A method, device, equipment and storage medium for detecting activation of a neural pathway

Abstract

The present invention discloses a method, device, equipment and storage medium for detecting the activation of a neural pathway. The method comprises: obtaining pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject; 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; determining the behavior change data of the experimental subject based on the video data to be detected of the experimental subject; determining the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data. The embodiment of the present invention solves the problem of high misjudgment rate of the neural pathway activation detection method that relies solely on behavioral data, and improves the credibility of the research results on brain function.

Description

Technical Field

[0001] The present invention relates to the field of brain science technology, and in particular to a method, device, equipment and storage medium for detecting activation of a neural pathway. Background Art

[0002] The brain's nervous system is an extremely complex, efficient and precise system, and its perception and cognitive brain mechanisms have always been a hot topic in brain science research. Among them, animal behavior is closely related to the activation of related neural pathways. When conducting experimental research, the experimental subjects are usually given certain stimuli to make them produce behaviors corresponding to the stimuli, and the changes in their behavior are used as a basis for judging whether the related neural pathways are activated.

[0003] However, changes in animal behavior may be caused by stimulation or spontaneous behavior unrelated to stimulation. In addition, some behaviors of experimental subjects cannot uniquely identify a neural pathway. For example, running behavior may correspond to a neural pathway related to fear or anxiety. Therefore, relying solely on behavioral changes as a basis for determining whether the relevant neural pathway is activated will result in a certain rate of misjudgment, making the credibility of research results on brain function low. Summary of the invention

[0004] The embodiments of the present invention 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 credibility of research results on brain function.

[0005] According to one embodiment of the present invention, a method for detecting activation of a neural pathway is provided, the method comprising:

[0006] Acquire pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

[0007] 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;

[0008] Determining behavior change data of the experimental subject based on the video data to be detected of the experimental subject;

[0009] Based on the connection strength change data and the behavior change data, the activation state of the neural pathway to be detected is determined.

[0010] According to another embodiment of the present invention, there is provided a neural pathway activation detection device, the device comprising:

[0011] A field potential data acquisition module, used to acquire pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

[0012] A connection strength change data determination module, 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;

[0013] A behavior change data determination module, used to determine the behavior change data of the experimental subject based on the to-be-detected video data of the experimental subject;

[0014] An activation state determination module is used to determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data.

[0015] According to another embodiment of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the neural pathway activation detection method described in any embodiment of the present invention.

[0019] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable a processor to implement the neural pathway activation detection method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention determines the connection strength change data of the neural pathway to be detected based on the acquired pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject, determines the behavior change data of the experimental subject based on the pre-stimulation video data and post-stimulation video data of the experimental subject, determines the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data, and comprehensively determines the activation state of the neural pathway to be detected from two dimensions of brain function connection strength and behavioral information, thereby solving the problem of high misjudgment rate of neural pathway activation detection methods that rely solely on behavioral data, and improving the credibility of research results on brain function.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a neural pathway activation detection method provided in Embodiment 1 of the present invention;

[0024] Figure 2 A flowchart of a neural pathway activation detection method provided in Embodiment 2 of the present invention;

[0025] Figure 3 A schematic diagram of posture feature points of an experimental object provided in the second embodiment of the present invention;

[0026] Figure 4 A flowchart of a specific example of a neural pathway activation detection method provided in Embodiment 2 of the present invention;

[0027] Figure 5 A schematic diagram of the structure of a neural pathway activation detection device provided in Embodiment 3 of the present invention;

[0028] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 This is a flowchart of a neural pathway activation detection method provided in Embodiment 1 of the present invention. This embodiment can be applied to detecting the activation state of a neural pathway in a nervous system. The method can be performed by a neural pathway activation detection device. The neural pathway activation detection device can be implemented in the form of hardware and / or software. The neural pathway activation detection device can be configured in a terminal device. Figure 1 As shown, the method includes:

[0033] S110, obtaining pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject.

