WO2024050920A1 - Method for testing neural pathway activation, apparatus, device, and storage medium - Google Patents

Abstract

The present application discloses a method for testing neural pathway activation, an apparatus, a device, and a storage medium. The method comprises: acquiring pre-stimulation field potential data and post-stimulation field potential data corresponding to a neural pathway to be tested of an experimental subject; on the basis of the pre-stimulation field potential data and the post-stimulation field potential data, determining connection strength change data of the neural pathway to be tested; on the basis of video data to be tested of the experiment subject, determining behavior change data of the experiment subject; and on the basis of the connection strength change data and the behavior change data, determining the activation state of said neural pathway.

Description

Activation detection method, device, equipment and storage medium of neural pathways

This application claims priority to the Chinese patent application with application number 202211094734.1, which was submitted to the China Patent Office on September 8, 2022. The entire content of this application is incorporated into this application by reference.

Technical field

This application relates to the technical field of brain science, for example, to a neural pathway activation detection method, device, equipment and storage medium.

Background technique

The nervous system of the brain is an extremely complex, efficient and precise system, and its brain mechanism of perception and cognition has always been a hot issue in brain science research. There is a close connection between the behavior of animals and the activation of related neural pathways. When conducting experimental research, experimental subjects are usually given certain stimuli to make the experimental subjects produce behaviors corresponding to the stimuli, and changes in their behavior serve as relevant neural pathways. The basis for judging whether it is activated.

However, changes in animal behavior may be caused by stimulation, or they may be spontaneous behaviors unrelated to stimulation. Moreover, some behaviors of experimental subjects cannot uniquely calibrate a neural pathway. For example, running behavior may correspond to neural pathways related to fear. pathways, which may also correspond to neural pathways related to anxiety. Therefore, relying solely on changes in behavior as a basis for judging whether relevant neural pathways are activated will have a certain misjudgment rate, making the research results on brain function less credible.

Contents of the invention

Embodiments of the present application provide a neural pathway activation detection method, device, equipment and storage medium to solve the problem of high misjudgment rate in neural pathway activation detection methods that rely solely on behavioral data, and improve the understanding of brain function. The credibility of the research results.

According to an embodiment of the present application, a method for detecting activation of neural pathways is provided. The method includes: obtaining pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject; based on the pre-stimulation field potential The data and the post-stimulation field potential data are used to determine the connection strength change data of the neural pathway to be detected; based on the video data to be detected of the experimental subject, the behavioral change data of the experimental subject is determined; based on the connection strength change data and the behavioral change data to determine the activation state of the neural pathway to be detected.

According to another embodiment of the present application, a neural pathway activation detection device is provided. The device includes: a field potential data acquisition module configured to acquire pre-stimulation field potential data and post-stimulation field potential corresponding to the to-be-detected neural pathway of the experimental subject. data; the connection strength change data determination module is configured to determine the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data; the behavior change data determination module is configured to determine based on the pre-stimulation field potential data and the post-stimulation field potential data. The video data of the experimental subject to be detected determines the behavioral change data of the experimental subject; the activation state determination module is configured to determine the activation of the neural pathway to be detected based on the connection strength change data and the behavioral change data. state.

According to another embodiment of the present application, an electronic device is provided. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores information that can be processed by the at least one processor. A computer program executed by a processor. The computer program is executed by the at least one processor, so that the at least one processor can execute the neural pathway activation detection method described in any embodiment of the present application.

According to another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement any of the embodiments of the present application when executed by a processor. Activation detection method of neural pathways.

Description of the drawings

The drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without exerting creative efforts, Under the premise, other drawings can also be obtained based on these drawings.

Figure 1 is a flow chart of a neural pathway activation detection method provided in Embodiment 1 of the present application;

Figure 2 is a flow chart of a neural pathway activation detection method provided in Embodiment 2 of the present application;

Figure 3 is a schematic diagram of the posture feature points of an experimental object provided in Embodiment 2 of the present application;

Figure 4 is a flow chart of an example of a neural pathway activation detection method provided in Embodiment 2 of the present application;

Figure 5 is a schematic structural diagram of a neural pathway activation detection device provided in Embodiment 3 of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of this application.

It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "include" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

Embodiment 1

Figure 1 is a flow chart of a neural pathway activation detection method provided in Embodiment 1 of the present application. This embodiment can be applied to detecting the activation status of neural pathways in the nervous system. The method can be performed by an activation detection device for neural pathways. The activation detection device for neural pathways can be implemented in the form of hardware and/or software. , the neural pathway activation detection device can be configured in the terminal device. As shown in Figure 1, the method includes the following steps.

