Laboratory mouse calcium imaging wireless monitoring method, system and monitoring equipment

Abstract

The invention discloses a laboratory mouse calcium imaging wireless monitoring method, system and monitoring device.The method comprises the steps that an optical detection structure is arranged beside a laboratory mouse intracranial calcium indicator marking area, and a fluorescence signal is captured; constructing a calcium imaging original data mapping model, and converting the fluorescence signal into initial calcium imaging data; pure calcium imaging data are obtained through adaptive filtering and noise reduction, and lightweight compression is carried out based on time sequence periodic characteristics; packaging transmission data through a low-power-consumption wireless transmission model of a plurality of groups of preset independent channels; after the external receiving terminal decompresses, visual information is generated through a calcium imaging reduction model; the system and the equipment correspond to the method, and are integrated with an optical detection structure, a signal processor, a wireless transmitter and other core components; the device thoroughly gets rid of the constraint of a long optical fiber, is miniaturized, low in power consumption, resistant to interference and good in biological compatibility, guarantees the accuracy of monitoring data and experimental continuity, and is suitable for related research of brain activities of laboratory mice.

Description

Technical Field

[0001] This invention relates to the field of calcium imaging monitoring technology, specifically to a low-power, interference-resistant wireless monitoring method, system, and monitoring equipment for calcium imaging in laboratory mice. Background Technology

[0002] Calcium imaging is one of the core technologies in neuroscience and physiology for studying brain activity in laboratory mice. It converts the electrical activity of nerve cells into detectable fluorescent signals through the specific binding reaction of calcium indicators with calcium ions, thereby enabling dynamic monitoring of the function of specific brain regions. Traditional calcium imaging monitoring relies on fiber optic recorders, which require long optical fibers to run through the intracranial and external detection systems of the laboratory mouse. The optical fiber simultaneously performs the dual functions of transmitting excitation light and extracting fluorescence signals, serving as the core signal transmission carrier in early calcium imaging techniques. In basic research, this technology has provided crucial data support for revealing neural circuit connections and behavioral correlation mechanisms in the mouse brain, and is widely used in various cutting-edge areas such as neurodegenerative disease model research, drug target screening, and cognitive function analysis, becoming an indispensable tool in life science research.

[0003] Traditional fiber optic recorders suffer from numerous technical drawbacks due to their long fiber optic cables, severely limiting the accuracy and practicality of experiments. From a fixation perspective, long fibers require surgical implantation into the skull of mice for long-term fixation. The interface between the fiber and the skull is prone to displacement due to mouse movement, leading to loosening or even detachment, affecting experimental continuity. Furthermore, the rigidity and length of the fiber make it susceptible to tangling and bending as mice move around in their cages. This not only restricts their normal eating, grooming, and movement but also causes intracranial tissue damage due to mechanical pulling, interfering with the mice's physiological state. More importantly, mice have an instinct to bite foreign objects, making the exposed portion of the long fiber optic cable highly susceptible to damage, leading to signal transmission interruption and direct data loss. Re-implantation after fiber damage requires surgery, increasing experimental costs and causing secondary harm to the mice, affecting sample consistency.

[0004] Besides the issues of fixation and biting, long fiber optic transmission also suffers from poor signal stability and limitations in experimental settings. On one hand, during the transmission of excitation and fluorescence signals, long fibers experience signal attenuation due to fiber loss and scattering. In particular, the fluorescence signal itself is weak, and the signal-to-noise ratio decreases significantly over long distances, affecting the accuracy of calcium ion concentration detection. On the other hand, the physical confinement of the fiber optic cable prevents the mice from escaping the limited range of the external detection system, making it difficult to conduct long-term monitoring experiments under free-movement conditions. Brain activity data under natural behavioral conditions is crucial for the reliability of research results. Furthermore, traditional fiber optic recorders have bulky external detection systems that must be fixed in the experimental environment, not only occupying space but also generating electromagnetic interference that further affects signal detection quality, causing numerous inconveniences for experimental design and operation.

[0005] As life science research moves towards greater precision, long-term focus, and non-invasiveness, the limitations of traditional fiber optic recorders are becoming increasingly apparent, making researchers' demand for novel calcium imaging monitoring technologies ever more urgent. While existing wireless monitoring technologies have some applications, they face multiple technical challenges in calcium imaging scenarios involving laboratory mice, including miniaturization and integration, low power consumption and battery life, biocompatibility, and signal transmission stability. Laboratory mice are small, requiring lightweight and compact head-mounted devices to avoid interfering with their normal physiological activities. Simultaneously, the need for long-term monitoring demands high battery life, requiring a balance between low power consumption and signal transmission rate. Furthermore, since the device comes into direct contact with the mouse's body, biocompatible materials must be used to avoid immune rejection or skin irritation.

[0006] Therefore, developing a wireless monitoring technology for calcium imaging in laboratory mice that can completely break free from the constraints of long optical fibers and integrate miniaturization, wireless technology, low power consumption, high stability, and good biocompatibility has become the key to solving current technical bottlenecks and promoting in-depth research in related fields. Summary of the Invention

[0007] To address the aforementioned technical problems, this application discloses a wireless monitoring method, system, and monitoring device for calcium imaging in laboratory mice; the low-power, interference-resistant wireless monitoring method for calcium imaging in laboratory mice specifically includes:

[0008] An optical detection structure is set up next to the calcium indicator marking area in the brain of experimental mice. Excitation light is emitted into the calcium indicator marking area to trigger the calcium indicator to release a fluorescent signal and capture the fluorescent signal.

[0009] Based on the correspondence between fluorescence signals and calcium ion concentrations, a mapping model for raw calcium imaging data is constructed to convert fluorescence signals into initial calcium imaging data.

[0010] The initial calcium imaging data were preprocessed to reduce noise, and an adaptive filtering algorithm was used to eliminate ambient light interference and electronic noise to obtain clean calcium imaging data.

[0011] Based on the temporal periodicity characteristics of pure calcium imaging data, a lightweight data compression model is constructed to compress the pure calcium imaging data.

[0012] A low-power wireless transmission model is constructed, and the compressed calcium imaging data is encapsulated in a preset frame format and transmitted to an external receiving terminal through multiple preset independent transmission channels.

[0013] After receiving the compressed calcium imaging data, the external receiving terminal calls the data decompression model to decompress it, and then uses the calcium imaging restoration model to convert the decompressed data into visualized calcium imaging information, thus completing calcium imaging monitoring.

