1. Introduction
Neuromodulation techniques have shown their significant promise in diagnosing and treating brain diseases and may also contribute to the development of new artificial intelligence (AI) technologies and information industries [
1]. Luigi et al. first explored the electrophysiological mechanism of neuromodulation by stimulating the frog sciatic nerve [
2]. Since then, great progress has been made in the field of neuromodulation, enabling the treatment of brain diseases such as Parkinson’s disease, cerebrovascular accidents, and paralysis [
3,
4]. Typically, the neuromodulation system comprises two parts: the neural signal recording module and the neurostimulator module [
5,
6]. The neural signal recording module detects special neural spikes generated under certain physiological conditions, and the neurostimulator delivers stimulation accordingly [
7]. During stimulation, the neural signal recording module records the response neural signals to evaluate the effectiveness of the stimulation. Based on the recorded response neural signals, the neuromodulation system’s user can adjust the neuromodulator’s parameters to optimize the stimulation effect, thereby achieving the desired neuromodulation system.
Currently, the most widely used method of neural stimulation is electrical stimulation [
8,
9,
10,
11]. However, electrical stimulation faces two major challenges. Firstly, the scope of electrical stimulation cannot be confined to a single neuron. Secondly, the modulation method of electrical stimulation lacks neuron-type specificity, making it difficult to modulate cells with specific molecular signatures. In contrast, optogenetics solves both of these challenges. By expressing specific photosensitive proteins in cells, light can induce cells to generate cationic current, thereby modulating the action potentials of cells. Thus, precise control of target neurons through optogenetic stimulation can be achieved [
12].
The special optrode equipped with micro-LEDs is required for optogenetic neuromodulation and microelectrodes for neural signal recording. However, issues such as damage to the micro-LED during implantation, the optrode potentially touching the skull and causing damage to the micro-LED, and the corrosion of micro-LED caused by tissue potentially happening after implantation are the major challenges to the stability of the optogenetic neuromodulation system; the fracture of a micro-LED may cause leakage current flow into the brain tissue around the optrode. Small leakage current will stimulate neurons that will affect the optogenetic stimulation, and the offset voltage caused by the leakage current will also affect recording neural spikes. Moreover, a large leakage current will even damage the brain tissue. Thus, real-time monitoring of the micro-LED’s working state is necessary. The optogenetic neuromodulation circuit system should consist of three modules: the optogenetic neurostimulation module, the recording module, and the micro-LED detection module.
Extensive research has been conducted in designing miniaturized circuits for optogenetic control. In 2013, H. Cao et al. presented the optrode probe for optogenetic neuromodulation that could record the neural signal from the visual cortex of the mouse and stimulate neurons by the optogenetic method [
13]. The presented novel optrode was small in size, which minimized the damage caused by inserting the optrode in the brain tissue. They used a function generator to drive a micro-LED in the optrode for optogenetic stimulation and used commercial instruments (OmniPlex, Plexon. Inc., Brooklyn, NY, USA) for recording neural signals. The mice used for the experiment were fixed on the experimental platform because of the external instruments’ considerable bulk and physical size. Further in 2018, H. Zhao et al. designed an integrated circuit for driving micro-LEDs [
14]. The minimal physical size of the integrated circuit system relieved the constraints on the behavior of animals. The system was also equipped with a micro-LED detection module, which enabled the monitoring of the micro-LED under long-term implantation. Nonetheless, the function of the integrated circuit system is limited—the circuit system was not equipped with the recording module, which restricts its usage in advanced neuroscience domains, and the optogenetic stimulation module could not adjust the emitted light of the micro-LED. Furthermore, during the self-diagnostic state, the micro-LED detection circuit module interferes with the normal operation of the neurostimulator circuit module; therefore, these two modules could not work at the same time. Besides, the detection results of the micro-LED detection circuit module are pulses with different intervals, not the direct result, which needs the user to spend time analyzing the data to identify the working state of the micro-LED. Subsequently, in 2018, Adam E. Mendrelaj et al. designed an integrated system for optogenetic stimulation and used the commercial INTAN chip for recording [
15]. The system could adjust the intensity of the emitted light with a 10-bit resolution. However, the system still lacks the function of detecting the micro-LED working state. In summary, the existing works have shortcomings in the miniaturization and comprehensiveness of the system, as well as real-time monitoring of the working status of micro-LEDs.
