MPI-based method and system for extracting activated regions of a moving brain of a primate

Abstract

The present application belongs to the field of image processing, and particularly relates to a method and system for extracting primate motor brain function activation area based on MPI, which is used to solve the problems of low detection rate of subtle activation and weak or lost network connection in functional connectivity research in the prior art. The method comprises: making a subject target perform a preset action and continuously collecting brain images in time sequence to obtain brain MPI data; based on the brain MPI data, image reconstruction is performed to obtain a two-dimensional brain time sequence image sequence; comparing a reference brain anatomical diagram, frontal lobe images and occipital lobe images are extracted based on the two-dimensional brain time sequence image sequence of the subject target; the correlation of the signal intensity of each position of the frontal lobe images and the occipital lobe images with the preset action performed by the subject target is calculated respectively to obtain corresponding motor function activation areas. The advantage of the method is that the change of functional CBV with time can be directly monitored, and the activation of the brain area is more clearly and accurately displayed.

Description

Technical Field

[0001] This invention belongs to the field of image processing, specifically to a method and system for extracting functional activation areas of primate motor brain based on MPI. Background Technology

[0002] The neural mechanisms of tactile-motor control in primates, particularly rhesus monkeys, have been extensively studied. However, only a few kinematic descriptions of their grasping movements are available. Given the role of rhesus monkeys as a valuable model in biomedical research to study neuromotor disorders and brain mechanisms, as well as in developing brain-computer interfaces to facilitate arm control, a thorough understanding of their grasping movements is clearly crucial.

[0003] Magnetic resonance imaging (MRI) is the most commonly used imaging technique for studying brain functional activation. Functional magnetic resonance imaging (fMRI), based on blood oxygen level-dependent BOLD (Bloody Level-Dependent Imaging), has been widely used in research on grasping movements and brain mechanisms due to its non-invasiveness, high sensitivity, and ease of operation. It is also the most commonly used technique for constructing brain functional activation areas. However, both BOLD and fMRI techniques can only indirectly reflect neuronal activity. Neuronal activity causes a complex combination of effects, including cerebral blood flow (CBF), blood volume, and blood oxygen levels. Because the actual amount of residual oxygen in the blood vessels of the activated brain changes after stimulation, and because the BOLD signal is sensitive to the oxygen content of hemoglobin, changes in oxyhemoglobin and deoxyhemoglobin in the activated brain area can be detected through BOLD. However, because changes in cellular oxygen consumption are relatively insignificant, and because changes in the oxygen state of hemoglobin in the same activation area are affected not only by the microvessels surrounding the activated neural activity area but also by signals from surrounding large vessels, it is impossible to distinguish between the blood signals in the dilated microvessels and large vessels within the true neural activity area. While fMRI is a versatile imaging method capable of measuring in vivo hemodynamic changes associated with brain activation, its co-noise ratio (CNR) remains problematic. CNR leads to low detection rates of subtle activations in task-based studies and weak or lost network connectivity in functional connectivity studies. In response, group averaging is often used to detect population effects, which limits the utility of fMRI in personalized medicine to only the strongest activation paradigms and the most robust resting-state networks.

[0004] In summary, a non-radioactive imaging technique is needed to detect changes in blood flow in local microvessels of the macaque brain to reflect brain activity. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, namely that fMRI imaging can only indirectly project neuronal activity, cannot distinguish between dilated microvessels and blood signals within large vessels in areas of true neuronal activity, and that the low detection rate of subtle activations in task-based studies and weak or lost network connections in functional connectivity studies, this invention provides an MPI-based method for extracting functional activation areas in the primate motor brain:

[0006] Step S100, Data Acquisition; The subject is instructed to perform a preset action and brain images are continuously acquired in sequence to obtain brain MPI data;

[0007] Step S200, data processing; based on the brain MPI data, perform image reconstruction to obtain a two-dimensional brain temporal image sequence;

[0008] By comparing with a reference brain anatomy map, frontal and occipital lobe images were extracted based on a two-dimensional temporal image sequence of the subject's target brain.