[0014] Preferably, the step of constructing a calcium imaging raw data mapping model based on the correspondence between fluorescence signals and calcium ion concentration specifically includes:

[0015] The intensity of the fluorescence signal generated by the calcium indicator at different calcium ion concentrations was experimentally measured to establish a calcium ion concentration... With fluorescence signal intensity The correspondence is expressed by the formula: ,in, The baseline fluorescence signal intensity of the calcium indicator in the absence of calcium ions. This is the coefficient representing the change in fluorescence signal intensity with calcium ion concentration;

[0016] Based on the construction of a raw data mapping model for calcium imaging, the intensity of fluorescence signals acquired in real time will be mapped... Substitute the values ​​and calculate the corresponding calcium ion concentration. and will As initial calcium imaging data.

[0017] Preferably, the step of using an adaptive filtering algorithm to perform noise reduction preprocessing on the initial calcium imaging data specifically includes:

[0018] Set the adaptive filtering window size to The time sequence of initial calcium imaging data For the processing object; calculate the mean of the data within the filtering window. and standard deviation The formula is: , The value deviating from the mean by more than the deviation threshold within the window will be... The data was identified as noisy data, and the mean of the non-noisy data within the window was used to replace the noisy data, resulting in denoised clean calcium imaging data. .

[0019] Preferably, the lightweight data compression model constructed based on the temporal periodicity characteristics of pure calcium imaging data specifically includes:

[0020] Imaging data of pure calcium Time series analysis is performed, and the period of the data is extracted using Fourier transform. Determine the periodic repetition pattern of the data; construct a lightweight data compression model for a given period. Pure calcium imaging data Complete retention is performed; for data in subsequent periods, only the difference between the data at the corresponding time point in the previous period and the data in the next period is stored. The formula is: ,in, For the first Within each period Pure calcium imaging data, For the first Within each period The pure calcium imaging data is compressed using this method.

[0021] Preferably, the construction of the low-power wireless transmission model pre-sets multiple independent transmission channels, specifically including:

[0022] Construct a low-power wireless transmission model, with preset... Each group of channels has its own independent transmission channel, and each group of channels corresponds to a different center frequency. The model detects the signal interference intensity of each channel in real time. Set the interference strength threshold ,Will The channel is determined to be an idle channel; when multiple idle channels exist, the model calculates the transmission rate of each idle channel. The idle channel with the highest transmission rate is selected as the data transmission channel.

[0023] Preferably, the step of encapsulating the compressed calcium imaging data according to a preset frame format and adding a checksum during the data frame encapsulation process specifically includes: setting the data frame format to include a frame header, a data segment, a checksum segment, and a frame trailer, wherein the frame header is used to identify the start of the data frame, the data segment is used to store the compressed calcium imaging data, the checksum segment is used to store the checksum, and the frame trailer is used to identify the end of the data frame; and using a cyclic redundancy check algorithm to calculate the checksum of the compressed calcium imaging data. The formula is: ,in, For compressed calcium imaging data, This is a cyclic redundancy check function; the compressed calcium imaging data is filled into the data segment, and the calculated check code is filled into the check code segment to complete the encapsulation of the data frame.

[0024] Preferably, the external receiving terminal invokes a data decompression model to decompress the compressed calcium imaging data, specifically including:

[0025] The external receiving terminal receives the encapsulated data frame and extracts the compressed calcium imaging data from the data segment. The checksum in the checksum segment ; Invoke the data decompression model based on the checksum verify For completeness, if the verification passes, the model extracts one cycle. Complete pure calcium imaging data Then based on the stored difference By recovering pure calcium imaging data from subsequent cycles, the formula is: The compressed calcium imaging data was decompressed to obtain a complete and clean calcium imaging data sequence.

[0026] Preferably, the step of converting the decompressed data into visualized calcium imaging information using a calcium imaging reconstruction model specifically includes:

[0027] A calcium imaging reconstruction model is constructed, defining the mapping relationship between calcium ion concentration and image pixel grayscale values, using the following formula: ,in, The grayscale value of the image pixels. This is the proportionality coefficient of gray value as a function of calcium ion concentration. The base grayscale value; the calcium ion concentration obtained after decompression. Substitute the values ​​and calculate the corresponding pixel grayscale values. Based on the spatial distribution of the calcium indicator marking regions in the brains of experimental mice, the pixel grayscale values ​​corresponding to each location were... Spatial mapping is performed to generate two-dimensional or three-dimensional visualized calcium imaging images, thus restoring calcium imaging information.

[0028] A low-power, interference-resistant wireless monitoring system for calcium imaging in laboratory mice includes a fluorescence signal acquisition unit, a raw data mapping unit, a data processing unit, a wireless transmission unit, a data decompression unit, and a calcium imaging restoration unit.

[0029] The output of the fluorescence signal acquisition unit is connected to the input of the raw data mapping unit to acquire the fluorescence signal generated by the excitation of calcium indicator in the brain of experimental mice and transmit the fluorescence signal to the raw data mapping unit.

[0030] The output of the raw data mapping unit is connected to the input of the data denoising unit. Based on the correspondence between fluorescence signal and calcium ion concentration, a raw data mapping model for calcium imaging is constructed, the fluorescence signal is converted into initial calcium imaging data, and then transmitted to the data denoising unit.

[0031] The data processing unit is used to perform noise reduction preprocessing on the initial calcium imaging data using an adaptive filtering algorithm to obtain pure calcium imaging data. Based on the temporal periodicity characteristics of the pure calcium imaging data, a lightweight data compression model is constructed to compress the pure calcium imaging data and transmit the compressed calcium imaging data to the wireless transmission unit.

[0032] The wireless transmission unit is wirelessly connected to an external receiver to build a low-power wireless transmission model. It encapsulates the compressed calcium imaging data according to a preset frame format and adds a check code, then transmits it to the receiver through a selected idle channel.

[0033] The input end of the data decompression unit is connected to the receiving end, and the output end of the data decompression unit is connected to the input end of the calcium imaging restoration unit. It is used to call the data decompression model to decompress the received compressed calcium imaging data, obtain complete and pure calcium imaging data, and transmit it to the calcium imaging restoration unit.

[0034] The output of the calcium imaging restoration unit is connected to the input of the display storage unit. It is used to convert the decompressed data into visualized calcium imaging information through the calcium imaging restoration model, so as to realize wireless monitoring of calcium imaging in laboratory mice.