[0034] Among them, illustratively, the neural pathways to be detected can be the ventral tegmental area of ​​the midbrain-nucleus accumbens-prefrontal cortex, superior colliculus-pulvinar thalamus-lateral amygdala, hippocampus-mammillary body-anterior thalamic nucleus-cingulate gyrus, etc. There is no limitation on the neural pathways to be detected here, and users can customize settings according to actual needs.

[0035] Specifically, the pre-stimulation field potential data is the field potential data of the neural pathway to be detected in the pre-stimulation time period, and the post-stimulation field potential data is the field potential data of the neural pathway to be detected in the post-stimulation time period. Specifically, the field potential data can be collected by implanted electrodes or non-implanted electrodes, and the field potential data is used to characterize the potential changes generated by the target nucleus in the neural pathway to be detected.

[0036] The pre-stimulation time period can be determined based on the stimulation time point and the first duration, and the post-stimulation time period can be determined based on the stimulation time point and the second duration. The first duration and the second duration can be the same or different. For example, the first duration is 3 minutes and the second duration is 5 minutes. The specific values ​​of the first duration and the second duration are not limited here.

[0037] 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. The advantage of this arrangement is that it avoids the time error caused by manual observation and recording of the stimulation time point, thereby improving the accuracy of the recorded stimulation time point.

[0038] Specifically, the stimulus given to the experimental subject may be physical stimulus or drug stimulus, and illustratively, the physical stimulus may be electric shock, sound or light signal, etc. The type of stimulus is not limited here, and the user may customize the setting according to the correlation between the neural pathway to be detected and the stimulus.

[0039] S120. 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.

[0040] Specifically, the neural pathway to be detected contains at least one pair of neural nuclei. For example, assuming that the neural pathway to be detected is superior colliculus-thalamus pulvinar-lateral amygdala, the neural pathway to be detected corresponds to at least one pair of neural nuclei in superior colliculus-thalamus pulvinar, thalamus pulvinar-lateral amygdala, and superior colliculus-lateral amygdala. Correspondingly, the connection strength change data corresponding to the neural pathway to be detected contains connection strength change data corresponding to at least one pair of neural nuclei.

[0041] In an optional embodiment, when the neural pathway to be detected contains a neural nucleus pair, the pre-stimulus field potential data include first pre-stimulus field potential data and second pre-stimulus field potential data corresponding to the first nucleus and the second nucleus in the neural nucleus pair in the neural pathway to be detected, respectively, and the post-stimulus field potential data include first post-stimulus field potential data and second post-stimulus field potential data corresponding to the first nucleus and the second nucleus in the neural nucleus pair, respectively. Accordingly, based on the pre-stimulus field potential data and the post-stimulus field potential data, the connection strength change data of the neural pathway to be detected is determined, including: determining the pre-stimulus connection strength corresponding to the neural nucleus pair based on the first pre-stimulus field potential data and the second pre-stimulus field potential data; determining the post-stimulus connection strength corresponding to the neural nucleus pair based on the first post-stimulus field potential data and the second post-stimulus field potential data; determining the connection strength change data of the neural nucleus pair in the neural pathway to be detected based on the pre-stimulus connection strength and the post-stimulus connection strength.

[0042] This embodiment is exemplified by taking a neural pathway to be detected that includes a pair of neural nuclei. It is easy to understand that the connection strength change data of other neural nuclei pairs corresponding to the neural pathway to be detected can also be determined by the above method.

[0043] In an optional embodiment, the parameter type of the pre-stimulus connection strength or the post-stimulus connection strength is a Granger causality value.

[0044] Granger causality test is a statistical method for hypothesis testing, which can be used to test whether the first pre-stimulus field potential data (or the second pre-stimulus field potential data) is the cause of the second pre-stimulus field potential data (or the first pre-stimulus field potential data). The results of Granger causality analysis include three elements: the connection direction, connection strength and significance of the connection between the two nuclei.