S110. Obtain the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject.

For example, the neural pathways to be detected can be the midbrain ventral tegmental area-nucleus accumbens-prefrontal cortex, superior colliculus-thalamus-pulvinar-lateral amygdala, hippocampus-mammary body-anterior thalamic nucleus-cingulate gyrus, etc. There are no restrictions on the neural pathways to be detected, and users can customize settings according to actual needs.

The pre-stimulation field potential data is the field potential data of the neural pathway to be detected in the pre-stimulation period, and the post-stimulation field potential data is the field potential data of the neural pathway to be detected in the post-stimulation period. Field potential data can be collected through implanted electrodes or non-implanted electrodes. Field potential data are used to characterize the potential changes produced by target nuclei in the neural pathways to be detected.

The pre-stimulation time period may be determined based on the stimulation time point and the first duration, and the post-stimulation time period may be determined based on the stimulation time point and the second duration. The first duration and the second duration may be the same or different. For example, the first duration is 3 minutes and the second duration is 5 minutes. The values of the first duration and the second duration are not limited here.

In an optional embodiment, when a stimulation signal is detected, a marker is used to generate a marking signal to record the stimulation time point. This setting avoids time errors caused by artificial observation and recording of stimulation time points, thereby improving the accuracy of recorded stimulation time points.

The stimulation given to the experimental subject can be physical stimulation or drug stimulation. For example, the physical stimulation can be electric shock, sound or light signal, etc. There is no limit to the type of stimulation here, and users can customize settings based on the correlation between the neural pathway to be detected and the stimulation.

S120. Based on the pre-stimulation field potential data and the post-stimulation field potential data, determine the connection strength change data of the neural pathway to be detected.

The neural pathway to be detected contains at least one pair of nerve nuclei. For example, assuming that the neural pathway to be detected is the superior colliculus - pulvinar thalamus - lateral amygdala, the neural pathway to be detected corresponds to at least one of the superior colliculus - pulvinar thalamus - lateral amygdala, and the superior colliculus - lateral amygdala Team pair. The connection strength change data corresponding to the neural pathway to be detected includes connection strength change data corresponding to at least one pair of nerve nuclei.

In an optional embodiment, when the neural pathway to be detected contains a pair of nerve nuclei, the pre-stimulation field potential data includes the first nucleus and the second nucleus respectively corresponding to the pair of nerve nuclei in the neural pathway to be detected. The first pre-stimulation field potential data and the second pre-stimulation field potential data, and the post-stimulation field potential data include the first post-stimulation field potential data and the second post-stimulation field potential data corresponding to the first and second nuclei in the nerve nucleus pair. The post-stimulation field potential data is used to determine the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data, including: based on the first pre-stimulation field potential data and the second pre-stimulation field potential data, determine The pre-stimulation connection strength corresponding to the nerve nuclei pair; based on the first post-stimulation field potential data and the second post-stimulation field potential data, determine the post-stimulation connection strength corresponding to the nerve nuclei pair; based on the pre-stimulation connection strength and post-stimulation connection strength , determine the connection strength change data of the nerve nucleus pairs in the neural pathway to be detected.

This embodiment illustrates that the neural pathway to be detected includes a pair of nerve nuclei. It is easy to understand that the connection strength change data of other pairs of nerve nuclei corresponding to the neural pathway to be detected can also be determined by the above method.

In an optional embodiment, the parameter type of the pre-stimulus connection strength or the post-stimulation connection strength is a Granger causality value.

The Granger causality test is a statistical method for hypothesis testing that can be used to test whether the field potential data before the first stimulus is the cause of the field potential data before the second stimulus or whether the field potential data before the second stimulus is the cause of the field potential data before the first stimulus. field potential data. The results of Granger causality analysis include three elements: connection direction, connection strength and connection significance between two nuclei.

For example, the Multivariate Granger Causality (MVGC) toolbox in the Matlab software can be used to determine at least one neural nucleus based on the pre-stimulus field potential data corresponding to multiple nuclei. The pre-stimulation Granger Causality (GC) values corresponding to the cluster pairs are determined. Based on the post-stimulation field potential data corresponding to multiple nuclei clusters, the post-stimulation Granger Causality (GC) values corresponding to at least one pair of neural nuclei are determined. J causality value.

