CN115049044A

CN115049044A - Default mode network construction method based on magnetic nanoparticle imaging system

Info

Publication number: CN115049044A
Application number: CN202210657567.0A
Authority: CN
Inventors: 田捷; 张慧; 赵婧; 侯晓媛
Original assignee: Beihang University
Current assignee: Beihang University
Priority date: 2022-06-10
Filing date: 2022-06-10
Publication date: 2022-09-13

Abstract

The invention belongs to the technical field of magnetic nanoparticle imaging, and particularly relates to a default mode network construction method based on a magnetic nanoparticle imaging system, aiming at solving the problem that the prior art does not have a nonradioactive imaging technology to detect the blood flow change state of local cerebral capillaries. The invention comprises the following steps: acquiring an MPI one-dimensional voltage signal of a target to be modeled in a resting state; carrying out image reconstruction by generating a countermeasure network to obtain a three-dimensional reconstruction image; rigid registration is carried out, brain areas are divided, and therefore a signal time sequence of the areas is obtained; calculating correlation coefficients among the signals based on the partition signal time sequence to obtain a function connection matrix, and further drawing a default mode network; and converting the functional connection matrix into a z value and performing single-sample t-test statistical analysis to obtain the significance of the functional connection. The invention avoids the problem of attenuation caused by BOLD signals in the fMRI signals adopted by the traditional technical means, and improves the accuracy of analysis.

Description

Default mode network construction method based on magnetic nanoparticle imaging system

Technical Field

The invention belongs to the technical field of magnetic nanoparticle imaging, and particularly relates to a default mode network construction method, system and equipment based on magnetic nanoparticle imaging.

Background

When the brain is in a rest state and does not engage in a specific behavior task, the brain region corresponding to a Default-mode Network (DMN) is active high, and the Network is activated. Currently, studies in which the brain is at rest, i.e., resting state, have been shown to be associated with a variety of neurological disorders, such as alzheimer's disease, epilepsy, depression, etc., while DMN plays a central role in brain resting state studies. Meanwhile, the DMN directly affects the psychological activities of people, and is mainly responsible for the functions of monitoring the internal and external environments of the brain, processing emotion, introspection, maintaining consciousness and the like. Therefore, the construction of DMN networks plays an important role in the study of human health and disease.

When the brain is stimulated, the activity of cerebral cortex of the corresponding area is increased, so that the blood flow volume, the flow rate and the cerebral vascular volume (CBV) of cortical micro-vessels of a brain functional area are increased, the increase of the oxygen consumption of cells is relatively unobvious, and the disproportionate increase of the two results in the increase of oxygenated hemoglobin and the decrease of deoxygenated hemoglobin of the functional active area. Conventional imaging methods for acquiring DMN include Positron Emission Tomography (PET) technology and functional magnetic resonance imaging (fMRI). PET technology is based on changes in blood flow in a localized region of the brain, the neural activity of which is reflected by the acquired signals. Although PET technology has high contrast, the injected tracer is radioactive and has low spatial resolution. Functional magnetic resonance imaging (fMRI) based on Blood Oxygen Level Dependent (BOLD), due to its non-destructive, high sensitivity and easy operability, has been widely used in the study of spontaneous brain activity in resting state, and is also the most commonly used technique for constructing DMN networks. However, the BOLD fMRI technique only indirectly reflects neuronal activity, which is a complex effect of a combination of Cerebral Blood Flow (CBF), blood volume, and blood oxygen level caused by neuronal activity. Since the actual amount of oxygen remaining in the blood vessels of the brain activation site changes after stimulation, the BOLD signal is sensitive to the amount of oxygen of hemoglobin, and thus changes in oxyhemoglobin and deoxyhemoglobin of the brain activation site can be detected by BOLD. However, since the change of the oxygen consumption of cells is relatively insignificant, and the change of the oxygen state in hemoglobin in the same activation region is influenced not only by the microvessels around the active nerve activity region but also by the signals of the peripheral great vessels, it is impossible to distinguish the dilated microvessels in the actual nerve activity region from the blood signals in the great vessels. Although fMRI is a general imaging method capable of measuring in vivo hemodynamic changes associated with brain activation, CNR of fMRI remains problematic. Low CNR results in low detection rates for subtle activation in mission-based studies and weak or lost network connectivity in functional connectivity studies. In response, group averaging is often used to discover population effects, which limits the utility of fMRI in personalized medicine to only the strongest activation paradigm and the strongest network of resting states.