[0045] Among them, exemplarily, the MVGC toolbox in the MATLAB software can be used to determine the Granger causality value (denoted as GC value) before stimulation corresponding to at least one pair of neural nuclei based on the pre-stimulation field potential data corresponding to each nucleus, and to determine the Granger causality value after stimulation corresponding to at least one pair of neural nuclei based on the post-stimulation field potential data corresponding to each nucleus.

[0046] Wherein, illustratively, the connection strength change data of the neural nucleus pair includes the connection strength before stimulation and the connection strength after stimulation, or the connection strength change data of the neural nucleus pair includes the strength difference or change trend corresponding to the connection strength before stimulation and the connection strength after stimulation. Wherein, specifically, when the connection strength before stimulation is less than the connection strength after stimulation, the strength difference is greater than 0, and the change trend is an increasing trend, and when the connection strength before stimulation is greater than the connection strength after stimulation, the strength difference is less than 0, and the change trend is a weakening trend.

[0047] S130: Determine the behavior change data of the experimental subject based on the video data to be detected of the experimental subject.

[0048] In an optional embodiment, the video data to be detected includes pre-stimulation video data and post-stimulation video data. Specifically, 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 duration of the pre-stimulation time period may be the same as or different from the duration of the post-stimulation time period. 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 timestamp.

[0049] Among them, exemplarily, 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-stimulus video data is input into the target classification model to obtain the output post-stimulus behavior type, and based on the pre-stimulus behavior type and the post-stimulus behavior type, the behavioral change data of the experimental subject is determined.

[0050] For example, the behavior types include but are not limited to stillness, stillness trembling, eating, circling, grooming, running, defecation, and social behavior, etc. The categorizable behavior types are not limited here.

[0051] S140. Determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data.

[0052] In an optional embodiment, the activation state of the neural pathway to be detected is determined based on the connection strength change data and the behavior change data, including: when the connection strength change data meets the preset connection condition, judging whether the behavior change data exists in the preset behavior change data set; if so, setting the activation state of the neural pathway to be detected to an activated state; if not, setting the activation state of the neural pathway to be detected to an unactivated state.

[0053] In an optional embodiment, when the connection strength change data includes the connection strength before stimulation and the connection strength after stimulation, the preset connection condition is that the connection strength before stimulation is less than the connection strength after stimulation. In another optional embodiment, when the connection strength change data includes the strength difference or change trend corresponding to the connection strength before stimulation and the connection strength after stimulation, the preset connection condition is that the strength difference satisfies the preset difference range or the change trend is an enhancing trend. Exemplarily, the preset difference range can be greater than a preset strength threshold, and exemplary, the preset strength threshold can be 0 or 1.

[0054] Specifically, when the connection strength change data of the neural pathway to be detected includes the connection strength change data of a neural nucleus pair, the connection strength change data satisfies the preset connection condition, indicating that the connection strength change data of the neural nucleus pair satisfies the preset connection condition. When the connection strength change data of the neural pathway to be detected includes the connection strength change data corresponding to at least two neural nucleus pairs, the connection strength change data satisfies the preset connection condition, indicating that the connection strength change data corresponding to each neural nucleus pair all satisfies the preset connection condition.

[0055] In an optional embodiment, the preset behavior change data set includes at least one preset behavior change data corresponding to the stimulus given to the experimental subject. Exemplarily, when the stimulus is a fear stimulus, the preset behavior change data set includes stillness-stillness trembling, stillness-running, eating-running, eating-excretion, etc.; when the stimulus is a depression stimulus, the preset behavior change data set includes eating-stillness, social behavior-stillness, etc.

[0056] In another optional embodiment, the preset behavior change data set includes a pre-stimulus behavior data set and a post-collection behavior data set corresponding to the stimulus given to the experimental subject. Exemplarily, when the stimulus is a fear-type stimulus, the pre-stimulus behavior data set includes stillness, eating, grooming, and social behaviors, and the post-stimulus behaviors include stillness, trembling, circling, defecation, and running. Specifically, if the pre-stimulus behavior data set includes the pre-stimulus behavior type in the behavior change data and the post-stimulus behavior data set includes the post-stimulus behavior type in the behavior change data, then it is considered that the preset behavior change data set contains behavior change data.

