Detection of Neuronal Defensive Discharges Information Transmission and Characteristics in the Periaqueductal Gray Double-Subregions Using PtNPs/PEDOT:PSS-modified Microelectrode Arrays

doi:10.21203/rs.3.rs-2479482/v1

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Detection of Neuronal Defensive Discharges Information Transmission and Characteristics in the Periaqueductal Gray Double-Subregions Using PtNPs/PEDOT:PSS-modified Microelectrode Arrays

https://doi.org/10.21203/rs.3.rs-2479482/v1

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published 31 May, 2023

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Appropriate defensive behaviors were selected and rapidly executed to survive in threatened animals. It was accepted the midbrain periaqueductal grey (PAG) presents an essential part of the circuitry that organize defensive behavioral responses. However, the role and correlation of different PAG subregions in the expression of different defensive behaviors remained largely unexplored. Here, we designed and manufactured a microelectrode array (MEA) to simultaneously detect the activities of dPAG and vPAG neurons in freely-behaving rats. The PtNPs/PEDOT:PSS nanocomposites were modified onto the MEAs to improve the detection performances of the MEAs. Subsequently, the predator odor was used to induce the rat's innate fear, and the changes and information transmission in neuronal activities were detected in dPAG and vPAG. Our results showed that dPAG and vPAG both participated in the innate fear, but the activation degree was distinct in different defense behavior. During the flight, neuronal responses were stronger and earlier in the dPAG compared with the vPAG, while vPAG neurons responded greater during freezing. The application of high-performance MEA detected that neural information spread from activated dPAG to weakly activated vPAG. Our research also revealed that dPAG and vPAG neurons had different defensive discharges characteristics, and dPAG neurons participated in the regulation of defense response with burst-firing patterns. The slow activation and continuous-firing of vPAG neurons were matched with the regulation of long-term freezing response. The results demonstrated the important role of PAG neuronal activities in the control of different aspects of defensive behaviors and provided novel insights for investigating defense from the electrophysiological perspective.

Physical sciences/Nanoscience and technology/Nanobiotechnology/Biosensors

Physical sciences/Nanoscience and technology/Nanobiotechnology/Nanofabrication and nanopatterning

MEA

PAG

double-region detection

innate fear

PEDOT

Fear is a conservative state in the process of biological evolution, which triggers the defensive behavior to effectively avoid or reduce potential damage to animals^1,2. After encountering predators, rodents express different defensive behavior patterns according to surrounding factors and threat imminence such as the existence of shelters³. When predatory imminence and potential attack are high in the environment, rodents present a circa-strike defense (jumping and flight). With the decline of emerging threats, rodents change to a freezing state^4,5. Many brain regions involved in different aspects and levels of controlling defensive behaviors. These include, but are not limited to, the cortex, hypothalamus, central amygdala (CEA), and continuous strip of midbrain structures, which corporately realize functions of generating innate fear and executing specific defensive actions^6-9.

Previous studies demonstrated that the periaqueductal gray (PAG) was the critical node of coordinate survival in the central network, which mediated fear-evoked and defensive behaviors ^10,11. Anatomically, the PAG is organized in longitudinally organized functional columns, consisting of the ventral PAG (vPAG), which are separated from the dorsal PAG (dPAG) by the lateral column^12,13. There is sufficient evidence that dPAG participates in the motor and autonomous response of defensive states: electrical stimulation of dPAG can induce aversion reactions such as freezing and escape in rats^14,15. Conversely, the vPAG seems to be the indispensable structure to express conditioned fear and learned behavior, and lesions of vPAG reduced the freezing reaction to the neutral stimulus with footshock, but do not affect the flight response^16-18. PAG receives inputs from crucial brain regions involved in fear regulation, such as the hypothalamus ¹⁹ and CEA ²⁰. However, the functional role of the PAG subregion in the expression of active and passive defensive behaviors remained poorly understood, including the lack of firing characteristics and exact functional role of PAG neurons in different subregions, and the neuronal microcircuitry and correlation in the PAG subregion during the expression of different predator odor-induced defensive behaviors⁸.