[0009] Step S300: Determine the activation region; calculate the correlation between the signal intensity at each position in the frontal lobe image and the subject performing a preset action, and obtain the corresponding motor function activation region.

[0010] In some preferred embodiments, the preset action includes:

[0011] Action 1: Instruct the subject to perform directional grasping, hand action, or passing;

[0012] Action 2: Instruct the subjects to observe the human experimenter performing the same directional grasping, manipulating, or passing actions as in Action 1.

[0013] Action 3: Perform an insensitive / intransitive version of the same action as Action 1.

[0014] Before executing a preset action, a resting state of preset duration must be included.

[0015] In some preferred embodiments, the preset actions include: performing action one, action two, and action three in a preset order and repeating them a preset number of times.

[0016] In some preferred embodiments, step S300 specifically includes:

[0017] Step S310: Based on the frontal lobe image or occipital lobe image, divide it into m nodes with a size of 1mm×1mm, and combine the signal intensity at all times at the same location to form the concentration value time series Y at that location;

[0018] Step S320: Construct a behavioral time series X based on the time it takes for the subject to perform a preset action;

[0019] Step S330: Calculate the correlation coefficient between X and Y;

[0020] Step S340: The positions corresponding to the concentration value time series Y with correlation coefficients greater than a preset threshold are set as activation nodes, and all activation nodes are combined to form the corresponding motion function activation area.

[0021] In some preferred embodiments, the calculation of the correlation coefficient between X and Y specifically involves:

[0022] Calculated using the correlation function:

[0023]

[0024] Where β(X,Y) represents the correlation value between X and Y, Cov(X,Y) represents the covariance between X and Y, Var[X] represents the variance of X, and Var[Y] represents the variance of Y.

[0025] In some preferred embodiments, a step of verifying the experimental results is also included;

[0026] The relationship between X and Y is:

[0027]

[0028] Among them, Y1, Y2, ..., Y M This represents a time series showing the change in signal strength over time at different locations, where M = 1, 2, 3...1000 represents different locations; β1, β2, ..., β... M Representing the correlation coefficients between X and Y at different node positions, ζ1, ζ2, ..., ζ M Representing different correlation constants;

[0029] A t-test is performed on the correlation values ​​β between a preset number of X and Y values. If the p-value is less than 0.05, the experiment is considered effective. The positions corresponding to the concentration value time series Y with M greater than a preset value, experimental validity, and correlation coefficient greater than a preset threshold are set as activation nodes. All activation nodes are combined to form the corresponding motor function activation area.

[0030] In some preferred embodiments, the continuous acquisition of brain images is performed through an aperture of 19 cm and a field of view (FOV) of 15 × 15 cm. 2 The large-aperture MPI imaging device, which includes an excitation magnetic field, a gradient magnetic field, and a receiving magnetic field, is used for acquisition. The excitation magnetic field signal frequency is set to 25KHz and the magnitude is 5mT. The gradient magnetic field gradient is set to 0.2T / m in each direction. The imaging depth is 15cm and the imaging resolution is 10mm. Real-time imaging is performed at a speed of 10fps.

[0031] In some preferred embodiments, prior to step S200, an image interpolation method is included, specifically a bilinear interpolation method:

[0032] Based on the brain MPI data, scaling is performed using a scaling factor, and the current pixel (x, y) at the original image position corresponding to the scaled coordinates is calculated. Interpolation is then performed on the four pixels closest to the current pixel (x, y), including pixel interpolation along the x-axis.

[0033]

[0034]

[0035] Pixel interpolation along the y-axis:

[0036] Where (x1,y1) represents Q 11 The position (x2, y2) represents Q. 22 The position, f(x,y1) represents the pixel value at point (x,y1), f(x,y2) represents the pixel value at point (x,y2), f(Q) 11 ),f(Q 21 ),f(Q 12 ),f(Q 22 The numbers () represent the Q values ​​used for interpolation of the four nearest pixels to the current pixel (x, y). 11 Q 21 Q 12 Q 22 Pixel value at;

[0037] High-resolution brain MPI data is obtained by interpolating the four nearest pixels to each pixel in the scaled brain MPI image corresponding to the original image location.

