Neuron data processing method and system and storage medium

Abstract

The invention provides a neuron data processing method, a neuron data processing system and a storage medium. The neuron data processing method comprises the following steps: acquiring first space coordinates of a plurality of pixel points on a neuron to establish a space model of the neuron; analyzing the spatial model to determine a first feature point about a neuron cell body, a second feature point about neuron bifurcation, and a third feature point about a neuron end; according to the first feature point, the second feature point and the third feature point, establishing a morphological model of the neuron; acquiring a second spatial coordinate of at least one brain region about the neuron; and performing data processing on the neurons and the at least one brain region according to the morphological model and the second space coordinates. By adopting the above configuration, the processing method can efficiently and accurately represent the brain structure and the brain network connection rule, provides an accurate navigation map for deep analysis of brain functions and brain diseases, and provides important reference for design of a novel brain-like intelligent system.

Description

Technical Field

[0001] The present invention relates to data analysis technology of neurons and brain regions, and in particular to a method for processing neuron data, a system for processing neuron data, and a computer-readable storage medium. Background Art

[0002] Better knowledge and understanding of the brain can not only help treat brain diseases, but also provide new ideas for the development of artificial intelligence. Neurons are one of the most basic components of the brain. The connections between them form a complex neural network, which constitutes the structural basis of brain function. Therefore, clarifying the complex projection patterns of neurons and their basic laws is of great significance for understanding the organizational structure and information processing mechanism of the brain.

[0003] The information exchange between different brain regions of the cerebral cortex depends on the long-range projection of neurons, and neurons with different projection patterns often participate in different brain functions. Therefore, studying the projection patterns of neurons and their basic laws is of great significance for analyzing the organizational structure and information processing mechanism of the brain. Previous studies have used group neuron tracing technology to conduct extensive studies on the whole-brain projection of mouse cortical neurons, and found that cortical projection neurons can be divided into telencephalic medial tract neurons, pyramidal tract neurons, and cortical thalamic neurons. However, recent studies have shown that among these traditional neuron types, there are more complex neuron subtypes with more sophisticated functional divisions, and neurons of different subtypes often have different cell body distributions, neuronal morphology, and projection patterns, but the group neuron connection map can no longer meet the current scientific research needs.

[0004] In order to overcome the above-mentioned defects of the existing technology, this field is in urgent need of a neuronal data processing technology that can perform high-precision analysis of the cell body position, projection structure and rules of neurons at the single neuron level, and systematically draw more detailed projection maps at the single neuron level of the brain, to help discover new neuronal subtypes and connection rules of brain networks, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and then more effectively understand the brain structure and brain network connection rules, provide accurate navigation maps for in-depth analysis of brain functions and brain diseases, and provide important references for the design of new brain-like intelligent systems. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or decisive elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a neuronal data processing technology, which can perform high-precision analysis on the cell body position, projection structure and regularity of neurons at the single neuron level, and systematically draw more detailed projection maps at the single neuron level of the brain, to help discover new neuronal subtypes and connection rules of brain networks, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and further more effectively understand the brain structure and brain network connection rules, providing an accurate navigation map for in-depth analysis of brain functions and brain diseases, and providing an important reference for the design of new brain-like intelligent systems.

[0007] Specifically, the method for processing the above-mentioned neuron data provided in accordance with the first aspect of the present invention includes the following steps: obtaining the first spatial coordinates of multiple pixel points on the neuron to establish a spatial model of the neuron; parsing the spatial model to determine a first feature point about the neuron cell body, a second feature point about the neuron bifurcation, and a third feature point about the neuron terminal; establishing a morphological model of the neuron based on the first feature point, the second feature point and the third feature point; obtaining the second spatial coordinates of at least one brain region of the neuron; and performing data processing on the neuron and the at least one brain region based on the morphological model and the second spatial coordinates.

[0008] Preferably, in one embodiment of the present invention, the step of obtaining the first spatial coordinate and / or the second spatial coordinate includes: determining the spatial coordinate range occupied by multiple brain regions and multiple neurons in the brain based on the anatomical data of the brain; establishing a spatial model of the brain according to the spatial coordinate range occupied by multiple brain regions and multiple neurons in the brain; generating an information file recording the index, type and spatial coordinates of each brain region and each neuron in the brain based on the spatial model of the brain; and reading the first information file recording the index, type and spatial coordinates of the neuron to determine the first spatial coordinate, and / or reading the second information file recording the index, type and spatial coordinates of the brain region to determine the second spatial coordinate.