In order to address the drawbacks of previous works, this paper proposes a miniaturized and comprehensive optogenetic neuromodulation system composed of integrated circuits and a customized optrode. This ASIC consists of a 4-channel micro-LED driver module for driving micro-LEDs to stimulate neural cells, a 16-channel neural signal recording module for recording neural signals, and a micro-LED detection module for detecting the micro-LED’s working state in real time. The micro-LED driver model can adjust the driving current for the micro-LED, thus adjusting the luminosity of the light of the micro-LED. The micro-LED detection module could detect the micro-LED’s working state in real time without interfering with the neurostimulator circuits, and direct readout of the detection results, thereby avoiding subsequent complex data analysis of detection results.
This paper is arranged as the following structure.
Section 2 presents the components of the optogenetic neuromodulation system.
Section 3 presents the post-simulation results of the circuit system and the in situ normal saline experiment result.
Section 4 discusses this work and summarizes the paper.
2. System Architecture
As shown in
Figure 1, the top-level block of the presented optogenetic neuromodulator system consists of four modules: a customized optrode, a 4-channel micro-LED driver, a 4-channel micro-LED self-detection module, and a 16-channel neural signal recording module.
Two PWM control signals from external sources are utilized to adjust the working condition of the optogenetic stimulation circuit, where the low-frequency PWM (1–10 Hz) is for controlling the micro-LED driver circuit’s switching between the hibernation phase and the simulation phase, and the high-frequency PWM (1–10 MHz) is for adjusting the output current of the stimulation circuit during the stimulation phase.
The optrode may be broken during or after implantation, thus making the micro-LED not work accurately, so each optogenetic stimulation module is equipped with a consistently running self-detection module to evaluate the working state of the micro-LED. The user could justify three possible working states of micro-LED directly by the output voltage of two output nodes of the detection circuit.
The neuron activity could be monitored by the 16-channel neural signal recording circuit before, during, and after optogenetic stimulation. Monitoring neuron activity is not only useful for medical uses but can also help evaluate the optogenetic stimulation efficiency in real time; the user can adjust the optogenetic stimulation intensity to the minimum required level to reduce power consumption by the feedback of the neural signal recording circuit. An extra reference channel is equipped to reduce the interference of external noise.
2.1. Light Requirement and Optrode Characteristics
The light source used for stimulation needs to reach a certain threshold intensity to activate the simulated neurons. Simulation implies that the major contributor to the high energy demand for initiating an action potential is the restricted conductivity of ChR2 [
16]. In 2003, Nagel et al. described that the activation threshold of cells that express ChR2 is 0.7 mW/mm
2. However, different power intensity thresholds for stimulation have been published since then, and the most commonly used threshold is 1 mW/mm
2, which can achieve strong neurostimulation.
The customized planar optrode for the neuromodulation system was designed (see
Figure 2), which was based on a sapphire substrate and processes the characteristics of high stiffness, high transparency, and high optogenetic density [
17,
18]. The optrode has a total length of L = 13 mm, with an 8 mm segment specifically reserved for the insertion into rodent brain tissue. The optrode comprised 4 GaN/InGaN multiple quantum-wells (MQWs) blue micro-LEDs serving as an excitation light source, along with 16 microelectrodes having a diameter of 10 µm. The optogenetic power density of a micro-LED is directly propotional to the driving current. At a current of 5 mA, the optogenetic power density can reach 1 mW/mm
2 [
17,
18], which meets the threshold requirement for the strong optical neurostimulation mentioned earlier. These microelectrodes are comparable in size to the neurons.
Based on the experiments, the micro-LED’s I-V curve was measured. According to the measured I-V curve, the micro-LED’s equivalent circuit was designed, which is used to replicate the characteristic I-V curve of the customized optrode, for the micro-LED driver circuit’s simulation. In the micro-LED equivalent circuit, an ideal comparator is utilized to create the turn-on voltage of the micro-LED, while the resistor Rs and a bipolar junction transistor are employed to generate an exponential relationship between the input voltage and the output current. The measured and simulated micro-LED I-V curves are presented in
Figure 3.
2.2. Micro-LED Driver Module
The micro-LED Driver Module is designed to provide a current output for driving micro-LEDs in an optrode for effective optogenetic stimulation. It has been observed that the luminance of a micro-LED has an explicit linear relationship to the current intensity [
18], and the integral value of the irradiant density received by neurons within a short period of time will affect the response of neurons significantly [
19,
20]. Therefore, this variable-current-output Integrated Circuit (IC) is designed to precisely adjust the luminance of the micro-LED, thus enabling precise optogenetic neuromodulation stimulation. The structural diagram of this micro-LED driver circuit is presented in
Figure 4.
