KR102339382B1 - Method and apparatus for regulating brain fucntion using self-adaptive brain model - Google Patents

Abstract

The present invention relates to a method for regulating brain function using a self-adaptive brain model to provide an optimal treatment method in which adaptive changes in the brain is estimated. According to the present invention, the method comprises the following steps: determining the strength of synapses between nodes in a brain network model of a patient; selecting a node to be treated in the brain network model and acquiring brain activity data according to stimulus by applying a continuous stimulus to the selected node; estimating a self-adaptation parameter unique to the patient by comparing the acquired brain activity data with brain activity data for stimulation of a non-disease predicted through simulation; and providing a stimulus that activity of each node of the patient is close to the activity of the non-disease or the synapse strength between the nodes of the patient is close to the synapse strength between nodes of the non-disease on the basis of the estimated self-adaptation parameter.

Description

Brain function control method and device using self-adaptive brain model {METHOD AND APPARATUS FOR REGULATING BRAIN FUCNTION USING SELF-ADAPTIVE BRAIN MODEL}

The present invention relates to a method and apparatus for regulating brain function using a self-adaptive brain model.

The goal of clinical treatment for the brain is to modulate brain circuits to achieve desirable brain function, and it is essential to find a treatment suitable for a patient in order to induce desired brain function. In other words, clinical treatment for the brain can be said to be a matter of controlling brain circuits.

On the other hand, as a treatment method to achieve desirable brain function, drugs, surgical surgery, radiosurgery, focused ultrasound treatment, invasive electrical stimulation treatment such as deep brain stimulation (DBS) or vagus nerve stimulation, transcranial magnetic stimulation (TMS) or Various treatment methods such as non-invasive electrical stimulation such as transcranial direct current stimulation have been developed, but compared to these development results, the understanding of the brain system responding to treatment is lacking.

In order to understand the brain system, various experiments must be performed, but since various experiments are impossible for the human brain, there is a limit to understanding the brain system and to suggesting the optimal treatment plan for each individual characteristic.

The description underlying the invention has been prepared to facilitate understanding of the invention. It should not be construed as an admission that the matters described in the background technology of the invention exist as prior art.

Korea Patent Publication No. 10-2015-0026629 (2015.03.11.)

Accordingly, the conventional brain treatment method was made through a simple comparison of the normal group and the disease group, and did not consider the plasticity and homeostasis of the brain system accompanying the response to the treatment, so the brain system according to the original treatment plan There is a problem in that it cannot be fully recovered.

Accordingly, there is a need for a new method that regards the brain as a system that actively changes according to treatment and provides an optimal treatment method accordingly.

The problem to be solved in the embodiment of the present invention is to estimate the patient's own self-adaptation parameters by comparing the brain activity data actually obtained from the patient with the brain activity data predicted through simulation with respect to the stimulus applied to the node. It is to provide a method and apparatus.

Another problem to be solved in the embodiment of the present invention is a method for rapidly estimating appropriate parameters for each patient by reducing the time for estimating the patient's own self-adaptation parameter through the Bayesian technique in the case of a patient without response data to treatment and to provide an apparatus.

The problems of the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood from the description below.

In order to solve the problems as described above, there is provided a brain function control method of an adaptive brain model according to an embodiment of the present invention. The method includes the steps of determining the strength of a synapse between nodes in a brain network model of a person with a disease, selecting a node to be treated in the brain network model, and applying continuous stimulation to the selected node, brain activity data according to the stimulation obtaining, comparing the acquired brain activity data with the brain activity data for the stimulation of the non-disease predicted through simulation, and estimating the patient's own self-adaptation parameter based on the estimated self-adaptation parameter It is configured to include the step of providing a stimulus such that the activity of each node approaches the activity of the non-disease, or the synaptic strength between nodes of the diseased person approaches the synapse strength between nodes of the non-disease.

According to a feature of the present invention, the brain network model has a structure in which one node and at least one node are connected, and a diseased person and a non-diseased person may have different synaptic strengths between nodes.

According to another feature of the present invention, according to the comparison result in the estimating step, the value of the stimulus applied to the node is changed or the stimulus applied to the node different from the node is changed, and the changed stimulus or the stimulus applied to the other node is changed. The method may further include acquiring brain activity data according to the stimulus.

According to another feature of the present invention, the brain activity data includes time-dependent change in potential membrane voltage (Membrane potential) self-change data with respect to stimulation and self-adaptation parameters of different intensities - The self-change data is the synaptic strength between nodes. A method of regulating brain function, comprising - reflecting autologous changes.

According to another feature of the present invention, before acquiring the brain activity data, the method may further include determining a range of stimulation to be applied to the node to be treated and a range of the self-adaptation parameter.