[0009] Preferably, in one embodiment of the present invention, the step of establishing the morphological model of the neuron based on the first feature point, the second feature point and the third feature point includes: determining the first spatial pose of the neuron cell body based on the first feature point indicating the neuron cell body; determining the second spatial pose of at least one axon or dendrite of the neuron with at least one second feature point as the starting point and with at least one corresponding third feature point as the end point; and determining the morphological model of the neuron based on the first spatial pose of the neuron cell body and the second spatial pose of each of the axons and each of the dendrites.

[0010] Preferably, in one embodiment of the present invention, the step of establishing the morphological model of the neuron based on the first feature point, the second feature point and the third feature point also includes: superimposing a plurality of concentric circles with different diameters on the neuron to calculate the number of intersections between the morphological model of the neuron and each circle; determining the branching pattern of each of the dendrites and each of the axons based on the number of intersections between the morphological model of the neuron and each circle; and quantitatively analyzing the branching pattern of each of the dendrites and each of the axons, and determining the morphological characteristics of the neuron based on the branch number information, endpoint number information, pixel number information and / or neuron type information of the neuron.

[0011] Preferably, in one embodiment of the present invention, the step of performing data processing on the neuron and the at least one brain region based on the morphological model and the second spatial coordinates includes: determining the brain region to which the corresponding neuron belongs based on the first spatial pose of the neuron cell body and the second spatial coordinates of each of the brain regions; determining the upstream brain region of the corresponding neuron based on the second spatial pose of the dendrite and the second spatial coordinates of each of the brain regions; and / or determining the downstream brain region of the corresponding neuron based on the second spatial pose of the axon and the second spatial coordinates of each of the brain regions.

[0012] Preferably, in one embodiment of the present invention, the step of performing data processing on the neuron and the at least one brain region according to the morphological model and the second spatial coordinates also includes: determining the projection information of the corresponding neuron in the brain region to which it belongs, the upstream brain region and the downstream brain region according to the first spatial posture, the second spatial posture, and the second spatial coordinates of each of the brain regions, wherein the projection information includes projection length, axon length, dendrite length, axon order, dendrite order, number of axon bifurcations, number of dendrite bifurcations, and number of presynapses; and performing projection weight simulation of the neuron and each of the brain regions according to the projection information to determine the projection relationship between the neuron and each of the brain regions.

[0013] Preferably, in one embodiment of the present invention, the step of simulating the projection weights of the neurons and each of the brain regions based on the projection information to determine the projection relationship between the neurons and each of the brain regions includes: performing cluster analysis on the morphological features of a plurality of the neurons to determine at least one mutually related neuronal cluster; and performing projection weight simulation between the neuronal cluster and each of the brain regions based on the projection information of each of the neurons in the neuronal cluster to determine the projection relationship between the neuronal cluster and each of the brain regions.

[0014] Preferably, in one embodiment of the present invention, each of the neurons in the neuron cluster is distributed in a first brain region, and the step of performing data processing on the neurons and the at least one brain region based on the morphological model and the second spatial coordinates also includes: performing projection weight simulation of the first brain region and the remaining at least one second brain region based on the projection information of the plurality of neurons contained in the first brain region to determine the projection relationship between the first brain region and each of the second brain regions.

[0015] Preferably, in one embodiment of the present invention, the step of performing data processing on the neuron and the at least one brain region according to the morphological model and the second spatial coordinates also includes: reading the inclusion relationship and / or upstream and downstream relationship between at least one brain region and at least one neuron from an information file about the brain; judging whether the neuron cell body and / or neuron terminal of the neuron is located in the corresponding brain region, upstream brain region and / or downstream brain region according to the morphological model and the second spatial coordinates.

[0016] Preferably, in one embodiment of the present invention, the following steps are also included: rendering and displaying a simulated image of the brain according to the spatial coordinate ranges occupied by multiple brain regions and the spatial coordinates occupied by multiple neurons in the brain; and in response to any of the brain regions or any of the neurons obtaining user focus, rendering and displaying an updated simulated image according to the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions and between the target brain region and the remaining neurons.

