System for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient

Abstract

The present invention provides a system (100) for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient (P), comprising: a stimulation unit (10) comprising a stimulation means (12) configured to deliver neuromodulation / neurostimulation to brain structures of the patient (P); a measurement unit (14) comprising a measurement means (16) configured to measure at least one set of metrics in response to delivered neuromodulation / neurostimulation for at least one activity carried out by the patient (P), and a control unit (18), operatively connected to the stimulation unit (10) and the measurement unit (14), wherein the control unit (18) comprises a control means (20) that is configured to execute an optimization algorithm for defining optimized stimulation parameters for the patient (P) by implementing an adaptive learning process comprising: controlling the stimulation unit (10) to deliver neuromodulation / neurostimulation to the patient (P) based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit (14) for the at least one activity carried out by the patient (P); updating stimulation parameters for the at least one activity carried out by the patient (P) based on the analyzed at least one set of metrics, and iterating the process.

Description

[0001] System for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient

[0002] The present invention belongs to the technical field of neural decoding and delivery of neuromodulation / neurostimulation therapy to brain structures of a patient in need thereof.

[0003] As a non-limiting example, neuromodulation / neurostimulation therapy can be delivered to mitigate symptoms of neurological disorders such as Parkinson’s Disease (PD), and / or psychiatric disorders such as Obsessive-Compulsive Disorders (OCD).

[0004] PD is a neurodegenerative disease characterized by the loss of dopaminergic neurons within the substantia nigra of the brain.

[0005] Clinically, the disease manifests mainly with motor impairments, including rigidity, tremor, bradykinesia and postural instability, although non-motor symptoms can lead to significant morbidity.

[0006] Large epidemiologic studies revealed around 10 million people with PD worldwide, affecting all races and cultures [1],

[0007] The age of onset is usually over 60, but it is estimated that one in 10 people is diagnosed even before the age of 50, with slightly more men than women affected.

[0008] Neurological disorders such as PD are characterized by structural and functional abnormalities in multiple brain areas involving several distinct brain systems.

[0009] These abnormalities can be referred to as brain neuronal network deficiencies (BNND), which can broadly vary in causes, type of dysfunction, and clinical manifestation, e.g., existence of deleterious networks in the brain in case of neurological and psychiatric disorders, such as Alzheimer disease, Parkinson’s disease, Huntington’s chorea, depression, and / or schizophrenia. Network abnormalities are also described in patients diagnosed with, or with high-risk of, schizophrenia, attention deficit hyperactivity disorder, bipolar disorder, and / or OCD [2],

[0010] In PD, changes observed in the dynamic functional connectivity are suggestive of a reduction in functional segregation among brain networks and are correlated with the clinical severity of PD symptoms. Additionally, a higher variability in network global efficiency is observed, suggesting an abnormal global integration of brain networks [3],

[0011] Deleterious neural networks are formed based on intimate interactions among key pathogenic factors. These deleterious networks could be the common pathway of these brain disorders and, when triggered by aging, and genetic and environmental factors, may lead to a cascade of pathological alterations that characterize illnesses.

[0012] The brain is a highly complex system that is poorly understood, even more so in pathology.

[0013] Disorders induced by BNND may explain many neuropsychiatric and neurodegenerative diseases, but are presently far from being completely understood.

[0014] Treatments approaching these conditions from a brain network perspective offer a promising alternative to single-target pharmacological therapies that, so far, have largely proven insufficient [4], [5],

[0015] Following an initial diagnosis, PD subjects are typically treated with Levodopa (a precursor of dopamine), with dopamine agonists or monoamine oxidase-B inhibitors. These represent different classes of drugs, and are all characterized by different benefit-risk profiles.

[0016] Subjects using these medications over a long term may experience drug-induced side effects, such as motor fluctuations, dyskinesia, or psychiatric complications [6],

[0017] Usually, these side effects emerge three to five years after starting of medication therapy.

[0018] However, it must be noted that those complications may be also due to disease progression.

[0019] The term “dyskinesia” denotes involuntary, erratic, and writhing movements of the face, arms, legs, and / or trunk which, usually, occur one to two hours after a dose of medication is absorbed into the bloodstream of the subject, and is thus expressing its peak clinical effect.

[0020] Also, many patients are prone to develop drug resistance over time. Therefore, drug therapy alone becomes no longer effective in controlling the symptoms of PD.

[0021] Some neuromodulation therapies, approved by the USA Food and Drug Administration (FDA) and currently applied in clinical routines, have proven able to impact dysfunctional networks, providing successful therapeutic outcomes. At present, local brain stimulation has proven effective in reducing pathological symptoms in most PD patients.

[0022] For instance, Deep Brain Stimulation (DBS) has emerged as an option for PD patients to be an adjunct to medications and provide additional benefit.

[0023] DBS may target either the subthalamic nucleus (STN) or the internal globus pallidus (GPi).

[0024] DBS is a technological evolution of the irreversible surgical ablation of the basal ganglia, a more invasive treatment that was originally used to treat PD-affected patients.

[0025] Instead of permanently damaging some target brain areas, DBS utilizes electrodes that are implanted in the same areas to interfere with pathological signals and thereby improve movements.

[0026] Once a patient is selected as a candidate for DBS, imaging is performed to determine target site(s) for lead placement.

[0027] STN is chosen as the target for treatment of most PD patients. However, GPi may be a better option in some other cases, depending on the observed symptoms.

[0028] Off-period motor symptoms and tremor improve in both targets.

[0029] Advantages of targeting STN target include more medication reduction, less frequent IPG changes, and a more favorable economic profile.

[0030] On the other hand, advantages of targeting GPi include more robust dyskinesia suppression, easier programming, and greater flexibility in medication adjustment [7],

[0031] Once a target site is chosen, SOC surgical stereotactic techniques are used to implant lead(s) at the target site. Here, a frame or a robotic arm is used to control insertion angle and depth. Surgery is planned with a preoperative image with fiducial markers and dedicated image processing.

[0032] Current DBS surgery involves two different surgical steps, i.e., lead implantation and neurostimulation. The leads are placed through burr holes in the skull of the patient and connected to extensions that are tunneled down to a pacemaker-like implantable pulse generator (IPG) that is generally placed in the chest below the clavicle. All of the existing approved DBS systems for this patient population use this basic setup, and contemplate a fully-implanted system.

[0033] DBS with commercially-available leads is typically delivered by using a setup including 4 to 8 metal electrodes delivering energy.

[0034] The selection is based on localization within the target area and, most importantly, on the capacity to induce a therapeutic effect.

[0035] Usually, PD patients who are selected for DBS already attempted one or more pharmacological therapeutic approaches for disease control, which have proven not completely effective.

[0036] DBS was established to be an efficient therapy for the treatment of PD symptoms, in particular with documented improvements in motor control, functionality, and quality of life.

[0037] Moreover, DBS allows on average to reduce Levodopa treatment dose, leading to lower Levodopa-related adverse effects and higher compliance [8],

[0038] Although STN DBS and Levodopa independently lessened motor severity in PD patients to a similar magnitude, their combined effect has proven greater than either treatment alone, thereby suggesting therapeutic synergism [9],

[0039] For this reason, DBS efficacy is generally assessed in off-medication conditions to evaluate effects of DBS, and in combination with antiparkinsonian drugs to assess real-life patients benefits.