[0038] In some preferred embodiments, the extraction method further includes, after step S300:

[0039] Step S400: Extract the associated motor function activation area based on the two-dimensional brain temporal image sequence;

[0040] Specifically:

[0041] Step S410: Based on the two-dimensional brain temporal image sequence, extract images of other brain regions; the images of other brain regions include temporal lobe images;

[0042] Step S420: Extract the signal intensity time series U of each location in other brain region images. Let the time series of signal intensity changes over time at different locations in the occipital lobe image be T1, T2, ... T aThe time series of signal intensity changes over time at different locations in the frontal lobe image are Z1, Z2, ... Z. b , where a represents the number of pixels in the occipital lobe image and b represents the number of pixels in the frontal lobe image;

[0043] The relationship between the signal intensity time series U at each location in other brain region images and each pixel in the occipital lobe image and the frontal lobe image is as follows:

[0044]

[0045] The obtained a+b correlation coefficients δ1, δ2, ... δ a+b A t-test was performed. If the p-value obtained was less than 0.05, it indicated that the experiment was effective. This means that the signal intensity time series U of other brain regions images with a+b greater than the preset value and the experiment was effective, and the correlation coefficient was greater than the preset threshold, was activated by the preset behavior. This is denoted as the activated signal intensity time series U' of other brain regions.

[0046] Step S430: Calculate the time series U′ of the signal intensity of the other activated brain regions, and determine whether each location is occipital lobe-related activation or frontal lobe-related activation, specifically:

[0047] Based on the time series U′ of signal intensity from other activated brain regions, the classification hyperplane line is calculated:

[0048] w T m+n=0

[0049] Where m represents (m1, m2, ..., m) i The i-dimensional vector of ), i.e., the x-coordinate of a point on the hyperplane, w T This represents a column vector with the same dimensions as m, and their product is a scalar. n represents the bias value of the plane.

[0050] The correlation coefficient δ = (δ1, δ2, ..., δ) is used to... a+b The two-dimensional spatial distribution of the pixels corresponding to ) is given by the distance d from the two-dimensional spatial point δ to the straight line of the classification hyperplane:

[0051]

[0052] in,

[0053] By calculating the distance δ from each two-dimensional spatial point to the hyperplane, several d values ​​are obtained, where r is the closest distance to the hyperplane. The values ​​are then classified by comparing the magnitudes of d and r.

[0054] Classification is performed using support vectors based on the aforementioned correlation coefficient:

[0055]

[0056] Where c represents the binary classification result, c = -1 indicates that the corresponding position belongs to occipital lobe-related activation, and c = 1 indicates that the corresponding position belongs to frontal lobe-related activation.

[0057] A second aspect of the present invention proposes an MPI-based system for extracting functional activation areas of primate motor brains, comprising:

[0058] The data acquisition module is configured to instruct the subject to perform preset actions and continuously acquire brain images in a time sequence to obtain brain MPI data;

[0059] The data processing module is configured to perform image reconstruction based on the brain MPI data to obtain a two-dimensional brain temporal image sequence;

[0060] By comparing with a reference brain anatomy map, frontal and occipital lobe images were extracted based on a two-dimensional temporal image sequence of the subject's target brain.

[0061] The activation region extraction module is configured to calculate the correlation between the signal intensity at each location in the frontal lobe image and the subject performing a preset action, thereby obtaining the corresponding motor function activation region.