In the existing research on the relationship between animal behavior and neural activities in different regions of the brain, traditional nerve detection electrodes which lacked spatial and temporal resolution, including metal wire and single channel electrodes²¹, were unable to identify and functionally characterize the interactions between multiple regions²². With the development of Micro-Electro-Mechanical system (MEMS) technology, implantable microelectrode arrays are widely used in neural information recording due to their long-term stability, and high resolution ²³. To reduce brain tissue damage and improve the detection performance of MEA²⁴, the surface of MEA is usually modified with composite conductive materials as functional layers. Poly(3,4-ethylene dioxythiophene): poly(styrene sulfonate)(PEDOT:PSS) is a conductive polyelectrolyte complex with good biological compatibility and electrical stability^25,26. Although it has many excellent characteristics when applied to the neural interface, some studies have found that its adhesion to metal electrodes and mechanical stability are poor, which is not conducive to the long-term detection of the neural interface. Platinum nanoparticles (PtNPs) can provide rough and stable surface structures for electrode surfaces²⁷. In this study, PtNPs and PEDOT: PSS composite coatings were used to improve the adhesion area and adhesion of PEDOT: PSS, which was helpful to improve the quality of recorded signals, and improved the signal-to-noise ratio (STR) and long-term detection performance.

The development of MEAs with multiple channels and multiple brain regions promotes the research of fear-related neural circuit mechanisms and fear-related diseases^28,29. Based on the above， we designed a kind of MEA with high spatial and temporal resolution for simultaneously recording neuronal information in dPAG and vPAG of rats with innate fear and modified the surface of microelectrodes with PtNPs /PEDOT: PSS nanocomposite. Combined with the changes in rat defense behavior output and the neural dynamics of dPAG and vPAG recorded by MEAwe explored the changes of neuronal correlation and activity mode of dPAG and vPAG in the process of defense behavior. The results showed that both dPAG and vPAG were involved in the regulation of the defensive behavior induced by innate fear in rats. We found that the firing patterns of dPAG and vPAG neurons were significantly different, and dPAG neurons participated in the active defense regulation with explosive firing patterns. Overall, our research elucidated the integration mechanism of dPAG and vPAG participating in defense behavior from the perspective of electrophysiology, discussed the influence of neuronal firing patterns on dPAG and VPAG participating in different defense behaviors, and provided new ideas for the study of PAG subregion relationships and fear related diseases.

2.1 Regent and Apparatus

2-methyl-2-thiazoline (2MT) was obtained from Shanghai Acmec Biochemical Co., Ltd (China). The chloroplatinic acid (H₂PtCl₆) and lead acetate [Pb (CH₃COO)₂] were obtained from Sinopharm Chemical Reagent Co., Ltd (China). 3,4-ethylene dioxythiophene (EDOT) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd (China). Poly(styrene sulfonate) (PSS) was obtained from Shanghai Herochem Co,. Ltd (China).

The stereotactic apparatus (51600) was purchased from Stoelting (USA). 128-channel neuroelectrophysiological recording system was purchased from Blackrock Microsystems (USA). Micropositioner (model 2662) was purchased from David KOPF instrument (USA).

2.2 Fabrication and Nanomodification of the MEA

Based on the demand for dual brain region detection, we prepared a 16-channel MEA array for the detection of dPAG and vPAG in conscious rats in the fear state (Figure S2a). The length of the four shanks was 8.5, 8.5, 7.7, and 7.6 mm respectively. The distance between the shanks was 110 mm. Each microelectrode can be used for the detection of electrophysiological signals. The manufacturing process adopted MEMS technology. The brief description was provided below with the MEA fabricating in Figure S1.

