Spatial location finding for neuromodulation in transcranial ultrasound

Abstract

Apparatuses, methods and computer programs are provided. An apparatus comprises one or more processors and memory. The memory stores computer program instructions that, when executed by the one or more processors, cause the one or more processors to perform a method. The method comprises: causing one or more ultrasound transducers to sequentially neuromodulate a plurality of candidate target volumes; performing an analysis based, at least in part, on one or more biomarker responses of the person in one or more biomarker signals associated with each candidate target volume; identifying, based at least in part on the analysis, one or more candidate target volumes of interest; determining a plurality of candidate target sub-volumes, each candidate sub-volume being a sub-volume of the one or more candidate target volumes of interest; causing the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes; performing a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with each candidate target sub-volume; and determining one or more therapeutic target locations based, at least in part, on the further analysis.

Description

[0001] NEUROMODULATION

[0002] TECHNOLOGICAL FIELD

[0003] Examples of the disclosure relate to neuromodulation. At least some relate to spatial location finding for neuromodulation in transcranial ultrasound.

[0004] BACKGROUND

[0005] Transcranial ultrasound is a non-invasive neuromodulatory technique that uses ultrasound waves to interact with a person’s brain, for example, to non-invasively excite and / or inhibit neural activity. This is performed by directing ultrasound signals to particular regions of the brain.

[0006] BRIEF SUMMARY

[0007] According to various, but not necessarily all, examples there is provided an apparatus, comprising: one or more processors; and memory storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to: cause one or more ultrasound transducers to sequentially neuromodulate sets of candidate target volumes, the candidate target volumes in a set being collectively neuromodulated by steering ultrasound signals to each candidate target volume in the set contemporaneously; perform an analysis based, at least in part, on one or more biomarker responses of the person in one or more of the biomarker signals associated with each set of collectively neuromodulated candidate target volumes; and determine one or more therapeutic target locations based, at least in part, on the analysis.

[0008] Causing the one or more ultrasound transducers to neuromodulate each candidate target volume in the set contemporaneously may comprise causing the one or more ultrasound transducers to neuromodulate each candidate target volume in the set substantially simultaneously.

[0009] A time period between neuromodulating an initial candidate volume in each set and neuromodulating a final candidate volume in the set may be less than one minute.

[0010] Neuromodulating each candidate target volume in the set contemporaneously may comprise neuromodulating candidate target volumes in the set sequentially within the time period.Neuromodulating each candidate target volume in the set contemporaneously may comprise neuromodulating each candidate target volume in the set simultaneously.

[0011] Neuromodulating a candidate target volume may comprise focusing one or more ultrasound signals at a candidate target volume, such that each candidate target volume in a set is a different focus for the one of more ultrasound transducers.

[0012] Each candidate target volume in the set may be spatially separated from the other candidate target volumes in the set.

[0013] The analysis may be based, at least in part, on one or more biomarker responses in one or more of the biomarker signals associated with each set of collectively neuromodulated candidate target volumes relative to one or more responses of the one or more biomarkers in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes.

[0014] The determination of one or more therapeutic target locations may be based, at least in part, on a determination as to whether one or more criteria have been satisfied. The satisfaction of the one or more criteria may depend, at least in part, on the biomarker signals associated with each set of collectively neuromodulated candidate target volumes.

[0015] The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: identify, based at least in part on the analysis, at least one set of candidate target volumes of interest; and determine a plurality of subsets of candidate target volumes of interest, wherein each subset includes candidate target volumes from the at least one set of candidate target volumes of interest; and cause the one or more ultrasound transducers to sequentially neuromodulate the subsets of candidate target volumes, the at least one candidate target volume in a subset being collectively neuromodulated by steering ultrasound signals to each candidate target volume in the subset contemporaneously; perform a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more further biomarker signals associated with each subset of collectively neuromodulated candidate target volumes, wherein the one or more therapeutic target locations is determined based, at least in part, on the further analysis.The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: determine the sets of candidate target volumes based, at least in part, on a probability distribution of candidate target locations indicating a probability that a candidate target location is likely to represent a therapeutic target location.

[0016] The probability distribution may depend, at least in part, on data specific to the person. The probability distribution may depend, at least in part, on prior biomarker responses to neuromodulation during at least one prior ultrasound neuromodulation session.

[0017] The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: update the probability distribution based, at least in part, on the analysis of the biomarker signals. The plurality of sets of candidate target volumes of interest may be identified, based at least in part, on the updated probability distribution.

[0018] A density of the candidate target volumes may depend, at least in part, on the probability distribution.

[0019] The density of the candidate target volumes may be greater in one or more anatomical regions which are indicated in the probability distribution to be more likely to include a suitable therapeutic target location relative to one or more anatomical regions which are indicated in the probability distribution to be less likely to include a suitable therapeutic target location.

[0020] In one or more anatomical regions that are indicated in the probability distribution to be more likely to include a suitable therapeutic target location relative to one or more other anatomical regions, there may be more sets of the determined sets of candidate target volumes than there are in the one or more other anatomical regions, and those sets include fewer candidate target volumes than the sets in the one or more other anatomical regions.

[0021] The biomarker signals may be indicative of one or more physiological biomarkers of the person.

[0022] According to various, but not necessarily all, examples there is provided a method, comprising: causing one or more ultrasound transducers to sequentially neuromodulate sets of candidate target volumes, the candidate target volumes in a set being collectively neuromodulated by steering ultrasound signals to each candidate target volume in the set contemporaneously; and determiningone or more therapeutic target locations based, at least in part, on an analysis of biomarker signals associated with each set of collectively neuromodulated candidate target volumes.

[0023] Causing the one or more ultrasound transducers to neuromodulate each candidate target volume in the set contemporaneously may comprise causing the one or more ultrasound transducers to neuromodulate each candidate target volume in the set substantially simultaneously.

[0024] A time period between neuromodulating an initial candidate volume in each set and neuromodulating a final candidate volume in the set may be less than one minute.

[0025] Neuromodulating each candidate target volume in the set contemporaneously may comprise neuromodulating candidate target volumes in the set sequentially within the time period.

[0026] Neuromodulating each candidate target volume in the set contemporaneously may comprise neuromodulating each candidate target volume in the set simultaneously.

[0027] Each candidate target volume in the set may be spatially separated from the other candidate target volumes in the set.

[0028] The analysis may be based, at least in part, on one or more biomarker responses in one or more of the biomarker signals associated with each set of collectively neuromodulated candidate target volumes relative to one or more responses of the one or more biomarkers in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes.

[0029] The determination of one or more therapeutic target locations may be based, at least in part, on a determination as to whether one or more criteria have been satisfied. The satisfaction of the one or more criteria may depend, at least in part, on the biomarker signals associated with each set of collectively neuromodulated candidate target volumes.

[0030] According to various, but not necessarily all, examples there is provided computer program instructions that, when executed by one or more processors, cause the one or more processors to perform the method described above.According to various, but not necessarily all, examples there is provided an apparatus, comprising: one or more processors; and memory storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to: cause one or more ultrasound transducers to sequentially neuromodulate a plurality of candidate target volumes; perform an analysis based, at least in part, on one or more biomarker responses of the person in one or more biomarker signals associated with each candidate target volume; identify, based at least in part on the analysis, one or more candidate target volumes of interest; determine a plurality of candidate target sub-volumes, each candidate sub-volume being a sub-volume of the one or more candidate target volumes of interest; cause the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes; perform a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with each candidate target sub-volume; and determine one or more therapeutic target locations based, at least in part, on the further analysis.

[0031] Determining the candidate target sub-volumes may comprise dividing at least one of the one or more candidate target volumes of interest to form the candidate target sub-volumes.

[0032] The candidate sub-volumes may be wholly located within at least one of the one or more candidate target volumes of interest.

[0033] The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: cause the one or more ultrasound transducers to neuromodulate at least one of the candidate target volumes using a first focal volume; and cause the one or more ultrasound transducers to neuromodulate at least one of the candidate target sub-volumes using a second focal volume. The second focal volume may be smaller than the first focal volume.

[0034] The determination of the one or more therapeutic target locations may depend, at least in part, on one or more further biomarker responses in one or more of the further biomarker signals associated with each neuromodulated candidate target sub-volume relative to one or more further biomarker responses in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volume.

[0035] The determination of one or more therapeutic target locations may be based, at least in part, on a determination made in the further analysis that one or more criteria have been satisfied. Thesatisfaction of the one or more criteria may depend, at least in part, on the further biomarker signals associated with the neuromodulated candidate target sub-volumes.

[0036] Identifying one or more candidate target volumes of interest may comprise identifying at least a first candidate target volume of interest and a second candidate target volume of interest. Determining a plurality of candidate target sub-volumes may comprise determining a plurality of first candidate target sub-volumes in respect of each of the first target volume of interest and determining a plurality of second candidate target sub-volumes in respect of each of the second target volume of interest. Causing the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes may comprise causing the one or more ultrasound transducers to neuromodulate the first plurality of candidate target sub -volumes and the second plurality of candidate target sub-volumes. The further analysis may be based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with the first candidate target sub-volume and the second candidate target sub-volume.

[0037] The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: determine the candidate target volumes based, at least in part, on a probability distribution of candidate target locations indicating a probability that a candidate target location is likely to represent a therapeutic target location. A size of the candidate target volumes may be determined based at least in part of the probability distribution.

[0038] The probability distribution may indicate that at least a first candidate target location has a first probability that the first candidate target location is likely to represent a therapeutic target location and indicates that a second candidate target location has a second probability that the second candidate target location is likely to represent a therapeutic target location. The first and second probabilities may indicate that the first candidate target location is more likely to represent a therapeutic target location than the second candidate target location. Based at least in part on the first and second probabilities, the size of the candidate target volume that includes the first candidate target location may be determined to be smaller than the size of the candidate target volume that includes the second candidate target location.

[0039] The probability distribution may depend, at least in part, on data specific to the person. The data specific to the person may include data indicating prior biomarker responses to neuromodulation during at least one prior neuromodulation session.The computer program instructions, when executed by the one or more processors, may cause the one or more processors to: update the probability distribution based, at least in part, on the analysis of the biomarker signals. The one or more candidate target volumes of interest may be identified, based at least in part, on the updated probability distribution.

[0040] The candidate target sub-volumes may be determined based, at least in part, on the updated probability distribution.

[0041] The biomarker signals may be indicative of one or more physiological biomarkers of the person.

[0042] According to various, but not necessarily all, examples there is provided a method, comprising: causing one or more ultrasound transducers to sequentially neuromodulate a plurality of candidate target volumes; performing an analysis based, at least in part, on one or more biomarkers responses of the person in one or more of the biomarker signals associated with each candidate target volume; identifying, based at least in part on the analysis, one or more candidate target volumes of interest; determining a plurality of candidate target sub-volumes, each candidate sub volume being a sub-volume of the one or more candidate target volumes of interest; causing the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes; performing a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with each candidate target sub-volume; and determining one or more therapeutic target locations based, at least in part, on the further analysis.

[0043] Determining the candidate target sub-volumes may comprise dividing a candidate target volume of interest to form the candidate target sub -volumes.

[0044] The method may further comprise: causing the one or more ultrasound transducers to neuromodulate at least one of the candidate target volumes using a first focal volume; and causing the one or more ultrasound transducers to neuromodulate at least one of the candidate target sub-volumes using a second focal volume . The second focal volume may be smaller than the first focal volume.

[0045] The determination of the one or more therapeutic target locations may depend, at least in part, on one or more further biomarker responses in one or more of the further biomarker signals associated with each neuromodulated candidate target sub-volume relative to one or more furtherbiomarker responses in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volume.

[0046] The determination of one or more therapeutic target locations may be based, at least in part, on a determination made in the further analysis that one or more criteria have been satisfied. The satisfaction of the one or more criteria may depend, at least in part, on the further biomarker signals associated with the neuromodulated candidate target sub-volumes.

[0047] The method may further comprise: determining the candidate target volumes based, at least in part, on a probability distribution of candidate target locations indicating a probability that a candidate target location is likely to represent a therapeutic target location.

[0048] A size of the candidate target volume may be determined based at least in part of the probability distribution.

[0049] The method may further comprise: updating the probability distribution based, at least in part, on the analysis of the biomarker signals. The one or more candidate target volumes of interest may be identified, based at least in part, on the updated probability distribution.

[0050] The candidate target sub-volumes may be determined based, at least in part, on the updated probability distribution.

[0051] According to various, but not necessarily all, examples there is provided computer program instructions that, when executed by one or more processors, cause the one or more processors to perform the method described above.

[0052] According to various, but not necessarily all, embodiments there is provided an apparatus comprising means for performing at least part of one or more methods described herein. The description of a function and / or action should additionally be considered to also disclose any means suitable for performing that function and / or action. Functions and / or actions described herein can be performed in any suitable way using any suitable method.

[0053] According to various, but not necessarily all, embodiments there is provided examples as claimed in the appended claims.While the above examples of the disclosure and optional features are described separately, it is to be understood that their provision in all possible combinations and permutations is contained within the disclosure. It is to be understood that various examples of the disclosure can comprise any or all the features described in respect of other examples of the disclosure, and vice versa. Also, it is to be appreciated that any one or more or all the features, in any combination, may be implemented by / comprised in / performable by an apparatus, a method, and / or computer program instructions as desired, and as appropriate. The description of a function should additionally be considered to also disclose any means suitable for performing that function.

