Neural activity controlled virtual reality system

Abstract

A medical instrument is disclosed, comprising an activity measurement system configured to measure brain activity data from the subject, and a memory storing machine-executable instructions and storing the simulation model, the simulation model configured to generate simulation data, the simulation model modifying the simulation data using model parameters, a virtual reality system (106, 106', 106'', 106''', 106'''') configured to render the simulation data, and a computing system for controlling the medical instrument.

Description

Technical Field

[0001] The present invention relates to magnetic resonance imaging, and more particularly to controlling a virtual reality system using measured brain activity.

Background Art

[0002] Brain activity of a subject can be measured using various measurement modalities. Functional magnetic resonance imaging is one embodiment of a means for directly measuring which parts of a subject's brain are active. A large static magnetic field is used by a magnetic resonance imaging (MRI) scanner to align the nuclear spins of atoms as part of a procedure for generating an image of a patient's body. This large static magnetic field is called the B0 field or the main magnetic field. Many functional magnetic resonance imaging (fMRI) techniques directly measure brain activity by detecting changes in blood flow in the brain.

Summary of the Invention

Problems to be Solved by the Invention

[0003] European Patent Application Publication EP3659499A discloses a medical device comprising an activity measurement system configured to measure brain activity data from a subject. The medical device further comprises a stimulus presentation system configured to apply a sensory stimulus to the subject. The medical device further comprises a memory for storing machine-executable instructions and for storing a stimulus enhancement database. The stimulus enhancement database contains entries. Each entry contains a command configured to control the stimulus presentation system to provide a sensory stimulus to the subject. The medical device further comprises a processor for controlling the medical device. Execution of the machine-executable instructions causes the processor to control the stimulus presentation system having a set of entries selected from the stimulus enhancement database to repeatedly provide a sensory stimulus to the subject, control an activity measurement system for performing measurement of brain activity data during each sensor stimulus, select an entry selected from the set of entries using the brain activity data, and store the selected entry in the memory.

[0004] US2005 / 283053 discloses a system in which VR data is modified based on brain activity. [Means for solving the problem]

[0005] The present invention provides a medical device, a computer program, and a method as described in the independent claims. Embodiments are given in the dependent claims.

[0006] Many types of therapy rely on a psychologist or therapist to provide sensory stimuli to a subject and then adjust the stimuli to the subject based on the subject's subjective observations. Embodiments may provide medical devices or tools that can be used to better evaluate and control the sensory stimuli provided to a subject. This can be achieved by measuring brain activity data using an activity measurement system. The brain activity data is then used to calculate a neural activity metric. The neural activity metric is then used to modify or control model parameters. Adjusting the model parameters modifies the simulation data generated by the simulation model. The virtual reality system then renders the model data. When the subject is exposed to stimuli provided by the virtual reality system, brain activity data is reacquired and the process continues as a closed loop. Embodiments adjust the model parameters to maintain the neural activity metric between a first predetermined value and a second predetermined value. For example, the medical device can adjust the model parameters as the subject adapts to the stimuli provided by the virtual reality system.

[0007] In one embodiment, the present invention provides a medical device comprising an activity measurement system configured to measure brain activity data from a subject. The activity measurement system can take different forms in different embodiments. For example, there may be an activity measurement system that measures the functional activity of the brain, such as functional magnetic resonance imaging. In other embodiments, the activity measurement system may include electrical measurements, such as an EEG system.

[0008] The medical device has memory for storing machine-executable instructions and further for storing a simulation model. The simulation model is configured to generate simulation data. The simulation model is also configured to modify the simulation data using model parameters. The simulation data may be for providing data for a virtual reality system. The medical device further has a virtual reality system configured to render the simulation data. The virtual reality system may take different forms in different examples. The virtual reality system may be configured to provide visual, tactile, temperature, pressure, and / or auditory stimuli to a subject. Rendering the simulation data sets up or manages the virtual reality system to provide these sensory stimuli to the subject.

[0009] The memory further includes a control algorithm configured to output model parameters in response to receiving a neural activity metric, a first predetermined value, and a second predetermined value. Execution of machine-executable instructions further causes the computing system to determine the model parameters by inputting the neural activity metric, the first predetermined value, and the second predetermined value into the control algorithm. This is beneficial because an advanced control algorithm can provide a feedback loop and be used to control the neural activity metric.

[0010] In known medical devices, there is only a simple relationship between activity metrics and stimulus modulation. When representing this with a lookup table, a single lookup table with only two columns works (as described, a given activity level always produces the same stimulus). However, in the present invention, the same stimulus modulation may be associated with many different activity levels depending on the previous history. For example, a patient may respond well to a slow increase in the intensity of the stimulus, or it may extend too far and / or too quickly, requiring tracking of a very gentle stimulus (or, in fact, any of the other feedback principles described later). As a result, multiple lookup tables (or multidimensional lookup tables) are required to account for this previous history, as different tables are referenced at different moments in time.

[0011] The medical device further comprises a computing system for controlling the medical device. Execution of machine-executable instructions causes the computing system to set model parameters to initial values. These initial values ​​may be global in some cases, specific to a particular subject in other cases, or loaded for a particular subject. These initial values ​​can, for example, set a simulation model to an initial state for starting the method.

[0012] The execution of machine-executable instructions further causes the computing system to repeatedly generate simulation data using a simulation model. During the first loop, the simulation model is controlled using model parameters that are set to initial values. In other loops, the model parameters are updated. The execution of machine-executable instructions further causes the computing system to repeatedly render the simulation data using a virtual reality system. This can be done by inputting the simulation data into the virtual reality system. The execution of machine-executable instructions further causes the computing system to repeatedly measure brain activity data using an activity measurement system.

[0013] The execution of machine-executable instructions further causes the computing system to repeatedly calculate a neural activity metric description of the subject's neural activity from brain activity data. The neural activity metric may be, for example, one or more numerical values ​​used to describe or characterize the subject's neural activity. The execution of machine-executable instructions further causes the system to repeatedly increase model parameters to adjust the simulation model if the neural activity metric falls below a first predetermined value, and to decrease model parameters to adjust the simulation model if the neural activity exceeds a second predetermined value. Thus, the goal of the system is to maintain the neural activity metric between the first predetermined value and the second predetermined value.

[0014] In some examples, model parameters may have one or more values ​​or a vector of values ​​that are input into the simulation model. This single value or vector of values ​​can be used to control or modify the behavior of the simulation model, which in turn causes changes in the simulation data and what the subject experiences through the virtual reality system. This embodiment may be beneficial because it provides a closed feedback loop that can be used to control a neural activity metric. This can be used to maintain the neural activity metric between a setpoint represented by a first predetermined value and a second predetermined value. This may be useful, for example, in various types of psychotherapy.

[0015] In another embodiment, the brain activity measurement system comprises a magnetic resonance imaging system. The memory further includes pulse sequence commands configured to acquire k-space data from the excited region of interest of the subject according to a functional magnetic resonance imaging protocol. When magnetic resonance imaging is performed, the magnetic resonance imaging system is controlled using pulse sequence commands so that a region of the subject is excited or its spin is excited, and then data from this excited region of interest is read. Thus, a given excited region of the subject is equivalent to a region of the subject being actively imaged.

[0016] Functional magnetic resonance imaging protocols are magnetic resonance imaging protocols that can be used to measure a subject's typical response when the brain or nerve tissue is currently being used. For example, functional magnetic resonance imaging can be used to determine which parts of a person's brain are active when the subject is being stimulated.