[0062] The beneficial effects of this invention are:

[0063] This invention uses magnetic nanoparticle imaging technology (MPI), which has advantages such as linear quantification, high sensitivity, and high temporal resolution;

[0064] This invention uses SPION as a tracer and employs MPI technology to detect and track the concentration of local SPION. Therefore, it can directly monitor the relationship between changes in functional cerebral blood volume (CBV) and time, and more clearly and accurately reflect the activation status of functional brain regions. Attached Figure Description

[0065] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0066] Figure 1 This is a flowchart illustrating the method for extracting functional activation areas of primate motor brain based on MPI in an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram illustrating the principle of inducing the subject to perform a preset action according to an embodiment of the present invention;

[0068] Figure 3 This is a schematic diagram of the behavioral time series X of the present invention;

[0069] Figure 4This is a schematic diagram illustrating the principle of extracting frontal lobe and occipital lobe images by comparing a reference brain anatomy diagram in an embodiment of the present invention;

[0070] Figure 5 This is a schematic diagram illustrating the principle of verifying the experimental results of an embodiment of the present invention;

[0071] Figure 6 This is a schematic diagram illustrating the principle of image interpolation of MPI data in an embodiment of the present invention;

[0072] Figure 7 This is a schematic diagram illustrating the principle of using the support vector method to determine whether the associated brain regions are occipital lobe-related activation or frontal lobe-related activation in an embodiment of the present invention. Detailed Implementation

[0073] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0074] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0075] The following is combined Figure 1 The flowchart of the method for extracting functional activation areas of primate motor brain based on MPI in the embodiments of the present invention is described, the method including:

[0076] This embodiment uses a novel imaging device, MPI, to image the monkey brain. By having the macaques perform different hand grasping experiments, MPI images of the macaque brain are continuously obtained, thereby enabling the localization of brain functional areas for specific cognitive tasks.

[0077] Participant selection: An 8-year-old male rhesus monkey, trained for an extended period, was selected and restrained in a specialized monkey chair. Simultaneously, six human subjects (3 men and 3 women) were recruited. All human subjects were neurologically and psychiatically normal and reported being right-handed. The human subjects demonstrated the behavior only to the primate subjects.

[0078] Step S100, Data Acquisition; The subject is instructed to perform a preset action and brain images are continuously acquired in sequence to obtain brain MPI data;

[0079] In this embodiment, the preset action includes:

[0080] Action 1: Instruct the subject to perform directional grasping, hand action, or passing;

[0081] Action 2: Instruct the subjects to observe the human experimenter performing the same directional grasping, manipulating, or passing actions as in Action 1.

[0082] Action 3: Have the subject perform an insensitive / intransitive version of the same action as Action 1;

[0083] Before executing the preset actions, there must be a resting state of preset duration. Execute actions one, two, and three in preset order and repeat the preset number of times.

[0084] Set behavioral tasks for macaques: such as Figure 2 As shown, macaques were subjected to three functional neuroimaging conditions: (1) a manual, transitive (object orientation) grasping action. In the executive condition, the macaques performed the grasping action with their right hand, with the object placed on a table; (2) observing a human subject demonstrate the same action, with the human participant sitting at a table and instructed to perform the same task, including grasping the same object with a precise grip. To reproduce the time constraints that affected the macaques' responses to group play, a metronome was introduced to modulate the human participant's actions. The macaques were trained in the executive condition using behavioral chains in a motor task. They were also trained similarly in the observation condition, sitting still and watching the demonstrated action; and (3) performing an insensitive version of the action, in which the grasping action was performed without touching any object. This intransitive condition also included the observation of the object's movement, so any difference in activation between the transitive and intransitive observation conditions could be specifically attributed to the presence or absence of object orientation grasping.

[0085] In this embodiment, the continuous acquisition of brain images is performed through an aperture of 19cm and a field of view (FOV) of 15×15cm. 2 The large-aperture MPI imaging device, which includes an excitation magnetic field, a gradient magnetic field, and a receiving magnetic field, is used for acquisition. The excitation magnetic field signal frequency is set to 25KHz and the magnitude is 5mT. The gradient magnetic field gradient is set to 0.2T / m in each direction. The imaging depth is 15cm and the imaging resolution is 10mm. Real-time imaging is performed at a speed of 10fps.

