A method for analyzing the vector of an intracerebral wave propagation and related device

Abstract

The application belongs to the technical field of computers, and particularly relates to a brain wave propagation vector analysis method and related device, wherein phase transformation is performed on frame-by-frame pixel values of a brain imaging video; wave propagation vector calculation is performed according to a phase transformation result, brain wave propagation wave speed and direction are obtained, the influence of extreme singular values and noise is reduced, the calculation process no longer highly depends on video imaging quality and professional personnel intervention, sampling cost and human cost are greatly reduced. Meanwhile, the wave starting area source is searched and wave propagation is visually presented, the wave propagation process is more intuitive, the calculation process is generalized, and the algorithm has high universality.

Description

Technical Field

[0001] This application belongs to the field of neuroscience technology, and specifically relates to a method and related device for analyzing the propagation vector of brain waves. Background Technology

[0002] In the field of neuroscience, analyzing brain imaging data is crucial for realizing neurological theories and mechanisms, providing a strong quantitative and qualitative analytical foundation for studying brain function, disease causes, substance diffusion, signal transduction, and many other practical aspects. In particular, the correlation between brightness and numerical values ​​in fluorescence imaging of animal brains has led to the concept of wave propagation in vision. Understanding and analyzing the process of wave propagation in brain imaging, and comparing differences in speed and direction during wave propagation, can reveal mechanisms in many neuroscience problems.

[0003] In existing, widely used technologies, researchers and technicians typically study wave formation and decay processes based on the pixel values ​​of brain imaging videos. However, this wave propagation brain imaging analysis method is often sensitive to extreme singularities or requires very high image quality. In real-world applications, obtaining high-quality imaging videos is often difficult, leading to significant inaccuracies in the numerical values. Furthermore, extreme singularities with large numerical ranges may appear due to noise and other factors, impacting the analysis process. This analysis also requires researchers to delve into the data itself, examine numerical ranges, and manually set relevant thresholds for filtering and elimination, thus demanding domain knowledge. The need for domain experts to set different thresholds during processing makes it susceptible to human interference, resulting in poor algorithm generalization and difficulty in forming a universal framework. Existing technologies have shortcomings. Summary of the Invention

[0004] The main objective of this application is to provide a method and related apparatus for analyzing the propagation vector of brain waves, aiming to solve the problems of existing wave propagation brain imaging analysis methods being sensitive to extreme singular values, having high requirements for imaging quality, and having poor generalization.

[0005] To achieve the above objectives, this application provides a method for analyzing the propagation vector of brain waves, comprising:

[0006] Obtain at least one brain imaging video;

[0007] Phase transformation is performed on the pixel values ​​of each frame in the brain imaging video;

[0008] Based on the phase transformation results, the wave propagation vector is calculated to obtain the wave velocity and direction of wave propagation within the brain.

[0009] On the other hand, this application also includes a brain wave propagation vector analysis device, comprising: an image acquisition unit configured to acquire a brain imaging video image to be processed; and a processing unit configured to invoke a target brain wave propagation vector analysis method to perform vector analysis on the brain imaging video image to be processed; wherein the brain wave propagation vector analysis method is any one of the brain wave propagation vector analysis methods described above.

[0010] On the other hand, this application also includes a computer-readable storage medium, wherein the computer instructions are configured to cause the computer to perform the brain wave propagation vector analysis method described in any of the preceding claims.

[0011] On the other hand, this application also includes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the brain wave propagation vector analysis method described in any of the above claims. In the above technical solution of this application, by performing phase transformation on the frame-by-frame pixel values ​​of the brain imaging video; and calculating the wave propagation vector based on the phase transformation results, the wave velocity and direction of brain wave propagation are obtained. This reduces the influence of extreme singular values ​​and noise, making the calculation process less dependent on video imaging quality and professional personnel intervention, greatly reducing sampling and labor costs. Simultaneously, it enables the search for the wave origin region and the visualization of wave propagation, making the wave propagation process more intuitive. The fully automated algorithm also enhances the generalization of the calculation process, giving it high versatility. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the logical steps of a method for analyzing the propagation vector of brain waves according to an embodiment of this application;

[0014] Figure 2 A supplementary before-and-after imaging comparison diagram for this application;

[0015] Figure 3 This is a supplementary before-and-after comparison diagram for another imaging in this application;

