Method for reconstructing eye-brain visual pathway model in vitro

Abstract

The invention discloses a method for reconstructing an eye-brain visual pathway model in vitro, belongs to the technical field of visual neural pathways, and realizes visual information transmission simulation from a retina to a cortex in an in-vitro environment through structural connection and multi-modal design of a functional network. According to the multi-modal construction mode and the unique electrical stimulation coding method, a flexible and accurate experimental model is provided for basic research of visual neuroscience, and important technical support is provided for research and development of visual rehabilitation, brain-computer interfaces and related nerve drugs. Through innovative signal transmission path optimization, PCA screening and frequency coding methods, the simulation effect of an in-vitro model on a real biological system is improved, and a solid theoretical and technical foundation is laid for future development of visual restoration, an artificial visual system and a man-machine interaction technology.

Description

Technical Field

[0001] The present invention relates to the technical field of visual nerve pathways, and in particular to a method for reconstructing an eye-brain visual pathway model in vitro. Background Art

[0002] The processing and transmission of visual information is a highly complex neural process involving multi-level processing from the retina to the visual cortex of the brain. Traditionally, the study of visual neural pathways has mainly relied on in vivo experiments. Although such studies provide rich physiological information, they have problems such as complex operations and ethical restrictions. With the rapid development of technologies such as brain-computer interfaces and visual prostheses, researchers hope to build an eye-brain visual pathway model in an in vitro environment in order to study the transmission mechanism of visual information under controllable conditions and provide experimental support for visual neural repair, visual impairment rehabilitation and human-computer interaction technology. Summary of the invention

[0003] The purpose of the present invention is to overcome the technical problems existing in the prior art and provide a method for reconstructing an eye-brain visual pathway model in vitro.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] A method for reconstructing an eye-brain visual pathway model in vitro is provided, which uses structural connections and functional networks to establish different connection modes, including the following three construction modes:

[0006] Structural connection method: Cultivate eye cells and brain cells simultaneously on a single multi-electrode array MEA, so that the eye cells and brain cells form a physical connection through natural or guided growth;

[0007] Functional network construction based on virtual retinal model: Use virtual retinal model to simulate the response of retina to light stimulation, convert the response into electrical stimulation to MEA epithelial cells through encoding, and construct functional network connection;

[0008] Construction of functional network based on separate culture of real eyes and brains: separate culture of real eye cells and brain cells, use of real eye cells to encode the response to light stimulation, and convert the encoding into electrical stimulation of brain cells, simulating eye-brain functional connection in vitro.

[0009] In some embodiments, the structural connection method specifically includes:

[0010] Retinal primary cells and cerebral cortex primary cells are cultured separately on MEA at the same time. When they grow to a certain stage, the separation membrane is removed and direct physical connection is formed through natural growth or guided culture to reproduce the structural connection between the eye and the brain.

[0011] In some embodiments, converting the response into electrical stimulation of MEA epithelial cells by encoding comprises:

[0012] The signal responded by the retinal model first calculates the Euclidean distance to the cortical neurons based on the retinal position, and selects the cortical neurons closest to it as the main information transmission object.

[0013] In some embodiments, selecting the closest cortical neuron as the main information transmission object includes:

[0014] Principal component analysis was applied to all retinal neuron response data for dimensionality reduction, and the six most important cortical neurons were selected according to their contribution, and their signals were transmitted to the corresponding six MEA electrodes.

[0015] In some embodiments, the step of converting the response into electrical stimulation of MEA epithelial cells by encoding further comprises:

[0016] For the selected cortical neurons, a 500-ms time window was taken and the number of spikes within the time window was calculated;

[0017] Clustering is used to classify the number of spikes responding to each time window, and multiple frequency categories corresponding to different stimulation frequencies are obtained, thereby simulating the transmission of visual information in the cortex.

[0018] Preferably, the clustering method is K-means clustering.

[0019] In some embodiments, by adjusting the stimulation frequency, neural signal transmission patterns of different response intensities are simulated.