Exemplarily, the connection strength change data of a pair of nerve nuclei includes the connection strength before stimulation and the connection strength after stimulation, or the connection strength change data of the pair of nerve nuclei includes the intensity difference corresponding to the connection strength before stimulation and the connection strength after stimulation, or Trend. When the pre-stimulation connection strength is less than the post-stimulation connection strength, the strength difference is greater than 0, and the change trend is an increasing trend. When the pre-stimulation connection strength is greater than the post-stimulation connection strength, the strength difference is less than 0, and the change trend is a weakening trend.

S130. Based on the video data to be detected of the experimental object, determine the behavioral change data of the experimental object.

In an optional embodiment, the video data to be detected includes pre-stimulation video data and post-stimulation video data. The pre-stimulation video data is the video data of the experimental subject in the pre-stimulation time period, and the post-stimulation video data is the video data of the experimental subject in the post-stimulation time period. The length of the pre-stimulation period can be the same as the length of the post-stimulation period, or they can be different. In this embodiment, the recording of the video data to be detected and the recording of the field potential data are synchronously recorded based on the same time stamp.

For example, the pre-stimulus video data is input into a pre-trained target classification model to obtain the output pre-stimulus behavior type, and the post-stimulation video data is input into the target classification model to obtain the output post-stimulus behavior type. Based on the stimulation Pre-behavior type and post-stimulus behavior type to determine the behavioral change data of the experimental subject.

Examples of behavior types include, but are not limited to, immobility, resting shaking, eating, circling, grooming, running, elimination, and social behaviors. There is no limit to the types of behaviors that can be classified here.

S140. Based on the connection strength change data and behavior change data, determine the activation state of the neural pathway to be detected.

In an optional embodiment, determining the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data includes: when the connection strength change data meets the preset connection conditions, determining the preset behavior change data set Whether there is behavior change data; if there is behavior change data in the preset behavior change data set, the activation state of the neural pathway to be detected is set to the activated state; if there is no behavior change data in the preset behavior change data set, the activation state of the neural pathway to be detected is set to the activated state; The activation state of the neural pathway is set to the inactive state.

In an optional embodiment, when the connection strength change data includes pre-stimulation connection strength and post-stimulation connection strength, the preset connection condition is that the pre-stimulation connection strength is less than the post-stimulation connection strength. In another optional embodiment, when the connection strength change data includes the strength difference or change trend corresponding to the pre-stimulation connection strength and the post-stimulation connection strength, the preset connection condition is that the strength difference satisfies the preset difference range or change. The trend is an increasing trend. For example, the preset difference range may be greater than the preset intensity threshold. For example, the preset intensity threshold may be 0 or 1.

In the case where the connection strength change data of the neural pathway to be detected includes the connection strength change data of a nerve nucleus pair, the connection strength change data satisfying the preset connection condition means that the connection strength change data of the nerve nucleus pair satisfies the preset connection condition. . In the case where the connection strength change data of the neural pathway to be detected includes connection strength change data corresponding to at least two pairs of nerve nuclei, the connection strength change data satisfying the preset connection conditions represents the connection corresponding to at least two pairs of nerve nuclei. The intensity change data all meet the preset connection conditions.

In an optional embodiment, the preset behavior change data set contains at least one preset behavior change data corresponding to the stimulus given to the experimental subject. For example, when the stimulus is a fear stimulus, the preset behavior change data set includes stillness-still trembling, stillness-running, eating-running, eating-excretion, etc.; when the stimulus is a depression stimulus, the preset behavior change data set The concentration includes eating-stillness, social behavior-stillness, etc.

In another optional embodiment, the preset behavior change data set includes a pre-stimulus behavior data set and a post-stimulus behavior data set corresponding to the stimulation given to the experimental subject. For example, when the stimulus is a fear-type stimulus, the pre-stimulus behavior data set includes resting, eating, grooming, and social behaviors, and the post-stimulus behavior includes resting, shaking, turning in circles, excreting, and running. Behavior change data is considered to exist in the preset behavior change data set if the pre-stimulus behavior data set contains the pre-stimulus behavior type in the behavior change data and the post-stimulus behavior data set contains the post-stimulus behavior type in the behavior change data.

Based on the above embodiment, the method further includes: if the connection strength change data does not meet the preset connection conditions, setting the activation state of the neural pathway to be detected to an inactive state. In the case where the connection strength change data of the neural pathway to be detected includes connection strength change data corresponding to at least two pairs of nerve nuclei, the connection strength change data does not meet the preset connection conditions, which is characterized by the presence of a connection of at least one pair of nerve nuclei. The intensity change data does not meet the preset connection conditions.

