PAC is measured using a Modulation Index (MI), which can quantify the magnitude of the coupling. MI also provides a general measurement method that can compare different forms of PAC at different frequencies (e.g. monomodal versus bimodal). MI is calculated as the Kullback-Leibler divergence between the uniform distribution (i.e. pure entropy) and the observed probability density p (j), which describes the normalized average amplitude at a given binary phase (see p (j) below). The pairwise calculation of MI for the two frequency series will yield a modulation map. MI is calculated as follows:

wherein D_KLIs the Kullback-Leibler divergence, P is the observed phase amplitude probability density function, Q is the uniform distribution, and N is the number of phase bins. P follows the following formula:

whereinIs a phase signalIs detected by the average amplitude signal fA at the phase bin j. The phase is divided into 18 frequency bins spaced 20 degrees apart.

To determine the PAC frequency pairs of interest, the trials were ranked by the emotion indicated by EMA and divided into quartiles from best emotion to worst emotion. We use the signals from the highest and lowest mood measure quartiles to generate a p (j) distribution of normalized amplitudes for each combined phase from which MI can be calculated.

Statistical analysis

Band limited power and PAC time series comparison

Cluster candidates are generated using the t statistic to test for a null hypothesis, i.e., no difference between the classes of each sample. If the sample t statistic exceeds the 5% alpha level, the invalid hypothesis for the sample is rejected and considered as a cluster candidate. Temporally adjacent cluster candidates are grouped into one cluster, and their t statistics are added to generate a cluster statistic. A permutation distribution test was performed on the cluster statistics of the observed data. To generate the permutation distribution, the test tags (e.g., valid and invalid) were shuffled and randomly reassigned 10000 times. For each shuffle, cluster candidates and cluster statistics are generated as described above. The maximum cluster statistics from each shuffle are used to create a rank distribution. The value of P is calculated for the observed cluster using the formula P ═ r + 1)/(n +1), where r is the number of shuffled cluster statistics greater than the observed cluster statistics and n is the total number of shuffled combinations used. Multiple comparisons across cortical sites can be corrected using a False Discovery Rate (FDR) correction method.

Phase amplitude coupled comparison

A two-dimensional non-parametric permutation test is used to perform cluster-based statistical inference on the coordination graph based on the difference between positive and negative sentiment tests. First, 1500 random distributions were generated for each cortical site by randomly reassigning the mood measurements to trial, classification, and quartile, and calculating the absolute difference in the tone map of the elevated and depressed mood quartiles, as follows:

the mixed variance is inThe distribution in each frequency pair is used to determine a cutoff threshold specific to each frequency pair. Adjacent pairs of super-threshold frequencies are grouped together in clusters and summed with the t statistic. Using a two-dimensional cluster-based permutation test, the null hypothesis was tested, i.e., the shuffled data did not differ from the observed data, where the diagonals were not considered adjacent. PAC time series were calculated using MI in 50 ms increments over a 500 ms sliding window. The difference between PAC time series of emotion categories is calculated by the one-dimensional cluster-based permutation test described above.

The second method comprises the following steps: amplitude modulation

A second approach is to identify emotion related physiological biomarkers, including assessing amplitude changes at specific frequencies. Using the above method, amplitude changes can also be determined to correlate with emotional states. This can be done for different amplitudes of different frequencies on a single electrode, or for different frequencies at different electrode locations.

Method for determining emotion index by using amplitude change

The original signal is high-pass filtered at a frequency of 0.05 hz using a third order Butterworth filter. The electrodes containing too much noise will be removed from further analysis. In addition, the time period in which artifacts are contained in most electrodes is discarded. The average of the noise-free electrodes was regressed from the signal of each electrode.

The Power Spectral Density (PSD) of the cortical signal from each electrode is estimated using the welch method. The window width of welch is 2 seconds (frequency resolution 0.5 hz) and the overlap rate is 50%. The power spectra are combined into standard bands (delta band: 0.1 to 4 Hz, theta band: 4.5 to 8 Hz, alpha band: 8.5 to 12 Hz, sigma band: 12.5 to 15 Hz, beta band: 15.5 to 25 Hz, low gamma band: 25.5 to 50 Hz, and high gamma band: 70 to 110 Hz), and then normalized by the total power of all bands.

Spatial spectral differences between states

Sensitivity (or discriminability) index d 'in signal detection theory is used'_b,cCortical electrophysiological differences between elevated mood and depressed mood states (defined by EMA) were examined for each subject in the frequency domain:

wherein, mu_b，oAnd σ_b，oThe average band-limited power (BLP) and the standard deviation of BLP for all periods in which the cognitive state is specified on the band b and the electrode c, respectively. ρ is the proportion of data belonging to each category.

Logistic regression model for state estimation

Using logistic regression, models are built that can accurately predict emotional states given cortical signals. The cortical signal from each behavioral epoch is divided into 120-second non-overlapping segments or samples. PSD is calculated for each sample and combined into a band to derive a set of featuresWhere C is the number of electrodes and B is the number of frequency bands. Tag y the characteristics and categories of all samples from a particular time period⁽ⁱ⁾(-1 for mood low, +1 for no mood low) were randomly placed into the training or test set as a group to preserve the category distribution in each set, so that approximately 80% of the total number of samples in all epochs were in the training set (approximately 20% in the test set). Five-fold cross validation (five-fold cross validation) was used to learn the model. Each fold (fold) has a unique test set.

In one folding, each feature is centered on the mean of the features of all training samples and normalized by the feature euclidean norm of all training samples:

wherein, χ⁽ⁱ⁾ _b,cIs the mean of the normalized features that is centered,is the BLP of sample i at band b and electrode c,is the average BLP over the training set within a fold. The feature means and norms calculated from the training set are also used to centralize and normalize the test set.

