Neuron rapid screening and identification method and system for auditory signal

Abstract

The invention discloses a neuron rapid screening and identification method and system for auditory signals, and the method comprises the steps: (a) two-photon microscopic imaging video data extraction: carrying out two-photon microscopic imaging on a biological tissue sample or a living body transfected with fluorescent protein, and capturing corresponding video sequence data; (b) performing neuron segmentation and activity information acquisition: realizing automatic segmentation of neurons in the two-photon imaging data, and calculating activity curves of the neurons one by one; (c) determining ASI and numerical analysis thereof: quantifying the neuron activity curve one by one, and obtaining ASI of a certain neuron in a specific time domain; and (d) acquiring a fixed frequency stimulation response functional unit. According to the method, screening and information identification are carried out on neurons under auditory signal stimulation, and the limitation that neuron signals are difficult to extract and data processing complexity is high in a traditional method is overcome. And meanwhile, the specific frequency sound stimulation neurons can be accurately classified, and the method has important significance for deeply exploring the auditory neuroscience field.

Description

Technical Field

[0001] The present application relates to the field of biological imaging / medical image processing, and in particular to a neuron rapid screening and identification system for auditory signals. Background Art

[0002] Auditory signal stimulation experiments use fluorescent marker proteins to label neurons, and then use a two-photon microscopy system to observe and record, which can capture the activities of single neurons and neuron clusters with subcellular spatial resolution and fast temporal resolution. The auditory stimulation experiment analysis platform has great application potential in the field of auditory neuroscience and is a new development direction.

[0003] At present, the main challenges faced by the auditory signal stimulation experiment analysis platform are: 1. The auditory system needs to extract behavior-related information from a complex sound environment, which involves non-isomorphic conversion of sound signals and encoding and decoding of sound signals; 2. How to efficiently extract neural activity curves from auditory stimulation experimental data, and then more quickly and accurately screen and identify neuronal functional units, is an important problem that needs to be solved urgently, and it is of great significance to the exploration of brain science research such as auditory neuroscience. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a system for rapid neuron screening and identification for auditory signals, so as to solve the limitations of traditional methods in neuronal signal extraction and data processing, and at the same time achieve efficient segmentation and accurate functional classification of neuronal populations for specific sound frequencies, significantly improve the detection accuracy and analysis accuracy of neuronal activity, and have guiding significance for the applied research of auditory neuroscience.

[0005] To this end, the present invention adopts the following technical solution: a neuron rapid screening and identification system for auditory signals, and its specific steps are as follows:

[0006] 1. A rapid neuron screening and identification system for auditory signals

[0007] include:

[0008] Two-photon microscopy video data extraction module: used to perform two-photon microscopy imaging on biological tissue samples or living bodies transfected with fluorescent proteins and capture corresponding video sequence data;

[0009] Neuron segmentation and activity information acquisition module: used to automatically segment all neurons in two-photon microscopy video data and calculate their activity curves one by one;

[0010] ASI data acquisition module: obtains the sound selectivity index (ASI) of a neuron in a specific time domain by quantifying the neuron activity curve one by one;

[0011] Fixed frequency stimulus response functional unit acquisition module: used to obtain the activity curves and sound selectivity index ASI of all neurons under specific frequency acoustic stimulation, and define the neuron group with ASI between 95% and 100% as the functional unit that has a significant response to the current specific frequency acoustic stimulation.

[0012] 2. A method for rapid neuron screening and identification of auditory signals

[0013] The specific steps include:

[0014] Step 1: Extraction of two-photon microscopy video data: Perform two-photon microscopy imaging on biological tissue samples or living organisms transfected with fluorescent proteins and capture the corresponding video sequence data;

[0015] Step 2: Neuron segmentation and activity information acquisition: Automatically segment all neurons in the two-photon microscopy video data obtained in step 1, and calculate their activity curves (△F / F curves) one by one;

[0016] Step 3: Determine ASI and its numerical analysis: quantify the neuron activity curves obtained in step 2 one by one, and obtain the sound selectivity index (ASI) of a neuron in a specific time domain;

[0017] Step 4: Obtaining fixed-frequency stimulus response functional units: For the neurons to be screened, under the action of acoustic stimulation of a specific frequency, the activity curves of all neurons are analyzed in detail. Based on step 3, the neuron group with an ASI between 95% and 100% is defined as a functional unit that has a significant response to the current specific frequency acoustic stimulation.