[0054] BRIEF DESCRIPTION

[0055] Some examples will now be described with reference to the accompanying drawings in which: FIG. 1 illustrates a schematic of an apparatus;

[0056] FIG. 2 illustrates at least some aspects of the apparatus being applied to a person;

[0057] FIG. 3 is a schematic which shows ultrasound signals being directed towards the brain of the person;

[0058] FIG. 4 illustrates a first spatial target finding method that comprises a plurality of candidate target volumes in a set being collectively neuromodulated contemporaneously;

[0059] FIG. 5 illustrates different sets and subsets of candidate target volumes being collectively neuromodulated contemporaneously;

[0060] FIG. 6 illustrates a second spatial target finding method that comprises neuromodulation of candidate target volumes and subsequent neuromodulation of candidate target sub-volumes; FIG. 7A illustrates a plurality of candidate target volumes determined in respect of the second spatial target finding method; and

[0061] FIG. 7B illustrates a plurality of candidate target sub-volumes of one of the candidate target volumes determined in respect of the second spatial target finding method.

[0062] The figures are not necessarily to scale. Certain features and views of the figures can be shown schematically or exaggerated in scale in the interest of clarity and conciseness. For example, the dimensions of some elements in the figures can be exaggerated relative to other elements to aid explication. Similar reference numerals are used in the figures to designate similar features. For clarity, all reference numerals are not necessarily displayed in all figures.DETAILED DESCRIPTION

[0063] Neurological and psychiatric disorders may manifest as disruptions in specific brain circuits, where dysfunction in discrete neural populations can propagate throughout broader networks to produce pathological symptoms. Clinical evidence from various therapeutic interventions has demonstrated that targeting specific anatomical regions of the brain, often with millimeter precision, can provide substantial therapeutic benefit.

[0064] Transcranial ultrasound (also known as "transcranial ultrasound stimulation", TUS, "transcranial focused ultrasound", tFUS, "low-intensity focused ultrasound pulsation", LIFUP, and "low-intensity focused ultrasound", LIFU) may be used to direct ultrasonic energy to target locations to neuromodulate brain tissue at the target locations, wherein neuromodulation comprises altering the state, function, or activity of neural and non-neural cells through one or more mechanisms including, but not limited to: mechanical forces, thermal effects, cavitation, changes in membrane permeability, alterations in blood-brain barrier integrity, modifications to cellular signaling pathways, changes in protein conformation, perturbations of ion channel dynamics, alterations in neurotransmitter release, changes in synaptic plasticity, modifications to gene expression, and alterations in cellular metabolism. Such changes may be acute or sustained and may be reversible or result in longer-term modifications.

[0065] Spatial target finding approaches for transcranial ultrasound may rely heavily on magnetic resonance imaging and computed tomography. While these approaches can provide high anatomical resolution, they present significant limitations including high equipment costs, limited accessibility particularly in resource -constrained settings, contraindications for patients with metallic implants or claustrophobia, and practical challenges in scaling treatment delivery. Furthermore, anatomical imaging alone may not fully capture the functional aspects of target engagement, as individual variation in functional anatomy means that structurally -defined targets may not perfectly align with optimal stimulation locations. This challenge is particularly relevant for psychiatric conditions where optimal targets may be defined more by functional connectivity patterns than anatomical landmarks, and in movement disorders where individual variations in circuit architecture can affect optimal target location.

[0066] In embodiments of the invention, spatial target finding is performed using biomarker feedback. This feedback-based approach enables validation of target engagement, where changes in relevantbiomarkers can be used to determine whether a desired target has been successfully engaged and to optimize stimulation parameters accordingly.

[0067] Treatment may require precise targeting to engage specific neural populations. The dorsolateral prefrontal cortex (DLPFC) of the brain, a common target for depression treatment, has a surface area o the order of 2000mm2. Ignoring depth, and assuming the lateral beam width of the ultrasound focal volume is on the order of 20mm2, this creates a search space of 100 potential target locations. Sequential scanning of each potential target with sufficient dwell time for biomarker assessment (for example, several minutes) would be prohibitively time-consuming for clinical applications. Simply increasing the focal volume size of the therapeutic target is not generally a viable solution, as stimulating larger volumes can produce confounding effects due to the simultaneous activation of functionally distinct neural populations. This is particularly problematic given the presence of local inhibitory circuits and competitive network interactions, where broader stimulation may diminish therapeutic effects by engaging opposing neural mechanisms. Additionally, larger stimulation volumes may recruit compensatory network effects that mask or interfere with the desired therapeutic response.

[0068] The therapeutic response is often discrete in nature. For example, clinical evidence from tremor treatment for Essential Tremor indicates that effective stimulation of the ventral intermediate nucleus produces immediate and substantial tremor reduction when the precise anatomical target is engaged, with minimal therapeutic effect in immediately adjacent locations. This spatially specific, binary -like response pattern, where targets are effectively "hit" or "missed" rather than producing a continuous gradient of effectiveness, represents a distinct characteristic of some therapeutic applications.

[0069] The improved spatial target finding provided by embodiments of the invention enables a precise spatial target to be determined in a manner that is quicker, more efficient and / or more effective than other approaches.

[0070] FIG. 1 illustrates a schematic of an ultrasound apparatus / system 1000. The ultrasound apparatus 1000 comprises a computer apparatus / system 10 and an ultrasound apparatus / system 500.

[0071] The ultrasound apparatus 500 comprises one or more ultrasound control processors 502 and one or more ultrasound transducers 504. The ultrasound control processor(s) 502 and the ultrasoundtransducer(s) 504 are operationally coupled and any number or combination of intervening elements can exist (including no intervening elements).

[0072] The one or more ultrasound transducers 504 are configured to output steerable ultrasonic signals. That is, the ultrasound control processor(s) 502 are configured to control the ultrasound transducer(s) 504 to output ultrasound signals and to steer the output ultrasound signals. The ultrasound control processor(s) 502 may also be configured to control other aspects of the output ultrasound signals, including one or more of: the phase, the amplitude, pulse duration, pulse duty cycle and frequency of the ultrasound signals. The output ultrasound signals are acoustic waves. The frequency of the output signals may, for example, be in the range 200-800 kHz.

[0073] The ultrasound transducers 504 may be arranged to direct ultrasonic signals towards an aspect of a person’s nervous system, such as aspects of the peripheral nervous system or the central nervous system. With regard to the latter, the ultrasound transducers 504 may be arranged to direct ultrasonic signals towards the brain of a person.

[0074] In some examples, the one or more ultrasound transducers 504 include a plurality of ultrasound transducers which function as a phased array. In such examples, the ultrasound control processor(s) 502 electronically steer the output ultrasound signals by controlling the amplitude and / or phase of pulses from the ultrasound transducers, such that constructive interference of the pulses causes an ultrasound beam to be directed in particular directions.

[0075] Alternatively, or additionally, the one or more ultrasound transducers 504 may include one or more ultrasound transducers that are mechanically movable to steer the output ultrasound signals. In these instances, the ultrasound control processor(s) 502 steer ultrasound signals output by the ultrasound transducer(s) 504 by controlling a mechanism to move the transducer(s) 504. The ultrasound transducers 504 may include one or more ultrasound transducers that are moved by hand, such as under the guidance of a neuro-navigation system.

[0076] Alternatively, or additionally, the one or more ultrasound transducers 504 may include an acoustic lens arrangement, such as a volumetric or thin element acoustic lens arrangement. In these examples, the ultrasound control processor(s) 502 may be configured to change a frequency of ultrasound signals output by the ultrasound transducer(s) 504, and the acoustic lens arrangement may be configured to steer the ultrasound signals based at least in part on the frequency. In some examples, the volumetric or thin element acoustic lens arrangement may include one or moreinterchangeable lenses, where steering is performed by interchanging the lenses. The lenses may be interchanged manually or in an automated manner.

[0077] The acoustic lens arrangement may cause scattering of ultrasound signals input into the acoustic lens arrangement which plays a role in changing the direction of the input ultrasound signals.

[0078] A wearable headset may be provided which houses the one or more ultrasound transducers 504. The ultrasound transducer(s) 504 may be arranged in the wearable headset to direct ultrasound signals towards the wearer, such as towards the wearer’s brain.

[0079] The computer apparatus 10 comprises at least one processor 12 and at least one memory 14. The processor 12 and the memory 14 are operationally coupled and any number or combination of intervening elements can exist (including no intervening elements).

[0080] While a single processor 12 is described hereinafter for clarity, multiple processors 12 may be provided to perform the described actions. Although the processor 12 is illustrated as a single component / circuitry it may be implemented as one or more separate components / circuitry some or all of which may be integrated / removable. The processor 12 may be a single core or multi -core processor.

[0081] The processor 12 may be implemented in hardware alone or can be a combination of hardware and software (including firmware).

[0082] The processor 12 may be implemented using instructions that enable hardware functionality, for example, by using executable instructions in a general -purpose or a special-purpose processor that may be stored on a machine -readable storage medium (disk, memory etc.) to be executed by such a processor 12.

[0083] The processor 12 is configured to read from and write to the memory 14. The processor 12 may also comprise an output interface via which data and / or commands are output by the processor 12 and an input interface via which data and / or commands are input to the processor 12.

[0084] The memory 14 is illustrated as storing at least one computer program 16 comprising computer program instructions 18 and a probability distribution 20 in the form of data.The computer program instructions 18 stored in the memory 14 may control the operation of the computer apparatus 10 when loaded into the processor 12 and executed by the processor 12. The computer program instructions provide the logic and routines that enables the apparatuses 10, 500, 1000 to perform the methods illustrated in the accompanying FIGs.

[0085] As illustrated in FIG. 1, the computer program instructions 18 may arrive at the apparatus 10 via any suitable delivery mechanism 30. The delivery mechanism 30 may be, for example, a machine-readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a Compact Disc Read-Only Memory (CD-ROM) or a Digital Versatile Disc (DVD) or a solid-state memory, and / or an article of manufacture that comprises or tangibly embodies the computer program(s) 16. The delivery mechanism 30 may be a signal configured to reliably transfer the computer program 16. The delivery mechanism 30 may include the Internet. The apparatus 10 may propagate or transmit the computer program 16 as a computer data signal.

[0086] In some but not necessarily all examples, the computer program instructions 18 may be distributed over more than one computer program 16.

[0087] Although the memory 14 is illustrated as a single component / circuitry, it may be implemented as one or more separate components / circuitry, some or all of which may be integrated / removable and / or may provide permanent / semi-permanent / dynamic / cached storage.

[0088] The probability distribution 20 stored in the memory 14 may be a probability distribution of candidate target locations which indicates probabilities that candidate target locations (e.g., in a person’s brain) are likely to represent a therapeutic target location. The probability distribution might include discrete values or it may be continuous in nature (such as a spatial function). The probability distribution will be discussed in further detail below.

[0089] The processor 12 of the computer apparatus 10 is configured to control the output of ultrasound signals from the ultrasound apparatus 500. In this regard, the processor 12 may cause the ultrasound transducer(s) 504 to output ultrasound signals having particular characteristics. The processor 12 is configured to provide control instructions to the ultrasound control processor(s) 502 which, in turn, control the output of the ultrasound transducer(s) 504 based at least in part on the control instructions received from the processor 12. Actual output of the ultrasound signals might or might not be controlled by a human user, such that when output is activated by the humanuser, the ultrasound signals are output in accordance with the control instructions provided by the processor 12. The control instructions provided by the processor 12 may control the phase, the amplitude, pulse timing parameters (for example, pulse duration and pulse duty cycle), and / or frequency of the ultrasound signals output by the ultrasound transducer(s) 504, and / or the position of the ultrasound transducer(s) 504. By way of example, the control instructions provided by the processor 12 may steer the transmitted ultrasound signals in a particular direction, and / or may change the focal size (e.g., focal volume), and / or may control an amplitude of the ultrasound signals.

[0090] Any number of intervening elements may exist between the processor 12 and the ultrasound control processor(s) 502 (including no intervening elements). The functionality of the ultrasound apparatus 500 and the computer apparatus 10 may be combined into a single apparatus in some embodiments. In such embodiments, the functionality of the processor 12 and the ultrasound control processor(s) 502 may be provided in the same processor or processors 12.

[0091] If any intervening elements exist between the processor 12 and the ultrasound control processor(s) 502, the control instructions provided by the processor 12 may be processed before being received at the ultrasound control processor(s) 502. By way of example, one or more transmitters / transceivers may be provided to transmit the control instructions from the computer apparatus 10, and one or more receivers / transceivers may be provided to receive the transmitted control instructions at the ultrasound apparatus 500. That is, the processor 12 of the computer apparatus 10 may control a transmitter / transceiver to transmit the control instructions to a receiver / transceiver of the ultrasound apparatus 500. The ultrasound control processor(s) 502 may receive the control instructions from that receiver / transceiver. The control instructions may be communicated from the computer apparatus 10 to the ultrasound apparatus 500 over a wireless connection or in a wired connection, such as via a cable and / or a bus.

[0092] The ultrasound apparatus 1000 comprises one or more sensors 22. The processor 12 is configured to receive inputs from the one or more sensors 22. For example, the processor 12 may be configured to receive biomarker signals generated by the one or more sensors 22.

[0093] The one or more sensors 22 are configured to generate biomarker signals by sensing one or more biomarkers of a person. The sensors 22 may sense the biomarker(s) of the person non-invasively. The biomarker signals are indicative of the one or more biomarkers of the person. For example,a biomarker signal may vary in accordance with a particular biomarker or particular biomarkers of the person.

[0094] The biomarker signals may be indicative of one or more physiological biomarkers. The one or more biomarkers may include one or more biomarkers of the central nervous system and / or one or more biomarkers of the peripheral nervous system. The one or more biomarkers may include one or more biomarkers of the autonomic nervous system and / or one or more biomarkers of the somatic nervous system. The biomarkers may reflect immediate or delayed responses to stimulation and may be measured through direct or indirect means. The biomarkers may indicate local effects at the stimulation site and / or systemic physiological responses.