[0086] In this embodiment, since the resolution of the directly reconstructed MPI image is 64*64, which does not match the resolution of the macaque brain anatomical image, direct comparison of the obtained brain region images is prone to errors. This embodiment reduces the impact of resolution mismatch by interpolating the images, specifically:

[0087] Before step S200, the method for interpolating the image is included, specifically the bilinear interpolation method:

[0088] Based on the brain MPI data, scaling is performed using a scaling factor, and the current pixel (x, y) at the original image position corresponding to the scaled coordinates is calculated. Interpolation is then performed on the four pixels closest to the current pixel (x, y), such as... Figure 6 As shown, this includes pixel interpolation along the x-axis:

[0089]

[0090]

[0091] Pixel interpolation along the y-axis:

[0092]

[0093] Where (x1,y1) represents Q 11 The position (x2, y2) represents Q. 22 The position, f(x,y1) represents the pixel value at point (x,y1), f(x,y2) represents the pixel value at point (x,y2), f(Q) 11 ),f(Q 21 ),f(Q 12 ),f(Q 22 The numbers () represent the Q values ​​used for interpolation of the four nearest pixels to the current pixel (x, y). 11 Q 21 Q 12 Q 22 Pixel value at;

[0094] High-resolution brain MPI data is obtained by interpolating the four nearest pixels to each pixel in the scaled brain MPI image corresponding to the original image location.

[0095] Step S200, data processing; based on the brain MPI data, perform image reconstruction to obtain a two-dimensional brain temporal image sequence;

[0096] like Figure 3 As shown, by comparing with a reference brain anatomy map, frontal lobe and occipital lobe images were extracted based on the two-dimensional temporal image sequence of the subject's target brain.

[0097] Step S300: Determine the activation region; calculate the correlation between the signal intensity at each position in the frontal lobe image and the subject performing a preset action, and obtain the corresponding motor function activation region.

[0098] In this embodiment, step S300 specifically includes:

[0099] Step S310: Based on the frontal lobe image or occipital lobe image, divide it into m nodes of size 1mm × 1mm, and combine the signal intensities at all times at the same location to form a concentration value time series Y for that location, such as... Figure 5 As shown;

[0100] Step S320: Based on the time required for the subject to perform a preset action, construct a behavioral time series X; such as... Figure 3 As shown, the acquisition time for macaque brain images was 60 minutes in total, with each paradigm lasting 5 minutes. The three behavioral tasks were repeated twice. Between each task, the macaque subjects were allowed to rest freely in an enclosure without any specific motor task, sensory stimulus, or behavioral instruction. However, it is noteworthy that this resting state did include a low level of motor activity and the ability to observe the normal environment, compared to a highly controlled standard fMRI task, because the macaques' movement and vision were not practically or ethically restricted.

[0101] Step S330: Calculate the correlation coefficient between X and Y;

[0102] In this embodiment, the calculation of the correlation coefficient between X and Y specifically involves:

[0103] Calculated using the correlation function:

[0104]

[0105] Where β(X,Y) represents the correlation value between X and Y, Cov(X,Y) represents the covariance between X and Y, Var[X] represents the variance of X, and Var[Y] represents the variance of Y.

[0106] Step S340: The positions corresponding to the concentration value time series Y with correlation coefficients greater than a preset threshold are set as activation nodes, and all activation nodes are combined to form the corresponding motion function activation area.

[0107] In this embodiment, the method also includes a step of verifying the experimental results, such as... Figure 4 As shown;

[0108] The relationship between X and Y is:

[0109]

[0110] Among them, Y1, Y2, ..., Y M This represents a time series showing the change in signal strength over time at different locations, where M = 1, 2, 3...1000 represents different locations; β1, β2, ..., β... M Representing the correlation coefficients between X and Y at different node positions, ζ1, ζ2, ..., ζ M Representing different correlation constants;

[0111] A t-test is performed on the correlation values ​​β between a preset number of X and Y values. If the p-value is less than 0.05, the experiment is considered effective. The positions corresponding to the concentration value time series Y with M greater than a preset value, experimental validity, and correlation coefficient greater than a preset threshold are set as activation nodes. All activation nodes are combined to form the corresponding motor function activation area.