In summary, there is a need for a non-radioactive imaging technique to detect blood flow changes in local microvessels of the brain to reflect brain activity.

The magnetic nanoparticle imaging (MPI) technique is a novel imaging technique, and the principle of the technique is that superparamagnetic oxidized nanoparticles (SPIONs) are directly scanned to serve as imaging tracers to position and quantify the tracers of an interested region, so that the magnetic nanoparticle imaging (MPI) technique has the characteristics of linear quantification, high sensitivity, high time resolution and the like. In the experiment, the SPION is used as a tracer to be injected into blood, and MPI can directly monitor the relation between the change of functional CBV and time by detecting and tracking the local SPION concentration, thereby reflecting the activation condition of a functional brain area.

Disclosure of Invention

In order to solve the above problems in the prior art, that is, the prior art does not have a non-radioactive imaging technique for detecting the blood flow change state of local microvessels of the brain, the present invention provides a default mode network construction method based on magnetic nanoparticle imaging, which includes:

applying a tracer to a target to be modeled, and setting scanning parameters;

acquiring an MPI one-dimensional voltage signal of a target to be modeled in a resting state based on the scanning parameters;

based on the MPI one-dimensional voltage signal, image reconstruction is carried out by generating a countermeasure network to obtain a three-dimensional reconstruction image;

based on the three-dimensional reconstruction image, carrying out rigid registration and dividing a brain region so as to obtain a partition signal time sequence;

calculating correlation coefficients among the signals based on the partition signal time sequence to obtain a functional connection matrix, and drawing a default mode network on the three-dimensional reconstruction image according to the functional connection matrix;

and converting the correlation coefficient of the function connection matrix into a z value, and performing single-sample t-test statistical analysis on the z value to obtain the significance of the function connection of the target to be modeled.

In some preferred embodiments, the three-dimensional reconstructed image is obtained by:

the MPI one-dimensional voltage signal is a one-dimensional voltage signal vector obtained by scanning frame by frame, image reconstruction is carried out on the MPI one-dimensional voltage signal through a generation countermeasure network at the speed of 10 frames per second, and 1 three-dimensional reconstruction image is obtained every 4 seconds.

In some preferred embodiments, the time sequence of the partition signals is obtained by:

based on the three-dimensional reconstruction image, carrying out rigid registration with the existing standard anatomical brain region template image to obtain a three-dimensional reconstruction image after registration;

selecting a seed region with a preset size according to the registered three-dimensional reconstruction image and the existing standard anatomical brain region template image so as to obtain a partition reconstruction image;

the partitioned reconstructed image is composed of a plurality of voxel signals, and each voxel signal is arranged according to time to obtain a partitioned signal time sequence.

In some preferred embodiments, the function connection matrix is calculated by:

calculating a Pearson correlation coefficient of each voxel signal with the rest of the voxel signals of the same partition based on the partition signal time series, wherein the ith voxel ROI _i With jth voxel ROI _j The correlation coefficient of (a) is:

wherein, ROI _i (t) the value at the t-th time point in the time series of the partition signal of voxel i, ROI _j (t) represents the value at the t-th time point in the time series of the partition signal of voxel j,

representing voxel ROI _i The mean of the voxel signals of the upper T time points,

representing voxel ROI _j Mean value of voxel signals of the upper T time points, N representing the number of voxel values of the region;

all voxel signals are combined with the pearson correlation coefficients of the remaining voxel signals to obtain a functional connection matrix.

In some preferred embodiments, the converting the functional connection matrix into z values and performing a single-sample t-test statistical analysis on the z values specifically includes:

performing Fisher z value transformation on the functional connection matrix to obtain a z value;

the z-values were subjected to a single sample t-test statistical analysis with a P-value of 0.05 to obtain significance of the functional linkage.

In some preferred embodiments, the setting of the scanning parameters specifically includes:

setting the excitation magnetic field to be 5mT, the excitation frequency to be 25Hz, and the magnetic field gradient to be: the Z direction: 0.2T/m, X direction: 0.1T/m, Y direction: 0.1T/m.

In some preferred embodiments, the target to be modeled is administered with a tracer, specifically by injecting the tracer at a dose of 8-12mg/kg based on the body weight of the target to be modeled, and allowing the target to be modeled to be in a resting state.