[0040] DBS benefits on motor symptoms are most commonly evaluated using both subjective assessment from users and objective assessment by clinicians, e.g., neurologists, using Unified Parkinson’s Disease Rating Scale (UPDRS) and, more particularly, the third part of the scale evaluating motor functions, rather than reporting on individual motor signs.

[0041] Measurement of DBS benefits in PD patients has been established by several class I studies, e.g., in a randomized double-blind design with sham control clinical trial, as discussed, e.g., in Vitek et al.

[0010] (the clinical trial being also known under the name of INTREPID).

[0042] In the context of this clinical trial, PD patients were implanted with a DBS system according to SOC, and were randomly assigned in a 3:1 ratio to receive either active therapeutic stimulation settings (active group) or subtherapeutic stimulation settings (control group) for a 3-month blinded period. During the blinded period, patients were not allowed to increase their antiparkinsonian medications per study protocol

[0010] ,

[0043] The primary efficacy outcome of the study was the difference in mean change from baseline visit to 3 months post-randomization between the active and control groups in the mean number of waking hours per day with good symptom control and no troublesome dyskinesias, as recorded by the user in a dedicated diary, with no increase in antiparkinsonian medications

[0010] .

[0044] The study reported that baseline duration for the active group was 7.78 hours, which increased to 12.37 hours after 12 weeks (3.74 hours improvement on average)

[0010] ,

[0045] Considering that patients were reporting to sleep 7.2 hours per day on average preoperatively, further considering that DBS therapy allows for about 1 more hour of sleep

[0011] , the total awake time after 12 weeks of treatment for active group was estimated around 15.8 hours on average, leading to a ceiling improvement from baseline of time with good symptoms control of about 8 hours

[0010] ,

[0046] Otherwise stated, the 3.74 hours change from baseline reported for active group after 12 weeks of DBS use was less than half of the maximum possible improvement.

[0047] The results of the clinical trial discussed in Vitek et al.

[0010] are resumed in the diagram of Fig. 1.

[0048] LIPDRS III scores were assessed in on- and off-medication conditions at baseline and at 12 weeks for both active and control groups. Change from baseline comparison between groups was performed as secondary endpoint, and showed significant reduction of LIPDRS III score in on-stim / off-meds condition for active group, concluded to be clinically meaningful.

[0049] However, no significant reduction was observed in on-stim / on-meds condition.

[0050] This could be explained by the fact that individual motor scores for tremor (61 %), rigidity (51 %), bradykinesia (32%), and gait (39%) were all improved in the on-stim / off-meds, while only tremor and rigidity were significantly better in the on-stim / on-meds condition

[0010] ,

[0051] These results emphasize the importance of antiparkinsonian medication in symptoms management, and also confirm the role of DBS as an adjunctive therapy for PD treatment, supporting effects of medications on tremor and rigidity, as other symptoms are better managed by medication. From a safety perspective, DBS surgery is overall assumed as characterized by a reasonable risk profile.

[0052] The above-mentioned INTREPID clinical trial discussed in Vitek et al.

[0010] , for example, reported 74 serious adverse events among 177 involved participants. Infection has been the most commonly reported serious adverse event associated with device-hardware / procedure (8 events, representing 2.7% of subjects). There were three events (each) of device- hardware / procedure-related serious adverse events of peri-operative intracranial hemorrhage (representing 1 % of subjects) and seizure (representing 1 % of subjects). After 12 weeks of use, 3 more cerebral hemorrhages and 6 more device-related infections were reported as non- serious events.

[0053] Other meta-analysis and reviews on large cohorts of patients implanted with similar DBS systems (although not all suffering from PD) demonstrated similar outcomes

[0012] -

[0013] , as shown in Table 1.

[0054] It is noticeable that the rate of infections, surgical site complications, and device complications are lower in the most recent reports, showing improvements in the surgical procedure and devices over time.

[0055] Still, more than 10% device complication rate is reported in Rasiah et al.

[0013] , suggesting that there is room for further improvement of device safety and reliability.

[0056] It is indeed a higher revision rate when compared, for example, to established head-mounted implanted active stimulator such as cochlear implants, showing device failure rate below 5% in adults and children, with a cumulative revision rate for primary implants at all ages increased linearly by 1% per year

[0014] ,

[0057] The difference in device complication rate between the results shown in Table 1 and the outcome reported in the context of the INTREPID clinical trial

[0010] may also be reasonably explained in the light of the short follow-up period for the INTREPID clinical trial (only amounting to 12 weeks).

[0058] On the other hand, adverse events related to stimulation are much more frequent.

[0059] In fact, the INTREPID clinical trial

[0010] reported a high incidence of 53.6% stimulation-related adverse events, most frequently involving dyskinesia (16,5%), balance disorders (5%), and dysphagia (3.3%). On the other hand, the results reported in Appleby et al.

[0012] , referring to a clinical trial carried out on a large cohort of participants with longer follow-up time, showed a lower rate of 26.9% for stimulation-related adverse events, most frequently involving speech disturbance (4.8%) and delirium / confusion (4.1%).

[0060] Horn et al.

[0015] evaluated connectivity between the stimulation site and other brain regions in PD patients treated with DBS by identifying the structural and functional connectivity profile of effective DBS to the STN.

[0061] This evaluation was made by estimating, for each patient, the volume of tissue activated by the DBS electrode from individual postoperative images, and then applying a combination of normative and PD pathologic averaged brain connectome to estimate brain networks activated from a given stimulated volume of tissue

[0015] ,

[0062] In particular, the approach described in Horn et al.

[0015] included the following steps: acquiring pre- / postoperative imaging (Fig. 2A), localizing DBS electrodes in standard space (Fig. 2B), calculating the volume of tissue activated (VTA) based on stimulation parameters (Fig. 2C), and calculating functional (Fig. 2D) and structural (Fig. 2E) connectivity from the VTA to the rest of the brain using high-quality normative connectome data.

[0063] In the context of Fig. 2, positive correlations are denoted with (a), whereas negative correlations (anticorrelations) are denoted with (b).

[0064] As a result, the Authors observed that a specific pattern of structural and functional connectivity with STN DBS electrodes correlates with clinical outcome across patients in PD. Moreover, the authors could appreciate the prognostic property of this approach by successfully predicting outcomes in an independent cohort of DBS users

[0015] ,

[0065] These findings support the notion that the targets of therapeutic brain stimulation might be brain networks, and not individual brain regions, such as the STN or GPi.

[0066] Another study (described in Boutet et al.

[0016] ), implemented by using functional Magnetic Response Imaging (fMRI) in response to DBS in PD patients, supports this notion and the importance of brain network activation to deep nucleus stimulation.

[0067] The primary purpose of this study was to identify specific markers of optimal DBS using fMRI, to ease device fitting (in particular, to select the optimal electrode and voltage level). The activation of ipsilateral primary motor cortex, ipsilateral thalamus, and contralateral cerebellum was identified as a maker of optimal STN DBS, suggesting a large brain network activation to STN DBS, further optimized with control of activation of this network at different levels

[0016] ,

[0068] This evidence points toward the importance of monitoring brain network response at various relevant locations, to evaluate efficacy of stimulation and potentially adjust delivered therapy accordingly.