[0016] Figure 4This is a schematic diagram showing the optical flow color sampling standard and the corresponding vector direction of the optical flow color blocks in this application;

[0017] Figure 5 This is a schematic diagram of one frame from the optical fluidization visualization video of this application;

[0018] Figure 6 This is another schematic diagram of a frame from the optical fluidization visualization video of this application;

[0019] Figure 7 This is another schematic diagram of a frame from the optical fluidization visualization video of this application;

[0020] Figure 8 This is an instantaneous schematic diagram of the diffusion and decay process of the wave in this application;

[0021] Figure 9 This is another instantaneous schematic diagram of the diffusion and decay process of the wave in this application;

[0022] Figure 10 This is another instantaneous schematic diagram of the diffusion and decay process of the wave in this application;

[0023] Figure 11 This is another instantaneous schematic diagram of the diffusion and decay process of the wave in this application;

[0024] Figure 12 This is a schematic diagram of the origin point region of wave propagation in an imaging video.

[0025] Figure 13 This is a schematic diagram of the origin point region of wave propagation in another imaging video. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] It should be noted that all directional indicators (such as up, down, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0028] Furthermore, the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0029] See Figure 1 As shown, according to one aspect of this application, this application provides a method for analyzing the propagation vector of brain waves, comprising the following steps:

[0030] S1. Obtain brain imaging videos;

[0031] S2, Phase Transformation Preprocessing

[0032] S3. Perform phase transformation on the frame-by-frame pixel values ​​of the brain imaging video;

[0033] S4. Phase Transformation Post-processing

[0034] S5. Calculate the wave propagation vector based on the phase transformation results to obtain the wave propagation speed and direction within the brain.

[0035] The step "S1, acquiring brain imaging video" refers to acquiring at least one brain imaging video, which can be two or more segments. As one embodiment of this application, two 4600-frame mouse brain imaging videos are acquired, named "iAdo2m" and "jRGEC01a" respectively, representing adenosine and calcium.

[0036] The "S2, Phase Transformation Preprocessing Step" includes the following sub-steps:

[0037] S21. Imaging lacks a supplementary step. Specifically, in the brain imaging video, pixels with a value of NaN are identified frame by frame. In the corresponding frame, the pixel is supplemented based on the surrounding pixels. Because the presence of blood vessels in brain imaging makes it difficult to analyze the natural and coherent propagation of wave signals, a filling technique is needed to eliminate the influence of pixels in locations such as blood vessels on wave analysis. To effectively eliminate the interference caused by pixels in specific locations such as blood vessels, a filling technique is used for compensation. Specifically, pixels with NaN (Not a Number) values ​​in the image are first accurately selected, and then filled based on the surrounding pixels. Figure 2-3 As shown, the effects of using filling techniques to remove blood vessel interference in imaging videos are illustrated. Before processing, from... Figure 2 and 3 As shown in the left figure, the distribution of cerebral blood vessels causes irregular segmentation of the imaging field, resulting in discontinuities in numerical analysis and causing analytical difficulties. Through supplementary processing, from... Figure 2 and 3 As shown in the right figure, the imaging field of view forms a coherent range, and the numerical path from any pixel to the remaining pixels becomes continuous. Furthermore, the supplementary processing does not affect the image quality itself. Figure 2 and 3 As can be seen from the legend and colors, the pixel values ​​of the supplemented image remain consistent with the original image. This application performs padding processing on the entire video frame.

[0038] S22, Domain Transformation Step: The pixel values ​​of the brain imaging video are processed frame by frame using a phase transformation filtering algorithm to perform a domain transformation on the non-zero pixel values. The phase transformation filtering algorithm includes:

[0039] S221. Use the Discrete Fourier Transform to convert the time-domain signal into the frequency domain and separate the amplitude and phase information;

[0040] S222. Set the adjustable cutoff frequency according to the preset requirements, and achieve low-pass filtering through a Butterworth filter;

[0041] Specifically, a fourth-order Butterworth filter is used, and its amplitude response function f is:

[0042]

[0043] The normalized cutoff frequency is: fc = freq_band / (0.5 × Fs), where Fs is the sampling rate and freq_band is the user-defined frequency band, which can be set according to user preset requirements. For algorithm parameter selection, a freq_band of 0.02Hz is recommended, covering typical neural wave frequencies (0.01-0.1Hz).