[0017] Measuring brain activity data using an activity measurement system involves controlling a magnetic resonance imaging system to acquire k-space data. This embodiment may be beneficial because functional magnetic resonance imaging can directly measure which parts of the subject's brain are currently active. This may be useful when directly measuring how the subject is responding to stimuli provided by a virtual reality system. This may allow for objective measurement of neural activity metrics without input from a psychologist or other medical professional or the subject itself. The subject's response to stimuli provided by the virtual reality system is measured directly.

[0018] In another embodiment, the magnetic resonance imaging protocol is a blood oxygenation level-dependent functional magnetic resonance imaging protocol. This protocol is configured to measure neural activity in one or more volumes selected within a region of interest. One or more volumes are regions or subregions of the region of interest. For example, when a subject is experiencing fear, different parts of the subject's brain may become active. Measuring multiple locations and correlating their behavior to determine a neural activity metric may have advantages.

[0019] In another embodiment, one or more volumes may include the amygdala.

[0020] In another embodiment, one or more volumes may include the prefrontal cortex.

[0021] In another embodiment, one or more volumes include the fronto-parietal network. During the duration of a demanding cognitive workload with cognitive fatigue, humans typically exhibit the time-on-task (TOT) effect, and their performance steadily deteriorates over the period of task engagement. There is a persistent effect of cognitive fatigue in the fronto-parietal network after a period of intense mental work, indicating the important role of this attentional network in mediating the TOT effect. Such cognitive fatigue tasks also result in cumulative cortisol levels.

[0022] In another embodiment, one or more volumes include the hippocampus. This may be useful, for example, in the treatment of patients with post-traumatic stress disorder (PTSD). In PTSD patients, the volume of the hippocampus (associated with placing memories in the correct context of space and time) is significantly reduced.

[0023] In another embodiment, one or more volumes include the default mode network. A study including fMRI data from 848 patients with major depressive disorder (MDD) and 794 normal controls reported a decrease in functional connectivity in the default mode network in recurrent MDD, confirming the important role of the default mode network in MDD. The decrease in functional connectivity in the default mode network was positively correlated with symptom severity in recurrent MDD.

[0024] In another embodiment, one or more volumes include both the amygdala and the prefrontal cortex. It is the natural neural activity after the activation of fear memories (such as claustrophobia). It is the significant enhanced functional connectivity between the amygdala and the ventromedial prefrontal cortex in the fear memory activation group.

[0025] In another embodiment, the activity measurement system includes a magnetoencephalogram system.

[0026] In another embodiment, the activity measurement system includes an electroencephalogram recording system.

[0027] In another embodiment, the activity measurement system comprises an optical pumping magnetometer magnetoencephalogram brain measurement system.

[0028] In another embodiment, the activity measurement system includes a functional near-infrared spectroscopy system.

[0029] In another embodiment, the activity measurement system comprises an eye dilation measurement system.

[0030] In another embodiment, the activity measurement system comprises a heart rate measurement system.

[0031] In another embodiment, the activity measurement system comprises a respiration rate system.

[0032] In another embodiment, the activity measurement system comprises a subject motion detection system.

[0033] Some of the above are not necessarily measurements directly performed on the subject's brain or brain. However, things such as the subject's eye dilation, heart rate, respiration, and involuntary movements may also be included when calculating the activity metric.

[0034] In another embodiment, the control algorithm is implemented as a look-up table.

[0035] In another embodiment, the control algorithm is implemented as a multi-dimensional look-up table.

[0036] In another embodiment, the control algorithm is implemented as a neural network. Measurements of medical devices and neural activity metrics from many subjects as functions of model parameters can be used as data for training the neural network.

[0037] In another embodiment, the control algorithm is implemented as a feedback loop. For example, the control algorithm can implement a negative feedback control system. In other examples, the control algorithm implements a proportional-integral-derivative controller or a closed-loop transfer function.

[0038] In another embodiment, the virtual reality system includes a visual display.

[0039] In another embodiment, the virtual reality system includes a three-dimensional display. This may be, for example, a flat three-dimensional display or other system that provides an image to each eye of the subject.

[0040] In another embodiment, the virtual reality system includes a virtual reality headset or an augmented reality headset. This may be beneficial because it can provide the subject with lifelike or realistic stimuli.

[0041] In another embodiment, the virtual reality system includes headphones, audio speakers, or a subwoofer. Each of these may be beneficial as it can provide auditory stimuli to the subject.

[0042] In another embodiment, the virtual reality system includes a haptic feedback system that can be used, for example, to simulate the interaction between a subject and a stimulus.

[0043] In another embodiment, the virtual reality system includes a heater.

[0044] In another embodiment, the virtual reality system includes a cooler.

[0045] In another embodiment, the virtual reality system further comprises a command display for providing manual haptic feedback commands. For example, an assistant or helper can look at the command display and then manually provide haptic feedback to the subject.

[0046] In another embodiment, the virtual reality system further comprises an olfactory stimulus generator which can be used, for example, to emulate the smells that a subject would smell.

[0047] In another embodiment, the virtual reality system is configured to render multiple sensory stimuli using rhythmically synchronized stimuli. In this example, the multiple sensory stimuli are provided to be synchronized with one another. This may have the advantage of improving the reliability of the simulation and enhancing psychological responses.

[0048] In another embodiment, the simulation model is configured such that an increase in model parameters triggers an initial increase in a neural activity metric. For example, when a scar image is moved closer to or modified on the subject, the subject may initially experience anxiety or increased activity in the parts of the brain that increase when the subject experiences anxiety.

[0049] In another embodiment, the decrease in the model parameter when the neural activity metric exceeds a second predetermined value is a function of the rate at which the neural activity metric exceeds the second predetermined value. For some types of neural stimulation, if the subject is overstimulated, the stimulation can be counterproductive. Therefore, slowly exceeding the second predetermined value may have a different function or effect on the subject's psychology than rapidly exceeding it. If the neural activity metric exceeds the second predetermined value too quickly, for example, it may be beneficial to reduce it to a lower value than if the model parameter were exceeded slowly.

[0050] In another embodiment, the decrease in the model parameter decreases as the rate of the neural activity metric increases when it exceeds the value of a second parameter. This embodiment may be useful, for example, to help prevent overstimulation of the subject.

[0051] In another embodiment, the present invention provides a computer program comprising machine-executable instructions for execution by a computing system controlling a medical device. The medical device comprises an activity measurement system configured to measure brain activity data from a subject. The medical device further comprises a virtual reality system configured to render simulation data or to provide simulation data to a subject. The medical device further comprises a memory for storing a simulation model. The simulation model is configured to generate simulation data.

[0052] The simulation model is configured to modify the simulation data using model parameters. Execution of machine-executable instructions causes the computing system to set the model parameters to their initial values. Further execution of machine-executable instructions causes the computing system to iteratively generate simulation data using the simulation model. Further execution of machine-executable instructions causes the computing system to iteratively render the simulation data using a virtual reality system. Further execution of machine-executable instructions causes the computing system to iteratively measure brain activity data using an activity measurement system.

[0053] The execution of machine-executable instructions further causes the computing system to repeatedly calculate a neural activity metric that describes the subject's neural activity from brain activity data. If the neural activity metric falls below a first predetermined value, the execution of machine-executable instructions further causes the computing system to repeatedly increase model parameters to adjust the simulation model. If the neural activity metric exceeds a second predetermined value, the execution of machine-executable instructions further causes the computing system to repeatedly decrease model parameters to adjust the simulation model.