[0069] Presently, most brain stimulation therapy solutions, e.g., DBS for PD, Vagus nerve stimulation (VNS) for epilepsy, or spinal cord stimulation for pain, rely on delivery of constant stimulation.

[0070] Here, stimulation is delivered continuously (or, at best, according to a specific pre-established duty cycle), with no real-time adjustment of stimulation parameters and no specificity with respect to individual patient’s biomarkers.

[0071] In particular, therapy parameters are pre-set by a clinician, and then only refined over time in an office setting.

[0072] These known therapeutic approaches are unable to conform to changes in pathology, symptoms severity, and / or daily activities carried out by patient (e.g., routine activities such as sleeping, standing, walking, or the like).

[0073] That is, fine tuning of delivered therapy is still very sparse in time, and usually occurs in timeframes that are much longer than those needed to provide the patient with a feeling that is close to natural as much as possible.

[0074] Symptomatic activity usually varies over time, e.g., during the day or over multiple days.

[0075] This is explained in Powers et al.

[0017] , describing the results of a study that was carried out using smartwatch-based motor symptoms detectors for a 6-month period on a large cohort of (non-implanted) PD patients.

[0076] Here, large fluctuations of dyskinesia and tremor and effects of medications were observed over a day in all patients with variability across patients, and within patients from one day to another, as schematically illustrated in Fig. 3

[0017] ,

[0077] Moreover, different activities (e.g., drawings, sleeping, standing, walking, running, cycling, eating, drinking, driving, or the like) are carried out by the patient during the day.

[0078] Open-loop therapeutic approaches are unable to properly consider changes in symptoms and / or side effects and / or activities that the patient may face over time, even on a daily basis. Open-loop approaches also have several drawbacks, e.g., in terms of tolerability-related issues, reduction of the therapeutic effect over time, and battery depletion.

[0079] More sophisticated, dynamic neurostimulation therapies (so-called “adaptive” or “closed-loop” therapies) have been developed and demonstrated in literature and previous art for PD

[0018] , and pain

[0019] ,

[0080] However, these known therapeutic approaches are still quite limited because they typically only modulate a single therapy parameter (usually, amplitude) in response to a single biomarker (e.g., beta-band power), describing a single symptom or an evoked response elicited by applied stimulation.

[0081] However, patients experience a multitude of symptoms, evolving over time.

[0082] Symptoms may vary depending, e.g., on the activity that is carried out by the patient, the time of the day, and time passed since the last medication intake.

[0083] Other therapeutic approaches are based on reinforcement learning (RL).

[0084] Solutions of this kind are described, e.g., in EP3341073B1 and US20230191130A1.

[0085] Current programming of neuromodulation / neurostimulation therapies relies on clinician expertise and is often limited by scarce availability of clear biomarkers and read out measures that can be used to optimize therapy, and are typically programmed and optimized in non- naturalistic settings (i.e., a clinician’s office or other clinical facility).

[0086] This applies to spinal cord stimulation for pain, VNS for epilepsy, DBS for PD, and other emerging therapies, e.g., spinal cord stimulation to restore locomotion, DBS for psychiatric conditions, or the like.

[0087] In case of DBS for PD treatment, programming sessions typically involve a trial-and-error evaluation of therapeutic response (i.e., clinical benefit vs. unwanted side effects) for numerous stimulation settings.

[0088] In particular, the current standard for programming and therapy optimization is based on clinical visits (e.g., 3 to 6) spanned across a time frame of about 6 to 24 months.

[0089] During the first session (i.e., the initial programming), the clinician usually relies on so-called “monopolar review”. That is, the clinician proceeds with testing each individual contact on a quadripolar (or octopolar) DBS lead against classical measurements of PD symptoms, such as rigidity, bradykinesia and tremor.

[0090] The evaluation is typically based on MDS LIPDRS clinical score (e.g., visual observations, manipulation of joints or the like)

[0020] ,

[0091] The parameter space to be explored for DBS (as well as for VNS, or spinal cord stimulation) is quite broad due to the possibility of customization of several stimulation parameters including contact configuration (cathode and anode selections), current amplitude, pulse width, and frequency, allowing to compensate for patient variability and variations in electrode placement, differences in local anatomy, symptom type, and severity.

[0092] As emphasized in Bonizzatto et al.

[0021] , providing optimal DBS treatment is a complex, multivariate problem. During programming clinician determines which electrical contact(s) to use, stimulation amplitude, frequency, and pulse duration. Multiple therapeutic targets (e.g., tremor, gait, and balance) need to be taken into consideration while rejecting stimulation- induced side-effects. This is a clinical process that relies on the programmer’s experience as well as the patient’s cooperation. In some cases, clinicians are assisted in this process by relatively simple software means that are embedded by manufacturers within a DBS programming device.

[0093] This not only configures a lengthy process, but also requires the patient to withdraw from medication at least 12 hours before the evaluation.

[0094] Moreover, as mentioned, this known approach is subjective to the expertise of the clinician, and broadly relies on the patient’s collaboration.

[0095] Still further, patients are required to travel to clinical facilities such as expert DBS centers, which in several cases may be located far away from their home.

[0096] This initial session and subsequent follow-up optimization sessions are conducted in a limited time span (oftentimes, clinician have limited time to dedicate to a single patient) and a constrained setting (e.g., the clinician’s office).

[0097] Also, during visit times, the patient has no or very limited ability to verify and improve therapy effectiveness. To date, initial programming (mostly based on monopolar review) often results in suboptimal therapeutic outcome.

[0098] Several times, known approaches have proven unable to consider naturalistic use (e.g., at home), and do not empower patients to adjust therapy over time.

[0099] By contrast, follow up sessions in clinical facilities are required (e.g., in the following year or so) to fine-tune therapy parameters.

[0100] Moreover, this fine-tuning remains limited to a generic single therapy, proving well in office setting.

[0101] In order to provide more precise and flexible solutions, therapeutic approaches have been developed involving multi-contact electrode arrays, e.g., including more than two, preferably more than four independent electrode contacts, current fractionation, widened pulse width range, and anodic stimulation.

[0102] These therapeutic approaches significantly expanded the parameter space, thereby making programming even more complex and time-consuming.

[0103] Known approaches based on monopolar review can only be implemented with low-density electrode arrays, and not multi-contact electrode arrays.

[0104] The provision of optimized, patient-specific neuromodulation / neurostimulation therapy still configures a complex, multivariate problem.

[0105] To date, several attempts have been made in the art to support clinicians in the programming phase.

[0106] For example, US11478633B2 discloses a system for optimization of DBS programming for, based upon a plurality of previously-acquired directional LFP signals measured in a plurality of different directions at a directional sensor lead located in a predetermined region of a patient's brain, determining optimized patient-specific programming parameters for programming a directional stimulation lead with parameters for stimulating the said region.

[0107] Further known approaches aimed at supporting the clinician during the programming phase may include

[0021] : - biomarkers-based programming, e.g., based on kinematics (for instance, by using a sensor means such as accelerometers or the like)

[0022] , or metabolic brain changes measured with functional MRI

[0023] ;

[0108] - local field potential-based programming (including supervised closed-loop DBS)

[0024] ;

[0109] - algorithm-driven online optimization, e.g., relying on a closed-loop based on kinematics (e.g., tremor)

[0025] , and

[0110] - neuroimaging-based optimization, e.g., based on qualitative volume of activated tissue or a quantitative algorithm.

