CN113967023A - Closed-loop optogenetic intervention system and intervention method - Google Patents

Closed-loop optogenetic intervention system and intervention method Download PDF

Info

Publication number
CN113967023A
CN113967023A CN202111254993.1A CN202111254993A CN113967023A CN 113967023 A CN113967023 A CN 113967023A CN 202111254993 A CN202111254993 A CN 202111254993A CN 113967023 A CN113967023 A CN 113967023A
Authority
CN
China
Prior art keywords
signal
module
signals
optogenetic
electrodes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111254993.1A
Other languages
Chinese (zh)
Other versions
CN113967023B (en
Inventor
陆巍
王桥
吴怡
盛涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fudan University
Original Assignee
Fudan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fudan University filed Critical Fudan University
Priority to CN202111254993.1A priority Critical patent/CN113967023B/en
Publication of CN113967023A publication Critical patent/CN113967023A/en
Application granted granted Critical
Publication of CN113967023B publication Critical patent/CN113967023B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/388—Nerve conduction study, e.g. detecting action potential of peripheral nerves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • A61B5/293—Invasive
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/294—Bioelectric electrodes therefor specially adapted for particular uses for nerve conduction study [NCS]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/386—Accessories or supplementary instruments therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6867—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive specially adapted to be attached or implanted in a specific body part
    • A61B5/6868—Brain
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N5/0601—Apparatus for use inside the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/06—Radiation therapy using light
    • A61N5/0613—Apparatus adapted for a specific treatment
    • A61N5/0622—Optical stimulation for exciting neural tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/40—Animals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/42—Evaluating a particular growth phase or type of persons or animals for laboratory research
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biophysics (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Psychiatry (AREA)
  • Neurology (AREA)
  • Neurosurgery (AREA)
  • Psychology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physiology (AREA)
  • Signal Processing (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

The invention discloses a closed-loop optogenetic intervention system and an intervention method, wherein the system comprises: a plurality of electrodes for implantation within the brain of an animal; the biological data recording device is communicated with the electrode signals and collects signals of the plurality of electrodes; the pulse stimulator is connected with the animal brain through an optical fiber and is used for performing light stimulation on the animal brain; and a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: and reading the signal of the biological data recording device, processing the signal, and outputting a corresponding signal to the pulse stimulator to perform optical stimulation on the brain of the animal after the signal reaches the optogenetic triggering condition. The system can analyze the LFP activities of a plurality of recording electrodes in real time, and realize closed-loop stimulation by taking functional connection between brain areas as a trigger condition.