[0057] On the basis of the above embodiment, the method further includes: if the connection strength change data does not satisfy the preset connection condition, setting the activation state of the neural pathway to be detected to an inactivated state. Specifically, when the connection strength change data of the neural pathway to be detected includes connection strength change data corresponding to at least two neural nucleus pairs, the connection strength change data does not satisfy the preset connection condition, indicating that there is at least one neural nucleus pair whose connection strength change data does not satisfy the preset connection condition.

[0058] The technical solution of the present embodiment determines the connection strength change data of the neural pathway to be detected based on the acquired pre-stimulus field potential data and post-stimulus field potential data corresponding to the neural pathway to be detected of the experimental subject, determines the behavior change data of the experimental subject based on the pre-stimulus video data and post-stimulus video data of the experimental subject, determines the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data, and comprehensively determines the activation state of the neural pathway to be detected from two dimensions of brain function connection strength and behavioral information, thereby solving the problem of high misjudgment rate of neural pathway activation detection methods that rely solely on behavioral data, and improving the credibility of research results on brain functions.

[0059] Embodiment 2

[0060] Figure 2 This is a flow chart of a neural pathway activation detection method provided by Embodiment 2 of the present invention. The video data to be detected in this embodiment includes video data collected by at least three video acquisition devices. This embodiment further optimizes the "determining the behavior change data of the experimental subject based on the video data to be detected of the experimental subject" in the above embodiment. Figure 2 As shown, the method includes:

[0061] S210, obtaining pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject.

[0062] In an optional embodiment, obtaining pre-stimulus field potential data and post-stimulus field potential data corresponding to a neural pathway to be detected of an experimental subject includes: obtaining at least two channel field potential data of a target nucleus in the neural pathway to be detected within a preset time period; performing a dimensionality reduction operation on each channel field potential data to obtain at least two reduced-dimensionality field potential data, and using the channel field potential data corresponding to the reduced-dimensionality field potential data with the largest principal component as the target field potential data corresponding to the target nucleus; wherein, when the target nucleus is the first nucleus and the preset time period is the pre-stimulus time period, the target field potential data is the first pre-stimulus field potential data, when the target nucleus is the first nucleus and the preset time period is the post-stimulus time period, the target field potential data is the first post-stimulus field potential data, when the target nucleus is the second nucleus and the preset time period is the pre-stimulus time period, the target field potential data is the second pre-stimulus field potential data, and when the target nucleus is the second nucleus and the preset time period is the post-stimulus time period, the target field potential data is the second post-stimulus field potential data.

[0063] Specifically, a multi-channel electrode is used to collect at least two channel field potential data corresponding to at least two nuclei in the neural pathway to be detected. Exemplarily, the multi-channel electrode can be implanted into the corresponding nuclei based on a brain map.

[0064] Specifically, the dimension reduction algorithm adopted by the dimension reduction operation is a PCA (Principal Components Analysis) algorithm.

[0065] The advantage of such a setting is that the pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected can contain more characteristic information, thereby improving the accuracy of the subsequent connection strength change data.

[0066] On the basis of the above-mentioned 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 also includes: performing preprocessing operations on the pre-stimulation field potential data and the post-stimulation field potential data, respectively, to obtain preprocessed pre-stimulation field potential data and post-stimulation field potential data; wherein the preprocessing operation includes filtering operation and / or downsampling operation.

[0067] Exemplarily, the filtfilt function in the matlab software can be used to implement zero-phase digital filtering of the pre-stimulation field potential data or the post-stimulation field potential data by inputting the pre-stimulation field potential data or the post-stimulation field potential data in the forward direction and the reverse direction, respectively.

[0068] Exemplarily, the resample function in the 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.