[0111] Recent works validated the use of some of these tools, e.g., based on imaging and simulation

[0026] , in controlled studies with success.

[0112] However, these known approaches heavily rely on imaging and current assumptions of therapy effects vs. 3D-positioning of the electrodes.

[0113] This is, however, not as clear yet.

[0114] In particular, while positioning of the electrodes relative to the STN seems to explain some of the consequent therapeutic effects, the same not always holds true when side-effects are concerned

[0027] ,

[0115] Therefore, improved and more reliable measurements of therapeutic outcome are needed.

[0116] Other recent approaches, as described in Sarikhani et al.

[0028] , rely on machine learning algorithms, in particular Bayesian optimization, to support programming based on objective measurement of patient’s symptoms, such as tremor.

[0117] In particular, Sarikhani et al.

[0028] describes and evaluates an automated, closed-loop, patientspecific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on recommendations from a closed-loop optimization algorithm, thereby eliminating the need for an expert clinician.

[0118] Nonetheless, the approach described in Sarikhani et al.

[0028] , as well as those described above, only look at the “acute” testing of parameters optimized over certain cardinal symptoms, such as tremor and rigidity. In particular, with “acute testing” it is intended that parameters are only tested on the spot in the presence of a clinician in the clinician’s office, while they are not evaluated over a longer term, e.g., during use in a home environment. Accordingly, this approach may work well in the short term (e.g., during testing in the clinician’s office), but may be suboptimal over a longer term, e.g., while implementing routine activities in a home environment. However, none among these known approaches considers the complexity of optimizing parameters associated with patient’s preferences, specific symptoms, pathology, side-effects, and everyday life activities such as walking, sleeping, doing sport activities, speaking, or the like.

[0119] In the light of the above, there is a strongly-felt need for an improved therapeutic approach that facilitates programming, reduces programming time, and does not require the intervention of an expert clinician, facilitates iterative and more frequent therapy optimization in naturalistic settings (without limitations and needs associated with travelling to clinical facilities such as expert centers), and which properly considers the patient’s preferences, lifestyle, and specific symptomatology and / or side effects, thus enabling the definition of a more effective and patient-specific therapeutic approach.

[0120] Accordingly, it is an object of the present invention to provide a system for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient allowing obtaining an optimized, highly-personalized therapeutic approach, thus enhancing therapeutic outcome.

[0121] According to the invention, a system for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient comprises: a stimulation unit comprising a stimulation means configured to deliver neuromodulation / neurostimulation to brain structures of the patient; a measurement unit comprising a measurement means configured to measure at least one set of metrics in response to delivered neuromodulation / neurostimulation for at least one activity carried out by the patient, and a control unit, operatively connected to the stimulation unit and the measurement unit, wherein the control unit comprises a control means that is configured to execute an optimization algorithm for defining optimized stimulation parameters for the patient by implementing an adaptive learning process comprising: controlling the stimulation unit to deliver neuromodulation / neurostimulation to the patient based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit for the at least one activity carried out by the patient; updating stimulation parameters for the at least one activity carried out by the patient based on the analyzed at least one set of metrics, and iterating the process.

[0122] The present invention provides a system for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient in need thereof.

[0123] As a non-limiting example, the patient may be a PD subject.

[0124] According to a further, non-limiting example, the patient may be a subject affected by a psychiatric disorder, e.g., OCD.

[0125] The system comprises a stimulation unit.

[0126] The stimulation unit comprises a stimulation means.

[0127] In particular, said stimulation means is configured to deliver neuromodulation / neurostimulation to brain structures of the patient.

[0128] As a non-limiting example, said stimulation means may be configured to deliver DBS to a target brain structure of the patient.

[0129] The system further comprises a measurement unit.

[0130] The measurement unit comprises a measurement means.

[0131] In particular, said measurement means is configured to measure at least one set of metrics in response to delivered neuromodulation / neurostimulation for at least one activity carried out by the patient.

[0132] As a non-limiting example, the patient’s activity can be a predefined task.

[0133] As a further, non-limiting example the patient’s activity can be a gamified task or test.

[0134] The system further comprises a control unit.

[0135] The control unit is operatively connected to the stimulation unit and the measurement unit. The control unit comprises a control means that is configured to execute an optimization algorithm for defining optimized stimulation parameters for the patient by implementing an adaptive learning process, comprising: controlling the stimulation unit to deliver neuromodulation / neurostimulation to the patient based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit for the at least one activity carried out by the patient; updating stimulation parameters for the at least one activity carried out by the patient based on the analyzed at least one set of metrics, and iterating the process.

[0136] The invention is based on the basic idea that, by the provision of an optimization algorithm adapted to implement an adaptive learning process as defined above, an optimized, patientspecific therapeutic approach can be provided, which is characterized by an improved level of adaptability to the patient’s preferences, lifestyle, and evolving symptoms and pathology.

[0137] Also, programming complexity and time can be significantly reduced, even in case of high- density arrays. Hence, programming can be successfully carried out without requiring the intervention of an expert clinician.

[0138] Still further, stimulation parameters are automatically adapted and optimized by the system based on at least one set of metrics from the measurement unit, without the need of specific sessions requiring the patient to travel to a clinical facility such as an expert clinic.

[0139] Conveniently, the control means may be configured to execute the optimization algorithm to automatically define a maximum threshold value for each stimulation parameter.

[0140] This allows defining safety boundaries to ensure tissue integrity.

[0141] Otherwise stated, a parameter space is designed to prevent occurrence of excessive heat, tissue burning, or the like, allowing the system to operate safely.

[0142] Alternatively, the system control means may be configured to determine maximum threshold values for each stimulation parameter based on a user input from an operator, e.g., a clinician, caregiver, or the like. Conveniently, the adaptive learning process can be a sequential learning process.

[0143] Preferably, said sequential learning process is a Bayesian learning process.

[0144] This configures a sample-efficient global optimization process, capable of implementing adaptive learning based on the patient’s response to applied therapy, and selecting / determining updated / optimized stimulation parameters accordingly.

[0145] The maximum threshold value for the stimulation parameters may as well be automatically determined by implementing a Bayesian learning process.

[0146] Conveniently, the control means may be configured to define initial stimulation parameters based on prior information regarding the patient.

[0147] In this case, the system may further comprise a sensor means for recording brain signals.

[0148] Here, the control means is further configured to define prior information regarding the patient based on detected brain signals from said sensor means.

[0149] As a non-limiting example, a prior phase can be implemented, where sensor means adapted to detect brain signals are used to sense a neural response from the patient to delivered stimulation for each electrode or group of electrodes comprised in the stimulation means of the stimulation unit.

[0150] This neural response allows defining which electrodes are comprised in a region of interest that needs therapy fine-tuning.

[0151] This information is then fed into the algorithm, so that initial stimulation parameters can be determined accordingly.

[0152] During subsequent iterations of the adaptive learning process, the stimulation parameters are adjusted based on the actual patient’s response to delivered therapy

[0153] Additionally or alternatively, the control means may be configured to define the initial stimulation parameters based on prior information regarding a population of patients having the same or similar symptoms and / or affected by the same or similar pathology.