[0112] Because of its high sensitivity, MPI is more sensitive to brain activity, enabling the observation of additional brain region activation that is impossible with traditional fMRI methods.

[0113] It can analyze brain regions other than the frontal and occipital lobes and classify them using the support vector method to observe whether the set actions cause other brain regions to have associated responses with the occipital and frontal lobes.

[0114] In this embodiment, after step S300, the extraction method further includes:

[0115] Step S400: Extract the associated motor function activation area based on the two-dimensional brain temporal image sequence;

[0116] Specifically:

[0117] Step S410: Based on the two-dimensional brain temporal image sequence, extract images of other brain regions; the images of other brain regions include temporal lobe images;

[0118] Step S420: Extract the signal intensity time series U of each location in other brain region images. Let the time series of signal intensity changes over time at different locations in the occipital lobe image be T1, T2, ... T a The time series of signal intensity changes over time at different locations in the frontal lobe image are Z1, Z2, ... Z. b , where a represents the number of pixels in the occipital lobe image and b represents the number of pixels in the frontal lobe image;

[0119] The relationship between the signal intensity time series U at each location in other brain region images and each pixel in the occipital lobe image and the frontal lobe image is as follows:

[0120]

[0121] The obtained a+b correlation coefficients δ1, δ2, ... δ a+b A t-test was performed. If the p-value obtained was less than 0.05, it indicated that the experiment was effective. This means that the signal intensity time series U of other brain regions images with a+b greater than the preset value and the experiment was effective, and the correlation coefficient was greater than the preset threshold, was activated by the preset behavior. This is denoted as the activated signal intensity time series U′ of other brain regions.

[0122] Step S430: Calculate the time series U′ of the signal intensity of the other activated brain regions, and determine whether each location is occipital lobe-related activation or frontal lobe-related activation, specifically:

[0123] Based on the time series U' of signal intensity from other activated brain regions, the classification hyperplane line is calculated:

[0124] w T m+n=0

[0125] Where m represents (m1, m2, ..., m) i The i-dimensional vector of ), i.e., the x-coordinate of a point on the hyperplane, w T This represents a column vector with the same dimensions as m, and their product is a scalar. n represents the bias value of the plane.

[0126] The correlation coefficient δ = (δ1, δ2, ..., δ) is used to... a+b The two-dimensional spatial distribution of the pixels corresponding to ) is given by the distance d from the two-dimensional spatial point δ to the straight line of the classification hyperplane:

[0127]

[0128] in,

[0129] By calculating the distance δ from each two-dimensional spatial point to the hyperplane, several d values ​​are obtained, where r is the closest distance to the hyperplane. The values ​​are then classified by comparing the magnitudes of d and r.

[0130] Classification is performed using support vectors based on the aforementioned correlation coefficient:

[0131]

[0132] Where c represents the binary classification result, c = -1 indicates that the corresponding location belongs to occipital lobe-related activation, and c = 1 indicates that the corresponding location belongs to frontal lobe-related activation, such as Figure 7 As shown.

[0133] Step S400 enables the detection of brain region activations that cannot be detected by fMRI, resulting in more sensitive images and the discovery of brain region activity that cannot be detected by other methods.

[0134] A second embodiment of the present invention discloses a system for extracting functional activation areas of primate motor brain based on MPI, characterized in that the system comprises:

[0135] The data acquisition module is configured to instruct the subject to perform preset actions and continuously acquire brain images in a time sequence to obtain brain MPI data;

[0136] The data processing module is configured to perform image reconstruction based on the brain MPI data to obtain a two-dimensional brain temporal image sequence;

[0137] By comparing with a reference brain anatomy map, frontal and occipital lobe images were extracted based on a two-dimensional temporal image sequence of the subject's target brain.

[0138] The activation region extraction module is configured to calculate the correlation between the signal intensity at each location in the frontal lobe image and the subject performing a preset action, thereby obtaining the corresponding motor function activation region.