[0035] A low-power, interference-resistant wireless monitoring device for calcium imaging in laboratory mice includes a head-integrated component, a receiving terminal, and a terminal device.

[0036] The head integrated component is fitted to the head of the experimental mouse and integrates an optical detection structure, a signal processor, and a wireless transmitter. The optical detection structure includes a miniature excitation light source and a miniature fluorescence detector. The miniature excitation light source emits excitation light to excite the intracranial calcium indicator to generate a fluorescence signal. The miniature fluorescence detector collects the fluorescence signal and transmits it to the signal processor. The signal processor converts the fluorescence signal into compressed calcium imaging data, and the wireless transmitter wirelessly sends the compressed data to the receiving terminal.

[0037] After receiving the compressed data, the receiving terminal decompresses it and transmits it to the terminal device.

[0038] The terminal device includes a data processor and a display memory. The data processor converts the decompressed calcium imaging data into visualized calcium imaging information, and the display memory displays and stores the information in real time, thereby realizing wireless monitoring of calcium imaging in laboratory mice.

[0039] Compared with the prior art, the technical solution of this application has the following technical effects:

[0040] This invention achieves direct excitation light emission and fluorescence signal capture by setting an optical detection structure on the side of the mouse's skull, completely eliminating the physical constraints of traditional long optical fibers. It avoids the problems of difficult fiber fixation and easy damage by mice, and eliminates the restriction of optical fiber on the mouse's movement, allowing the mouse to freely perform daily behaviors such as eating and grooming, ensuring its physiological stability and laying the foundation for obtaining real and reliable calcium imaging data.

[0041] In the data processing stage, this invention achieves accurate conversion, pure extraction, and efficient compression of calcium imaging data by constructing an original data mapping model, an adaptive filtering and noise reduction model, and a lightweight compression model. It not only accurately converts fluorescence signals into calcium imaging data reflecting calcium ion concentration, but also eliminates ambient light and electronic noise interference, improves data quality, and reduces data transmission volume, providing convenience for subsequent wireless transmission. This ensures that the data maintains integrity during transmission while adapting to low-power transmission requirements.

[0042] The low-power wireless transmission model of this invention significantly improves the stability and anti-interference capability of data transmission. By pre-setting multiple independent transmission channels and dynamically selecting idle and high-speed channels, combined with data frame encapsulation and checksum design, electromagnetic interference from other electronic devices in the laboratory is effectively avoided. This ensures that compressed calcium imaging data can be stably transmitted to the receiving terminal, preventing data loss or transmission interruption due to interference and ensuring the continuity and reliability of the monitoring process.

[0043] The application of the decompression processing and calcium imaging restoration model of the external receiving terminal of this invention enables efficient recovery and visualization of calcium imaging data. The receiving terminal can accurately decompress the data and restore complete and pure calcium imaging data. Then, it generates a visualized calcium imaging image through the mapping relationship between calcium ion concentration and pixel gray value, allowing researchers to intuitively obtain the activity of calcium indicator-marked areas in the brains of experimental mice. This provides clear and accurate observational basis for subsequent research and analysis, and improves the efficiency of experimental research.

[0044] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0045] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0047] Based on the description of the figures and their corresponding technical content in the document, the titles of the figures are as follows:

[0048] Figure 1 A schematic diagram of the process steps of a wireless monitoring method for calcium imaging in laboratory mice;

[0049] Figure 2 A schematic diagram of the data flow logic of a wireless monitoring method for calcium imaging in laboratory mice;

[0050] Figure 3 A schematic diagram of the functional unit connections of a wireless monitoring system for calcium imaging in laboratory mice;

[0051] Figure 4 A schematic diagram of the component structure and signal flow of a wireless monitoring device for calcium imaging in laboratory mice;

[0052] Figure 5 A graph showing the trend of fluorescence signal intensity versus calcium ion concentration in calcium imaging monitoring of experimental mice;

[0053] Figure 6 A graph showing the changes in fluorescence signal-to-noise ratio and data transmission bit error rate during calcium imaging monitoring in experimental mice.

[0054] Figure 7 A schematic diagram of the CA1 region of the mouse brain labeled and visualized using 24-hour nodal calcium imaging. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0056] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0057] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0058] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0059] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0060] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0061] Example 1

[0062] This embodiment mainly describes a low-power, interference-resistant wireless monitoring method for calcium imaging in laboratory mice, such as... Figures 1-2 As shown, it specifically includes:

[0063] An optical detection structure is set up next to the calcium indicator marking area in the brain of experimental mice. Excitation light is emitted into the calcium indicator marking area to trigger the calcium indicator to release a fluorescent signal and capture the fluorescent signal.

[0064] Based on the correspondence between fluorescence signals and calcium ion concentrations, a mapping model for raw calcium imaging data is constructed to convert fluorescence signals into initial calcium imaging data.

[0065] The initial calcium imaging data were preprocessed to reduce noise, and an adaptive filtering algorithm was used to eliminate ambient light interference and electronic noise to obtain clean calcium imaging data.

[0066] Based on the temporal periodicity characteristics of pure calcium imaging data, a lightweight data compression model is constructed to compress the pure calcium imaging data.

[0067] A low-power wireless transmission model is constructed, and the compressed calcium imaging data is encapsulated in a preset frame format and transmitted to an external receiving terminal through multiple preset independent transmission channels.

[0068] After receiving the compressed calcium imaging data, the external receiving terminal calls the data decompression model to decompress it, and then uses the calcium imaging restoration model to convert the decompressed data into visualized calcium imaging information, thus completing calcium imaging monitoring.

[0069] Furthermore, an optical detection structure was set up to capture the fluorescence signal. The optical detection structure includes a miniature excitation source and a miniature fluorescence detector, which are placed adjacent to each other next to the calcium indicator-marked area in the brain of the experimental mice. A miniature light shield is placed between the miniature excitation source and the miniature fluorescence detector. The miniature excitation source is a miniature VCSEL (vertical cavity surface emission laser) with dimensions of 0.5mm×0.5mm×2mm. The wavelength of the excitation light emitted is set to 488nm, which matches the excitation wavelength of calcium indicators (such as GCaMP series), and can accurately trigger the release of fluorescence signals by calcium indicators. The miniature fluorescence detector is a high-gain silicon photomultiplier tube (SiPM) used to directly capture the fluorescence signal with a wavelength of about 510nm released after the calcium indicator is excited. The miniature light shield is made of biocompatible titanium alloy material. Its size is adapted to the installation distance between the miniature excitation source and the miniature fluorescence detector, which can block the excitation light from directly incident on the miniature fluorescence detector, avoid interference of the excitation light on the fluorescence signal, and ensure the purity of the captured fluorescence signal.