Using all featuresAnd the characteristicsA subset of learning models. For example, a unique model is learned for a set of features belonging to each frequency band. For each training set in n samples of a patientOr R ═ x⁽¹⁾，...，x⁽ⁿ⁾；y⁽¹⁾，...，y⁽ⁿ⁾The system models the probability that the patient is in a depressed state or a non-depressed state, e.g. sample i, using a linear model transformed by a sigmoid function (commonly referred to as logistic regression):

wherein z is x or x_bAnd w is the weight vector of the parameterized model. The system solves for these weights by maximizing the probability of correctly predicting each reading:

or equivalently, by minimizing the sum in the case where the probability is negative log:

modeling the probabilities can naturally represent prediction uncertainty, which has practical value for BCI applications because it is safer if the BCI remains in the off state when the system is not certain of the user's cognitive state.

Determining optimal cortical locations

The optimal electrode position for estimating the emotional state is determined by a constrained optimization problem. By adding weight to featuresMixed norm, system forced convergenceA solution using BLP from all bands but from a sparse set of electrodes.Mixed norm canonical logistic regression is shown below:

wherein lambda is more than or equal to 0, and the prediction precision of the training set is balanced by the sparsity of the electrode weight values. Similarly, for features that only use one particular frequency band as input x_bModel of (1), system using₁A model of a regular logistic regression,

the electrode sparsity varies independently from one to four electrodes (or more if desired). A binary search of each folded training set is used to learn the corresponding hyper-parameter lambda. Initially, an arbitrary value is assigned to λ, and then a subsequent model is constructed. If the model is more sparse than desired, λ is reduced to reduce the effect of the constraints on the model. Conversely, if the sparsity of the model is lower than expected, λ is increased. This process is repeated systematically until λ converges to a value that provides the required electrode sparsity for the model.

Model prediction and performance

The output of each model is the probability that the subject is in a depressed state (equation (7) above; y)⁽ⁱ⁾1). Thus, the state is evaluated using the following rules:

model performance was quantified by evaluating the accuracy, sensitivity, and specificity of each fold test set.

Referring now to fig. 6, fig. 6 is a flow diagram illustrating steps of a method of brain stimulation therapy by processing sensed cortical activity and ecological transient mood assessment data of a patient, in accordance with some embodiments of the methods of the present application.

The system (e.g., system 10) initiates and senses (and records) cortical electrical signals (step 300). The cortical region may be the right DLPFC, the left DLPFC or any other region of the PFC. The system 10 processes the recorded cortical signals (step 302). The system then checks whether a biomarker for depression is detected in the recorded signals (step 304). The marker may be the modulation index MI or any other suitable cortical biomarker (e.g., a state estimate in which the patient has a probability of depression equal to or greater than 0.5, as disclosed in detail above). If no biomarker is detected (e.g., if the probability of patient depression is less than 0.5), the system returns the steps to step 300 and continues to sense and process cortical signals. If a biomarker is detected, the system then checks whether the value of the currently calculated mood index MX is equal to or less than a threshold value (step 306). The threshold may be determined during the test or may be set by a caregiver or a doctor. According to some embodiments, the sentiment index value may be calculated as follows:

MX＝(aA₁+bB₂+cC₃+…+mM_n)/n

wherein:

n is the total number of biomarker parameters used (including cortical signal biomarkers and/or one or more biomarker parameter values sensed by one or more auxiliary sensors 15);

a. b, c, … m are n weighting factors;

and A, B, C, … M are based on correlation with patient reported EMA data, normalizing actual biomarker values to a range of 1 to 10.

If the value of the mood index (MX) is greater than the threshold, the system moves the step to step 300. If the value of the mood index (MX) is equal to or less than the threshold, the system delivers cortical stimulation (step 308). The stimulus may be delivered to the right DLPFC and/or left DLPFC and/or any other selected region of the PFC. The system then checks whether biomarkers for depression are still detected (step 310). If a biomarker of depression is still detected, the system transfers the step to step 308 to continue cortical stimulation. If no biomarker of depression is detected, the system checks whether the value of the mood index MX is greater than a threshold value (step 312). If the value of the mood index MX is greater than the threshold, the system terminates the stimulation (step 314) and returns the step to step 300. If the value of MX is not greater than the threshold, the system moves the step to step 308 to continue delivering cortical stimulation.

Referring now to fig. 7, fig. 7 is a flow diagram illustrating steps of a method for assessing a correlation between one or more parameters of recorded cortical signals and an emotion index calculated from ecological instantaneous emotion assessment (EMA) data of a patient, according to some embodiments of the method of the present application.

The testing method includes sensing and recording cortical signals from one or more cortical regions (step 320). The cortical areas sensed may include the right DLPFC and/or the left DLPFC and/or any other selected areas of the PFC.

The system receives and records the EMA data and/or other biomarker data (e.g., biomarkers sensed by any auxiliary sensors 15) from the patient and calculates an emotion index from the EMA data and/or other biomarker data (step 322).

The system may then process and analyze the recorded cortical signal and the mood index to detect one or more positive correlations between one or more parameters of the cortical signal and the calculated mood index (step 324).

The system then determines one or more parameters of cortical signals suitable for use as one or more biomarkers of depression from the detected positive correlations (step 326).

It is noted that while it is possible to deliver an antidepressant therapeutic treatment using a single type of stimulation paradigm, in some embodiments of the method, the system may deliver a graded stimulation paradigm as the antidepressant therapeutic treatment.

Referring now to fig. 8A-8B, fig. 8A-8B are flow diagrams illustrating method steps for providing a graded brain stimulation therapy to a patient by processing the patient's perceived cortical activity and ecological transient mood assessment data, according to some embodiments of the methods of the present application.

The system may begin by setting the value of parameter C to zero (step 340). The system then presents the patient with a request for an emotional assessment (step 342). The request may be in the form of a screen on the mobile phone 70 or laptop 9 asking the patient to provide a mood self-assessment representing the patient's subjective perception of whether or not he/she is depressed and the degree of depression. For example, in some embodiments of the method, the patient may enter a number in the range of 1 to 10, where the number 10 represents the most severe state of depression and the number 1 represents a mood that is not depressed at all.