[0018] The step 1 is specifically as follows:

[0019] Step 1.1) For the biological tissue sample or living animal to be observed, the specific brain area corresponding to its auditory cortex is accurately located and transfected by injecting GCaMP6s fluorescent protein.

[0020] (The current experiment provides the situation that the injection was performed in the M2 brain region of C57 mice)

[0021] Step 1.2) After the fluorescent protein is fully expressed, imaging observation is performed through a two-photon microscope and corresponding video data is obtained.

[0022] The step 2 is specifically as follows:

[0023] Step 2.1) Calculate the corresponding threshold value (Threshold Value) for all neurons in the two-photon microscopy video using the following formula, thereby obtaining the corresponding threshold curve, and take the minimum value on the curve as the segmentation threshold:

[0024]

[0025] Where F(t) represents the accumulation of all pixel values ​​in the area marked by the current neuron in the current frame (time t); F0 represents the average value of F(t); σ represents the standard deviation;

[0026] Step 2.2) based on the segmentation threshold obtained in step 2.1), all neurons in the two-photon fluorescence microscopy video are automatically segmented and the outline of each neuron is marked;

[0027] Step 2.3) Using the segmentation results, the segmented neurons are calculated one by one using the following formula to obtain the activity curve (△F / F curve) corresponding to each neuron:

[0028]

[0029] Among them, F(t) represents the accumulation of all pixel values ​​in the area marked by the current neuron in the current frame number (time t), and F0 represents the average value of F(t).

[0030] The step 3 is specifically as follows:

[0031] Step 3.1) Analyze the extracted neuronal activity curve using two-photon microscopy data and calculate the number of firings of all neuronal stimulation responses one by one;

[0032] In step 3.1), the number of times the neuron stimulates a response is calculated by the derivative method, specifically:

[0033] Derivative the neuron firing curve, find the points where the derivative is zero and the second-order derivative is less than zero, use these points as neuron firing points, and determine the number of neuron firings by counting the number of these points.

[0034] Step 3.2) Define every a second in the imaging data as a specific time window. If the number of neuron firings reaches 2 times in this time window, it is recorded as 1, otherwise it is recorded as 0; calculate the number of times the neuron is marked in t / a time windows, where t is the total duration of the stimulus (in seconds); calculate the sound selectivity index ASI of the current neuron according to the following formula:

[0035]

[0036] If the neuron is marked as 1 in t / a time windows, its sound selectivity index ASI is 100%.

[0037] The step 4 is specifically as follows:

[0038] Step 4.1) Select a specific frequency acoustic stimulus according to research requirements, obtain the activity curves of all neurons to be screened under the specific frequency acoustic stimulus according to step 2, and analyze the dynamic information of the neurons;

[0039] Step 4.2) According to the method proposed in step 3, the neurons whose ASI values ​​are in the range of 95% to 100% under the acoustic stimulation of a specific frequency are counted, and these neurons are defined as functional units that have a significant response to the current acoustic stimulation of a specific frequency.

[0040] Beneficial effects of the present invention:

[0041] Aiming at the widely used auditory signal stimulation experiment, the present invention proposes a neuron rapid screening and identification system for auditory signals to solve the limitations of traditional methods in neuron signal extraction and data processing, achieve efficient segmentation and accurate functional classification of neuron groups for specific sound frequencies, restore the encoding mechanism of auditory information in the brain, and significantly improve the detection accuracy and analysis accuracy of neuron activity, which is of great significance for in-depth exploration of the field of auditory neuroscience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the neuron rapid screening and identification system for auditory signals provided by the present invention.

[0043] Figure 2 It is a schematic diagram of determining ASI and its numerical analysis provided by the present invention.