[0017] Preferably, in one embodiment of the present invention, in response to any of the brain regions or any of the neurons obtaining user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain region or target neuron obtaining the user focus and the remaining brain regions and between the target brain region and the remaining neurons includes: in response to any of the brain regions obtaining user focus, rendering and displaying the target brain region obtaining the user focus and / or at least one target neuron contained therein in a first color and / or a first visibility; and / or in response to any of the brain regions obtaining user focus, rendering and displaying related brain regions and / or related neurons that have a projection relationship with the target brain region in a second color and / or a second visibility; and / or in response to any of the brain regions obtaining user focus, hiding irrelevant brain regions and / or irrelevant neurons that have no projection relationship with the target brain region.

[0018] Preferably, in one embodiment of the present invention, in response to any of the brain areas or any of the neurons obtaining user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain area or target neuron obtaining the user focus and the remaining brain areas and between the target brain area and the remaining neurons includes: in response to any of the neurons obtaining user focus, rendering and displaying the target neuron obtaining the user focus and / or the brain area to which the target neuron belongs in a third color and / or a third visibility; and / or in response to any of the neurons obtaining user focus, rendering and displaying a related brain area that has a projection relationship with the target neuron in a fourth color and / or a fourth visibility; and / or in response to any of the neurons obtaining user focus, hiding an irrelevant brain area that has no projection relationship with the target neuron.

[0019] Preferably, in one embodiment of the present invention, in response to any one of the brain regions or any one of the neurons obtaining user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain region or target neuron obtaining user focus and the remaining brain regions and between the target brain region and the remaining neurons also includes: determining the color of rendering and displaying the relevant brain region according to the strength of the projection relationship between the target brain region obtaining user focus and its related brain regions.

[0020] In addition, the neuron data processing system provided according to the second aspect of the present invention includes a memory and a processor. The memory stores computer instructions. The processor is connected to the memory and is configured to execute the computer instructions stored in the memory to implement the neuron data processing method provided in any one of the above embodiments.

[0021] In addition, the computer-readable storage medium provided according to the third aspect of the present invention stores computer instructions. When the computer instructions are executed by the processor, the method for processing neuron data provided by any one of the above embodiments is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0023] Figure 1 A flowchart of a method for processing neuron data provided according to some embodiments of the present invention is shown.

[0024] Figure 2 A schematic diagram of brain region visualization of a neuron data processing system provided according to some embodiments of the present invention is shown.

[0025] Figure 3 A neuron visualization schematic diagram of a neuron data processing system provided according to some embodiments of the present invention is shown.

[0026] Figures 4A to 4B A schematic visualization diagram of a neuron cluster of a neuron data processing system provided according to some embodiments of the present invention is shown.

[0027] Figures 5A-5B A schematic diagram showing the visualization of the projection relationship between target neurons and other brain regions of a neuron data processing system provided according to some embodiments of the present invention. DETAILED DESCRIPTION

[0028] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description.

[0029] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] In addition, the terms "upper", "lower", "left", "right", "top", "bottom", "horizontal" and "vertical" used in the following description should be understood as the directions shown in the paragraph and the related drawings. Such relative terms are only used for the convenience of description and do not mean that the device described therein must be manufactured or operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0031] It is understood that although the terms "first", "second", "third", etc. may be used herein to describe various components, regions, layers and / or parts, these components, regions, layers and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers and / or parts. Therefore, the first component, region, layer and / or part discussed below may be referred to as a second component, region, layer and / or part without departing from some embodiments of the present invention.

[0032] As mentioned above, the information exchange between different brain regions of the cerebral cortex depends on the long-range projection of neurons, and neurons with different projection patterns often participate in different brain functions. Therefore, studying the projection patterns of neurons and their basic laws is of great significance for analyzing the organizational structure and information processing mechanism of the brain. Previous studies have used group neuron tracing technology to conduct extensive studies on the whole-brain projection of mouse cortical neurons, and found that cortical projection neurons can be divided into telencephalic medial tract neurons, pyramidal tract neurons, and cortical thalamic neurons. However, recent studies have shown that among these traditional neuron types, there are more complex neuron subtypes with more refined functional divisions, and neurons of different subtypes often have different cell body distributions, neuronal morphology, and projection patterns, but the group neuron connection map can no longer meet the current scientific research needs.