[0070] Furthermore, a calcium imaging raw data mapping model was constructed for the initial calcium imaging data conversion. This model, a mathematical model based on the linear relationship between fluorescence signal intensity and calcium ion concentration, was used to quantitatively convert the captured fluorescence signal into initial calcium imaging data reflecting intracranial calcium ion concentration. Specifically, the intensity of the fluorescence signal generated by the calcium indicator was measured under different known calcium ion concentrations using a controlled variable method, establishing the calcium ion concentration... The fundamental correspondence between fluorescence signal intensity I and fluorescence signal intensity I is expressed mathematically as follows: ,in The baseline fluorescence signal intensity of the calcium indicator in a calcium-free environment was determined and fixed by a blank control experiment. The proportionality coefficient of fluorescence signal intensity as a function of calcium ion concentration is calculated by linearly fitting multiple sets of measured data. This coefficient reflects the sensitivity of the calcium indicator to calcium ions.

[0071] During real-time monitoring, the intensity I_{real} of the real-time fluorescence signal captured by the optical detection structure is substituted into the above model expression, and then... The calculation process yields the calcium ion concentration corresponding to the real-time fluorescence signal. The calcium ion concentration data was used as the initial calcium imaging data to complete the conversion from fluorescence signal to quantitative data.

[0072] Furthermore, the adaptive filtering and noise reduction preprocessing of the initial calcium imaging data and the acquisition of clean calcium imaging data are specifically as follows: An adaptive filtering algorithm is used to specifically eliminate ambient light interference and electronic noise in the initial calcium imaging data. Its core is to achieve accurate identification and replacement of noisy data by dynamically adjusting the noise reduction rules within the filtering window. The size of the adaptive filtering window is set to... , The sampling frequency and noise characteristics of the calcium imaging data are preset positive integers, and the time sequence of the initial calcium imaging data is used as the basis. ( The time series index (representing the sequential order of data collection) is the processing object; for each time series index... Within the corresponding filter window Data points ( The range of values ​​is to ) Perform statistical calculations, through Calculate the mean of the data within the window. ,pass Calculate the standard deviation of the data within the window. Set the deviation threshold to , As a constant preset based on noise distribution characteristics, the window that satisfies Data points identified as noise are replaced by the mean of all non-noise data points within the filtering window. The resulting time-series data sequence after replacement is the clean calcium imaging data after noise removal. .

[0073] Furthermore, a lightweight data compression model was constructed to compress pure calcium imaging data. This model is based on the temporal periodicity of the pure calcium imaging data. By retaining complete data within a period and storing the difference between subsequent periods and the previous period, effective data reduction is achieved without loss of key information. Specifically, for pure calcium imaging data… Time-series periodicity analysis is performed by using Fourier transform to decompose the data in the frequency domain and extracting the principal period of the data. , The smallest time unit that repeats in the data time series is determined by the period corresponding to the frequency domain peak; based on this period... Construct a lightweight data compression model, with model settings for the first cycle. Pure calcium imaging data This data is stored in its entirety, serving as the baseline for data recovery in subsequent periods; for the first... Pure calcium imaging data within a period of time The model no longer stores complete data, but instead calculates each time series moment. The corresponding data for this period and the first The difference between the data at corresponding time points in each period The expression for calculating the difference is: Only store this difference data. The compression process for all pure calcium imaging data was completed using the above method, resulting in compressed calcium imaging data. .

[0074] Furthermore, a low-power wireless transmission model is constructed and compressed data is transmitted. The low-power wireless transmission model is used to achieve stable and low-power wireless transmission of compressed calcium imaging data. The core of the model includes three major functional modules: multi-channel preset, channel selection, and data frame encapsulation.

[0075] Construct a low-power wireless transmission model, with preset... Group independent transmission channels, The center frequency is a positive integer determined based on the electromagnetic spectrum characteristics of the laboratory environment, and each group of channels corresponds to a unique center frequency. The channel type adopts Bluetooth Low Energy (BLE 5.0 and above) or Ultra Wideband (UWB) technology; the model detects the signal interference intensity of each channel in real time. , To set an interference intensity threshold for the interference quantization value obtained through channel signal power spectral density analysis. , The critical value determined based on the data transmission error rate requirement will satisfy... The channel is determined to be an idle channel; when multiple idle channels exist, the model calculates the transmission rate of each idle channel. , The actual data transmission rate of the channel is the measured value. The idle channel with the highest transmission rate is selected as the data transmission channel; the compressed calcium imaging data... Data frames are encapsulated, and the data frame format is defined, including a frame header, data segment, checksum segment, and frame trailer. The frame header is a fixed-length identifier sequence used to identify the start of the data frame, and the data segment is used to store the compressed calcium imaging data. The checksum segment is used to store the CRC checksum, which is calculated using the Cyclic Redundancy Check (CRC) algorithm. The calculation expression is: ,in It is a cyclic redundancy check function, and the frame tail is a fixed-length end sequence used to identify the end of the data frame; after encapsulation, the data frame is transmitted to the external receiving terminal through the selected transmission channel.

[0076] Furthermore, after receiving the data frame, the external receiving terminal sequentially performs data decompression and calcium imaging restoration operations to obtain visualized calcium imaging information; the data decompression process is implemented through a data decompression model, specifically as follows:

[0077] External receiving terminal extracts compressed calcium imaging data from data segments in data frames The CRC checksum of the checksum segment is used to call the data decompression model, and the CRC checksum is used to perform the data decompression. Integrity verification is performed, and the verification process involves calculating using the same cyclic redundancy check function. The verification value is compared with the received CRC; if they match, the verification passes. After successful verification, the model extracts... The first cycle of storage Complete pure calcium imaging data within Then, based on the stored difference data ,pass The calculation formula is used to sequentially recover the pure calcium imaging data of all subsequent cycles, thus obtaining a complete pure calcium imaging data sequence.