[0054] In another embodiment, the present invention provides a method for controlling a medical device. The medical device comprises an activity measurement system configured to measure brain activity data from a subject. The medical device further comprises a virtual reality system configured to render simulated data to the subject. The medical device further comprises a memory for storing a simulation model. The simulation model is configured to generate simulation data. The simulation model is configured to modify the simulation data using model parameters.

[0055] This method includes setting model parameters to initial values. This method further includes iteratively generating simulation data using a simulation model. This method further includes iteratively rendering the simulation data using a virtual reality system. This method further includes iteratively measuring brain activity data using an activity measurement system. This method further includes iteratively calculating a neural activity metric that describes the subject's neural activity from the brain activity data. This method further includes iteratively increasing model parameters to adjust the simulation model if the neural activity metric falls below a first predetermined value. This method further includes iteratively decreasing model parameters to adjust the simulation model if the neural activity metric exceeds a second predetermined value.

[0056] In another embodiment, the method further includes rendering simulation data using a virtual reality system, and selecting a simulation model such that it elicits an emotional or psychological response from a subject which is initially increased by an increase in model parameters. The simulation model can be selected, for example, by having the subject complete a questionnaire. In another example, the simulation model may be provided by a healthcare provider. In yet another example, the simulation model can be selected by exposing the subject to a library containing different simulation models, which can be measured using an activity system to determine which of these models elicits a greater emotional response.

[0057] In another embodiment, the emotional or psychological response is fear.

[0058] In another embodiment, the emotional or psychological response is anxiety.

[0059] In another embodiment, the emotional or psychological response is stress.

[0060] In another embodiment, the emotional or psychological response is a depression.

[0061] In another embodiment, the emotional or psychological response is pain.

[0062] In another embodiment, the emotional or psychological response includes addictive thoughts.

[0063] In another embodiment, the emotional or psychological response includes maladaptive thinking.

[0064] In another embodiment, the emotional or psychological response includes an obsessive response or thought.

[0065] It is understood that one or more of the above-described embodiments of the present invention can be combined, provided that the combined embodiments are not mutually exclusive.

[0066] As will be understood by those skilled in the art, aspects of the present invention can be embodied as apparatus, methods, or computer program products. Accordingly, aspects of the present invention can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware embodiments, all of which can be generally referred to herein as “circuits,” “modules,” or “systems,” and further, aspects of the present invention can take the form of computer program products embodied in one or more computer-readable media having computer executable code embodied thereon.

[0067] Any combination of one or more computer-readable media may be used. Computer-readable media may be computer-readable signal media or computer-readable storage media. As used herein, “computer-readable storage media” encompasses any tangible storage media capable of storing instructions executable by the processor or computing system of a computing device. Computer-readable storage media may also be referred to as computer-readable non-temporary storage media. Computer-readable storage media may also be referred to as tangible computer-readable media. In some embodiments, computer-readable storage media may also be capable of storing data that can be accessed by the computing system of a computing device. Examples of computer-readable storage media include, but are not limited to, floppy disks (registered trademark) disks, magnetic hard disk drives, solid-state hard disks, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and register files of computing systems. Examples of optical disks include compact disks (CDs) and digital multipurpose disks (DVDs), such as CD-ROMs, CD-RWs, CDRs, DVD-ROMs, DVD-RWs, or DVD-R disks. The term computer-readable storage medium also refers to various types of recording media that can be accessed by computer devices via a network or communication link. For example, data may be retrieved via a modem, the internet, or a local area network. Computer executable code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, fiber optic cable, RF, or any suitable combination thereof.

[0068] A computer-readable signal medium may include, for example, a propagating data signal having computer-executable code embodied therein, either in the baseband or as part of a carrier wave. Such a propagating signal may take any of various forms, including but not limited to electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium, rather than a computer-readable storage medium, that can communicate, propagate, or transfer programs for use by or in connection with an instruction execution system, apparatus, or device.

[0069] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory that is directly accessible to the computing system. "Computer storage device" or "storage" is a further example of a computer-readable storage medium. A computer storage device is any non-volatile memory computer-readable storage medium. In some embodiments, the computer storage device may be computer memory, and vice versa.

[0070] As used herein, “computational system” encompasses electronic components capable of executing programs, machine-executable instructions, or computer-executable code. References to computational systems, including examples of “computational systems,” should be interpreted as including, in some cases, two or more computational systems or processing cores. A computational system may, for example, be a multi-core processor. A computational system may also refer to a collection of computational systems within a single computer system or distributed across multiple computer systems. The term computational system should also probably be interpreted as referring to a collection or network of computing devices, each having a processor or computational system. Machine-executable code or instructions may be executed by multiple computational systems or processors, which may be within the same computing device or distributed across multiple computing devices.

[0071] Machine-executable instructions or computer-executable code may comprise instructions or programs that cause a processor or other computing system to perform aspects of the present invention. Computer-executable code for performing operations for aspects of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the C programming language or similar languages, and may be compiled into machine-executable instructions. In some cases, computer-executable code may be in the form of a high-level language or a pre-compiled form and may be used in conjunction with an interpreter that generates machine-executable instructions on the fly. In other cases, machine-executable instructions or computer-executable code may be the formation of a program for a programmable logic gate array.

[0072] Computer executable code can run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be made to an external computer (for example, via the Internet using an Internet service provider).

[0073] Aspects of the present invention will be described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block or part of a block in a flowchart, diagram, and / or block diagram can be implemented by computer program instructions in the form of computer executable code, where applicable. Furthermore, it will be noted that combinations of blocks in different flowcharts, diagrams, and / or block diagrams may be combined if they are not mutually exclusive. These computer program instructions can be provided to a computing system of a general-purpose computer, a dedicated computer, or other programmable data processing device so that the machine can generate means for instructions executed via the computing system of the computer or other programmable data processing device to perform functions / operations specified in the flowcharts and / or block diagram blocks or blocks.

[0074] These machine-executable instructions or computer program instructions may be stored in a computer-readable medium that can instruct a computer, other programmable data processing device, or other device to function in a particular way, and as a result, the instructions stored in the computer-readable medium generate a product containing instructions that perform functions / operations specified in one or more blocks of a flowchart and / or block diagram.

[0075] Machine-executable instructions or computer program instructions can also be loaded onto a computer, other programmable data processing device, or other device to generate a computer implementation process, such that the instructions executed on the computer or other programmable device perform a series of action steps on the computer, the other programmable device, or the other device, providing a process for implementing functions / actions specified within a flowchart and / or block diagram block or block.

[0076] As used herein, “user interface” is an interface that enables a user or operator to interact with a computer or computer system. A “user interface” may also be called a “human interface device,” and a user interface can provide information or data to an operator and / or receive information or data from an operator. A user interface can enable input from an operator to be received by a computer and can provide output from a computer to a user. In other words, a user interface can enable an operator to control or operate a computer, and the interface can enable the computer to demonstrate the effects of the operator’s control or operation. Displaying data or information on a display or graphical user interface is one example of providing information to an operator. Receiving data via a keyboard, mouse, trackball, touchpad, pointing stick, graphics tablet, joystick, gamepad, webcam, headset, pedal, wired glove, remote control, and accelerometer are all examples of user interface components that enable the reception of information or data from an operator.