The MEA was fabricated with the silicon-on-insulator (SOI) wafer (25 µm Si/1 µm SiO₂/550 µm Si) substrate and a 200 nm layer of SiO₂ was deposited on the substrate to insulate devices from the SOI substrate. A layer of photoresist was spin-coated on the wafer. This was followed by sputtering of 250 nm Pt and 30 nm Ti as the metal layer and lift-off processing to form recording sites, pads, and microwires. Next, an insulating layer (500 nm Si₃N₄ / 300 nm SiO₂) was deposited. Then, the MEA was shaped by photolithography and reactive ion etching (RIE). Finally, individual MEA was separated by KOH (80 ℃, 30%) back wet etching of the SOI wafer.

After the MEAs were fabricated, we modified the surface of all recording sites with PtNPs /PEDOT: PSS to improve the detection performance and biocompatibility of the MEAs. The PtNPs layer was deposited on recording sites by chronoamperometric method (CA, − 1.0 V, 60 s) in the electroplating solution involving 48 mM chloroplatinic acid and 4.2 mM lead acetate. Then 0.1 M PSS solution and 20 mM EDOT were mixed for 1 h ultrasonic dispersion. The deposition was carried out by cyclic voltammetry (CV) between 0 and 0.95 V at the rate of 50 mV/s for 10 cycles in the mixed solution. The electroplating of PtNPs /PEDOT: PSS on the surface of microelectrodes was realized.

2.3 Animals and surgical Procedures

All animal experiments were conducted with the permission of the Beijing Association on Laboratory Animal Care and approved by the Institutional Animal Care and Use Committee at the Aerospace Information Research Institute, Chinese Academy of Science (AIRCAS). A total of 15 adult male Sprague-Dawley (SD) rats (230~350 g, 8- to 12-week-old; Vital River Laboratory Animal Technology Co., Ltd, Beijing, China) were used in this study. All rats were housed in a temperature-controlled (22~26 ℃ and 50~80% humidity) maintained on a 12/12 h dark/light cycle. Food and water was available ad libitum.

Rats were deeply anesthetized with 5% isoflurane in oxygen-enriched air before the surgical operation. Each rat was fixed in a stereotactic apparatus with non-traumatic ear bars and was anesthetized with 1-2% isoflurane in oxygen-enriched. During the operation, the depth of anesthesia was checked regularly by the corneal and paw withdrawal reflexes. An incision was cut from the midline scalp to expose the skull. After drilling a 2 X 2 mm in the skull, MEA was implanted into PAG (AP: –7 mm, ML: –0.9 mm) at the constant speed of 2 μm/s using a micropositioner, and paused for 2~3 minutes every 200 μm implanted. MEA was attached to the skull with 5 screws and dental cement (Figure 1a). To improve the detection STR, metal wires were used to connect the ground with skulls and MEA. After craniotomy, rats were intraperitoneally injected with 45,000 units of penicillin to reduce inflammation. Rats recovered for 3~5 days before the start of behavioral testing (Figure 1b).

2.4 2MT-evoked innate fear assay

Odor extracts or analogs from the fur and feces of predators have been widely used to stimulate the innate fear of rats³⁰. Fox odor 2,4,5-trimethyl thiazoline (TMT) and 2-methyl-2-thiazoline (2MT) are the most widely used efficient predator odors, which trigger strong defensive behavior in rats^31-33. In this study, 2-MT was used to induce innate fear in rats (Figure 1c). During the experiment, we used a digital video camera (30 frames/s, 1080 × 1920 pixels) and a 128-channel neuroelectrophysiological recording system simultaneously record the behavior and neuroelectrophysiological activities of rats. Predator odor was introduced into the test cage by gently dropping water or 2-MT onto the filter paper placed in the corner of the test cage. To reduce external electromagnetic interference, the outside of the test cage was covered with a metal screen and grounded. Before the experiment, rats have 5~10 minutes of free time to adapt to the experimental cage. First, 80 μL water was added onto the filter paper gently. Rats were free to explore in the test cage for 10 minutes. After 10 minutes, 80 μL 2MT was dripped onto the filter paper for ten minutes. The behavior of rats was monitored by the digital camera above the test cage. After the experiment, we used 75% alcohol and water to deeply clean the test chamber and dried it to prevent the smell of the previous experiment from affecting the next experiment.