[0139] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0140] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0141] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0142] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for extracting functional activation areas of the primate motor brain based on MPI, characterized in that, The method includes: Step S100, Data Acquisition; The subject is instructed to perform a preset action and brain images are continuously acquired in sequence to obtain brain MPI data; Based on the brain MPI data, scaling is performed using a scaling factor, and the current pixel (x, y) at the original image position corresponding to the scaled coordinates is calculated. Interpolation is then performed on the four pixels closest to the current pixel (x, y), including pixel interpolation along the x-axis. ； ； Pixel interpolation along the y-axis: ； in,( , )express The location, ( , )express Location, Representing a point ( The pixel value at ) Representing a point ( The pixel value at ) , , , These represent the data used for interpolation of the four closest pixels (x, y) to the current pixel. , , , Pixel value at; High-resolution brain MPI data is obtained by interpolating the four nearest pixels to each pixel in the scaled brain MPI image corresponding to the original image location. Step S200, data processing; based on the brain MPI data, perform image reconstruction to obtain a two-dimensional brain temporal image sequence; By comparing with a reference brain anatomy map, frontal and occipital lobe images were extracted based on a two-dimensional temporal image sequence of the subject's target brain. Step S300: Determine the activation region; calculate the correlation between the signal intensity at each position in the frontal lobe image and the subject performing a preset action, and obtain the corresponding motor function activation region.

2. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 1, characterized in that, The preset actions include: Action 1: Instruct the subject to perform directional grasping, hand action, or passing; Action 2: Instruct the subjects to observe the human experimenter performing the same action as in Action 1; Action 3: Have the subject perform an insensitive / intransitive version of the same action as Action 1; Before executing a preset action, a resting state of preset duration must be included.

3. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 2, characterized in that, The preset actions include: performing action one, action two, and action three in a preset order and repeating them a preset number of times.

4. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 1, characterized in that, Step S300 specifically includes: Step S310: Based on the frontal lobe image or occipital lobe image, divide it into m groups of size... The nodes at the same location are used to form a time series Y of the concentration value at that location, which consists of the signal strength at all times at the same location. Step S320: Construct a behavioral time series X based on the time it takes for the subject to perform a preset action; Step S330: Calculate the correlation coefficient between X and Y; Step S340: The positions corresponding to the concentration value time series Y with correlation coefficients greater than a preset threshold are set as activation nodes, and all activation nodes are combined to form the corresponding motion function activation area.

5. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 4, characterized in that, The calculation of the correlation coefficient between X and Y is specifically as follows: Calculated using the correlation function: ； in, This represents the correlation value between X and Y. This represents the covariance of X and Y. express X variance This represents the variance of Y.

6. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 5, characterized in that, It also includes the step of verifying the experimental results; The relationship between X and Y is: ； in, , ... This represents a time series showing the change in signal strength over time at different locations, where M=1, 2, 3...1000 represents different locations; , ... This represents the correlation coefficient between X and Y at different node positions. , ... Representing different correlation constants; The correlation values ​​of a preset number of X and Y values. Perform a t-test. If the p-value is less than 0.05, the experiment is considered effective. Set the location nodes corresponding to the concentration value time series Y with M greater than the preset value, experimental validity, and correlation coefficient greater than the preset threshold as activation nodes. All activation nodes are combined to form the corresponding motor function activation area.