[0069] The advantage of this setting is that it can remove the noise in the field potential data and reduce the lag value in the Granger causality test process.

[0070] S220. 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.

[0071] S230 . For each video data, determine the two-dimensional skeleton data of the experimental object in each key image frame in the video data.

[0072] In this embodiment, at least three video acquisition devices are used to respectively acquire video data of the experimental subject during the experiment. In an optional embodiment, the number of video acquisition devices is 4. The number of video acquisition devices is not limited here.

[0073] Specifically, the two-dimensional skeleton data is used to characterize 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 key image frame output and the position coordinates corresponding to at least one posture feature point in each key image frame.

[0074] Exemplarily, the model type of the feature point recognition model is a DeepLabCut deep learning model, and the at least one posture feature point includes limbs, nose, ears, head, trunk, and tail, etc. The model type of the feature point recognition model and the setting of the posture feature points are not limited here.

[0075] Figure 3 This is a schematic diagram of the posture feature points of an experimental object provided by the second embodiment of the present invention. Specifically, Figure 3 The number of posture feature points shown is 16, namely, nose, left ear, right ear, neck, limbs, 5 trunk feature points, tail root, tail middle and tail tip. Among them, each posture feature point can be distinguished and displayed by using dots of different colors.

[0076] S240: Determine the three-dimensional posture data of the experimental object based on the camera parameter data corresponding to each video acquisition device and each two-dimensional skeleton data.

[0077] On the basis of the above embodiment, the method further includes: for each video acquisition device, obtaining a preset number of standard chessboard images acquired by the video acquisition device, using the StereoCameraCalibratorGUI toolbox in the matlab software, performing a calibration operation based on each standard chessboard image, and obtaining the camera parameter data corresponding to the video acquisition device. Wherein, by way of example, the specification of the standard chessboard is 12*9, the preset number is 60, and the camera parameter data includes internal reference data and external reference data.

[0078] In an optional embodiment, a triangulation algorithm is used to determine the three-dimensional posture data of the experimental object based on the camera parameter data corresponding to each video acquisition device and each two-dimensional skeleton data.

[0079] S250: Determine behavior change data of the experimental subject based on the three-dimensional posture data.

[0080] In an optional embodiment, based on the three-dimensional posture data, the behavior change data of the experimental object is determined, including: obtaining pre-stimulation posture data corresponding to the pre-stimulation time period and post-stimulation posture data corresponding to the post-stimulation time period in the three-dimensional posture data, and inputting the pre-stimulation posture data into a pre-trained target classification model to obtain an output pre-stimulation behavior type, inputting the post-stimulation posture data into the target classification model to obtain an output post-stimulation behavior type, and determining the behavior change data of the experimental object based on the pre-stimulation behavior type and the post-stimulation behavior type.

[0081] In another optional embodiment, based on the three-dimensional posture data, determining the behavior change data of the experimental subject includes: using an unsupervised machine learning algorithm to segment the three-dimensional posture data to obtain time posture data corresponding to at least two time series respectively; performing a dimensionality reduction operation on each time posture data to obtain at least two reduced-dimensional posture data, and performing a clustering operation on each reduced-dimensional posture data to obtain time behavior data; based on the time behavior data and the stimulation time point, determining the behavior change data of the experimental subject. Wherein, the time behavior data represents the behavior type corresponding to the at least two time series respectively.

[0082] Among them, exemplarily, the unsupervised machine learning algorithm can be a behavior atlas (BeA) algorithm, the dimensionality reduction algorithm used in the dimensionality reduction operation is a UMAP dimensionality reduction algorithm, and the clustering algorithm used in the clustering operation is a hierarchical clustering algorithm.