Description

Closed-loop optogenetic intervention system and intervention method
Technical Field
The invention relates to the field of optogenetics, in particular to a closed-loop optogenetics intervention system and an intervention method.
Background
The closed-loop control triggered by the neural electric signal is a strategy for adjusting external intervention in real time according to the electric signal of an organism, and is an effective method for researching the dynamics of a neural loop. The open-loop control is to perform corresponding processing according to preset control input, and the closed-loop control is to adjust the control input in the system according to the difference between the output and the desired target, and the control input needs to be adjusted in real time according to the state of the system itself.
In neuroscience research, this process can be summarized as comparing signals recorded in a living body, such as behavior parameters, electric signals or imaging signals, with target signals, inputting the difference result into a specific controller for processing, and performing intervention of neural activity by the controller on stimulation signals input by the living body. In practical situations, the connections of the neural circuits often have directionality, for example, in a certain behavioral paradigm, there is a functional connection between the brain area a and the brain area B, but the brain can only send commands from the brain area a to the brain area B when functioning. Such connections with directivity are often not obtained by direct measurement. The method intervenes the nerve activity of each brain area through closed-loop control, and is an effective way for testing the causal relationship between functional connection and behaviours of the brain areas.
Deep Brain Stimulation (DBS) has been used clinically for decades in the direction of closed-loop electrical stimulation to treat dyskinesia. Research aiming at closed-loop DBS can be traced back to 2001 at first, Osorio and the like design a closed-loop control system for stimulating through detecting epileptic seizure by electroencephalogram signals, and the effectiveness of the closed loop is researched and verified in 2002-2005 in a large amount. These treatments had a positive effect on 11 of 27 epileptic patients with fewer side effects on the brain than the open-loop approach. Rosin et al clinically treated Parkinson's Disease (PD) patients with closed-loop DBS in the hypothalamus (STN), triggering of electrical stimulation by real-time detection of the envelope of beta rhythm (12-35 Hz). In 2013, Little et al showed that eight PD patients had 27% (p-0.005) to 29% (p-0.03) higher motor scores during closed-loop DBS than open-loop, respectively. In addition to these therapeutic benefits, closed-loop stimulation time is reduced by 56% compared to open-loop DBS, reducing energy requirements.
Although optogenetic stimulation has been triggered for many years since the beginning using electrical signals or behaviours, only a few papers have implemented feedback control in this way. This is because the combination of closed-loop control theory and neuroscience is highly interdisciplinary. All closed loop systems require a real time processing component that can detect the occurrence of a target transient event in a timely manner and respond within a defined time. Solutions based on integrated circuits, programmable gate arrays (FPGAs), Digital Signal Processors (DSPs) or other custom hardware are well suited for real-time processing, and can meet stringent real-time requirements. However, such custom hardware systems are difficult to validate in a cross-laboratory environment and suffer from the following drawbacks:
1. at present, no method for realizing closed-loop intervention based on functional connection exists. The closed-loop optogenetic intervention on the neuron activity of each brain area of the hippocampus and the cortex is an effective way for testing the causal relationship between functional connection and behaviours of brain intervals.
2. These methods have already determined the detection method when the program is burned into the motherboard, and cannot be adjusted in real time. Some laboratories have developed hardware-customized systems for simultaneously implementing electrical signal recording and closed-loop optogenetic stimulation in closed-loop systems. Although many of these settings are quite smart, their implementation in other laboratories is not so easy, as control systems typically require an experienced technician to assemble the required electronics, and to debug other necessary hardware and software.
Disclosure of Invention
The invention aims to provide a closed-loop optogenetic intervention system and an intervention method, which are used for solving the problems in the prior art.
To solve the above-mentioned problems, according to an aspect of the present invention, there is provided a closed-loop optogenetic intervention system, the system comprising:
a plurality of electrodes for implantation within the brain of an animal;
a biological data recording device in signal communication with the electrodes and collecting signals from the plurality of electrodes;
the pulse stimulator is connected with the brain of the animal through an optical fiber and is used for performing light stimulation on the brain of the animal;
a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: and reading the signal of the biological data recording device, processing the signal, and outputting a corresponding signal to the pulse stimulator to perform optical stimulation on the brain of the animal after the signal reaches the optogenetic trigger condition.
In one embodiment, the controller includes:
the command receiving and analyzing unit is used for receiving and analyzing an external command and allowing the trigger threshold value to be changed according to the requirement of a user;
the storage unit is used for storing the signals in real time; and
and the processing unit is used for processing data in real time and continuously detecting whether the optogenetic trigger condition is met currently.
In one embodiment, the controller further comprises a display unit, and the processing result of the processing unit is displayed in real time through the display unit.
In one embodiment, the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the filtering module is used for filtering the read-in signals;
the computing module is used for computing the filtered signals;
a judging module configured to judge whether the optogenetic trigger condition is satisfied according to the data calculated by the calculating module; and
and the output module sends laser pulses to the pulse stimulator according to the output state determined by the judgment module.
In one embodiment:
the data read-in module is configured to: obtaining an actual signal y of the ith electrode from the read signal of the ith electrode read by the following formula 1iEquation 1: y isi＝di×scaleiX 1000, wherein, scaleiThe amplification factor of the analog signal of the ith electrode;
the filtering module is configured to: actual signal y to the ith electrodeiFiltering and obtaining a filtered signal Ri；
The computing module is configured to: the root mean square RMS1 of the filtered signal over the first time length window is calculated by equation 2 belowiAnd root mean square RMS2 of the filtered signal within a second window of time lengthsiEquation 2:
Figure BDA0003323570860000041
wherein W is the number of data points in the corresponding window;
the determination module is configured to: comparing the root mean square RMS1 of the filtered signals within a window of the first time lengthiWhether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length windowiThree times that of the original.
In one embodiment, the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
a calculation module: the calculation module is configured to calculate a correlation between any two read-in signals,
a filtering module for calculating and smoothing the correlation obtained by the calculating module;