The technical solution of this embodiment determines the connection strength change data of the neural pathway to be detected based on the obtained pre-stimulation field potential data and post-stimulation field potential data corresponding to the experimental subject's neural pathway to be detected. Video data and post-stimulation video data are used to determine the behavioral change data of the experimental subject. Based on the connection strength change data and behavioral change data, the activation status of the neural pathway to be detected is determined, and the activation status of the neural pathway to be detected is determined comprehensively from the two dimensions of brain functional connection strength and behavioral information. The activation status of neural pathways solves the problem of high misjudgment rates in neural pathway activation detection methods that rely solely on behavioral data, and improves the credibility of research results on brain function.

Embodiment 2

Figure 2 is a flow chart of a neural pathway activation detection method provided in Embodiment 2 of the present application. The video data to be detected in this embodiment includes video data collected by at least three video collection devices. This embodiment is based on the "video data to be detected based on the experimental object to determine the behavioral change data of the experimental object" in the above embodiment. Be explained. As shown in Figure 2, the method includes the following steps.

S210. Obtain the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject.

In an optional embodiment, obtaining the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject includes: obtaining at least 10% of the target nuclei in the neural pathway to be detected within a preset time period. Two channels of field potential data; perform dimensionality reduction operations on at least two channels of field potential data to obtain at least two dimensionality reduction field potential data, and compare the dimensionality reduction field potential with the largest principal component among the at least two dimensionality reduction field potential data. The channel field potential data corresponding to the data is used as the target field potential data corresponding to the target nucleus; among them, when the target nucleus is the first nucleus and the preset time period is the pre-stimulation period, the target field potential data is before the first stimulation. Field potential data, when the target nucleus is the first nucleus and the preset time period is the post-stimulation time period, the target field potential data is the field potential data after the first stimulation, when the target nucleus is the second nucleus and the preset time period is When the time period is the pre-stimulation period, the target field potential data is the second pre-stimulation field potential data. When the target nucleus is the second nucleus and the preset time period is the post-stimulation period, the target field potential data is the second Poststimulus field potential data.

Multi-channel electrodes are used to collect at least two channel field potential data corresponding to at least two nuclei in the neural pathway to be detected. For example, multi-channel electrodes can be implanted into corresponding nuclei based on brain maps.

The dimensionality reduction algorithm used in the dimensionality reduction operation is the Principal Components Analysis (PCA) algorithm.

This setting can make the obtained pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected contain more characteristic information, thereby improving the accuracy of the subsequently obtained connection strength change data.

Based on the above embodiment, before determining the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data, the method further includes: comparing the pre-stimulation field potential data and the post-stimulation field potential data. Perform preprocessing operations respectively to obtain preprocessed pre-stimulation field potential data and post-stimulation field potential data; wherein the preprocessing operations include filtering operations and/or downsampling operations.

For example, the zero-phase digital filter (filtfilt) function in matlab software can be used to input the pre-stimulation field potential data or the post-stimulation field potential data in the forward and reverse directions respectively, so as to realize the pre-stimulation field potential data or post-stimulation field data. Zero-phase digital filtering of potentiometric data.

For example, the resample function in matlab software can be used to reduce the sampling rate of the pre-stimulation field potential data or the post-stimulation field potential data to 1000 Hz.

This setting can remove the noise in the field potential data and reduce the lag value during the Granger causality test.

S220. Based on the pre-stimulation field potential data and the post-stimulation field potential data, determine the connection strength change data of the neural pathway to be detected.

S230. Based on each video data, determine the two-dimensional skeleton data of the experimental object in the key image frame in each video data.

In this embodiment, at least three video collection devices are used to collect video data of the experimental subjects during the experiment. In an optional embodiment, the number of video collection devices is 4. There is no limit on the number of video capture devices here.

The two-dimensional skeleton data is used to represent the position coordinates of at least one posture feature point of the experimental object in the key image frame. In an optional embodiment, the target acquisition video is input into a pre-trained feature point recognition model to obtain at least one output key image frame and the position coordinates corresponding to at least one posture feature point in each key image frame.

For example, the model type of the feature point recognition model is the DeepLabCut deep learning model, and at least one posture feature point includes limbs, nose, ears, head, torso, tail, etc. There are no restrictions on the model type of the feature point recognition model and the settings of the posture feature points.

FIG. 3 is a schematic diagram of posture feature points of an experimental subject provided in Embodiment 2 of the present application. The number of posture feature points shown in Figure 3 is 16, which are nose, left ear, right ear, neck, limbs, 5 trunk feature points, tail base, tail middle and tail tip. At least one posture feature point can be displayed differently using dots of different colors.