[0083] Specifically, based on the temporal behavioral data and the stimulation time point, the behavioral change data of the experimental subject is determined, including: based on the stimulation time point and the first duration, the pre-stimulation time period is determined, and based on the stimulation time point and the second duration, the post-stimulation time period is determined; the behavior type corresponding to the time series with the highest overlap rate with the pre-stimulation time period is used as the pre-stimulation behavior type, and the behavior data corresponding to the time series with the highest overlap rate with the post-stimulation time period is used as the post-stimulation behavior type, and based on the pre-stimulation behavior type and the post-stimulation behavior type, the behavioral change data of the experimental subject is determined.

[0084] S260: Determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data.

[0085] Figure 4 A flowchart of a specific example of an activation detection method for a neural pathway provided in Embodiment 2 of the present invention. Specifically, based on the brain map, a multi-channel electrode is implanted into at least two nuclei corresponding to the neural pathway 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 a pre-processing operation on the multi-channel field potential data, and obtain the pre-stimulation field potential data and the post-stimulation field potential data, wherein, exemplarily, the pre-processing operation includes a principal component analysis operation, a filtering operation, and a downsampling operation. Based on the pre-stimulation field potential data and the post-stimulation field potential data, determine the pre-stimulation connection strength and the post-stimulation connection strength corresponding to at least two nuclei in the neural pathway to be detected, wherein the parameter type of the pre-stimulation connection strength or the post-stimulation connection strength is a Granger causality value (GC value), and based on each pre-stimulation connection strength and each post-stimulation connection strength, determine the connection strength change data of the neural pathway to be detected.

[0086] At the same time, the video data to be detected of the experimental subject is obtained, wherein the video data to be detected includes video data respectively collected by at least three video acquisition devices. For each video data, the two-dimensional skeleton data of the experimental subject in each key image frame in the video data is determined; based on the camera parameter data corresponding to each video acquisition device, each two-dimensional skeleton data is three-dimensionally reconstructed to obtain the three-dimensional posture data of the experimental subject, and based on the three-dimensional posture data, the behavior change data of the experimental subject is determined. Finally, based on the connection strength change data and the behavior change data, the activation state of the neural pathway to be detected is determined.

[0087] The video data collected by a single video acquisition device only contains the two-dimensional posture information of the experimental object. Since many behaviors of the experimental object are relatively similar, the accuracy of the behavior type of the experimental object determined based on the two-dimensional posture information is poor. The technical solution of this embodiment uses at least three video acquisition devices to collect video data respectively, and for each video data, determines the two-dimensional skeleton data of the experimental object in each key image frame in the video data, determines the three-dimensional posture data of the experimental object based on the camera parameter data corresponding to each video acquisition device and each two-dimensional skeleton data, and determines the behavior change data of the experimental object based on the three-dimensional posture data, which 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, thereby improving the accuracy of the behavior change data and increasing the number of distinguishable behavior types.

[0088] Embodiment 3

[0089] Figure 5 This is a schematic diagram of the structure of a neural pathway activation detection device provided by Embodiment 3 of the present invention. Figure 5 As shown, 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.

[0090] The field potential data acquisition module 310 is used to acquire the pre-stimulation field potential data and the post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

[0091] A connection strength change data determination module 320, for 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;

[0092] A behavior change data determination module 330 is used to determine the behavior change data of the experimental subject based on the video data to be detected of the experimental subject;

[0093] The activation state determination module 340 is used to determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data.

[0094] The technical solution of the present embodiment determines the connection strength change data of the neural pathway to be detected based on the acquired pre-stimulus field potential data and post-stimulus field potential data corresponding to the neural pathway to be detected of the experimental subject, determines the behavior change data of the experimental subject based on the pre-stimulus video data and post-stimulus video data of the experimental subject, determines the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data, and comprehensively determines the activation state of the neural pathway to be detected from two dimensions of brain function connection strength and behavioral information, thereby solving the problem of high misjudgment rate of neural pathway activation detection methods that rely solely on behavioral data, and improving the credibility of research results on brain functions.