the judging module is used for judging whether the functional connection strength of the channel pairs higher than a certain proportion is higher than a given threshold value; and
and the output module is used for sending laser pulses to the pulse stimulator according to the output state determined by the judgment module.
In one embodiment of the present invention,
the filtering module is configured to: determining a trigger condition according to the 'baseline', correcting and smoothing the correlation obtained by the calculation module by using a 200-500 millisecond moving average filter, thereby generating real-time functional connection strength;
the determination module is configured to: when the channel pairing functional connection strength exceeding 25% is higher than a given threshold value, starting to trigger the pulse stimulator to emit laser pulses; and
the output module is configured to: the delay from reaching the threshold to the start of the pulsed stimulus is set at 20-40 milliseconds; and after the laser pulse stimulation is triggered, continuing until the connection strength drops below the threshold value again.
In one embodiment, the biological data recording device is an OmniPlex multichannel recording system, and the pulse stimulator is a Master-9 pulse stimulator, and the controller is implemented using Matlab software. In one embodiment, the controller is configured to increase the response speed through parallel computing, and call the Nvidia GPU for parallel computing by means of the CUDA toolkit to accelerate the online detection speed.
In one embodiment, the closed-loop optogenetic intervention system includes a plurality of electrodes and a plurality of optical fibers and uses multiple-input multiple-output closed-loop control.
According to another aspect of the present invention, there is also provided a closed-loop optogenetic intervention method comprising the steps of:
implanting a plurality of electrodes into the brain of the animal;
communicating signals with the electrodes and collecting signals of the plurality of electrodes by using a biological data recording device;
reading the signal of the biological data recording device, processing the signal in real time, and sending laser pulse to perform optical stimulation on the animal brain after reaching the optogenetic triggering condition.
In one embodiment, the step of "reading the signal of the biological data recording device and processing the signal in real time, and sending a laser pulse to perform optical stimulation on the brain of the animal after reaching the optogenetic trigger condition" comprises the following sub-steps:
reading signals of a plurality of electrodes from the biological data recording device;
filtering the read-in signals;
calculating the filtered signals;
judging whether the optogenetic triggering condition is met; and
laser pulses are sent for optical stimulation.
In one embodiment:
in the step of "reading signals of a plurality of electrodes from the biological data recording apparatus", an actual signal y of the ith electrode is acquired from a read-in signal of the ith electrode read in by the following formula 1iEquation 1: y isi＝di×scaleiX 1000, wherein, scaleiThe amplification factor of the analog signal of the ith electrode;
in the step "filtering the read signal", the actual signal y of the i-th electrode is subjected toiFiltering and obtaining a filtered signal Ri；
In the step "calculating the filtered signal", the root mean square RMS1 of the filtered signal in the first time length window is calculated by the following equation 2iAnd root mean square RMS2 of the filtered signal within a second window of time lengthsiEquation 2:
Figure BDA0003323570860000051
wherein W is the number of data points in the corresponding window; and
in the step "judge whether or not fullIn the optogenetic trigger condition ", the root mean square RMS1 of the filtered signal is compared over a window of a first time lengthiWhether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length windowiThree times that of the original.
In one embodiment, the step of "reading the signal of the biological data recording device and processing the signal in real time, and sending a laser pulse to perform optical stimulation on the brain of the animal after reaching the optogenetic trigger condition" comprises the following sub-steps:
reading signals of a plurality of electrodes from the biological data recording device;
calculating the correlation between any two read-in signals;
calculating and smoothing the obtained correlation;
judging whether the functional connection strength of the channel pairs higher than a certain proportion is higher than a given threshold value; and
laser pulses are sent for optical stimulation.
In one embodiment, in the step "calculating and smoothing the obtained correlation", the triggering condition is determined according to the "baseline", and the correlation obtained by the calculation module is corrected and smoothed by using a moving average filter of 200-;
in the step of "calculating the correlation between any two read-in signals", when the channel pairing function connection strength exceeding 25% is higher than a given threshold value, the pulse stimulator is started to be triggered to emit laser pulses; and
in the step "sending laser pulses for optical stimulation", the time delay from reaching the threshold to the start of the pulse stimulation is set to 20-40ms and after the laser pulse stimulation is triggered, it continues until the connection strength again falls below the threshold.
The closed-loop optogenetic intervention system and the closed-loop optogenetic intervention method can process data flow in an experiment in real time, LFP activities of a plurality of recording electrodes can be analyzed in real time through the system or the method, closed-loop stimulation with functional connection between brain areas as a trigger condition is realized, and experiment parameters can be modified according to personal requirements. In addition, GPU parallel computation can be called, so that the requirements of multi-channel photoelectric combined electrode multiple input and multiple output can be met.
Drawings
FIG. 1 is a connection block diagram of a closed-loop optogenetic intervention system in accordance with one embodiment of the present invention.
Fig. 2 is a flow chart of LFP preprocessing according to an embodiment of the present invention.
Fig. 3 shows a connection block diagram of units of the controller in one embodiment of the present invention.
FIG. 4 is a block diagram of the connections of the various modules of the processing unit in accordance with one embodiment of the present invention.
FIG. 5 is a flow diagram of the operation of a processing unit in accordance with one embodiment of the present invention.
FIG. 6 is a block diagram of the connection of modules of a processing unit in accordance with another embodiment of the present invention.
Fig. 7 shows a schematic diagram of a process of parallel computing using Matlab.
FIG. 8 is a graph of the practical waveform and experimental results of behavior of an exemplary application of the closed-loop optogenetic intervention system of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the objects, features and advantages of the invention can be more clearly understood. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely intended to illustrate the spirit of the technical solution of the present invention.
In the following description, for the purposes of illustrating various disclosed embodiments, certain specific details are set forth in order to provide a thorough understanding of the various disclosed embodiments. One skilled in the relevant art will recognize, however, that the embodiments may be practiced without one or more of the specific details. In other instances, well-known devices, structures and techniques associated with this application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.
Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In the following description, for the purposes of clearly illustrating the structure and operation of the present invention, directional terms will be used, but terms such as "front", "rear", "left", "right", "outer", "inner", "outer", "inward", "upper", "lower", etc. should be construed as words of convenience and should not be construed as limiting terms.
For closed-loop optogenetic control, research focus and difficulty are mainly shown in the following two aspects:
(1) closed-loop control for realizing multiple input and multiple output
The number of possible combinations of connectivity patterns is quite large, even for only a few tens of recording sites. Worse yet, functional connections are difficult to predict. The functional connections may change dramatically with changes in motor activity, sensory input, and internal brain states. As the number of neurons recorded increases, to run a model of a large complex network in real time, the original model needs to be decomposed into smaller subsystems or simpler lower-order models, so that they can run in parallel on a millisecond-scale time scale.
(2) Ensuring stability of detection method
Neurodynamics are very unstable and often exhibit random transient phenomena such as short-time synchronization of action potentials in cells or extracellular field potentials. Therefore, the design of closed-loop control needs to fully consider the stability of the detection method, and the selection of recording and stimulation parameters is very important. Furthermore, the relationship between optogenetic modulation and recorded neural activity is difficult to quantify, which prevents the control algorithm from effectively adjusting the stimulation parameters, resulting in under-or over-stimulation of the target cells. Therefore, it is important to realize simultaneous electrical signal recording and closed-loop optogenetic stimulation.
The invention provides a closed-loop optogenetic intervention system and a closed-loop optogenetic intervention method, which can process data streams in an experiment in real time. The method can analyze the Local Field Potential (LFP) activities of a plurality of recording electrodes in real time, realize closed-loop stimulation with functional connection between brain areas as a trigger condition, and modify parameters according to personal requirements. In addition, the GPU can be called for parallel computation, and the requirement of multi-channel photoelectric combined electrode multiple input and multiple output can be met.
Fig. 1 is a connection block diagram of a closed-loop optogenetic intervention system according to an embodiment of the present invention, and as shown in fig. 1, the closed-loop optogenetic intervention system 100 includes a plurality of electrodes 10, a biological data recording device 20, a pulse stimulator 30, and a controller 40. The plurality of electrodes 10 are used for being implanted into the brain of an animal and collecting electroencephalogram signals of a specific brain area, the biological data recording device 20 is in signal connection with the electrodes 10 and receives the electroencephalogram signals collected by the plurality of electrodes, and the pulse stimulator 30 is connected with the brain of the animal through an optical fiber 50 and is used for performing light stimulation on a specified area of the brain of the animal. The controller 40 is in signal connection with the biological data recording device 20 and the pulse stimulator 30 and is configured to: the electroencephalogram signal of the biological data recording device 20 is read and processed, and after the optogenetic trigger condition is reached, the corresponding output signal is output to the pulse stimulator 30 to perform optical stimulation on the animal brain.
The plurality of electrodes 10 are microwire electrodes or silicon electrodes which can detect electrical activity of neurons at deep positions of the brain by being implanted into the brain, the high frequency part of signals recorded by the electrodes reflects information of neuron discharge (spike) around the position, and the low frequency part, namely Local Field Potential (LFP), reflects changes of functional activity of cell populations, and the amplitude is influenced by the number, type, distribution and geometric form of cells around the recording position. LFP reflects the combined effect of cellular membrane potential at the site in the hundreds of microns around the electrode, and is very sensitive to the act of synchronized firing of neuronal clusters, whose activity is closely related to the functional state and behavioral tasks of the brain.
In one embodiment, the biological data recording device 20 is an OmniPlex multichannel recording system from Plexon. Before recording, whether the whole set of instrument can work normally or not is required to be tested, a Headset test board HTB-10 can be used for simulating a neuron discharge signal, waveform parameters are compared, and the fact that the whole set of system can record normally is confirmed. Thereafter, OmniPlex (version 1.11.3, Plexon, USA) was turned on for signal acquisition, and Plexocontrol (version 1.11.3, Plexon, USA) was turned on for display and signal preservation. The headstage was then inserted into the jellyfish harvest signal on the electrode cap, and the electrical signal (20 kHz harvest frequency) was then stored in a computer in the format of pl2 and introduced into a NeuroExplorer (version 5.115, Nex Technologies, USA) for pre-processing of the data.
Alternatively, the neural electrical signals collected by OmniPlex, which contain a variety of information such as broadband signals, spike signals, event sequences, etc., can be stored on a computer in the format of pl 2. Alternatively, the LFP preprocessing may be performed by NeuroExplorer software. The specific flow of this preprocessing is described below with reference to fig. 2.
As shown in fig. 2, first, the state of the animal is determined according to the video record of the animal, for example, in the case of taking the brain of the rat as the acquisition object, it is necessary to select the signal of the rat in the waking and resting state to obtain the raw data: and (4) a wideband. Next, LFPs are extracted from all signals by low pass filtering in NeuroExplorer and saved in a format that Matlab (version 2014b, MathWorks Inc, USA) can handle. And thirdly, removing power frequency interference. Then, the average power of the signal of each channel in all the time is calculated, and the problem channel with poor contact is removed. And finally, detecting head movement interference, wherein the absolute value of the normally recorded local field potential is less than 20mV, if the signal fluctuates greatly, the data in the time period is subjected to the head movement interference, and the data section is discarded.
It should be noted that the OmniPlex-64 multichannel recording system is a biological data recording system that assembles complex electronic components of Plexon, including synchronized clocks between system components and amplifier digital interface cards. The OmniPlex host computer adopts a powerful digital interface, supports up to 64 inputs and one auxiliary analog input, and supports 64 or more non-neural experimental signal inputs.
The controller 40 implements real-time soft processing of the multi-channel electroencephalogram signals, can operate on the input data stream, and produces an output that can be used for closed-loop control.
Fig. 3 shows a connection block diagram of modules of the controller 40 in an embodiment of the present invention, and as shown in fig. 3, in an embodiment, the controller 40 includes a command receiving and parsing unit 41, a storage unit 42, and a processing unit 43. The command receiving and parsing unit 41 is used for receiving and parsing an external command, and optionally, the command receiving and parsing unit 41 allows the trigger threshold to be changed according to the requirement of the user. The storage unit 42 is used for real-time storage of the recording signal. The processing unit 43 is used to process the data stream in real time and continuously detect whether the current state satisfies the optogenetic trigger condition, and if necessary, parallel processing can be used to increase the computation speed.