According to another feature of the present invention, the range of the stimulation may be determined within a range that affects the self-adaptation parameter set of the patients.

According to another feature of the present invention, in the step of determining the stimulus value, when there is no information about the self-adaptation characteristic of the brain network to be treated, it is stimulated by applying a conditional probabilistic marginalization technique. may be a step in determining

According to another feature of the present invention, the method further comprises determining a treatment condition based on the provided stimulation, wherein the treatment condition is selected from among a quantity of a drug suitable for the disease, a brain location to be stimulated, and an intensity of stimulation. The time or number of times or times of presentation of the drug or stimulus with at least one.

In order to solve the problems as described above, there is provided a brain function control method according to another embodiment of the present invention. The method includes determining the strength of synapses between nodes in a brain network model of a person with a disease, a plurality of stimuli obtained using a node to be treated in the brain network model, and brain activity data for the plurality of stimuli based on the It is configured to include the steps of estimating a self-adaptation parameter unique to the patient, and providing a stimulus so that the inter-node synapse strength of the diseased person can approach the inter-node synaptic strength of the non-disease based on the estimated self-adaptation parameter.

According to a feature of the present invention, the brain network model has a structure in which one node and at least one node are connected, and a diseased person and a non-diseased person may have different synaptic strengths between nodes.

According to another feature of the present invention, the brain activity data includes a voltage signal dependent imaging (VSDI) signal calculated based on a change value of a membrane potential of a node to be treated, a calcium image (CaI), It may include at least one of magnetic resonance imaging (fMRI), electroencephalogram (EEG), and electroencephalogram (MEG).

According to another feature of the present invention, in the estimating step, the self-variable parameter is estimated by using the first VSDI signal, at least one signal of CaI, fMRI, EEG, and MEG for the first stimulus defined as the treatment value. It may be a step to

According to another feature of the present invention, in the estimating step, the self-adaptation of the self-variable parameter that minimizes the Mean Squared Error (MSE) value or the user-defined optimal function value based on the first stimulus is self-adapted to the patient. It may be a step of estimating with parameters.

According to another feature of the present invention, the method further comprises determining a treatment condition based on the provided stimulation, wherein the treatment condition is selected from among a quantity of a drug suitable for the disease, a brain location to be stimulated, and an intensity of stimulation. The time or number of times or times of presentation of the drug or stimulus with at least one.

In order to solve the problems as described above, there is provided an apparatus for controlling brain functions according to another embodiment of the present invention. The apparatus includes a communication interface, a memory, and a processor operatively connected to the communication interface and the memory, wherein the processor determines a synaptic strength between nodes in a brain network model of a patient with a disease, and a treatment target in the brain network model Select a node to become the target node, apply continuous stimulation to the selected node, acquire brain activity data according to the stimulation, and compare the acquired brain activity data with the brain activity data for the stimulation of a non-ill patient predicted through simulation Thus, the patient's own self-adaptation parameter is estimated, and the activity of each node of the diseased person is close to that of the non-disease based on the estimated self-adaptation parameter, or the synapse strength between nodes of the diseased person is the internode synapse of the non-disease. and may be configured to provide a stimulus that can approach intensity.

In order to solve the problems as described above, there is provided an apparatus for controlling brain functions according to another embodiment of the present invention. The apparatus includes a communication interface, a memory, and a processor operatively connected to the communication interface and the memory, wherein the processor determines a synaptic strength between nodes in a brain network model of a patient with a disease, and a treatment target in the brain network model Based on the plurality of stimuli obtained using the node that becomes the It may be configured to provide a stimulus close to the activity of the non-disease, or the synaptic strength between the nodes of the diseased person can be close to the synaptic strength between the nodes of the non-disease.

Details of other embodiments are included in the detailed description and drawings.

The present invention sets as variables neuroplasticity and homoeostasis, which occur in response to stimuli applied to the brain, and based on this, the brain system can be controlled as a normal system in drug/surgical/non-invasive stimulation treatment. By estimating the treatment parameters present, it is possible to provide an optimal treatment method that predicts adaptive changes in the brain.

In addition, the present invention estimates unique variables (parameters) for each patient, and as the optimal treatment method is provided accordingly, the patient can obtain a rapid treatment effect.

In addition, the present invention can provide an appropriate treatment plan to all patients by estimating the patient's own parameters through simulation even in the case of a patient with no brain activity data according to treatment.

In addition, in the case of a patient without a treatment history, the present invention can shorten the time for estimating patient-specific parameters by using the Bayesian technique.

The effect according to the present invention is not limited by the contents exemplified above, and more various effects are included in the present invention.