S240. Determine the three-dimensional posture data of the experimental object based on the camera parameter data corresponding to at least three video acquisition devices and the two-dimensional skeleton data of the experimental object in the key image frame in the video data collected by at least three video acquisition devices respectively.

Based on the above embodiment, the method further includes: based on each video collection device, obtaining a preset number of standard checkerboard images collected by each video collection device, and using the stereo camera calibration graphical user interface in the matlab software (StereoCameraCalibrator Graphical User Interface, StereoCameraCalibrator GUI) toolbox performs calibration operations based on a preset number of standard checkerboard images to obtain camera parameter data corresponding to each video capture device. For example, the standard checkerboard size is 12*9, the preset number is 60, and the camera parameter data includes internal parameter data and external parameter data.

In an optional embodiment, a triangulation algorithm is used, based on camera parameter data corresponding to at least three video acquisition devices and the experimental object in the key image frame in the video data collected by at least three video acquisition devices respectively. Two-dimensional skeleton data determines the three-dimensional posture data of the experimental object.

S250. Based on the three-dimensional posture data, determine the behavioral change data of the experimental subject.

In an optional embodiment, determining the behavioral change data of the experimental subject based on the three-dimensional posture data includes: obtaining the pre-stimulation posture data corresponding to the pre-stimulation time period in the three-dimensional posture data and the post-stimulation posture corresponding to the post-stimulation time period. data, and input the pre-stimulus posture data into the pre-trained target classification model to obtain the output pre-stimulus behavior type. Input the post-stimulation posture data into the target classification model to obtain the output post-stimulus behavior type. Based on the pre-stimulus Behavior type and post-stimulus behavior type determine the behavioral change data of the experimental subject.

In another optional embodiment, determining the behavioral change data of the experimental subject based on the three-dimensional posture data includes: using an unsupervised machine learning algorithm to segment the three-dimensional posture data to obtain times corresponding to at least two time series. Attitude data; perform dimensionality reduction operations on the time posture data corresponding to at least two time series to obtain at least two dimensionality reduction posture data, and perform clustering operations on at least two dimensionality reduction posture data to obtain time behavior data; based on time Behavioral data and stimulation time points to determine behavioral change data of experimental subjects. The time behavior data represents behavior types corresponding to at least two time series respectively.

For example, the unsupervised machine learning algorithm can be the Behavior Atlas (BeA) algorithm, and the dimensionality reduction algorithm used in the dimensionality reduction operation is the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction algorithm. The clustering algorithm used in class operations is hierarchical clustering algorithm.

Based on the temporal behavioral data and the stimulation time point, determine the behavioral change data of the experimental subject, including: determining the pre-stimulation time period based on the stimulation time point and the first duration, and determining the post-stimulation time period based on the stimulation time point and the second duration; The behavior type corresponding to the time series with the highest overlap rate of the pre-stimulus time period is regarded as the pre-stimulus behavior type, and the behavioral data corresponding to the time series with the highest overlap rate of the post-stimulus time period is regarded as the post-stimulus behavior type. Based on the pre-stimulus behavior type and Post-stimulus behavior type determines the behavioral change data of the experimental subject.

S260. Determine the activation state of the neural pathway to be detected based on the connection strength change data and behavior change data.

FIG. 4 is a flow chart of an example of a neural pathway activation detection method provided in Embodiment 2 of the present application. Based on the brain atlas, multi-channel electrodes are implanted into at least two nuclei corresponding to the neural pathways to be detected, and at least three cameras are calibrated respectively to obtain camera parameter data. Give the experimental subject a certain stimulus, obtain the multi-channel field potential data in the pre-stimulation time period and the multi-channel field potential data in the post-stimulation time period, perform preprocessing operations on the multi-channel field potential data, and obtain the pre-stimulation field potential data and Poststimulus field potential data. Exemplarily, preprocessing operations include principal component analysis operations, filtering operations, and downsampling operations. Based on the pre-stimulation field potential data and the post-stimulation field potential data, determine the pre-stimulation connection strength and post-stimulation connection strength corresponding to at least two nuclei in the neural pathway to be detected, where the parameters of the pre-stimulation connection strength or post-stimulation connection strength are The type is Granger causality value (GC value), which determines the connection strength change data of the neural pathway to be detected based on the pre-stimulation connection strength and post-stimulation connection strength corresponding to at least two nuclei.