[0077] As used herein, “hardware interface” encompasses interfaces that enable a computer system’s computing system to interact with and / or control external computing devices and / or equipment. A hardware interface may enable a computer system to send control signals or instructions to external computing devices and / or equipment. A hardware interface may also enable a computing system to exchange data with external computing devices and / or equipment. Examples of hardware interfaces include, but are not limited to, universal serial buses, IEEE 1394 ports, parallel ports, IEEE 1284 ports, serial ports, RS232 ports, IEEE 488 ports, Bluetooth connections, wireless local area network connections, TCP / IP connections, Ethernet connections, control voltage interfaces, MIDI interfaces, analog input interfaces, and digital input interfaces.

[0078] As used herein, “display” or “display device” includes an output device or user interface adapted to display images or data. A display may output visual data, audio data, and / or haptic data. Examples of displays include, but are not limited to, computer monitors, television screens, touchscreens, haptic electronic displays, braille screens, cathode ray tubes (liquid), memory tubes, bistable displays, electronic paper, vector displays, flat panel displays, vacuum fluorescent displays (VF), light-emitting diode (LED) displays, electroluminescent displays (ELD), plasma display panels (PDP), liquid crystal displays (LCD), organic light-emitting diode (OLED) displays, projectors, and head-mounted displays.

[0079] The activity measurement systems used in this specification are devices capable of measuring brain activity data. Brain activity data is data that describes the neural activity of a subject.

[0080] The virtual reality (VR) systems used herein are devices capable of simulating sensory stimuli for a subject.

[0081] VR systems often include a three-dimensional display or virtual reality glasses, but may also include, or be formed from, other devices capable of providing sensory stimuli, such as haptic feedback systems or generators, temperature control systems, such as heaters or coolers, sound systems, such as headphones and / or speaker systems, and / or olfactory K-space data, which are defined herein as recorded measurements of high-frequency signals emitted by atomic spins using the antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan. Magnetic resonance data is an example of tomographic image data.

[0082] Magnetic resonance imaging (MRI) images, or MR images, are defined herein as reconstructed two-dimensional or three-dimensional visualizations of anatomical data contained within k-space data. These visualizations can be performed using a computer. Functional magnetic resonance imaging is a magnetic resonance image that includes mappings describing spatially dependent neural or brain activity.

[0083] In the following, preferred embodiments of the present invention will be described with reference to the drawings, merely as examples. [Brief explanation of the drawing]

[0084] [Figure 1] An example of a medical device is shown. [Figure 2] A flowchart illustrating how to operate the medical device shown in Figure 1 is provided. [Figure 3] Further examples of medical devices are shown. [Figure 4] An example of rendering simulation data is shown. [Figure 5]An example of rendering simulation data is shown. [Figure 6] An example of rendering simulation data is shown. [Figure 7] An example of rendering simulation data is shown. [Modes for carrying out the invention]

[0085] Elements with similar numbering in these diagrams are equivalent elements or perform the same function. The aforementioned elements, if functionally equivalent, are not necessarily discussed in later diagrams.

[0086] Figure 1 shows an example of a medical device 100. A subject 102 is shown lying on a subject support 104 to use the medical device 100. The medical device 100 comprises a virtual reality system. The virtual reality system may comprise different components. In one example, the virtual reality system comprises virtual reality glasses 106. The virtual reality system may further comprise an olfactory stimulus generator 106'. The olfactory stimulus generator may release an aerosol or other stored substance or material that stimulates the subject's sense of smell.

[0087] The virtual reality system may further include headphones 106'' or other systems for providing audio signals to the subject 102. The virtual reality system may further include a haptic feedback system 106''''. The virtual reality system may further include a temperature control system 106'''' which can be, for example, a heater and / or cooling system for heating or cooling a part of the subject 102's body. The virtual reality system may consist of any one of these components or a combination thereof.

[0088] The medical device 100 is further shown to include an activity measurement system 110 configured to measure brain activity data of a subject 102. Here, the activity measurement system 110 is representative. For example, it may be a magnetic resonance imaging system, a magnetoencephalography (MEG) system, an electroencephalogram (EEG) recording system, an optical pumping magnetometer / MEG brain measurement system, a functional near-infrared spectroscopy system, an eye dilation measurement system, a heart rate measurement system, a respiratory rate system, a subject movement detection system, or a combination thereof.

[0089] Various components of the virtual reality system and activity measurement system 110 are shown as being connected to a hardware interface 116 of the computer system 112. The computer 112 comprises a computing system 114. The computing system 114 is intended to represent one or more computing systems or processing cores that may be located in one or more locations. The computing system 114 is shown as being connected to a hardware interface 116. The hardware interface 116 allows the computing system 114 to exchange data as well as control other components of the medical device 100. The computing system 114 is also shown as being connected to an optional user interface 118 and memory 120. Memory 120 is intended to represent any type of memory accessible to the computing system 114.

[0090] Memory 120 is shown as containing machine-executable instructions 130. The machine-executable instructions 130 enable the computing system 114 to control other components and perform various tasks, such as performing data and image processing tasks. Memory 120 is further shown as containing a simulation model 132. The simulation model 132 is configured to generate simulation data 134. The simulation data 134 used herein includes data that can be used to control various components of a virtual reality system to provide sensory stimuli to a subject 102 in order to provide a virtual environment. Memory 120 is shown as containing the simulation data 134 generated by the simulation model 132.

[0091] Memory 120 is further shown to contain brain activity data 136 measured by the activity measurement system 110. Memory 120 is further shown to contain a neural activity metric 138 calculated from the brain activity data 136. Memory 120 may contain, for example, an algorithm that calculates one or more numerical values ​​from the brain activity data 136, which may be the neural activity metric 138. Memory 120 is further shown to contain model parameters 140. Model parameters 140 are parameters for controlling the simulation model 132. By changing the model parameters 140, the output of the simulation model 132 in forming the simulation data 134 can be changed. Memory 120 is further shown to contain initial values ​​142. These may be initial settings when the model parameters 140 are set when the subject 102 begins using the medical device 100. Memory 120 is further shown to contain a first predetermined value 144 and a second predetermined value 146. The model parameter 140 is adjusted so that the neural activity metric 138 remains between a first predetermined value 144 and a second predetermined value 146. Various control algorithms can be used to achieve this.

[0092] Figure 2 is a flowchart illustrating how to use the medical device 100 shown in Figure 1. First, in step 200, the model parameter 140 is set to an initial value of 142. Next, in step 202, simulation data 134 is generated using the simulation model 132. In step 204, the simulation data 134 is rendered using the components of the virtual reality system 106, 106', 106'', 106''', and 106''''. Next, in step 206, brain activity data 136 is measured using the activity measurement system 110. Next, in step 208, a neural activity metric 138 is calculated from the brain activity data 136. Then, in step 210, if the neural activity metric 138 is below a first predetermined value 144, the model parameter is increased. Next, in step 212, if the neural activity metric 138 is above a second predetermined value 146, the model parameter 140 is decreased. After step 212 is completed, the method returns to step 202, which can be completed multiple times as a closed control loop.

[0093] Figure 3 shows a further example of the medical device 300. The medical device 300 is similar to that shown in Figure 1, except that it is shown comprising a magnetic resonance imaging system 302 and an optional EEG system 301. Brain activity data 136 can be provided using both the magnetic resonance imaging system 302 and, optionally, the optional EEG system 301.