[0095] The sensors 22 may include one or more sensors for sensing neural activity, such as neural activity in the brain of a person. Such sensors 22 may include one or more of: an electroencephalography device, a magnetoencephalography device, a magnetic resonance imaging device, a near-infrared spectroscopy device, a positron emission tomography device, an optical imaging device, a voltage -sensitive dye imaging device, and a calcium imaging device.

[0096] The sensors 22 may include one or more of: an electromagnetic sensor, an optical sensor, a chemical sensor, a mechanical sensor, a motion sensor, a thermal sensor, an ultrasound sensor, and an acoustic sensor. These sensors 22 may, for example, sense electrical potentials, electrical currents, magnetic fields, optical properties, chemical concentrations, mechanical properties, acoustic properties, motion, thermal signatures and acoustic emissions from biological tissues.

[0097] The sensors 22 may include sensors that are configured to sense heart rate, heart rate variability, galvanic skin response, electrical activity of muscles (electromyography), blood pressure dynamics, blood flow patterns, pupil movement and / or pupil size (pupillometry), respiratory patterns, mechanical tissue properties, and molecular biomarkers in biological fluids including blood, cerebrospinal fluid, saliva, and interstitial fluid.

[0098] The sensors 22 may also be configured to sense aspects of tissue metabolism, neurotransmitter levels, receptor binding, protein expression, gene activation patterns, cellular metabolism, membrane properties, ion channel function, synaptic transmission, and network dynamics.

[0099] The sensors 22 that are used may depend on the therapeutic application / malady of the person. For example, in connection with a therapeutic application for Essential Tremor, the sensors 22 mayinclude one or more motion sensors, such as one or more accelerometers, for sensing essential tremor. In connection with a therapeutic application for psychiatric disorders, where neurocardiac guided stimulation is employed, the sensors 22 may, for instance, include a sensor for sensing heart rate and / or heart rate variability. In relation to a therapeutic application for epilepsy, the sensors 22 may include may include electroencephalography (EEG) sensors for detecting seizure activity patterns, as well as sensors for monitoring autonomic changes that often precede or accompany seizures, such as heart rate variability and galvanic skin response . In relation to a therapeutic application for post-amputation phantom limb pain, the sensors 22 may include electromyography (EMG) sensors for detecting muscle activity patterns at the residual limb, and localized temperature sensors for measuring sympathetic responses associated with neuroma sites.

[0100] Any number of intervening elements may exist between the processor 12 and the sensors 22 (including no intervening elements). If any intervening elements exist, the biomarker signals may be processed before being received by the processor 12. By way of example, one or more transmitters / transceivers may be provided to transmit generated biomarker signals to the computer apparatus 10, and one or more receivers / transceivers may be provided to receive the transmitted generated biomarker signal at the computer apparatus 10. The generated biomarker signals may be communicated over a wireless connection or in a wired connection, such as via a cable and / or a bus.

[0101] The sensors 22 are shown as not being part of the computer apparatus 10 or the ultrasound apparatus 500. One, some or all of the sensors 22 could, however, be provided in the computer apparatus 10 and / or the ultrasound apparatus 500. If the functionality of the computer apparatus 10 and the ultrasound apparatus 500 is provided in a single combined apparatus as described above, one, some or all of the sensors 22 might be provided in that combined apparatus.

[0102] FIG. 2 illustrates an example of the ultrasound apparatus 1000. In the illustration, the apparatus 1000 is being applied to the person 200. FIG. 3 is a schematic which shows ultrasound neuromodulation taking place by a first ultrasound signal 35 being directed towards a first candidate target volume 40 in the DLPFC 204 of the brain of the person 200, and a second ultrasound signal 37 being directed towards a second candidate target volume 42 in the DLPFC 204 of the brain of the person 200. The ultrasound signals 35, 37 neuromodulate brain tissue at the target locations.The example illustrated in FIGs 2 and 3 relates to a therapeutic application for psychiatric disorders, where neuro-cardiac guided stimulation is employed. In this regard, the one or more sensors 22 include one or more heart rate sensors for sensing heart rate and / or heart rate variability. In the illustrated example, the one or more ultrasound transducers 504 are housed by a wearable headset 300. The headset 300 includes nasion and helix root locators 302, 304 to enable the headset 300 to be correctly positioned on the head 202 of the wearer 200.

[0103] FIG. 3 schematically illustrates a functional connection between the DLPFC 204, the subgenual anterior cingulate cortex (sgACC) 206, the vagus nerve 208 and the heart 210. In this example, a candidate target volume 40, 42 may be determined to be a therapeutic target location if the transmitted ultrasound signals 35, 37 cause a particular change in heart rate or heart rate variability through the functional connection between the DLPFC 204 and the heart 210. For example, the functional connection may cause a decrease in heart rate, which is determined by the processor 12 through analysis of biomarker signals generated by the heart rate sensor 22. This is discussed in further detail below.

[0104] FIG. 4 illustrates a first spatial target finding method 400 that comprises a plurality of candidate target volumes in a set being collectively neuromodulated contemporaneously. In the method 400, at block 402 in FIG. 4, the processor 12 optionally determines a probability distribution 20. This may involve, for example, retrieving the probability distribution 20 from the memory 14.

[0105] The probability distribution 20 is a probability distribution of candidate target locations indicating a probability that a candidate target location (e.g., in a person’s brain) is likely to represent a therapeutic target location. The probability distribution may reference a plurality of candidate target locations (e.g., in a person’s brain) and may indicate, for each candidate target location, a likelihood that that candidate target location will represent an appropriate therapeutic target location for ultrasound treatment. As explained above, the probability distribution 20 may be continuous and / or may include discrete values. In some implementations, the probability distribution 20 may define variances in probability in three-dimensional space. The three-dimensional space may correspond with an anatomical region of search in a person, such as a region of the brain. The probability distribution 20 may define how the likelihood that a candidate target location represents a therapeutic target location varies across the three-dimensional space. In an implementation including discrete values, the granularity with which the candidate target locations are specified to have a probability in the probability distribution 20 can vary depending on the implementation. The candidate target locations in the probability distribution 20 may bereferenced relative to one or more anatomical landmarks (e.g., anatomical landmarks of aperson’s head). Each candidate target location could be a point location or a volume.

[0106] In some examples, the probability distribution 20 may be a generic probability distribution that is applicable to a whole population of people or a category within a population of people. In other examples, the probability distribution 20 may be a specific probability distribution that is specific to a particular person. The processor 12 may be configured to update a probability distribution 20 to make the probability distribution 20 (more) specific to a particular person. For example, spatial target finding in respect of a particular person may commence by using a generic probability distribution (for instance, that is determined at least in part using population-based anatomical or functional distributions derived from imaging or tractography studies).

[0107] The probability distribution might be made specific to the person by adjusting the probability distribution to take account of characteristics of the person, such as age-related factors, sex, comorbidities, previous treatment history, concurrent medications, and so on.

[0108] That probability distribution may be made (more) specific to the person by updating the probability distribution based on feedback received during a spatial target finding process, as described below in connection with block 410. In instances where at least one prior ultrasound neuromodulation session has been carried out prior to block 402, either for spatial target finding or a therapeutic purpose, the probability distribution may be made (more) specific to the person such that it depends, at least in part, on prior biomarker responses to neuromodulation at some or all of the candidate target locations during that / those prior neuromodulation session(s).

[0109] In block 404 in FIG. 4, the processor 12 determines sets of candidate target volumes to collectively neuromodulate contemporaneously. The candidate target volumes determined by the processor 12 may have the same level of granularity or a different level of granularity as the candidate target locations referenced in the probability distribution. For example, each candidate target volume may correspond to a single candidate target location in the probability distribution, multiple candidate target locations in the probability distribution, or a portion of a continuous function defined in the probability distribution. The candidate target volumes may be determined by the processor 12 as volumes or point locations. Those skilled in the art will appreciate that even if the candidate target volumes are determined as point locations by the processor 12, an ultrasound signal directed to such a point location will cause neuromodulation of a three-dimensional volume around the point location in accordance with the focal size of the transmitted ultrasound signal.

[0110] Each candidate target volume may have a location in a person’s brain. A plurality of sets of candidate target locations are determined, where each set includes multiple candidate target volumes. In some implementations, each candidate target volume in a set may be spatially separated from the other candidate target volumes in the set. In some implementations, none of the candidate target volumes in a set spatially overlap. In other implementations, it is possible that some or all of the candidate target volumes in a set overlap one or more other candidate target volumes in that set.

[0111] In examples where a probability distribution 20 is used, the density of the candidate target volumes may depend, at least in part, on the probability distribution 20. For instance, the density of candidate target volumes may be greater in anatomical regions (e.g., of the person’s brain) which are indicated in the probability distribution 20 to have a greater likelihood of including a suitable therapeutic target location relative to other anatomical regions (which may also be in the person’s brain) indicated in the probability distribution 20 to be less likely to include a suitable therapeutic target location. This may create gaps in the sampling pattern, with the spacing between candidate target volumes potentially being adjusted to maintain safe acoustic exposure levels while concentrating measurements in the most promising areas.

[0112] The spacing between adjacent candidate target volumes may depend on one or more characteristics of the ultrasound signals output by the ultrasound transducer(s) 504 and / or tissue properties of the person (e.g., the person’s brain). The ultrasound transducer(s) 504 may output one or more ultrasound signals where the foci of the signals have a particular focal width. Each of the candidate target volumes may be spatially separated by at least 3 times the focal width, such as 5 times the focal width or 10 times the focal width.

[0113] The manner in which the sets are determined may vary depending on the implementation. In some examples, in one or more anatomical regions of the person (e.g., the person’s brain) that are indicated to be more likely to include a suitable therapeutic target location relative to one or more other anatomical regions (which may also be in the person’s brain), there may be more sets of candidate target volumes and the number of candidate target volumes in a set may be smaller. This may advantageously help to accelerate to search for a suitable therapeutic target location.Neuromodulation of a candidate target volume comprises directing ultrasound signals to the candidate target volume using the one or more ultrasound transducers 504 to (potentially) modulate neural activity at the candidate target volume. Directing ultrasound signals to the candidate target volume may comprise focusing one or more ultrasound signals at the candidate target volume (e.g., in accordance with one or more pulse timing parameters).

[0114] Each candidate target volume within a set is to be collectively neuromodulated contemporaneously. That is, the candidate target volumes are to be neuromodulated such that the biomarker response by the person (as sensed by the one or more sensors 22) will pertain, over a period of time, to the neuromodulation of all of the candidate target volumes in the set rather than individual ones of the candidate target volumes in the set and not others. This may be achieved by neuromodulating the candidate target volumes in a set substantially simultaneously.

[0115] The expressions “contemporaneously” and “substantially simultaneously” are intended to encompass both the simultaneous neuromodulation of the candidate target volumes in a set and a sequential neuromodulation of the candidate target volumes that occurs quickly enough to cause a biomarker response by the person that can be considered to pertain, over a period of time, to all of the sequentially neuromodulated candidate target volumes. For instance, a change in a biomarker signal sensed by the sensors 22 (e.g., a magnitude of change) may be attributable to all of the neuromodulated candidate target volumes in the set rather than individual ones of the neuromodulated candidate target volumes in the set.

[0116] When the candidate target volumes in a set are neuromodulated contemporaneously and / or substantially simultaneously, a time period between neuromodulating an initial candidate volume in the set and neuromodulating a final candidate volume in that set may be less than one minute. In some examples, it could be less than 45 seconds, less than 30 seconds, less than 15 seconds or less than 5 seconds. In any case, the time period between neuromodulating an initial candidate volume in the set and neuromodulating a final candidate volume in that set is less than the timescale over which a physiological response (as sensed by the one or more sensors 22) occurs.

[0117] In some implementations, the ultrasound signals may be pulsed, with pulses sequentially delivered to each candidate target volume in the set, such that each volume receives pulsed neuromodulation as part of the overall stimulation sequence. This pulsing pattern may enable systematic coverage of multiple target volumes while maintaining temporal coordination across the set of candidate volumes.In block 406 of FIG. 4, the processor 12 causes the one or more ultrasound transducers 504 to sequentially neuromodulate the sets of candidate target volumes (e.g., located in the person’s brain), where the candidate target volumes in a particular set are collectively neuromodulated by steering ultrasound signals to each candidate target volume in the set contemporaneously. That is, the sets are neuromodulated sequentially, but the candidate target volumes with a particular set are neuromodulated contemporaneously. The processor 12 may be configured to control the ultrasound transducers 504 such that the (rest) time period that elapses between the collective neuromodulation of one set and the collective neuromodulation of the next set is longer than the time period over which a biomarker response to neuromodulation can develop and be distinguished from responses to previous neuromodulation.

[0118] As explained above, the processor 12 causing the one or more ultrasound transducers 504 to sequentially neuromodulate the sets of candidate target volumes may involve the processor 12 providing control instructions on which the ultrasound signal output by the ultrasound transducers 504 is based, at least in part. Actual output of the ultrasound signals might or might not be controlled by a human user, such that when output is activated by the human user, the ultrasound signals are output in accordance with the control instructions provided by the processor 12.

[0119] Each candidate target volume in a set may be a different focus for the one or more ultrasound transducers 504.

[0120] The order in which the sets are neuromodulated may, for example, be deterministically derived from the probability distribution 20, or estimated using a stochastic procedure such as a Monte Carlo procedure. The order can be randomized or systematically varied to prevent sequential testing artifacts and account for potential temporal dependencies in the physiological response. The apparatus 1000 may employ adaptive parameter refinement based on measured responses (or response gradients), allowing more efficient convergence on optimal targets while respecting safety bounds. Safety constraints include limits on continuous stimulation duration, cumulative exposure at each location, and thermal dose calculations based on acoustic field modeling.