[0033] In order to overcome the above-mentioned defects of the prior art, the present invention provides a neuronal data processing technology, which can perform high-precision analysis on the cell body position, projection structure and rules of neurons at the single neuron level, and systematically draw more detailed projection maps at the single neuron level of the brain, to help discover new neuronal subtypes and connection rules of brain networks, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and then more effectively understand the brain structure and brain network connection rules, provide accurate navigation maps for in-depth analysis of brain functions and brain diseases, and provide important reference for the design of new brain-like intelligent systems.

[0034] In some non-limiting embodiments, the above-mentioned neuron data processing method provided in the first aspect of the present invention can be implemented via the above-mentioned neuron data processing system provided in the second aspect of the present invention. Specifically, the neuron data processing system can be configured with a memory and a processor. The memory includes but is not limited to the above-mentioned computer-readable storage medium provided in the third aspect of the present invention, on which computer instructions are stored. The processor is connected to the memory and is configured to execute the computer instructions stored on the memory to implement the neuron data processing method provided in the first aspect of the present invention.

[0035] Please refer to Figure 1 , Figure 1 A flowchart of a method for processing neuron data provided according to some embodiments of the present invention is shown.

[0036] like Figure 1 As shown, the neuron data processing system first obtains the first spatial coordinates of multiple pixel points on the neuron to establish a spatial model of the neuron.

[0037] Here, the processing system of neuron data determines the spatial coordinate ranges occupied by multiple brain regions and multiple neurons in the brain based on the anatomical data of the brain, and establishes a spatial model of the brain according to the spatial coordinate ranges occupied by multiple brain regions and multiple neurons in the brain. Based on the spatial model of the brain, an information file is generated that records the index, type and spatial coordinates of each brain region and each neuron in the brain. The file can be a file configured using a json file. In a non-limiting embodiment, the file is named info.json. Further, the processing system of neuron data can directly read the relationship between neurons and brain regions from the information file (e.g., info.json file). This information file records the relationship between neurons and related brain regions, such as the brain region to which the neurons belong (source region), input region (input_region), and output region (output_region). It also records the relationship between brain regions and related neurons, such as owned neurons (ownedneuron), projecting region (projecting region), and receiving region (recipient region). It also records the relationship between brain regions, such as upstream region and downstream region (downstream region), and the relationship between neurons, such as upstream neuron and downstream neuron.

[0038] Furthermore, the neuron data processing system can also read a first information file that records the index, type and spatial coordinates of the neuron to determine the first spatial coordinates. The first information file can be a neuron file with the suffix *.swc. The neuron file can also record basic information about the neuron. The basic information about the neuron includes the number of branches, the number of endpoints, the number of points, the neuron type, etc.

[0039] Afterwards, the analysis module of the neuron data processing system is started to analyze the established neuron spatial model to calculate the neuron morphological parameters to determine the first characteristic point of the neuron soma, the second characteristic point about the neuron bifurcation, and the third characteristic point about the neuron terminal. Based on the first characteristic point, the second characteristic point and the third characteristic point, the neuron morphological model is established.

[0040] Specifically, based on the first characteristic point indicating the neuron cell body, the first spatial pose of the neuron cell body is determined, and then with at least one second characteristic point as the starting point and at least one corresponding third characteristic point as the end point, the second spatial pose of at least one axon or dendrite of the neuron is determined, and based on the first spatial pose of the neuron cell body and the second spatial pose of each axon and each dendrite, the morphological model of the neuron is determined.

[0041] In addition, to establish a morphological model of a neuron, multiple concentric circles with different diameters can be superimposed on the neuron to calculate the number of intersections between the morphological model of the neuron and each circle, and then the branching pattern of each dendrite and each axon can be determined based on the number of intersections between the morphological model of the neuron and each circle. The branching pattern of each dendrite and each axon is quantitatively analyzed, and the morphological characteristics of the neuron are determined based on the branch number information, endpoint number information, pixel number information and / or neuron type information of the neuron.

[0042] Please continue to refer to Figure 1 After the morphological model is established, the second spatial coordinates of at least one brain region related to the neuron are obtained, and data processing on the neuron and at least one brain region is performed based on the morphological model and the second spatial coordinates.

[0043] Here, the neuron data processing system can read a second information file recording the index, type and spatial coordinates of the brain region to determine the second spatial coordinates of at least one brain region of the neuron, and the second information file is a file configured as a json file.