[0154] As an alternative, the initial stimulation parameters can be randomly selected by the control means. Conveniently, the system may further comprise a user input device for providing a user’s feedback on delivered neuromodulation / neurostimulation.

[0155] Here, the control means is configured to execute the optimization algorithm to update stimulation parameters for the at least one activity carried out by the patient based on the analyzed at least one set of metrics and a user’s feedback provided through the user input device.

[0156] Therefore, stimulation parameters can be updated / adjusted with enhanced accuracy and reliability.

[0157] Conveniently, the user input device may comprise a display means of the touch-screen type.

[0158] This allows the user, e.g., the patient him / herself, a caregiver, or the like, to easily and intuitively provide a feedback.

[0159] For instance, a questionnaire including multiple choices can be displayed on the display means of the user input device, allowing the user to select a displayed option, e.g., by tapping.

[0160] The user input device can be a smartphone, a tablet computer, or the like.

[0161] For instance, the user’s feedback may comprise one or more among a patient’s physical and / or psychological state during and / or after neuromodulation / neurostimulation, increase / reduction in side effects, increase / reduction in physical and psychological symptoms, quality assessment of a performed activity (e.g., distance walked, hours of sleep and / or REM phase duration and / or other sleep metrics, fatigue, or the like) and information on medication intake.

[0162] In a simplified, basic configuration the user’s feedback can be a binary feedback indicating which among a plurality (e.g., two) stimulation sequences is deemed preferable.

[0163] Here, a plurality (e.g., two) stimulations sequences are subsequently delivered to the patient through the stimulation means of the stimulation unit. Then, the patient determines which stimulation sequence he / she prefers.

[0164] Iteratively, the algorithm learns to order the best stimulation parameters as reported by the patient until the best combination is found.

[0165] Conveniently, the measurement means of the measurement unit may comprise a sensor means. For instance, said sensor means may be configured to capture audio recordings of the patient.

[0166] Here, said sensor means may include a microphone.

[0167] The microphone can be either implantable or non-implantable.

[0168] Additionally or alternatively, said sensor means may be configured to capture video recordings of the patient.

[0169] Here, said sensor means may include a camera.

[0170] Conveniently, said sensor means, e.g., a microphone and / or a camera, may be embedded into a smartphone, tablet computer, or the like.

[0171] Additionally or alternatively, said sensor means may include an accelerometer.

[0172] For example, the accelerometer can be non-implantable, e.g., embedded into a smartphone, tablet computer, or the like.

[0173] Alternatively, the accelerometer may be implantable.

[0174] Audio recordings can be used for defining the patient’s mood, e.g., based on the types of words used and / or intonation.

[0175] Video recordings may enable to analyze metrics of the patient’s face and / or expression.

[0176] Together, video and audio recordings can be used to evaluate psychiatric effects of delivered therapy.

[0177] This can be used alone and / or in combination with a requested feedback from the patient by using specific surveys (e.g., a multiple-choice questionnaire displayed on the display means of the user input device), aimed at collecting metrics concerning the patient’s state, mood or the like during and / or after stimulation.

[0178] The at least one set of metrics may comprise at least one motor symptom in response to delivered neuromodulation / neurostimulation.

[0179] Additionally or alternatively, the at least one set of metrics may comprise at least one nonmotor symptom in response to delivered neuromodulation / neurostimulation. For instance, said at least one motor symptom may comprise one or more among tremor, rigidity, bradykinesia, gate, balance, dyskinesia, and paresthesia. For instance, said at least one non-motor symptom may comprise one or more among mood, face expression, energy or fatigue level, speech ability, emotional status.

[0180] Additionally or alternatively, said at least one set of metrics may comprise increase / decrease in side-effects in response to delivered neuromodulation / neurostimulation.

[0181] Conveniently, the control means may be configured to execute the optimization algorithm to update stimulation parameters for different motor and / or non-motor symptoms independently.

[0182] An activity that is carried out by the patient may involve different motor and / or non-motor symptoms.

[0183] For instance, a task requiring the patient to draw a spiral, e.g., on the screen of a smartphone or tablet computer, may give metrics on bradykinesia (drawing speed) and tremor (lack of drawing smoothness).

[0184] Stimulation parameters can be independently defined and optimized for different metrics (in the example, bradykinesia and tremor).

[0185] Accordingly, the applied therapy can be optimized / adjusted with enhanced accuracy.

[0186] Conveniently, said at least one activity comprises one or more daily activities such as sleeping, standing, walking, running, cycling, speaking, swallowing, reading, driving, or swimming.

[0187] Also, said at least one activity may as well include a gamified activity / task, such as drawing, spiral drawing, finger tapping, pronation / supination of a hand and / or foot, hand opening / closing, arm extension / flexion, touching nose with a finger while extending and flexing the arm, repeating words / sentences, move a cursor on a screen, follow a marker on a screen, and standing on a single leg.

[0188] For instance, said at least one activity may include a task that is performed in short term by the patient (e.g., spiral drawing, finger tapping, walking a short distance).

[0189] A dedicated application embedded in a smartphone, tablet computer, or the like can be used to guide the patient in performing the task.

[0190] Alternatively, said at least one activity may include a task that is performed in a longer term, such as standing, walking, running, or the like. Here, stimulation parameters are tested over a certain period of time.

[0191] The patient’s activity is not necessarily a predefined task (such as spiral drawing, finger tapping, walking a short distance, or the like).

[0192] For instance, therapy can be applied to the patient for a certain time while performing a task, e.g., going for a walk. Then, after the walk, the patient may provide a feedback comprising an evaluation of the performed task, e.g., length walked, quality of walking, duration of the walk, which may be used as metrics for the algorithm to provide an adjusted / optimized set of parameters to be used / tested in another walking session.

[0193] This allows an optimization over longer time frame for a specific activity.

[0194] Additionally or alternatively, metrics on the performed activity may as well be derived from sensor measurement signals, e.g., from an accelerometer or other sensor means, e.g., embedded in a wearable or portable device such as a smartphone or the like.

[0195] Also, a specific symptom reduction target or activity enhancement target may not be defined or may be impossible to observe. Yet, stimulation parameters may be sought (e.g., to define safety boundaries for the stimulation that do not cause side effects).

[0196] In these cases, a target is defined by a user in terms of stimulation parameters to optimize / maximize, e.g., the amount of total charge discharged by the stimulator over a certain period of time, and the algorithm iteratively models a safety boundary for maximum user- defined target stimulation values that do not cause side effects.

[0197] The algorithm builds the boundary by having values reported by the user in terms of sideeffects.

[0198] Here, the algorithm does not need to explore all possible combinations of effects, but may instead extrapolate the boundary by assuming continuity and smoothness of unexplored values to those explored reducing exploration time.

[0199] Said set of stimulation parameters may comprise one or more among frequency, amplitude, pulse width, electrode configuration, and selection between low-resolution or high-resolution stimulation.

[0200] In particular, the selection between low-resolution or high-resolution stimulation (which is rendered possible by the high dimensional space available in high-density arrays) allows for defining multiple fidelity levels in stimulation parameters optimization. That is, stimulation therapy can be set / adjusted so that relationships between using a single channel or multiple channels for stimulation and their parameters are incorporated in the adaptive learning process, and the algorithm is enabled to make decision on what fidelity level (i.e., low-resolution stimulation by using channel groups, or high-resolution, by using individual channels) is to be selected in order to gather the maximum amount of information in the least number of steps.