[0121] In block 408 of FIG. 4, the one or more sensors 22 generate one or more biomarker signals. The generated signals are indicative of the one or more biomarkers of the person. That is, they may indicate a biomarker response of the person to contemporaneous neuromodulation of the candidate target volumes in a set. The generated signals may be received by the processor 12, andstored in the memory 14 or a different memory. A generated signal may, for example, indicate a variance in one or more biomarkers over time or the trend of a biomarker over time .

[0122] One or more biomarker signals may be associated with each set of collectively neuromodulated candidate target volumes. A biomarker signal that is associated with a set of collectively neuromodulated candidate target volumes indicates the biomarker response, if any, caused by the neuromodulation of the candidate target volumes in the set. In this respect, a first portion of the biomarker signal that is stored by the processor 12 may correspond with that generated by the one or more sensors 22 (shortly) before neuromodulation of the candidate target volumes takes place. A second portion of the biomarker signal that is stored by the processor 12 may correspond with that generated during neuromodulation of the candidate target volumes in the set and / or (shortly) after the candidate target volumes in the set have been neuromodulated.

[0123] FIG. 5 illustrates an example in which different sets (i) - (iv) of candidate target volumes are collectively neuromodulated contemporaneously.

[0124] The manner in which the candidate target volumes are determined may vary depending on the implementation. In the example illustrated in FIG. 5, a search volume 100 in the person (e.g., in the person’s peripheral or central nervous system, such as in the person’s brain) is divided, by the processor 12, into a multiplicity of candidate target volumes 101-116, and each of the candidate target volumes 101 - 116 are assigned to a set (i) - (iv) . The search volume 100 could, for example, be divided into a cubic grid pattern. The candidate target volumes may be spaced uniformly or non-uniformly. In some implementations, (substantially) the whole of the search volume 100 may be searched. That is, the candidate target volumes may substantially cover a continuous whole of the search volume 100. In other example, the whole of the search volume might not be searched. That is, the candidate target volumes might not substantially cover a continuous whole of the search volume 100, such that there are unsearched gaps between the candidate target volumes.

[0125] It will be appreciated that the ultrasound foci of the signals could be arranged in various geometric patterns including linear patterns, circular arrangements, curved trajectories, or other spatial configurations. The individual foci may also vary in shape, including asymmetric focal patterns, including ellipsoidal foci, curved linear patterns, and arbitrary 3D distributions through appropriate phase control of the array or other wavefront shaping devices such as acoustic lenses. The choice of pattern geometry and focal spacing may be selected based on the shape and extentof the search volume and may be informed by expected anatomical features within that volume when such information is available.

[0126] In the illustrated example, there are sixteen candidate target volumes 101-116 assigned to the four sets (i)-(iv), and four candidate target volumes 101-116 in each set (i)-(iv). In other examples, there could be more or fewer candidate target volumes 101-116, more or fewer sets (i)-(iv) and / or more or fewer candidate target volumes 101-116 in each set (i)-(iv) .

[0127] A first plurality of candidate target volumes 101, 103, 109, 111 is assigned to the first set (i). It can be seen that, in the illustrated example, each candidate target volume 101, 103, 109, 111 in the first set (i) is spatially separated from the other candidate target volumes in the set. Each candidate target volume 101, 103, 109, 111 might be spatially separated such that there is no overlap between the candidate target volumes. The candidate target volumes 101, 103, 109, 111 are collectively neuromodulated contemporaneously, such as simultaneously or substantially simultaneously. A graphic 201 illustrates the biomarker response sensed by the one or more sensors 22 and communicated to the processor 12 when the candidate target volumes 101, 103, 109, 111 in the first set (i) are collectively neuromodulated contemporaneously. In this example, the biomarker response is relatively high. For example, the magnitude of the biomarker response may be relatively high.

[0128] In embodiments where the ventral intermediate nucleus (VIM) or dentato-rubro-thalamic tract (DRT) is targeted for the purpose of finding one or more therapeutic target locations in connection with treating Essential Tremor, some or all of the candidate target volumes 101-116 may be located in the VIM and / or the DRT, or in the expected location of the VIM and / or the DRT. In such embodiments, the biomarker being sensed might be a kinetic tremor, the sensors 22 might be one or more motion sensors such as one or more accelerometers, and the biomarker response might be a reduction in tremor power. A high biomarker response might be considered to be a large reduction in tremor power.

[0129] In embodiments where the DLPFC and / or the sgACC is targeted for the purpose of finding one or more therapeutic target locations in connection with treating a psychiatric disorder, some or all of the candidate target volumes 101-116 may be located in the expected location of the DLPFC and / or the sgACC. In such embodiments, the biomarker being sensed might be heart rate and / or heart rate variability, the sensors 22 might be one or more heart rate sensors such as one or more electrical sensors and / or one or more optical sensors. The biomarker response might be adeceleration in heart rate. A high biomarker response might be considered to be a large deceleration in heart rate.

[0130] In embodiments where the anterior nucleus of the thalamus (ANT) is targeted for the purpose of finding one or more therapeutic target locations in connection with treating epilepsy, some or all of the candidate target volumes 101-116 may be located in the ANT or its expected location. In such embodiments, the biomarker being sensed might be epileptiform activity including interictal epileptiform discharges and pathological oscillations, and the sensors 22 might be one or more scalp electroencephalography (EEG) electrodes. The biomarker response might be a reduction in epileptiform activity frequency, amplitude, or spread pattern compared to pre -stimulation measurements. A high biomarker response might be considered to be a substantial reduction in epileptiform activity parameters or disruption of pathological network synchronization.

[0131] In embodiments where a peripheral neuroma site in the residual limb is targeted for the purpose of finding one or more therapeutic target locations in connection with treating post -amputation phantom limb pain, some or all of the candidate target volumes 101-116 may be located at the expected location of the terminal ends of identified nerve trunks in the residual limb. In such embodiments, the biomarker being sensed might be local muscle hyperactivity and sympathetically-maintained pain responses, and the sensors 22 might be one or more electromyography (EMG) sensors and one or more localized temperature sensors. The biomarker response might be a reduction in spontaneous EMG activity and an increase in local skin temperature. A high biomarker response might be considered to be a substantial reduction in spontaneous EMG firing combined with a consistent increase in local skin temperature above baseline.

[0132] A second plurality of candidate target volumes 102, 104, 110, 112 is assigned to the second set (ii). It can be seen that, in the illustrated example, each candidate target volume 102, 104, 110, 112 in the second set (ii) is spatially separated from the other candidate target volumes in the set. Each candidate target volume 102, 104, 110, 112 might be spatially separated such that there is no overlap between the candidate target volumes. The candidate target volumes 102, 104, 110, 112 are collectively neuromodulated contemporaneously, such as simultaneously or substantially simultaneously. A graphic 202 illustrates the biomarker response sensed by the one or more sensors 22 and communicated to the processor 12 when the candidate target volumes 102, 104, 110, 112 in the second set (ii) are collectively neuromodulated contemporaneously. In this example, the biomarker response is lower than the biomarker response caused by thecontemporaneous neuromodulation of the candidate target volumes 101, 103, 109, 111 in the first set (i). For example, the magnitude of the biomarker response may be lower than the biomarker response caused by the contemporaneous neuromodulation of the candidate target volumes 101, 103, 109, 111 in the first set (i).

[0133] A third plurality of candidate target volumes 105, 107, 113, 115 is assigned to the third set (iii). It can be seen that, in the illustrated example, each candidate target volume 105, 107, 113, 115 in the third set (iii) is spatially separated from the other candidate target volumes in the set. Each candidate target volume 105, 107, 113, 115 might be spatially separated such that there is no overlap between the candidate target volumes. The candidate target volumes 105, 107, 113, 115 are collectively neuromodulated contemporaneously, such as simultaneously or substantially simultaneously. A graphic 203 illustrates the biomarker response sensed by the one or more sensors 22 and communicated to the processor 12 when the candidate target volumes 105, 107, 113, 115 in the third set (iii) are collectively neuromodulated contemporaneously. In this example, the biomarker response is lower than the biomarker response caused by the contemporaneous neuromodulation of either the candidate target volumes 101, 103, 109, 111 in the first set (i) and the candidate target volumes 102, 104, 110, 112 in the second set (ii) . For example, the magnitude of the biomarker response may be lower than the biomarker response caused by the contemporaneous neuromodulation of the candidate target volumes 101, 103, 109, 111 in the first set (i) and the candidate target volume 102, 104, 110, 112 in the second set (ii).

[0134] A fourth plurality of candidate target volumes 106, 108, 114, 116 is assigned to the fourth set (iv). It can be seen that, in the illustrated example, each candidate target volume 106, 108, 114, 116 in the fourth set (iii) is spatially separated from the other candidate target volumes in the set. Each candidate target volume 106, 108, 114, 116 might be spatially separated such that there is no overlap between the candidate target volumes. The candidate target volumes 106, 108, 114, 116 are collectively neuromodulated contemporaneously, such as simultaneously or substantially simultaneously. A graphic 204 illustrates the biomarker response sensed by the one or more sensors 22 and communicated to the processor 12 when the candidate target volumes 106, 108, 114, 116 in the fourth set (iv) are collectively neuromodulated contemporaneously. In this example, the biomarker response is lower than the biomarker response caused by the contemporaneous neuromodulation of either the candidate target volumes 101, 103, 109, 111 in the first set (i) and the candidate target volumes 102, 104, 110, 112 in the second set (ii), and higher than the biomarker response caused by the contemporaneous neuromodulation of the candidate target volumes 105, 107, 113, 115 in the third set (iii). For example, the magnitude ofthe biomarker response may be lower than the biomarker response caused by the contemporaneous neuromodulation of the candidate target volumes 101, 103, 109, 111 in the first set (i) and the candidate target volume 102, 104, 110, 112 in the second set (ii), and higher than the biomarker response caused by the contemporaneous neuromodulation of the candidate target volumes 105, 107, 113, 115 in the third set (iii).

[0135] In block 410 in FIG. 4, the processor 12 performs an analysis and, in block 412 in FIG. 4, the processor 12 determines whether it is possible to determine one or more therapeutic target locations.

[0136] The analysis performed by the processor 12 is based, at least in part, on one or more responses of one or more biomarkers in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116. For example, the analysis may be based on one or more responses of one or more biomarkers in one or more of the biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 relative to one or responses of the one or more biomarkers in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116.

[0137] The determination of one or more therapeutic target locations in block 412 in FIG. 4 may depend on at least one characteristic (e.g., the magnitude) of the biomarker response(s) in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 relative to that / those characteristics of the biomarker response(s) in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116.

[0138] The analysis may comprise making a comparison of at least one characteristic (e.g., the magnitude) of the biomarker response(s) in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 with that / those characteristics of the biomarker response in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116. The determination of the one or more therapeutic target locations may be based, at least in part, on this comparison.

[0139] In some implementations, in block 410 the processor 12 might integrate biomarkers from multiple biomarker signals received from multiple different sensors 22 into a composite biomarkerresponse metric. For example, in respect of treating anxiety disorders, the system might combine heart rate variability, electrodermal activity, and pupillary response into a weighted biomarker response metric. The weighting factors can be dynamically adjusted based on signal quality and relative response magnitude. The system may employ machine learning algorithms to identify optimal feature combinations from the physiological signals and adapt the feedback processing in real-time based on accumulated biomarker response data.

[0140] The biomarker signals including the biomarker responses might be processed and / or analyzed using a range of mathematical and statistical techniques. These could include time-domain analyses such as root mean square amplitude, peak-to-peak measurements, and envelope detection; frequency-domain methods including Fourier transforms, wavelet analysis, and spectral power distributions; non-linear metrics such as sample entropy, detrended fluctuation analysis, and Poincare plots; state-space reconstructions for dynamical system analysis; and learned models using neural networks or other universal function approximators. The processor 12 may measure absolute threshold values, or relative improvement, selecting locations producing the strongest desired physiological response. These processing methods can be applied across multiple time scales simultaneously, from immediate neural effects occurring within milliseconds to slower autonomic changes over seconds to minutes. Temporal constraints might include minimum dwell times at each set based on response latency for the specific feedback measure, with suitable intervals between stimulation epochs to prevent tissue heating and account for physiological recovery periods.

[0141] In some embodiments, the processor 12 may employ adaptive signal processing techniques to optimize the physiological measurements in real-time. As a non-exhaustive list of examples, this could include dynamic filtering based on signal-to-noise characteristics, automated artifact rejection using machine learning algorithms to distinguish between stimulation -induced responses and confounding physiological variations, and adaptive thresholding based on baseline variability. Processing parameters may be continuously updated based on the quality and consistency of measured responses, allowing the apparatus 1000 to maintain reliable targeting even as physiological conditions change during the procedure. Advanced processing approaches might incorporate dimensionality reduction methods like principal component analysis, independent component analysis for artifact removal, and adaptive filtering for noise reduction, all operating within the real-time constraints of the targeting procedure.In block 412 in FIG. 4, the processor 12 determines whether one or more therapeutic target locations can be determined and that determination might or might not be based, at least in part, on the analysis performed in block 410. The processor 12 may, for example, determine whether one or more criteria have been satisfied to enable one or more therapeutic target locations to be determined. The determination as to whether the one or more criteria have been satisfied depends, at least in part, on the biomarker response in the one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116.

[0142] The analysis performed in block 410 depends on the biomarker responses associated with each collectively neuromodulated set of candidate target volumes 101-116, and can, but need not, include a direct comparison of those biomarker responses with one another.

[0143] For instance, in some embodiments, the processor 12 may compare the biomarker response(s) in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 with the biomarker response(s) in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116. The comparison may comprise comparing one or more characteristics of the biomarker responses, such as the magnitude.