The system then checks whether a response has been received from the patient to the request (step 344). If the patient's response is not received (within an assigned response period (e.g., 2 minutes), the system returns the step to step 342 to re-present the request if the patient's response is received within the assigned response period in time, the system calculates and stores the value of the received self-assessed emotional index and calculates the value of the emotional index MX based on the modulation index MI, the EMA data, and the patient's self-assessed value in parameter MI1 (step 346) — after a preset period of time (e.g., two hours), the system presents another emotional assessment request to the patient (step 348) — then the system checks whether the patient's response is received within the assigned response period (step 350) — if the patient's response is not received within the assigned response period, the system returns the step 348 to re-present the request, the system then calculates the value of the mood index MX from the modulation index MI, the EMA data and the new self-assessed value of the patient and stores the calculated value of the mood index MX in the parameter MI2 (step 352).

The system then checks whether MI2 ≧ MI1 (step 354). If MI2 ≧ MI1, the system stores the value of MI2 in MI1 (step 358), sets the value of MI2 to zero (step 360), and transitions the step to step 348. If MI2 is MI1, the system selects stimulation example C from a look-up table (LUT) comprising N hierarchical stimulation examples and initiates cortical stimulation using stimulation example C (step 356). The system records the values of MI1, MI2, and C in memory (step 362) to provide the caregiver with recorded stimulation history information. The system then checks if the parameter C is N. If C ═ N, the system terminates the stimulation (step 366), and may optionally present a warning signal (either visually or audibly, e.g., an audible sound or warning screen on the mobile phone 70 or laptop 9) to the patient and/or caregiver (step 367).

If C is not equal to N, the system sets the value of C to C +1 (step 368) and moves the step to step 358.

In the method, prior to operation, a program operating on the system may load a LUT comprising N stimulation paradigms having increasing efficacy for treating depression, such as in a test cycle, evaluating various stimulation paradigms and determining their efficacy in treating a depressed mood. For example, if the stimulation paradigm includes delivering a list of supra-threshold stimulation pulses to the stimulated cortical region, the grading may be performed by using an increased pulse frequency for different stimulation paradigms. In some embodiments, the number and location of electrodes may be varied to deliver stimulation therefrom, in some embodiments, allowing the implant to stimulate deep brain structures, such as system 140 and system 160 (and in fig. 12-17) disclosed below, a graded efficacy stimulation paradigm may be achieved by varying the area of the cortex being stimulated and/or the deep brain structures being stimulated. For example, if it is found experimentally that stimulating right DLPFC is less effective than stimulating right DLPFC and anterior cingulate cortex, and stimulating left DLPFC during a trial period, the ventral caudate nucleus is more effective in treating depressed mood, possibly using this different stimulation paradigm to deliver a graded stimulation paradigm in response to an increase in the patient's emotional severity. Any suitable combination and/or sub-combination of such fractionation methods may be used in the methods. For example, the number and location of stimulation electrodes may be varied, while varying the stimulation pulse frequency and/or varying the particular combination of regions being stimulated.

In this way, in some embodiments, the system starts with the least efficient stimulation paradigm (C ═ 0), and if no relief in emotional severity is detected, the system will continue to use the more efficient stimulation paradigm until the most efficient stimulation paradigm is used, at which time the system will stop stimulating and notify the patient and/or caregiver. Alternatively, if the most efficient stimulation paradigm is used without successfully reducing the severity of the depressed mood, the system may (optionally) reset C so that C is 0 and start a new graduated stimulation cycle (not shown in fig. 8A-8B).

It is noted that although as described above the spectral power over a plurality of different frequency bands may be used to calculate the modulation index MI, this is not essential and some methods may use only spectral power over a single selected frequency band.

Referring now to fig. 9A-9B, are flow diagrams illustrating steps of a method of providing brain stimulation therapy to a patient by using power values at the gamma band (P γ) of the sensed cortical signals and ecological instantaneous emotion assessment (EMA) data of the patient, according to some embodiments of the methods of the present application.

In the method described in fig. 9A-9B, the system then presents a request for an emotional assessment to the patient (step 370). The request may be in the form of a screen on the mobile phone 70 or laptop 9 requesting the patient to provide a mood self-assessment representing the patient's subjective perception of whether or not he/she is depressed and the degree of depression. For example, in some embodiments of these methods, the patient may enter a number in the range of 1 to 10, where the number 10 represents the most severe state of depression and the number 1 represents a mood that is not depressed at all.

The system then checks whether a response to the request has been received from the patient (step 372). If the patient's response is not received (within an assigned response period (e.g., 3 minutes), the system returns the step to step 370 to make the request again, if the patient's response is received within the assigned response period in time, the system calculates and stores the value of the received self-assessed emotional index, and calculates the value of the emotional index MX based on the modulation index MI, the EMA data, and the patient self-assessed value in parameter MI1 (step 374). after a preset period of time (e.g., one hour), the system makes another emotional assessment request to the patient (step 376). then, the system checks whether the patient's response is received within the assigned response period (step 378). if the patient's response is not received within the assigned response period, the system returns the step to step 376 to make the request again, the system calculates the value of the mood index MX from the modulation index MI, the EMA data and the new self-assessed value of the patient and stores the calculated value of the mood index MX in the parameter MI2 (step 380).

The system then checks whether MI2 ≧ MI1 (step 382). If MI2 ≧ MI1, the system stores the value of MI2 in MI1 (step 384), sets the value of MI2 to zero (step 386), and transfers control to step 376. If MI2 is MI1, the system senses signals in one or more cortical regions (step 388), performs a Fast Fourier Transform (FFT) on the recorded cortical signals (step 390), and calculates the power at the gamma band P γ from the resulting power spectrum (step 392). The system then checks whether P γ is ≦ threshold (step 394). The threshold may be a preset threshold determined during the test that relates the value of P γ to EMA data and/or a self-assessment of mood received from the patient.

If P γ ≦ threshold, the system begins stimulating the target brain region (step 396) and transfers control to step 384. The target brain region for stimulation may be selected from any cortical region disclosed in the present application and/or any deep brain structure disclosed in the present application and/or any combination or sub-combination thereof, as disclosed above. If P γ > threshold, the system transfers control to step 388 to continue sensing cortical signals.