[0095] Based on the above embodiment, optionally, the activation state determination module 340 is specifically used to:

[0096] When the connection strength change data satisfies the preset connection condition, determining whether there is behavior change data in the preset behavior change data set;

[0097] If yes, the activation state of the neural pathway to be detected is set to an activated state;

[0098] If not, the activation state of the neural pathway to be detected is set to an inactive state.

[0099] On the basis of the above embodiment, optionally, the pre-stimulation field potential data includes first pre-stimulation field potential data and second pre-stimulation field potential data corresponding to the first nucleus and the second nucleus in the pair of neural nuclei in the neural pathway to be detected, and the post-stimulation field potential data includes first post-stimulation field potential data and second post-stimulation field potential data corresponding to the first nucleus and the second nucleus in the pair of neural nuclei, respectively. Accordingly, the connection strength change data determination module 320 is specifically used to:

[0100] Determine the pre-stimulus connection strength corresponding to the pair of neural nuclei based on the first pre-stimulus field potential data and the second pre-stimulus field potential data;

[0101] Determining the corresponding post-stimulation connection strength of the neural nucleus pair based on the first post-stimulation field potential data and the second post-stimulation field potential data;

[0102] Based on the connection strength before stimulation and the connection strength after stimulation, the connection strength change data of the neural nucleus pair in the neural pathway to be detected is determined.

[0103] Based on the above embodiment, optionally, the field potential data acquisition module 310 is specifically used for:

[0104] Acquiring at least two channel field potential data of a target nucleus in a neural pathway to be detected within a preset time period;

[0105] Performing dimensionality reduction operations on each channel field potential data respectively to obtain at least two dimensionality-reduced field potential data, and taking the channel field potential data corresponding to the dimensionality-reduced field potential data with the largest principal component as the target field potential data corresponding to the target nucleus;

[0106] Among them, when the target nucleus is the first nucleus and the preset time period is the 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 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.

[0107] Based on the above embodiment, optionally, the parameter type of the pre-stimulus connection strength or the post-stimulus connection strength is a Granger causality value.

[0108] Based on the above embodiment, optionally, the device further includes:

[0109] The preprocessing module is used to perform preprocessing operations on the pre-stimulus field potential data and the post-stimulus field potential data respectively before determining the connection strength change data of the neural pathway to be detected based on the pre-stimulus field potential data and the post-stimulus field potential data, so as to obtain the preprocessed pre-stimulus field potential data and the post-stimulus field potential data; wherein the preprocessing operation includes filtering operation and / or downsampling operation.

[0110] On the basis of the above embodiment, optionally, the video data to be detected includes video data respectively collected by at least three video collection devices, and accordingly, the behavior change data determination module 330 is specifically used to:

[0111] For each video data, determining the two-dimensional skeleton data of the experimental object in each key image frame in the video data;

[0112] Based on the camera parameter data corresponding to each video acquisition device and each two-dimensional skeleton data, the three-dimensional posture data of the experimental object is determined, and based on the three-dimensional posture data, the behavior change data of the experimental object is determined.

[0113] The neural pathway activation detection device provided in the embodiment of the present invention can execute the neural pathway activation detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0114] Embodiment 4

[0115] Figure 6This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown in the embodiments of the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0116] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. 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 through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] A number of components in the electronic device 10 are connected to the I / O interface 15, including: 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 a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a 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 telecommunication networks.

[0118] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for detecting activation of a neural pathway.

[0119] In some embodiments, the activation detection method of a neural pathway may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the 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 execute the activation detection method of the neural pathway in any other appropriate manner (e.g., by means of firmware).

[0120] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can 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.

[0121] The computer programs for implementing the activation detection method of the neural pathway of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0122] Embodiment 5

[0123] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute a method for detecting activation of a neural pathway, the method comprising:

[0124] Acquire pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject;

[0125] 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;

[0126] Determine the behavior change data of the experimental subject based on the video data to be detected of the experimental subject;

[0127] Based on the connection strength change data and the behavior change data, the activation state of the neural pathway to be detected is determined.