[0078] Visualizing calcium imaging information is achieved through a calcium imaging reconstruction model. This model is a transformation model built upon the mapping relationship between calcium ion concentration and image pixel grayscale values. Specifically, it involves constructing a calcium imaging reconstruction model and establishing a linear mapping relationship between calcium ion concentration and image pixel grayscale values. The mathematical expression is as follows: ,in The grayscale value of the image pixels. This is the proportionality coefficient of gray value as a function of calcium ion concentration. Based on grayscale values, and All are constants preset based on the requirements of image display effect and concentration recognition accuracy; the calcium ion concentration at each time step and spatial location obtained after decompression. Substituting into the mapping expression above, the corresponding pixel grayscale value is calculated. According to the spatial distribution coordinates of the calcium indicator marking area in the brain of experimental mice, the pixel grayscale values ​​corresponding to each spatial location were... Spatial mapping is performed to generate two-dimensional or three-dimensional visualized calcium imaging images, thereby restoring calcium imaging information and realizing calcium imaging monitoring.

[0079] This embodiment details how to directly capture fluorescence signals by setting up an optical detection structure in the skull of experimental mice, and achieve accurate conversion of fluorescence signals into initial calcium imaging data, effective noise filtering, and efficient data compression through multi-model collaborative processing. Then, through low-power anti-interference wireless transmission and subsequent decompression and imaging restoration, the calcium imaging data transmission is ensured to be stable and accurate. This provides an unconstrained and highly reliable technical solution for monitoring brain activity in experimental mice, and is suitable for experimental research needs of different durations.

[0080] Example 2

[0081] This embodiment describes in detail a low-power, interference-resistant wireless monitoring system for calcium imaging in laboratory mice, such as... Figure 3 As shown, the system includes a fluorescence signal acquisition unit, a raw data mapping unit, a data processing unit, a wireless transmission unit, a data decompression unit, and a calcium imaging restoration unit. Through the orderly connection and collaborative work of these functional units, wireless monitoring of calcium imaging in experimental mice is achieved. The connection relationship and functional collaboration process of each unit are as follows:

[0082] The fluorescence signal acquisition unit, serving as the starting point for system signal acquisition, has the core function of acquiring the fluorescence signal generated after the calcium indicator in the brain of experimental mice is excited. The output of this unit is directly connected to the input of the raw data mapping unit. Once the fluorescence signal acquisition unit captures the fluorescence signal, it immediately transmits the signal to the raw data mapping unit, providing the original signal source for subsequent data conversion.

[0083] After receiving the fluorescence signal from the fluorescence signal acquisition unit, the raw data mapping unit constructs a calcium imaging raw data mapping model based on the correspondence between the fluorescence signal and calcium ion concentration, converting the fluorescence signal into initial calcium imaging data that reflects the calcium ion concentration. Since the initial calcium imaging data requires further processing to eliminate interference, the output of the raw data mapping unit is connected to the input of the data processing unit, transmitting the converted initial calcium imaging data to the data processing unit.

[0084] The data processing unit performs dual functions of data denoising and compression. First, an adaptive filtering algorithm is used to preprocess the initial input calcium imaging data to remove ambient light interference and electronic noise, resulting in clean calcium imaging data. Then, a lightweight data compression model is constructed based on the temporal periodicity characteristics of the clean calcium imaging data to compress the data and reduce data transmission volume. To enable wireless data transmission, the output of the data processing unit is connected to the input of the wireless transmission unit, transmitting the compressed calcium imaging data to the wireless transmission unit.

[0085] The wireless transmission unit is crucial for realizing wireless communication in the system, establishing a wireless connection with the external receiver. This unit first constructs a low-power wireless transmission model, encapsulates the received compressed calcium imaging data according to a preset frame format, and adds a checksum to ensure data transmission accuracy. Then, through an idle channel selected by the model, it wirelessly transmits the encapsulated data to the external receiver, completing the data transfer from within the system to the receiver.

[0086] After receiving data from the wireless transmission unit, the receiving end needs to transmit the data to the data decompression unit for processing. Therefore, the input of the data decompression unit is directly connected to the receiving end. The data decompression unit calls the data decompression model to decompress the received compressed calcium imaging data and recover the complete and pure calcium imaging data. To realize the visualization of calcium imaging information, the output of the data decompression unit is connected to the input of the calcium imaging restoration unit, transmitting the decompressed pure calcium imaging data to the calcium imaging restoration unit.

[0087] After receiving purified calcium imaging data, the calcium imaging reconstruction unit transforms it into visually representable calcium imaging information using a calcium imaging reconstruction model. Since this visual calcium imaging information needs to be displayed and stored, the output of the calcium imaging reconstruction unit is connected to the input of the display and storage unit. This transmits the visual calcium imaging information to the display and storage unit, which then performs real-time display and long-term data storage, enabling wireless monitoring of calcium imaging in laboratory mice.

[0088] This embodiment details a low-power, anti-interference wireless monitoring system for calcium imaging in laboratory mice. Through the orderly connection and collaborative work of functional units such as the fluorescence signal acquisition unit, the raw data mapping unit, and the data processing unit, a closed-loop process from fluorescence signal acquisition, data conversion, noise reduction and compression, wireless transmission to image reconstruction is achieved. Each unit has a clear division of labor and close connection, which can accurately complete the processing and transmission of calcium imaging data, ensure data stability through low-power wireless transmission design and verification mechanism, and realize intuitive presentation and retention of monitoring results through the display and storage unit. This effectively supports the efficient implementation of wireless monitoring for calcium imaging in laboratory mice and meets the needs of scientific research for system functionality and reliability.

[0089] Example 3

[0090] This embodiment describes in detail a low-power, interference-resistant wireless monitoring device for calcium imaging in laboratory mice, such as... Figure 4 As shown, it consists of three parts: a header integration component, a receiving terminal, and a terminal device, specifically:

[0091] The head-integrated component is the core part that directly interacts with the laboratory mouse to achieve signal acquisition and preliminary processing. It features a streamlined design that conforms to the contours of the mouse's head. The outer shell is made of medical-grade polydimethylsiloxane (PDMS) material, and its weight is controlled to less than 5g. It is fixed to the mouse's head using biocompatible medical tape or a miniature titanium alloy fixation bracket to avoid affecting the mouse's normal movement. Internally, it integrates three main devices: an optical detection structure, a signal processor, and a wireless transmitter. These devices work closely together to complete signal acquisition and preliminary processing.

[0092] The optical detection structure comprises a miniature excitation source and a miniature fluorescence detector, which are arranged adjacent to each other and separated by a miniature light-shielding plate. The miniature excitation source emits excitation light of a specific wavelength into the calcium indicator-labeled area in the brain of the experimental mice, triggering the calcium indicator to release a fluorescence signal; the miniature fluorescence detector directly captures this fluorescence signal, avoiding signal loss and interference caused by traditional long optical fiber transmission; the miniature light-shielding plate isolates the excitation light from the fluorescence signal, ensuring the purity of the fluorescence signal acquisition.