[0044] Afterwards, the neuron data processing system can determine the brain region to which the corresponding neuron belongs based on the first spatial position of the neuron cell body and the second spatial coordinates of each brain region. Specifically, in response to the distribution of the neuron cell body in a brain region, the brain region is determined to be the brain region to which the corresponding neuron belongs. The upstream brain region of the corresponding neuron can also be determined based on the second spatial position of the dendrite and the second spatial coordinates of each brain region. Specifically, in response to the spatial interaction between the dendrite and a brain region, the brain region is determined to be the upstream brain region of the corresponding neuron, and the corresponding neuron receives information from the brain region. In addition, the downstream brain region of the corresponding neuron can also be determined based on the second spatial position of the axon and the second spatial coordinates of each brain region. Specifically, in response to the spatial interaction between the axon and a brain region, the brain region is determined to be the downstream brain region of the corresponding neuron, and the corresponding neuron outputs information to the brain region.

[0045] Specifically, according to the first spatial posture, the second spatial posture, and the second spatial coordinates of each brain region, the projection information of the corresponding neuron in the brain region to which it belongs, the upstream brain region and the downstream brain region is determined respectively, wherein the projection information includes projection length, axon length, dendrite length, axon order, dendrite order, number of axon bifurcations, number of dendrite bifurcations, and presynaptic number (Presynaptic Site Count). Here, projection length includes total projection length (Projection Length), ipsilateral projection length (Ipsilateral Projection Length), contralateral projection length (ContralateralProjection Length), ipsilateral / contralateral projection length (lpsi- / contralateral Projection Length); axon length includes total axon length (Axonal Length), ipsilateral axon total length (Ipsilateral Axonal Length), contralateral axon total length (Contralateral Axonal Length); dendrite length includes total dendrite length (Dendritic Length), ipsilateral dendrite total length (Ipsilateral Dendritic Length), contralateral dendrite total length (Contralateral Dendritic Length); axon order includes minimum axon order (Min Axonal Branch Order), ipsilateral minimum axon order (Ipsilateral Min Axonal Branch Order), contralateral minimum axon order (Contralateral Min Axonal Branch Order), maximum axon order (Max Axonal Branch Order), ipsilateral maximum axon order (Ipsilateral Max Axonal Branch Order), Contralateral Max Axonal Branch Order;Dendrite order includes the minimum dendrite order (MinDendritic Branch Order), the minimum ipsilateral dendrite order (Ipsilateral Min Dendritic Branch Order), the minimum contralateral dendrite order (Contralateral Min Dendritic Branch Order), the maximum dendrite order (Max Dendritic Branch Order), the maximum ipsilateral dendrite order (Ipsilateral Max DendriticBranch Order), and the maximum contralateral dendrite order (Contralateral Max Dendritic Branch Order); the number of axonal branches includes the total axonal branch number (Axonal Branch Count), the ipsilateral axonal branch number (IpsilateralAxonal Branch Count), and the contralateral axonal branch number (ContralateralAxonal Branch Count); the number of dendritic branches includes the total dendritic branch number (Dendritic Branch Count), the ipsilateral dendritic branch number (Ipsilateral Dendritic Branch Count), and the contralateral dendritic branch number (Contralateral DendriticBranch Count). ;

[0046] Afterwards, the projection weights of neurons and each brain region are simulated according to the projection information to determine the projection relationship between neurons and each brain region.

[0047] Furthermore, cluster analysis is performed on the morphological features of multiple neurons to determine at least one mutually related neuron cluster; and projection weight simulation between the neuron cluster and each brain region is performed based on the projection information of each neuron in the neuron cluster to determine the projection relationship between the neuron cluster and each brain region. Furthermore, when each neuron in the above-mentioned neuron cluster is distributed in the first brain region, the data processing of the neuron data processing system can also perform projection weight simulation between the first brain region and at least one remaining second brain region based on the projection information of multiple neurons contained in the first brain region to determine the projection relationship between the first brain region and each second brain region.

[0048] In addition, the data processing about neurons and at least one brain region also includes reading the inclusion relationship and / or upstream and downstream relationship between at least one brain region and at least one neuron from the information file about the brain, and then judging whether the neuron cell body and / or neuron terminal of the neuron is located in the corresponding brain region, upstream brain region and / or downstream brain region based on the morphological model and the second spatial coordinate.