[0094] The magnetic resonance imaging system 302 is equipped with a magnet 304. The magnet 304 is a superconducting cylindrical magnet having a type bore 306. The use of different types of magnets is also possible; for example, both a segmented cylindrical magnet and a so-called open magnet can be used. A segmented cylindrical magnet is similar to a standard cylindrical magnet except that the cryostat is divided into two sections to allow equal-plane access to the magnet, and such a magnet can be used, for example, in conjunction with charged particle beam therapy. An open magnet has two magnet sections, one above the other, with a space between them large enough to accommodate the subject, i.e., an arrangement of two section regions similar to the region of a Helmholtz coil. Open magnets are popular because they result in less subject confinement. Inside the cryostat of a cylindrical magnet is an assembly of superconducting coils.

[0095] Within the bore 306 of the cylindrical magnet 304 is an imaging zone 308 with a strong and sufficiently uniform magnetic field for performing magnetic resonance imaging. The region of interest 309 is shown within the imaging zone 308. The acquired magnetic resonance data is typically obtained for the region of interest. The subject 102 is shown supported by the subject support 104 so that the brain region of the subject 102 is within the imaging zone 308 and the excited region of interest 309. The EEG system 301 is shown having electrodes directed toward the head region of the subject 102.

[0096] Within the magnet's bore 306 is a set of gradient coils 310 used for acquiring preliminary magnetic resonance data to spatially encode the magnetic spins within the imaging zone 308 of the magnet 304. The gradient coils 310 are connected to a gradient coil power supply 312. The gradient coils 310 are intended to be representative. Typically, the gradient coils 310 include three separate sets of coils for spatial encoding in three orthogonal spatial directions. The gradient power supply provides current to the gradient coils. The current supplied to the gradient coils 310 is controlled as a function of time and can be ramped or pulsed.

[0097] Adjacent to the imaging zone 308 is a radio frequency coil 314 for manipulating the orientation of magnetic spins within the imaging zone 308 and for receiving radio transmissions from spins within the imaging zone 308. The radio frequency antenna may include multiple coil elements. The radio frequency antenna is sometimes referred to as a channel or antenna. The radio frequency coil 314 is connected to a radio frequency transceiver 316. The radio frequency coil 314 and radio frequency transceiver 316 can be replaced by separate transmitting and receiving coils, as well as separate transmitters and receivers. It is understood that the radio frequency coil 314 and radio frequency transceiver 316 are representative. The radio frequency coil 314 is also intended to represent dedicated transmitting and receiving antennas. Similarly, the transceiver 316 may represent separate transmitters and receivers. The radio frequency coil 314 may also have multiple receiving / transmitting elements, and the radio frequency transceiver 316 may have multiple receiving / transmitting channels. For example, when parallel imaging techniques such as detection are performed, the radio frequency 314 may have multiple coil elements.

[0098] The transceiver 316 and the tilt controller 312 are shown as being connected to the hardware interface 116 of the computer 112.

[0099] Memory 120 is further shown as storing pulse sequence commands 320. Pulse sequence commands 320 may be used, for example, to measure k-space data 322 according to a functional magnetic resonance imaging protocol. The functional magnetic resonance imaging protocol acquires data that can be used to infer where brain activity is occurring within the brain of subject 102. Memory 120 is further shown as containing k-space data 322 acquired by controlling the magnetic resonance imaging system 302 with pulse sequence commands 320. Memory 120 is further shown as containing functional magnetic resonance imaging data provided by reconstructing it from the k-space data 322. Memory 120 is also shown as optionally containing EEG data 326 acquired by the EEG system 301. Functional magnetic resonance images 324 and sometimes optional EEG data 326 may also be used to provide brain activity data 136.

[0100] The memory is also shown to include an optional control algorithm 328 that can be used to set the values ​​of the model parameters after each iteration loop. This can be implemented, for example, as a feedback loop, a neural network, or other control algorithm.

[0101] Figures 4 through 7 are used to illustrate how the rendering of the simulation data 134 may change as the model parameter 140 changes. Figure 4 shows a representation of the three-dimensional image 400. This figure shows a spider 402 in the distance, inside a cage 404. This could be an example of the initial value 142, for example. The spider in this image is not a threat. This may cause only a very small amount of anxiety in, for example, a person who is afraid of spiders. The simulation could include the spider 402 moving around inside the cage.

[0102] As the model parameters are changed, Figure 5 shows the changes in the 3D image 500. The spider 402 is still far away, but is no longer inside the cage 404. As the subject adapts to this, brain activity data 136 decreases, as does the neural activity metric 138. The model parameter 140 is then changed to induce at least a transient increase in the neural activity metric 138. The simulation can, for example, move the spider to a distance far from the subject.

[0103] The model parameter 140 is increased, causing the spider to move closer. Figure 6 shows a 3D image with the closer spider 602. The depicted spider has a more friendly and anthropological appearance. The spider has only two eyes, no hair on its legs or body, and a smile instead of fangs.

[0104] As the subject adapts to this 3D image or virtual reality simulation, the model parameters are changed again. Figure 7 shows a further 3D image 700, a further figure of the spider 702. Spider 702 is less friendly or has a scararious appearance and has various features that are modified. For example, spider 702 in this example has additional leg hairs 704. As the model parameters 140 are changed, the simulation deforms between the various images shown in Figures 4 to 7. By varying this degree, the subject may become more accustomed to his or her fear of spiders and eventually lose his or her fear.

[0105] Real-time fMRI brain signals can be used, for example, as a tool to adjust virtual reality content during treatment. Existing adjustment methods consist only of selecting image or music fragments from a limited number of predetermined fixed image and music fragments.

[0106] Therapeutic literature demonstrates that the transfer of learning skills from the therapeutic environment to the real world is maximized when the stimulating content is immersive and, specifically, individualized to the relevance of each patient (subject 102).

[0107] The example demonstrates how the shortcomings of current approaches can be overcome by providing virtual reality therapy optimized according to the patient's individual needs. It comprises a system and method for closed-loop personalization of immersive virtual reality environment therapy. Personalized virtual reality therapy is provided by a stimulation system that dynamically adapts one or more variables in a feature table (e.g., proximity, size, etc.) according to real-time measurements based on real-time brain activity measurements (e.g., real-time fMRI, EEG, fNIRS, etc.).

[0108] Virtual reality approaches have been shown to be beneficial in many aspects of psychiatry and mental disorders, including anxiety disorders, substance abuse, and obsessive-compulsive disorder. Sometimes, we begin by describing an example using an anxiety disorder as a carrier.

[0109] Anxiety disorders can cause significant fear of specific situations, circumstances, or individuals. For example, arachnoids are a fear of spiders, and claustrophobia is a fear of small spaces. Many different types of anxiety disorders exist that can lead to abnormal thoughts, feelings, or behaviors. Psychologists have long studied ways to overcome fears of non-harmful situations and objects. Exposure therapy is an effective approach to treating anxiety disorders and facilitates an inhibitory learning approach called fear extinction. Fear extinction is defined as a decrease in the conditioned fear response (CR) after unreinforced exposure to a fear-conditioned stimulus (CS). Evidence of behavior indicates that extinction is a form of inhibitory learning. The extinct fear response reappears with the passage of time (natural recovery), changes in context (renewal), and no signal presentation of the unconditioned stimulus (recovery). For this reason, it is important that fear extinction occurs in the presence of all difficult situations and objects, over long periods, and in the real world, in order to extend and maintain inhibition.