1 and 2 are schematic diagrams for explaining the outline of a brain function control method using a self-adaptive brain model according to an embodiment of the present invention.
3 is a block diagram showing the configuration of a brain function control apparatus according to an embodiment of the present invention.
4 is a schematic diagram for explaining connectivity between brain network nodes according to an embodiment of the present invention.
5 is a schematic diagram for explaining a treatment outline of a method for regulating brain function according to an embodiment of the present invention.
6 is a flowchart of a method for regulating brain function of a patient without a treatment history according to an embodiment of the present invention.
7 is a graph for explaining a method of estimating, by the apparatus for controlling brain function according to an embodiment of the present invention, a self-specific parameter of a patient without treatment data.
8 is a flowchart of a method for regulating brain function of a patient with a history of treatment according to an embodiment of the present invention.
9 is a schematic diagram for explaining the overall sequence of the brain function control method according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be embodied in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the art to which the present invention pertains It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, like reference numerals may be used for like components.

In this document, expressions such as "have," "may have," "includes," or "may include" refer to the presence of a corresponding characteristic (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In this document, expressions such as “A or B,” “at least one of A or/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, "A or B," "at least one of A and B," or "at least one of A or B" means (1) includes at least one A, (2) includes at least one B; Or (3) it may refer to all cases including both at least one A and at least one B.

As used herein, expressions such as "first," "second," "first," or "second," may modify various elements, regardless of order and/or importance, and refer to one element. It is used only to distinguish it from other components, and does not limit the components. For example, the first user equipment and the second user equipment may represent different user equipment regardless of order or importance. For example, without departing from the scope of rights described in this document, the first component may be named as the second component, and similarly, the second component may also be renamed as the first component.

A component (eg, a first component) is "coupled with/to (operatively or communicatively)" to another component (eg, a second component); When referring to "connected to", it will be understood that the certain element may be directly connected to the other element or may be connected through another element (eg, a third element). On the other hand, when it is said that a component (eg, a first component) is "directly connected" or "directly connected" to another component (eg, a second component), the component and the It may be understood that other components (eg, a third component) do not exist between other components.

The expression "configured to (or configured to)" as used in this document, depending on the context, for example, "suitable for," "having the capacity to ," "designed to," "adapted to," "made to," or "capable of." The term “configured (or configured to)” may not necessarily mean only “specifically designed to” in hardware. Instead, in some circumstances, the expression “a device configured to” may mean that the device is “capable of” with other devices or parts. For example, the phrase "a processor configured (or configured to perform) A, B, and C" refers to a dedicated processor (eg, an embedded processor) for performing the corresponding operations, or by executing one or more software programs stored in a memory device. , may mean a generic-purpose processor (eg, a CPU or an application processor) capable of performing corresponding operations.

Terms used in this document are only used to describe specific embodiments, and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meanings as commonly understood by one of ordinary skill in the art described in this document. Among terms used in this document, terms defined in a general dictionary may be interpreted with the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, ideal or excessively formal meanings is not interpreted as In some cases, even terms defined in this document cannot be construed to exclude embodiments of the present document.

Each feature of the various embodiments of the present invention may be partially or wholly combined or combined with each other, and as those skilled in the art will fully understand, technically various interlocking and driving are possible, and each embodiment may be implemented independently of each other, It may be possible to implement together in a related relationship.

For clarity of interpretation of the present specification, terms used herein will be defined below.

As used herein, the term “adaptive brain model” may refer to a brain network model having characteristics that actively change according to stimuli, and “self-adaptation parameter” refers to brain characteristics that actively change according to stimuli for each patient. It can mean a variable expressed numerically.

1 and 2 are schematic diagrams for explaining the outline of a brain function control method using a self-adaptive brain model according to an embodiment of the present invention.

Referring to FIG. 1 , the brain function control method according to an embodiment of the present invention is a method for finding a treatment method suitable for a patient in consideration of the characteristic that the brain function changes inconsistent with the treatment intention, the brain structure is defined A neural dynamics model that controls the stimuli and a measurement model that defines changes in response to stimuli can be used. Specifically, the brain function control method according to an embodiment of the present invention compares an expected response (measured data 1) to a response actually measured (measured data 2) to any one stimulus, and the patient's own nerve Kinetics model parameters can be estimated. Here, the measurement data is data obtained through various brain imaging techniques, and may include EEG and fMRI measurement data.