[0144] Thus, in the context of the example illustrated in FIG. 5, the processor 12 may compare the biomarker response(s) in the biomarker signal(s) associated with the first set (i) of collectively neuromodulated candidate target volumes 101, 103, 109, 111 with the biomarker response(s) in the biomarker signal(s) associated with the second, third and fourth sets (ii), (iii), (iv) of neuromodulated candidate target volumes.

[0145] The processor 12 may also perform a corresponding comparison of the biomarker response(s) in the biomarker signal(s) associated with the second set (ii) of collectively neuromodulated candidate target volumes 102, 104, 110, 112 with that / those associated with the first, third and fourth sets (i), (iii), (iv) of neuromodulated candidate target volumes, a corresponding comparison of the biomarker response(s) in the biomarker signal(s) associated with third set (iii) of collectively neuromodulated candidate target volumes 105, 107, 113, 115 with that / those associated with the first, second and fourth sets (i), (ii), (iv) of neuromodulated candidate target volumes, and a corresponding comparison of the biomarker response(s) in the biomarker signal(s) associated with the fourth set (iv) of collectively neuromodulated candidate target volumes 106,108, 114, 116 with that / those associated with the first, second and third sets (i), (ii), (iii) of neuromodulated candidate target volumes.

[0146] In the example illustrated in FIG. 5, the processor 12 determines from the comparisons that the biomarker response to the collective neuromodulation of the candidate target volumes 101, 103, 109, 111 in the first set (i) is greater than the biomarker response to the collective neuromodulation of the candidate target volumes in the others sets (ii), (iii), (iv). For instance, the magnitude of the biomarker response might be greater in respect of the candidate target volumes 101, 103, 109, 111 in the first set (i) than in respect of the candidate target volumes in the others sets (ii), (iii), (iv).

[0147] In other embodiments, processor 12 might not directly compare one or more response(s) of one or more biomarkers in one or more biomarker signals associated with each set (i)-(iv) of collectively neuromodulated candidate target volumes 101-116 with one or more response(s) of the one or more biomarkers in one or more biomarker signals associated with the other sets (i)-(iv) of collectively neuromodulated candidate target volumes 101-116. For instance, instead, a comparison of probabilities from the probability distribution 20 may be made after the probability distribution 20 has been updated in dependence on the response(s) of the one or more biomarkers in the received biomarker signals.

[0148] In these embodiments, the processor 12 might use the response(s) of the one or more biomarkers in the biomarker signal(s) associated with each set (i)-(iv) of collectively neuromodulated candidate target volumes 101-116 to update the probability distribution 20. As indicated above, the probability distribution 20 may indicate probabilities that candidate target locations in a person (e.g., in a person’s brain) are likely to represent atherapeutic target location. The manner in which the processor 12 updates the probability that a candidate target location represents a therapeutic target location depends on the responses of the one or more biomarkers in biomarker signals associated with the set (i)-(iv) that the candidate target location belongs to. For example, the processor 12 could use a Bayesian inference method to update the beliefs about the location of the therapeutic target. In this case, when neuromodulation is applied at candidate target volumes within sets (i)-(iv), the measured biomarker responses are used to update this spatial distribution according to Bayes' rule. The spatial correlation structure of the biomarker response can be modeled through various approaches - for example, using a Gaussian process with a kernel function that captures how the similarity of neural responses decays with distance. If in one instance the responses to neuromodulation in the one or more biomarkers is relatively high forthe candidate target volumes in a particular set (i)-(iv), and in another instance the responses to neuromodulation in the one or more biomarkers is relatively low for the candidate target volumes in a particular set (i)-(iv), the posterior probability distribution is updated to reflect both of these measurements and their spatial relationship, such that the posterior probability is higher in relation to locations corresponding with the first instance candidate target volumes and lower in relation to locations corresponding with the second instance candidate target volumes. This yields a revised probability distribution that combines the initial spatial prior with the observed responses. The methodology can accommodate different choices for modeling the likelihood of observations and the spatial correlation structure. The Gaussian process framework being one specific implementation that provides closed-form expressions for both the posterior mean (the best estimate of target locations) and the associated uncertainty across the spatial map.

[0149] The one or more criteria that are assessed in block 412 of FIG. 4 depend upon the implementation. For example, in some implementations the criteria include at least one criterion that is not based at all on the analysis performed in block 410. That criterion might be a determination as to whether the method 400 has reached a stage where a single candidate target volume can be determined as a therapeutic target location from a choice of two candidate target volumes (e.g., the single candidate target volume eliciting the most significant / greatest biomarker response). In such embodiments, the purpose of performing the analysis in block 410 is in connection with identifying the candidate target volume(s) of interest in block 414. If the method 400 has not reached a stage where a single candidate target volume can be determined as a therapeutic target location from a choice of two candidate target volumes, the method 400 may proceed to block 414.

[0150] In some implementations, the criteria used in block 412 may include a determination as to whether a biomarker response (e.g., one or more characteristics of a biomarker response, such as the magnitude) in a biomarker signal meets or exceeds a threshold. In some implementations, the criteria may include a determination as to whether a probability in the probability distribution meets or exceeds a probability threshold. In some implementations, it may be that no such threshold is used. For example, the processor 12 might be configured to determine which candidate target location (or locations, if a plurality of locations is sought) represents the best option(s) as a therapeutic target location, for example the location eliciting the stronger biomarker response, and this might or might not be coupled with a requirement that the best option(s) meet or exceed a threshold (e.g., a biomarker response threshold or a probability threshold) in order to be considered as a therapeutic target location.In the context of the example in FIG. 5, in block 412 in FIG. 4, the processor 12 is not able to determine one or more therapeutic target locations, for instance because one or more criteria have not been satisfied. In response, the processor 12 may continue the search for one or more therapeutic target locations. Thus, the method proceeds to block 414 of FIG. 4, where the search is continued by determining at least one set of candidate target volumes of interest based at least in part on the analysis performed in block 410.

[0151] The determination as to whether a set of candidate target volumes is of interest may depend on at least one characteristic of (e.g., the magnitude of) the biomarker response(s) in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 relative to that / those characteristic(s) of the biomarker response(s) in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116.

[0152] As explained above, the analysis in block 410 in FIG. 4 may comprise making a comparison of at least one characteristic of biomarker response(s) in one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes 101-116 with that / those characteristic(s) of biomarker response(s) in one or more of the biomarker signals associated with each other set of collectively neuromodulated candidate target volumes 101-116. The determination of the candidate target volumes of interest may be based, at least in part, on this comparison.

[0153] The processor 12 may determine whether one or more criteria have been satisfied when determining whether a set of candidate target volumes is of interest. The determination as to whether the one or more criteria have been satisfied depends, at least in part, on the biomarker response in the one or more biomarker signals associated with each set of collectively neuromodulated candidate target volumes.

[0154] As explained above, the analysis performed in block 410 in FIG. 4 depends on the biomarker responses associated with each collectively neuromodulated set of candidate target volumes 101-116, and can, but need not, include a direct comparison of those biomarker responses with one another. Thus, the determination of the candidate target volumes of interest may be based, at least in part, on such a comparison.It was also explained above that in block 410 in FIG. 4 the processor 12 might not directly compare biomarker responses of sets of collectively neuromodulated candidate target volumes 101-116 with biomarkers responses of other sets of collectively neuromodulated candidate target volumes 101-116. It was explained that, instead, a comparison of probabilities from the probability distribution 20 may be made after the probability distribution 20 has been updated in dependence on the response(s) of the one or more biomarkers in the received biomarker signals. The determination of the one or more target candidate volumes of interest in block 414 might be based, at least in part, on such a comparison.

[0155] The one or more criteria that are assessed in block 414 of FIG. 4 depend upon the implementation. In some implementations, the criteria may include a determination as to whether one or more characteristics of a biomarker response (e.g., the magnitude of a biomarker response) in a biomarker signal meets or exceeds a threshold. In some implementations, the criteria may include a determination as to whether a probability in the probability distribution 20 meets or exceeds a probability threshold. In some implementations, it may be that no such threshold is used. For example, the processor 12 might be configured to determine which candidate target location (or locations, if a plurality of locations is sought) represents the best option(s) as a therapeutic target location, and this might or might not be coupled with a requirement that the best option(s) meet or exceed a threshold (e.g., a biomarker response threshold or a probability threshold) in order to be considered as a therapeutic target location.

[0156] In the context of the example illustrated in FIG. 5, the processor 12 determines that set (i) is of interest and sets (ii), (iii) and (iv) are not of interest, based at least in part on the relative biomarker responses in the biomarker signals associated with sets (i)-(iv). While a single set (i) of interest is identified in this example, a greater number of sets of interest might be identified in other examples.

[0157] In block 416 in FIG. 4, the processor 12 then determines a plurality of subsets of candidate target volumes of interest, where each subset may include at least one candidate target volume from the at least one candidate target volumes of interest. For instance, the processor 12 may divide each set of candidate volumes of interest into a plurality of subsets. Each subset may include one or more candidate volumes of interest, such as a plurality of candidate volumes of interest. It may be that every candidate target volume in a set of interest is placed into a subset by the processor 12.The processor 12 may apply one or more criteria when deciding how to divide a set into subsets. The criteria may depend, at least in part, on the probability distribution 20 and / or on the relative spacing of candidate target volumes in a set. For instance, candidate target volumes that are closer to one another may be placed into the same set by the processor 12.

[0158] In the context of the example illustrated in FIG. 5, the processor 12 divides the set (i) of candidate target volumes 101, 103, 109, 111 into two subsets (v), (vi). A first plurality of candidate target volumes 101, 109 are assigned to a first subset (v) and a second plurality of candidate target volumes 103, 111 are assigned to a second subset (vi).

[0159] The method then proceeds back to block 406 in FIG. 4, where blocks 406, 408, 410 and 412 in FIG. 4 are repeated for the subsets of candidate target volumes determined in block 416. In the context of the example illustrated in FIG. 5, the processor 12 is not able to determine one or more therapeutic target locations in block 412 in FIG. 4, for instance because one or more criteria have not been satisfied. In the context of the example illustrated in FIG. 5, the processor 12 then identifies that the second subset (vi) is of interest and the first subset (v) is not of interest in block 414 of FIG. 4, based at least in part on the relative biomarker responses in the biomarker signals associated with the subsets (v) and (vi). While a single subset (v) of interest is identified in this example, a greater number of subsets of interest might be identified in other examples if the total number of subsets is greater than two.

[0160] In block 416 in FIG. 4, the processor 12 then determines a plurality of further subsets of candidate target volumes of interest, where each further subset may include at least one candidate target volume from the at least one candidate target volumes of interest. For instance, the processor 12 may divide each subset of candidate target volume of interest into a plurality of further subsets. Each further subset may include one or more candidate volumes of interest, such as a plurality of candidate volumes of interest. It may be that every candidate target volume in a subset of interest is placed into a further subset by the processor 12. The process for dividing a subset into further subsets may be the same as that described above for dividing a set into subsets in relation to block 416.

[0161] In the context of the example illustrated in FIG. 5, the processor 12 divides the subset (vi) of candidate target volumes into two further subsets (vii), (viii). A first candidate target volume 103 is assigned to a first further subset (vii) and a second candidate target volume 111 is assigned to a second further subset (viii).The method then proceeds back to block 406 in FIG. 4, where blocks 406, 408, 410 and 412 in FIG. 4 are repeated for the further subsets of candidate target volumes determined in block 416. The processor 12 may loop through blocks 414, 416, 406, 408, 410 and 412 until the processor 12 is able to determine one or more therapeutic target locations in block 412 in FIG. 4.

[0162] In the context of the example illustrated in FIG. 5, in block 412 in FIG. 4, the processor 12 determines that the candidate target location 111 in the second further subset set (viii) is a suitable therapeutic target location (and / or the best candidate target location to select as a therapeutic target location) based at least in part on the analysis performed in block 410 in the manner described above, for instance by determining that the candidate target location 111 satisfies one or more criteria. The processor 12 may determine that the candidate target location 103 in the first further subset (vii) is not a suitable therapeutic target location (and / or is not the best candidate target location to select as a therapeutic target location) based at least in part on the analysis performed in block 410. The determination that the candidate target location 111 in the second further subset set (viii) is a suitable therapeutic target location can be considered to be based, at least in part, on the analysis that was performed by the processor 12 each time block 410 in the method 400 was reached. In this regard, it should be understood that re-analysis of all prior biomarker responses is not required the final time block 410 is reached prior to at least one therapeutic target location being determined in block 412. Instead, the determination of the therapeutic target location(s) in block 412 can be considered to depend on the analysis that was performed each time block 410 in the method was reached, because those analysis / analyses were used to filter the candidate target volumes and to an eventual determination of the candidate target location 111 in the second further subset set (viii) as a suitable therapeutic target location.

[0163] Following the determination of the one or more therapeutic target locations, the method proceeds to block 418 in FIG. 4, and spatial target finding ends. The processor 12 may then cause the one or more ultrasound transducers to neuromodulate the one or more therapeutic target locations (by directing one or more ultrasound signals to the one or more therapeutic target locations). In some cases, a therapeutic ultrasound session such as this may immediately follow the spatial target finding method of FIG. 4 following the determination of one or more therapeutic target locations in block 412 of FIG. 4. In other cases, a therapeutic ultrasound session such as this may be performed on a different occasion (e.g., later in the same day, or on a different day). When therapy is performed and the one or more ultrasound transducers 504 are controlled to direct ultrasound signals to the determined one or more therapeutic target locations, the processor 12 may monitor biomarker signals from the one or more sensors 22 to determine whether the determined one ormore therapeutic target locations have been successfully engaged. For instance, the processor 12 may analyze one or more biomarker responses in the biomarker signals to determine whether successful engagement has been made. In some implementations, the one or more sensors 22 from which biomarker signals are received and analyzed for treatment are the same as that or those which were used for spatial target finding in the method of FIG. 4. In some other implementations, the one or more sensors 22 from which biomarker signals are received and analyzed for treatment are different from that or those which were used for spatial target finding in the method of FIG.