Reference is now made to fig. 10, which is a flow diagram illustrating a method for providing fractionated stimulation therapy to a patient in response to processing cortical signals, EMA data, and other sensor data, in accordance with some embodiments of the methods of the present application.

The system first sets the values of the following parameters K ═ 1 and N ═ N, where K isIs a counter parameter, n is the available stimulation pattern SR_KThe number of (step 400). Then, the system initiates a stimulation mode SR_K(step 404). The system then receives the cortical signals and the EMA data and, optionally, sensor data received from any auxiliary sensor(s) 15 of the system (step 406). The system then calculates the current value of the mood index MX from the currently available cortical signals and EMA data and/or (optional) sensor data. The system then checks whether MX ≦ T, where T is the threshold determined in the appropriate system test period for empirically finding an acceptable threshold above which the stimulus should increase.

If MX is>T, then the system transfers control to step 406. If MX ≦ T, the system checks if K ≦ n, indicating that the most effective stimulation pattern has been used. The system signals an alarm to the patient and reports to the caregiver and/or patient (using the audio or video signals detailed above (step 414), sets the value of the counter K to 1 (step 416), and transfers control to step 404 to use the stimulation mode SR_K＝SR₁The stimulation is continued. If K ≠ n, the system sets K ≠ K +1 (step 418) and returns control to step 404.

In this approach, the n stimulation patterns may be stored in a suitable LUT, as described above. With increasing n, the stimulation pattern SR_KThe efficiency of treating a depressed mood is improved (where n is an integer). Thus, SR₁，SR₂，SR₃，…，SR_nIn order of increasing effectiveness of the therapy for the depressed mood.

The stimulation pattern may be any of the different stimulation paradigms disclosed above.

Reference is now made to fig. 11, which is a schematic flow diagram illustrating a method of providing intermittent brain stimulation therapy to a patient in response to processing cortical signals, EMA data, and additional sensor data, in accordance with some embodiments of the methods of the present application.

The system starts and receives and processes the cortical signal, EMA data, and (optionally) sensor data received from one or more auxiliary sensors 15 of the system (step 420). The system then calculates the current value of the mood index MX from the sensed cortical signal and EMA data and (optionally) sensor data (step 422). The system then checks whether MX ≦ T, where T is the preset threshold as described above (step 424).

If MX > T, the system transfers control to step 420. If MX ≦ T, the system initiates a therapeutic stimulation session (step 426). The time period may be any suitable time period empirically found (during preliminary testing for each individual patient) to be sufficient to produce a therapeutic effect on depressed mood. Such therapeutic stimulation periods may range from minutes to hours, depending on the type of stimulation delivered, the area of the brain stimulated, and other stimulation parameters.

While the stimulus is being applied, the system checks whether MX > T (step 428). If MX > T, the system terminates stimulation (step 432) and transfers control to step 420. If MX ≦ T, the system checks whether the therapeutic stimulation session is over (step 430). If the stimulation period has not ended, the system returns control to step 426 (while continuing to stimulate). If the stimulation period has ended, the system terminates stimulation (step 432) returning control to step 428 and returning control to step 420.

It should be noted that the method of fig. 11 always uses the same stimulation type (which can be programmed by the caregiver before starting the operation of the method). The stimulation type may be any of the stimulation types disclosed above in any suitable combination of stimulation targets, but the stimulation type is not modified or changed during operation of the program or method unless terminated before the end of the therapeutic stimulation cycle due to the detection of the condition MT > T in step 428.

It should be noted that the system of the present application is not limited to stimulating cortical areas (e.g., left DLPFC and/or right DLPFC). In some embodiments, deep brain structures may also be stimulated as part of a therapeutic stimulus for treating mood disorders.

Reference is now made to fig. 12 to 15. Fig. 12 shows a block schematic diagram of a system for treating a mood disorder, including scalp electrodes for transcranial frequency interferential stimulation of cortical and/or brain deep structures, and an array of intracranial-implanted ECOG electrodes for sensing and/or stimulating one or more cortical areas, according to some embodiments of the system of the present application. Fig. 13 shows a block schematic diagram of functional components of an intracranial portion of the system of fig. 12. Fig. 14 shows a schematic diagram of a system for treating a mood disorder having multiple intracranial ECOG arrays for performing sensing in one or more cortical regions and for performing Transcranial Frequency Interference Stimulation (TFIS) on one or more deep brain structures and/or direct stimulation on one or more cortical regions, according to some embodiments of the system of the present application. FIG. 15 shows a functional block diagram of a number of functional components included in the system of FIG. 14.

Returning to fig. 12, system 140 includes an extracranial module 141 and an intracranial module 135 that are in wireless communication with each other. The extracranial module 141 also includes one or more processors/controllers 114 suitably coupled to the memory/data storage device 116. The extracranial module 141 also includes a power supply 143 for powering the components of the extracranial module. The stimulus generator 118 is suitably electrically connected to four stimulus electrodes 145A, 145B, 147A and 147B, which are attached to the skin surface of the user's head 4 at four different locations. The stimulating electrodes 145A, 145B, 147A and 147B may be electrically coupled to the skin surface of the head 4 by using any suitable conductive gel or paste (e.g., any EEG electrode gel or paste). The stimulation electrodes 145A, 145B, 147A and 147B are connected to the stimulation generator 118 by suitable electrically conductive insulated wires 139A, 139B, 137A and 137B, respectively. A first stimulation current at a first frequency f may be applied by the stimulation generator 118 to the first electrode pair 145A and 145B, and a second stimulation current at a second frequency f + Δ f may be applied by the stimulation generator 118 to the second electrode pair 147A and 147B. The frequency ranges for both frequencies f and f + Δ f are too high to generate nerve discharges (e.g., f and f + Δ f ≧ 1 kHz). The stimulus generator 118 is suitably electrically connected to the processor/controller 114, which controls the operation of the stimulus generator 118.