[0020] Preferably, the different stimulation frequencies include 8 Hz, 6 Hz, 4 Hz, and 2 Hz.

[0021] In some embodiments, encoding the response of real eye cells to light stimulation and converting the encoding into electrical stimulation of brain cells comprises:

[0022] The response of real eye cells to light stimulation was calculated using Euclidean distance and PCA dimensionality reduction, and the six cortical neurons with the highest contribution were selected and transmitted to the corresponding MEA electrodes.

[0023] In some embodiments, it also includes:

[0024] Verify the feasibility of different construction modes.

[0025] It should be further explained that the technical features corresponding to the above embodiments can be combined or replaced with each other to form a new technical solution without conflict.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The present invention proposes three different construction methods, respectively using structural connections and functional networks, and provides a variety of means to reconstruct in vitro visual pathways. Each method has a unique implementation method that adapts to different experimental needs and research scenarios. By constructing an eye-brain visual pathway model in vitro, the present invention provides a highly controllable experimental platform for the study of the visual nervous system, helping scientists to deeply understand the process of visual information transmission from the retina to the cortex. This model not only contributes to basic research, but also provides an experimental basis for visual rehabilitation, the development of brain-computer interface technology and neuro-drug screening. Specific application values ​​include:

[0028] Visual rehabilitation and therapy: Simulating normal visual pathways provides potential technical support for neural repair and rehabilitation of patients with visual impairment.

[0029] Brain-computer interface (BCI): It provides an experimental platform for decoding and transmitting visual information in brain-computer interface, which can promote the development of human-computer interaction technology.

[0030] Neurodrug screening: As a testing platform for new neurodrugs, it helps evaluate the effects of drugs on the visual nervous system.

[0031] Visual neuroscience research: This invention provides a flexible and highly controllable experimental model that can be used to study the process of visual signal transmission between the eye and the brain in vitro, providing a new tool for understanding the working mechanism of the visual nervous system.

[0032] (2) In the method of constructing a functional network, the present invention designs a unique electrical stimulation coding method to efficiently transmit retinal signals and stimulate cortical cells. Among them, through Euclidean distance calculation and PCA screening, the effectiveness and accuracy of signal transmission are ensured, making the model closer to the neural information transmission method in biological systems. Improved the flexibility of frequency coding: frequency coding of different response intensities is performed through cluster analysis, and a more sophisticated functional network connection is established, which improves the bionic authenticity of the in vitro visual pathway model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of constructing an in vitro eye-brain visual pathway model according to an embodiment of the present invention;

[0034] Figure 2 This is a flow chart of constructing an in vitro eye-brain visual pathway model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0037] In view of the technical problems pointed out in the background technology, based on the National Key R&D Program (2022YFF1202900), the embodiments provided by the present invention are as follows:

[0038] Provide a method for reconstructing an eye-brain visual pathway model in vitro, referring to Figure 1 , including the following three build modes:

[0039] Structural connection method: Cultivate eye cells and brain cells simultaneously on a single multi-electrode array MEA, so that the eye cells and brain cells form a physical connection through natural or guided growth; specifically, separate and culture retinal primary cells and cerebral cortex primary cells on a single MEA at the same time, remove the separation membrane when they grow to a certain stage, and form a direct physical connection through natural growth or guided culture, thus reproducing the structural connection between the eye and the brain. The electrical response of eye cells to light stimulation is collected by MEA and transmitted to brain cells, thus constructing a structurally connected in vitro visual pathway model.

[0040] Functional network construction based on virtual retinal model: A virtual retinal model is used to simulate the response of the retina to light stimulation, and the response is converted into electrical stimulation of the MEA epithelial cells through encoding. This method establishes functional connections through the response of the virtual retina to light stimulation, simulates the information transmission process between the retina and the cortex, and provides a flexible way to simulate the visual pathway.

[0041] Construction of functional network based on separate culture of real eyes and brains: separate culture of real eye cells and brain cells, use the response of real eye cells to light stimulation to encode, and convert the code into electrical stimulation of brain cells to simulate eye-brain functional connection in vitro. This method is based on the response data of real biological cells, which enhances the biological rationality of the model.