Use Ethvision (Blackrock Microsystems, SaltLake, UT, USA) behavior analysis software to post-process the captured video. If it was detected that the rat has not moved for more than 2 seconds, the rat had entered the freezing state. The freezing rate was the percentage of freezing time in the total duration, which was used to measure the freezing degree and was independently analyzed by computer software. The software could automatically obtain the rats’ body center to calculate the position and movement speed. The neural signals were collected by preamplifier and differential amplifier circuit, which were divided into action potential spike and local field potential (LFP). The digital sampling rate of the spike signal was 30 kHz, and it was extracted by a 200 Hz high-pass filter. Spike waveform characteristics, including amplitude, peak interval, and power spectrum, were derived from the sampled waveform by clustering K-mean analysis. The sampling rate of the local field potential signal was 1 kHz, and it was extracted by using a 200 Hz low-pass filter, with the ground as the reference.

2.5. Histology

After the behavioral tests, animals were deeply anesthetized and terminated by transcardial perfusion (0.9% Sodium chloride and 4% paraformaldehyde). The brains were extracted and postfixed with 20% sucrose solution overnight at 4 ℃. After post-fixation, the brain was cryoprotected in the 30% sucrose solution. Coronal sections were cut into 50 μm with the cryotome. Figure S3b and c showed the histological validation diagram of the target location.

2.6 Statistical methods

All neuroelectrophysiological data were analyzed offline with NeuroExplorer software (NexTechnologies, USA) and OfflineSorter (Plexon, USA). ORIGIN (OriginLab, USA) and Matlab (The Mathworks, USA) were used for statistical analysis. Unless otherwise specially illustrated, values were expressed as mean ± SEM. Significance tests of different groups were determined using Student’s t-test, or one-way ANOVA with Tukey’s post hoc tests Significance levels were indicated as follows: *p < 0.05; **p < 0.01; ***p < 0.001; ns was not significant (p > 0.05).

The trough-to-right peak latency was calculated from the time interval between the peak and trough of the average peak waveform. The spike asymmetry index was the ratio of the difference between left and right baseline-to-peak amplitudes of the average peak waveform and their sum^34,35.

3.1 Electrical performance characterization of MEAs

Figure 2a and b showed the packaged MEA and an enlarged view of the modified MEA tip under the microscope. Black modification materials were uniformly distributed on each microelectrode site. The scanning electron microscopy (SEM) image indicated that PtNPs/PEDOT:PSS nanocomposite adhered closely to the surface of the microelectrode (Figure 2c and d). The PtNPs improved the roughness of the microelectrode, and PEDOT:PSS structure covered the entire surface of the electrode, which was conducive to improving the stability of the electrode. The modification of PEDOT also improved the biocompatibility and long-term detection capability of microelectrodes in vivo (Figure S2).

We compared the electrical performance of bare microelectrodes and modified microelectrodes in the frequency range from 10 Hz to 1 MHz. The phase delay and impedance of the bare microelectrode were both large in the frequency domain from 10 Hz to 1 MHz (Figure 3a and b), which was not conducive to the detection of electrophysiological signals in vivo. The phase delay and impedance of the PtNPs/PEDOT:PSS-modified microelectrodes were significantly better than the bare microelectrodes and the PEDOT:PSS-modified microelectrode at 10 Hz to 1 MHz. The central frequency of the neural activities is around 1 kHz, at which the impedance and phase delay of the three were compared in this work. The microelectrodes impedance decreased from 847.07 ± 334.3 kΩ (bare) to 156.18 ± 64.14 kΩ (PEDOT:PSS) and then to 25.55 ± 10.33 kΩ (PtNPs/PEDOT:PSS) (Figure 3d). There was no significant difference in phase between PEDOT:PSS-modified microelectrodes (-31.41 ± 6.28°) and PtNPs/PEDOT:PSS-modified microelectrodes (-28.89 ± 1.15°), which indicated that PEDOT played a major role in phase delay (Figure 3e). In addition, the Nyquist plots of the impedance spectra showed that the PtNPs/PEDOT:PSS-modified microelectrode had intense diffusion characteristics (Figure 3c). Our results meant that the PtNPs/PEDOT:PSS-modified microelectrodes had better electrical performance, which was helpful for the detection of neuronal information transmission.