[0110] Following the elimination of fear, it can be noted that in depressive states, maladaptive thoughts may be induced. Illusions can be presented in psychosis, and mirror visual feedback can be provided in pain therapy, and so on. This is where virtual reality can play a significant role.

[0111] Virtual reality can create realistic yet inherently harmless training situations for therapeutic environments, helping therapists and patients with mental health problems gradually increase their exposure to frightening situations or subjects.

[0112] To optimize therapeutic effects, it is beneficial to track the emotional levels of patients with mental health issues. The goal of exposure therapy is to gradually become accustomed to / relaxed to objects or situations that are not harmful but induce fear. Therefore, it is beneficial to individualize therapeutic sessions based on the intensity of fear experienced. For example, for a patient with a fear of spiders, as exemplified by spiders 402, 602, and 702, the size of the spider (images 402 vs. 602), the amount of hair on the spider (704), the proximity of the spider, color, etc., are all variables that can be adapted to the therapy. These variables are best specified in a functional table that drives the content within the virtual environment. Alongside visual variability, one can also consider tactile variability, such as the sensation of a spider stinging on the hand, or the sound of a spider. Multiple modalities (visual, tactile, auditory, etc.) can add characteristics to the content in "virtual reality." The more realistic the content in virtual reality, the better the therapeutic effect due to the transmission effect of learned (ecologically effective) context to the real world.

[0113] fMRI neurofeedback can be applied to learn and regulate emotions. Music can be used to induce emotional responses (i.e., tenderness and pain trials), and subjects can spontaneously regulate the induced emotions by providing them with real-time information about their brain activation. Induced by color changes in the virtual environment BCI during rtfMRINFB, participants can increase activity in the septal hypothalamic region and amygdala in real time during ROI-based rtfMRINFB, and can successfully induce distribution patterns of brain activity classified as tenderness and anger during SVM-based rtfMRINFB.

[0114] The goal of exposure therapy is to gradually increase comfort in situations or objects that are not harmful but evoke fear. To achieve this, virtual reality tools can render controlled training situations in which a therapist can guide the patient to mental health issues as they cope with the fear-inducing situations. Optimization of therapeutic effects can be achieved by real-time generation of content that best resonates with the patient's fear level. Examples can be used in exposure therapy in some cases.

[0115] For other therapies, virtual reality (VR) tools may be applied for different purposes; for example, in substance abuse therapy, VR regimes are used as stimuli to guide craving levels in a controlled, non-harmful manner. The transfer of learning skills from the therapeutic environment to the real world is greatest when the stimulus content is immersive and, more specifically, individualized to the relevance of each individual patient.

[0116] Taking anxiety as an example, the personalization of therapy can be done based on subjective fear levels (through questionnaires, such as visual analog scales, to assess fear levels).

[0117] However, applying subjective fear levels as a driver for feature tables has several drawbacks / problems / disadvantages.

[0118] One challenge is that tracking subjective emotions continuously over time is rather impractical. The patient's attention should be gradually reduced during the frightening content, and preferably not a continuous judgment of subjective fear levels.

[0119] Another drawback is the fact that patients cannot easily provide an objective assessment of their fear. This can lead to an overshoot of virtual reality content, i.e., the patient is exposed to situations or subjects that are too frightening, thereby halting the therapeutic effect and causing the patient to shut down for further exposure.

[0120] For other disorders, a similar approach to personalization encounters the same problem of subjectivity in patients' responses to VR content.

[0121] For example, the above drawbacks could potentially be overcome by providing immersive, personalized stimulation devices and methods to assist in therapies that utilize real-time brain signals.

[0122] An example could provide an approach consisting of methods and systems for optimizing virtual reality treatment for mental disorders such as anxiety or for individuals suffering from mental disorders. Treatment optimization is achieved by means of an intrabore closed-loop stimulation system and method. The stimulation system readapts immersive multisensory virtual reality stimuli in real time to provide real-time input from fMRI to a personalized system.

[0123] A concrete example is the provision of a stimulation system and method that enables real-time adaptation of stimulation conditions (parameters and features) according to detected brain activity.

[0124] Therefore, the example can provide a stimulation system in which real-time measurement of brain activity is used by a controller to dynamically and parameterically adapt variables in a feature table, thereby driving content within an immersive, multisensory, personalized virtual environment.

[0125] The example may include one or more of the following characteristics:

[0126] Functional brain activation monitoring units (e.g., fMRI, EEG, fNIRS, OPM, MEG, etc.).

[0127] Virtual reality treatment unit (medical equipment 100, 300).

[0128] A device for providing virtual content 106 to a user, namely, a headset or 3D television capable of generating immersive content having depth cues (either MR compatible or utilizing mirrors to project onto an MR bore).

[0129] Devices for providing sensory stimulation, or combinations of multisensory hardware for stimulation (e.g., a tactile stimulation device 106'' for generating the sensation of a stinging spider).

[0130] Preferably, any extension to hardware capable of stimulating a motor system integrated with sensory stimuli.

[0131] Actuator unit.

[0132] Information gathering from the monitoring unit.

[0133] Analysis of collected information.

[0134] Information transmission to virtual reality units.

[0135] Therefore, the example may comprise a system and method for a virtual reality unit, i.e., a 3D display for rendering an immersive virtual environment, in which the content to be exposed to a psychiatric patient is defined by one or more variables in a feature table (e.g., proximity, size, etc., of an emotional object or situation) and a brain recording and processing unit (e.g., real-time fMRI, EEG, fNIRS, etc.).

[0136] The system may include other monitoring units, such as sensory units for monitoring physiological parameters like heart rate, respiratory rate, skin conductance, eye movements, and pupillary width.

[0137] Several examples are described below, but the description of the technical features of the examples is primarily for general faults. Further examples define system details—specific faults.

[0138] Example 1: Closed-loop system. Real-time adaptation of virtual reality stimuli based on brain activity measurement. Real-time measurement of the patient's brain activity is used to adapt the virtual reality stimuli presented to the patient.

[0139] The virtual reality stimulation system preferably includes a set of stimuli or sensory stimuli, which may include at least a visual stimulus.

[0140] Visual stimuli can be generated by a virtual reality headset or any other display.

[0141] In a preferred embodiment, a display or VR headset renders 3D image information, as described in more detail in Embodiment 2.

[0142] In more advanced systems, virtual reality stimuli may include other types of stimuli, such as audio or tactile stimuli, using standard components such as loudspeakers and haptic actuators like vibration devices and tapping devices.

[0143] Brain activity monitoring unit. The determination of a patient's brain activity can be performed in real time by one of the following systems:

[0144] an fMRI system in which the subject is placed inside the bore of an MRI machine to facilitate a functional scan of the subject's brain (at least a specified region).

[0145] An EEG system in which a subject wears a cap having one or more electrodes to measure the electrical activity of the subject's brain.

[0146] The OPM, in which the subject wears a cap equipped with one of several optical pumping magnetometers, measures the electrical activity of the subject's brain.

[0147] An fNIRS system in which a subject wears a cap having one or more light-emitting diodes that optically measure blood flow in the subject's brain.

[0148] The workflow or method may comprise one or more of the following steps:

[0149] Step 1: The subject is positioned so that their brain activity can be measured in real time.

[0150] Step 2: A baseline measurement of the subject's brain activity is established. This baseline may include non-emotional stimuli.

[0151] Step 3: The first therapeutic stimulus is presented to the subject, and changes in brain activity relative to baseline are measured.