In this regard, referring to FIG. 2 , in the brain function control method according to an embodiment of the present invention, measurement data may be acquired through the imaging apparatus 200 . For example, the imaging apparatus 200 may include a Magnetic Resonance Image (MRI) apparatus, a Functional MRI apparatus, an Electrocorticogram (ECoG) measurement apparatus, and a Magnetic EEG (Magnetoencephalography, MEG) apparatus. It may be a device, and the measurement data is a two-dimensional image, a three-dimensional volume image, a still image of one cut, a video consisting of a plurality of cuts, and a plurality of images having various cross-sectional images, which can observe the dynamic changes of the brain according to the stimulus. may include

When the brain function control device 100 provides a specific stimulus to the imaging device 200 that takes a medical image of a person, it is possible to acquire brain activity data (measured data) according to the brain function from the imaging device 200, The brain function control apparatus 100 may estimate a patient's unique self-adaptation parameter based on this and determine a treatment condition. Here, the stimulation may include invasive stimulation such as deep brain stimulation, non-invasive stimulation using magnetic fields and ultrasound such as transcranial magnetic stimulation and vagus nerve stimulation, and stimulation through drug injection.

That is, the brain function control apparatus 100 may determine the patient's own self-adaptation parameter based on the response to the stimulus and provide appropriate treatment to the patient. It may be different for patients without a history of treatment and patients with a history of treatment.

Hereinafter, the brain function control apparatus 100 for determining the patient's own self-adaptation parameters and establishing an optimal treatment plan accordingly will be described.

Figure 3 is a block diagram showing the configuration of a brain function control apparatus according to an embodiment of the present invention, Figure 4 is a schematic diagram for explaining the connectivity between brain network nodes according to an embodiment of the present invention, Figure 5 is this It is a schematic diagram for explaining a treatment outline of a method for controlling brain function according to an embodiment of the present invention, and FIG. 6 is a schematic diagram for explaining a self-adaptation process of the brain system according to an embodiment of the present invention.

Referring to FIG. 3 , the brain function control device 100 may include a communication interface 110 , a memory 120 , an I/O interface 130 , and a processor 140 , and each configuration includes one or more communication buses Alternatively, they may communicate with each other via signal lines.

The communication interface 110 may be connected to the image capturing apparatus 200 through a wired/wireless communication network to exchange data. For example, the communication interface 110 may transmit the intensity, frequency, period, etc. of stimulation to be provided to the patient to the imaging device 200 , and may receive brain activity data according to the stimulus from the imaging device 200 . can receive

On the other hand, the communication interface 110 that enables the transmission and reception of such data includes a wired communication port 111 and a wireless circuit 112, wherein the wired communication port 111 is one or more wired interfaces, for example, Ethernet , Universal Serial Bus (USB), FireWire, and the like. Also, the wireless circuit 212 may transmit/receive data to and from an external device through an RF signal or an optical signal. In addition, the wireless communication may use at least one of a plurality of communication standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol.

The memory 120 may store various data used in the brain function control apparatus 100 . For example, the memory 120 includes various data used to control brain functions, such as brain activity data for stimulation (treatment), a response prediction model to a stimulus, a brain network model structure for a diseased/non-diseased person, and a treatment history for each patient. can be saved.

Meanwhile, in an embodiment of the present invention, brain activity data is expressed based on Dynamic Casual Modeling (DCM), and interactions between nodes according to stimuli, ie, changes in synaptic strength, can be confirmed through the brain activity data.

In various embodiments, memory 120 may include a volatile or non-volatile recording medium capable of storing various data, commands, and information. For example, the memory 120 may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage. , cloud, and may include at least one type of storage medium of a blockchain database.

In various embodiments, the memory 120 may store a configuration of at least one of the operating system 121 , the communication module 122 , the user interface module 123 , and one or more applications 124 . Here, the operating system 121 (eg, embedded operating systems such as LINUX, UNIX, MAC OS, WINDOWS, VxWorks, etc.) controls and manages general system operations (eg, memory management, storage device control, power management, etc.) It may include various software components and drivers, and may support communication between various hardware, firmware, and software components.

The communication module 123 may support communication with another device (eg, the image capturing apparatus 200 ) through the communication interface 110 . The communication module 120 may include various software components for processing data received by the wired communication port 111 or the wireless circuit 112 of the communication interface 110 .

The user interface module 123 may receive a user's request or input from a keyboard, a touch screen, a microphone, etc. through the I/O interface 130 , and may provide a user interface on a display.

Applications 124 may include programs or modules configured to be executed by one or more processors 140 . Here, an application capable of estimating parameters and providing treatment conditions to adjust brain functions according to intention may be on a server farm.

The I/O interface 130 may connect an input/output device (not shown) of the brain function control apparatus 100 , for example, at least one of a display, a keyboard, a touch screen, and a microphone, with the user interface module 123 . The I/O interface 130 may receive a user input (eg, a voice input, a keyboard input, a touch input, etc.) together with the user interface module 123 and process a command according to the received input.