[0049] Also, please refer to Figure 2 , Figure 3 and Figures 4A to 4B , Figure 2 A schematic diagram of brain region visualization of a neuron data processing system provided according to some embodiments of the present invention is shown. Figure 3 A neuron visualization schematic diagram of a neuron data processing system provided according to some embodiments of the present invention is shown. Figures 4A to 4B A schematic visualization diagram of a neuron cluster of a neuron data processing system provided according to some embodiments of the present invention is shown.

[0050] The neuron data processing system can also render and display a simulated image of the brain according to the spatial coordinate ranges occupied by multiple brain regions and the spatial coordinates occupied by multiple neurons, such as Figure 2 and Figure 3 As shown. Figure 2 In the embodiment shown, parameters such as the capacity and color of the brain region can be set. Figure 3 In the illustrated embodiment, the method of neuron rendering and display can be increased by setting "Add Coloring Attribute", and the neurons can be rendered and displayed according to the position and / or morphological model and / or structural features of the neurons. For example, "Set Color by NeuronStructure" can be selected to set the color of a certain structure of a certain neuron or to set the color of a certain structure of all neurons. "Mirror Left to Right" and "Mirror Right to Left" can also be set to mirror the neurons from the left (right) to the right (left).

[0051] In addition, in Figure 4A In the embodiment shown, neurons can also be divided into multiple categories by analyzing the morphological characteristics of neurons. The above neuron file can also record a cluster information table, a cluster node information table and a corresponding neuron name information table, and these information tables can be csv files. According to the recorded cluster information table, cluster node information table and corresponding neuron name information table, the user sets the number of clusters that the neuron is required to be divided into, so as to export the visualization results, such as Figure 4B shown.

[0052] In response to any brain region or any neuron obtaining user focus (for example, the mouse hovers over its image, or clicks on the image), an updated simulation image is rendered and displayed based on the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions, and between the target brain region and the remaining neurons. For example, in a neuron data processing system provided in a non-limiting embodiment, the "Set color by projection weight" option can be selected, so that in response to any brain region obtaining user focus, the neuron data processing system renders and colors according to the projection weight between the target brain region and the remaining brain regions.

[0053] Furthermore, in response to any brain region obtaining user focus, the target brain region obtaining user focus and / or at least one target neuron contained therein are rendered and displayed in a first color and / or a first visibility, and in response to any brain region obtaining user focus, related brain regions and / or related neurons having a projection relationship with the target brain region may be rendered and displayed in a second color and / or a second visibility, and optionally, in response to any brain region obtaining user focus, irrelevant brain regions and / or irrelevant neurons having no projection relationship with the target brain region may be hidden.

[0054] In a non-limiting embodiment, the relevant neurons that have a projection relationship with the target brain area include but are not limited to neurons projecting to the target brain area, neurons whose cell bodies are in the target brain area, and neurons that receive information from the target brain area. For example, in the neuron data processing system, the target brain area is selected by a click operation, and in response to the target brain area obtaining user focus, "Show Only Region" is selected in the pop-up menu, and then "owned Neurons" is selected, so that the relevant neurons belonging to the target brain area are rendered and displayed in a second color, and irrelevant brain areas and irrelevant neurons that have no projection relationship with the target brain area are hidden.

[0055] In addition, in response to any neuron obtaining user focus, the target neuron obtaining user focus and / or the brain area to which the target neuron belongs are rendered and displayed in a third color and / or a third visibility, and in response to any neuron obtaining user focus, the related brain area that has a projection relationship with the target neuron can be rendered and displayed in a fourth color and / or a fourth visibility. Optionally, in response to any neuron obtaining user focus, irrelevant brain areas that have no projection relationship with the target neuron are hidden.

[0056] Please refer to Figures 5A-5B , Figures 5A-5B A schematic diagram showing the visualization of the projection relationship between target neurons and other brain regions of a neuron data processing system provided according to some embodiments of the present invention.

[0057] like Figure 5A As shown, it includes the target neuron and the complete brain region. In the neuron data processing system, the target neuron is selected by clicking, and in response to the target neuron obtaining the user focus, "Show Only Neuron" is selected in the pop-up menu, and then "Projecting Regions" is selected, so that the projected brain region belonging to the target neuron is rendered and displayed in the second color, and irrelevant brain regions and irrelevant neurons that have no projection relationship with the target neuron are hidden, and a visualization schematic diagram is obtained as shown below. Figure 5B shown.