[0201] Conveniently, the stimulation means may comprise a high-density implantable neural interface.

[0202] Preferably, said implantable neural interface comprises at least two electrodes per cm2.

[0203] More preferably, said implantable neural interface comprises at least four electrodes per cm2.

[0204] The neural interface may be configured to be implanted cortically.

[0205] Alternatively, the neural interface may be configured to be implanted subcortically.

[0206] Conveniently, the neural interface may be configured to deliver neuromodulation / neurostimulation and sense brain signals of the patient.

[0207] That is, the same neural interface can be used for both delivering neuromodulation / neurostimulation and sensing brain signals.

[0208] The neural interface may comprise a support.

[0209] For example, the support may at least partially be and / or comprise a thin film and / or be made of a nanostructure and / or a microstructure.

[0210] In particular, the term thin film is to be understood as a stack of layers of materials ranging from fractions of a nanometer to several micrometers in thickness.

[0211] Thin film layers can be obtained applying a variety of thin-film deposition / preparation techniques including sputtering, evaporation, chemical vapor deposition, molecular beam epitaxy, atomic layer deposition, spin coating, 3D printing, or the like.

[0212] The stack of layers exhibits a variety of functionalities, such as conductive layers, insulating layers, passivating layers, or the like.

[0213] The support, e.g., a thin film, may include and / or carry an electrode array comprising a plurality of electrodes.

[0214] The electrodes can be made of reduced graphene oxide (rGO). Also, each electrode of said plurality of electrodes may be characterized by an area in the micrometer range.

[0215] Conveniently, the area of each electrode may be comprised between 1.0 and 0.0005 mm2.

[0216] Conveniently, the electrode array may comprise at least 20 electrodes.

[0217] For example, the electrode array may include 40 electrodes.

[0218] Alternatively, the electrode array may include 50 electrodes.

[0219] Alternatively, the electrode array may include from 128 to 1024 electrodes.

[0220] As mentioned, the provision of micrometer-range rGO electrodes allows achieving precise stimulation while allowing decoding of neural signals with high signal-to-noise ratio.

[0221] Conveniently, the electrode array may comprise a plurality of electrodes having different areas in the micrometer range.

[0222] For example, one or more electrodes having a smaller area can be used for sensing smaller areas targeting single- and multi-unit activity, optimizing SNR and enlarging bandwidth. On the other hand, one or more electrodes having a larger area can be used for a more extended activation area targeting larger groups of neurons and measure, e.g., LFPs.

[0223] Also, electrodes having different areas can be selected depending on characteristics of the stimulation target, spatial selectivity, charge injection, current density, required compliance voltage, and / or impedance.

[0224] Conveniently, the thin film can be made of a flexible material.

[0225] Preferably, said flexible material may include metal or a metal alloy.

[0226] Additionally or alternatively, said flexible material may include a polymer material.

[0227] Conveniently, said polymer material may include one among Polylmide, Parylene, or Polydimethylsiloxane (PDMS).

[0228] The neural interface may allow sensing a large variety of brain signals over time.

[0229] For instance, said brain signals may include brain oscillations (e.g., beta, gamma, HFO).

[0230] Additionally or alternatively, said brain signals may include evoked responses to subcortical stimulation.

[0231] These recordings are characterized by a high resolution, both spatially and temporally. Spatial high resolution can be obtained through the provision of a quite large number of rGO electrodes, arranged to form a high-density neural interface allowing recording / sensing activity of different locations nearby the implantation site.

[0232] On the other hand, temporal high resolution is mostly enabled by the properties of graphene.

[0233] In particular, rGO electrodes in the micrometer scale having low impedance allow for a high signal-to-noise ratio, enabling recording / sensing of local neural activity at both low and high frequencies (with supporting sensing electronics).

[0234] According to a comparative example, an adaptive learning process for stimulation parameters adjustment / optimization can be implemented by using the system described above, said method comprising: controlling the stimulation unit to deliver neuromodulation / neurostimulation to the patient based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit for the at least one activity carried out by the patient; updating stimulation parameters for the at least one activity carried out by the patient based on the analyzed at least one set of metrics, and iterating the process.

[0235] Also, a computer-readable non-volatile storage medium can be provided, comprising computer-readable instructions that, when executed by a processing means, causes said processing means to implement and / or process and / or perform the method steps defined above.

[0236] Further details and advantages of the present invention shall now be disclosed in connection with the drawings, where.

[0237] Fig. 1 is a diagram showing the results of the clinical trial carried out on a plurality of PD patients, discussed in Vitek et al.

[0010] , Reproduced from Vitek et al.

[0010] ;

[0238] Fig. 2 is a diagram illustrating a method of identifying DBS connectivity. Processing steps include: A. acquiring pre- / postoperative imaging; B. localizing DBS electrodes in standard space; C. calculating the volume of tissue activated (VTA) based on stimulation parameters; D. calculating functional and E. structural connectivity from the VTA to the rest of the brain using high-quality normative connectome data. The processing stream using the connectome datasets defined in healthy subjects is shown and was used in all primary analyses. Reproduced from Horn et al. (2017)

[0015] ; Fig. 3 is a diagram showing smartwatch symptom profiles (% per 15-min window) aggregated across multiple days according to medication schedules for a given subject. Upper plot: Higher tremor levels were observed in the morning and mid-afternoon, which matched patient-reported "off1times. Lower plot: A reduction in the amount of tremor was seen during the patient’s mid-afternoon “off" time on the new medication schedule (day 20 onward). Reproduced from Powers et al. (2021)

[0017] ;

[0239] Fig. 4 is a diagram illustrating a system for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient according to an embodiment of the invention;

[0240] Fig. 5 is a diagram showing a programming and stimulation parameters optimization cycle, implemented through the system of Fig. 4.

[0241] Fig. 4 schematically illustrates a system 100 for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient P in need thereof.

[0242] As a non-limiting example, the patient may be a PD subject.

[0243] According to a further, non-limiting example, the patient P may be a subject affected by a psychiatric disorder, e.g., OCD.

[0244] The system 100 comprises a stimulation unit 10.

[0245] The stimulation unit comprises a stimulation means 12.

[0246] Said stimulation means 12 are configured to deliver neuromodulation / neurostimulation to brain structures of the patient P.

[0247] As a non-limiting example, the stimulation means 12 may be configured to deliver DBS to target brain structures of the patient P.

[0248] The system further comprises a measurement unit 14.

[0249] The measurement unit 14 comprises a measurement means 16.

[0250] The measurement means 16 is configured to measure at least one set of metrics in response to delivered neuromodulation / neurostimulation for at least one activity carried out by the patient P.

[0251] For instance, said at least one activity may comprise one or more daily activities such as sleeping, standing, walking, running, cycling, speaking, swallowing, reading, or driving, and swimming. Also, said at least one activity may as well include a gamified activity / task, such as drawing, spiral drawing, finger tapping, pronation / supination of a hand and / or foot, hand opening / closing, arm extension / flexion, touching nose with a finger while extending and flexing the arm, repeating words / sentences, move a cursor on a screen, follow a marker on a screen, and standing on a single leg.

[0252] Said at least one set of metrics may comprise at least one motor symptom and / or non-motor symptom in response to delivered neuromodulation / neurostimulation.

[0253] In particular, said at least one motor symptom may comprise one or more among tremor, rigidity, bradykinesia, gate, balance, dyskinesia, and paresthesia.

[0254] In particular, said at least one non-motor symptom may comprise one or more among mood, face expression, energy or fatigue level, speech ability, emotional status.

[0255] Nevertheless, different motor and / or non-motor symptoms may also be considered according to the invention.

[0256] Additionally or alternatively, said at least one set of metrics may comprise increase / decrease in side-effects in response to delivered neuromodulation / neurostimulation.

[0257] The system 100 further comprises a control unit 18.

[0258] The control unit 18 is operatively connected to the stimulation unit 10 and the measurement unit 14, as shown in Fig. 4.

[0259] The control unit 18 comprises a control means 20.

[0260] The control means 20 is configured to execute an optimization algorithm for defining optimized stimulation parameters for the patient P by implementing an adaptive learning process comprising: controlling the stimulation unit 10 to deliver neuromodulation / neurostimulation to the patient P based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit 14 for the at least one activity carried out by the patient P; updating stimulation parameters for the at least one activity carried out by the patient P based on the analyzed at least one set of metrics, and iterating the process. Accordingly, optimized, patient-specific stimulation therapy can be provided, tailored on the patient’s preferences, specific symptoms, pathology, side-effects, and / or everyday life activities.

[0261] Also, programming complexity and time can be significantly reduced, even in case of high- density arrays. Hence, programming can be successfully carried out without requiring the intervention of an expert clinician.

[0262] Still further, stimulation parameters optimization can be straightforwardly carried out in a home environment, without requiring the patient to travel to a clinical facility or the like. Also, because stimulation parameter optimization is carried out by the system 100 automatically (in particular, by implementing the adaptive learning process), the intervention of a specialized clinician is not requested.

[0263] In the present embodiment, said adaptive learning process a sequential learning process, in particular a Bayesian learning process.

[0264] Nevertheless, different kinds of adaptive learning processes are also possible according to the invention.

[0265] In the present embodiment, the control means 20 is configured to execute the optimization algorithm to automatically define a maximum threshold value for each stimulation parameter.

[0266] Accordingly, safety boundaries can be determined for each stimulation parameter.

[0267] Consequently, tissue damages, e.g., caused by excessive heat, tissue burning, or the like, can be largely prevented.

[0268] Alternatively, maximum threshold values for the stimulation parameters can be defined based on a user input, e.g., from a clinician, caregiver, or the like.

[0269] In the present embodiment, as shown in Fig. 4, the system 100 further comprises a user input device 22 for providing a user’s feedback on delivered neuromodulation / neurostimulation.

[0270] Not shown is that, in the present embodiment, the user input device 22 comprises a display means of the touch-screen type.

[0271] The user input device 22 can be a smartphone, a tablet computer, or the like.

[0272] Nevertheless, different configurations for the user input device 22 are also possible according to the invention.

[0273] As a non-limiting example, a multiple-choice questionnaire can be displayed on the display means, allowing the user, e.g., the patient him / herself, a caregiver, or the like, to select a desired option, e.g., by tapping on the touch-screen with his / her finger or a tool. As a non-limiting example, the user’s feedback may comprise one or more among a patient’s physical and / or psychological state during and / or after neuromodulation / neurostimulation, increase / reduction in side effects, increase / reduction in physical and psychological symptoms, quality assessment of a performed activity (e.g., distance walked, hours of sleep and / or REM phase duration and / or other sleep metrics, fatigue, or the like, and information on medication intake.

[0274] In the present embodiment, the control means 20 is configured to execute the optimization algorithm to update stimulation parameters for the at least one activity carried out by the patient P based on the analyzed at least one set of metrics and a user’s feedback provided through the user input device 22.

[0275] Because the optimization algorithm considers both the at least one set of metrics and the user’s feedback, stimulation parameters adjustment / optimization can be implemented with enhanced accuracy and reliability.

[0276] In the present embodiment, the control means 20 is configured to define said initial stimulation parameters based on prior information regarding the patient P.

[0277] In particular, in the present embodiment, the system 100 may further comprise a sensor means for recording brain signals, and the control means 20 is further configured to define prior information regarding the patient P based on detected brain signals from said sensor means.

[0278] Additionally or alternatively, the control means 20 may be configured to define said initial stimulation parameters based on prior information regarding a population of patients having the same or similar symptoms and / or affected by the same or similar pathology.

[0279] In yet another alternative, the initial stimulation parameters may be randomly selected.

[0280] An example of programming and stimulation parameters optimization cycle, implemented through the system 100, is shown in Fig. 5.

[0281] As a first step, initial stimulation parameters are defined (either randomly or based on prior information).

[0282] Then, stimulation is applied based on the defined initial stimulation parameters.

[0283] Stimulation with such parameters is applied while the patient performs an activity, e.g., a gamified task that may allow for quantification of desired symptoms.

[0284] In the example of Fig. 5, a gamified task includes spiral drawing. Here, the patient is requested to draw a spiral, e.g., with a finger or with a suitable writing tool, on a touch-screen type display means of a smartphone, tablet computer, or the like (a smartphone is exemplarily illustrated in Fig. 5).

[0285] While stimulation is still ongoing, the patient and / or the clinician provides a user input, e.g., including a subjective feedback on the applied therapy along with an indication of the activity / task carried out by the patient while stimulation is applied.

[0286] This can be done by selecting an option among a plurality of options displayed on a display means of the touch-screen type (e.g., by tapping).

[0287] In the shown example, the user input includes:

[0288] - therapy feedback (e.g., “bad”, “same”, “worse”),

[0289] - time of the last medication intake, and

[0290] - performed activity (e.g., “sleep”, “walk”, “speak”, “eat”, “sport”, “other”).

[0291] The provision of alternative and / or additional parameters is also possible according to the invention.

[0292] At least one set of metrics along with the user’s input are fed to the optimization algorithm that updates / adjusts the stimulation parameters accordingly (by implementing the adaptive learning process).

[0293] The adaptive learning process is then iterated to further tune stimulation parameters based on the patient’s status and response to applied therapy.

[0294] Not shown is that, in the present embodiment, the measurement means 14 comprises a sensor means.

[0295] In particular, the sensor means is configured to capture audio and / or video recordings of the patient P.

[0296] For instance, said sensor means may include a microphone to capture audio recordings of the patient P.

[0297] The microphone can be either implantable or non-implantable.

[0298] Additionally or alternatively, said sensor means may includer a camera for capturing video recordings of the patient P.

[0299] Not shown is that said sensor means, e.g., a microphone and / or a camera, may be conveniently embedded into a smartphone, tablet computer, or the like.

[0300] Additionally or alternatively, said sensor means may include an accelerometer. For example, the accelerometer can be non-implantable, e.g., embedded into a smartphone, tablet computer, or the like.

[0301] Alternatively, the accelerometer may be implantable. Audio recordings can be used for defining the patient’s mood, e.g., based on the types of words used and intonation.

[0302] Video recordings may enable to analyze metrics of the patient’s face and / or expression.

[0303] Together, video and audio recording can be used to evaluate psychiatric effects of delivered therapy.

