CN111406706A - Method for constructing brain-computer interface behavioural model - Google Patents
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Abstract
The invention relates to a method for constructing a brain-computer interface behavioural model. The construction method comprises the following steps: an operational behavior phase and a mind control phase. And training the mouse to obtain reward through specific behaviors in different time periods, and completing the construction of a brain-computer interface behavioural model. The construction method has high success rate and stable model, and provides a new research carrier for brain-computer interface learning.
Description
Technical Field
The invention relates to the technical field of brain-computer interfaces, in particular to a method for constructing a behavioral model of a brain-computer interface.
Background
A novel information exchange and control channel is established between the brain and external equipment by a brain-machine interface (BMI), so that the direct interaction between the brain and the outside is realized, and the technology does not depend on a conventional spinal cord/peripheral neuromuscular system. In 1969, Fetz, E.E detected the activity of cortical neurons, and constructed volitional control by strengthening the operative conditions leading to increased discharge of cortical neurons. In 1999, real brain interaction with the outside world is established by detecting and decoding rat cortical M1 neurons and controlling external mechanical arms to obtain reward by using electric signals of M1 cortical neurons. Over the past decade, studies on brain-computer interfaces have been vigorously conducted using rats, mice, non-human primates, and paralyzed patients, and studies on clinical pre-experiments on brain-computer interfaces have been ongoing, while neural artificial limbs have been successfully controlled in mice, apes, and paralyzed patients using real-time control of external devices. While current research has made great progress in signal detection, signal decoding, and feedback of BMI learning, greatly improving BMI learning, the interaction of the machine and the brain is not only to restore a disconnected connection, but also involves learning and neural adaptation of the neural prosthetic. Control and use of a neural prosthetic control does not approximate normal motion. The study of the neural artificial limb is to control the neural artificial limb by means of idea in a target-oriented way through a compulsive learning theory. Therefore, the learning of the brain-computer interface is a long process, and at present, no better method is available for promoting the learning of the brain-computer interface except for strengthening training, improving decoding and recording neural signals by an engineering method.
Disclosure of Invention
Based on the method, the invention provides a new method for constructing the brain-computer interface behavioural model, the success rate of the construction method is high, and the constructed model is stable.
The specific technical scheme is as follows:
a method for constructing a brain-computer interface behavioural model comprises the following steps:
an operational behavior phase: training a mouse to obtain reward through an operative behavior in T1 after the prompt appears, wherein the number of the operative behaviors for successfully obtaining the reward is N, and simultaneously, respectively recording a calcium signal F of a mouse M1 cortical neuron when the N operative behaviors occur1Values, calcium signal of mouse M1 cortical neurons at the time of the cue was recorded as F0Calculating the rate of change (F) of said calcium signal in said N operational behaviors1-F0)/F0Setting the average value to a threshold value;
a idea control stage: training the mice to control the calcium signal of the mouse M1 cortical neuron in the T2 after the prompt appears through the idea, and recording the calcium signal F of the mouse M1 cortical neuron in the T22Value, rate of change of calcium signal(F2-F0)/F0When the threshold value is exceeded, the mouse obtains reward, the reward is recorded as a successful test, the success rate of the mouse in the test every day is counted, and the total time for completing M successful tests every day is recorded;
and finishing the construction of the brain-computer interface behavioural model by taking the success rate in the test every day and the total time required for finishing M successful tests as evaluation indexes.
In one embodiment, the time interval T1 is 0-30s or 0-20s after the prompt appears.
In one embodiment, the time interval T2 is 0-30s or 0-20s after the prompt appears.
In one embodiment, the idea control phase further comprises:
training mice to control calcium signals of mouse M1 cortical neurons in the T3 before the suggestion appears through idea, and recording calcium signals F of mouse M1 cortical neurons in the T33Value of F to be3＜F1And the rate of change of the calcium signal (F)2-F0)/F0Beyond the threshold, the mouse received a reward, which was recorded as a successful trial.
In one embodiment, the time T3 is within 15s before the prompt appears.
In one embodiment, whether the brain-computer interface behavioral model is successfully constructed is judged by comparing the success rate in each day test with the total time required for completing M successful experiments.