[0044] Figure 3 It is a schematic diagram of a fixed frequency stimulus response functional unit defined by the present invention. DETAILED DESCRIPTION

[0045] The process of the method for rapid neuron screening and identification of auditory signals is as follows Figure 1 As shown, the specific steps are as follows:

[0046] Step 1: Extracting two-photon microscopic imaging video data: Perform two-photon microscopic imaging on biological tissue samples or living organisms transfected with fluorescent proteins, and capture corresponding video sequence data;

[0047] Step 2: Neuron segmentation and activity information acquisition: Automatically segment active neurons in two-photon imaging data and calculate their activity curves (△F / F curves) one by one;

[0048] Step 3: Determine the ASI and its numerical analysis: quantify the neuron activity curves in step (b) one by one, and obtain the ASI (Auditory selectivity index) of a neuron in a specific time domain;

[0049] Step 4: Obtaining fixed-frequency stimulus response functional units: Under the action of acoustic stimulation of a specific frequency, the activity curves of all neurons are analyzed in detail. According to step (c), the neuron population with an ASI between 95% and 100% is defined as a functional unit that has a significant response to the current stimulus.

[0050] In the step 1, two-photon microscopy video data extraction is performed as follows:

[0051] For the C57BL / 6INifidc mice to be observed, for the biological tissue samples or living animals to be observed, transfection is performed by injecting GCaMP6s specific fluorescent protein in the specific area of ​​the mouse M2 brain region.

[0052] After the fluorescent protein is fully expressed, imaging observation is performed through a two-photon microscope and video data is obtained.

[0053] In the step 2, proceed as follows:

[0054] All active neurons in two-photon fluorescence microscopy videos are automatically segmented using a deep learning-based neuron segmentation algorithm, and the outline of each active neuron is marked.

[0055] Using the segmentation results, the activity curves (△F / F curves) of all neurons are calculated one by one using the following formula:

[0056]

[0057] Among them, F(t) represents the accumulation of all pixel values ​​in the marked area of ​​the neuron in the current frame number (time t), and F0 represents the average value of F(t).

[0058] In step 3, the ASI and its numerical analysis are determined as follows:

[0059] like Figure 2 As shown, the neuronal activity curve extracted by two-photon microscopy imaging data analysis is used to calculate the number of neuronal stimulation responses one by one.

[0060] Define a (1 second) as a specific time window. If the number of neuron firing reaches 2 times in this time window, it is recorded as 1, otherwise it is recorded as 0. Assuming that the total duration of the stimulus t is 60 seconds, calculate the number of times the neuron is marked in t / a (60) time windows. If the neuron is marked as 1 in t / a time windows, its sound selectivity index (ASI) is 100%.

[0061] In the step 4, proceed as follows:

[0062] like Figure 3 As shown, referring to step (b), the activity curves of all neurons under the acoustic stimulation of a specific frequency of 21 kHz are obtained to analyze the dynamic information of neurons.

[0063] According to the method proposed in step (c), the neurons whose ASI values ​​are in the range of 95% to 100% under the specific frequency stimulation are counted, and these neurons are defined as functional units that have a significant response to the current specific stimulation.

[0064] It can be used to explore which neurons will show significant responses when different frequencies (e.g. 7kHz, 14kHz, 21kHz, 28kHz) are stimulated, and these neurons with significant responses are classified as neuronal functional unit groups under the corresponding frequency stimulation, and further analyze the mapping relationship between different frequency stimulations and specific neuronal functional unit groups.

Claims

1. A neuron rapid screening and identification system for auditory signals, characterized in that: include: Two-photon microscopy video data extraction module: used to perform two-photon microscopy imaging on biological tissue samples or living bodies transfected with fluorescent proteins and capture corresponding video sequence data; Neuron segmentation and activity information acquisition module: used to automatically segment all neurons in two-photon microscopy video data and calculate their activity curves one by one; ASI data acquisition module: obtains the sound selectivity index (ASI) of a neuron in a specific time domain by quantifying the neuron activity curve one by one; Fixed frequency stimulus response functional unit acquisition module: used to obtain the activity curves and sound selectivity index ASI of all neurons under specific frequency acoustic stimulation, and define the neuron group with ASI between 95% and 100% as the functional unit that has a significant response to the current specific frequency acoustic stimulation.