[0164] 4. For example, galvanic skin response could be used for target finding and heart rate and / or heart rate variability could be used for treatment. It may be that no sensors are used during treatment.

[0165] In some implementations, the spatial target finding method can serve as a preparatory process for various therapeutic implementations beyond ultrasound neuromodulation. Once one or more target therapeutic locations have been identified through the biomarker feedback-guided search process, this can guide the placement or targeting of other therapeutic modalities. These may include but are not limited to: precise targeting of ablative treatments (radiofrequency, focused ultrasound, or laser ablation), optimal placement of deep brain stimulation electrodes, guidance of magnetic stimulation, or delivery of focused drug therapies.

[0166] In some circumstances, the entire spatial target finding method 400 of FIG. 4 or aspects of it might be repeated to ensure and account for temporal variations in physiological responses. These repeated searches might involve neuromodulating sets of candidate target volumes in different orders to prevent systematic biases from sequential testing effects. Multiple search iterations also enable signal averaging or other statistical ensemble processing to improve response measurement accuracy, which is particularly useful when dealing with physiological signals with low signal-to-noise ratios or high variability. In some circumstances, for example, if a suitable target volume is not found, then the initial search volume may be modified before repeating the spatial target finding method 400.

[0167] FIG. 6 illustrates a second spatial target finding method 600 that comprises neuromodulation of candidate target volumes and subsequent neuromodulation of candidate target sub-volumes. The second spatial target finding method 600 illustrated in FIG. 6 represents an alternative spatial target finding method 600 to that illustrated in FIG. 4 and discussed above.

[0168] In the method illustrated in FIG. 6, at block 602, the processor 12 optionally determines a probability distribution 20. This may involve, for example, retrieving the probability distribution20 from the memory 14. The probability distribution 20 may be the same as that described above in relation to FIG. 4. That is, it may have some or all of the features of the probability distribution described above in relation to FIG. 4.

[0169] In block 604 in FIG. 6. the processor 12 determines a plurality of candidate target volumes to neuromodulate sequentially. Each candidate target volume may have a location in a person’s brain. The candidate target volumes might or might not spatially overlap. In some implementations, the size of each of candidate target volume is the same as the other candidate target volumes. In other implementations, the size of one or more of the candidate target volumes is different from the others. In examples where a probability distribution 20 is used, the size of one or more candidate target volumes may be dependent on the probability distribution. For example, the size of a candidate target volume might be smaller in some anatomical regions (which may be in the person’s brain) which are indicated in the probability distribution 20 to have a greater likelihood of including a suitable therapeutic target location relative to other anatomical regions (which may also be in the person’s brain) indicated in the probability distribution 20 to be less likely to include a suitable therapeutic target location.

[0170] FIG. 7A illustrates an example in which a plurality of candidate target volumes 710, 720, 730 within a search volume 700 of a person (e.g., in the person’s peripheral or central nervous system, such as such as a person’s brain) are determined. The search volume 700 could, for example, be in any of the regions discussed above in relation to FIG. 4.

[0171] The plurality of candidate target volumes 710, 720, 730 include first, second and third candidate target volumes 710, 720, 730 in the illustrated example. There might be more or fewer candidate target volumes 710, 720, 730 in other examples.

[0172] In block 606 in FIG. 6, the processor 12 causes the one or more ultrasound transducers 504 to sequentially neuromodulate the plurality of candidate target volumes 710, 720, 730 located in the person, such as the brain of the person.

[0173] As explained above, the processor 12 causing the one or more ultrasound transducers 504 to sequentially neuromodulate the plurality of candidate target volumes 710, 720, 730 may involve the processor 12 providing control instructions on which the ultrasound signal output by the ultrasound transducers 504 is based, at least in part. Actual output of the ultrasound signals might or might not be controlled by a human user, such that when output is activated by the human user,the ultrasound signals are output in accordance with the control instructions provided by the processor 12.

[0174] In the context of the example illustrated in FIG. 7A, the processor 12 may cause the ultrasound transducer(s) 504 to neuromodulate the first candidate target volume 710 for a first dwell time (by directing ultrasound signals to the first candidate target volume 710), then there may be a first rest period over which no neuromodulation is performed, then the ultrasound transducer(s) 504 may neuromodulate the second candidate target volume 720 for a second dwell time (by directing ultrasound signals to the second candidate target volume 720), then there may be a second rest period over which no neuromodulation is performed, then the ultrasound transducer(s) 504 may neuromodulate the third candidate target volume 730 for a third dwell time (by directing ultrasound signals to the third candidate target volume 730), then there may be a third rest period over which no neuromodulation is performed. Each dwell time might be the same and each rest period might be the same.

[0175] Each candidate target volume 710, 720, 730 may be a different focus for the one or more ultrasound transducers 504.

[0176] The order in which the candidate target volumes are neuromodulated may, for example, be deterministically derived from the probability distribution 20, or estimated using a stochastic procedure such as a Monte Carlo procedure. The order can be randomized or systematically varied to prevent sequential testing artifacts and account for potential temporal dependencies in the physiological response. The apparatus 1000 may employ adaptive parameter refinement based on measured responses (or response gradients), allowing more efficient convergence on optimal targets while respecting safety bounds. Safety constraints include limits on continuous stimulation duration, cumulative exposure at each location, and thermal dose calculations based on acoustic field modeling.

[0177] In block 608 in FIG. 6, the one or more sensors 22 generate one or more biomarker signals. These biomarker signals may be received by the processor 12 and stored in the memory 14. The biomarker signals may be generated by the one or more sensors 22 prior to the neuromodulation of a particular candidate target volume 710, 720, 730 (while no neuromodulation is being performed), during the neuromodulation of a particular candidate target volume 710, 720, 730 and / or after the neuromodulation of a particular candidate target volume 710, 720, 730.One or more biomarker signals may be associated with each neuromodulated candidate target volume 710, 720, 730. A biomarker signal that is associated with a particular neuromodulated candidate target volume 710, 720, 730 indicates the biomarker response, if any, caused by neuromodulation of that candidate target volume 710, 720, 730. In this respect, a first portion of the biomarker signal that is stored by the processor 12 may correspond with that generated by the one or more sensors 22 (shortly) before neuromodulation of the candidate target volume 710, 720, 730 takes place. A second portion of the biomarker signal that is stored by the processor 12 may correspond with that generated during neuromodulation of the candidate target volume 710, 720, 730 and / or (shortly) after the candidate target volume 710, 720, 730 has been neuromodulated.

[0178] The process of generating, receiving and / or storing the biomarker signals may be the same as that described above in the context of FIG. 4. It will be appreciated that the foci of the ultrasound signals could be arranged in various geometric patterns, as described above in relation to FIG. 4.

[0179] In block 610 in FIG. 6, the processor 12 performs an analysis and, in block 612 in FIG. 6, the processor 12 identifies one or more candidate target volumes of interest based, at least in part, on the analysis.

[0180] The analysis performed by the processor 12 in block 610 is based, at least in part, on one or more responses of one or more biomarkers in one or more biomarker signals associated with the neuromodulated candidate target volumes 710, 720, 730. For example, the analysis may be based on one or more responses of one or more biomarkers in one or more of the biomarker signals associated with each neuromodulated candidate target volume 710, 720, 730 relative to one or responses of the one or more biomarkers in one or more of the biomarker signals associated with each other neuromodulated candidate target volume 710, 720, 730.

[0181] The identification of one or more candidate target volumes of interest in block 612 in FIG. 6 may depend on one or more characteristics of (e.g. the magnitude of) the biomarker response(s) in one or more biomarker signals associated with each neuromodulated candidate target volume 710, 720, 730 relative to the that / those characteristics of that / those biomarker response(s) in one or more of the biomarker signals associated with each other neuromodulated candidate target volume 710, 720, 730.

[0182] The analysis may comprise making a comparison that comprises comparing one or more characteristics of (e.g., the magnitude of) the biomarker response(s) in one or more biomarkersignals associated with each neuromodulated candidate target volume 710, 720, 730 with the characteristic(s) of (e.g., the magnitude of) the biomarker response(s) in one or more of the biomarker signals associated with each other neuromodulated candidate target volume 710, 720, 730. The determination of the one or more candidate target volumes of interest may be based, at least in part, on this comparison.

[0183] In some implementations, in block 610 the processor 12 might integrate biomarkers from multiple biomarker signals received from multiple different sensors 22 into a composite biomarker response metric, as described above in relation to block 410 in FIG. 4.

[0184] The processor 12 may determine whether one or more criteria have been satisfied in order to identify a candidate target location of interest. The determination as to whether the one or more criteria have been satisfied depends, at least in part, on the biomarker response in the one or more biomarker signals associated with each neuromodulated candidate target volume 710, 720, 730.

[0185] The analysis performed in block 610 depends on the biomarker responses associated with each neuromodulated candidate target volume 710, 720, 730, and can, but need not, include a direct comparison of those biomarker responses with one another.

[0186] For instance, in some embodiments, the processor 12 may compare at least one characteristic of (e.g., the magnitude of) the biomarker responses in one or more biomarker signals associated with each neuromodulated candidate target volume 710, 720, 730 with the same characteristic(s) of the biomarker response(s) in one or more of the biomarker signals associated with each other neuromodulated candidate target volume 710, 720, 730.

[0187] Thus, in the context of the example illustrated in FIG. 7A, the processor 12 may compare the biomarker responses in the biomarker signals associated with the first, second and third neuromodulated candidate target volumes 710, 720, 730 with one another.

[0188] In the example illustrated in FIG. 7A, the processor 12 determines from the comparisons that the biomarker response to the neuromodulation of the second candidate target volume 720 is greater than the biomarker response to the neuromodulation of the first and third candidate target volume 710, 730. For instance, the magnitude of the biomarker response might be greater in respect of the second candidate target volume 720 than in respect of the first and third candidate target volumes 710, 730.In other embodiments, processor 12 might not directly compare the responses of one or more biomarkers in one or more biomarker signals associated with each neuromodulated candidate target volume 710, 720, 730 with one another. For instance, instead, a comparison of probabilities from the probability distribution 20 may be made after the probability distribution 20 has been updated in dependence on the response(s) of the one or more biomarkers in the received biomarker signals. In these embodiments, the processor 12 might use the responses of the one or more biomarkers in the biomarker signals associated with each neuromodulated candidate target volumes 710, 720, 730 to update the probability distribution 20.

[0189] As indicated above, the probability distribution 20 may indicate probabilities that candidate target locations in a person (such as in a person’s brain) are likely to represent a therapeutic target location. The manner in which the processor 12 updates the probability that a candidate target location represents a therapeutic target location depends on the responses of the one or more biomarkers in biomarker signals associated with candidate target volume that the candidate target location belongs to.

[0190] The processor 12 could, for example, use a Bayesian inference method to update the beliefs about the location of the therapeutic target. In this case, when neuromodulation is applied at the candidate target volumes 710, 720, 730, the measured biomarker responses are used to update this spatial distribution according to Bayes' rule. The spatial correlation structure of the biomarker response can be modeled through various approaches - for example, using a Gaussian process with a kernel function that captures how the similarity of neural responses decays with distance.

[0191] If in one instance where the responses to neuromodulation in the one or more biomarkers is relatively high for the candidate target locations in a particular candidate target volume and in another instance the responses to neuromodulation in the one or more biomarkers is relatively low for the candidate target locations in a particular candidate target volume, the posterior probability distribution is updated to reflect both of these measurements and their spatial relationship, such that the posterior probability is higher in relation to locations corresponding with the first instance candidate target volumes and lower in relation to locations corresponding with the second instance candidate target volumes. This yields a revised probability distribution that combines the initial spatial prior with the observed responses. The methodology can accommodate different choices for modeling the likelihood of observations and the spatial correlation structure. The Gaussian process framework being one specific implementation thatprovides closed-form expressions for both the posterior mean (the best estimate of target locations) and the associated uncertainty across the spatial map.

[0192] The one or more criteria that may be assessed in block 612 of FIG. 6 depend upon the implementation. In some implementations, the criteria may include a determination as to whether at least one characteristic of a biomarker response (e.g., the magnitude of a biomarker response) in a biomarker signal meets or exceeds a threshold. In some implementations, the criteria may include a determination as to whether a probability in the probability distribution 20 meets or exceeds a probability threshold. In some implementations, it may be that no such threshold is used. For example, the processor 12 might be configured to determine which candidate target location (or locations, if a plurality of locations is sought) represents the best option(s) as a therapeutic target location, and this might or might not be coupled with a requirement that the best option(s) meet or exceed a threshold (e.g., a biomarker response threshold or a probability threshold) in order to be considered as a therapeutic target location.

[0193] In the context of the example illustrated in FIG. 7A, the processor 12 determines that the second candidate target volume 720 is of interest and the first and third candidate target volumes 710, 730 are not of interest. While a single candidate target volume 720 of interest is identified in this example, a greater number of candidate target volumes of interest might be identified in other examples.

[0194] In block 614 in FIG. 6, the processor 12 determines a plurality of candidate sub-volumes. Each candidate sub-volume is a sub-volume of the candidate target volume(s) of interest identified in block 612.