[0152] Step 4: Based on this change in brain activity, parameterize at least one feature of the stimulus (such as contrast, size, speed, or color).

[0153] Step 5: A new stimulus is presented to the subject, and changes in brain activity are measured relative to baseline (or as a response to the previous stimulus).

[0154] Step 6: Steps 4 and 5 are repeated for a series of stimuli.

[0155] Step 7: Once the desired sequence of stimuli is complete (to induce the desired brain activity), the subject is removed from the system.

[0156] In particular, step 4 of the process can be adapted to the patient's specific disorder, which will be explained in detail below. However, in many cases, the following configurations are possible.

[0157] If the measured brain activity response in a brain region of interest (one or more volumes selected from within the region of interest excitation) is in the expected direction (below the second threshold and moving toward the first threshold), the feature may be adapted to slightly enhance the effect of the new stimulus. This indicates that the feedback is positive.

[0158] If the measured brain activity response in the brain region of interest is neutral (or not in the expected direction), the feature may be adapted to strongly enhance the effect of the new stimulus. This represents a neutral feedback loop.

[0159] If the measured brain activity response in the brain region of interest is neutral (or not in the expected direction), and if a strong response exceeding a second threshold is measured in an unexpected area of ​​the brain, the feature is adapted to weaken the effect of the new stimulus. This represents a negative feedback (i.e., the subject is responding negatively to the stimulus). If this situation persists, the series of stimuli may be discontinued.

[0160] Example 2: Presenting a horror object using a 3D depth cue.

[0161] As mentioned above, one of the main factors in the manipulation of virtual visual stimuli is the perceived distance of the object from the subject.

[0162] For this reason, in another example, the system may include a 3D display equipped with lenses or polarized glasses. In either case, real-time brain activity is used to modify one or more features of the 3D image, particularly those related to 3D depth cues. Such features include, for example, the 3D depth of the image, the position of the image relative to the plane of the display, and other binocular cues. In this way, it is possible to manipulate the position of the image to be closer to or further away from the user, for example, as a result of the user's measured skin conductance. To achieve the best effect, the 3D display should be configured to show up to 3D depth, more specifically, the ability to render objects far in front of the screen. Such a configuration can be achieved by specific choices of lens pitch, lens tilt, pixel size, and image rendering on the pixels.

[0163] When a 3D display with lenses is used in conjunction with fMRI, the display is viewed by the subject using a 45-degree angled mirror, most advantageously positioned above the subject's head within the scanner bore. As a result, the image on the actual display must be inverted so that the image is rendered correctly to the subject (i.e., so that objects react to gravity in the correct way).

[0164] Example 3: Multisensory rhythmic synchronized stimulation. In this embodiment, the stimulus includes a combination of stimuli such as visual, auditory, and tactile stimuli in a specific rhythmically synchronized manner.

[0165] There is evidence that rhythmic, synchronous combinations of sensory modalities help patients focus on sensory stimuli. The use of rhythmic synchronous signals for the present invention was derived from basic neuroscience experiments by one of the authors of this ID proposal on the behavioral effects of signal matching across sensory brain modalities. Aneuploid congruence is an efficient means in the brain for identifying signal relevance in the impact of sensory signals. There exists a machine of neurons specifically dedicated to combining perceptual functions across sensory modalities. These neurons play a crucial role in perceptual control that influences conscious experience. Signal compatibility facilitates diverse mechanisms of spontaneous perceptual control because there is more support for a particular perception when there is information from another sensory modality that fits with spontaneous perceptual control. Indeed, experiments have shown that the ability to spontaneously select one of two competing perceptions is greatly enhanced when such congruence is present (an increase of over 400% in control over perception in some observers). Furthermore, internal Philips' research has shown that rhythmically synchronized visual and auditory stimuli have a positive effect on respiratory rate. Additionally, patients are more relaxed because they can increasingly focus on the stimulus rather than the stress [in fact, multisensory stimulation has been shown to lower blood pressure]. Overall, multisensory rhythmically synchronized stimulation increases the permeability of the stimulus, thereby better penetrating the perceptual brain. For example, the multisensory system can mimic the movement of a spider.

[0166] Example 4: Stimulation device for anxiety disorders.

[0167] In this example, the adaptation of a stimulus system is considered to allow for parametric modification of stimuli to suit subjects with anxiety disorders. Here, the potential goal of the process is to monitor limbic system activity after the presentation of anxiety-inducing stimuli and to achieve one of the following states:

[0168] Activity decreases as the same stimulus is presented. and / or

[0169] The activity remains constant, or even decreases as the intensity of the stimulus increases (i.e., should induce more anxiety).

[0170] To explain the operating principle, let's consider the case of anxiety (phobia) induced by the presence of a visual stimulus (in this case, a spider).

[0171] Here, the virtual presence of the object of fear (e.g., spider 402 in cage 404) is adapted based on the subject's measured real-time brain activity. In this case, the feature to be adapted is the perceived distance of the object from the subject (see the embodiments below for details on how this is achieved using 3D object rendering).

[0172] The system operates as follows:

[0173] Step 1: The system begins by presenting a frightening object located at a first distance (e.g., 1 meter) from the user. This is stimulus 1.

[0174] Step 2: Monitor limbic system activity after presentation of stimulus 1. An increase in activity is expected.

[0175] If the increase in activity is higher than expected, the stimulus is thought to induce excessive anxiety, and the new stimulus should be considered at a second distance beyond the first distance (e.g., 2 meters).

[0176] If the increase in activity is lower than expected, the stimulus is thought to not induce much anxiety, and the new stimulus can be considered at a second distance, which is less than the first distance (e.g., 0.8 meters).

[0177] If the increase in activity is roughly as expected, the stimulus is thought to induce an acceptable level of anxiety, and repetition of the first stimulus is initiated. Depending on the new level of activity, the stimulus is then brought closer or moved further away, as described above.

[0178] Step 3: Repeat Steps 1 and 2 until the desired reduction in limbic system activity is achieved.

[0179] The parameter being calculated determines its value from one or more variables in the feature table. In this example, the variable in the feature table could be "proximity to the frightening object." Other features in the table could be the size of the frightening object, or the amount of spider hair, etc.

[0180] Example 5: Stimulant devices for a wide range of mental disorders, including addiction, pain, and obsessive-compulsive disorder. This embodiment can also be used for other purposes such as treating addiction, subjective pain perception, obsessive-compulsive disorder, other phobias, PTSD, sleep disorders, eating disorders, bipolar disorder, schizophrenia, and other common mental disorders.

[0181] The combination of stimuli can be specifically (re)adapted to the mental disorder at hand.

[0182] Example 6: The presentation of a frightening object is adapted based on physiological measurements such as heart rate and respiratory rate.

[0183] This example is very similar to the previous embodiment, but it does not use real-time signals from a brain scanner. Instead, it uses real-time signals from physiological measurements such as heart rate and respiratory rate, which are collected via an MR-compatible sensor.

[0184] Example 7: Integrating a motor system.

[0185] This example preferably involves a motor system integrated with a sensory system. Here, we can consider the motor actions of a patient attempting to swat a spider by moving their hand. Here, the stimulator requires hand monitoring, which can be done in a conventional manner. In a relevant embodiment, the patient performs an exercise such as cycling within a brain scanner.

[0186] Example 8: Odor content.

[0187] This embodiment includes the involvement of olfaction depending on the distance of the object to be imitated.