In another aspect of the present invention, a default-mode network construction system based on magnetic nanoparticle imaging is provided, which includes:

a scanning preparation module configured to apply a tracer to a target to be modeled and set scanning parameters;

the MPI scanning module is configured to acquire an MPI one-dimensional voltage signal of a target to be modeled in a resting state based on the scanning parameters;

the three-dimensional reconstruction module is configured to perform image reconstruction by generating a countermeasure network based on the MPI one-dimensional voltage signal to obtain a three-dimensional reconstruction image;

the brain region division module is configured to perform rigid registration and divide a brain region based on the three-dimensional reconstruction image so as to obtain a partition signal time sequence;

the function connection matrix acquisition module is configured to calculate correlation coefficients among the signals based on the partition signal time sequence, acquire a function connection matrix, and draw a default mode network on the three-dimensional reconstruction image according to the function connection matrix;

and the significance authentication module is configured to convert the correlation coefficient of the functional connection matrix into a z value, and perform single-sample t-test statistical analysis on the z value to obtain the significance of the target functional connection to be modeled.

In a third aspect of the present invention, an electronic device is provided, including: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the magnetic nanoparticle imaging-based default mode network construction method described above.

In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for being executed by the computer to implement the above-mentioned default mode network construction method based on magnetic nanoparticle imaging.

The invention has the beneficial effects that:

(1) according to the method, the brain signals are obtained by using a new imaging technology MPI capable of directly reflecting blood volume change, and the image reconstruction is carried out by generating the countermeasure network, so that the problem of attenuation caused by the adoption of fMRI signals due to BOLD signals in the traditional technical means is solved;

(2) according to the method, the brain signals are obtained by using the MPI, and the images have no background signals of the brain, so that the contrast of the obtained images is improved, and the accuracy of analysis is further improved;

(3) the invention avoids using radioactive medicines and has safer and more reliable implementation process.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of a default-mode network construction method based on magnetic nanoparticle imaging according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a training process for generating a confrontation model according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a model structure of a generator in an embodiment of the invention;

FIG. 4 is a schematic diagram of the structure of the model for generating the countermeasure model VGG16 of the present invention;

FIG. 5 is a schematic diagram of rigid registration in an embodiment of the present invention;

FIG. 6 is a schematic diagram of a time sequence of a partition signal obtained from a three-dimensional reconstructed image according to an embodiment of the present invention;

FIG. 7 is a schematic diagram illustrating the principle of calculating the correlation coefficient between signals by the default-mode network construction method based on magnetic nanoparticle imaging in the embodiment of the present invention;

FIG. 8 is a diagram illustrating the effect of a default mode network generated in an embodiment of the present invention;

FIG. 9 is a block diagram of a computer system of a server for implementing embodiments of the method, system, and apparatus of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

The invention provides a magnetic nanoparticle imaging-based default mode network construction method, which is characterized in that a new imaging technology MPI capable of directly reflecting blood volume changes is used for acquiring brain signals, and image reconstruction is carried out by generating a countermeasure network, so that the problem of attenuation caused by a BOLD signal due to fMRI signals adopted in the traditional technical means is solved, and the accuracy of the default mode network is improved.

The invention discloses a default mode network construction method based on magnetic nanoparticle imaging, which comprises the following steps:

applying a tracer to a target to be modeled, and setting scanning parameters;

carrying out image reconstruction by generating a countermeasure network based on the MPI one-dimensional voltage signal to obtain a three-dimensional reconstruction image;

In order to more clearly explain the method for constructing the default-mode network based on magnetic nanoparticle imaging of the present invention, the following description is made with reference to fig. 1 and the steps in the embodiment of the present invention.

The method for constructing the default mode network based on magnetic nanoparticle imaging in the first embodiment of the invention comprises steps S100-S600, and the steps are described in detail as follows:

step S100, applying a tracer to a target to be modeled, and setting scanning parameters;

in this embodiment, the tracer is applied to the target to be modeled, specifically, tracer injection is performed based on the weight of the target to be modeled, the dose is 8-12mg/kg, and the target to be modeled is in a resting state. The specific operation steps are that macaques are selected as experimental subjects, 20 macaques are recruited, wherein 5 female monkeys and 15 male monkeys are selected, and the selected macaques have no serious somatic diseases, no brain diseases and no medical alcohol dependence history. According to the weight of a macaque, injecting SPION with a corresponding dose at a calf vein, waiting for about 15 minutes after injection, and allowing the SPION to circulate to the brain along with the vein, in order to acquire data in a resting state, placing the head of the macaque in an MPI imaging cavity with the aperture of 19cm and the length and width of 15 x 15cm after injecting anesthetic into the macaque, fixing the head of the macaque by adopting equipment, and ensuring that the head does not move during data acquisition as much as possible. And setting acquisition parameters of the MPI equipment, such as a gradient magnetic field, an excitation frequency and the like. And after the setting is finished, the equipment is opened, and the signal acquisition is started.