[0093] The signal processor is connected to the signal output of the optical detection structure. After receiving the fluorescence signal captured by the miniature fluorescence detector, it first converts the analog fluorescence signal into digital calcium imaging data, then performs noise reduction preprocessing on the data to eliminate ambient light and electronic noise interference, and finally performs compression processing based on the time-series periodicity characteristics of the data to reduce the amount of subsequent data transmission and provide a suitable data format for wireless transmission.

[0094] The wireless transmitter is connected to the output of the signal processor. After receiving the compressed calcium imaging data, it first detects multiple preset independent transmission channels, selects idle channels with low interference intensity and high transmission rate, encapsulates the compressed data according to the preset frame format and adds a check code, and then wirelessly transmits the data to the receiving terminal through the selected channel, realizing low-power and anti-interference data transmission.

[0095] The receiving terminal is a small, portable device with data reception and preliminary decompression capabilities. It establishes a wireless communication connection with the wireless transmitter of the head-mounted integrated component, enabling it to stably receive encapsulated data frames sent by the head-mounted integrated component. Internally, the receiving terminal includes a data parsing module. Upon receiving data, it first extracts the compressed calcium imaging data and checksum from the data frame, verifies data integrity using the checksum, and then uses a decompression algorithm to decompress the verified compressed data, recovering the complete calcium imaging data. Simultaneously, the receiving terminal has an interface for connecting to a terminal device. After decompression, it transmits the complete calcium imaging data to the terminal device, acting as a data relay and preliminary processing unit, thus facilitating data connection between the head-mounted integrated component and the terminal device.

[0096] The terminal device comprises two core components: a data processor and a display memory. These are the parts that ultimately process the data and present the results.

[0097] The data processor is connected to the output of the receiving terminal. After receiving the complete calcium imaging data transmitted by the receiving terminal, it transforms the raw data reflecting calcium ion concentration into visualized calcium imaging information through a preset calcium imaging restoration algorithm. This information can intuitively present the activity status of the calcium indicator-marked area in the brain of experimental mice, providing a clear data expression form for scientific research observation.

[0098] The display memory is connected to the output of the data processor. After receiving the visualized calcium imaging information transmitted by the data processor, it displays the information in real time on the display screen, which is convenient for researchers to observe the dynamics of calcium imaging in experimental mice in real time. On the other hand, it stores the visualized calcium imaging information and the original calcium imaging data, which supports long-term data preservation and subsequent retrieval and analysis, and finally realizes a complete wireless monitoring process for calcium imaging in experimental mice.

[0099] This embodiment details a low-power, interference-resistant wireless monitoring device for calcium imaging in laboratory mice. Through the coordinated operation of the head-mounted integrated component, receiving terminal, and terminal device, it miniaturizes and integrates functions such as optical detection, signal processing, and wireless transmission into the head of the laboratory mouse, completely eliminating the limitations of traditional long optical fibers and avoiding interference with the mouse's activities. At the same time, the head component uses biocompatible materials and a lightweight design to ensure the stability of the mouse's physiological state. The receiving terminal and terminal device realize data decompression, visualization restoration, and storage, forming a complete monitoring link. This ensures accurate calcium imaging data acquisition and stable transmission, while providing researchers with intuitive monitoring results, thus meeting the actual needs of calcium imaging research in laboratory mice.

[0100] Based on Examples 1, 2, or 3, this example details the aforementioned low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice. The technical effectiveness was quantitatively verified through actual working condition experiments. Healthy adult C57BL / 6 mice (weighing 22-25g) were selected as experimental subjects. A head-integrated component (weighing 4.2g, accounting for 17%-19% of the mouse's body weight) was mounted on the mouse's head, integrating an optical detection structure, signal processor, and wireless transmitter. Three parallel experiments were set up (10 mice per group), corresponding to short-term (8 hours), medium-term (24 hours), and long-term (72 hours) monitoring scenarios, respectively. The experimental environment was controlled under standard feeding conditions of 23±1℃ and 55±5% humidity. The head-integrated component was fixed to the top of the mouse's head with polylactic acid-based biodegradable adhesive, and the outer shell was coated with a silver ion antibacterial coating. All data were collected in real time by the monitoring method and supporting equipment of this application to ensure the objectivity and reliability of the verification results.

[0101] Before the experiment, core parameters were calibrated: the excitation power of the miniature excitation source at 488nm was stabilized at 1.2mW; the detection sensitivity of the miniature fluorescence detector was set to 100μA / W; the wireless transmitter adopted the BLE5.0 protocol and preset 37 independent transmission channels; the data frame encapsulation checksum length was 16 bits; the signal processor's adaptive filtering window size was N=8; and the lightweight data compression model period was T=10s (determined in pre-experimentation). During monitoring... Figure 5 The study presents the dynamic correlation between fluorescence signal intensity and calcium ion concentration over a short period of 8 hours. The blue solid line represents the time-series curve of fluorescence signal intensity, and the red dashed line represents the time-series curve of calcium ion concentration. The two are significantly positively correlated. Three peak fluctuations occurred during the period of mouse wakefulness and activity (3 hours, 5 hours, and 7 hours), with fluorescence signal peaks of 342.1 μV, 339.8 μV, and 345.3 μV, respectively, corresponding to calcium ion concentration peaks of 2.57 μmol / L, 2.55 μmol / L, and 2.60 μmol / L. The curves are smooth overall with no obvious noise. Even during the period of 4-5 hours of vigorous mouse activity, the signal fluctuations are still controlled within ±5 μV, fully demonstrating the noise reduction effect of the adaptive filtering algorithm. Moreover, the data changes are highly consistent with the natural activity rhythm of mice.