In one embodiment, the training time for the operational behavior phase is 5-8 days.
In one embodiment, the training time for the mental control phase is 8-15 days.
In one embodiment, the number of times N is 40-60 times.
In one embodiment, the number of M times is 40 to 60 times.
In one embodiment, the operative behavior is a plunger behavior.
In one embodiment, the prompt is an audio prompt.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a new method for constructing a brain-computer interface behavioural model, and the successful construction of the model provides a new research carrier for brain-computer interface learning. Through tests, the construction method has high success rate, particularly keeps higher success rate in the idea control stage, and has stable model.
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FIG. 1 is a diagram showing the identification of GCaMP6 f-expressing cells;
FIG. 2 is a schematic diagram of a brain-computer interface behavioural model construction method;
FIG. 3 is a graph showing the test results of example 1;
FIG. 4 is a graph showing the test results of example 2, example 3 and example 4;
FIG. 5 is a schematic representation of the test method and test results of example 5.
Detailed Description
The following adenosine A of the present invention is described with reference to the specific examples2AThe novel use of the receptor antagonist drugs is further described in detail. The present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Example 1 construction of a brain-computer interface behavioral model Using calcium signals from mouse M1 cortical neuron population cells
The brain-computer interface can help paralyzed patients to recover motor functions, and the construction of a behavioural model of the brain-computer interface (neural artificial limb) is the core of the adaptation of the brain-computer interface. In this embodiment, a brain-computer interface behavioral model (fig. 3A-D) is constructed in a free-moving mouse by using a calcium signal of a mouse M1 cortical group neuron as a "controller", and the specific method is as follows:
first, AAV9-syn-GCaMP6f-WPRE-SV40(AAV virus) was used to express genetically encoded Ca in mouse M1 cortical neurons2+GCaMP6f was shown to be selectively expressed in L-6 cortical (L/L/L) neurons and co-expressed with the neuronal marker NeuN, rather than with the microglia marker (GFPA +) and the microglia marker (IBA1) (FIGS. 1A-L). A fiber optic recording system was used to record changes in calcium signal from M1 cortical neuron L (FIG. 2A) in real time and this calcium signal was used as a "controller" to construct a brain-machine interface behavioral model (FIG. 2B).
Second, since it is difficult for a mouse to learn a working structure that does not exist originally, a learning manner (operational behavior phase model construction and idea control phase model construction) applying two-step transformation helps the mouse to learn brain-computer interface learning (fig. 2C-D).
(1) An operational behavior phase: mice were trained to receive reward by the operative act (compression bar) for a specific period of time, accompanied by the appearance of specific calcium signaling changes in the M1 cortical neurons.
The specific method comprises the following steps:
mice expressing GCaMP6f from M1 cortical neurons were unconditionally given sugar water at regular intervals (half an hour), and then were forced to learn for three days, with sugar water being obtained by compression of the compression bar 50 times a day (if not done 50 times, but the total training time reached 1 hour, it was also considered to be complete for the daily trial). Then, the training is performed for the operational behavior of 6 days, the voice prompt is added, the mouse pressure bar can obtain sugar water only within 30s after the voice prompt appears (fig. 2C), the success rate of the mouse in the test per day and the total time required for obtaining the sugar water 50 times per day at this stage are counted (if the test is not completed 50 times, but the total training time reaches 1 hour, the test per day is considered to be completed, and the total time is recorded as 1 hour), and it is known that the success rate of the mouse pressure bar obtaining the sugar water is continuously increased (fig. 3A, n is 8: one way anova <0.0001), and the training time per day (the total time for obtaining the sugar water 50) is decreased (fig. 3B, n is 8: one way ANOV P < 0.0001). Research shows that the success rate reaches the plateau period on the next day of training, and the success rate is maintained to be more than 95% on the following four days.