Due to the interference of two different oscillations of the electric field resulting from simultaneous stimulation by the two first electrode pairs 145A and 145B and the second electrode pairs 147A and 147B of different frequencies, selective neuron activation can be achieved in deep brain structures located in defined regions where interference between the electric fields can result in a significant electric field envelope modulated at a differential frequency Δ f.

This selective stimulation method is known as Transcranial Interferential (TI) stimulation, described in detail in the paper by Grossman n. The exact location of the electrodes on the user or patient's head 4, as well as the stimulation intensity and frequency, may be determined, among other things, by the location of the deep brain structures being stimulated in the brain, the thickness of the skull, and other physical and electrical parameters (which may vary significantly between different users at different age stages), and may be determined empirically experimentally by conducting appropriate tests on each individual user/patient.

Since the size and shape of the area of neuronal recruitment zone in NTIS can be varied by adjusting or changing the location set of the stimulation electrodes 145A, 145B, 147A and 147B and/or the stimulation frequency and intensity (amplitude) parameters, one deep brain structure or several deep brain structures can be stimulated by appropriately changing the size, shape and location of the neuronal recruitment zone, as disclosed in detail by Grossman et al.

The system 140 may also include auxiliary sensor(s) 15, as disclosed in detail with reference to the system 10 of fig. 1. The auxiliary sensor(s) 15 may be in wireless communication with the wireless communication device(s) 100, for example with the mobile phone 70 and/or the laptop 9 and/or the AR headset 11.

Extracranial module 141 also includes a telemetry unit 117 suitably connected to processor/controller 114 for bi-directional communication with intracranial module 135. Optionally, the telemetry unit 117 may also be in two-way communication with the portable communication device 100 (e.g., with the mobile phone 70 and/or laptop 9 and/or AR headset 11). Extracranial module 141 and intracranial module 135 (optionally, portable communication device 100) may telemetrically exchange data, control signals, and status signals therebetween.

Intracranial module 135 can include an intracranial implanted electronic circuit module 152, two Ecog electrode arrays 144 and 146 suitably electrically connected to electronic circuit module 152, and an intracranial induction coil 146 (which can be similar to induction coil 16 of fig. 1) suitably electrically coupled to electronic circuit module 152 to provide electrical energy to electronic circuit module 152 as is, as disclosed in greater detail above. As shown in fig. 12, Ecog array 142 may be disposed on the left DLPFC and Ecog array 144 may be disposed on the right DLPFC. For clarity of illustration, the cortical hemisphere is not shown in detail in fig. 12.

Referring to fig. 13, the electronic circuit module 152 includes one or more processors/controllers 124; a power conditioning and storage unit 177 electrically coupled to the intracranial induction coil 146; a telemetry unit 17 suitably electrically coupled to the processor/controller; a memory/data storage unit 16 suitably electrically connected to said processor/controller 124; and a signal conditioning and digitizing unit 126 electrically connected to the Ecog arrays 142 and 144 for receiving sensed signals from the electrodes of the Ecog arrays 142 and 144. The conditioning and digitizing unit 126 is also connected to the processor/controller 124 to provide the digitized data of the sensed Ecog signal to the processor/controller 124.

Telemetry unit 17 may be in two-way communication with telemetry unit 117 of extracranial module 141, enabling two-way wireless transmission of data, control signals and status signals between processor/controller 114 and processor controller 124.

It is noted that the power conditioning and storage unit 177 may comprise suitable circuitry (not shown in detail in fig. 12) for conditioning the current induced in the intracranial induction coils 146 by a second induction coil (not shown in fig. 12-13 for clarity of illustration, but see induction coil 19 of fig. 1 for example) placed extracranially, which may be placed on the scalp of the patient's head 4. Alternating current flowing in such a second induction coil placed outside the skull induces an alternating current in the intracranial first induction coil. The alternating current flowing within the intracranial induction coil 146 can be rectified by a suitable current rectifying diode bridge circuit (not shown) included in the power conditioning and storage unit 177, and can be stored by any suitable charge storage device (not shown), such as a supercapacitor, capacitor, or rechargeable electrochemical cell included within the power conditioning and storage unit 177. The power conditioning and storage unit 177 is used to energize any current requiring electrical components of the electronic circuit module 152. Note that for clarity of illustration, electrical connections that provide power to the components of the electronic circuit module 152 are not shown in fig. 12-13.

In operation, system 140 may deliver therapeutic stimuli for treating the mood disorder using any of the methods disclosed in the present application. For example, Ecog arrays 142 and 144 may sense signals from the left and/or right DLPFCs, respectively, and the sensed signals may be conditioned (amplified and/or filtered) and digitized by signal conditioning and digitizing unit 126 and fed to processor/controller 124 for processing (according to any of the processing methods disclosed herein). If the processor/controller 124 of the system 140 detects patient depression. The system 140 may stimulate one or more deep brain structures using the NTIS method described above, using the extracranial module 141, via the electrodes 145A, 145B, 147A and 147B and the stimulation generator 118. The extracranial module 141 may then be used to stimulate any of the deep brain structures disclosed in this application to treat the patient's depressed mood. Alternatively and/or additionally, the system 140 may use any of the Ecog arrays 142 and 144 to deliver cortical stimulation to the left DLPFC and/or the right DLPFC, respectively, and/or both simultaneously.

Having a sensing/stimulation device (e.g., Ecog array 142) for sensing/stimulating the left DLPFC and another sensing/stimulation device (e.g., Ecog array 144) for sensing/stimulating the right DLPFC may allow for simultaneous machine learning to optimize the stimulation frequency delivered to the right DLPFC for sTMS-like stimulation and the stimulation frequency delivered to the left DLPFC for rTMS-like stimulation, both of which may have independent efficacy in treating depression.

It should be noted that the systems disclosed herein are not limited to sensing and stimulation in left and/or right DLPFCs using an intracranial implanted ECOG array, but may use other types of more or less invasive stimulation/sensing devices. For example, two intracranial implants (such as, but not limited to, implant 20 of fig. 1) may be implanted in the skull covering the left and right DLPFCs and may be used to sense and stimulate the left and right DLPFCs, respectively. Other types of injectable electronics that may be used in the sensing/stimulation device may include mesh-type injectable electronics, nerve dust, and stent thick wire electrode arrays, among others.

The construction and methods of using such different types of electrodes and electrode arrays and their associated electronic circuits that may be used in the system for treating mood disorders of the present application are described in detail, inter alia, in some of the following references:

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US patent US 8,121,694 to Molnar et al entitled "treatment control based on patient movement status".

Although system 140 uses NTIS to non-invasively stimulate one or more deep brain structures and one or more invasive electrode sets, such as Ecog electrode arrays 142 and 144 (or other types of electrode arrays, such as UTAH electrode arrays, whose electrodes can penetrate the cortical surface), the exemplary configuration is not a necessary configuration to implement the methods disclosed herein. While the non-invasive nature of the stimulation electrodes in NTIS simplifies the stimulation procedure, the user must be restrained on the extracranial module 141 (in the case where the module 141 is a large static module), or may have to carry (or wear, where the module 141 is implemented as a small lightweight module that can be carried by the user) the module. Furthermore, performing NTIS using extracranial electrodes may be inconvenient to the user, may be significantly unsightly, and may also require frequent maintenance and care to avoid inadvertent electrode movement or undesirable changes in the electrical coupling characteristics of such extracranial stimulation electrodes to the skin.

Referring to fig. 14-15, all components of the system 160 are disposed intracranial, except for the portable communication device unit 100 (e.g., mobile phone 70 and/or laptop 9 and/or AR headset 11) disposed outside the patient and some or all of the auxiliary sensors 15 that may be attached to or implanted in or worn by the patient, as disclosed in detail above. The portable communication device 100 may be wirelessly connected to the cloud 31 and may exchange data and/or control signals/commands (not shown in fig. 13) with a remote processor in the cloud 31, as disclosed in detail above with respect to the system 10 of fig. 1.

System 160 may include an intracranial implanted electronics module 162, three intracranial implanted Ecog electrode arrays 164, 166, and 168 electrically connected to electronics module 162, and an intracranial induction coil 146 electrically connected to electronics module 162. The Ecog electrode array 168 may be disposed on the DLPFC or on a portion or portion of the PFC. According to some embodiments of the system 160, the Ecog electrode array 168 may be disposed on the PFC region of both cortical hemispheres, as shown in fig. 14, enabling selective sensing and/or stimulation of one of the left and/or right DLPFCs by appropriate selection of the individual electrodes 168A of the Ecog electrode array 168 for sensing and/or stimulation. Alternatively, according to some embodiments of system 160, Ecog electrode array 168 may be disposed on the PFC of the right cortical hemisphere or a portion thereof (for sensing and/or stimulating the right DLPFC). Alternatively, according to other embodiments of system 160, Ecog electrode array 168 may be disposed on the PFC of the left cortical hemisphere or a portion thereof (for sensing and/or stimulating the left DLPFC).

In some embodiments, Ecog electrode array 164 may be disposed on or over a portion of the left cortical hemisphere, and Ecog electrode array 166 may be disposed on or over a portion of the right cortical hemisphere.

Turning now to fig. 15, a system 160 may include one or more processors/controllers 14; a memory/data storage 16 suitably connected to the processor/controller 14; a telemetry unit 17, suitably connected to the processor/controller 14, is used to wirelessly transmit data and/or control signals to the portable communication device 100 (disposed outside the patient's body). The system 160 may also include a power conditioning and storage unit 177 suitably electrically connected to the induction coil 146 to receive alternating current therefrom (as described in detail with respect to the induction coil 16 of fig. 1). The structure and operation of the power conditioning and storage unit 177 is as described in detail above with respect to the power conditioning and storage unit 177 of fig. 13.

The system 160 may also include a stimulation generation module 170 suitably connected to the processor/controller 14 and controlled by the processor/controller 14. The stimulation generation module 170 includes a direct cortical stimulation generator 172 and a frequency interferential stimulation generator 174 adapted to provide the different frequencies required to stimulate the deep brain structures. The system 160 may also include one or more multiplexing units 176. The multiplexing unit 176 is suitably connected to the stimulus generation module 170 and the processor/controller 14 for controlling the delivery of stimulation from the frequency interference stimulus generator 174 to deep brain structures and controlling the delivery of direct cortical stimulation from the direct cortical stimulus generator 172 to selected electrodes of the Ecog electrode arrays 164, 166 and 168.

The system 160 may also include one or more sensed signal conditioning and digitizing units 126, suitably electrically connected to the Ecog sensor arrays 164, 166, and 168, for conditioning signals received from electrodes included in the Ecog arrays 164, 166, and 168, as disclosed in detail above with respect to fig. 13.

The power conditioning and storage unit 177 may provide power for the operation of the electronic module 162. However, for clarity of illustration, the connections providing power to the various components of the electronic module 162 are not shown in detail in FIG. 15.

Portable communication device 100 is any suitable communication device capable of telemetric communication with telemetry unit 17 of electronic module 162 (e.g., mobile phone 70 and/or laptop 9 and/or AR headset 11 of fig. 14), or any other handheld or portable device, including processing and control and wireless communication components, as described in detail above with respect to system 10 of fig. 1.

In operation, the system 160 may sense electrical signals from one or more cortical areas of the user by using one or more of the Ecog electrode arrays 164, 166, and 168 (e.g., electrode array 168 is sensed in the patient's left and/or right DLPFC). The sensed signal may then be conditioned (e.g., by optionally filtering and amplifying it) and then digitized by a sensed signal conditioning and digitizing unit 126 and fed to processor/controller 14 for processing (according to any of the processing methods disclosed herein). If the processor/controller 14 detects a depressed mood based on the processing of the sensed signals and the EMA data and (optionally) based on data from the auxiliary sensor 15, the processor/controller 14 may control the stimulation generator module 170 to stimulate one or more deep brain structures as described below. The processor/controller unit 14 may control the multiplexing unit 176 to select the two spaced apart electrodes 164A and 164B of the Ecog electrode array 164 and to select the two spaced apart electrodes 166A and 166B from the Ecog electrode array 166. After selecting the electrodes, the processor/controller 14 controls the frequency-interfering stimulus generator 174 to apply an oscillating current or voltage signal having an oscillating frequency f between the electrode pair 164A and 164B, and simultaneously apply an oscillating current or voltage signal having an oscillating frequency f + Δ f. The two frequencies f and f + af may be greater than or equal to 1 khz.