3.2 Characterization of behavioral and electrophysiological signals during 2MT-induced defensive behavior

To interrogate the role of dPAG and vPAG in the defense behaviors related to innate fear, we combined the electrophysiological activities of dPAG and vPAG neurons recorded by MEA with the behavior changes recorded by the digital camera before and during the defense behavior evoked by exposure to 2MT. We associated the changes in motor output related to the defense behaviors with their neural activity patterns, which allowed us to evaluate the endogenous neural mechanism of dPAG and vPAG under different defense behaviors. After exposure to 2MT, we observed two different innate defense behaviors in rats (Figure 4a and d). Velocity and freezing rate are important indexes of defense behavior (flight and freezing) in rats. The rats showed the obvious flight avoidance reaction, which was characterized by an extremely low freezing rate and high moving velocity. We define this state as flight. Then, after the rats found that there was no hope of escape, they stopped running and stayed in place almost motionless and began to enter the freezing state. This freezing mechanism made the prey more likely to avoid being captured when threatened. As shown in Figure 4e, we also counted the temporal heatmap of the spatial position of the rat. It could be found that the rats in the control moved evenly throughout the space, and the rats in the flight circled the space. When rats entered the freezing stage, they stayed far away from the filter paper (the source of predator odor) for a long time.

Our fabricated MEA was a brain-computer interface (BCI) with a high spatiotemporal resolution that provided a powerful strategy for monitoring real-time neuronal electrophysiological activities. The recording sites of MEA were in close contact with the neurons in the target area, so the neural information recorded in each channel could accurately reflect the neuronal activities. Figure 4b and c showed the neural spikes and LFP recorded by the same rat during the experiment. Compared with the control, the firing density of spikes was significantly more intensive and LFP had higher amplitude and frequent fluctuation during the flight in the dPAG, while the spike firing and LFP amplitude during the freezing were between the control and flight. The difference was that in the vPAG, the release density of spikes was the most intensive in the freezing, and the fluctuation and amplitude of LFPs increased to the most frequent. The above results indicate that dPAG and vPAG have different activation levels in flight and freezing.

In conclusion, the above results suggested that the highly efficient predator odor analog 2MT induced innate fear (defense) behaviors in rats. Both dPAG and vPAG were significantly activated during the defensive behavior. dPAG was highly activated during the flight, while vPAG was highly activated during freezing.

3.3 Information Transmission and Functional Analysis of vPAG and dPAG neurons in Defense State

To explore the activities of dPAG and vPAG neurons in the whole process of defensive behavior, we counted the change in the average spike firing rate during the experiment (Figure 5a). After exposure to 2MT, the spike firing rate of dPAG neurons reached a peak during the flight, and gradually decreased and stabilized after the peak. It indicated that dPAG was fastly activated to a high degree after exposure to 2MT, and then the activation degree was gradually stable during the freezing. The spike firing rate of vPAG neurons began to increase after exposure to 2MT, which indicated that the activation degree of vPAG neurons gradually increased after exposure to 2MT. LFP reflects the real-time transmission of information across the neural network and is an important supplement to measure neural information activity. The energy change of the dPAG and vPAG LFP spectrogram is consistent with the changing trend of the spike firing rate in the defense state (Figure 5b). To analyze the correlation and spread of defense behavior regulation in PAG subregions, the time when peaks of LFP and spike-firing occurred in the dPAG and vPAG were compared after 2MT-induced innate fear (Figure 5c). LFP peak and spike-firing first appeared in dPAG and gradually transmitted to the vPAG. In addition, the LFP waveforms of dPAG and vPAG had roughly the same components. The transmission was found in multiple channels and accompanied by the release of spikes. The transmission time between typical channels was about 80 ms.