[0195] In the context of the example illustrated in FIG. 7A, FIG. 7B illustrates a plurality of candidate target sub-volumes 722, 724, 726 determined in block 614 following the second candidate target volume 720 being identified as of interest in block 612. Each of the candidate target sub-volumes 722, 724, 726 determined in block 614 is a sub-volume of the candidate target volume(s) 720 that was / were identified as being of interest in block 612.

[0196] Determining candidate target sub-volumes may comprise dividing a candidate target volume. For instance, in the context of the example illustrated in FIGs 7A and 7B, the second candidate target 720 may be divided to form the candidate target sub-volumes 722, 724, 726. The candidate sub-volumes 722, 724, 726 might or might not overlap. Each of the candidate sub-volumes 722, 724, 726 might be wholly located within the candidate volume(s) of interest 720, as shown in FIG. 7B.

[0197] In some implementations, the size of each of candidate target sub-volume 722, 724, 726 is the same as the other candidate target sub-volumes 722, 724, 726. In other implementations, the size of one or more of the candidate target volumes 722, 724, 726 is different from the others.

[0198] In some implementations, the determination of the candidate target sub-volumes 722, 724, 726 may depend on the probability distribution 20 which, as explained above, may have been updated during the analysis performed at block 610. In examples where a probability distribution 20 is used, the size of one or more candidate target sub-volumes 722, 724, 726 may depend on the probability distribution 20. For instance, the size of a candidate target sub-volume 722, 724, 726 may be smaller in some anatomical regions of the person (such as the person’s brain) which are indicated in the probability distribution 20 to have a greater likelihood of including a suitable therapeutic target location relative to other anatomical regions (which may also be in a person’s brain) indicated in the probability distribution 20 to be less likely to include a suitable therapeutic target location. This may help to accelerate the determination of one or more therapeutic target locations.

[0199] In block 616 in FIG. 6, the processor 12 causes the one or more ultrasound transducers 504 to sequentially neuromodulate each of the candidate target sub-volumes 722, 724, 726. As explained above, the processor 12 causing the one or more ultrasound transducers 504 to sequentially neuromodulate the plurality of candidate target sub-volumes 722, 724, 726 may involve the processor 12 providing control instructions on which the ultrasound signal output by the ultrasound transducers 504 is based, at least in part. Actual output of the ultrasound signals might or might not be controlled by a human user, such that when output is activated by the human user, the ultrasound signals are output in accordance with the control instructions provided by the processor 12.

[0200] In the context of the example illustrated in FIG. 7B, the processor 12 may cause the ultrasound transducers 504 to neuromodulate the first candidate target sub-volume 722 for a fourth dwell time (by directing ultrasound signals to the first candidate target sub-volume 722), then there may be a fourth rest period over which no neuromodulation is performed, then the ultrasound transducers 504 may neuromodulate the second candidate target sub-volume 724 for a fifth dwell time (by directing ultrasound signals to the second candidate target sub-volume 724), then theremay be a fifth rest period over which no neuromodulation is performed, then the ultrasound transducers 504 may neuromodulate the third candidate target sub-volume 726 for a sixth dwell time (by directing ultrasound signals to the third candidate target sub-volume 726), then there may be a sixth rest period over which no neuromodulation is performed. Each dwell time might be the same and each rest period might be the same.

[0201] Each candidate target sub-volume 722, 724, 726 may be a different focus for the one or more ultrasound transducers 504. The processor 12 may cause the ultrasound transducer(s) 504 to neuromodulate the candidate target volumes 710, 720, 730 using a first focal volume and cause the ultrasound transducer(s) 504 to neuromodulate the candidate target sub-volumes 722, 724, 726 using a second focal volume, where the second focal volume is smaller than the first focal volume.

[0202] In block 618 in FIG. 6, the one or more sensors 22 generate one or more further biomarker signals. These further biomarker signals may be received by the processor 12 and stored in the memory 14.

[0203] The further biomarker signals may be generated by the one or more sensors 22 prior to the neuromodulation of a particular candidate target sub-volume 722, 724, 726 (while no neuromodulation is being performed), during the neuromodulation of a particular candidate target sub-volume 722, 724, 726 and / or after the neuromodulation of a particular candidate target subvolume 722, 724, 726.

[0204] One or more further biomarker signals may be associated with each neuromodulated candidate target sub-volume 722, 724, 726. A further biomarker signal that is associated with a particular neuromodulated candidate target sub-volume 722, 724, 726 indicates the biomarker response, if any, caused by neuromodulation of that candidate target sub-volume 722, 724, 726. In this respect, a first portion of the further biomarker signal that is stored by the processor 12 may correspond with that generated by the one or more sensors 22 (shortly) before neuromodulation of the candidate target sub-volume 722, 724, 726 takes place. A second portion of the further biomarker signal that is stored by the processor 12 may correspond with that generated during neuromodulation of the candidate target sub-volume 722, 724, 726 and / or (shortly) after the candidate target sub-volume 722, 724, 726 has been neuromodulated.The process of generating, receiving and / or storing the further biomarker signals in block 618 may be the same as that described above in respect of generating, receiving and / or storing the biomarker signals in relation to block 608.

[0205] In block 620 in FIG. 6, the processor 12 performs a further analysis and, in block 622 in FIG. 6, the processor 12 determines whether one or more therapeutic target locations can be determined. The decision as to whether to determine one or more therapeutic target locations in block 622 of FIG. 6 might depend on one or more criteria being satisfied, and the one or more criteria might or might not depend on the further analysis performed in block 620 of FIG. 6.

[0206] In the context of the example illustrated in FIGs. 7A and 7B, at block 622 in FIG. 6, the processor 12 determines one or more therapeutic target locations based at least in part on the further analysis performed in block 620.

[0207] The further analysis performed by the processor 12 in block 620 is based, at least in part, on one or more responses of one or more biomarkers in one or more further biomarker signals associated with the neuromodulated candidate target sub-volumes 722, 724, 726. For example, the analysis may be based on one or more responses of one or more biomarkers in one or more of the further biomarker signals associated with each neuromodulated candidate target sub-volume 722, 724, 726 relative to one or more responses of the one or more biomarkers in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volume 722, 724, 726.

[0208] The determination of one or more therapeutic target locations in block 622 in FIG. 6 may depend on (at least one characteristic of, such as the magnitude of) the biomarker response(s) in one or more further biomarker signals associated with each neuromodulated candidate target sub-volume 722, 724, 726 relative to the same characteristic(s) of biomarker response(s) in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub -volume 722, 724, 726.

[0209] The analysis may comprise making a comparison comprising comparing at least one characteristic of (e.g., the magnitude of) the biomarker response(s) in one or more further biomarker signals associated with each neuromodulated candidate target sub-volume 722, 724, 726 relative to the same characteristic(s) of the biomarker response(s) in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volume 722, 724, 726. Thedetermination of the one or more therapeutic target locations may be based, at least in part, on this comparison.

[0210] As explained above, the processor 12 may, for example, determine in block 622 of FIG. 6 whether one or more criteria have been satisfied when determining one or more therapeutic target locations. The determination as to whether the one or more criteria have been satisfied may depend, at least in part, on the biomarker response in the one or more further biomarker signals associated with each neuromodulated candidate target sub-volumes 722, 724, 726.

[0211] The analysis performed in block 620 depends on the biomarker responses associated with each neuromodulated candidate target sub-volumes 722, 724, 726, and can, but need not, include a direct comparison of those biomarker responses with one another.

[0212] For instance, in some embodiments, the processor 12 may compare at least one characteristic of (e.g., the magnitude of) the biomarker response(s) in one or more further biomarker signals associated with each neuromodulated candidate target sub-volumes 722, 724, 726 with the same characteristic(s) of the biomarker response(s) in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volumes 722, 724, 726.

[0213] Thus, in the context of the example illustrated in FIG. 7A and 7B, the processor 12 may compare the biomarker response(s) in the further biomarker signal(s) associated with the first, second and third neuromodulated candidate target sub-volumes 722, 724, 726 with the biomarker response(s) in the further biomarker signal(s) associated with each other. The processor 12 may, for instance, compare the magnitude of the response.

[0214] In the example illustrated in FIG. 7A and 7B, during block 620 the processor 12 determines from the comparisons that the biomarker response to the neuromodulation of the first candidate target sub-volume 722 is greater than the biomarker response to the second or third candidate target sub volumes 724, 726. For instance, the magnitude of the biomarker response might be greater in respect of the first candidate target sub-volume 722 than in respect of the second or third candidate target sub-volumes 724, 726.

[0215] In other embodiments, processor 12 might not directly compare one or more biomarker response(s) in one or more further biomarker signals associated with each neuromodulated candidate target sub-volumes 722, 724, 726 with one or more biomarker response(s) in one ormore further biomarker signals associated with the other neuromodulated candidate target volumes 722, 724, 726. For instance, instead, a comparison of probabilities from the probability distribution 20 may be made after the probability distribution 20 has been updated in dependence on the biomarker response(s) in the received further biomarker signals.

[0216] In these embodiments, the processor 12 might use the biomarker response(s) in the further biomarker signal(s) associated with each neuromodulated candidate target sub-volume to update the probability distribution 20. As indicated above, the probability distribution 20 may indicate probabilities that candidate target locations in a person are likely to represent a therapeutic target location. The manner in which the processor 12 updates the probability that a candidate target location represents a therapeutic target location depends on the biomarker response(s) in the further biomarker signal(s) associated with the candidate target sub-volume 722, 724, 726 that the therapeutic target location belongs to. In instances where the responses to neuromodulation in the one or more biomarkers is relatively high for a candidate target sub-volume 722, 724, 726, the probability / probabilities that each of the candidate target locations in that candidate target sub-volume 722, 724, 726 represents a therapeutic target location may be increased in the probability distribution 20. In instances where the responses to neuromodulation in the one or more biomarkers is relatively low for the candidate target locations in a candidate target sub-volume 722, 724, 726, the probability / probabilities that each of the candidate target locations in that candidate target sub-volume 722, 724, 726 represents a therapeutic target location may be decreased in the probability distribution 20.

[0217] The one or more criteria that are assessed in block 622 of FIG. 6 depend upon the implementation. In some implementations, the criteria may include a determination as to whether a biomarker response (or one or more characteristics of a biomarker response) in a further biomarker signal meets or exceeds a threshold. In some implementations, the criteria may include a determination as to whether a probability in the probability distribution meets or exceeds a probability threshold. In some implementations, it may be that no such threshold is used. For example, the processor 12 might be configured to determine which candidate target location (or locations, if a plurality of locations is sought) represents the best option(s) as a therapeutic target location, and this might or might not be coupled with a requirement that the best option(s) meet or exceed a threshold (e.g., a biomarker response threshold or a probability threshold) in order to be considered as a therapeutic target location.In the context of the example in FIGs 7A and 7B, in block 612 in FIG. 6, the processor 12 is able to determine one or more therapeutic target locations, for instance because one or more criteria have been satisfied, and then the method proceeds to block 624 where spatial target finding ends.

[0218] In the event that the processor 12 is not able to determine one or more therapeutic target locations in block 622 in FIG. 6. The method proceeds back to block 612 in FIG. 6, where blocks 612, 614.

[0219] 616, 618, 620 and 622 are repeated.

[0220] It was explained above that, in some implementations, one or more criteria that are applied in block 622 in FIG. 6 (e.g., at least the first time that block 622 in FIG. 6 is reached) might include at least one criterion that is not based on the further analysis performed in block 620 and may instead relate to one or more other criteria, such as the size of the candidate target sub -volumes 722, 724, 726. For instance, in this regard, in block 622 the processor 12 might assess the size of the candidate sub-volumes 722, 724, 726 and, if the size of the candidate sub-volumes 722, 724, 726 are above a threshold size, the processor 12 might conclude that one or more therapeutic target locations cannot be determined in block 622 (because they are presently too large) and therefore the method might proceed to block 612 on this basis. In such embodiments, the purpose of performing the analysis in block 620 is in connection with identifying one or more further candidate target sub-volume(s) of interest in block 612.

[0221] If the processor 12 is not able to determine one or more therapeutic target locations in block 622 in FIG. 6, then the method proceeds back to block 612 in FIG. 6 in which one or more of the candidate target sub-volumes 722, 724, 726 are identified by the processor 12 as being of interest, based at least in part on the further analysis performed in block 620 of FIG. 6. The identification of at least one candidate sub-volume of interest might depend on one or more criteria being satisfied which relate to the further analysis performed in block 620 in FIG. 6. The identification of at least one candidate sub-volume of interest might be performed in any of the ways described above in relation to the identification of at least one candidate target volume of interest with reference to block 612.

[0222] The method then proceed to block 614 in FIG. 6, where the processor 12 determines a plurality of further candidate sub-volumes. Each further candidate sub-volume is a sub-volume of the candidate target volume(s) of interest identified in block 612. The process of determining the plurality of further candidate sub-volumes in block 614 may be performed in any of the ways described above in relation to the determination of the candidate sub-volumes with reference toblock 614. The processor 12 will then proceed through blocks 616, 618 and 620, which will be performed with respect to the determined further candidate sub-volumes in accordance with the description provided above in relation to blocks 616, 618, 620. The processor 12 will determine in block 622 whether one or more therapeutic target locations can be determined in the manner previously described. If so, the method proceeds to block 624. If not, the method will loop through blocks 612, 614, 616, 618, 620 and 622 as many times as necessary until one or more therapeutic target locations can be determined in block 622.

[0223] The one or more therapeutic target locations can be considered to be based, at least in part, on the analysis that was performed by the processor 12 in block 610 and the analysis that was performed by the processor 12 each time block 620 in the method 600 was reached. In this regard, it should be understood that re-analysis of all prior biomarker responses is not required at block 620 prior to at least one therapeutic target location being determined in block 622. Instead, the determination of the therapeutic target location(s) in block 622 can be considered to depend on the analyses that were performed in block 610 and each time block 620 in the method was reached, because those analyses were used to fdter the candidate target volumes before an eventual determination in block 622 of one or more suitable therapeutic target locations.