The processor 140 is connected to the communication interface 110 , the memory 120 , and the I/O interface 130 to control the overall operation of the brain function control device 100 , and may use applications stored in the memory 120 or Through the program, various commands for estimating self-adaptation parameters unique to the patient according to the presence/absence of a treatment history may be performed.

The processor 140 may correspond to a computing device such as a central processing unit (CPU) or an application processor (AP). In addition, the processor 140 may be implemented in the form of an integrated chip (IC), such as a system on chip (SoC) in which various computing devices are integrated. Alternatively, the processor 140 may include a module for calculating an artificial neural network model, such as a Neural Processing Unit (NPU).

According to an embodiment, the processor 140 considers the tendency (neural plasticity and homeostasis) of the entire brain system to return to its original state according to a stimulus in a brain network model composed of a plurality of nodes (regions for each function), and can be provided by estimating other changed stimulus values.

Hereinafter, with reference to FIGS. 4 and 5, the structure and connectivity of the brain network model and an overview of treatment taking this into account will be described.

Referring to FIG. 4 , the connection structure between nodes in the brain network model can be confirmed through the hippocampal circuit of the animal. Specifically, the brain network model has a structure in which one node is connected to one or more other nodes. It can be confirmed that an excitation signal or an inhibitory signal is sent to the connected node. For example, the E31 node connected to the I32, I31, and E21 nodes may send an excitation signal to the I32 and I31 nodes and receive the excitation signal from the E21 node in response to an electrical stimulus. Here, the difference in thickness of the arrow may mean a difference in intensity of the excitation signal or the inhibitory signal.

Referring to FIG. 5, when the hippocampal circuit has a synaptic strength difference between the following nodes, as shown in the upper left of (a), if the connectivity between different nodes of the E21 node is changed, the brain system changes the neuroplasticity and It can be seen that the synaptic strength gradually changes in the order of A1>S1>A2>S2>A3>S3 by homeostasis. In addition, when stimulation is applied to one E21 node, it can be confirmed that the brain activity data of the neural group including the node and the plurality of other neural groups are gradually changed as shown in (b).

As such, when a stimulus is applied to any one node, the brain system changes the strength of synapses with other nodes connected to it like a mesh, and at the same time tries to return to the original state. Accordingly, the goal of the processor 140 is to optimize the brain system to have the same effect as the target system (brain network model for a non-disease) when stimulation (treatment) is applied to the original system (brain network model of a patient). It is to find a stimulus, and in the process, effect distortion by self-correction can be considered.

Applying a stimulus (treatment) to the original system can cause the distorted changes (self-adjustment after treatment) shown in the box below by neural plasticity (Heb's plasticity) and homeostasis (self-adjustment after treatment). In this case, activity-dependent plasticity (neural plasticity) may be defined as [Equation 1], and homeostasis plasticity may be defined as [Equation 2].

where i is the number of repetitions of stimulus presentation, n _t is the node to be stimulated, e _nt is the directly affected edge node, A _n is the absolute amount of synaptic strength of n nodes, and r is the node-to-node response updated at each iteration after the stimulus is provided It means the speed, and γ _m means the total amount of connection signal (strength) coming into the node.

The processor 140 assumes that synaptic strength adjustment between nodes is performed to balance the number of synapses in one node or nodes connected thereto, regardless of the type of signal, such as an excitatory signal or an inhibitory signal, to the node by the brain system. Optimal parameters can be estimated while changing the variables defining activity-dependent plasticity (neural plasticity) and homeostasis (self-adaptation parameters of neurodynamic models) during continuous stimulation of the node.

That is, the processor 140 applies stimulation i times to the node nt as shown in Equation 3 below _{, and changes the maximum postsynaptic potential (Hn t} ) in the node or nerve group while changing the desired treatment It is possible to estimate the optimal parameters that can produce an effect.

Here, α is the rate at which the stimulus is increased/decreased compared to the maximum PSP at the node applying the stimulus (n _{t ), Hn t} is the processing effect at the node, and Hn _t * is the final processing effect at the node.

On the other hand, the method of searching for the optimal treatment condition for each disease patient according to this treatment outline may vary depending on the presence or absence of a treatment history. A method of regulating brain function in an adaptive brain model of a patient with a disease will be described.

Case 1. Patients with no treatment history

6 is a flowchart of a method for regulating brain function of a patient without a treatment history according to an embodiment of the present invention, and FIG. These are graphs for explaining a method for estimating parameters.

Referring to FIG. 6 , the processor 140 may determine the synapse strength between nodes in the brain network model of the patient ( S110 ). Here, the brain network model represents the connection structure between each region (node) of the brain, and the connectivity between nodes may be defined as synaptic strength.