[0128] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] 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 CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types 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 input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0130] The systems and techniques described herein may be implemented in a computing system that includes backend 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 frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0132] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0133] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting activation of a neural pathway, characterized in that: include: Acquire pre-stimulation field potential data and post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject; 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; Determining behavior change data of the experimental subject based on the video data to be detected of the experimental subject; Determining the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data; 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: In the case where the connection strength change data meets the preset connection condition, determining whether the behavior change data exists in the preset behavior change data set; If yes, setting the activation state of the neural pathway to be detected to an activated state; If not, the activation state of the neural pathway to be detected is set to an inactive state.

2. The method according to claim 1, characterized in that The pre-stimulus field potential data include first pre-stimulus field potential data and second pre-stimulus field potential data corresponding to the first nucleus and the second nucleus in the pair of neural nuclei in the neural pathway to be detected, respectively; the post-stimulus field potential data include first post-stimulus field potential data and second post-stimulus field potential data corresponding to the first nucleus and the second nucleus in the pair of neural nuclei, respectively; accordingly, determining the connection strength change data of the neural pathway to be detected based on the pre-stimulus field potential data and the post-stimulus field potential data includes: Determine the pre-stimulus connection strength corresponding to the pair of neural nuclei based on the first pre-stimulus field potential data and the second pre-stimulus field potential data; Determine the post-stimulation connection strength corresponding to the pair of neural nuclei based on the first post-stimulation field potential data and the second post-stimulation field potential data; Based on the pre-stimulation connection strength and the post-stimulation connection strength, the connection strength change data of the neural nucleus pair in the neural pathway to be detected is determined.

3. The method according to claim 2, characterized in that The step of obtaining the pre-stimulation field potential data and the post-stimulation field potential data corresponding to the neural pathway to be detected of the experimental subject includes: Acquiring at least two channel field potential data of the target nucleus in the neural pathway to be detected within a preset time period; Performing a dimensionality reduction operation on each of the channel field potential data to obtain at least two dimensionality-reduced field potential data, and taking the channel field potential data corresponding to the dimensionality-reduced field potential data with the largest principal component 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 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 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.

4. The method according to claim 2, characterized in that: The parameter type of the pre-stimulus connection strength or the post-stimulus connection strength is a Granger causality value.

5. The method according to claim 1, characterized in that Before determining the connection strength change data of the to-be-detected neural pathway based on the pre-stimulation field potential data and the post-stimulation field potential data, the method further includes: Preprocessing operations are performed on the pre-stimulation field potential data and the post-stimulation field potential data respectively to obtain preprocessed pre-stimulation field potential data and post-stimulation field potential data; wherein the preprocessing operation includes a filtering operation and / or a downsampling operation.

6. The method according to any one of claims 1 to 5, characterized in that: The video data to be detected includes video data respectively collected by at least three video acquisition devices. Accordingly, the determining of the behavior change data of the experimental subject based on the video data to be detected of the experimental subject includes: For each video data, determining the two-dimensional skeleton data of the experimental object in each key image frame in the video data; Based on the camera parameter data corresponding to each of the video acquisition devices and the two-dimensional skeleton data, the three-dimensional posture data of the experimental object is determined, and based on the three-dimensional posture data, the behavior change data of the experimental object is determined.

7. A neural pathway activation detection device, characterized in that: include: A field potential data acquisition module, used to acquire 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, 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; A behavior change data determination module, used to determine the behavior change data of the experimental subject based on the to-be-detected video data of the experimental subject; An activation state determination module, used to determine the activation state of the neural pathway to be detected based on the connection strength change data and the behavior change data; Among them, the activation state determination module is specifically used to: when the connection strength change data meets the preset connection condition, determine whether the behavior change data exists in the preset behavior change data set; if so, set the activation state of the neural pathway to be detected to an activated state; if not, set the activation state of the neural pathway to be detected to an unactivated state.

8. An electronic device, characterized in that: The electronic device comprises: 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, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for detecting activation of a neural pathway according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The 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 6 when executed.

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