[0224] When the method has reached block 624 and spatial target finding has ended, a therapeutic ultrasound session or a different therapy may be performed in the manner described above in relation to FIG. 4.

[0225] As was explained in the context of the method of FIG. 4, In some circumstances, the entire spatial target finding method 600 illustrated in FIG. 6 or aspects of it might be repeated to ensure and account for temporal variations in physiological responses. These repeated searches involve neuromodulating candidate target volumes and sub-volumes in different orders to prevent systematic biases from sequential testing effects. Multiple search iterations also enable signal averaging to improve response measurement accuracy, particularly useful when dealing with physiological signals with low signal-to-noise ratios or high variability. In some circumstances, for example, if a suitable target volume is not found, then the initial search volume may be modified before repeating the spatial target finding method 600.

[0226] While a single candidate target volume 720 of interest was identified in block 612 of FIG. 6 in the example illustrated in FIG. 7A and 7B, in other examples multiple candidate target volumes of interest might be identified in other examples and then the method will continue byinvestigating each of the candidate target volumes of interest in blocks 614, 616, 620 and 622 of FIG. 6. For example, if first and second candidate target volumes of interest are determined in block 612 of FIG. 6, in block 614 a first plurality of candidate target sub-volumes may be determined in respect of the first candidate target volume of interest and a second plurality of candidate target sub-volumes may be determined in respect of the second candidate target volume of interest. In block 616, the first and second pluralities of candidate target sub-volumes may be neuromodulated, and their associated biomarker signals may be generated, received, stored and analyzed in blocks 618 and 620 of FIG. 6.

[0227] In some implementations, the spatial target finding techniques of FIG. 4 and FIG. 6 might be combined. For instance, spatial target finding may be performed by arranging candidate target volumes into sets in the manner described above in relation to FIG. 4, and then volume -reduction spatial target finding may be performed in relation to one or more candidate target volumes of interest in the manner described in relation to FIG. 6 in order to determine one or more therapeutic target locations. Alternatively, volume -reduction spatial target finding may be performed in respect of a plurality of candidate target volumes in the manner described above in relation to FIG. 6, and then candidate target volumes may be arranged into sets in the manner described above in relation to FIG. 4 in order to determine one or more therapeutic target locations.

[0228] Methods, apparatuses and computer programs have been described above which may enable ultrasound spatial target finding (e.g., transcranial ultrasound spatial target finding) to be performed in a manner that is quicker, more efficient and / or more effective than other approaches.

[0229] References to ‘computer-readable storage medium’, ‘computer program product’, ‘tangibly embodied computer program’ etc. or a ‘controller’, ‘computer’, ‘processor’ etc. should be understood to encompass not only computers having different architectures such as single / multiprocessor architectures and sequential (Von Neumann) / parallel architectures but also specialized circuits such as field-programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other processing circuitry. References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed -function device, gate array or programmable logic device etc.The blocks illustrated in the accompanying Figs may represent steps in a method and / or sections of code in the computer program 16. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some blocks to be omitted.

[0230] Where a structural feature has been described, it may be replaced by means for performing one or more of the functions of the structural feature whether that function or those functions are explicitly or implicitly described.

[0231] The term ‘comprise’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising Y indicates that X may comprise only one Y or may comprise more than one Y. If it is intended to use ‘comprise’ with an exclusive meaning then it will be made clear in the context by referring to ‘comprising only one...’ or by using ‘consisting.’

[0232] In this description, the wording ‘connect’, ‘couple’ and ‘communication’ and their derivatives mean operationally connected / coupled / in communication. It should be appreciated that any number or combination of intervening components can exist (including no intervening components), i.e., to provide direct or indirect connection / coupling / communication. Any such intervening components can include hardware and / or software components.

[0233] As used herein, the term "determine / determining" (and grammatical variants thereof) can include, not least: calculating, computing, processing, deriving, measuring, investigating, identifying, looking up (for example, looking up in a table, a database, or another data structure), ascertaining and the like. Also, "determining" can include receiving (for example, receiving information), accessing (for example, accessing data in a memory), obtaining and the like. Also, " determine / determining" can include resolving, selecting, choosing, establishing, and the like.

[0234] In this description, reference has been made to various examples. The description of features or functions in relation to an example indicates that those features or functions are present in that example. The use of the term ‘example’ or ‘for example’ or ‘can’ or ‘may’ in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus ‘example’, ‘for example’, ‘can’, or ‘may’ refers to a particular instance in a class of examples. A property of the instance can be a property of onlythat instance or a property of the class or a property of a sub-class of the class that includes some but not all the instances in the class. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example as part of a working combination but does not necessarily have to be used in that other example.

[0235] As used herein, “at least one of the following: ” and “at least one of” and similar wording, where the list of two or more elements are joined by “and” or “or” mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0236] Although examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims. For instance, while examples have been described in which neuromodulation is performed by directing ultrasonic signals towards a person’s brain, in other examples, neuromodulation might be performed by directing ultrasonic signals to other aspects of the central nervous system or the peripheral nervous system.

[0237] Features described in the preceding description may be used in combinations other than the combinations explicitly described above.

[0238] Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not.

[0239] The description of a feature, such as an apparatus or a component of an apparatus, configured to perform a function, or for performing a function, should additionally be considered to also disclose a method of performing that function. For example, description of an apparatus configured to perform one or more actions, or for performing one or more actions, should additionally be considered to disclose a method of performing those one or more actions with or without the apparatus.

[0240] Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not.

[0241] The term ‘a’, ‘an’ or ‘the’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a / an / the Y indicates that X may comprise only one Y ormay comprise more than one Y unless the context clearly indicates the contrary. If it is intended to use ‘a’, ‘an’ or ‘the’ with an exclusive meaning then it will be made clear in the context. In some circumstances the use of ‘at least one’ or ‘one or more’ maybe used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer any exclusive meaning.

[0242] The presence of a feature (or combination of features) in a claim is a reference to that feature or (combination of features) itself and to features that achieve substantially the same technical effect (equivalent features). The equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. The equivalent features include, for example, features that perform substantially the same function, in substantially the same way to achieve substantially the same result.

[0243] In this description, reference has been made to various examples using adjectives or adjectival phrases to describe characteristics of the examples. Such a description of a characteristic in relation to an example indicates that the characteristic is present in some examples exactly as described and is present in other examples substantially as described.

[0244] The above description describes some examples of the present disclosure however those of ordinary skill in the art will be aware of possible alternative structures and method features which offer equivalent functionality to the specific examples of such structures and features described herein above and which for the sake of brevity and clarity have been omitted from the above description. Nonetheless, the above description should be read as implicitly including reference to such alternative structures and method features which provide equivalent functionality unless such alternative structures or method features are explicitly excluded in the above description of the examples of the present disclosure.

[0245] Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance, the applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and / or shown in the drawings whether or not emphasis has been placed thereon.

Claims

1. CLAIMS1. An apparatus, comprising:one or more processors; andmemory storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to:cause one or more ultrasound transducers to sequentially neuromodulate a plurality of candidate target volumes;perform an analysis based, at least in part, on one or more biomarker responses of the person in one or more biomarker signals associated with each candidate target volume;identify, based at least in part on the analysis, one or more candidate target volumes of interest;determine a plurality of candidate target sub-volumes, each candidate sub-volume being a sub-volume of the one or more candidate target volumes of interest;cause the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes;perform a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with each candidate target sub-volume; anddetermine one or more therapeutic target locations based, at least in part, on the further analysis.

2. The apparatus of claim 1, wherein determining the candidate target sub-volumes comprises dividing at least one of the one or more candidate target volumes of interest to form the candidate target sub -volumes.

3. The apparatus of claim 1 or 2, wherein the candidate sub-volumes are wholly located within at least one of the one or more candidate target volumes of interest.

4. The apparatus of claim 1, 2 or 3, wherein the computer program instructions, when executed by the one or more processors, cause the one or more processors to:cause the one or more ultrasound transducers to neuromodulate at least one of the candidate target volumes using a first focal volume; andcause the one or more ultrasound transducers to neuromodulate at least one of the candidate target sub-volumes using a second focal volume, wherein the second focal volume is smaller than the first focal volume.

5. The apparatus of any of the preceding claims, wherein the determination of the one or more therapeutic target locations depends, at least in part, on one or more further biomarker responses in one or more of the further biomarker signals associated with each neuromodulated candidate target sub-volume relative to one or more further biomarker responses in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub volume.

6. The apparatus of any of the preceding claims, wherein the determination of one or more therapeutic target locations is based, at least in part, on a determination made in the further analysis that one or more criteria have been satisfied, wherein the satisfaction of the one or more criteria depends, at least in part, on the further biomarker signals associated with the neuromodulated candidate target sub-volumes.

7. The apparatus of any of the preceding claims, wherein:identifying one or more candidate target volumes of interest comprises identifying at least a first candidate target volume of interest and a second candidate target volume of interest; determining a plurality of candidate target sub-volumes comprises determining a plurality of first candidate target sub-volumes in respect of each of the first target volume of interest and determining a plurality of second candidate target sub-volumes in respect of each of the second target volume of interest; andcausing the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes comprises causing the one or more ultrasound transducers to neuromodulate the first plurality of candidate target sub-volumes and the second plurality of candidate target sub-volumes, whereinthe further analysis is based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with the first candidate target sub-volume and the second candidate target sub-volume.

8. The apparatus of any of the preceding claims, wherein the computer program instructions, when executed by the one or more processors, cause the one or more processors to:determine the candidate target volumes based, at least in part, on a probability distribution of candidate target locations indicating a probability that a candidate target location is likely to represent a therapeutic target location.

9. The apparatus of claim 8, wherein a size of the candidate target volumes is determined based at least in part of the probability distribution.

10. The apparatus of claim 9, wherein the probability distribution indicates that at least a first candidate target location has a first probability that the first candidate target location is likely to represent a therapeutic target location and indicates that a second candidate target location has a second probability that the second candidate target location is likely to represent a therapeutic target location, wherein the first and second probabilities indicate that the first candidate target location is more likely to represent a therapeutic target location than the second candidate target location, and, based at least in part on the first and second probabilities, the size of the candidate target volume that includes the first candidate target location is determined to be smaller than the size of the candidate target volume that includes the second candidate target location.

11. The apparatus of claim 8, 9 or 10, wherein the probability distribution depends, at least in part, on data specific to the person.

12. The apparatus of claim 11, wherein the data specific to the person includes data indicating prior biomarker responses to neuromodulation during at least one prior neuromodulation session.

13. The apparatus of any of claims 8 to 12, wherein the computer program instructions, when executed by the one or more processors, cause the one or more processors to:update the probability distribution based, at least in part, on the analysis of the biomarker signals, wherein the one or more candidate target volumes of interest are identified, based at least in part, on the updated probability distribution.

14. The apparatus of claim 13, wherein the candidate target sub-volumes are determined based, at least in part, on the updated probability distribution.

15. The apparatus of any of the preceding claims, wherein the biomarker signals are indicative of one or more physiological biomarkers of the person.

16. A method, comprising:causing one or more ultrasound transducers to sequentially neuromodulate a plurality of candidate target volumes;performing an analysis based, at least in part, on one or more biomarkers responses of the person in one or more of the biomarker signals associated with each candidate target volume; identifying, based at least in part on the analysis, one or more candidate target volumes of interest;determining a plurality of candidate target sub-volumes, each candidate sub-volume being a sub-volume of the one or more candidate target volumes of interest;causing the one or more ultrasound transducers to sequentially neuromodulate each of the candidate target sub-volumes;performing a further analysis based, at least in part, on one or more further biomarker responses of the person in one or more of the further biomarker signals associated with each candidate target sub-volume; anddetermining one or more therapeutic target locations based, at least in part, on the further analysis.

17. The method of claim 16, wherein determining the candidate target sub-volumes comprises dividing a candidate target volume of interest to form the candidate target sub-volumes.

18. The method of claim 16 or 17, further comprising:causing the one or more ultrasound transducers to neuromodulate at least one of the candidate target volumes using a first focal volume; andcausing the one or more ultrasound transducers to neuromodulate at least one of the candidate target sub-volumes using a second focal volume, wherein the second focal volume is smaller than the first focal volume.

19. The method of any of claims 16, 17 or 18, wherein the determination of the one or more therapeutic target locations depends, at least in part, on one or more further biomarker responses in one or more of the further biomarker signals associated with each neuromodulated candidate target sub-volume relative to one or more further biomarker responses in one or more of the further biomarker signals associated with each other neuromodulated candidate target sub-volume20. The method of any of claims 16 to 19, wherein the determination of one or more therapeutic target locations is based, at least in part, on a determination made in the further analysis that one or more criteria have been satisfied, wherein the satisfaction of the one or more criteria depends, at least in part, on the further biomarker signals associated with the neuromodulated candidate target sub-volumes.

21. The method of any of claims 16 to 20, further comprising:determining the candidate target volumes based, at least in part, on a probability distribution of candidate target locations indicating a probability that a candidate target location is likely to represent a therapeutic target location.

22. The method of claim 21, wherein a size of the candidate target volumes is determined based at least in part of the probability distribution.

23. The method of claim 21 or 22, further comprising:updating the probability distribution based, at least in part, on the analysis of the biomarker signals, wherein the one or more candidate target volumes of interest are identified, based at least in part, on the updated probability distribution.

24. The method of claim 23, wherein the candidate target sub-volumes are determined based, at least in part, on the updated probability distribution.

25. Computer program instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any of claims 16 to 24.

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