In the brain network model, synaptic strengths between nodes are different for any one continuous stimulus, and the processor 140 may determine synaptic strengths between nodes through estimation of a mathematical model for brain activity. For example, the processor 140 may estimate a mathematical model of brain activity using various measurement data such as EEG, ECoG, and fMRI.

According to an embodiment, the diseased person and the non-disease person have different inter-node synaptic strengths in the brain network model, and the processor 140 uses a method so that the synaptic strength between nodes of the diseased person is equal to the synaptic strength between nodes of the non-disease person. can explore.

The processor 140 may select a node to be treated in the brain network model, apply continuous stimulation to the selected node, and acquire brain activity data according to the stimulation (S120), and simulate the acquired brain activity data By comparing the brain activity data for the stimulation of the non-disease predicted through , it is possible to estimate the self-adaptation parameter unique to the patient (S130). Since there is no standard for estimating self-adaptation parameters by providing a range of stimulation for a patient with no treatment history, the processor 140 sets the variable range input from the user through the I/O interface 130 . Based on this, it is possible to determine the value of any stimulus to be provided to the patient.

The processor 140 may determine a range of stimulation to be applied to a node to be treated and a range of a self-adaptation parameter, and the range of stimulation may be determined within a range that affects the self-adaptation parameter set of patients with disease. Accordingly, the processor 140 confirms the comparison result in the estimating step, changes (reduces or increases) the value of the stimulus in a direction in which the effect of the stimulus becomes the same as that of the non-disease, and receives brain activity data according to the changed stimulus. can be obtained According to an embodiment, when the effect according to the stimulus is not the same, the processor 140 may apply the stimulus to a node different from the previously selected node and acquire brain activity data accordingly.

In more detail, the processor 140 compares the actual brain activity data for the stimulus with the brain activity data for the stimulus of the non-disease through the graph defined by the following [Equation 4] to estimate the self-adaptation parameter. It is possible to determine the stimulus value for

Here, R and T are VSDI (Voltage Signal Dependent Imaging) signal y, which is another expression formula of brain activity data, which is defined as the change value of the membrane potential with time for the intensity/self-adaptation parameter of the stimulus. It means the total number of regions and the total number of time samples, y _t denotes a predicted signal, and y _s denotes an actual measured signal.

That is, the processor 140 determines the accuracy of the VSDI signal (y _{s signal) calculated based on the actual brain activity data for the stimulus and the VSDI signal (y t} ) calculated based on the predicted brain activity data for the stimulus of the non-disease. A value of an appropriate stimulus to be provided to the patient can be determined through a graph representing the difference (MSE graph).

In this regard, referring to FIG. 7 , as shown in (a), the initial brain system (A _initial ) and the target brain system (A _target ) of the patient have the following synaptic strength difference (ΔA) between nodes, The processor 140 may determine the node E21 having the largest difference value as the node to be treated.

The processor 140 may determine the range of stimulation to be applied to the node to be treated and the range of the self-adaptation parameter, and based on this, generate an MSE graph as shown in (b), and an appropriate stimulus to be provided to the patient through the MSE graph. can determine the value of Specifically, the processor 140 may determine a stimulus value suitable for estimating the self-adaptation parameter from among a wide range of stimuli determined arbitrarily by using a Bayesian estimation technique based on a Gaussian process model. It is possible to shorten the time to find a treatment condition suitable for a disease patient. In particular, the processor 140 applies the marginalization technique defined in [Equation 5] to the most probabilistically appropriate self-adaptation parameter when there is no information on the self-adaptation characteristics of the brain network to be treated. After selecting , it is possible to determine the value of the stimulus to be provided to the patient.

Here, β _LB and β _UB denote the upper and lower limits of the self-adaptation parameter input from the user, and α* denotes the value of the stimulus to be provided to the patient.

(c) Referring to the graph, the processor 140 may determine α*=0.133 having the smallest MSE value according to the selected self-adaptation parameter as a stimulus value to be provided to the patient, and through this, the patient's own self-adaptation parameter can be estimated. That is, as can be seen from the graphs of solutions (d) and (e), when the MSE value has a smaller value as 4.304 instead of 9.015, the difference between the VSDI signal graph calculated from the diseased patient and the VSDI signal graph predicted from the non-diseased patient is small, and accordingly, the synaptic strength of each node (region) of the brain network becomes the same, and it can be confirmed that the same effect can be realized. For reference, the graph signal connected to one may be a VSDI signal calculated/predicted in the Hilus, CA1, and CA3 regions of the hippocampal circuit shown in FIG. 4 , respectively.