At the same time, the video data to be detected of the experimental subject is obtained, where the video data to be detected includes video data collected by at least three video collection devices. Based on each video data, determine the two-dimensional skeleton data of the experimental object in the key image frame in each video data; based on the corresponding camera parameter data of at least three video collection devices, compare the videos collected by at least three video collection devices. The two-dimensional skeleton data of the experimental subject in the key image frame in the data is three-dimensionally reconstructed to obtain the three-dimensional posture data of the experimental subject. Based on the three-dimensional posture data, the behavioral change data of the experimental subject is determined. Finally, based on the connection strength change data and behavioral change data, the activation state of the neural pathway to be detected is determined.

The video data collected using a single video acquisition device only contains the two-dimensional posture information of the experimental subject. Since many behaviors of the experimental subject are relatively close, the accuracy of the behavior type of the experimental subject determined based on the two-dimensional posture information is poor. The technical solution of this embodiment is to use at least three video collection devices to collect video data respectively, and based on each video data, determine the two-dimensional skeleton data of the experimental object in the key image frame in each video data, based on at least three video collections The camera parameter data corresponding to the device and the two-dimensional skeleton data of the experimental object in the key image frame in the video data collected by at least three video acquisition devices respectively determine the three-dimensional posture data of the experimental object, and based on the three-dimensional posture data, determine the experiment The object's behavior change data solves the problem of a large misjudgment rate in distinguishing behavior types based on two-dimensional video data. The three-dimensional posture data contains more posture information of the experimental object, which can improve the accuracy of the behavior change data and increase the number of behavioral changes. The number of types of distinguishable behavior types.

Embodiment 3

FIG. 5 is a schematic structural diagram of a neural pathway activation detection device provided in Embodiment 3 of the present application. As shown in Figure 5, the device includes: a field potential data acquisition module 310, a connection strength change data determination module 320, a behavior change data determination module 330 and an activation state determination module 340.

The field potential data acquisition module 310 is configured to acquire the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected in the experimental subject; the connection strength change data determination module 320 is configured to obtain the pre-stimulation field potential data and post-stimulation field potential data based on the pre-stimulation field potential data and post-stimulation field potential data. The field potential data determines the connection strength change data of the neural pathway to be detected; the behavior change data determination module 330 is configured to determine the behavior change data of the experimental subject based on the video data to be detected; the activation state determination module 340 is configured to determine the behavior change data of the experimental subject based on the video data to be detected. Connection strength change data and behavioral change data determine the activation status of the neural pathway to be detected.

The technical solution of this embodiment determines the connection strength change data of the neural pathway to be detected based on the obtained pre-stimulation field potential data and post-stimulation field potential data corresponding to the experimental subject's neural pathway to be detected. Video data and post-stimulation video data are used to determine the behavioral change data of the experimental subject. Based on the connection strength change data and behavioral change data, the activation status of the neural pathway to be detected is determined, and the activation status of the neural pathway to be detected is determined comprehensively from the two dimensions of brain functional connection strength and behavioral information. The activation status of neural pathways solves the problem of high misjudgment rates in neural pathway activation detection methods that rely solely on behavioral data, and improves the credibility of research results on brain function.

Based on the above embodiment, optionally, the activation state determination module 340 is configured to: when the connection strength change data meets the preset connection conditions, determine whether there is behavior change data in the preset behavior change data set; if If there is behavior change data in the preset behavior change data set, the activation state of the neural pathway to be detected is set to the activated state; if there is no behavior change data in the preset behavior change data set, the activation state of the neural pathway to be detected is set to Inactive status.

On the basis of the above embodiment, optionally, the pre-stimulation field potential data includes the first pre-stimulation field potential data and the third pre-stimulation field potential data respectively corresponding to the first nucleus and the second nucleus of the pair of nerve nuclei in the neural pathway to be detected. 2. Pre-stimulation field potential data and post-stimulation field potential data include the first post-stimulation field potential data and the second post-stimulation field potential data corresponding to the first and second nuclei in the pair of nerve nuclei respectively, and the connection strength change data. The determination module 320 is configured to: determine the pre-stimulation connection strength corresponding to the nerve nucleus pair based on the first pre-stimulation field potential data and the second pre-stimulation field potential data; based on the first post-stimulation field potential data and the second post-stimulation field potential data. The field potential data determines the post-stimulation connection strength corresponding to the nerve nucleus pair; based on the pre-stimulation connection strength and the post-stimulation connection strength, the connection strength change data of the nerve nucleus pair in the neural pathway to be detected is determined.