As noted above, this time-perturbed approach to stimulation is somewhat similar to, but not identical to, the NTIS approach of Grossman et al, but differs from the NTIS approach in certain respects. The first difference between the two methods is that while NTIS uses an extracranial non-invasive stimulation electrode to achieve non-invasive deep brain stimulation, another method described herein with respect to system 160 uses an intracranial stimulation electrode (an intracranial implanted Ecog electrode array or other intracranial electrode array) to stimulate one or more deep brain structures. To clearly distinguish the method of using the intracranial stimulation electrodes disclosed herein from the NTIS method, we refer to the second method as intracranial temporal interference stimulation (ICTIS) throughout the application.

Another advantageous difference between NTIS and ICTIS is that in NTIS, the extracranial electrodes are fixed at the same position of the head, and the stimulation electrodes used can be changed very quickly by simply controlling the multiplexing unit 176 to select different electrode pairs from any Ecog electrode array as stimulation electrode pairs, and to transmit two different interfering oscillation frequencies to any desired configuration of stimulation electrode pairs. This advantage may enable better control and modulation of the size, shape and location of neuronal recruitment focus areas formed within the brain.

Furthermore, the configuration of system 160 allows for additional control over stimulation, as stimulation electrodes can be changed almost immediately by applying stimulation oscillations having a frequency f to one of two different electrode groups having any desired number and configuration of electrodes selected from Ecog electrode array 164, and simultaneously applying stimulation oscillations having a frequency f + Δ f to the other of two different electrode groups having any desired number and configuration of electrodes selected from Ecog electrode array 166. This method of electrode grouping variation in each pair of stimulation electrodes may allow for better control of parameters of the neuronal recruitment envelope region than the NTIS method with statically fixed sized pairs of stimulation electrodes.

Furthermore, another advantage of the ICTIS approach is that the configuration and location of electrode group pairs or individual electrode pairs can be rapidly alternated, allowing for rapid alternating changes in the location and/or size and/or shape of neuronal recruitment areas between stimulation group pairs at different locations or individual electrode pairs at different locations, which may result in alternating stimulation of deep brain structures at different locations within the user's brain. Such changes can also be used to achieve finer temporal control of deep brain structures if desired (which means that it is possible to stimulate different deep brain structures at different times after the detection of the indications disclosed above.

Another feature of the system 160 is that it may not only allow stimulation of deep brain structures by NTIS or by ICTIS, but may also stimulate selected areas of certain cortical areas by applying stimulation signals (e.g., pulses or stimulation pulse sequences) directly to any selected electrode (or electrode pair or electrode group). For example, the processor/controller 14 may control the multiplexing unit 176 and the direct cortical stimulation generator 172 to deliver direct stimulation to any desired cortical area under the Ecog electrode arrays 164 and 166, and/or to the DLPFC or any portion thereof through the electrodes of the Ecog electrode array 168, or to any selected combination of the right DLPFC, left DLPFC, and other cortical areas under the Ecog electrode arrays 164 and 166.

Furthermore, by using appropriate multiplexing controls, multiple types of stimulation patterns may be performed, including, for example, simultaneous stimulation of one or more deep brain structures and one or more cortical areas (e.g., left DLPFC and right DLPFC), simultaneous stimulation of only one or more different cortical areas (e.g., right DLPFC and left DLPFC), stimulation of a single deep brain structure (by ICTIS), stimulation of a single cortical area or a portion thereof by direct stimulation of a selected one of Ecog electrode arrays 164, 166, and 168. Any combination and permutation of such stimulation patterns/methods may be performed.

Another advantage of using ICTIS rather than NTIS for any selected combination of deep brain structure stimulation and direct stimulation of one or more cortical areas is that in NTIS the electrodes are connected to the scalp by a conductive gel or paste, it is difficult to keep the stimulation electrodes in exactly the same position on the scalp for long periods of time due to accidental slipping or displacement of the stimulation electrodes, the above problems can be at least partially alleviated by the internal positioning of the intracranial electrode arrays (such as Ecog arrays or other intracranial arrays). In addition, intracranial placement can be performed by the Ecog array used in ICTIS, thereby addressing the problem of undesirable changes in scalp electrode impedance due to dry coupling gel or paste used to electrically couple the stimulation electrodes to the patient's scalp involved in NTIS.

It is further noted that in some embodiments of the system of the present application, the intracranial electrode arrays (e.g., Ecog arrays 144, 142, 164, 166, and 168) may be replaced with suitable Intracranial (IC) implants that are semi-invasively implanted inside the skull without damaging or completely penetrating the inner plate 6 of the skull 13. Advantages of using such IC implants may include reduced risk of patient complications, simpler and cheaper implantation procedures, ability to be performed in an outpatient day clinic, no need for hospitalization, and less trauma to the patient. Such IC implants for stimulation of deep brain structures in ICTIS or for sensing/stimulation of cortical regions, as disclosed in detail above for IC implant 20, advantageously increase the stability of the electrodes (as shown by IC implant 20 in fig. 5), reduce the tissue mass under the stimulation electrodes of the IC implant (compared to the scalp electrodes used in NTIS), reduce the stimulation current required, and greatly simplify and shorten the implantation procedure, reduce patient inconvenience, reduce or eliminate hospital stay, due to the anchoring of the IC implant to outer plate 5 of skull 13.

IC implants useful in the system of the present application may be similar to IC implant 20 configured for sensing and stimulating cortical regions, but may also be different IC implants specifically configured for delivering stimulation of deep structures of the brain and/or sensing/stimulating cortical regions.