Referring back to FIG. 6 , the processor 140 determines that the activity of each node of the diseased person is close to the activity of the non-disease based on the estimated self-adaptation parameter, or the synaptic strength between nodes of the diseased person is the internode synapse strength of the non-disease. It is possible to provide a stimulus to get closer to (S140). Here, providing the stimulus may be understood as selecting a stimulus suitable for the patient.

According to an embodiment, the processor 140 may determine a treatment condition based on the provided stimulation, wherein the treatment condition includes at least one of a quantity of a drug suitable for the disease, a brain location targeted for stimulation, and an intensity of stimulation; together may include the time or number of times the drug or stimulus is given.

As such, even when there is no treatment data, the processor 140 may rapidly estimate the patient's own self-adaptation parameter using the Bayesian estimation technique, and determine the optimal treatment condition accordingly.

Hereinafter, a method of controlling a brain function in an adaptive brain model of a patient with a treatment history through the processor 140 will be described.

Case 2. Patients with a history of treatment

8 is a flowchart of a method for regulating brain function of a patient with a history of treatment according to an embodiment of the present invention.

Referring to FIG. 8 , the processor 140 may determine the synaptic strength between nodes in the brain network model of the patient ( S210 ). Here, the brain network model represents the connection structure between each region (node) of the brain, and the connectivity between nodes may be defined as synaptic strength.

The processor 140 may estimate a self-adaptation parameter unique to the patient based on a plurality of stimuli obtained by using a treatment target node in brain network models and brain activity data for the plurality of stimuli ( S220 ). At this time, the brain activity data is data observed for changes in connectivity between nodes with respect to the stimulus, and according to an embodiment, the brain activity data is VSDI ( voltage signal dependent imaging) signal, calcium imaging (Cal), magnetic resonance imaging (fMRI), electroencephalogram (EEG), and electroencephalogram (MEG) may be included.

The processor 140 may estimate a self-variable parameter by utilizing at least one of the first VSDI signal, Cal fMRI, EEG, and MEG for the first stimulus defined as the treatment value. More specifically, the processor ( 140) may estimate a self-variable parameter having a minimum value in the MSE graph as a self-adaptation parameter unique to the patient. Here, the MSE graph may be a graph indicating a difference in accuracy between treatment data (VSDI signal) of a diseased patient and brain activity data (VSDI signal) for a stimulus predicted from a non-diseased patient.

As such, when there is a treatment history, the processor 140 can quickly estimate the self-variable parameters based on the brain activity data, and the processor 140 determines the activity of each node of the patient based on the estimated self-adaptation parameters. It is possible to provide a stimulus that approaches the activity of the non-disease, or that the synaptic strength between the nodes of the diseased person approaches the synapse strength between the nodes of the non-disease (S230). Here, providing the stimulus may be understood as selecting a stimulus suitable for the patient.

According to an embodiment, the processor 140 may determine a treatment condition based on the provided stimulation, wherein the treatment condition includes at least one of a quantity of a drug suitable for the disease, a brain location targeted for stimulation, and an intensity of stimulation; together may include the time or number of times the drug or stimulus is given.

9 is a schematic diagram for explaining the overall sequence of the brain function control method according to an embodiment of the present invention.

Referring to FIG. 9 , the brain function control device 100 is the first optimal treatment ( After exploring treatment 1), estimating the patient's own self-adaptation parameters, and then establishing an optimal treatment plan, the self-adaptation parameters that change and the An optimal treatment plan can be established.

Although one embodiment of the present invention has been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and various modifications may be made within the scope without departing from the technical spirit of the present invention. have. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

100: brain function control device
110: communication interface
111: wired communication port 112: wireless circuit
120: memory
121: operating system 122: communication module
123: user interface module 124: application
130: I/O interface
140: processor
200: video recording device

Claims (16)