[0304] In the present embodiment, the control means 20 may be configured to execute the optimization algorithm to update stimulation parameters for different motor and / or non-motor symptoms independently.

[0305] An activity that is carried out by the patient P may involve different motor and / or non-motor symptoms.

[0306] For instance, a task requiring the patient P to draw a spiral on the screen of a smartphone or tablet computer (as illustrated in the diagram of Fig. 5) may provide metrics on both bradykinesia (speed of drawing) and tremor (lack of drawing smoothness).

[0307] Here, stimulation parameters can be independently defined and / or optimized for the different metrics for the implemented task (in this example, bradykinesia and tremor).

[0308] As a non-limiting example, the stimulation parameters may comprise one or more among frequency, amplitude, pulse width, electrode configuration, and selection between low- resolution or high-resolution stimulation.

[0309] In particular, the selection between low-resolution or high-resolution stimulation (which is rendered possible by the high dimensional space available in high-density arrays) allows for defining multiple fidelity levels in stimulation parameters optimization.

[0310] That is, stimulation therapy can be set / adjusted so that relationships between using a single channel or multiple channels for stimulation and their parameters are incorporated in the adaptive learning process, and the algorithm is enabled to make decision on what fidelity level (i.e. , low-resolution stimulation by using channel groups, or high-resolution by using individual channels) is to be selected in order to gather the maximum amount of information in the least number of steps.

[0311] Not shown is that, in the present embodiment, the stimulation means 12 comprises a high- density implantable neural interface. Preferably said implantable neural interface comprises at least two, more preferably at least four, electrode contacts per cm2.

[0312] Conveniently, said neural interface is configured to deliver neuromodulation / neurostimulation and sense brain signals of the patient P.

[0313] That is, the same neural interface can be used for both delivering neuromodulation / neurostimulation and sensing brain signals.

[0314] Nevertheless, a different configuration for the stimulation means 12 is also possible according to the invention.

[0315] In a comparative example, the system 100 can be used to implement an adaptive learning method for adjustment / optimization of stimulation parameters for a patient, said method comprising: controlling the stimulation unit 10 to deliver neuromodulation / neurostimulation to the patient P based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit 14 for the at least one activity carried out by the patient P; updating stimulation parameters for the at least one activity carried out by the patient P based on the analyzed at least one set of metrics, and iterating the process.

[0316] According to a further comparative example, a computer-readable non-volatile storage medium can be provided, comprising computer-readable instructions that, when executed by a processing means, causes said processing means to implement and / or process and / or perform the method steps defined above.

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[0012] and Rasiah et al.

[0013] )

[0346] References

[0347] System

[0348] Stimulation unit

[0349] Stimulation means

[0350] Measurement unit

[0351] Measurement means

[0352] Control unit

[0353] Control means

[0354] User input device

[0355] Patient

Claims

Claims1. System (100) for delivery of neuromodulation / neurostimulation therapy to brain structures of a patient (P), comprising: a stimulation unit (10) comprising a stimulation means (12) configured to deliver neuromodulation / neurostimulation to brain structures of the patient (P); a measurement unit (14) comprising a measurement means (16) configured to measure at least one set of metrics in response to delivered neuromodulation / neurostimulation for at least one activity carried out by the patient (P), and a control unit (18), operatively connected to the stimulation unit (10) and the measurement unit (14), wherein the control unit (18) comprises a control means (20) that is configured to execute an optimization algorithm for defining optimized stimulation parameters for the patient (P) by implementing an adaptive learning process comprising: controlling the stimulation unit (10) to deliver neuromodulation / neurostimulation to the patient (P) based on initial stimulation parameters; receiving and analyzing at least one set of metrics from the measurement unit (14) for the at least one activity carried out by the patient (P); updating stimulation parameters for the at least one activity carried out by the patient (P) based on the analyzed at least one set of metrics, and iterating the process.

2. The system (100) according to claim 1 , characterized in that the control means (20) is configured to execute the optimization algorithm to automatically define a maximum threshold value for each stimulation parameter.

3. The system (100) according to claim 1 or 2, characterized in that said adaptive learning process is a sequential learning process, preferably wherein said sequential learning process is a Bayesian learning process.

4. The system (100) according to any of the preceding claims, characterized in that the control means (20) is configured to define said initial stimulation parameters based on prior information regarding the patient (P) and / or a population of patients having the same or similar symptoms and / or affected by the same or similar pathology, preferably wherein the system (100) further comprises a sensor means for recording brain signals, and the control means (20) is further configured to define prior information regarding the patient (P) based on detected brain signals from said sensor means.

5. The system (100) according to any of the preceding claims, characterized in that the system (100) further comprises a user input device (22) for providing a user’s feedback on delivered neuromodulation / neurostimulation, wherein the control means (20) is configured to execute the optimization algorithm to update stimulation parameters for the at least one activity carried out by the patient (P) based on the analyzed at least one set of metrics and a user’s feedback provided through the user input device (22).

6. The system (100) according to claim 5,characterized in that the user input device (22) comprises a display means of the touch-screen type.

7. The system (100) according to claim 5 or 6, characterized in that the user’s feedback comprises one or more among a patient’s physical and / or psychological state during and / or after neuromodulation / neurostimulation, increase / reduction in side effects, increase / reduction in physical and psychological symptoms, quality assessment of a performed activity, and information on medication intake.

8. The system (100) according to any of the preceding claims, characterized in that the measurement means (14) comprises one or more among a sensor means configured to capture video recordings of the patient (P), an implantable or nonimplantable microphone configured to capture audio recordings of the patient (P), and an implantable or non-implantable accelerometer.

9. The system (100) according to any of the preceding claims, characterized in that said at least one set of metrics comprises at least one motor symptom and / or nonmotor symptom, and / or increase / decrease in side-effects in response to delivered neuromodulation / neurostimulation, preferably wherein said at least one motor symptom comprises one or more among tremor, rigidity, bradykinesia, gate, balance, dyskinesia, and paresthesia, and / or wherein said at least one non-motor symptom comprises one or more among mood, face expression, energy or fatigue level, speech ability, emotional status.

10. The system (100) according to claim 9, characterized in that the control means (20) is configured to execute the optimization algorithm to update stimulation parameters for different motor and / or non-motor symptoms independently.11 . The system (100) according to any of the preceding claims, characterized in that said at least one activity comprises one or more among sleeping, standing, walking, running, cycling, speaking, swallowing, reading, driving, swimming, drawing, spiral drawing, finger tapping, pronation / supination of a hand and / or foot, hand opening / closing, arm extension / flexion, touching nose with a finger while extending and flexing the arm, repeating words / sentences, move a cursor on a screen, follow a marker on a screen, and standing on a single leg.

12. The system (100) according to any of the preceding claims, characterized in that said stimulation parameters comprise one or more among frequency, amplitude, pulse width, electrode configuration, and selection between low-resolution or high-resolution stimulation.

13. The system (100) according to any of the preceding claims, characterized in that the stimulation means (12) comprises a high-density implantable neural interface, preferably wherein said implantable neural interface comprises at least two, more preferably at least four, electrode contacts per cm2.

4. The system (100) according to claim 13, characterized in that said neural interface is configured to deliver neuromodulation / neurostimulation and sense brain signals of the patient (P).

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