Referring now to fig. 16-17, fig. 16 illustrates a human skull with an implanted intracranial implant for delivering deeper brain stimulation to the brain of a patient implanted in the skull of the skull, according to some embodiments of the intracranial implant of the present application. FIG. 17 is a top view of the skull shown in FIG. 16.

Note that in fig. 16-17, other components of a system in which the illustrated IC implant 180 may be used are not shown and are provided to indicate the location of the IC implant 180 and its components in the skull of the skull. Such system components may include a portable communication device 100, an effector device 14, and an auxiliary sensor 15 as disclosed for the system 10 of fig. 1.

The IC implant 180 may include a housing 190 similar to the housing 202 and four elongated flexible intracranial electrode arrays 182, 184, 186, and 188. The intracranial electrode array 188 has a plurality of conductive electrodes 182A disposed therealong. The intracranial electrode array 188 has a plurality of conductive electrodes 184A disposed therealong. The intracranial electrode array 188 has a plurality of conductive electrodes 186A disposed therealong. The intracranial electrode array 188 has a plurality of conductive electrodes 188A disposed therealong. The shell 190 may be made of a material similar to that disclosed above for the shell 202 of the implant 200.

When the IC implant 180 is implanted, cancellous bone of the outer plate 5 and the skull 13 may be present7 to receive the housing 190 therein. Four elongate channels (not shown) may then be drilled or laser ablated within the cancellous bone layer 7 in a direction generally parallel to the plane of the inner surface 6 to accommodate the four flexible elongate electrode arrays 182, 184, 186 and 188 therein. Preferably, the channel is made close to or abutting the outer surface 6B of the inner plate 6. The flexible electrode arrays 182, 184, 186 and 188 can then be inserted into the four channels and then the housing 190 inserted into the opening drilled in the outer plate 5 so that it is flush with the outer surface 5A of the outer plate. As shown in fig. 5 (see fig. 5), and sealed and attached to the outer plate 5 with a biocompatible sealant or glue, as described in detail with respect to the implant 20.

IC implant 180 may also include a microelectronic module 191 shown in phantom to indicate that it is disposed within housing 190. The electronics module 191 may include all of the components of the extracranial module 141 of fig. 12, and in addition to being miniaturized to fit within the housing 190, the electronics module may also include a multiplexing unit(s) 176 (fig. 15) connected between the processor (s)/controller 114 of the electronics module 191 and all of the electrodes 182A, 184A, 186A and 188A of the elongated electrode arrays 182, 184, 186 and 188, respectively.

The multiplexing unit 176 may allow any selected pair of electrodes 182A, 184A, 186A and 188A to be connected to the stimulation generator 118 of the electronics module 191 to deliver ICTIS stimulation to any selected region of the brain, including deep brain structures and/or cortical regions. Optionally, in some embodiments, the electronics module 191 may also include a signal conditioning and digitizing unit 126 of the circuitry module 152 (fig. 13), which may be suitably connected to the multiplexing unit 176 and the processor/controller 114, such that cortical signals from selected electrodes of the elongate electrode arrays 182, 184, 186, and 188 can be sensed.

The electronics module 191 of the implant 180 may be suitably connected to the induction coil 146 by suitable insulated conductive leads 197, as disclosed in detail above, for receiving power from another induction coil located on the scalp (the scalp not shown for clarity of illustration). The elongate electrode arrays 182, 184, 186 and 188 are suitably sealingly connected to the housing 191 and comprise a plurality of isolation lines (not shown in figures 16 to 17 for clarity of illustration) allowing the multiplexing unit 176 of the electronic module 191 to "address" each electrode.

The electronics module 191 can perform stimulation of deep brain structures by the same frequency interference methods disclosed above with respect to the systems 140 and 160 herein. Selecting specific electrode pairs at different locations to deliver stimulation at frequencies f and f + Δ f may allow fine-tuning of stimulation of deep brain structures as necessary, and may allow greater flexibility in stimulating selected deep brain structures and shallower cortical regions (e.g., right and left DLPFCs). Thus, the use of IC implant 180 may allow for sensing cortical areas and stimulating deep brain structures and/or cortical areas by interleaving sensing and stimulation time periods.

It is noted that although the methods and systems disclosed above may specifically stimulate the left and/or right DLPFC regions (which may or may not be combined with stimulating one or more deep brain structures), in some embodiments of these methods and systems, different cortical stimulation targets may be used. For example, other areas of the prefrontal cortex (PFC) may be cortical stimulation targets. Such stimulation of other PFC regions may or may not be combined with stimulation of deep brain structures.

Evidence of the effectiveness of sTMS can be found in Klein et al (1999), which is incorporated by reference into the following list of references.

It should be noted that although in the system disclosed herein the portable communication device 100 is shown as comprising a mobile phone 70, a laptop computer 9 and an AR1 headset 1, this is not limiting to the implementation of the present invention and the communication device 100 may comprise any suitable type of portable communication device, such as a smartphone, tablet, phablet, laptop, mobile computer, AR headset with communication capabilities or any other similar type of portable device with processing capabilities, communication capabilities and the ability to display content to a patient. Furthermore, if the patient has a mobile phone or smartphone for providing EMA input and patient self-assessment data, the laptop 9 may be replaced by a non-portable computer, such as a desktop computer, workstation, or remote server or remote personal computer, for providing recorded patient data and/or warning signals and/or patient status information to the caregiver.

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 in any other described embodiment suitable for use with the invention. The particular features described herein in the context of the various embodiments are not considered essential features of those embodiments, unless the embodiments are inoperative without those elements.

Various embodiments and objects of the present invention as described above and as described in the claims below are supported experimentally in the following examples.

All publications, patents and patent applications mentioned in this specification, including the references listed below, are herein incorporated by reference in their entirety. To the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference herein. In addition, citation or identification of any reference shall not be construed as an admission that such reference is available as prior art to the present invention. The headings in this application are used herein to facilitate the understanding of this description and should not be construed as necessarily limiting.

In addition, any priority documents of the present application are herein incorporated by reference in their entirety.

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