Based on the above embodiments, optionally, the field potential data acquisition module 310 is configured to: acquire at least two channels of field potential data within a preset time period of the target nuclei in the neural pathway to be detected; Perform dimensionality reduction operations on the two channel field potential data respectively to obtain at least two dimensionality reduction field potential data, and use the channel field potential data corresponding to the dimensionality reduction field potential data with the largest principal component among the at least two dimensionality reduction field potential data as the target. Target field potential data corresponding to the nucleus; among them, when the target nucleus is the first nucleus and the preset time period is the pre-stimulation period, the target field potential data is the first pre-stimulation field potential data. When the target nucleus is When the first nucleus is the first nucleus and the preset time period is the post-stimulation time period, the target field potential data is the first post-stimulation field potential data. When the target nucleus is the second nucleus and the preset time period is the pre-stimulation time period, The target field potential data is the second pre-stimulation field potential data. When the target nucleus is the second nucleus and the preset time period is the post-stimulation time period, the target field potential data is the second post-stimulation field potential data.

Based on the above embodiment, optionally, the parameter type of the pre-stimulation connection strength or the post-stimulation connection strength is a Granger causality value.

Based on the above embodiments, optionally, the device further includes:

The preprocessing module is configured to separately perform preprocessing operations on the pre-stimulation field potential data and the post-stimulation field potential data before determining the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data, Preprocessed pre-stimulation field potential data and post-stimulation field potential data are obtained; wherein the preprocessing operation includes a filtering operation and/or a downsampling operation.

Based on the above embodiment, optionally, the video data to be detected includes video data collected by at least three video collection devices. The behavior change data determination module 330 is configured to: based on each video data, determine each The two-dimensional skeleton data of the experimental object in the key image frame in the video data; based on the camera parameter data corresponding to at least three video acquisition devices and the experimental object in the key image frame in the video data collected by at least three video acquisition devices. The two-dimensional skeleton data determines the three-dimensional posture data of the experimental subject, and based on the three-dimensional posture data, determines the behavioral change data of the experimental subject.

The neural pathway activation detection device provided by the embodiments of the present application can execute the neural pathway activation detection method provided by any embodiment of the present application, and has functional modules corresponding to the execution method.

Embodiment 4

FIG. 6 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application. Electronic device 10 is intended to represent many forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (eg, helmets, glasses, watches, etc.), and other similar computing devices. The components shown in the embodiments of the present application, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application as described and/or claimed herein.

As shown in Figure 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (Read-Only Memory, ROM) 12, a random access memory (Random Access Memory, RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor 11. The processor 11 can perform the processing according to the computer program stored in the ROM 12 or loaded from the storage unit 18 into the RAM 13. Performs a variety of appropriate actions and processes. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via the bus 14. An input/output (I/O) interface 15 is also connected to the bus 14 .

Multiple components in the electronic device 10 are connected to the I/O interface 15. The multiple components include: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as Disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunications networks.

Processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (GPU), a variety of dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, a variety of running Machine learning model algorithm processor, digital signal processor (Digital Signal Processor, DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs a plurality of methods and processes described above, such as neural pathway activation detection methods.

In some embodiments, the neural pathway activation detection method may be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as the storage unit 18 . In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the activation detection method of the neural pathway described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the neural pathway activation detection method in any other suitable manner (eg, by means of firmware).

Various implementations of the systems and techniques described above may be implemented in digital electronic circuit systems, integrated circuit systems, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Parts (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and/or they realized in a combination. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.

The computer program used to implement the neural pathway activation detection method of the present application can be written using any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the computer program, when executed by the processor, causes the functions/operations specified in the flowcharts and/or block diagrams to be implemented. A computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

Embodiment 5

Embodiment 5 of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions. The computer instructions are used to cause the processor to execute a neural pathway activation detection method. The method includes: obtaining an experimental subject. The pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected are determined; based on the pre-stimulation field potential data and post-stimulation field potential data, the connection strength change data of the neural pathway to be detected is determined; based on the video of the experimental subject to be detected data to determine the behavioral change data of the experimental subject; based on the connection strength change data and behavioral change data, determine the activation state of the neural pathway to be detected.

In the context of this application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, laptop disks, hard drives, RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), or Flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having a display device (e.g., a cathode ray tube (CRT) or liquid crystal) for displaying information to the user. A display (Liquid Crystal Display, LCD monitor); and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., A user's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), blockchain network, and the Internet.

Computing systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, also known as cloud computing server or cloud host. It is a host product in the cloud computing service system to solve the problems that exist in traditional physical host and virtual private server (VPS) services. It has the disadvantages of difficult management and weak business scalability.