As shown in Figure 5d, the difference in the average spike firing rate of dPAG and vPAG was not significant during the control. During the flight, the average spike firing rate of dPAG and vPAG neurons increased significantly, but dPAG neurons were significantly higher than that vPAG neurons. During the freezing, the average firing rate of vPAG neurons continued to increase, while that of dPAG neurons decreased compared with the flight and was lower than that of vPAG neurons. The average power of LFP in dPAG and vPAG was calculated during different behaviors. Compared with the control state, the average LFP power of dPAG in the flight and freezing state increased significantly, the LFP power in the flight state increased to 0.49 ± 0.07mW, and the LFP power in the freezing decreased to 0.36 ± 0.05 (Figure 5e). In the vPAG (Figure 5f), the average LFP power in the flight and freezing increased significantly compared with the control, and the LFP power in the freezing state (0.34 ± 0.03 mW) was slightly higher, but there was no significant difference compared with the flight (0.32 ± 0.04 mW). Then, we normalized the power of LFPs in three states and analyzed their relative characteristics in the 0-20 Hz range. We divided it into the delta frequency band (1-4 Hz), theta frequency band (4-8 Hz), alpha frequency band (8-13 Hz), and beta frequency band (13-30 Hz) (Figure S4b). After exposure to 2MT, the energy of dPAG was more concentrated in the delta band, and the proportion of the delta band in the flight was significantly higher than freezing and control, while the change of vPAG was reflected in the increase of the proportion of theta band.

The results showed that the rapid activation of dPAG was consistent with the flight behavior after exposure to 2MT, and accompanied by the transmission of neuronal defense information from dPAG to vPAG. When entering the freezing state, the activation degree of vPAG neurons gradually rose, and the activation degree of dPAG neurons began to decline. And the spike firing rate of vPAG neurons was highly correlated with the freezing rate (Figure S6). In other words, the activities of dPAG may be more related to sports and active defense response, while vPAG was consistent with the passive defensive.

3.4 Comparison and Analysis of dPAG and vPAG Neurons Spike Characteristics

As we detected that dPAG and vPAG neurons had different activation degrees and exhibited different regulation modes during flight and freezing, this work compared and analyzed the diverse firing pattern of the two types of neurons in defense information transmission. The signal of 84 channels recorded was separated into spike units using principal component analysis (PCA) and valley searching methods (VSM). We calculated the average waveform of individual spike units and analyzed their autocorrelation. As shown in Figure 6a, the average waveform of dPAG neurons had shorter peak intervals and a narrower waveform than that of vPAG neurons. The spike-time autocorrelogram of dPAG neurons had a shorter latency to firing than that of vPAG neurons (Figure 6b). Pooling all dPAG and vPAG neurons, we calculated the trough to right peak latency and peak amplitude asymmetry of the average waveform (see Statistical methods for the calculation method). Figure 6c showed that there was a clear distinction between the waveform types of dPAG and vPAG neurons. The trough-to-right peak latency of the dPAG neurons' average waveform was generally shorter than that of vPAG neurons, and the spike asymmetry index was higher.

In summary, The firing characteristics of dPAG and vPAG neurons were obviously different. The waveform of dPAG neurons was narrow and the spike-time autocorrelograms latency period was shorter, which also confirms that dPAG neurons were more likely to exhibit burst or burst-like firings, thus providing physiological conditions for controlling the flight behavior that requires the instant response.

3.5 Changes in spike characteristics of neurons in different defense states

The spike-firing characteristics of neurons provided the physiological basis for neurons in the regulation of defense behavior. To study the characteristic changes of neuronal spikes under different defense states, we also calculated the average spike waveform under different behavior states (Figure 6d). After exposure to 2MT, the average amplitude of spikes dPAG-neurons spikes during control was significantly lower than during flight and freezing. The amplitude was the highest during flight, and the amplitude during freezing was between flight and control. For the vPAG average spike waveform, the amplitude of flight and freezing state was similar, which was slightly greater than the control state. The above changes confirmed that the waveform of PAG neurons changed significantly under the defensive state. It was noteworthy that dPAG neurons showed more significant waveform changes, which seemed that dPAG neurons were more susceptible to being affected by innate fear.