[0044] S223. Perform signal centering processing on the filtered signal to achieve zero mean and eliminate signal offset.

[0045] S3 involves performing phase transformation on the frame-by-frame pixel values ​​of the brain imaging video;

[0046] The phase transformation includes using the Hilbert algorithm to extract the instantaneous phase information of pixel values ​​in frame-by-frame images of the brain imaging video, transforming the pixel values ​​to the interval [-π, π] to obtain the phase domain.

[0047] Since different imaging methods present different dimensional values ​​or require different calculation methods under different systems, this is detrimental to developing a unified algorithm for analyzing wave diffusion and decay. Therefore, domain transformation is necessary. By using the pixels of the imaging to calculate the phase, the range is constrained to [-π, π], thus achieving dimensional balancing and allowing the pixels in the imaging to be compared under the same metric.

[0048] Specifically, construct the analytical signal:

[0049]

[0050] in The Hilbert transform operator is a linear operator that produces a function H(y)(t) with the same domain as the function y(t), where y(t) represents the current signal. More precisely, the Hilbert transform of the signal y(t) (i.e., applying the Hilbert transform operator to the signal y(t)) is:

[0051]

[0052] Where pv represents the use of Cauchy principal value in Hilbert transform, and τ represents the time variable.

[0053] Calculate the instantaneous phase using the Hilbert transform signal z(t) obtained from the two formulas above:

[0054]

[0055] Arctan2(#,*) is a two-parameter arctangent function used to calculate the argument (the angle between the point (*,#) and the positive real axis). This function correctly handles all quadrants (avoiding zero denominators or sign confusion) and returns an angle value in the range (-π,π]. Im(z(t)) and Re(z(t)) represent the imaginary and real parts of the signal, respectively. This refers to the phase of each pixel within the wide-field imaging obtained by the final calculation in this invention.

[0056] Then, information such as angular velocity, time, and speed are obtained based on the phase of the pixels.

[0057] Among them, S4, the post-processing step of phase transformation, includes the following sub-steps:

[0058] S41. Perform phase unwrapping processing on the phase conversion result to eliminate 2π jumps. Phase unwrapping aims to solve the phase jump problem caused by the periodicity (usually 2π) of the phase data and restore the true continuous phase distribution.

[0059] S42. Perform isotopic regression optimization on the phase transformation results to ensure the monotonically increasing characteristic of the phase. Specifically, isotopic regression is a regression method based on data order constraints, and its core objective is to optimize model performance while maintaining the monotonicity of the predicted values.

[0060] Step S5 involves calculating the wave propagation vector based on the phase transformation results to obtain the wave propagation velocity and direction within the brain. The wave propagation vector calculation includes:

[0061] Based on the phase conversion results, the phase, angular velocity, time, and velocity information of each pixel are obtained. The velocity of each pixel in the brain imaging video is calculated frame by frame to obtain the wave propagation vector velocity, which includes the wave propagation speed, magnitude, and direction.

[0062] Specifically, after obtaining the calculated phase, this application calculates the wave vector velocity based on the phase. Assume there are two distinct pixels on the image, and that there is wave propagation or decay from pixel 1 to pixel 2. By calculating the phase difference between the different pixels, the wave propagation velocity can be obtained.

[0063] The calculation method for this application is as follows:

[0064] At any time t, the phases of any two pixels are respectively and It's possible Within a very small time interval Δt, there exists

[0065] Starting from the physical definition of velocity, we have:

[0066]

[0067] Where Δs 12 This represents the physical distance between two pixels.

[0068] At the same time, by introducing the concept of angular velocity, we obtain the calculation formula under the physical definition:

[0069]

[0070] The angular velocity ω2(t) can be derived from the instantaneous phase. It is obtained from the derivative of .

[0071] Based on the above formulas and methods, this application calculates the wave vector velocity, including both horizontal and vertical velocities, using the following formula:

[0072]

[0073] As a preferred embodiment, selecting 5 frames for comparison and 10 neighboring pixels provides the most robustness during the calculation process. This application applies the above formula to obtain the velocity vector of each pixel in a full-frame wide-field imaging video.