In this embodiment, the setting of the scanning parameters specifically includes:

S200, acquiring an MPI one-dimensional voltage signal of a target to be modeled in a resting state based on the scanning parameters;

step S300, based on the MPI one-dimensional voltage signal, image reconstruction is carried out through a generation countermeasure network to obtain a three-dimensional reconstruction image;

in this embodiment, the three-dimensional reconstructed image is obtained by:

In this embodiment, the image reconstruction of the MPI one-dimensional voltage signal by generating the countermeasure network specifically includes:

inputting the MPI one-dimensional voltage signal as an input signal into a generation countermeasure network to obtain a three-dimensional reconstruction image;

the generation countermeasure network comprises a generator and an arbiter;

the generator comprises a full connection layer, three coding modules and a linear layer which are connected in sequence; the encoding module comprises a Transformer encoder and an up-sampling unit;

the discriminator comprises N convolution layers and a full-connection layer construction which are sequentially connected, wherein N represents a positive integer.

The training method of the generation countermeasure network is shown in fig. 2, and specifically includes:

a100, generating different types of binary images through a preselected model in MATLAB; simulating the binary image to generate a corresponding one-dimensional MPI signal; the binary image comprises a geometric image, a letter image and a resolution image;

in this embodiment, firstly, 10000 binary images of three different models are generated by MATLAB, which specifically includes: obtaining a binary image (namely the binary image comprises a geometric image, an alphabetic image and a resolution image, as shown in fig. 2) through the geometric model, the alphabetic model and the resolution model respectively, and generating a corresponding one-dimensional signal (namely an MPI one-dimensional voltage signal) from the binary image by using simulation software; the MPIRF simulation software is preferred in the simulation software, and in other embodiments, the MPIRF simulation software can be selected according to actual needs.

A200, taking the binary image as a truth label, and combining a corresponding MPI one-dimensional voltage signal to construct a training sample to obtain a training data set;

in the present embodiment, 8000 pairs of 10000 pairs of paired data (i.e. MPI one-dimensional voltage signals and their corresponding binary image truth labels) are used as training data, and the remaining 2000 pairs are used as a test data set. Meanwhile, the MPI equipment was used to actually collect 10 sets of data of different models as a test data set.

A300, inputting the one-dimensional MPI signal into a pre-constructed generator to generate a two-dimensional MPI image as a predicted image; meanwhile, inputting a binary image corresponding to a one-dimensional MPI signal input into a pre-constructed generator into a pre-constructed VGG16 model to obtain the category of the binary image, wherein the category of the binary image is used as the condition constraint of the generator to enable the image generation to be carried out towards a specified direction; in the embodiment, the generation of the confrontation network model comprises a generator and a discriminator; the generator is constructed on the basis of a full connection layer, three coding modules and a linear inverse flattening layer which are connected in sequence; the encoding module comprises a Transformer encoder; the image processing method includes the following steps of three upsampling units, wherein the upsampling units are located between every two transform encoders, and one upsampling unit (namely 2 × upsampling in fig. 3, 2 × representing that the dimension of an image is increased, for example, the dimension of a matrix before input is 8 × 8, and the matrix becomes 16 × 16 after upsampling), as shown in fig. 3, wherein the linear inverse flattening layer is used for changing the dimension of a feature matrix encoded by the transform encoders into a two-dimensional image;

the generator constructed as above has the advantages that: 1. the calculation and memory amount can be reduced; the resolution of the features can be increased step by step but the dimensions of the embedding layer are reduced (better features are extracted, image resolution is improved). The generator is mainly used for performing image reconstruction on the input one-dimensional MPI signal to obtain a corresponding two-dimensional MPI image (i.e., a reconstructed image in FIG. 2).

While generating the two-dimensional MPI image, inputting the binary image corresponding to the input one-dimensional MPI signal of the generator into the pre-constructed VGG16 model to obtain the category of the binary image, as shown in FIG. 4. Since there are 3 types of images, it is necessary to automatically classify the images, for example, the image classification result of the letter is 00, the image classification result of the resolution is 01, the image classification result of the geometric image is 10, and if there is no way to distinguish the category, the image classification result is 11.

The overall structure of the VGG16 model is as shown in fig. 4 (size in fig. 4 indicates size, conv indicates convolutional layer, pool indicates pooling, fc indicates fully-connected layer, and for example, 3 × 3conv in fig. 4, total 64 channels indicate convolutional layer with convolutional kernel size of 3 × 3), where the convolutional kernel size (kernels) of the convolutional layer in the VGG16 model is 3 × 3, the step size (stride) is 1, and there is no information loss. During training, the multi-classification cross-loss function is used for optimization, and the loss function of the VGG16 is shown as an equation (1):

Loss＝α1*loss1+α2*loss2+α3*loss3 (1)

therein, loss _i (i ═ 1, 2, 3) respectively denote cross entropy loss functions of the alphabetical branch, the resolution branch and the geometric branch, and α 1, α 2, α 3 denote preset weights corresponding to the different branches. The existing research shows that the identification precision of the whole or single task can be improved by setting the proper weight for different branches in the multi-task.

A400, obtaining a loss value through a pre-constructed generator loss function based on the category of the binary image and the prediction image, and updating the network parameters of a generator; in this embodiment, the generator loss function is shown in equation (2):

wherein the content of the first and second substances,

representing the loss function corresponding to the generator, G representing the generator, D representing the discriminator, y representing the class of the binary image, x representing the real image, G (z | y) representing the image generated by the generator, z representing the input one-dimensional MPI signal,

a probability distribution function, λ, representing x _G Denotes a constant coefficient, L _MAE The mean absolute error function is represented.

A500, inputting the true value labels of the predicted image and the corresponding binary image into a pre-constructed discriminator to discriminate the truth of the predicted image;

in this embodiment, the discriminator is constructed based on N convolutional layers and all-connected layers connected in sequence; n is a positive integer. In the present invention, N is preferably set to 4.

And inputting the truth labels of the predicted image and the corresponding binary image into a pre-constructed discriminator to discriminate the truth of the predicted image.

A600, combining a true and false judgment result corresponding to the predicted image, obtaining a loss value through a pre-constructed discriminator loss function, and updating network parameters of a discriminator;

in this embodiment, different discriminant loss functions are constructed for different inputs to the generator.

If the input of the generator is a one-dimensional MPI signal of the letter image, the discriminant loss function is as shown in equation (3):

wherein the content of the first and second substances,

represents the corresponding loss function of the discriminator, z represents the input MPI one-dimensional voltage signal,

a probability distribution function, L, representing z _BCE Representing a two-class cross-entropy loss function, t _letter Representing features of the extracted letter image, D representing a discriminator, lambda _D Representing a preset second constant coefficient;

if the input of the generator is a one-dimensional MPI signal of the geometric image, the discriminant loss function is as shown in equation (4):

wherein, t _geometry Features representing the extracted geometric image;

if the input of the generator is a one-dimensional MPI signal of the resolution image, the loss function of the discriminator is shown in formula (5):

wherein, t _resolution Representing features of the extracted resolution image.

And A700, circulating A300-A600 until a trained generated confrontation network model is obtained.

Step S400, based on the three-dimensional reconstruction image, rigid registration is carried out and brain areas are divided, so that a partition signal time sequence is obtained; this embodiment still includes: before rigid registration, the method also comprises the step of preprocessing the three-dimensional reconstruction image, wherein the preprocessing comprises time correction, head motion correction, registration, space standardization, smoothing and filtering;

in this embodiment, the specific obtaining method of the partition signal time sequence is as follows:

based on the three-dimensional reconstruction image, carrying out rigid registration with the existing standard anatomical brain region template image to obtain a three-dimensional reconstruction image after registration; the standard anatomical brain region template adopted in this example is monkey F99;

in this embodiment, the specific method of rigid registration shown in fig. 5 is as follows:

and (3) setting that only translational changes exist between the three-dimensional reconstruction image and the standard anatomical brain region template, and selecting the translational changes as a registration method, wherein the registration formula is shown as a formula (6):

wherein x ', y ' and z ' represent coordinates of pixels on a template of a standard anatomical brain region, and x, y and z represent pixels on a three-dimensional reconstructed imageCoordinate value of, Δ _x 、Δ _y And Δ _z Is the translational change coefficient to be solved;

and calculating a registration formula by using any three coordinate values of the three-dimensional reconstruction image and the standard anatomical brain region template.

Taking a seed region with a preset size according to the registered three-dimensional reconstruction image and the existing standard anatomical brain region template image so as to obtain a partition reconstruction image; the DMN network mainly comprises a brain area which comprises posterior cingulate gyrus cortex, anterior cuneiform lobe, medial prefrontal cortex, inferior parietal lobe and bilateral temporal cortex, and the brain area is selected as an analyzed region of interest; taking the posterior cingulate cortex as an example, selecting the posterior cingulate cortex as a central point, and selecting a brain area with a preset size of 10mm as a seed area based on the central point;

the partitioned reconstructed image is composed of a plurality of voxel signals, and each voxel signal is arranged according to time to obtain a partitioned signal time sequence. The size of each voxel in this example is 1 mm. The process of acquiring a time series of partition signals based on the partition reconstructed images is shown in fig. 6;

step S500, calculating correlation coefficients among signals based on the partition signal time sequence to obtain a function connection matrix, and drawing a default mode network on the three-dimensional reconstruction image according to the function connection matrix as shown in FIG. 7; in this embodiment, the functional connection between voxels is drawn on the MPI image, and the finally obtained default mode network is shown in fig. 8, where L represents the left hemisphere of the brain and R represents the right hemisphere of the brain in fig. 8; the final default mode network reflects the activation of the functional connection area in a resting state;

in this embodiment, the function connection matrix is shown in fig. 7, and the calculation method thereof is as follows:

calculating a Pearson correlation coefficient of each voxel signal with the rest of the voxel signals of the same partition based on the partition signal time series, wherein the ith voxel ROI _i With jth voxel ROI _j The correlation coefficient of (2) is shown in equation (7):

wherein, ROI _i (t) values representing the t-th time point in the time series of the partition signal of voxel i, ROI _j (t) represents the value at the t-th time point in the time series of the partition signal of voxel j,

representing voxel ROI _i The voxel signal average of the upper T time points,

and combining the Pearson correlation coefficients between all the voxel signals and the rest voxel signals to obtain a functional connection matrix. The Pearson correlation coefficient reflects the synchronism between two signals, and the finally obtained function connection matrix is a symmetrical correlation coefficient matrix, wherein the larger the numerical value is, the stronger the function connection is; the more non-0 values, the more activation of the corresponding region is indicated.

Calculating the function connection matrix of each brain area by a calculation method of the function connection matrix, and further drawing a default mode network of all brain areas;

and S600, converting the correlation coefficient of the function connection matrix into a z value, and carrying out single-sample t-test statistical analysis on the z value to obtain the significance of the function connection of the target to be modeled.

In this embodiment, the converting the functional connection matrix into a z value and performing single-sample t-test statistical analysis on the z value specifically includes:

performing Fisher z value transformation on the function connection matrix to obtain a z value;

and (4) carrying out single-sample t-test statistical analysis on the z value with the P value of 0.05 to obtain the significance of the target function connection to be modeled. The method is used for verifying the reliability of the default mode network constructed by the invention according to the significance of the target function connection to be established.

The MPI as an imaging technology for constructing a default mode network has the following advantages: 1. the MPI imaging device has higher sensitivity, can directly reflect the change of blood volume, and simultaneously avoids the attenuation caused by the BOLD signal in the fMRI signal. 2. MPI images only SPIONs, and there is no background signal of the brain in the image, and thus has a high contrast. 3. SPION is a safe tracer, free of radioactive substances, and metabolised in the body. At the same time SPIONs have been used clinically. The effect pair ratios of the various methods are shown in table 1.

TABLE 1 comparison of the results of MPI method, FMRI method and PET method

	MPI	fMRI	PET
				Spatial resolution	10mm	2mm	20mm
Temporal resolution	>10 frames/s	25 frames/s	Several minutes
				Sensitivity of the probe	Height of	Is higher than	Is low with
Radioactivity	Is free of	Is free of	Is provided with

Although PET technology can reflect blood changes in the brain by the metabolic status of glucose, the injected tracer is radioactive and the spatial resolution of PET is low. fMRI can reflect changes in oxygenated hemoglobin and deoxygenated hemoglobin in the blood flow, but cannot distinguish between signals from microvessels and large blood vessels in the brain's active region, resulting in errors in localization.