In one embodiment, the controller 40 described above may be written in Matlab language and may operate on an OmniPlex-based signal recording system. For example, real-time threshold selection may be accomplished using the Matlab handle and Timer. The user can know the waveform of the recording electrode in real time, set a threshold value according to the animal individual difference, and can switch between the two states of threshold value detection and optogenetic stimulation.
An embodiment of the processing unit 43 with ripple as a trigger condition is described below with reference to fig. 4. As shown in fig. 4, the processing unit 43 includes a data reading module 431, a filtering module 432, a calculating module 433, a judging module 434 and an output module 435. The data reading module 431 is used for reading signals of the plurality of electrodes from the biological data recording device 30, and the filtering module 432 is used for filtering the read signals. The calculation module 433 performs calculation on the filtered signal. The determination module 434 is configured to determine whether the optogenetic trigger condition is satisfied based on the data calculated by the calculation module 433, and the output module 435 is configured to send the laser pulse to the pulse stimulator 30 based on the output status determined by the determination module 434.
Alternatively, the data read-in module 431 mayThe data format was read in real time from the OmniPlex server using the matlabcelllientdevilpkit software development kit. The obtained data format can be read using the print _ pars () function. They include three types: SPIKE _ TYPE, EVENTS _ TYPE, and continuuous _ TYPE. Wherein, SPIKE _ TYPE is a SPIKE waveform separated by a SPIKE debugging program carried by the server itself, event _ TYPE records an event time sequence, and here we mainly focus on the control _ TYPE. The CONTINUOUS _ TYPE includes a broadband signal (sampling rate of 40kHz) and a local field potential signal (down-sampled to 1 kHz). Simultaneously reading in the preset analog signal amplification factor scale of the ith recording electrode in the AD conversion processiIf the multiple is not 1, the actual signal size of the ith recording electrode and the read-in data diThe relationship of (1) is:
yi＝di×scalei×1000
wherein, yiThe unit of (b) is mV.
Reading in input signals from N recording electrodes
Figure BDA0003323570860000101
Detection of the closed loop control trigger condition may then commence, but before that it is often necessary to confirm the magnitude of the trigger threshold. Due to the variability of animal individuals, the adjustment needs to be carried out by experienced users according to actual conditions. The mathab-based coherence threshold aided debugging can display waveforms and to-be-measured values in real time according to electrode channels selected by a user, the refreshing time is 50 milliseconds/frame, and a cursor is moved to select a threshold value required to be set. The default settings are one mean and three standard deviations without additional modifications.
In one embodiment, the filtering module 432 selects a band pass filter with a cutoff frequency of 80-250 Hz. The filter is provided with two sliding windows, the lengths of the two sliding windows are respectively 8ms and 2s, the two sliding windows are respectively recorded as a first time length window and a second time length window, the sampling frequency is set to be 1000Hz, and then the number of data points in the first time length window is 8, and the number of data points in the second time length window is 2000. For each channel i of the multi-channel electrode, first, it will takeCollected LFP signal yiThrough the filter, obtain the signal R of ripple frequency bandi。
The calculation module 433 calculates the root mean square of the signals in the two time length windows, i.e. the root mean square RMS1 of the signal in the first time length windowiAnd root mean square RMS2 of the signal for the second time length windowi. Root mean square RMS for W windows of data pointsiThe calculation method comprises the following steps:
Figure BDA0003323570860000111
the decision module 434 is configured to compare the RMS1iAnd 3 × RMS2iIf RMS1iExceeding 3 x RMS2iIf the state of (c) continues for more than 8s, it is defined that a ripple event occurs, and the state (state) of the channel is defined as state 1, otherwise, it is defined as state 0. At one detection instant, one state is used for each channel. How to integrate this information depends on how closed-loop optogenetic logic is used. When multiple-input multiple-output (multiple-input multiple-output) closed-loop control is used, the states can be distributed to the corresponding optical fibers, and if multiple-input single-output (multiple-input and single-output) closed-loop control is used, the results from different channels need to be integrated.
The flow of the processing unit 43 relating to ripple as the trigger condition according to an embodiment of the present invention is described below with reference to fig. 5. FIG. 5 is a flowchart of the processing unit 43 relating to ripple as a trigger condition according to an embodiment of the present invention. As shown in fig. 5, the modules of the processing unit 43 perform the following steps:
step 100, starting a system;
step 200, initializing parameters;
step 300, the data reading module 431 reads data from OmniPlex;
step 400, the filtering module 432 passes the data through an 80-250Hz band-pass filter;
step 500, the calculation module 433 calculates root mean square RMS1 and RMS 2;
step 600, the determining module 434 determines whether the stimulation duration is reached, and if the stimulation duration is not reached, the following step 700 is performed, and if the stimulation duration is reached, the step 800 is performed;
step 700, comparing the magnitudes of RMS1 and 3 × RMS2, entering step 900 when RMS1 is >3 × RMS2, and returning to step 300 when RMS1 ≦ 3 × RMS 2;
step 800, inputting a 60ms pulse to an output module, such as a plexDo;
step 900, the OminiPLex connection is disconnected, and the process goes to step 1000.
And step 1000, ending.
An implementation of the processing unit 43A with functional connection as a trigger condition according to another embodiment of the present invention is described below with reference to fig. 6. As shown in fig. 6, the processing unit 43A includes:
a data reading module 431A for reading signals of the plurality of electrodes from the biological data recording device 20;
a calculating module 432A, configured to calculate a coherence between any two read-in signals;
a filtering module 433A, configured to calculate and smooth the correlation coherence obtained by the calculating module;
a determining module 434A, configured to determine whether there is a channel pair with a functional connection strength higher than 25% that is higher than a given threshold, and determine an output state when the channel pair with a functional connection strength higher than 25% that is higher than the given threshold; and
an output module 435A for sending the laser pulse to the pulse stimulator 30 according to the output status determined by the determination module 434A.
Where functional connectivity of the brain refers to connectivity between brain regions sharing functional attributes. More specifically, it can be defined as the temporal correlation that occurs in different spaces. The main index describing the functional connectivity status of neural networks in neuroscience is the correlation of signals between brain regions (coherence).
Enhanced and phase-synchronized simultaneous neural oscillations of some kind occur between brain regions, the brainThe information exchange between intervals provides a time window of rhythmicity reflecting changes in the functional connections of the brain. Coherence is often used in neuroscience to quantify the functional connection between two brain regions. To calculate coherence between the two LFP signals x (t) and y (t), the power spectrum of the signal may be obtained by using a Multi-window spectral estimation (Multi-person method). Fourier transformation for each orthogonal window (tap), x (t), (t ═ 1,2, …, N)