[0074] It also includes step S7, a visualization step, where, based on the vector velocity of wave propagation, an optical flow rendering process is used to obtain a visualized arrow vector diagram showing the velocity change. Specifically, such as... Figure 4 As shown, the rendering metrics used in the visualization are illustrated. The left image shows the optical flow color standard, and the right image shows the vector direction corresponding to the optical flow color patches. For example, a pure red patch in the left image corresponds to a horizontal leftward vector in the arrow diagram. That is, when a pure red area is seen in the optical flow diagram, it means that the velocity vector at that location is horizontally to the left. Furthermore, this application utilizes optical flow rendering and vector arrow drawing to qualitatively describe the visualization of wave propagation speed within a full-frame video frame, based on the quantitative analysis above. Figure 5-7 As shown, three frames from a video demonstrating the optical fluidization visualization of wave propagation velocity in mouse brain imaging illustrate the wave diffusion and dissipation process. Specifically, Figure 5 Indicating the initial stage of diffusion, the blue-purple areas in the diagram represent the wave spreading from top to bottom (reference). Figure 4 legend); Figure 6 This indicates the peak of the diffusion phase, during which a large range of waves appear in the brain, with wave diffusion occurring in different areas. Figure 7 This indicates the final stage of the dissipation process, where the wave disappears from bottom to top at the top of the brain region. A key technical point and innovation of this application lies in this: wave transformations in imaging videos can only provide researchers with changes and visualizations of pixel fluorescence values, making it difficult to capture the wave change trends of a specific pixel, brain region, or the entire brain. However, using this technology, researchers can observe the waveform movement trends of specific pixels, brain regions, and even the entire brain within mouse brain imaging, such as diffusion and decay, and the direction of movement.

[0075] To further demonstrate the effectiveness of the technical solution in this application, such as Figure 8-11 As shown, the wave propagation process from top to bottom and the receding process from bottom to top are illustrated sequentially. The arrow visualization, drawn from the calculated wave velocity vector in mouse brain imaging, clearly demonstrates the effectiveness of the velocity vector depicted in this application, showing a high degree of consistency with the actual propagation and divergence directions of the wave in the original imaging. The dark blue-purple represents the background area of ​​the mouse brain in the imaging, with a more pronounced green tint indicating a stronger wave. The velocity vector and arrow drawing obtained using this technique clearly demonstrate its accuracy and significant impact in visualizing wave propagation.

[0076] This application also includes step S6, propagation origin, which involves comparing the phase situation within the entire frame of the brain imaging video with the vector velocity of the wave propagation, and deriving the origin region of the wave propagation based on the diffusion and dispersion process of the wave propagation.

[0077] In addition to enabling quantitative calculation and qualitative characterization of waves, this application proposes a method for calculating the origin point of waves. By comparing the phase of each pixel, the final origin point is deduced based on the diffusion and dispersion process of wave propagation. The core idea is that each pixel completes a full lifecycle from -π to π, and an origin point should begin its phase increase relatively early and reach the final state of π at the latest. Using this principle, this application searches all pixels in the field and compares pixels that meet the criteria, deducing the origin region of wave propagation based on the diffusion and dispersion process of wave propagation.

[0078] like Figure 12 The image shows the region of origin for the waves of iAdo2m (adenosine) and jRGEC01a (calcium) identified by the method described in this application. The algorithm described in this application can complete the search in just 1.4 seconds using a 4600-frame, 359x359 wide-field brain imaging video.

[0079] Furthermore, this application also supports the identification of two or more waves occurring within a brain. For example... Figure 13 As shown, the arrow diagram illustrating the propagation direction of wave propagation in the origin region from a local perspective is qualitatively characterized. It can be seen that the method of this application has strong analytical power and performs well in imaging brain regions corresponding to biological meanings and biological experiments.

[0080] On the other hand, this application also includes a brain wave propagation vector analysis device, comprising: an image acquisition unit configured to acquire a brain imaging video image to be processed; and a processing unit configured to invoke a target brain wave propagation vector analysis method to perform vector analysis on the brain imaging video image to be processed; wherein the brain wave propagation vector analysis method is any one of the brain wave propagation vector analysis methods described above.

[0081] On the other hand, this application also includes a computer-readable storage medium, wherein the computer instructions are configured to cause the computer to perform the brain wave propagation vector analysis method described in any of the preceding claims.