7. The method for extracting primate motor brain functional activation areas based on MPI according to claim 6, characterized in that, The extraction method, after step S300, further includes: Step S400: Extract the associated motor function activation area based on the two-dimensional brain temporal image sequence; Specifically: Step S410: Based on the two-dimensional brain temporal image sequence, extract images of other brain regions; the images of other brain regions include temporal lobe images; Step S420: Extract the signal intensity time series U of each location in other brain region images. Let the time series of signal intensity changes over time at different locations in the occipital lobe image be U. , ... The time series of signal intensity changes over time at different locations in the frontal lobe image is as follows: , ... , where a represents the number of pixels in the occipital lobe image and b represents the number of pixels in the frontal lobe image; The relationship between the signal intensity time series U at each location in other brain region images and each pixel in the occipital lobe image and the frontal lobe image is as follows: ； The obtained a+b correlation coefficients A t-test is performed. If the p-value is less than 0.05, the experiment is considered valid. Time series of signal intensity at various locations in other brain region images that are greater than preset values, experimentally valid, and have correlation coefficients greater than preset thresholds The corresponding locations are activated by preset behaviors, and the signal intensity time series of other activated brain regions is recorded. ; Step S430: Calculate the time series of signal intensity in the other activated brain regions. The activation at each location was determined to be either occipital lobe-related or frontal lobe-related, specifically as follows: Time series of signal intensity from other activated brain regions Calculate the hyperplane line for classification: , in, express( , ,…… )of i A dimensional vector, that is, the x-coordinate of a point on the hyperplane. Indicates and Column vectors of the same dimension multiplied together form a scalar, where n represents the bias value of the plane; The correlation coefficient The distribution of the corresponding pixels in two-dimensional space, two-dimensional space points The distance d to the straight line of the classification hyperplane is: ； in, ; By calculating each two-dimensional space point The distance to the hyperplane is calculated, resulting in several d values, where r is the closest distance to the hyperplane. The hyperplane is then classified by comparing the magnitudes of d and r. Classification is performed using support vectors based on the aforementioned correlation coefficient: ； Where c represents the binary classification result, c=-1 indicates that the corresponding position belongs to occipital lobe-related activation, and c=1 indicates that the corresponding position belongs to frontal lobe-related activation.

8. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 1, characterized in that, The continuous acquisition of brain images is achieved through an aperture of 19 cm and a field of view (FOV) of 15 × 15 cm. 2 The large-aperture MPI imaging device, which includes an excitation magnetic field, a gradient magnetic field, and a receiving magnetic field, is used for acquisition. The excitation magnetic field signal frequency is set to 25KHz and the magnitude is 5mT. The gradient magnetic field gradient is set to 0.2T / m in each direction. The imaging depth is 15cm and the imaging resolution is 10mm. Real-time imaging is performed at a speed of 10fps.

9. The method for extracting functional activation areas of primate motor brain based on MPI according to claim 2 or 3, characterized in that, Action 1 includes a transitive condition, under which the subject is instructed to use their right hand to perform a directional grasping, manual manipulation, or passing action on an object placed on a table; Action 2 includes a transitive observation condition, under which the subject is instructed to sit still and observe a human experimenter perform the same action as Action 1 on the same object; Action 3 includes an intransitive condition, under which the subject is instructed to perform the same action as Action 1 but without touching the object.

10. A system for extracting functional activation areas of primate motor brain based on MPI, characterized in that, The system includes: The data acquisition module is configured to instruct the subject to perform preset actions and continuously acquire brain images in a time sequence to obtain brain MPI data; Based on the brain MPI data, scaling is performed using a scaling factor, and the current pixel (x, y) at the original image position corresponding to the scaled coordinates is calculated. Interpolation is then performed on the four pixels closest to the current pixel (x, y), including pixel interpolation along the x-axis. ； ； Pixel interpolation along the y-axis: ； in,( , )express The location, ( , )express Location, Representing a point ( The pixel value at ) Representing a point ( The pixel value at ) , , , These represent the data used for interpolation of the four closest pixels (x, y) to the current pixel. , , , Pixel value at; High-resolution brain MPI data is obtained by interpolating the four nearest pixels to each pixel in the scaled brain MPI image corresponding to the original image location. The data processing module is configured to perform image reconstruction based on the brain MPI data to obtain a two-dimensional brain temporal image sequence; By comparing with a reference brain anatomy map, frontal and occipital lobe images were extracted based on a two-dimensional temporal image sequence of the subject's target brain. The activation region extraction module is configured to calculate the correlation between the signal intensity at each location in the frontal lobe image and the subject performing a preset action, thereby obtaining the corresponding motor function activation region.

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