Figure BDA0003323570860000121
Comprises the following steps:
Figure BDA0003323570860000131
the cross-power spectrum between the two time series x (t) and y (t) is defined as:
Figure BDA0003323570860000132
coherence is defined as:
Figure BDA0003323570860000133
wherein S isxx(f) And Syy(f) The self-power spectral densities of x (t) and y (t), respectively, are derived in the same manner as the cross-spectral densities. The size of Coherence satisfies 0. ltoreq. Cxy (f). ltoreq.1. For LFP collected from different days after training, differences between coherence were compared by means of independent sample t test, when p<A significant difference between these functional linkages was considered at 0.05.
Optionally, the filtering module 433A corrects and smoothes the coherence obtained by the calculation using a 200-500ms moving average filter, so as to generate the real-time functional connection strength, and control the stimulation trigger through a customized threshold.
Optionally, the output module 435A is configured to have a delay of 20-40ms from reaching the threshold to the start of stimulation and to continue until the connection strength again drops below the threshold once the light stimulation is triggered.
In one embodiment, the output modules 435 and 435A are configured to: once the output state is calculated, PlexDO can be used to control the output of rows and columns on the device. Each bit can be independently set to a low level (0V) or a high level (+ 5V). For example, bit 0 (DIO0) may be set to 1 to indicate the start of the light stimulus and set to 0 at the end of the light stimulus. Throughout the process, the potentials remain in the set state (0V or 5V) until the user changes them. The PlexDO line number starts at 1 and the first line corresponds to the name GPCTR0 on the NIDAQ card.
Alternatively, the waveform output may have two modes: setting a fixed pulse length and adjusting the laser switch according to the actual detection condition. The former will output a length-determining pulsed square wave, which is typically 60ms in the ripple trigger condition and 200ms in the coherence trigger condition. The latter will keep the set state until the next time the output changes the value of the state.
Optionally, the controller is configured to increase the response speed by parallel computation. The CPU is responsible for transaction processing and serial calculation with strong logicality, and the CUDA toolkit is used for calling the Nvidia GPU to perform parallel operation, so that the online detection speed can be accelerated. In Matlab GPU computing, data between the Matlab working area and the Graphics Processing Unit (GPU) are transferred to each other, and they have independent memory address spaces, i.e. memory of the host and video memory of the device. The main calling method is to use the gpuArray () function to put the data on the GPU for operation, and then use the gather () function to retrieve the result. Matlab defines rich library functions on the GPU, and the invention mainly applies fast Fourier transform and matrix multiplication. Some complex functions cannot be accelerated by using Matlab internal codes for a GPU, and can be compiled into a mex file which can be called by Matlab through calling cu files and mixed programming of Matlab and C.
FIG. 7 illustrates the above process, as shown in FIG. 7, for input signals from N recording electrodes in order to split the final operation into a fast Fourier transform and a matrix multiplication
Figure BDA0003323570860000141
We integrate it into Datachannel_pair＝[yi；yj]Wherein y isi、yjRespectively representing the time series from the ith and jth electrodes, common to the N recording electrodes
Figure BDA0003323570860000142
And (4) combination. Similarly, the parameters from the K orthogonal windows are sorted into
Figure BDA0003323570860000143
Thus, each column of Data contains information of a certain set of recording electrode pairs, which can be processed by each core of the GPU respectively when performing correlation detection.
An example of a rat fear memory intervention using the closed loop optogenetic intervention system of the present invention is described below.
Subject: clean male SD rats at 28 days postnatal were selected for this study and purchased from the experimental animals center of the university of medical, tokyo. The photosensitive channel protein expression virus AAV9-CamkII alpha-NpHR-mCherry of the experiment entrusted the biological company to package, and simultaneously purchased the control virus AAV9-CamkII alpha-mCherry. After the virus injection, SD male rats were continuously raised to 8 weeks, and the virus transfection efficiency and the expression level of susceptible tract proteins were confirmed by observing fluorescence. 570nm yellow light was used to activate the NpHR receptor, inhibiting the activity of the cells. The raising, operation and other related operations of the experimental animals strictly comply with the regulations of the international animal ethics act and the relevant regulations of the animal feeding and management committee of southeast university.
The experimental method comprises the following steps: the photoelectric combined electrode is implanted in four cerebral areas of prefrontal cortex (mPF), Anterior Cingulate Cortex (ACC), Posterior Parietal Cortex (PPC) and hippocampus of a rat, so that the intervention on the activity of corresponding cerebral cells can be realized while recording LFP, sharp-wave ripple (SPW-R) of hippocampus is closely linked with memory reactivation or playback, the experiment adopts bilateral hippocampal CA1 area optogenetic intervention taking hippocampal ripple as a trigger condition, after training of conditional Fear (FC) memory of rats, the closed loop optogenetic intervention system of the invention is used for inhibiting hippocampal ripple from 0 th day to 7 th day after the experiment and synchronously recording the intervention signals, the inventor carries out statistics on the results of five experimental groups of rats, the detection delay time is below 10 ms., when the detection threshold is set to three times of standard deviation, the proportion of the average stimulation time to the total stimulation duration was 12.86%. Furthermore, when more than 25% of the channels (set to 16 in this experiment) had a signal amplitude exceeding 20mV, the signal was considered to have suffered from head movement disturbances, at which time no light stimulation would be given to the rats.
FIG. 8 shows the practical waveforms and behavioral experimental results of an exemplary application of the closed-loop optogenetic intervention system of the present invention. The left side view in FIG. 8 shows the waveform of optogenetic inhibition, and the amplitude of ripple frequency band (80-250 Hz) is effectively inhibited after receiving optical stimulation. The rate of the rigor-flat state of the rats in the closed loop intervention group (n-4) was significantly decreased compared to the control group (n-6) as measured by the FreezeFrame calculation of the ratio of the rigor-flat time to the total test time (p <0.01, t test). This shows that the closed-loop control system of the invention inhibits the ripple of the hippocampus in the memory formation stage, and effectively interferes with the formation of memory, thereby directly verifying the effectiveness of the closed-loop optogenetic intervention of the invention from experimental results.
While the preferred embodiments of the present invention have been illustrated and described in detail, it should be understood that various changes and modifications of the invention can be effected therein by those skilled in the art after reading the above teachings of the invention. Such equivalents are intended to fall within the scope of the claims appended hereto.