[0082] On the other hand, this application also includes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the brain wave propagation vector analysis method described in any of the preceding claims.

[0083] In the above-described technical solution of this application, phase transformation is performed on the pixel values ​​of each frame of the brain imaging video; and wave propagation vector is calculated based on the phase transformation results to obtain the wave propagation speed and direction within the brain. This reduces the influence of extreme singular values ​​and noise, making the calculation process less dependent on video imaging quality and professional personnel intervention, thus greatly reducing sampling and labor costs. Simultaneously, it enables the identification of wave origin regions and the visualization of wave propagation, making the wave propagation process more intuitive. The fully automated algorithm also enhances the generalization of the calculation process, giving it high versatility.

[0084] The above are merely preferred embodiments of this application and do not limit the scope of the patent application. Any equivalent structural transformations made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A method for analyzing the propagation vector of brain waves, characterized in that, include: Obtain at least one brain imaging video; Phase transformation is performed on the pixel values ​​of each frame in the brain imaging video; Based on the phase transformation results, the wave propagation vector is calculated to obtain the wave velocity and direction of wave propagation within the brain.

2. The brain wave propagation vector analysis method as described in claim 1, characterized in that, The steps following "acquire at least one brain imaging video" also include the following steps: The pixel with a value of Nan is identified in each frame of the brain imaging video. The pixel is then imaged in the corresponding frame based on the surrounding pixels to supplement any missing pixels.

3. The brain wave propagation vector analysis method as described in claim 1 or 2, characterized in that, The following steps are included before the step "perform phase transformation on the pixel values ​​of the brain imaging video frame by frame": The pixel values ​​of the brain imaging video are processed frame by frame using a phase transformation filtering algorithm, and the non-zero pixel values ​​are transformed by domain transformation.

4. The brain wave propagation vector analysis method as described in claim 3, characterized in that, The phase change filtering algorithm includes: The discrete Fourier transform is used to convert the time-domain signal into the frequency domain, separating the amplitude and phase information; The adjustable cutoff frequency is set according to preset requirements, and low-pass filtering is achieved through a Butterworth filter. The filtered signal is centered to achieve zero mean and eliminate signal offset.

5. The brain wave propagation vector analysis method as described in claim 1, characterized in that, The phase transformation includes: The instantaneous phase information of pixel values ​​in the brain imaging video frame by frame was extracted using the Hilbert algorithm, and the pixel values ​​were transformed into the interval [-π, π] to obtain the phase domain.

6. The brain wave propagation vector analysis method as described in claim 5, characterized in that, The phase conversion result is subjected to phase unwinding processing to eliminate the 2π jump and restore the continuous phase distribution.

7. The brain wave propagation vector analysis method as described in claim 5, characterized in that, The phase transformation results are optimized by order-preserving regression to ensure the monotonically increasing phase characteristic.

8. The method for analyzing the propagation vector of intracranial waves as described in claims 5-7, characterized in that, The wave propagation vector calculation includes: Based on the phase conversion results, the phase, angular velocity, time, and velocity information of each pixel are obtained. The velocity of each pixel in the brain imaging video is calculated frame by frame to obtain the wave propagation vector velocity, which includes the wave propagation speed, magnitude, and direction.

9. The method for analyzing the propagation vector of intracranial waves as described in claim 8, characterized in that, Based on the vector velocity of the wave propagation, the phase situation within the entire frame of the brain imaging video is compared, and the origin region of the wave propagation is deduced based on the diffusion and dispersion process of the wave propagation.

10. The brain wave propagation vector analysis method as described in claim 9, characterized in that, Based on the vector velocity of wave propagation, a visual arrow vector diagram of velocity changes is obtained through optical flow rendering.

11. A device for analyzing the propagation vector of brain waves, characterized in that, include: The image acquisition unit is configured to acquire brain imaging video images to be processed; The processing unit is configured to invoke a target brain wave propagation vector analysis method to perform vector analysis on the brain imaging video image to be processed; wherein the brain wave propagation vector analysis method is the brain wave propagation vector analysis method according to any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, The computer instructions are used to cause the computer to execute the brain wave propagation vector analysis method according to any one of claims 1-10.

13. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the brain wave propagation vector analysis method according to any one of claims 1-10.

Images