[0042] Among them, the structural connection method enables the eye and brain cells to form a direct physical connection, simulating the direct structural connection of visual information in biological systems. It can better reproduce the structural connection between the eye and the brain, and provide an experimental platform for studying the physical transmission mechanism of visual signals.

[0043] The functional network construction mode based on the virtual retina model specifically includes:

[0044] Method description: The response of the virtual retinal model to light stimulation is converted into electrical stimulation of cortical cells on MEA through a coding algorithm to establish functional network connections.

[0045] Electrical stimulation encoding method: The Euclidean distance of the retinal model response signal to the cortical neurons is first calculated based on the retinal position, and the cortical neurons with the closest distance are selected as the main information transmission objects. Considering the limitation of the number of MEA electrodes, principal component analysis (PCA) is applied to all retinal neuron response data for dimensionality reduction, and the six most important neurons are selected according to their contribution, and their signals are transmitted to the corresponding six MEA electrodes.

[0046] Time window and frequency encoding: For the selected retinal neurons, a 500ms time window is taken and the number of spikes in the time window is calculated. K-means clustering is used to classify the responses (number of spikes) of each time window to obtain four frequency categories (4, 3, 2, 1), corresponding to the stimulation frequencies of 8Hz, 6Hz, 4Hz, and 2Hz, respectively, thereby simulating the transmission of visual information in the cortex.

[0047] This model uses a virtual retinal model to construct an in vitro visual pathway, combines PCA and cluster analysis to optimize the transmission path of the stimulus signal, effectively simulates the functional information transmission from the retina to the cortex, does not require the use of real eye cells, and provides a flexible and controllable implementation method for functional network connections. A highly controllable and easily adjustable functional visual information transmission model is constructed.

[0048] Furthermore, the construction model of the functional network based on the separation and cultivation of real eyes and brains specifically includes:

[0049] Method description: Real eye cells and brain cells are cultured separately, and the responses of eye cells to light stimulation are collected and encoded according to the responses of the retina, which are converted into electrical stimulation of brain cells to simulate the eye-brain functional connection in vitro.

[0050] Electrical stimulation encoding method: The response of real eye cells to light stimulation is also processed by Euclidean distance calculation and PCA dimensionality reduction. The six neurons with the highest contribution are selected and transmitted to the corresponding MEA electrodes. K-means clustering is performed on the retinal response spike data within a 500ms time window to obtain four frequency categories (4, 3, 2, 1) corresponding to the stimulation frequencies of 8Hz, 6Hz, 4Hz, and 2Hz respectively.

[0051] This model is based on the response data of real cells, combined with electrical stimulation coding methods, and achieves functional connection through frequency regulation. This approach uses real eye and brain cells and is based on real biological reactions, which enhances the biological rationality of the model and reproduces the biological signal transmission mechanism between the eye and the brain to a certain extent, making the model closer to the real biological system. It can also further verify the effect of the virtual model constructed based on the functional network of the virtual retinal model.

[0052] Furthermore, combined with Figure 2 In the functional network (the second and third methods), the present invention designs a unique electrical stimulation encoding method to achieve efficient signal transmission from the retina to the cortex, specifically including:

[0053] Optimization of the distance between retina and cortex neurons: By calculating the Euclidean distance from each retinal neuron to the cortical neuron, cortical neurons with closer distances are preferentially selected as stimulation targets to ensure the effectiveness of information transmission, simulating the characteristic in biological systems that "the closer the distance, the more information is transmitted."

[0054] PCA dimensionality reduction processing: Considering the limitation of the number of MEA electrodes, principal component analysis (PCA) is performed on the response signals of all retinal neurons to calculate the contribution of each neuron, and the six neurons with the highest contribution are selected and mapped to the six electrodes of MEA for electrical stimulation. This method not only ensures the effectiveness of signal transmission, but also solves the problem of limited number of electrodes.