Claims (10)

1. An activation detection method for neural pathways, including:

   Obtain the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

   Based on the pre-stimulation field potential data and the post-stimulation field potential data, determine the connection strength change data of the neural pathway to be detected;

   Determine the behavioral change data of the experimental subject based on the video data to be detected of the experimental subject;

   Based on the connection strength change data and the behavior change data, the activation state of the neural pathway to be detected is determined.
2. The method of claim 1, wherein determining the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data includes:

   When the connection strength change data meets the preset connection conditions, determine whether the behavior change data exists in the preset behavior change data set;

   In response to the presence of the behavior change data in the preset behavior change data set, setting the activation state of the neural pathway to be detected to an activated state;

   In response to the fact that the behavior change data does not exist in the preset behavior change data set, the activation state of the neural pathway to be detected is set to an inactive state.
3. The method according to claim 1, wherein the pre-stimulation field potential data includes the first pre-stimulation field potential corresponding to the first nucleus and the second nucleus of the pair of nerve nuclei in the neural pathway to be detected. data and second pre-stimulation field potential data. The post-stimulation field potential data includes the first post-stimulation field potential data and the second post-stimulation field corresponding to the first nucleus and the second nucleus in the pair of neural nuclei. Potential data, which determines the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data, including:

   Based on the first pre-stimulation field potential data and the second pre-stimulation field potential data, determine the pre-stimulation connection strength corresponding to the pair of nerve nuclei;

   Based on the first post-stimulation field potential data and the second post-stimulation field potential data, determine the post-stimulation connection strength corresponding to the pair of nerve nuclei;

   Based on the pre-stimulation connection strength and the post-stimulation connection strength, the connection strength change data of the pair of nerve nuclei in the neural pathway to be detected is determined.
4. The method according to claim 3, wherein said obtaining the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject includes:

   Obtain at least two channel field potential data of the target nucleus in the neural pathway to be detected within a preset time period;

   Perform dimensionality reduction operations on the at least two channel field potential data respectively to obtain at least two dimensionality reduction field potential data, and obtain the dimensionality reduction field potential data corresponding to the largest principal component among the at least two dimensionality reduction field potential data. The channel field potential data is used as the target field potential data corresponding to the target nuclei;

   Wherein, when the target nucleus is the first nucleus and the preset time period is a pre-stimulation time period, the target field potential data is the first pre-stimulation field potential data. When the target nucleus is the first nucleus and the preset time period is a post-stimulation time period, the target field potential data is the first post-stimulation field potential data. When the target nucleus When it is the second nucleus and the preset time period is the pre-stimulation time period, the target field potential data is the second pre-stimulation field potential data. When the target nucleus is the third When there are two nuclei and the preset time period is a post-stimulation time period, the target field potential data is the second post-stimulation field potential data.
5. The method according to claim 3, wherein the parameter type of the pre-stimulation connection strength or the post-stimulation connection strength is a Granger causality value.
6. The method according to claim 1, before determining the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data, further comprising:

   Preprocessing operations are performed on the pre-stimulation field potential data and the post-stimulation field potential data respectively to obtain pre-processed pre-stimulation field potential data and post-stimulation field potential data; wherein the pre-processing operation includes filtering operations and At least one of the downsampling operations.
7. The method according to any one of claims 1 to 6, wherein the video data to be detected includes video data collected respectively by at least three video collection devices, and based on the video data to be detected of the experimental object, it is determined The behavioral change data of the experimental subjects include:

   Based on each video data, determine the two-dimensional skeleton data of the experimental object in the key image frame in each video data;

   Based on the camera parameter data respectively corresponding to the at least three video acquisition devices and the two-dimensional skeleton data of the experimental object in the key image frame of the video data respectively collected by the at least three video acquisition devices, the experimental object is determined. Three-dimensional posture data, and based on the three-dimensional posture data, determine the behavioral change data of the experimental subject.
8. A neural pathway activation detection device, including:

   The field potential data acquisition module is configured to obtain the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

   A connection strength change data determination module configured to determine the connection strength change data of the neural pathway to be detected based on the pre-stimulation field potential data and the post-stimulation field potential data;

   The behavior change data determination module is configured to determine the behavior change data of the experimental subject based on the video data to be detected of the experimental subject;

   An activation state determination module is configured to determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data.
9. An electronic device including:

   at least one processor; and

   a memory communicatively connected to the at least one processor; wherein,

   The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor, so that the at least one processor can execute any one of claims 1-7 The neural pathway activation detection method.
10. A computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the neural pathway activation detection method according to any one of claims 1 to 7 when executed.

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