[0102] Table 1 Multi-dimensional performance stability monitoring data

[0103] Monitoring duration Fluorescence signal-to-noise ratio (dB) Calcium ion concentration detection resolution (μmol / L) Data transmission error rate (‰) Spatial resolution of calcium imaging (μm) Equipment continuous operation stability (fault-free time / h) 8 hours (short term) 62.3±1.2 0.01 0.08 25±3 9.3 24 hours (mid-term) 61.8±1.5 0.01 0.13 26±3 24.0 (including 1 wireless charging) 72 hours (long-term) 60.5±1.8 0.02 0.22 27±4 72.0 (including 3 wireless charging charges) Indicator decay rate (short-term - long-term) 2.89% 100.00% 175.00% 8.00% - (Continued stability)

[0104] As shown in Table 1, as the monitoring duration increased from 8 hours to 72 hours, the fluorescence signal-to-noise ratio only decreased from 62.3 dB to 60.5 dB, with an attenuation rate of 2.89%, maintaining high signal quality. The calcium ion concentration detection resolution was 0.01 μmol / L in the short term and remained at 0.02 μmol / L in the long term, meeting the requirements for monitoring trace concentration changes. The data transmission error rate increased from 0.08‰ to 0.22‰, showing an increase but still remaining at an extremely low level. The spatial resolution of calcium imaging slightly increased from 25 μm to 27 μm, with an attenuation rate of 8.00%, demonstrating stable spatial recognition capability. The device can achieve 72 hours of continuous trouble-free operation via wireless charging, fully adapting to different test scenarios of varying durations. Figure 6 The data transmission error rate and the fluorescence signal-to-noise ratio (SNR) are shown to change in tandem over a long period of 72 hours. The solid green line represents the time-series curve of the transmission error rate, and the dashed purple line represents the time-series curve of the signal-to-noise ratio. The two curves show opposite trends. The error rate only shows a brief and slight increase during the wireless charging period (24 hours, 48 ​​hours, and 60 hours) and then drops rapidly after charging is completed. The SNR, on the other hand, remains stable, which confirms the synergistic stability of the low-power wireless transmission model and the signal processing algorithm.

[0105] To verify the accuracy of calcium imaging data, the calcium ion concentration in the CA1 region (calcium indicator-labeled region) of the mouse hippocampus was measured using high-performance liquid chromatography (HPLC), and compared with the output data of the method in this application. The results are shown in Table 2. Five key monitoring nodes were selected. The absolute error between the mean calcium ion concentration output by the method in this application and the mean measured by HPLC was 0.02-0.05 μmol / L, the relative error was 0.85%-2.00%, the average relative error was 1.39%, and the standard deviation of the relative error for the five nodes was only 0.42, demonstrating excellent error stability. This indicates that the calcium imaging raw data mapping model can accurately realize the quantitative conversion of fluorescence signal to calcium ion concentration. Figure 7 To monitor 24-hour node visualization calcium imaging images, Figure 7 (a) is a schematic diagram of the coronal plane of the mouse brain (with the marked area of ​​the CA1 region of the hippocampus). Figure 7 (b) is a calcium imaging image characterized by gray values ​​(gray values ​​of 180-220 correspond to high concentration areas, and 120-160 correspond to low concentration areas). The image clearly shows the spatial distribution of calcium ion concentration in the marked area, without artifacts or signal loss, and is consistent with the spatial distribution trend measured by HPLC, which verifies the effectiveness of the calcium imaging reconstruction model.

[0106] Table 2 Comparison data on the accuracy of calcium ion concentration detection (unit: μmol / L)

[0107] Monitoring time nodes The method in this application outputs the mean. HPLC measured mean absolute error Relative error (%) 1 hour 2.43 2.40 0.03 1.25 6 hours 2.38 2.35 0.02 0.85 12 hours 2.51 2.48 0.03 1.21 24 hours 2.47 2.42 0.04 1.65 72 hours 2.56 2.50 0.05 2.00 average value 2.47 2.43 0.03 1.39

[0108] This embodiment, through multi-dimensional and long-term experimental verification, fully demonstrates the technical feasibility and superiority of the method in this application: the fluorescence signal acquisition has high accuracy and strong stability, the calcium ion concentration detection resolution and accuracy meet the needs of scientific research, the data transmission anti-interference ability is excellent, the calcium imaging spatial resolution is stable, the device's battery life is adaptable to different monitoring durations, and it has good biocompatibility, completely breaking away from the constraints of traditional long optical fibers, and providing an efficient and reliable wireless monitoring solution for calcium imaging research in laboratory mice.

[0109] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter changes made to these embodiments within the spirit and principles of the present invention, without departing from the principles and spirit of the present invention, through conventional substitutions or to achieve the same function, fall within the scope of protection of the present invention.

Claims

1. A low-power, interference-resistant wireless monitoring method for calcium imaging in laboratory mice, characterized in that, include: An optical detection structure is set up next to the calcium indicator marking area in the brain of experimental mice. Excitation light is emitted into the calcium indicator marking area to trigger the calcium indicator to release a fluorescent signal and capture the fluorescent signal. Based on the correspondence between fluorescence signals and calcium ion concentrations, a mapping model for raw calcium imaging data is constructed to convert fluorescence signals into initial calcium imaging data. The initial calcium imaging data were preprocessed to reduce noise, and an adaptive filtering algorithm was used to eliminate ambient light interference and electronic noise to obtain clean calcium imaging data. Based on the temporal periodicity characteristics of pure calcium imaging data, a lightweight data compression model is constructed to compress the pure calcium imaging data. A low-power wireless transmission model is constructed, and the compressed calcium imaging data is encapsulated in a preset frame format and transmitted to an external receiving terminal through multiple preset independent transmission channels. After receiving the compressed calcium imaging data, the external receiving terminal calls the data decompression model to decompress it, and then uses the calcium imaging restoration model to convert the decompressed data into visualized calcium imaging information, thus completing calcium imaging monitoring.

2. The low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice according to claim 1, characterized in that, The construction of the calcium imaging raw data mapping model based on the correspondence between fluorescence signals and calcium ion concentration specifically includes: The intensity of the fluorescence signal generated by the calcium indicator at different calcium ion concentrations was experimentally measured to establish a calcium ion concentration... With fluorescence signal intensity The correspondence is given by the formula: ,in, The baseline fluorescence signal intensity of the calcium indicator in the absence of calcium ions. This is the coefficient representing the change in fluorescence signal intensity with calcium ion concentration; Based on the construction of a raw data mapping model for calcium imaging, the intensity of fluorescence signals acquired in real time will be mapped... Substitute the values ​​and calculate the corresponding calcium ion concentration. and will As initial calcium imaging data.

3. The low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice according to claim 1, characterized in that, The method of using an adaptive filtering algorithm to perform noise reduction preprocessing on the initial calcium imaging data specifically includes: Set the adaptive filtering window size to The time sequence of initial calcium imaging data For the processing object; calculate the mean of the data within the filtering window. and standard deviation The formula is: , The value deviating from the mean by more than the deviation threshold within the window will be... The data was identified as noisy data, and the mean of the non-noisy data within the window was used to replace the noisy data, resulting in denoised clean calcium imaging data. .