Claims (12)

1. A closed loop optogenetic intervention system, the system comprising:
a plurality of electrodes for implantation within the brain of an animal;
a biological data recording device in signal communication with the electrodes and collecting signals from the plurality of electrodes;
the pulse stimulator is connected with the brain of the animal through an optical fiber and is used for performing light stimulation on the brain of the animal; and
a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: and reading the signal of the biological data recording device, processing the signal, and outputting a corresponding signal to the pulse stimulator to perform optical stimulation on the brain of the animal after the signal reaches the optogenetic trigger condition.
2. The closed loop optogenetic intervention system of claim 1, wherein the controller comprises:
the command receiving and analyzing unit is used for receiving and analyzing an external command and allowing the trigger threshold value to be changed according to the requirement of a user;
the storage unit is used for storing the signals in real time; and
the processing unit is used for processing data in real time and continuously detecting whether the optogenetic trigger condition is met currently; optionally, the controller further includes a display unit, and the processing result of the processing unit is displayed in real time through the display unit.
3. The closed loop optogenetic intervention system of claim 2, wherein the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the filtering module is used for filtering the read-in signals;
the computing module is used for computing the filtered signals;
a judging module configured to judge whether the optogenetic trigger condition is satisfied according to the data calculated by the calculating module; and
and the output module sends laser pulses to the pulse stimulator according to the output state determined by the judgment module.
4. The closed loop optogenetic intervention system of claim 3, wherein:
the data read-in module is configured to: obtaining an actual signal y of the ith electrode from the read signal of the ith electrode read by the following formula 1iEquation 1: y isi＝di×scaleiX 1000, wherein, scaleiThe amplification factor of the analog signal of the ith electrode;
the filtering module is configured to: actual signal y to the ith electrodeiFiltering and obtaining a filtered signal Ri；
The computing module is configured to: the root mean square RMS1 of the filtered signal over the first time length window is calculated by equation 2 belowiAnd root mean square RMS2 of the filtered signal within a second window of time lengthsiEquation 2:
Figure FDA0003323570850000021
wherein W is the number of data points in the corresponding window;
the determination module is configured to: comparing the root mean square RMS1 of the filtered signals within a window of the first time lengthiWhether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length windowiThree times that of the original.
5. The closed loop optogenetic intervention system of claim 2, wherein the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
a calculation module: the calculation module is configured to calculate a correlation between any two read-in signals,
a filtering module for calculating and smoothing the correlation obtained by the calculating module;
the judging module is used for judging whether the functional connection strength of the channel pairs higher than a certain proportion is higher than a given threshold value; and
and the output module is used for sending laser pulses to the pulse stimulator according to the output state determined by the judgment module.
6. The closed loop optogenetic intervention system of claim 5,
the filtering module is configured to: determining a trigger condition according to the 'baseline', correcting and smoothing the correlation obtained by the calculation module by using a 200-500 millisecond moving average filter, thereby generating real-time functional connection strength;
the determination module is configured to: when the channel pairing functional connection strength exceeding 25% is higher than a given threshold value, starting to trigger the pulse stimulator to emit laser pulses; and
the output module is configured to: the delay from reaching the threshold to the start of the pulsed stimulus is set at 20-40 milliseconds; and after the laser pulse stimulation is triggered, continuing until the connection strength drops below the threshold value again.
7. The closed loop optogenetic intervention system of claim 2, wherein the biological data recording device is an OmniPlex multichannel recording system, the pulse stimulator is a Master-9 pulse stimulator, and the controller is implemented using Matlab software; optionally, the controller is configured to increase the response speed through parallel computing, and call the Nvidia GPU for parallel computing by means of the CUDA toolkit to accelerate the online detection speed.
8. A method of closed-loop optogenetic intervention, the method comprising the steps of:
implanting a plurality of electrodes into the brain of the animal;
communicating signals with the electrodes and collecting signals of the plurality of electrodes by using a biological data recording device;
reading the signal of the biological data recording device, processing the signal in real time, and sending laser pulse to perform optical stimulation on the animal brain after reaching the optogenetic triggering condition.
9. The closed-loop optogenetic intervention method of claim 8, wherein the step of "reading the signal of the biological data recording device and processing the signal in real time, and sending a laser pulse to photostimulate the animal brain after reaching the optogenetic trigger condition" comprises the sub-steps of:
reading signals of a plurality of electrodes from the biological data recording device;
filtering the read-in signals;
calculating the filtered signals;
judging whether the optogenetic triggering condition is met; and
and after the optogenetic trigger condition is reached, sending laser pulses to perform photostimulation on the animal brain.
10. The closed-loop optogenetic intervention method of claim 9, wherein:
in the step of "reading signals of a plurality of electrodes from the biological data recording apparatus", an actual signal y of the ith electrode is acquired from a read-in signal of the ith electrode read in by the following formula 1iEquation 1: y isi＝di×scaleiX 1000, wherein, scaleiThe amplification factor of the analog signal of the ith electrode;
in the step "filtering the read signal", the actual signal y of the i-th electrode is subjected toiFiltering and obtaining a filtered signal Ri；
In the step "calculating the filtered signal", the root mean square RMS1 of the filtered signal in the first time length window is calculated by the following equation 2iAnd root mean square RMS2 of the filtered signal within a second window of time lengthsiEquation 2:
Figure FDA0003323570850000041
wherein W is the number of data points in the corresponding window; and
in the step "determining whether the optogenetic trigger condition is satisfied", the root mean square RMS1 of the filtered signals in the first time length window is comparediWhether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length windowiThree times that of the original.
11. The closed-loop optogenetic intervention method of claim 8, wherein the step of "reading the signal of the biological data recording device and processing the signal in real time, and sending a laser pulse to photostimulate the animal brain after reaching the optogenetic trigger condition" comprises the sub-steps of:
reading signals of a plurality of electrodes from the biological data recording device;
calculating the correlation between any two read-in signals;
calculating and smoothing the obtained correlation;
judging whether the functional connection strength of the channel pairs higher than a certain proportion is higher than a given threshold value; and
laser pulses are sent for optical stimulation.
12. The closed-loop optogenetic intervention method of claim 11,
in the step of calculating and smoothing the obtained correlation, determining a trigger condition according to a baseline, and correcting and smoothing the correlation obtained by the calculation module by using a moving average filter of 200- & lt500 & gt milliseconds, so as to generate real-time functional connection strength;
in the step of "calculating the correlation between any two read-in signals", when the channel pairing function connection strength exceeding 25% is higher than a given threshold value, the pulse stimulator is started to be triggered to emit laser pulses; and
in the step "sending laser pulses for optical stimulation", the time delay from reaching the threshold to the start of the pulse stimulation is set to 20-40ms and after the laser pulse stimulation is triggered, it continues until the connection strength again falls below the threshold.
CN202111254993.1A 2021-10-27 2021-10-27 Closed-loop optogenetic intervention system and intervention method Active CN113967023B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111254993.1A CN113967023B (en) 2021-10-27 2021-10-27 Closed-loop optogenetic intervention system and intervention method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111254993.1A CN113967023B (en) 2021-10-27 2021-10-27 Closed-loop optogenetic intervention system and intervention method