2. A method for rapid neuron screening and identification of auditory signals based on the system of claim 1, characterized in that: The specific steps include: Step 1: Two-photon microscopy video data extraction: Perform two-photon microscopy imaging on biological tissue samples or living bodies transfected with fluorescent proteins and capture the corresponding video sequence data; Step 2: Neuron segmentation and activity information acquisition: Automatically segment all neurons in the two-photon microscopy video data obtained in step 1, and calculate their activity curves one by one; Step 3: Determine the ASI and its numerical analysis: quantify the neuron activity curves obtained in step 2 one by one, and obtain the sound selectivity index ASI of a neuron in a specific time domain; Step 4: Obtaining fixed-frequency stimulus response functional units: For the neurons to be screened, under the action of acoustic stimulation of a specific frequency, the activity curves of all neurons are analyzed in detail. Based on step 3, the neuron group with an ASI between 95% and 100% is defined as a functional unit that has a significant response to the current specific frequency acoustic stimulation.

3. The method for rapid neuron screening and identification of auditory signals according to claim 2, characterized in that: The step 1 is specifically as follows: Step 1.1) For the biological tissue sample or living animal to be observed, the specific brain area corresponding to its auditory cortex is accurately located and transfected by injecting fluorescent protein. Step 1.2) After the fluorescent protein is fully expressed, imaging observation is performed through a two-photon microscope and corresponding video data is obtained.

4. The method for rapid neuron screening and identification of auditory signals according to claim 2, characterized in that: The step 2 is specifically as follows: Step 2.1) Calculate the corresponding thresholds for all neurons in the two-photon microscopy video using the following formula, thereby obtaining the corresponding threshold curve, and take the minimum value on the curve as the segmentation threshold: Where F(t) represents the accumulation of all pixel values ​​in the area marked by the current neuron in the current frame; F0 represents the average value of F(t); σ represents the standard deviation; Step 2.2) based on the segmentation threshold obtained in step 2.1), all neurons in the two-photon fluorescence microscopy video are automatically segmented and the outline of each neuron is marked; Step 2.3) Using the segmentation results, the segmented neurons are calculated one by one using the following formula to obtain the activity curve corresponding to each neuron: Among them, F(t) represents the accumulation of all pixel values ​​in the area marked by the current neuron in the current frame number, and F0 represents the average value of F(t).

5. The method for rapid neuron screening and identification of auditory signals according to claim 2, characterized in that: The step 3 is specifically as follows: Step 3.1) Analyze the extracted neuronal activity curve using two-photon microscopy data and calculate the number of firings of all neuronal stimulation responses one by one; In step 3.1), the number of times the neuron stimulates a response is calculated by the derivative method, specifically: Derivative the neuron firing curve, find the points where the derivative is zero and the second-order derivative is less than zero, use these points as neuron firing points, and determine the number of neuron firings by counting the number of these points. Step 3.2) Define every a seconds in the imaging data as a specific time window. If the number of neuron firing reaches 2 times in this time window, it is recorded as 1, otherwise it is recorded as 0; calculate the number of times the neuron is marked in t / a time windows, where t is the total duration of the stimulation; The sound selectivity index ASI of the current neuron is calculated according to the following formula:

6. The method for rapid neuron screening and identification of auditory signals according to claim 2, characterized in that: The step 4 is specifically as follows: Step 4.1) Select a specific frequency acoustic stimulus according to research requirements, obtain the activity curves of all neurons to be screened under the specific frequency acoustic stimulus according to step 2, and analyze the dynamic information of the neurons; Step 4.2) According to the method proposed in step 3, the neurons whose ASI values ​​are in the range of 95% to 100% under the acoustic stimulation of a specific frequency are counted, and these neurons are defined as functional units that have a significant response to the current acoustic stimulation of a specific frequency.

Images