[0188] Example 9: Incorporation of neurofeedback. This embodiment involves neurofeedback that depends on voluntary neural activity induced by the subject.

[0189] Although the present invention is illustrated and described in detail in the drawings and the foregoing description, such illustrations and descriptions should be considered illustrative or non-limiting, and the present invention is not limited to the embodiments disclosed.

[0190] Other variations of the disclosed embodiments can be understood and achieved by those skilled in the art in carrying out the claimed invention, based on the study of the drawings, disclosures, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude the plural. A single processor or other unit can fulfill the functions of several items enumerated in the claims. The mere fact that certain means are described in mutually different dependent claims does not imply that combinations of these means cannot be used advantageously. Computer programs may be stored / distributed on suitable media such as optical storage media or solid-state media supplied together with or as part of other hardware, but may also be distributed in other forms such as the Internet or other wired or wireless telecommunications systems. No reference numerals in the claims should be construed as limiting in scope. [Explanation of symbols]

[0191] 100 Medical equipment 102 subjects 104 Subject Support 106 Virtual Reality Systems - Virtual Reality Glasses 106' Virtual Reality System - Olfactory Stimulus Generator 106'' Virtual Reality System - Headphones 106''' Virtual reality system - Haptic feedback system 106'''' Virtual reality system - Temperature control system (heater / cooling system) 110 Activity Measurement System 112 Computers 114 Computing Systems 116 Hardware Interfaces 118 User Interface 120 memory 130 Machine-Executable Instructions 132 Simulation Models 134 Simulation Data 136 Brain activity data 138 Neural Activity Metrics 140 Model Parameters 142 Initial value 144 First predetermined value 146 Second predetermined value Step 200: Set model parameters to their initial values. 202 Steps to generate simulation data using a simulation model 204 Steps to render simulation data using a virtual reality system 206 Steps to measure brain activity data using an activity measurement system 208 Calculate neural activity metrics that describe the subject's neural activity from brain activity data. 210 If the neural activity metric falls below a first predetermined value, the step of increasing the model parameters to adjust the simulation model. 212 If the neural activity metric exceeds a second predetermined value, the step of reducing the model parameters to adjust the simulation model. 300 Medical instruments 301 Optional EEG System 302 Magnetic Resonance Imaging System 304 Magnet 306 Magnet Bore 308 imaging zones 309 Excitation region of interest 310 Magnetic field gradient coil 312 Gradient Magnetic Field Coil Power Supply 314 High-frequency coil 316 Transceiver 320 Pulse Sequence Commands 322k spatial data 324 Functional Magnetic Resonance Imaging 326 Optionally selected EEG data 328 Control Algorithms 400 3D images 402 Spider (far away) 404 Cage 500 3D images 402 Spider (far away) 600 3D images 602 Non-hostile spider (close) 700 3D images 702 Spider (close) 704 Leg hair

Claims

1. an activity measurement system configured to measure brain activity data from a subject; a memory storing machine executable instructions and storing a simulation model, the simulation model being configured to generate simulation data; a virtual reality system configured to render the simulation data; and a computing system for controlling the medical instrument, execution of the machine-executable instructions causing the computing system to set the model parameters to initial values, execution of the machine-executable instructions further causing the computing system to: generating the simulation data using the simulation model; rendering the simulation data using the virtual reality system; measuring the brain activity data using the activity measurement system; calculating a neural activity metric, which is a numerical value describing the neural activity of the subject, from the brain activity data; if the neural activity metric is below a first predetermined value, increasing the model parameters to adjust the simulation model; if the neural activity metric exceeds a second predetermined value, decreasing the model parameters to adjust the simulation model; A computation system that repeatedly executes the above having the simulation model is configured to modify the simulation data using model parameters, the memory further comprising a control algorithm configured to output the model parameters in response to receiving a neural activity metric, a first predetermined value, and a second predetermined value, execution of the machine-executable instructions causes the computing system to determine the model parameters by inputting the neural activity metric, the first predetermined value, and the second predetermined value into the control algorithm; the brain activity measurement system comprises a magnetic resonance imaging system, the memory further comprising pulse sequence commands configured to acquire k-space data describing an excitation region of interest of the subject according to a functional magnetic resonance imaging protocol, and measuring the brain activity data using the activity measurement system comprises controlling the magnetic resonance imaging system to acquire the k-space data, the magnetic resonance imaging protocol being a blood oxygenation level dependent functional magnetic resonance imaging protocol configured to measure neural activity in one or more volumes selected within the excitation region of interest. medical equipment.

2. The medical device of claim 1, wherein the one or more volumes include any one of the amygdala, the ventromedial prefrontal cortex, the frontoparietal network, the hippocampus, and combinations thereof.

3. The medical device of claim 1, wherein the control algorithm is implemented as one of a lookup table, a multidimensional lookup table, a neural network, and a feedback loop.

4. The medical device of any one of claims 1 to 3, wherein the virtual reality system has any one of a visual display, a three-dimensional display, a virtual reality headphone set or an augmented reality headphone set, a headphone, an audio speaker, a subwoofer, a tactile feedback system, a heater, a cooler, an instruction display for providing manual tactile feedback instructions, an olfactory stimulus generator, and combinations thereof.

5. A medical device as described in any one of claims 1 to 4, wherein the virtual reality system is configured to render multiple sensory stimuli using rhythmically synchronized stimulation.

6. The medical device of any one of claims 1 to 5, wherein the simulation module is configured such that an increase in the model parameter induces an initial increase in the neural activity metric.

7. The medical device of claim 1, wherein the decrease in the model parameter when the neural activity metric exceeds the second predetermined value is a function of the rate at which the neural activity metric exceeds the second predetermined value.

8. The medical device of claim 7, wherein the value of the model parameter decreases as the rate of the neural activity metric increases when it exceeds the second parameter value.

9. A computer program having machine-executable instructions for execution by a computer system controlling a medical instrument, the computer program having a brain activity measurement system configured to measure brain activity data from a subject, the medical instrument further having a virtual reality system configured to render simulation data to the subject, the medical instrument further having a memory for storing a simulation model, and execution of the machine-executable instructions further comprising: generating the simulation data using the simulation model; rendering the simulation data using the virtual reality system; measuring the brain activity data using the activity measurement system; calculating a neural activity metric, which is a numerical value describing the neural activity of the subject, from the brain activity data; if the neural activity metric is below a first predetermined value, increasing the model parameters to adjust the simulation model; if the neural activity metric exceeds a second predetermined value, decreasing the model parameters to adjust the simulation model; Repeatedly execute the simulation model is configured to modify the simulation data using model parameters, the memory further comprising a control algorithm configured to output the model parameters in response to receiving a neural activity metric, a first predetermined value, and a second predetermined value, execution of the machine-executable instructions causes the computing system to determine the model parameters by inputting the neural activity metric, the first predetermined value, and the second predetermined value into the control algorithm; the brain activity measurement system comprises a magnetic resonance imaging system, the memory further comprising pulse sequence commands configured to acquire k-space data describing an excitation region of interest of the subject according to a functional magnetic resonance imaging protocol, and measuring the brain activity data using the activity measurement system comprises controlling the magnetic resonance imaging system to acquire the k-space data, the magnetic resonance imaging protocol being a blood oxygenation level dependent functional magnetic resonance imaging protocol configured to measure neural activity in one or more volumes selected within the excitation region of interest. Computer program.