The calcium signal of the cortical neuron of mouse M1 at the time of speech presentation was taken as the baseline of calcium signal (F)0) (ii) a Δ F is defined as the calcium signal F1At F0Change value (F) on the basis1-F0). By analysing Δ F/F [ (F)1-F0)/F0]The change in the pre-2 s and post-5 s of the compression bar, found that the calcium signal started to rise before the compression bar and started to fall after the compression bar (fig. 2C), therefore, it was concluded that the change in calcium signal and the compression bar behavior are closely related, indicating that the change in neuronal activity is triggered by the compression bar behavior. To further demonstrate the correlation between calcium signal changes and strut behavior, myoelectrical changes in the forelimb of mice were also recorded (fig. 2C). It was confirmed that the calcium signal in the cortex M1 was instantaneously and precisely coincident with the myoelectric increase or decrease of the mouse forelimb (FIG. 2C; P)<0.05), it was further demonstrated that the change in M1 neuronal calcium signal was correlated with the compression bar behavior of the forelimbs of the mice.
The values of the calcium signal F1 of mouse M1 cortical neurons at 50 successful compression strokes were recorded during each day of the operative behavioral phase. Calculating the rate of change (F) of the calcium signal1-F0)/F0Average value of (D), the rate of change of calcium signal (F) in the 6-day period of operative behavior1-F0)/F0The average value of (a) is set as a threshold value;
(2) a idea control stage: mice were trained to reward at specific times by calcium signaling similar to the appearance and operative behavior of the mind-controlling M1 cortical neurons.
The specific method comprises the following steps: after the 6-day training of the operative behavior was finished, on day 7, the rods were withdrawn, and the mice switched to training in the mental control phase of the brain-computer interface for 10 days. Within 30s after the training sound prompt appears, the mouse controls the calcium signal of M1 cortical neuron through the idea, and the training sound prompt will be sent toThe calcium signal of mouse M1 cortical neuron in 0-30s after the appearance of the sound prompt is recorded as F2. Mice need to demonstrate Δ F within 30s after the onset of audible cues2[(F2-F0)/F]When the sugar water level is increased to a set threshold value, the sugar water level is recorded as a successful experiment, the success rate of the mouse in each day experiment is counted, and the total time required for completing 50 successful experiments is recorded every day (if the test is not completed 50 times, but the total training time reaches 1 hour, the test is also regarded as completing each day experiment, and the total time is recorded as 1 hour).
Base line F0It is important for the mice to be rewarded for each success by increasing calcium signal beyond a set threshold, completing each experiment, because each audible cue appears, indicating the start of the experiment, and our defined baseline (F)0) Comprises the following steps: 10ms after the appearance of the audible cue, mouse M1 values for calcium signaling in cortical neurons, so F0It was uncertain in every experiment.
Only before the experiment begins (before the sound prompt appears) the mouse reduces the calcium signal, and after the sound prompt appears, the mouse increases the calcium signal, and meets the two conditions, the mouse can successfully complete the experiment task, and the mouse records the calcium signal of mouse M1 cortical neuron as F within 15s before the sound prompt appears3Training mice to reduce calcium signal F before audible cues3。
In the initial stage of thought training, the calcium signal has great variation, and the calcium signal changes slowly with the increase of training. The expression is as follows: the calcium signal starts to fall 10s before the onset of the tone and then starts to increase after the onset of the audible cue (fig. 3C-D; n-7). Finally, the results show that mice not only conditionally increase calcium signal beyond the set threshold after the occurrence of the audible cue, but also decrease calcium signal before the occurrence of the audible cue.
After training, two indexes are provided for judging whether the brain-computer interface behavioural model is successfully constructed: 1. after 10 days of training, whether the success rate is obviously different or not; 2. whether the completion time was significantly reduced after 10 days of training.
The results show that success rates increase with increasing number of training days (FIG. 3E; One-way ANOVA, P < 0.05). The time for each training also decreased during the 10 day ideogram training period (FIG. 3F; One-way ANOVA, P < 0.05). On the first day of training, mice did not significantly increase calcium signals after audible cues appeared (fig. 3C), but on the tenth day, a significant trend had occurred (fig. 3D), and the time to completion of each training on the tenth day (fig. 3G) was significantly lower than on the first day (fig. 3H). The mouse slowly learns to regulate the change of the calcium signal to complete the construction of the brain-computer interface behavioural model, and the model is stable.