A pulse-width-modulation (PWM) controlled current mode output driver is used to drive the micro-LED in the optrode. is biased by 2V5 voltage and works as a current source to supply a 10 mA current. Another MOSFET works as a switch, toggling the current source MOSFET on and off by the external control PWM signal to adjust the amplitude of the current flow into the micro-LED. The driving circuit adopts two kinds of PWM signal as the input control signal for adjusting the current duty cycle, magnitude, and frequency: the high-frequency PWM control signal is in the 1–10 MHz band, which is used to control the output current amplitude by adjusting the duty cycle of the PWM signal. The low-frequency PWM control signal is in the 1–10 Hz band, and it is used to control the duration of the stimulation time. The circuit uses an inverter for driving the switch to improve the switching speed. Since the optogenetic stimulation circuit is regulated by the PWM signal, the output current waveform inevitably has a ripple. In order to decrease the influence of the ripple, an off-chip filter capacitor is equipped at the output node.
2.3. Micro-LED Self-Detection Module
The micro-LED may be damaged during or after chronic implantation into the animal skull, which leads to an inability in the micro-LED to operate effectively and causes the luminance of the light to deviate from the predetermined value or the leakage current, which may affect neural signal recording and even damage brain tissue. Possible degradation mechanisms include open and short circuits of the micro-LED [
14].
Figure 5 shows the circuit models under normal and two abnormal working states. The short circuit state can be represented by a short circuit between the micro-LED anode and cathode, and the open circuit state can be represented by a micro-LED cathode serial connection with a 10 pF capacitor. In the open circuit state, the drive current cannot flow out from the cathode of the micro-LED, so the anode voltage of the micro-LED is the power voltage VDD. In the short circuit state, the anode and cathode of the micro-LED are short-circuited; hence, the voltage of the anode is the ground voltage. Under the normal working state, the anode voltage of the micro-LED is between its turn-on voltage and power supply voltage. The self-detection module can judge the working state of the micro-LED by monitoring the anode voltage when the micro-LED is working, without interfering with the normal operation of the micro-LED.
The structure of the self-detection module is shown in
Figure 6. The micro-LED anode voltage is compared with the power voltage and the ground voltage through two comparators, and the results are output at “SHORT” and “OPEN”. The output result of each output node is the power supply voltage (VDD) or ground voltage (GND), which, respectively, represents whether the micro-LED is in the OPEN state or SHORT. If the micro-LED operates normally, the output of both comparators is “0”. Specific possibilities and output results are shown in
Table 1.
2.4. Neural Signal Recording Module
Neural signals generally have an amplitude of 10 µV to 100 µV [
21], while the impedance of commonly used microelectrodes at 1 KHz is usually 100 K–100 MΩ. The electrochemical reaction between the microelectrode and the brain tissue will lead to several volts of DC offsets, which is much larger than the detected neural signal; thus, the capacitor-coupled input structure is used in the neural signal recording circuit to eliminate the DC offset. The neural signal spike has energy distribution within 100–7 KHz bandwidth [
21], so the operational amplifier with a low cut-off frequency lower than 100 Hz and a high cut-off frequency higher than 10 KHz is required to acquire the neural signal.
The 16-channel neural signal recording circuit is shown in
Figure 7. The front stage operational amplifier adopts a differential input to the single end output structure, the negative input terminal receives the neural signal sensed by the microelectrode, and the positive input terminal is connected to the reference channel’s output.
In order to decrease the low cutoff frequency of the amplifier to a very low level to acquire the neural signal as completely as possible, two MOSFETs are used to form a pseudo-resister device that has a very large resistance, and it is connected in parallel with the capacitor. The mid-band gain of the recorder circuit is C1/C2.
When the back-end stage is under heavy load (such as the probe of the oscilloscope), the quiescent point set by the circuit will shift, affecting the measurement results. In order to improve the driving capability of the circuit, the amplifier output is connected to a unity gain buffer stage, and the structure is also shown in
Figure 7.
There is also a tradeoff between area, power, and noise performance in the design.
The structure of the operational amplifier in the reference channel is mostly the same as that of the operational amplifier in the recording channel, with the capacitor-coupled input to reduce DC offsets, and all the circuit components in the reference channel have the same parameters as the 16 neural signal recording channels to form the same bandwidth as the recording channel. The reference channel adopts the buffer connection method and is used to detect environmental noise. By using the reference channel, the environmental noise could be seen as the common-mode signal and be rejected by the differential input of the amplifier, thus improving the quality of the detected neural signal.
In addition, this design uses an advanced process (TSMC 65 nm) for fabricating, and the negative effect of gate-oxygen leakage cannot be ignored under the advanced process. Therefore, thick gate-oxygen MOSFET is used in this design to reduce gate-oxygen leakage and improve circuit performance.