Although the foregoing embodiments describe the steps in the above sequential order, those skilled in the art will understand that, in order to achieve the effect of the present embodiments, the steps may not be executed in such an order, and may be executed simultaneously (in parallel) or in an inverse order, and these simple variations are within the scope of the present invention.

The default mode network construction system based on magnetic nanoparticle imaging of the second embodiment of the invention comprises:

the function connection matrix acquisition module is configured to calculate correlation coefficients among the signals based on the partition signal time sequence, acquire a function connection matrix, and draw a default mode network on the MPI image according to the function connection matrix; (ii) a

And the significance verification module is used for converting the correlation coefficient of the functional connection matrix into a z value and carrying out single-sample t-test statistical analysis on the z value to obtain the significance of the functional connection of the target to be modeled.

It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

It should be noted that, the default-mode network construction system based on magnetic nanoparticle imaging provided in the foregoing embodiment is only illustrated by the division of the above functional modules, and in practical applications, the above functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the above described functions. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.

An electronic apparatus according to a third embodiment of the present invention includes: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the magnetic nanoparticle imaging-based default mode network construction method described above.

A computer readable storage medium of a fourth embodiment of the present invention stores computer instructions for being executed by the computer to implement the above-mentioned default-mode network construction method based on magnetic nanoparticle imaging.

It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Reference is now made to FIG. 9, which illustrates a block diagram of a computer system of a server for implementing embodiments of the method, system, and apparatus of the present application. The server shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 9, the computer system includes a Central Processing Unit (CPU)901 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for system operation are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An Input/Output (I/O) interface 905 is also connected to bus 904.

The following components are connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 908 including a hard disk and the like; and a communication section 909 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The above-described functions defined in the method of the present application are executed when the computer program is executed by a Central Processing Unit (CPU) 901. It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.

The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can be within the protection scope of the invention.

Claims

1. A default mode network construction method based on magnetic nanoparticle imaging is characterized by comprising the following steps:

applying a tracer to a target to be modeled, and setting scanning parameters;

2. The method for constructing the default-mode network based on magnetic nanoparticle imaging according to claim 1, wherein the three-dimensional reconstructed image is obtained by:

3. The method for constructing a default-mode network based on magnetic nanoparticle imaging according to claim 1, wherein the time series of the partition signals is obtained by:

taking a seed region with a preset size according to the registered three-dimensional reconstruction image and the existing standard anatomical brain region template image so as to obtain a partition reconstruction image;

4. The method for constructing a default-mode network based on magnetic nanoparticle imaging according to claim 1, wherein the functional connection matrix is calculated by:

representing voxel ROI _i The voxel signal average of the upper T time points,

5. The magnetic nanoparticle imaging-based default-mode network construction method of claim 1, wherein the functional connectivity matrix is converted into z-values, and the z-values are subjected to single-sample t-test statistical analysis, specifically:

and carrying out single-sample t-test statistical analysis on the z value with the P value of 0.05 to obtain a default mode network.

6. The method according to claim 1, wherein the setting of scanning parameters comprises:

setting the excitation magnetic field to be 5mT, the excitation frequency to be 25Hz, and the magnetic field gradient to be: : the Z direction: 0.2T/m, X direction: 0.1T/m, Y direction: 0.1T/m.

7. The magnetic nanoparticle imaging-based default-mode network construction method according to claim 1, wherein the tracer is administered to the target to be modeled, specifically, tracer injection is performed based on the body weight of the target to be modeled, the dose is 8-12mg/kg, and the target to be modeled is in a resting state.

8. A default-mode network construction system based on magnetic nanoparticle imaging, the system comprising:

and the significance authentication module is used for converting the correlation coefficient of the functional connection matrix into a z value and carrying out single-sample t-test statistical analysis on the z value to obtain the significance of the functional connection of the target to be modeled.

9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the magnetic nanoparticle imaging-based default-mode network construction method of any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for execution by the computer to implement the magnetic nanoparticle imaging-based default-mode network construction method of any one of claims 1-7.