Finally, to confirm that the above brain-computer interface behavioural model is controlled by mouse mind, the following validation was performed:
(1) in the ideation control stage, myoelectricity of the left forelimb of the mouse and the change of the mouse M1 calcium signal are recorded simultaneously. A separation of myoelectric and calcium signals was observed (fig. 2D).
(2) F of mice successfully obtaining sugar water0The nearby calcium signal was lower than that of the failure, indicating that the increased success rate of the mice in the mental control was consciously in F0Reducing calcium signal.
(3) After the audible cue appeared, mice that successfully achieved sugar water had increased Δ F/F, and failed did not.
(4) The video results show that the mouse does not reach the set threshold value due to free movement and head-up. In video, it can be seen that the calcium signal does not change obviously in the free movement and head raising processes of the mouse, which indicates that the change of the calcium signal has no relation with the movement, and the calcium signal is increased only when the mouse wants to drink water, which indicates that the calcium signal is generated by idea.
Through the test verification, the brain-computer interface ethology model is proved to be controlled by the idea of the mouse.
Example 2
In the embodiment, adenosine A is injected into the abdominal cavity of a mouse participating in training in the operational behavior stage and the idea control stage of constructing a brain-computer interface ethology model2AReceptor antagonist KW6002(5mg/kg) was injected half an hour before training began, dailyOnce.
The results show that the success rate of the mice in obtaining sugar water during the operative behavior phase is not significantly different from the five-day training success rate except for a significant increase on the first day (FIG. 4A; two-way ANOVA, P < 0.050), and that the time of training per day is not significantly different from the four-day training time except for a significant decrease on the first two days (FIG. 4B). The success rate of the mice in obtaining sugar water was increased during the ideological control phase (fig. 4C; two-way ANOVA, P <0.051, P ═ 0.05). However, the time to complete the training does not vary significantly for each pass (fig. 4D).
Example 3
In this example, adenosine A was injected into the abdominal cavity of a mouse participating in training only at the mental control stage of constructing a brain-computer interface ethology model2AThe receptor antagonist KW6002(5mg/kg), was injected once daily starting half an hour before the start of training.
The results show that: in the ideogram control phase, the success rate of the mice getting sugar water increases, and compared with the results of example 2, it was found whether to inject KW6002 in the operational behavior phase, without affecting the training success rate in the ideogram control phase, which was only related to whether to inject KW6002 in the ideogram control phase (fig. 4E, two-way-ANOVA, P <0.05), while injection of KW6002 had no effect on the completion time of each training (fig. 4F).
Example 4
This example trains mice in two groups to construct a brain-computer interface behavioural model.
The training method of the first group is substantially the same as that of example 1, except that in the operational behavior phase, the training conditions are changed to: after the voice prompt is added, the mouse pressure bar can obtain the sweet water within 20s after the voice prompt appears. In the mind control stage, the training conditions are changed into: within 20s after the onset of the audible cue, the mouse needs to convert Δ F within 20s after the onset of the audible cue2The sugar water can be obtained only by increasing the sugar water to a set threshold value. Mice in this group were not injected with adenosine A during both the operative behavioral and the mental control phases2AReceptor antagonist KW 6002.
Of the second groupThe training method is substantially the same as the training method of the first group, except that: mice in this group were injected with adenosine A during both the operative behavioral and the mental control phases2AReceptor antagonist KW 6002.
The results show that the mice in the first group can not successfully complete the construction of the brain-computer interface behavioral model, and the training success rate of the mice in the first group is obviously different from that of the mice in the second group in the idea control stage (fig. 4G, two-way-ANOVA, P)<0.05), adenosine A was injected under the same training conditions2AThe mouse training success rate of the receptor antagonist KW6002 is high. There was also a clear difference in the time at which training was completed (FIG. 4H, two-way-ANOVA, P)<0.05), under the same training conditions, adenosine A was injected2AMice with the receptor antagonist KW6002 completed less time. The second group of mice successfully completed the construction of the brain-computer interface behavioral model, while the first group did not.