[0055] Response frequency coding: In the selected retinal neurons, a 500ms time window is set to count the number of spikes in the retinal neuron response, and K-means clustering is applied to divide the responses into four categories, representing different stimulation frequencies (8Hz, 6Hz, 4Hz, and 2Hz). This frequency coding method adjusts different stimulation frequencies to make the electrical signal transmission in the model closer to the response of the real biological system.

[0056] This electrical stimulation coding method has the following technical advantages: Optimized signal transmission path: Through Euclidean distance calculation and PCA screening, the effectiveness and accuracy of signal transmission are ensured, making the model closer to the neural information transmission method in biological systems. Improved the flexibility of frequency coding: Through cluster analysis, frequency coding of different response intensities is performed, and a more sophisticated functional network connection is established, which improves the bionic authenticity of the in vitro visual pathway model.

[0057] Through the above three methods, the present invention can construct an in vitro eye-brain visual pathway model under different conditions, providing a variety of options from structural connection to functional network, and providing a flexible experimental means for studying various mechanisms of the visual nervous system.

[0058] Furthermore, the present invention also analyzes the eye-brain signal transmission mechanism through information theory, verifies the feasibility of eye-brain visual pathway models constructed in different ways, analyzes functional networks through graph theory, and conducts in vitro clinical trials.

[0059] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for reconstructing an eye-brain visual pathway model in vitro, characterized in that: include: Structural connection method: Cultivate eye cells and brain cells simultaneously on a single multi-electrode array MEA, so that the eye cells and brain cells form a physical connection through natural or guided growth; Functional network construction based on virtual retinal model: Use virtual retinal model to simulate the response of retina to light stimulation, convert the response into electrical stimulation to MEA epithelial cells through encoding, and construct functional network connection; Construction of functional network based on separate culture of real eyes and brains: separate culture of real eye cells and brain cells, use of real eye cells to encode the response to light stimulation, and convert the encoding into electrical stimulation of brain cells, simulating eye-brain functional connection in vitro.

2. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 1, characterized in that: The structural connection method specifically includes: Retinal primary cells and cerebral cortex primary cells are cultured separately on MEA at the same time. When they grow to a certain stage, the separation membrane is removed and direct physical connection is formed through natural growth or guided culture to reproduce the structural connection between the eye and the brain.

3. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 1, characterized in that: The step of converting the response into electrical stimulation of MEA epithelial cells by encoding comprises: The signal responded by the retinal model first calculates the Euclidean distance to the cortical neurons based on the retinal position, and selects the cortical neurons closest to it as the main information transmission object.

4. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 3, characterized in that: The step of selecting the closest cortical neuron as the main information transmission object includes: Principal component analysis was applied to all retinal neuron response data for dimensionality reduction, and the six most important cortical neurons were selected according to their contribution, and their signals were transmitted to the corresponding six MEA electrodes.

5. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 4, characterized in that: The step of converting the response into electrical stimulation to the MEA epithelial cells by encoding further comprises: For the selected cortical neurons, a 500-ms time window was taken and the number of spikes within the time window was calculated; Clustering is used to classify the number of spikes responding to each time window, and multiple frequency categories corresponding to different stimulation frequencies are obtained, thereby simulating the transmission of visual information in the cortex.

6. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 5, characterized in that: The clustering method is K-means clustering.

7. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 5, characterized in that: By adjusting the stimulation frequency, the neural signal transmission patterns with different response intensities can be simulated.

8. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 5, characterized in that: The different stimulation frequencies include 8 Hz, 6 Hz, 4 Hz, and 2 Hz.

9. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 1, characterized in that: The method of encoding the response of real eye cells to light stimulation and converting the encoding into electrical stimulation to brain cells includes: The response of real eye cells to light stimulation was calculated using Euclidean distance and PCA dimensionality reduction, and the six cortical neurons with the highest contribution were selected and transmitted to the corresponding MEA electrodes.

10. The method for reconstructing an eye-brain visual pathway model in vitro according to claim 1, characterized in that: Also includes: Verify the feasibility of different construction modes.

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