[0058] In addition, the neuronal data processing system can also determine the color of the relevant brain area to be rendered and displayed based on the strength of the projection relationship between the target brain area that obtains the user's focus and its related brain areas.

[0059] For example, in a neuron data processing system, when a user selects "projecting neuron proportion" in "Set Color by Projection Weight", when any brain region obtains the user's focus, the brain region is rendered according to the proportion of the number of neurons projected from the brain region to other brain regions.

[0060] In summary, the neuron data processing method provided by the present invention digitizes the anatomical data of the brain of a real biological sample, and accordingly establishes a spatial model of the brain and the spatial models of each brain region and each neuron therein, and locates the neurons and brain regions therein in the same reference physical space and mines the projected information, thereby providing a complete set of interactive analysis and visualization solutions for creating an inter-brain connection network. The neuron data processing technology provided by the present invention is real-time, interactive, customizable, intuitive, easy to operate, and scientific research-guiding, and is suitable for projection analysis and visualization of brain atlas data based on neuron morphology of various animals.

[0061] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0062] Those skilled in the art will appreciate that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips cited throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0063] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0064] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0065] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0066] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0067] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing neuron data, characterized in that: The following steps are involved: Acquire first spatial coordinates of a plurality of pixel points on a neuron to establish a spatial model of the neuron; parsing the spatial model to determine a first characteristic point about a neuron cell body, a second characteristic point about a neuron bifurcation, and a third characteristic point about a neuron terminal; Establishing a morphological model of the neuron according to the first feature point, the second feature point and the third feature point; Acquiring second spatial coordinates of at least one brain region related to the neuron; as well as Data processing on the neurons and the at least one brain region is performed according to the morphological model and the second spatial coordinates.

2. The processing method according to claim 1, characterized in that The step of acquiring the first space coordinate and / or the second space coordinate comprises: Based on the anatomical data of the brain, determine the spatial coordinate ranges occupied by multiple brain regions and multiple neurons in the brain; Establishing a spatial model of the brain according to the spatial coordinate ranges occupied by the multiple brain regions and multiple neurons in the brain; Based on the spatial model of the brain, generating an information file recording the index, type and spatial coordinates of each brain region and each neuron in the brain; and Read a first information file recording the index, type and spatial coordinates of the neuron to determine the first spatial coordinates, and / or read a second information file recording the index, type and spatial coordinates of the brain region to determine the second spatial coordinates.

3. The processing method according to claim 1, characterized in that: The step of establishing the morphological model of the neuron according to the first feature point, the second feature point and the third feature point comprises: Determining a first spatial position of the neuron cell body according to a first feature point indicating the neuron cell body; Determine a second spatial position of at least one axon or dendrite of the neuron with at least one second feature point as a starting point and with at least one corresponding third feature point as an end point; and The morphological model of the neuron is determined according to the first spatial position of the neuron cell body and the second spatial position of each axon and each dendrite.

4. The processing method according to claim 3, characterized in that: The step of establishing the morphological model of the neuron according to the first feature point, the second feature point and the third feature point further comprises: superimposing a plurality of concentric circles with different diameters on the neuron to calculate the number of intersections between the morphological model of the neuron and each circle; Determining the branching pattern of each of the dendrites and each of the axons according to the number of intersections between the morphological model of the neuron and each circle; and The branching patterns of each of the dendrites and each of the axons are quantitatively analyzed, and the morphological characteristics of the neuron are determined based on the branch number information, endpoint number information, pixel number information and / or neuron type information of the neuron.

5. The processing method according to claim 3, characterized in that: The step of performing data processing on the neurons and the at least one brain region according to the morphological model and the second spatial coordinates comprises: Determining the brain region to which the corresponding neuron belongs according to the first spatial position of the neuron cell body and the second spatial coordinates of each of the brain regions; Determining the upstream brain region of the corresponding neuron according to the second spatial position of the dendrite and the second spatial coordinates of each of the brain regions; and / or According to the second spatial position of the axon and the second spatial coordinates of each of the brain regions, the downstream brain region of the corresponding neuron is determined.