The autocorrelograms of PAG neurons displayed behavior-associated firing changes under different defensive. dPAG neurons during flight had a higher probability of firing with short inter-spike intervals than during control and freezing (Figure 7a). The distribution of vPAG neuron inter-spike intervals was relatively uniform in freezing than in flight and control (Figure 7b). Pooling all dPAG and vPAG neurons, we also calculated the average autocorrelogram of neurons before and after exposure to 2MT. As shown in Figure 7c, compared with vPAG neurons, dPAG neurons had a higher probability of firing with short spike-inter intervals. We linearly fit the spike firing rate of dPAG and vPAG neurons to the amplitude and latency of their autocorrelogram peak respectively. We found that the spike firing rate was positively correlated with the amplitude of the peak value of the autocorrelogram (Figure S5a), and negatively correlated with the latency of the peak value of the autocorrelogram (Figure S5b).

These results indicated that a higher amplitude of the autocorrelogram peak and a shorter peak latency predicted a higher neuron firing rate. dPAG and vPAG neurons had changes in firing characteristics under different defense states. While dPAG was more prone to burst or burst-like firing in the flight. The spike firing time of vPAG neurons was more evenly distributed in the freezing state so that vPAG can provide stable and continuous neuroregulatory information to match the long-term freezing behavior.

In the study, we designed an implantable microelectrode array with high spatial and temporal resolution that can be used to record neural information in the dPAG and vPAG of free-moving rats, and exposed rats to the highly effective predator analog 2MT to induce innate fear, resulting in flight and freezing and recorded and analyzed the neuroelectrophysiological signals and behavioral information in the dPAG and vPAG under the defensive behavior. A general consensus has emerged that dPAG plays a major regulatory role in innate fear^36,37, whereas vPAG mainly participates in the integration of conditioned fear^38,39. The fabricated MEAs conduce to accurately detect and synchronously analyze the neural activities and defensive information transmission of dPAG and vPAG, avoiding the separation of them as previously studied. Using MEA as a tool, we found that dPAG and vPAG were involved in the control of predator odor-induced innate fear and defensive behaviors through electrophysiological data, including flight and freezing. However, dPAG neurons were a more robustly activated behavior than vPAG neurons in active defensive and transmitted neuronal defense information to vPAG. dPAG is responsible for organizing the defense behavior against predators and transmitting neural defense information. vPAG neurons were more strongly activated and dPAG activation was decreased when rats stopped movement and were freezing. The dominance of the freezing phase gradually shifted to vPAG. In this process, we also did not find that defense information was transmitted between two nuclear clusters, which seemed to be mediated by different pathways. It should however be noted that, after exposure to 2MT, the delta frequency band of dPAG was more active and vPAG was active in the theta frequency band, indicating that the frequency bands of neural regulation between the two were different.

Flight is a short-term and explosive behavior, while freezing is a long-term and stable state. dPAG and vPAG performance drove these two opposite types of behavior: the increase in vPAG activities was slow and matched with the long-term freezing response; the significant increase in dPAG activity was short-lasting matching with the short-term response. Therefore, we further studied the characteristics of dPAG and vPAG neurons to explore their cellular mechanisms. Our research results showed that the spike of dPAG neurons had a shorter peak duration and better waveform symmetry, and autocorrelation analysis shows shorter latency. A shorter peak duration indicated that the swifter alternation of neurons might be related to the rapidly increasing firing pattern of dPAG neurons during the flight. The spike peak of vPAG neurons lasts longer, and the refractory period of autocorrelation analysis is longer, which shows that the spike peak can be kept for a longer time. In addition, changes in the spike average waveforms of dPAG and vPAG neurons were also observed during exposure to the odor of predators. The amplitude of the average waveform peak of dPAG neurons increased more significantly than that of vPAG neurons. Pooling all the detected neurons, we found that spike firing rate was positively correlated with the peak value of the refractory period in autocorrelation analysis, and negatively correlated with the peak latency. On the other hand, the latency of the peak value of autocorrelogram analysis of dPAG neurons became shorter when exposed to predator odor, while the latency and peak refractory of autocorrelation analysis of vPAG neurons did not change significantly. These results showed that dPAG and vPAG have different firing characteristics, and dPAG neurons exhibit burst or burst-like firings characteristics to match with short-term flight response.