Example 5
This example utilizes Cre enzyme mediated floxed A2AMethod for receptor gene knockout to verify blockade A2AInfluence of the receptor on the construction of a brain-computer interface behavioural model. The specific method comprises the following steps:
get A2ATransgenic mice of flox injected with AAV-Cre knockdown A in the dorsal striatum2AExpression of the receptor (fig. 5A), and then, a brain-computer interface behavioral model was constructed in the above mouse in the same manner as in example 1.
The results show that A is knocked out in the dorsal striatum2AAfter the recipient, it was also able to promote the mice in the operative behavior (FIG. 5B, two-way-ANOVA, P)<0.05) and the mental control phase (FIG. 5D, two-way-ANOVA, P)<0.05), also had no significant effect on training completion time (fig. 5C and 5E; two-way-ANOVA, P>0.05)。
The above studies indicate that adenosine A2AThe receptor antagonist medicine can be used as medicine for promoting brain-computer interface learning, and can raise the success rate of building brain-computer interface behavioral model, in particular, adenosine A2AReceptor antagonists can be used as a central control of mental learning in the promotion of brain-computer interfaceThe success rate of completing the tasks in the idea control stage is improved by the aid of the constructed medicine of the segment model.
Further, adenosine A2AThe receptor antagonist drug can be selected from adenosine A2AThe receptor antagonist KW6002 can be selected from adenosine A2AOther adenosine A antagonists CPI-4442AOne or more of the receptor antagonists.
Adenosine A2AThe receptor antagonist KW6002 can be combined with pharmaceutically acceptable auxiliary materials to prepare medicines in various dosage forms, and is applied to learning of brain-computer interfaces.
Furthermore, researches show that the striatum-globus pallidus pathway (brief pathway) participates in brain-computer interface learning and regulates and controls A of high expression and indirect pathway2AThe receptor, can influence brain-computer interface learning.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The method for constructing the brain-computer interface behavioral model is characterized by comprising the following steps of:
an operational behavior phase: training a mouse to obtain reward through an operative behavior in T1 after the prompt appears, wherein the number of the operative behaviors for successfully obtaining the reward is N, and simultaneously, respectively recording a calcium signal F of a mouse M1 cortical neuron when the N operative behaviors occur1Values, calcium signal of mouse M1 cortical neurons at the time of the cue was recorded as F0Calculating the rate of change (F) of said calcium signal1-F0)/F0Setting the average value to a threshold value;
a idea control stage: training the mice to control the calcium signal of the mouse M1 cortical neuron in the T2 after the prompt appears through the idea, and recording the calcium signal F of the mouse M1 cortical neuron in the T22Value, rate of change of calcium signal (F)2-F0)/F0When the threshold value is exceeded, the mouse obtains reward, the reward is recorded as a successful test, the success rate of the mouse in the test every day is counted, and the total time for completing M successful tests every day is recorded;
and finishing the construction of the brain-computer interface behavioural model by taking the success rate in the test every day and the total time required for finishing M successful tests as evaluation indexes.
2. The method of claim 1, wherein the interval T1 is 0-30s or 0-20s after the prompt appears.
3. The method of claim 1, wherein the interval T2 is 0-30s or 0-20s after the prompt appears.
4. The build method of claim 1, wherein the idea control phase further comprises:
training mice to control calcium signals of mouse M1 cortical neurons in the T3 before the suggestion appears through idea, and recording calcium signals F of mouse M1 cortical neurons in the T33Value of F to be3＜F1And the rate of change of the calcium signal (F)2-F0)/F0Beyond the threshold, the mouse received a reward, which was recorded as a successful trial.
5. The building method according to claim 4, wherein the time T3 is within 15s before the prompt appears.
6. The construction method according to any one of claims 1 to 3, wherein the training time of the operational behavior phase is 5 to 8 days.
7. The construction method according to any one of claims 1 to 3, wherein the training time of the will control phase is 8 to 15 days.
8. The method of constructing according to any one of claims 1 to 3, wherein the N times are 40 to 60 times; and/or
The number of M times is 40-60 times.
9. A construction method according to any one of claims 1-3, wherein said operational behaviour is a strut behaviour.
10. A construction method according to any one of claims 1 to 3, wherein the prompt is an audible prompt.
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