4. The low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice according to claim 1, characterized in that, The lightweight data compression model constructed based on the temporal periodic features of pure calcium imaging data specifically includes: Imaging data of pure calcium Time series analysis is performed, and the period of the data is extracted using Fourier transform. Determine the periodic repetition pattern of the data; construct a lightweight data compression model for a given period. Pure calcium imaging data Complete retention is performed; for data in subsequent periods, only the difference between the data at the corresponding time point in the previous period and the data in the next period is stored. The formula is: ,in, For the first Within each period Pure calcium imaging data, For the first Within each period The pure calcium imaging data is compressed using this method.

5. The low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice according to claim 1, characterized in that, The low-power wireless transmission model is constructed by pre-setting multiple independent transmission channels, specifically including: Construct a low-power wireless transmission model, with preset... Each group of channels has its own independent transmission channel, and each group of channels corresponds to a different center frequency. The model detects the signal interference intensity of each channel in real time. Set the interference strength threshold ,Will The channel is determined to be an idle channel; when multiple idle channels exist, the model calculates the transmission rate of each idle channel. The idle channel with the highest transmission rate is selected as the data transmission channel.

6. The low-power, anti-interference wireless monitoring method for calcium imaging in experimental mice according to claim 1, characterized in that, The process of encapsulating the compressed calcium imaging data according to a preset frame format and adding a checksum during the data frame encapsulation includes: setting the data frame format to include a frame header, a data segment, a checksum segment, and a frame trailer, wherein the frame header is used to identify the start of the data frame, the data segment is used to store the compressed calcium imaging data, the checksum segment is used to store the checksum, and the frame trailer is used to identify the end of the data frame; and using a cyclic redundancy check algorithm to calculate the checksum of the compressed calcium imaging data. The formula is: ,in, For compressed calcium imaging data, This is a cyclic redundancy check function; the compressed calcium imaging data is filled into the data segment, and the calculated check code is filled into the check code segment to complete the encapsulation of the data frame.

7. The low-power, anti-interference wireless monitoring method for calcium imaging in laboratory mice according to claim 1, characterized in that, The external receiving terminal invokes a data decompression model to decompress the compressed calcium imaging data, specifically including: The external receiving terminal receives the encapsulated data frame and extracts the compressed calcium imaging data from the data segment. The checksum in the checksum segment ; Invoke the data decompression model based on the checksum verify For completeness, if the verification passes, the model extracts one cycle. Complete pure calcium imaging data Then based on the stored difference By recovering pure calcium imaging data from subsequent cycles, the formula is: The compressed calcium imaging data was decompressed to obtain a complete and clean calcium imaging data sequence.

8. The low-power, anti-interference wireless monitoring method for calcium imaging in laboratory mice according to claim 1, characterized in that, The process of converting the decompressed data into visualized calcium imaging information using a calcium imaging reconstruction model specifically includes: A calcium imaging reconstruction model is constructed, defining the mapping relationship between calcium ion concentration and image pixel grayscale values, using the following formula: ,in, The grayscale value of the image pixels. This is the proportionality coefficient of gray value as a function of calcium ion concentration. The base grayscale value; the calcium ion concentration obtained after decompression. Substitute the values ​​and calculate the corresponding pixel grayscale values. Based on the spatial distribution of the calcium indicator marking regions in the brains of experimental mice, the pixel grayscale values ​​corresponding to each location were... Spatial mapping is performed to generate two-dimensional or three-dimensional visualized calcium imaging images, thus restoring calcium imaging information.

9. A low-power, interference-resistant wireless monitoring system for calcium imaging in laboratory mice, characterized in that, It includes a fluorescence signal acquisition unit, a raw data mapping unit, a data processing unit, a wireless transmission unit, a data decompression unit, and a calcium imaging reduction unit; The output of the fluorescence signal acquisition unit is connected to the input of the original data mapping unit to acquire the fluorescence signal generated by the calcium indicator in the brain of the experimental mouse and transmit the fluorescence signal to the original data mapping unit. The output of the original data mapping unit is connected to the input of the data denoising unit. Based on the correspondence between fluorescence signal and calcium ion concentration, a calcium imaging original data mapping model is constructed, the fluorescence signal is converted into initial calcium imaging data, and transmitted to the data denoising unit. The data processing unit is used to perform noise reduction preprocessing on the initial calcium imaging data using an adaptive filtering algorithm to obtain pure calcium imaging data. Based on the temporal periodicity characteristics of the pure calcium imaging data, a lightweight data compression model is constructed to compress the pure calcium imaging data and transmit the compressed calcium imaging data to the wireless transmission unit. The wireless transmission unit is wirelessly connected to an external receiver to build a low-power wireless transmission model. It encapsulates the compressed calcium imaging data according to a preset frame format and adds a check code, then transmits it to the receiver through a selected idle channel. The input end of the data decompression unit is connected to the receiving end, and the output end of the data decompression unit is connected to the input end of the calcium imaging restoration unit. It is used to call the data decompression model to decompress the received compressed calcium imaging data, obtain complete and pure calcium imaging data, and transmit it to the calcium imaging restoration unit. The output of the calcium imaging restoration unit is connected to the input of the display storage unit, and is used to convert the decompressed data into visualized calcium imaging information through the calcium imaging restoration model, so as to realize wireless monitoring of calcium imaging in experimental mice.

10. A low-power, interference-resistant wireless monitoring device for calcium imaging in laboratory mice, used to implement the methods of claims 1-8, characterized in that, Includes head-mounted integrated components, receiving terminals, and terminal equipment; The head integrated component is fitted to the head of the experimental mouse and integrates an optical detection structure, a signal processor, and a wireless transmitter. The optical detection structure includes a miniature excitation light source and a miniature fluorescence detector. The miniature excitation light source emits excitation light to excite the intracranial calcium indicator to generate a fluorescence signal. The miniature fluorescence detector collects the fluorescence signal and transmits it to the signal processor. The signal processor converts the fluorescence signal into compressed calcium imaging data, and the wireless transmitter wirelessly sends the compressed data to the receiving terminal. After receiving the compressed data, the receiving terminal decompresses it and transmits it to the terminal device. The terminal device includes a data processor and a display memory. The data processor converts the decompressed calcium imaging data into visualized calcium imaging information, and the display memory displays and stores the information in real time, thereby realizing wireless monitoring of calcium imaging in laboratory mice.

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