Previous studies in different species have demonstrated the central role of the amygdala in dealing with predators' threats^40,41. Lesions of the medial CEA do block fear responses to predator odor⁴². After the amygdala receives the information of predator threat through the olfactory bulb, these signals are conveyed to dPAG for the organization of defense responses via the medial hypothalamus^43-45. The process requires the dPAG to respond quickly. The burst firing mode of dPAG contributes to projecting instructions to the brain stem and other downstream circuits in a short time. Dissimilarly, the CEA directly projects to the vPAG to suppress ongoing motivated behaviors and promote freezing. Consistent with this result, we found that the spike firing rate of vPAG neurons was positively and the spike firing rate of dPAG neurons was negatively correlated with freezing behavior (Figure S6). These findings revealed that different aspects of defensive responses seemed to be mediated by different pathways involving PAG, which also explained the different activation states of dPAG and vPAG under the same behavior.

In the present study, we designed and fabricated an implantable neural MEA, which was used to simultaneously detect dynamic neurophysiological activities of PAG double-subregions in the defensive state rats. Our results suggested that both dPAG and vPAG neurons were involved in regulating the innate fear response to the predators’ threat. dPAG neurons were most activated in the flight, and vPAG neurons were most activated in the freezing. Activated dPAG transmitted neural defense information to vPAG. The application of our MEAs revealed that the different cellular mechanisms of dPAG and vPAG were matched with their main regulated behaviors. Furthermore, these results demonstrated that PAG seems to participate in distinct pathways to control different types of defense responses. Our research laid the foundation for further research on the role of PAG in the regulation of congenital fear and precisely targeted treatment of fear-related diseases in the future.

Acknowledgments

This work was sponsored by the National Natural Science Foundation of China (T2293731, L2224042, 61988102, 62121003, 61960206012,62171434, 61971400, 61975206, and 61973292), the Frontier Interdisciplinary Project of the Chinese Academy of Sciences (XK2022XXC003), STI 2030 - Major Projects 2021ZD0201600, the National Key R&D Program of China (2022YFC2402501), the Scientific Instrument Developing Project of the Chinese Academy of Sciences (GJJSTD20210004).

Conflict of interest

The authors declare no competing interests

Author contributions

B.L. designed the experiments, fabricated the devices, analyzed the data, and drafted the manuscript. H.F. contributed to the study concept. M.L. and Y.D.helped with the device fabrication.W.L review and edited the manuscript. G.Y., F.M., Z.X., and J.S. assisted with the experiments. Y.S. helped complete the data analysis. J.L. Y.W., and X.C. guided the theoretical analysis, reviewed the manuscript, and checked it for final submission. All authors have approved the submitted version.

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Detection of Neuronal Defensive Discharges Information Transmission and Characteristics in the Periaqueductal Gray Double-Subregions Using PtNPs/PEDOT:PSS-modified Microelectrode Arrays

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Abstract

Figures

Introduction

Materials And Methods

2.1 Regent and Apparatus

2.2 Fabrication and Nanomodification of the MEA

2.3 Animals and surgical Procedures

2.4 2MT-evoked innate fear assay

2.5. Histology

2.6 Statistical methods

RESULT

3.1 Electrical performance characterization of MEAs

3.2 Characterization of behavioral and electrophysiological signals during 2MT-induced defensive behavior

3.3 Information Transmission and Functional Analysis of vPAG and dPAG neurons in Defense State

3.4 Comparison and Analysis of dPAG and vPAG Neurons Spike Characteristics

3.5 Changes in spike characteristics of neurons in different defense states

Discussion

Conclusions

Declarations

Acknowledgments

Conflict of interest

Author contributions

References

Additional Declarations

Supplementary Files

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