6. The processing method according to claim 5, characterized in that: The step of performing data processing on the neurons and the at least one brain region according to the morphological model and the second spatial coordinates further comprises: According to the first spatial position, the second spatial position, and the second spatial coordinates of each of the brain regions, respectively determine the projection information of the corresponding neuron in the brain region to which it belongs, the upstream brain region, and the downstream brain region, wherein the projection information includes projection length, axon length, dendrite length, axon order, dendrite order, axon bifurcation number, dendrite bifurcation number, and presynaptic number; and A projection weight simulation between the neuron and each of the brain regions is performed according to the projection information to determine the projection relationship between the neuron and each of the brain regions.

7. The processing method according to claim 6, characterized in that: The step of simulating the projection weights of the neurons and the brain regions according to the projection information to determine the projection relationship between the neurons and the brain regions comprises: Performing cluster analysis on the morphological features of the plurality of neurons to determine at least one mutually related neuronal cluster; and According to the projection information of each of the neurons in the neuron cluster, a projection weight simulation between the neuron cluster and each of the brain regions is performed to determine the projection relationship between the neuron cluster and each of the brain regions.

8. The processing method according to claim 7, characterized in that: Each of the neurons in the neuron cluster is distributed in a first brain region, and the step of performing data processing on the neurons and the at least one brain region according to the morphological model and the second spatial coordinates further includes: According to the projection information of the plurality of neurons included in the first brain region, a projection weight simulation of the first brain region and at least one remaining second brain region is performed to determine the projection relationship between the first brain region and each of the second brain regions.

9. The processing method according to claim 5, characterized in that: The step of performing data processing on the neurons and the at least one brain region according to the morphological model and the second spatial coordinates further comprises: Reading the inclusion relationship and / or upstream-downstream relationship between at least one brain region and at least one neuron from the information file about the brain; According to the morphological model and the second spatial coordinates, it is determined whether the neuron cell body and / or neuron terminal of the neuron is located in the corresponding brain region, upstream brain region and / or downstream brain region.

10. The processing method according to claim 1, characterized in that: The following steps are also included: Rendering and displaying a simulated image of the brain according to the spatial coordinate ranges occupied by multiple brain regions and the spatial coordinates occupied by multiple neurons in the brain; as well as In response to any of the brain regions or any of the neurons obtaining user focus, an updated simulation image is rendered and displayed based on the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions and between the target brain region and the remaining neurons.

11. The processing method according to claim 10, characterized in that: In response to any of the brain regions or any of the neurons obtaining the user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions and between the target brain region and the remaining neurons comprises: In response to any of the brain regions obtaining user focus, rendering and displaying the target brain region obtaining the user focus and / or at least one target neuron contained therein in a first color and / or a first visibility; and / or In response to any of the brain regions obtaining user focus, rendering and displaying the relevant brain regions and / or relevant neurons that have a projection relationship with the target brain region in a second color and / or a second visibility; and / or In response to any of the brain regions obtaining user focus, irrelevant brain regions and / or irrelevant neurons that have no projection relationship with the target brain region are hidden.

12. The processing method according to claim 10, characterized in that: In response to any of the brain regions or any of the neurons obtaining the user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions and between the target brain region and the remaining neurons comprises: In response to any of the neurons obtaining user focus, rendering and displaying the target neuron obtaining the user focus and / or the brain region to which the target neuron belongs in a third color and / or a third visibility; and / or In response to any of the neurons obtaining user focus, rendering and displaying a related brain region having a projection relationship with the target neuron in a fourth color and / or a fourth visibility; and / or In response to any of the neurons obtaining user focus, irrelevant brain areas that have no projection relationship with the target neuron are hidden.

13. The processing method according to claim 11 or 12, characterized in that: In response to any of the brain regions or any of the neurons obtaining the user focus, the step of rendering and displaying an updated simulation image according to the projection relationship between the target brain region or target neuron that obtains the user focus and the remaining brain regions and between the target brain region and the remaining neurons also includes: The color of the relevant brain area to be rendered and displayed is determined according to the strength of the projection relationship between the target brain area focused by the user and its relevant brain area.

14. A neuron data processing system, characterized in that: include: a memory having computer instructions stored thereon; as well as A processor is connected to the memory and is configured to execute computer instructions stored in the memory to implement the neuron data processing method according to any one of claims 1 to 13.

15. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the neuron data processing method according to any one of claims 1 to 13 is implemented.

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