A method for determining a stimulus value for controlling a brain function performed by a processor of an information providing device for controlling a brain function, the method comprising:
Determining the strength of synapses between nodes in the brain network model of the patient;
In the brain network model of the diseased person and the brain network model of the non-diseased person, through continuous stimulation applied to the target node, which has the largest synaptic strength difference value between nodes in response to stimulation, brain activity according to the continuous stimulation acquiring data;
Compare the acquired brain activity data with the brain activity data for the stimulation of the non-disease predicted through simulation, and based on the comparison result, the patient's own self-adaptation parameter through an estimation technique- The self-adaptation parameter is applied to the node. estimating the variables that define activity-dependent plasticity and homeostasis to stimuli; and
Based on the estimated self-adaptation parameter, the activity data of each node of the diseased person is close to the activity data of the non-disease, or the synaptic strength between nodes of the diseased person is close to the internode synapse strength of the non-disease Determination of a stimulus value to do; A method of providing information for brain function control based on a self-adaptive brain model comprising a. According to claim 1,
The brain network model is
A method for providing information for regulating brain function based on a self-adaptive brain model, having a structure in which one node and at least one node are connected, and wherein the diseased and the non-disease have different synaptic strengths between nodes. According to claim 1,
According to the result of the comparison in the estimating step, the value of the stimulus applied to the node is changed, or through the stimulus applied to a node different from the node,
Acquiring brain activity data according to the changed stimulus or the stimulus applied to the other node; 4. The method of claim 3,
The brain activity data,
Based on self-adaptive brain model, including self-change data of membrane potential with time for stimulation of different intensity and self-adaptation parameters, wherein the self-change data reflects self-change in synaptic strength between nodes A method of providing information for regulating brain function. According to claim 1,
Prior to acquiring the brain activity data,
and determining a range of a stimulus value applied to the target node and a range of the self-adaptation parameter. 6. The method of claim 5,
The range of the stimulus value is,
A method of providing information for brain function control based on a self-adaptive brain model that is determined within the range that affects the self-adaptive parameter set of patients with disease. 6. The method of claim 5,
The step of determining the stimulus value comprises:
When there is no information on self-adaptation characteristics in relation to the brain network model of the patient, conditionally probabilistically applying a marginalization technique to determine a stimulus value, a self-adaptive brain model-based brain function control How to provide information for delete A method for determining a stimulus value for controlling a brain function performed by a processor of an information providing device for controlling a brain function, the method comprising:
Determining the strength of synapses between nodes in the brain network model of the patient;
In the mean squared error (MSE) graph formed based on brain activity data for a plurality of stimuli obtained using a target node in the brain network model, the MSE value can be minimized in the patient's own self-adaptation parameter-the above estimating self-adaptation parameters - variables defining activity-dependent plasticity and homeostasis with respect to stimuli applied to the node; and
Based on the estimated self-adaptation parameter, the activity data of each node of the diseased person is close to the activity data of the non-disease, or the synaptic strength between nodes of the diseased person is close to the internode synapse strength of the non-disease is determined. to do; A method of providing information for brain function regulation based on self-adaptive brain model. 10. The method of claim 9,
The brain network model is
A method for providing information for regulating brain function based on a self-adaptive brain model, having a structure in which one node and at least one node are connected, and wherein the diseased and the non-disease have different synaptic strengths between nodes. 10. The method of claim 9,
The brain activity data,
A voltage signal dependent imaging (VSDI) signal, calcium image (CaI), magnetic resonance imaging (fMRI), electroencephalogram (EEG) and an EEG ( MEG), comprising at least one of the self-adaptive brain model-based information providing method for brain function control. delete 10. The method of claim 9,
The estimating step is
Information for brain function control based on a self-adaptive brain model, which is a step of estimating a self-variable parameter with the minimum user-defined optimal function value as a self-adaptive parameter unique to the patient based on a predefined first stimulus value Way. delete communication interface;
Memory; and
a processor operatively coupled to the communication interface and the memory; including,
The processor is
Determine the synaptic strength between nodes in the brain network model of the diseased person, and through continuous stimulation applied to the target node with the largest difference in synaptic strength between nodes in the brain network model of the diseased person and the brain network model of the non-diseased person , acquire brain activity data according to the continuous stimulation, compare the acquired brain activity data with the brain activity data for the stimulation of the non-disease predicted through simulation, and use the estimation technique based on the comparison result for the diseased person Intrinsic self-adaptation parameters-the self-adaptation parameters are variables that define activity-dependent plasticity and homeostasis with respect to stimuli applied to the node-, and based on the estimated self-adaptation parameters, the activity data of each node of the patient is not An information providing device for regulating brain function, configured to determine a stimulus value that approaches the active data of the diseased person, or that allows the inter-node synapse strength of the diseased person to approach the inter-node synaptic strength of the non-diseased person. communication interface;
Memory; and
a processor operatively coupled to the communication interface and the memory; including,
The processor is
The MSE value in the MSE (Mean Squared Error) graph formed based on the brain activity data for a plurality of stimuli obtained by determining the synaptic strength between nodes in the patient's brain network model, and using the target node in the brain network model. A patient-specific self-adaptation parameter that can be minimized is estimated, the self-adaptation parameter is a variable that defines activity-dependent plasticity and homeostasis to a stimulus applied to the node, and based on the estimated self-adaptation parameter, the patient's Information for regulating brain function, configured to determine a stimulus value at which the activity data of each node is close to the activity data of the non-disease, or the inter-node synapse strength of the diseased person approaches the inter-node synaptic strength of the non-disease provided device.

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