Publications (2)

Publication Number Publication Date
CN113967023A true CN113967023A (en) 2022-01-25
CN113967023B CN113967023B (en) 2023-08-29

Family

ID=79588673

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111254993.1A Active CN113967023B (en) 2021-10-27 2021-10-27 Closed-loop optogenetic intervention system and intervention method

Country Status (1)

Country Link
CN (1) CN113967023B (en)

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100094377A1 (en) * 2008-10-09 2010-04-15 Daniel Graupe Method and apparatus for closed-loop deep brain stimulation in treating neurological diseases
CN104225790A (en) * 2014-09-19 2014-12-24 清华大学 Closed loop nerve stimulation system
CN104436447A (en) * 2014-10-11 2015-03-25 中国科学院苏州生物医学工程技术研究所 Closed loop photostimulation system used for regulating and controlling functions of spinal cord
CN104874102A (en) * 2015-04-02 2015-09-02 中国科学院苏州生物医学工程技术研究所 Multi-channel wireless closed loop deep brain neural sensing and control system
CN105361880A (en) * 2015-11-30 2016-03-02 上海乃欣电子科技有限公司 Muscle movement event recognition system and method
CN105688333A (en) * 2016-01-12 2016-06-22 中国人民解放军军事医学科学院基础医学研究所 Nerve effect evaluation apparatus of external transcranial magnetic stimulation
CN109011148A (en) * 2018-08-29 2018-12-18 复旦大学 Portable closed loop brain depth stimulator system with intelligent adaptive function
US20190168021A1 (en) * 2016-07-28 2019-06-06 University Of Newcastle Upon Tyne Optogenetic System and Method
CN110464298A (en) * 2019-07-25 2019-11-19 深圳大学 A kind of EEG Processing device and method
CN111298304A (en) * 2020-03-20 2020-06-19 北京航空航天大学 Diaphragm nerve light stimulation system with variable light intensity
CN210963587U (en) * 2019-07-25 2020-07-10 武汉大学 Closed-loop regulating and controlling device for treating epilepsy based on optogenetics
CN112451854A (en) * 2020-12-01 2021-03-09 中国康复研究中心 Implanted diaphragm pacemaker and control method thereof
CN113181569A (en) * 2021-04-27 2021-07-30 燕山大学 Closed-loop transcranial brain stimulation system and method
CN113476060A (en) * 2021-06-16 2021-10-08 南京曦光信息科技研究院有限公司 Closed-loop feedback type photoacoustic and electromagnetic integrated brain working rhythm adjusting device

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100094377A1 (en) * 2008-10-09 2010-04-15 Daniel Graupe Method and apparatus for closed-loop deep brain stimulation in treating neurological diseases
CN104225790A (en) * 2014-09-19 2014-12-24 清华大学 Closed loop nerve stimulation system
CN104436447A (en) * 2014-10-11 2015-03-25 中国科学院苏州生物医学工程技术研究所 Closed loop photostimulation system used for regulating and controlling functions of spinal cord
CN104874102A (en) * 2015-04-02 2015-09-02 中国科学院苏州生物医学工程技术研究所 Multi-channel wireless closed loop deep brain neural sensing and control system
CN105361880A (en) * 2015-11-30 2016-03-02 上海乃欣电子科技有限公司 Muscle movement event recognition system and method
CN105688333A (en) * 2016-01-12 2016-06-22 中国人民解放军军事医学科学院基础医学研究所 Nerve effect evaluation apparatus of external transcranial magnetic stimulation
US20190168021A1 (en) * 2016-07-28 2019-06-06 University Of Newcastle Upon Tyne Optogenetic System and Method
CN109011148A (en) * 2018-08-29 2018-12-18 复旦大学 Portable closed loop brain depth stimulator system with intelligent adaptive function
CN110464298A (en) * 2019-07-25 2019-11-19 深圳大学 A kind of EEG Processing device and method
CN210963587U (en) * 2019-07-25 2020-07-10 武汉大学 Closed-loop regulating and controlling device for treating epilepsy based on optogenetics
CN111298304A (en) * 2020-03-20 2020-06-19 北京航空航天大学 Diaphragm nerve light stimulation system with variable light intensity
CN112451854A (en) * 2020-12-01 2021-03-09 中国康复研究中心 Implanted diaphragm pacemaker and control method thereof
CN113181569A (en) * 2021-04-27 2021-07-30 燕山大学 Closed-loop transcranial brain stimulation system and method
CN113476060A (en) * 2021-06-16 2021-10-08 南京曦光信息科技研究院有限公司 Closed-loop feedback type photoacoustic and electromagnetic integrated brain working rhythm adjusting device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
袁明军: "光遗传学无线光刺激器与闭环调控系统研制", 硕士电子期刊 *

Also Published As

Publication number Publication date
CN113967023B (en) 2023-08-29

Similar Documents

Publication Publication Date Title
CN111477299B (en) Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control
US10384064B2 (en) Biomimetic multichannel neurostimulation
US10258291B2 (en) Systems and methods for evaluation of neuropathologies
Newman et al. Closed-loop, multichannel experimentation using the open-source NeuroRighter electrophysiology platform
CN105938397A (en) Hybrid brain-computer interface method based on steady state motion visual evoked potential and default stimulation response
CN116570834B (en) Transcranial direct current stimulation method, device, terminal and medium
CN112951449A (en) Cloud AI (artificial intelligence) regulation diagnosis and treatment system and method for neurological dysfunction diseases
Hogri et al. A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats
Seu et al. Exploiting all programmable SoCs in neural signal analysis: A closed-loop control for large-scale CMOS multielectrode arrays
Chen A primer on neural signal processing
CN115192907A (en) Real-time biofeedback percutaneous vagus nerve electronic acupuncture device
CN113967023B (en) Closed-loop optogenetic intervention system and intervention method
Erbslöh et al. Artefact-suppressing analog spike detection circuit for firing-rate measurements in closed-loop retinal neurostimulators
CN113112017B (en) Electroencephalogram grading and prognosis FPGA decoding system based on neural manifold
Bonnet et al. NeuroPXI: A real-time multi-electrode array system for recording, processing and stimulation of neural networks and the control of high-resolution neural implants for rehabilitation
Filippov et al. Sound-induced changes of infraslow brain potential fluctuations in the medial geniculate nucleus and primary auditory cortex in anaesthetized rats
JP2019510568A (en) Brain tissue stimulation method, apparatus and computer program
Lim Bi-Directional Brain-Computer Interfaces: Stimulation Artifact Suppression Design and Walking Exoskeleton Implementation
Alzuhair Theta phase-specific closed-loop stimulation in implantable neuromodulation devices
Kameneva et al. Spike history neural response model
CN115154837B (en) Control method and device of sleep-assisting equipment, terminal and storage medium
CN110338784A (en) Test method, apparatus, computer equipment and the storage medium of sleep deprivation effect
CN110339448B (en) Method, device, computer equipment and storage medium for regulating and controlling animal state
US20230181109A1 (en) Neurostimulation programming and triage based on freeform text inputs
Piwkowska et al. Re-creating active states in vitro with a dynamic-clamp protocol

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant