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Citation: Wang, Y.; Wang, M.; Dai, Y.;
Song, Y.; Wang, Y.; Lu, B.; Li, Y.; Cai,
X. PtNPs/Short MWCNT-PEDOT:
PSS-Modified Microelectrode Array
to Detect Neuronal Firing Patterns in
the Dorsal Raphe Nucleus and
Hippocampus of Insomnia Rats.
Micromachines 2022,13, 488.
https://doi.org/10.3390/
mi13030488
Academic Editor: Colin Dalton
Received: 21 February 2022
Accepted: 16 March 2022
Published: 21 March 2022
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micromachines
Article
PtNPs/Short MWCNT-PEDOT: PSS-Modified Microelectrode
Array to Detect Neuronal Firing Patterns in the Dorsal Raphe
Nucleus and Hippocampus of Insomnia Rats
Yun Wang 1,2, Mixia Wang 1,2, Yuchuan Dai 1,2, Yilin Song 1,2, Yiding Wang 1,2, Botao Lu 1,2, Yinghui Li 3,*
and Xinxia Cai 1, 2, *
1
State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy
of Sciences, Beijing 100190, China; wangyun17@mails.ucas.ac.cn (Y.W.); wangmixia@mail.ie.ac.cn (M.W.);
daiyuchuan18@mails.ucas.ac.cn (Y.D.); ylsong@mail.ie.ac.cn (Y.S.); wangyiding18@mails.ucas.ac.cn (Y.W.);
lubotao18@mails.ucas.ac.cn (B.L.)
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,
Beijing 100049, China
3The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and
Training Center, Beijing 100094, China
*Correspondence: yinghuidd@vip.sina.com (Y.L.); xxcai@mail.ie.ac.cn (X.C.); Tel.: +86-135-0133-2556 (Y.L.);
+86-135-8195-8569 (X.C.)
Abstract:
Research on the intracerebral mechanism of insomnia induced by serotonin (5-HT) defi-
ciency is indispensable. In order to explore the effect of 5-HT deficiency-induced insomnia on brain
regions related to memory in rats, we designed and fabricated a microelectrode array that simultane-
ously detects the electrical activity of the dorsal raphe nucleus (DRN) and hippocampus in normal,
insomnia and recovery rats
in vivo
. In the DRN and hippocampus of insomnia rats, our results
showed that the spike amplitudes decreased by 40.16 and 57.92%, the spike repolarization slope
decreased by 44.64 and 48.59%, and the spiking rate increased by 66.81 and 63.40%. On a mesoscopic
scale, the increased firing rates of individual neurons led to an increased
δ
wave power. In the DRN
and hippocampus of insomnia rats, the
δ
wave power increased by 57.57 and 67.75%. Furthermore,
two segments’
δ
wave slopes were also increased in two brain regions of the insomnia rats. Our
findings suggest that 5-HT deficiency causes the hyperactivity of neurons in the hippocampus and
DRN; the DRN’s firing rate and the hippocampal neuronal amplitude reflect insomnia in rats more
effectively. Further studies on alleviating neurons affected by 5-HT deficiency and on achieving a
highly effective treatment for insomnia by the microelectrode array are needed.
Keywords: microelectrode array; insomnia; electrophysiology; dorsal raphe nucleus; serotonin
1. Introduction
Sleep is a common physiological rhythm phenomenon in humans and higher animals
to maintain the body’s health and normal functioning of the central nervous system [
1
].
There is growing evidence that people worldwide are experiencing a downward trend
in the average sleep duration, increasing the prevalence of insomnia and other sleep
disorders
[2–4]
. Severe/extreme nocturnal sleep problems are reported by more than 40%
of older adults in low-income settings [
5
]. There is a strong link between insomnia and
illness, especially sleep deprivation, which can greatly impair memory and cognitive
functions in the body [6–8].
The International Classification of Sleep Disorders (ICSD) specifies 11 subtypes of
insomnia: acute, psychophysiological, paradoxical, idiopathic, and substance-induced
insomnia [
9
]. The insomnia mechanism is still poorly understood, and abnormal neural
activity and neurotransmitter secretion in corresponding brain regions are the main causes
of insomnia [
10
]. Serotonin (5-HT) is an important neurotransmitter, and a lack of 5-HT in
Micromachines 2022,13, 488. https://doi.org/10.3390/mi13030488 https://www.mdpi.com/journal/micromachines
Micromachines 2022,13, 488 2 of 15
the brain is considered a major cause of insomnia [
11
,
12
]. Serotonergic neurons are widely
distributed in the brain. About two-thirds of the cells in the dorsal raphe nucleus (DRN) of
adult rats are serotonergic neurons [
13
], projecting axons to most of the brain areas, includ-
ing the cerebral cortex, hypothalamus, amygdala, and hippocampus [
14
]. The hippocampus
is the biological brain region responsible for processing memory and cognition [
15
–
17
].
Insomnia negatively affects hippocampus morphology and function [18,19].
Numerous neurons involved in the processing are endogenously activated during
insomnia [
20
,
21
]. Their activity in the brain can be captured with implantable microelec-
trode arrays (MEA) [
22
,
23
]. The real-time detection of neuronal electrophysiological signals
in vivo
is important for discovering and understanding brain function mechanisms. Michi-
gan electrodes are widely used to detect intracranial neurons with a high temporal and
spatial resolution and capture the dynamic changes of individual neuron signals in real
time [
23
]. In addition, with the development of chemical modification technology for the
electrode surface, electrode impedance is significantly reduced after modification, and
individual neuron activity can be detected more accurately [24,25].
We explored the effects of 5-HT deprivation-induced insomnia on neurons in brain
regions, corresponding to memory and cognition. Since insomnia alters biological EEG,
we hypothesized that the electrophysiological signals of the DRN and hippocampus of
insomnia rats also altered. For this study, we design and fabricated an MEA to detect
electrophysiological signals in the hippocampus and DRN simultaneously. The electro-
physiological activities of the hippocampus and DRN in rats under normal, insomnia, and
recovery conditions are detected
in vivo
using electrodes. Our purpose is to investigate
the effects of insomnia induced by 5-HT on neurons in the hippocampus and DRN by ana-
lyzing the captured electrophysiological activity, and verifying whether the hypothesis is
supported. Our research results provide new ideas for simultaneously detecting insomnia-
related brain regions in multiple brain regions over long distances. It also promotes research
on the intracerebral mechanism of insomnia caused by decreasing concentrations of 5-HT
in the brain, affecting memory.
2. Materials and Methods
2.1. Reagents and Apparatus
The surface modification of the sites was performed using 3,4-Ethylenedioxythiophene
(EDOT) (Aladdin, Shanghai, China), polystyrene sulfonate (PSS) (HEROCHEM, Shang-
hai, China), and short multi-walled carbon nanotubes (MWCNTs) (XFNANO, Nanjing,
China). Reference 600 (Gamry Instruments, Warminster, PA, USA) evaluated the MEA
site impedance. P-chlorophenylalanine (PCPA) (Sigma–Aldrich, Shanghai, China) and
phosphate-buffered saline (PBS) (Sigma–Aldrich, Shanghai, China) were used to induce
insomnia in rats. R520IP (RWD Life Science, Shenzhen, China) was used to anesthetize
the rats. KeyGEN BioTECH (Nanjing, China) provided cell membrane orange fluorescent
dye (DiI). Brain slices were manufactured by leica1950 (Leica Biosystems, Deer Park, IL,
USA). The brain slices were imaged by ZEISS Axio Observer (ZEISS, Jena, Germany). The
128-channel neural data recording system (Blackrock Microsystems, Tampa, FL, USA),
micropositioner (model 2662, David KOPF instrument, Los Angeles, CA, USA), and a
classic stereotaxic instrument for rats (Stoelting, Wood Dale, IL, USA) were used to capture
intracranial electrophysiological signals in rats.
2.2. Animal and Injection of P-Chlorophenylalanine
The schematic procedure of the experiment is shown in Figure 1. Twelve adult male
Sprague Dawley (SD) rats weighing 250 g were randomly assigned to 3 groups: 4 rats were
used as the control group, 4 rats were induced to become insomnia rats, and 4 recovered
rats were detected 14 days after modeling. The laboratory temperature was kept at 25
◦
C
to avoid the effects of low ambient temperatures [26].
Micromachines 2022,13, 488 3 of 15
Micromachines 2022, 13, x FOR PEER REVIEW 3 of 16
P-chlorophenylalanine (PCPA) was recognized as a chemical agent to induce insom-
nia in rats [27–29], and it inhibited 5-HT synthesis [30]. PCPA was dissolved in PBS solu-
tion (30 mg/mL), and the solution was centrifuged for 30 min before injection. Rats were
injected intraperitoneally with the solution (2.5 mL) on two consecutive days (24 h inter-
val). Thirty hours after the first injection of PCPA, the rats lost their circadian rhythm and
retained their insomnia, which verified the successful establishment of the model. All an-
imal care and usage protocols were performed under the direction of institutional animal
ethics committees.
Figure 1. Schematic diagram of the experimental arrangement.
2.3. Design and Modification of Microelectrode Arrays
In this study, the MEA was implanted into the rat brain at an angle of 36° at the hor-
izontal plane and 29° at the sagittal plane; the implantation depth was 11.8 mm (Figure
2a). The MEA consisted of four shanks, each with a U-shaped array of eight circular mi-
croelectrode sites (10 μm in diameter) (Figure 2b). The shank length was 12 mm, and the
sites were distributed into two shank positions so that the rats’ hippocampus and DRN
could be detected simultaneously. The site for detecting the hippocampus was 5.2 mm
from the shank tip. There were sixteen sites in each of the two brain regions. An additional
rectangular site was designed for each shank as the counter electrode. The MEA was im-
planted from the cortex of the rat brain (position coordinate, ML, 1.2 mm; AP, 0 mm; DV,
−1.5 mm), through the hippocampus (position coordinate, ML, 0.5 mm; AP, 3.8 mm; DV,
−4.9 mm), to the DRN (position coordinate, ML, 0 mm; AP, 6.4 mm; DV, −7.2 mm).
The MEAs were mass-fabricated in ultra-clean rooms by MEMS thin-film technology
(Figure 2c): The SiO2 (500 nm) insulating layer grew by thermal oxidation on a silicon
wafer on an SOI substrate; Ti/Pt (30 nm/250 nm) was sputtered and patterned using the
lift-off process as a conductive layer; SiO2/Si3N4 (300 nm/500 nm) was deposited by plasma
enhanced chemical vapor deposition as an insulating layer. The insulating layer on the
surface of the MEA sites and bonding pads was removed by SF6 reactive ion etching. The
probe shape was etched out using inductively coupled plasma reactive ion etching. The
upper surface of the wafer was covered with pitch, and the lower surface was wet etched
Figure 1. Schematic diagram of the experimental arrangement.
P-chlorophenylalanine (PCPA) was recognized as a chemical agent to induce insomnia
in rats [
27
–
29
], and it inhibited 5-HT synthesis [
30
]. PCPA was dissolved in PBS solution
(30 mg/mL), and the solution was centrifuged for 30 min before injection. Rats were
injected intraperitoneally with the solution (2.5 mL) on two consecutive days (24 h interval).
Thirty hours after the first injection of PCPA, the rats lost their circadian rhythm and
retained their insomnia, which verified the successful establishment of the model. All
animal care and usage protocols were performed under the direction of institutional animal
ethics committees.
2.3. Design and Modification of Microelectrode Arrays
In this study, the MEA was implanted into the rat brain at an angle of 36
◦
at the hori-
zontal plane and 29
◦
at the sagittal plane; the implantation depth was 11.8 mm (
Figure 2a
).
The MEA consisted of four shanks, each with a U-shaped array of eight circular micro-
electrode sites (10
µ
m in diameter) (Figure 2b). The shank length was 12 mm, and the
sites were distributed into two shank positions so that the rats’ hippocampus and DRN
could be detected simultaneously. The site for detecting the hippocampus was 5.2 mm
from the shank tip. There were sixteen sites in each of the two brain regions. An additional
rectangular site was designed for each shank as the counter electrode. The MEA was
implanted from the cortex of the rat brain (position coordinate, ML, 1.2 mm; AP, 0 mm; DV,
−
1.5 mm), through the hippocampus (position coordinate, ML, 0.5 mm; AP, 3.8 mm; DV,
−4.9 mm), to the DRN (position coordinate, ML, 0 mm; AP, 6.4 mm; DV, −7.2 mm).
The MEAs were mass-fabricated in ultra-clean rooms by MEMS thin-film technology
(Figure 2c): The SiO
2
(500 nm) insulating layer grew by thermal oxidation on a silicon wafer
on an SOI substrate; Ti/Pt (30 nm/250 nm) was sputtered and patterned using the lift-off
process as a conductive layer; SiO
2
/Si
3
N
4
(300 nm/500 nm) was deposited by plasma
enhanced chemical vapor deposition as an insulating layer. The insulating layer on the
surface of the MEA sites and bonding pads was removed by SF
6
reactive ion etching. The
probe shape was etched out using inductively coupled plasma reactive ion etching. The
upper surface of the wafer was covered with pitch, and the lower surface was wet etched
Micromachines 2022,13, 488 4 of 15
in a KOH solution (50%, 80
◦
C) until it stopped automatically; then, the buried oxide layer
(2
µ
m) was removed in HF buffer solution. The wafer was placed in a developer solution
to dissolve the pitch. Finally, the probes were soldered to the designed PCB and coated
with silicone rubber (Figure 2d).
Micromachines 2022, 13, x FOR PEER REVIEW 4 of 16
in a KOH solution (50%, 80 °C) until it stopped automatically; then, the buried oxide layer
(2 μm) was removed in HF buffer solution. The wafer was placed in a developer solution
to dissolve the pitch. Finally, the probes were soldered to the designed PCB and coated
with silicone rubber (Figure 2d).
Figure 2. MEA design and fabrication. (a) MEA implantation path diagram in the sagittal plane. The
blue circles are the location of the hippocampus and dorsal raphe nucleus. The smaller picture
shows the MEA implantation path in the horizontal plane; (b) Site distribution map of the MEA; (c)
The fabrication processes of the MEA. The processes are SOI wafer, thermal oxidation, photolithog-
raphy, sputtering and stripping, plasma chemical vapor deposition, photolithography, oxygen
plasma etching, photolithography, deep etching, black glue coating, and electrode release; (d) MEA
finished product drawing, and (e) optical image of the MEA site. The upper image shows the site of
the hippocampus, and the lower image shows the site of the dorsal raphe nucleus.
Figure 2.
MEA design and fabrication. (
a
) MEA implantation path diagram in the sagittal plane. The
blue circles are the location of the hippocampus and dorsal raphe nucleus. The smaller picture shows
the MEA implantation path in the horizontal plane; (
b
) Site distribution map of the MEA; (
c
) The
fabrication processes of the MEA. The processes are SOI wafer, thermal oxidation, photolithography,
sputtering and stripping, plasma chemical vapor deposition, photolithography, oxygen plasma
etching, photolithography, deep etching, black glue coating, and electrode release; (
d
) MEA finished
product drawing, and (
e
) optical image of the MEA site. The upper image shows the site of the
hippocampus, and the lower image shows the site of the dorsal raphe nucleus.
Micromachines 2022,13, 488 5 of 15
The naked sites are shown in Figure 2e. The purpose of surface modification of MEA
sites was to increase the site’s surface area. In addition, for the same tube diameter, the
shorter the tube length, the larger the specific surface area of the multi-walled carbon
nanotubes [
31
]. Therefore, we adopted PtNPs/short MWCNT-PEDOT: PSS as the modifi-
cation scheme for this study. A three-electrode setup was used in this experiment (MEA:
working electrode; Pt: counter electrode; Ag|AgCl: reference electrode). First, platinum
nanoparticles (PtNPs) were electrodeposited onto sites by chronoamperometry (CA,
−
1.0 V,
40 s) in a mixed solution of 48 mM chloroplatinic acid and 4.2 mM lead acetate. Then, the
electrodeposition of short-MWCNT-PEDOT: PSS was performed by cyclic voltammetry
sweeping (0–0.9 V) for 10 cycles in the mixture solution of PSS (0.2 M), EDOT (40 mM), and
short-MWCNTs (4 mg/mL).
To explore the electrochemical performance of PtNPs/short MWCNT-PEDOT: PSS-
modified MEA sites, the redox potentials of MEA sites modified with PtNPs/MWCNT-
PEDOT: PSS or PtNPs/short MWCNT-PEDOT: PSS were measured in the solution con-
tained dopamine (DA) (100
µ
M) and 5-HT (100
µ
M) via cyclic voltammetry. The minimum
detection limit and linear calibration curve were measured by chronoamperometry (0.495 V)
in a PBS solution after the multiple addition of 5-HT solution (10
µ
L) at different concen-
trations. The Nafion layer was coated on the MEA site, which blocked the interference of
anions in the brain. The selectivity was measured by chronoamperometry (0.495 V) in PBS
solution after adding 5-HT solution, DA solution, uric acid solution (UA), ascorbic acid
solution (AA), and 3,4-dihydroxyphenylacetic acid solution (DOPAC) in sequence.
2.4. Surgical Procedures and Implant Pathway Check
Rats were anesthetized by isoflurane and fixed in a stereotaxic frame during the
experiment. The craniotomy was conducted at the coordinates of 1.5 mm posterior to
bregma and 1.2 mm lateral from the midline. Another site was drilled into the skull’s
surface, and a stainless-steel needle was fixed there as the ground electrode.
After the collection of electrophysiological signals, the brain was dehydrated and
sectioned, and the DiI marked the wound trace. Sections with coordinates of 1.5, 4.9, and
7.2 mm posterior to bregma were viewed through a microscope. Sections of coordinates
1.5 mm after bregma were observed by a microscope and compared to the rat brain atlas to
verify whether the electrode implantation was exactly at the predetermined position.
2.5. δWave Slope Analysis
According to the classification of brain waves, the LFP’s power was divided into four
bands:
δ
(0–4 Hz),
θ
(4–7 Hz),
α
(8–12 Hz) and
β
(13–30 Hz) [
32
]. The steps to extracting
the slope of the
δ
wave [
33
]: (1) the signal was filtered into a
δ
wave by low-pass filtering
(4 Hz); (2) the zero-crossing point was determined; (3) the extreme values on both sides of
the zero-crossing point was determined; (4) the first segment slope of the
δ
wave was the
slope of the positive deflection through the zero-crossing point, and the second segment
slope of the
δ
wave was the slope of the negative deflection through the zero-crossing point;
(5) the sum of the durations of adjacent positive and negative deflections should be longer
than 0.25 s; and (6) the slopes of the two segments were averaged and counted.
2.6. Data Acquisition and Statistics
The stereotaxic frame with the rat was placed into a grounded and shielded box
during the measurement to reduce external interference. The MEA was connected to a
128-channel neural data recording system to simultaneously detect electrophysiological
signals from neurons in the DRN and hippocampus of rats. The sampling frequency was set
to 30 kHz to capture neurophysiological signals in real time. Then, the local field potential
(LFP) (low-pass filter of 250 Hz) and spike (high-pass filter of 250 Hz) were extracted from
the data. After recording the measurements, NeuroExplorer Version 4 (Nex Technologies,
Colorado Springs, CO, USA) was used to analyze the data. Statistical analyses and graphs
were performed with OriginPro 2021 (OriginLab Corporation, Northampton, MA, USA).
Micromachines 2022,13, 488 6 of 15
Data were calculated as the mean
±
SEM. A two-sample analysis was used. A statistical
significance of p< 0.05 was set for all analyses.
3. Results
3.1. Sensor Evaluation
The modification scheme of PtNPs/short MWCNT-PEDOT: PSS is shown in
Figure 3a
.
After modification, the surface area of the MEA site significantly increased (Figure 3b,c).
The electrochemical impedance spectrum (EIS) is an effective and basic electro-chemical cell
analysis method used to characterize the sensor impedance. Figure 3d details the impedance
of the MEA sites at 1 kHz before and after the PtNPs/short MWCNT-PEDOT: PSS mod-
ification. Before modification, the average impedance of 32 sites was
873.70 ±44.20 kΩ
.
After modification, the impedance decreased to 11.75
±
2.30 k
Ω
, significantly reducing to
74.36 times smaller.
Micromachines 2022, 13, x FOR PEER REVIEW 6 of 16
software were used to analyze the data. Statistical analyses and graphs were performed
with OriginPro 2021 (OriginLab Corporation, Northampton, MA, USA). Data were calcu-
lated as the mean ± SEM. A two-sample analysis was used. A statistical significance of p <
0.05 was set for all analyses.
3. Results
3.1. Sensor Evaluation
The modification scheme of PtNPs/short MWCNT-PEDOT: PSS is shown in Figure
3a. After modification, the surface area of the MEA site significantly increased (Figure
3b,c). The electrochemical impedance spectrum (EIS) is an effective and basic electro-
chemical cell analysis method used to characterize the sensor impedance. Figure 3d details
the impedance of the MEA sites at 1 kHz before and after the PtNPs/short MWCNT-PE-
DOT: PSS modification. Before modification, the average impedance of 32 sites was 873.70
± 44.20 kΩ. After modification, the impedance decreased to 11.75 ± 2.30 kΩ, significantly
reducing to 74.36 times smaller.
The cyclic voltammetry of the MEA sites modified with PtNPs/MWCNT-PEDOT:
PSS or PtNPs/short MWCNT-PEDOT: PSS is shown in Figure 4a. The redox curve of the
MEA site modified with PtNPs/MWCNT-PEDOT: PSS had only one oxidation peak (0.350
V), which cannot distinguish DA and 5-HT in solution. The redox curve of the PtNPs/short
MWCNT-PEDOT: PSS-modified MEA site has two oxidation peaks, which can clearly dis-
tinguish DA (0.250 V) and 5-HT (0.495 V) in the solution. When a 50 nM solution of 5-HT
was dropped in the PBS, the current changed significantly (signal-to-noise ratio > 3), so
the minimum detection limit of 5-HT for this MEA was 50 nM (Figure 4b). In addition, the
5-HT solution at a concentration of 10 nM–10 uM was sequentially dropped into the PBS
solution. When the linear range was 10 nM–35 μM, the sensitivity of MEA was 12.601
pA/μM (R = 0.999) (Figure 4c). Finally, MEA has excellent selectivity (Figure 4d). The se-
lectivity ratios of MEA to DOPAC, Glu, UA and AA were 95.5, 97.1, 97.5 and 98.0%, re-
spectively.
Figure 3. Morphology and impedance characteristics of the MEA sites. (a) MEA site modification
protocol; (b) Scanning electron microscopy images (magnification: 5 k) of the PtNPs/short MWCNT-
PEDOT: PSS-modified MEA sites; (c) Scanning electron microscopy images (magnification: 30 k) of
the PtNPs/short MWCNT-PEDOT: PSS-modified MEA sites; (d) The EIS was recorded in a fre-
quency range from 1 Hz to 1 MHz with a sinusoidal potential of 20 mV peak–peak amplitude. The
mean impedance of the MEA decreased from 873.70 ± 44.20 kΩ to 11.75 ± 2.30 kΩ (n = 32) at 1 kHz
after the PtNPs/short MWCNT-PEDOT: PSS modification.
Figure 3.
Morphology and impedance characteristics of the MEA sites. (
a
) MEA site modification
protocol; (
b
) Scanning electron microscopy images (magnification: 5 k) of the PtNPs/short MWCNT-
PEDOT: PSS-modified MEA sites; (
c
) Scanning electron microscopy images (magnification: 30 k) of
the PtNPs/short MWCNT-PEDOT: PSS-modified MEA sites; (
d
) The EIS was recorded in a frequency
range from 1 Hz to 1 MHz with a sinusoidal potential of 20 mV peak–peak amplitude. The mean
impedance of the MEA decreased from 873.70
±
44.20 k
Ω
to 11.75
±
2.30 k
Ω
(n= 32) at 1 kHz after
the PtNPs/short MWCNT-PEDOT: PSS modification.
The cyclic voltammetry of the MEA sites modified with PtNPs/MWCNT-PEDOT: PSS
or PtNPs/short MWCNT-PEDOT: PSS is shown in Figure 4a. The redox curve of the MEA
site modified with PtNPs/MWCNT-PEDOT: PSS had only one oxidation peak (0.350 V),
which cannot distinguish DA and 5-HT in solution. The redox curve of the PtNPs/short
MWCNT-PEDOT: PSS-modified MEA site has two oxidation peaks, which can clearly
distinguish DA (0.250 V) and 5-HT (0.495 V) in the solution. When a 50 nM solution of 5-HT
was dropped in the PBS, the current changed significantly (signal-to-noise ratio > 3), so the
minimum detection limit of 5-HT for this MEA was 50 nM (Figure 4b). In addition, the 5-HT
solution at a concentration of 10 nM–10 uM was sequentially dropped into the PBS solution.
When the linear range was 10 nM–35
µ
M, the sensitivity of MEA was 12.601 pA/
µ
M
(
R = 0.999
) (Figure 4c). Finally, MEA has excellent selectivity (Figure 4d). The selectivity
ratios of MEA to DOPAC, Glu, UA and AA were 95.5, 97.1, 97.5 and 98.0%, respectively.
Micromachines 2022,13, 488 7 of 15
Micromachines 2022, 13, x FOR PEER REVIEW 7 of 16
Figure 4. Electrochemical performance of the MEA. (a) The cyclic voltammetry of the MEA sites
modified with PtNPs/MWCNT-PEDOT: PSS or PtNPs/short MWCNT-PEDOT: PSS in the solution
contained DA (100 μM) and 5-HT (100 μM); (b) Minimum detection limit of the MEA tested at tiny
concentrations of 5-HT solution; (c) The fitting curve between the 5-HT response current and the
corresponding concentration; (d) Selective results of the 5-HT microelectrode arrays against com-
mon interferences. DA: dopamine; UA: uric acid; AA: ascorbic acid; DOPAC: 3,4-dihydroxy-
phenylacetic acid.
3.2. MEA Implantation Path
The MEA implantation path was more clearly determined using the staining micro-
scope. According to the stained area of the brain slice, it was found that the MEA reached
the DRN from the craniotomy position through the hippocampus. The pathways did not
shift from the expected positions after being compared to the brain atlas (Figure 5).
Figure 5. MEA implantation path verification. (a) Craniotomy location in the brain atlas; (b) Hippo-
campus in the brain atlas; (c) DRN in the brain atlas; (d) Craniotomy location in the brain slices; (e)
Hippocampus in the brain slices; (f) DRN in the brain slices; (g) DiI traces at the craniotomy location
Figure 4.
Electrochemical performance of the MEA. (
a
) The cyclic voltammetry of the MEA sites
modified with PtNPs/MWCNT-PEDOT: PSS or PtNPs/short MWCNT-PEDOT: PSS in the solution
contained DA (100
µ
M) and 5-HT (100
µ
M); (
b
) Minimum detection limit of the MEA tested at tiny
concentrations of 5-HT solution; (
c
) The fitting curve between the 5-HT response current and the
corresponding concentration; (
d
) Selective results of the 5-HT microelectrode arrays against common
interferences. DA: dopamine; UA: uric acid; AA: ascorbic acid; DOPAC: 3,4-dihydroxyphenylacetic acid.
3.2. MEA Implantation Path
The MEA implantation path was more clearly determined using the staining micro-
scope. According to the stained area of the brain slice, it was found that the MEA reached
the DRN from the craniotomy position through the hippocampus. The pathways did not
shift from the expected positions after being compared to the brain atlas (Figure 5).
Micromachines 2022, 13, x FOR PEER REVIEW 7 of 16
Figure 4. Electrochemical performance of the MEA. (a) The cyclic voltammetry of the MEA sites
modified with PtNPs/MWCNT-PEDOT: PSS or PtNPs/short MWCNT-PEDOT: PSS in the solution
contained DA (100 μM) and 5-HT (100 μM); (b) Minimum detection limit of the MEA tested at tiny
concentrations of 5-HT solution; (c) The fitting curve between the 5-HT response current and the
corresponding concentration; (d) Selective results of the 5-HT microelectrode arrays against com-
mon interferences. DA: dopamine; UA: uric acid; AA: ascorbic acid; DOPAC: 3,4-dihydroxy-
phenylacetic acid.
3.2. MEA Implantation Path
The MEA implantation path was more clearly determined using the staining micro-
scope. According to the stained area of the brain slice, it was found that the MEA reached
the DRN from the craniotomy position through the hippocampus. The pathways did not
shift from the expected positions after being compared to the brain atlas (Figure 5).
Figure 5. MEA implantation path verification. (a) Craniotomy location in the brain atlas; (b) Hippo-
campus in the brain atlas; (c) DRN in the brain atlas; (d) Craniotomy location in the brain slices; (e)
Hippocampus in the brain slices; (f) DRN in the brain slices; (g) DiI traces at the craniotomy location
Figure 5.
MEA implantation path verification. (
a
) Craniotomy location in the brain atlas; (
b
) Hip-
pocampus in the brain atlas; (
c
) DRN in the brain atlas; (
d
) Craniotomy location in the brain slices;
(
e
Hippocampus in the brain slices; (
f
) DRN in the brain slices; (
g
) DiI traces at the craniotomy location
by fluorescence microscopy; (
h
) DiI traces at the hippocampus by fluorescence microscopy; and (
i
) DiI
traces in the DRN by fluorescence microscopy. (a–i) correspond with the coronal plane.
Micromachines 2022,13, 488 8 of 15
3.3. Analysis of Neural Spikes and Local Field Potentials
MEA captured the electrophysiological signals of normal, insomnia, and recovery rats,
and spikes of over eight channels and were successfully recorded at a signal-to-noise ratio
of above 3.5 (Figure 6). The MEA sites were in close contact with neurons in brain regions,
so the information displayed by each channel clearly reflected neural information in the
hippocampus and DRN. The real-time neural spikes and local field potentials (LFPs) were
recorded simultaneously in eight different channels over a 30 s period. As seen in Figure 6,
spikes in both brain regions of normal rats fired at a relatively slow rate. Significantly, spike
discharges in insomniac rats increased and became fiercer, accompanied by a frequent
fluctuation of LFP.
Micromachines 2022, 13, x FOR PEER REVIEW 8 of 16
by fluorescence microscopy; (h) DiI traces at the hippocampus by fluorescence microscopy; and (i)
DiI traces in the DRN by fluorescence microscopy. (a–i) correspond with the coronal plane.
3.3. Analysis of Neural Spikes and Local Field Potentials
MEA captured the electrophysiological signals of normal, insomnia, and recovery
rats, and spikes of over eight channels and were successfully recorded at a signal-to-noise
ratio of above 3.5 (Figure 6). The MEA sites were in close contact with neurons in brain
regions, so the information displayed by each channel clearly reflected neural information
in the hippocampus and DRN. The real-time neural spikes and local field potentials (LFPs)
were recorded simultaneously in eight different channels over a 30 s period. As seen in
Figure 6, spikes in both brain regions of normal rats fired at a relatively slow rate. Signif-
icantly, spike discharges in insomniac rats increased and became fiercer, accompanied by
a frequent fluctuation of LFP.
Figure 6. Captures of neural spiking activity (upper panel) and local field potentials (LFPs) (lower
panel) across eight channels of rats in normal, insomnia, and recovery states.
Next, the spikes were analyzed in detail. According to Figure 7a,b, the spike ampli-
tude (64.63 μV, 57.00 μV) in the DRN and hippocampus of insomnia rats was significantly
smaller than normal (108.00 μV, 135.45 μV) and recovery rats (102.17 μV, 110.25 μV). In
addition, the firing rates of insomnia rats were consistently higher than those of normal
and recovery rats. After 14 days, spikes and LFPs were restored in both brain regions of
the recovery rats. We counted the spike’s firing rate and found that the hippocampal
spike’s firing rate of insomnia rats increased from 3.88 to 6.34 Hz after modeling, then
dropped to 4.80 Hz after 14 days. Similarly, insomnia rats’ DRN spike’s firing rate in-
creased from 4.76 to 7.94 Hz after modeling, then dropped to 4.95 Hz after 14 days (Figure
7c).
Figure 6.
Captures of neural spiking activity (upper panel) and local field potentials (LFPs) (lower
panel) across eight channels of rats in normal, insomnia, and recovery states.
Next, the spikes were analyzed in detail. According to Figure 7a,b, the spike amplitude
(64.63
µ
V, 57.00
µ
V) in the DRN and hippocampus of insomnia rats was significantly smaller
than normal (108.00
µ
V, 135.45
µ
V) and recovery rats (102.17
µ
V, 110.25
µ
V). In addition,
the firing rates of insomnia rats were consistently higher than those of normal and recovery
rats. After 14 days, spikes and LFPs were restored in both brain regions of the recovery rats.
We counted the spike’s firing rate and found that the hippocampal spike’s firing rate of
insomnia rats increased from 3.88 to 6.34 Hz after modeling, then dropped to 4.80 Hz after
14 days. Similarly, insomnia rats’ DRN spike’s firing rate increased from 4.76 to 7.94 Hz
after modeling, then dropped to 4.95 Hz after 14 days (Figure 7c).
The spikes’ action potential was divided into three parts: depolarization, repolarization,
and hyperpolarization. The spike duration in this study was defined as the time of repolariza-
tion. The DRN spike duration did not change significantly, and the spike durations were 0.33,
0.36, and 0.30 s in normal, insomnia, and recovery rats, respectively. The hippocampal spike
duration (0.34 s) in insomnia rats was much smaller than normal (0.40 s) and recovered rats
(0.39 s). The repolarization slopes of the DRN spikes in the three states were 318.18
±
26.39,
176.16
±
15.41, and 340.56
±
32.02. The repolarization slopes of the hippocampal spikes in the
three states were 317.50 ±26.83, 163.24 ±17.5, and 282.69 ±27.98 (Figure 7d).
3.4. Analysis of Powers
The power spectral density (PSD) of the LFP’s hippocampus and DRN power were
mainly distributed in the lower frequency band; the power significantly increased after the
insomnia model (Figure 8a,b). We have more refined statistics at the power of 0–30 Hz. The
δ
wave power of the hippocampus and DRN increased by 67.75 and 57.57% after insomnia
modeling. After 14 days of insomnia modeling, the
δ
wave power of the hippocampus and
Micromachines 2022,13, 488 9 of 15
DRN were only 10.91 and 4.95% higher than in normal rats. Meanwhile, the power changes
in other frequency bands were not significant (Figure 8c,d).
Micromachines 2022, 13, x FOR PEER REVIEW 9 of 16
Figure 7. Statistics for spike waveform and firing rate of the rats’ dorsal raphe nucleus (DRN) and
hippocampus in normal, insomnia and recovery states (n = 12). (a) Spike waveform statistics in the
DRN; (b) Spike waveform statistics in the hippocampus; (c) Statistics for neural firing rate in the
DRN and hippocampus; (d) Repolarization slope of spikes in the DRN and hippocampus. (* p < 0.05,
** p < 0.01, *** p < 0.001, compared to normal in the same brain region).
The spikes’ action potential was divided into three parts: depolarization, repolariza-
tion, and hyperpolarization. The spike duration in this study was defined as the time of
repolarization. The DRN spike duration did not change significantly, and the spike dura-
tions were 0.33, 0.36, and 0.30 s in normal, insomnia, and recovery rats, respectively. The
hippocampal spike duration (0.34 s) in insomnia rats was much smaller than normal (0.40
s) and recovered rats (0.39 s). The repolarization slopes of the DRN spikes in the three
states were 318.18 ± 26.39, 176.16 ± 15.41, and 340.56 ± 32.02. The repolarization slopes of
the hippocampal spikes in the three states were 317.50 ± 26.83, 163.24 ± 17.5, and 282.69 ±
27.98 (Figure 7d).
3.4. Analysis of Powers
The power spectral density (PSD) of the LFP’s hippocampus and DRN power were
mainly distributed in the lower frequency band; the power significantly increased after
the insomnia model (Figure 8a,b). We have more refined statistics at the power of 0–30
Hz. The δ wave power of the hippocampus and DRN increased by 67.75 and 57.57% after
insomnia modeling. After 14 days of insomnia modeling, the δ wave power of the hippo-
campus and DRN were only 10.91 and 4.95% higher than in normal rats. Meanwhile, the
power changes in other frequency bands were not significant (Figure 8c,d).
Figure 7.
Statistics for spike waveform and firing rate of the rats’ dorsal raphe nucleus (DRN) and
hippocampus in normal, insomnia and recovery states (n= 12). (
a
) Spike waveform statistics in the
DRN; (
b
) Spike waveform statistics in the hippocampus; (
c
) Statistics for neural firing rate in the
DRN and hippocampus; (
d
) Repolarization slope of spikes in the DRN and hippocampus. (* p< 0.05,
** p< 0.01, *** p< 0.001, compared to normal in the same brain region).
Micromachines 2022, 13, x FOR PEER REVIEW 10 of 16
Figure 8. Statistics for PSD and power of rats’ dorsal raphe nucleus (DRN) and hippocampus of rats
in normal, insomnia and recovery states (n = 12). (a) PSD statistics in DRN; (b) PSD statistics in
hippocampus; (c) Power statistics in the DRN; (d) Power statistics in the hippocampus. δ: 0–4 Hz;
θ: 4–7 Hz; α: 8–12 Hz; β: 13–30 Hz. (** p < 0.01, *** p < 0.001, compared to normal in the same brain
region).
3.5. δ Wave Slope Analysis
The δ wave slope is an LFP component closely related to sleep [33,34]. Representative
individual LFP delta waves were selected through our algorithm (Figure 9a).
By calculating the slopes, the first and second segment slopes in the DRN increased
from 83.72, 89.63 to 122.41, 109.91 after insomnia modeling (Figure 9b). The first and sec-
ond segment slopes in the hippocampus increased from 76.01, 78.94 to 128.85, 132.93 after
insomnia modeling (Figure 9c).
Figure 9. Calculations and analysis of δ wave slope in rats’ dorsal raphe nucleus (DRN) and hippo-
campus in normal, insomnia and recovery states (n = 12). (a) Representative individual LFP δ wave;
(b) first segment slopes in the DRN and hippocampus. (c) Second segment slopes in the DRN and
hippocampus. (** p < 0.01, *** p < 0.001, compared to normal in the same segment).
3.6. Analysis between Firing Rate and Local Field Potential
The spike’s firing rate increased in the hippocampus and DRN of insomnia rats. Sim-
ultaneously, the δ wave power in the hippocampus and DRN of insomnia rats also in-
creased. To determine whether there was a link between power and spike, we fitted the
relationship between neuronal firing rate and δ wave power in the DRN and hippocam-
pus.
Figure 8.
Statistics for PSD and power of rats’ dorsal raphe nucleus (DRN) and hippocampus of
rats in normal, insomnia and recovery states (n= 12). (
a
) PSD statistics in DRN; (
b
) PSD statistics
in hippocampus; (
c
) Power statistics in the DRN; (
d
) Power statistics in the hippocampus.
δ: 0–4 Hz
;
θ: 4–7 Hz
;
α
: 8–12 Hz;
β
: 13–30 Hz. (** p< 0.01, *** p< 0.001, compared to normal in the same
brain region).
3.5. δWave Slope Analysis
The
δ
wave slope is an LFP component closely related to sleep [
33
,
34
]. Representative
individual LFP delta waves were selected through our algorithm (Figure 9a).
Micromachines 2022,13, 488 10 of 15
Micromachines 2022, 13, x FOR PEER REVIEW 10 of 16
Figure 8. Statistics for PSD and power of rats’ dorsal raphe nucleus (DRN) and hippocampus of rats
in normal, insomnia and recovery states (n = 12). (a) PSD statistics in DRN; (b) PSD statistics in
hippocampus; (c) Power statistics in the DRN; (d) Power statistics in the hippocampus. δ: 0–4 Hz;
θ: 4–7 Hz; α: 8–12 Hz; β: 13–30 Hz. (** p < 0.01, *** p < 0.001, compared to normal in the same brain
region).
3.5. δ Wave Slope Analysis
The δ wave slope is an LFP component closely related to sleep [33,34]. Representative
individual LFP delta waves were selected through our algorithm (Figure 9a).
By calculating the slopes, the first and second segment slopes in the DRN increased
from 83.72, 89.63 to 122.41, 109.91 after insomnia modeling (Figure 9b). The first and sec-
ond segment slopes in the hippocampus increased from 76.01, 78.94 to 128.85, 132.93 after
insomnia modeling (Figure 9c).
Figure 9. Calculations and analysis of δ wave slope in rats’ dorsal raphe nucleus (DRN) and hippo-
campus in normal, insomnia and recovery states (n = 12). (a) Representative individual LFP δ wave;
(b) first segment slopes in the DRN and hippocampus. (c) Second segment slopes in the DRN and
hippocampus. (** p < 0.01, *** p < 0.001, compared to normal in the same segment).
3.6. Analysis between Firing Rate and Local Field Potential
The spike’s firing rate increased in the hippocampus and DRN of insomnia rats. Sim-
ultaneously, the δ wave power in the hippocampus and DRN of insomnia rats also in-
creased. To determine whether there was a link between power and spike, we fitted the
relationship between neuronal firing rate and δ wave power in the DRN and hippocam-
pus.
Figure 9.
Calculations and analysis of
δ
wave slope in rats’ dorsal raphe nucleus (DRN) and hip-
pocampus in normal, insomnia and recovery states (n= 12). (
a
) Representative individual LFP
δ
wave; (
b
) first segment slopes in the DRN and hippocampus. (
c
) Second segment slopes in the DRN
and hippocampus. (** p< 0.01, *** p< 0.001, compared to normal in the same segment).
By calculating the slopes, the first and second segment slopes in the DRN increased
from 83.72, 89.63 to 122.41, 109.91 after insomnia modeling (Figure 9b). The first and second
segment slopes in the hippocampus increased from 76.01, 78.94 to 128.85, 132.93 after
insomnia modeling (Figure 9c).
3.6. Analysis between Firing Rate and Local Field Potential
The spike’s firing rate increased in the hippocampus and DRN of insomnia rats.
Simultaneously, the
δ
wave power in the hippocampus and DRN of insomnia rats also
increased. To determine whether there was a link between power and spike, we fitted the
relationship between neuronal firing rate and
δ
wave power in the DRN and hippocampus.
Our results showed a positive proportional relationship between the firing rate and
δ
wave power. As shown in Figure 10, the slope of the fitted line was 1.066 for the DRN,
whereas the slope of the fitted line was 1.231 for the hippocampus.
Micromachines 2022, 13, x FOR PEER REVIEW 11 of 16
Our results showed a positive proportional relationship between the firing rate and
δ wave power. As shown in Figure 10, the slope of the fitted line was 1.066 for the DRN,
whereas the slope of the fitted line was 1.231 for the hippocampus.
Figure 10. The relationship between neuronal firing rate and δ wave power in the dorsal raphe nu-
cleus (DRN) and hippocampus. X: Firing rate; Y: δ wave power. The green line is the fitted line for
the DRN data. The blue line is the fitted line for the hippocampal data.
4. Discussion
In order to investigate the neural mechanisms associated with insomnia, we designed
a PtNPs/short MWCNT-PEDOT: PSS-modified MEA for simultaneously detecting firing
patterns in the hippocampus and DRN of rats. Twelve rats were divided equally into three
groups: normal, insomnia, and recovery. The designed MEA was implanted into the DRN
through the hippocampus at a set angle. We further explored electrophysiological signal
changes in the hippocampus and DRN of rats under three conditions: normal, insomnia,
and recovery. Our results showed that the spike amplitude in the hippocampus and DRN
of PCPA-induced insomnia rats was significantly reduced, whereas the spiking rate was
significantly increased. In addition, the 0–50 Hz power of the rats after the insomnia model
was significantly increased, and the main change was in the δ band. The firing rate of the
DRN and the hippocampus was also positive proportional to the δ wave power. Our find-
ings suggested that implanting MEA into the brain at specific angles simultaneously de-
tects multiple deep brain regions, and that 5-HT-induced insomnia altered neuronal firing
patterns in the hippocampus and DRN.
The shanks of the MEA were 12 mm long. It could not only detect superficial regions,
such as the cerebral cortex or hippocampus, but also had the ability to detect deep brain
regions, such as the DRN. Meanwhile, the MEA sites were only 10 μm in diameter so that
the activity of individual neurons was detectable [35]. In this study, the impedance of the
MEA sites was reduced to 11.75 ± 2.30 kΩ by modifying PtNPs/short MWCNT-PEDOT:
PSS. The impedance of the modified MEA site was only 1.34% of that of the bare MEA
site, which was superior to our previous design [36]. In this way, the MEA could clearly
capture the neuronal spikes in the DRN and hippocampus of insomnia rats in vivo. In
addition, the PtNPs/short MWCNT-PEDOT: PSS-modified MEA possessed the ability to
detect 5-HT and DA simultaneously due to the better electrochemical performance of
short MWCNTs [31]. The molecular structure of DA was very similar to 5-HT, and DA
could affect the current response of 5-HT. Our designed MEA had different oxidation po-
tentials for 5-HT and DA, which could exclude the interference of DA.
In this study, when 5-HT-deficient rats suffered from insomnia, spike discharges in
the DRN and hippocampus were more frequent, and LFP fluctuations were more intense
Figure 10.
The relationship between neuronal firing rate and
δ
wave power in the dorsal raphe
nucleus (DRN) and hippocampus. X: Firing rate; Y:
δ
wave power. The green line is the fitted line for
the DRN data. The blue line is the fitted line for the hippocampal data.
4. Discussion
In order to investigate the neural mechanisms associated with insomnia, we designed
a PtNPs/short MWCNT-PEDOT: PSS-modified MEA for simultaneously detecting firing
patterns in the hippocampus and DRN of rats. Twelve rats were divided equally into three
groups: normal, insomnia, and recovery. The designed MEA was implanted into the DRN
Micromachines 2022,13, 488 11 of 15
through the hippocampus at a set angle. We further explored electrophysiological signal
changes in the hippocampus and DRN of rats under three conditions: normal, insomnia,
and recovery. Our results showed that the spike amplitude in the hippocampus and DRN
of PCPA-induced insomnia rats was significantly reduced, whereas the spiking rate was
significantly increased. In addition, the 0–50 Hz power of the rats after the insomnia model
was significantly increased, and the main change was in the
δ
band. The firing rate of
the DRN and the hippocampus was also positive proportional to the
δ
wave power. Our
findings suggested that implanting MEA into the brain at specific angles simultaneously
detects multiple deep brain regions, and that 5-HT-induced insomnia altered neuronal
firing patterns in the hippocampus and DRN.
The shanks of the MEA were 12 mm long. It could not only detect superficial regions,
such as the cerebral cortex or hippocampus, but also had the ability to detect deep brain
regions, such as the DRN. Meanwhile, the MEA sites were only 10
µ
m in diameter so that
the activity of individual neurons was detectable [
35
]. In this study, the impedance of the
MEA sites was reduced to 11.75
±
2.30 k
Ω
by modifying PtNPs/short MWCNT-PEDOT:
PSS. The impedance of the modified MEA site was only 1.34% of that of the bare MEA
site, which was superior to our previous design [
36
]. In this way, the MEA could clearly
capture the neuronal spikes in the DRN and hippocampus of insomnia rats
in vivo
. In
addition, the PtNPs/short MWCNT-PEDOT: PSS-modified MEA possessed the ability to
detect 5-HT and DA simultaneously due to the better electrochemical performance of short
MWCNTs [
31
]. The molecular structure of DA was very similar to 5-HT, and DA could
affect the current response of 5-HT. Our designed MEA had different oxidation potentials
for 5-HT and DA, which could exclude the interference of DA.
In this study, when 5-HT-deficient rats suffered from insomnia, spike discharges in
the DRN and hippocampus were more frequent, and LFP fluctuations were more intense
(Figure 6). Many 5-HT neurons were present in the DRN, and the hippocampus was also a
brain region with efferent connections to 5-HT neurons [
14
]. Previous studies have shown
that 5-HT is a class of inhibitory neurons [
30
]. The rat brain was depleted of 5-HT, so
inhibited neuronal activity in the DRN and hippocampus increased significantly (Figure 6).
As a result, there was a significant increase in the spike firing rate on a microscopic level.
Our study also showed that, after insomnia in rats, the firing rate change (66.81%) of the
DRN was higher than (63.40%) the hippocampus; this may be due to the denser distribution
of 5-HT1A neurons in the DRN than in the hippocampus.
A detailed analysis of spike waveforms revealed that, after insomnia modeling, the
slope of the spike depolarization did not change (Figure 7a,b), indicating that sodium
currents in the DRN and hippocampus are associated with cellular electrical signaling
were not affected. However, the spike repolarization slope was significantly reduced after
insomnia modeling, possibly due to the lack of 5-HT activation for potassium currents [
37
].
In addition, the spike duration in hippocampal neurons was shortened due to increased
calcium currents in hippocampal neurons [
38
]. The same phenomenon was found in a
study on the Aplysia nervous system [
39
], which included 5-HT-induced widening of
spikes in sensory neurons.
The amplitudes of the spike depolarization were significantly reduced in both brain
regions, possibly due to the combined effect of changes in calcium and potassium currents
(Figure 7d). Likewise, 5-HT may amplify fast extensor tibiae motor neuron spikes by mod-
ulating the potassium conductance responsible for the spike repolarization, similar to the
results of this study [
40
]. In addition, after insomnia in rats, the amplitude change (57.92%)
of the hippocampus was higher (40.16%) than in the DRN (Figure 7). This phenomenon
suggested that the potassium conductance of neurons in the dorsal raphe nucleus was
more affected by 5-HT than hippocampal neurons. Altogether, the firing rate of DRN’s
spike and the hippocampal spike’s amplitude more effectively reflected the sleep situation
of 5-HT-deficient rats.
In this study, after insomnia modeling, increased power in the 0–50 Hz of the LFP
(Figure 8a,b) and individual sub-bands within each were observed from multiple channels
Micromachines 2022,13, 488 12 of 15
of the MEA, suggesting that PCPA-induced insomnia affected groups of neurons’ activity.
In addition, the increase in power mainly depended on the increase in
δ
wave power
(Figure 8c,d). The
δ
oscillation between regular frequency and large amplitude played an
important role in sleep. There was a marked increase in
δ
power when rats were not asleep
for extended periods, consistent with the present study [
41
,
42
]. Sleep studies in animals
surfaced that EEG
δ
power during non-rapid-eye-movement sleep increases proportionally
with wake duration [43,44]. In our study, after insomnia modeling, the change in δpower
in the hippocampus was larger than in the DRN, indicating that hippocampal
δ
power
reflected insomnia caused by 5-HT deficiency more effectively. Furthermore, our study
found that the two-segment slopes of the
δ
wave in insomnia rats increased (Figure 9b,c),
indicating that they could be used as two parameters to reflect the sleep state of rats [
33
].
Moreover, changes to the two-segment slopes in the hippocampus (41.01%, 40.62%) were
more effective in reflecting insomnia than changes in the DRN’s two-segment slopes
(31.61%, 18.45%).
The spike reflected the electrophysiological activity of a single neuron, and the LFP
reflected the electrophysiological activity of a group of neurons in the vicinity of the site.
As shown in Figure 10, increased firing rates of individual neurons led to increased
δ
power
in the DRN and hippocampus of insomnia rats, consistent with our previous study [
36
].
Studies have shown that the LFP power is proportional to the spike firing rate [
45
]. Similarly,
our study found that the change in
δ
power in the hippocampus was larger than in the DRN
for the same difference in firing rate. This finding might indicate that neurons in different
brain regions contribute differently to
δ
wave power. Much of the relationship between
δ
power and sleep has been validated by EEG signals; however, studies using EEG
δ
power
alone to measure sleep status remain sparse. The relationship between intracranial
δ
power
and sleep quality indicates the possibility of using
δ
power in multiple brain regions to
measure sleep quality.
Finally, we also found that the amplitude, firing rate, and repolarization slope of the
recovery rats’ hippocampus were significantly different from the normal rats’ hippocampus.
Studies have shown that insomnia clouds significantly affect the memory function of
rats [
15
,
46
,
47
]. The abnormal firing pattern of neurons in the hippocampus may involve
compensatory brain activation to maintain memory and cognition [
48
]. Therefore, the
negative effects of long-term insomnia on memory may be difficult to recover from quickly
or completely [49,50].
5. Conclusions
Essentially, an MEA was designed to detect the hippocampus and DRN of rats simul-
taneously. Electrophysiological signals from two brain regions of normal, insomnia, and
recovery rats were captured
in vivo
through designed and modified MEA. Our preliminary
findings showed that insomnia rats have more intensive spikes and severe LFP fluctuations.
Microscopically, after insomnia, the spike amplitudes decreased, cell repolarization slope
decreased, and the spike firing rate increased in the DRN and hippocampus. Then,
δ
wave
power increased in insomnia rats. In addition, two segment slopes of the
δ
wave in the
DRN and hippocampus also increased in insomnia rats. Finally, we found that the firing
rates of the DRN and hippocampus were positive in relation to the
δ
wave power. Our
research lays the foundation for the simultaneous electrophysiological signal detection
of deep multi-brain regions
in vivo
and preliminarily explores changes in the neuronal
firing patterns of insomnia-induced rats by 5-HT deficiency. Unfortunately, our study did
not combine cytology with the exploration of the causes of neuronal electrophysiological
signal changes in depth. We can combine chemical or electrical stimulation to achieve
high-efficiency treatment for insomnia in the future.
Author Contributions:
Conceptualization, Y.W. (Yun Wang), X.C. and Y.L.; methodology, Y.W.
(Yun Wang), X.C., Y.L. and Y.D.; software, Y.W. (Yiding Wang) and Y.S.; validation, Y.W. (Yun Wang);
formal analysis, Y.W. (Yun Wang) and M.W.; investigation, Y.W. (Yun Wang); resources, X.C. and
Y.L.; data curation, Y.W. (Yun Wang),Y.D., M.W. and B.L.; writing—original draft preparation,
Micromachines 2022,13, 488 13 of 15
Y.W. (Yun Wang); writing—review and editing, Y.W. (
Yun Wang
), X.C., Y.L., M.W., Y.S. and Y.W.
(
Yiding Wang
); visualization, Y.W. (Yun Wang) and M.W.; supervision, X.C. and Y.L.; project
administration, X.C. and Y.L.; funding acquisition, X.C. All authors have read and agreed to the
published version of the manuscript.
Funding:
This work was sponsored by the National Natural Science Foundation of China (Grant
No. 61960206012, 62121003, 61971400, 61771452, 61775216, 61975206, and 61973292), the Scientific
Instrument Developing Project of the Chinese Academy of Sciences (Grant No. GJJSTD20210004), the
National Key Research and Development Program (Grant No. 2017YFA0205902).
Institutional Review Board Statement:
All animal care protocol was under the guide of institutional
Animal Ethics Committee.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data are contained within the article.
Acknowledgments:
Thanks for the support of the National Natural Science Foundation of China,
the Scientific Instrument Developing Project of the Chinese Academy of Sciences and the National
Key Research and Development Program.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
De Zambotti, M.; Trinder, J.; Silvani, A.; Colrain, I.M.; Baker, F.C. Dynamic coupling between the central and autonomic nervous
systems during sleep: A review. Neurosci. Biobehav. Rev. 2018,90, 84–103. [CrossRef]
2.
Knutson, K.L.; Van Cauter, E.; Rathouz, P.J.; DeLeire, T.; Lauderdale, D.S. Trends in the prevalence of short sleepers in the USA:
1975–2006. Sleep 2010,33, 37–45. [CrossRef] [PubMed]
3. Pavlova, M.K.; Latreille, V. Sleep Disorders. Am. J. Med. 2019,132, 292–299. [CrossRef] [PubMed]
4.
Caudwell, L.; Himani, H.; Khaw, A.; Taylor, R.; White, J.; Rhodes, S.; Skinner, M. Attitudes and perceptions of health professionals
towards sleep health: A systematic review. Phys. Ther. Rev. 2020,25, 361–380. [CrossRef]
5.
Stranges, S.; Tigbe, W.; Gómez-Olivé, F.X.; Thorogood, M.; Kandala, N.B. Sleep problems: An emerging global epidemic? Findings
from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. Sleep
2012
,
35, 1173–1181. [CrossRef] [PubMed]
6. MacDonald, K.J.; Cote, K.A. Contributions of post-learning REM and NREM sleep to memory retrieval. Sleep Med. Rev. 2021,59, 13.
[CrossRef]
7.
Cousins, J.N.; Fernandez, G. The impact of sleep deprivation on declarative memory. In Sleep Deprivation and Cognition;
VanDongen, H.P.A., Whitney, P., Hinson, J.M., Honn, K.A., Chee, M.W.L., Eds.; Elsevier: Amsterdam, The Netherlands, 2019;
Volume 246, pp. 27–53.
8.
Marshall, L.; Cross, N.; Binder, S.; Thien, T.D.V. Brain Rhythms During Sleep and Memory Consolidation: Neurobiological
Insights. Physiology 2020,35, 4–15. [CrossRef]
9.
Sateia, M.J. International classification of sleep disorders-third edition: Highlights and modifications. Chest
2014
,146, 1387–1394.
[CrossRef]
10.
Ditmer, M.; Gabryelska, A.; Turkiewicz, S.; Bialasiewicz, P.; Malecka-Wojciesko, E.; Sochal, M. Sleep Problems in Chronic
Inflammatory Diseases: Prevalence, Treatment, and New Perspectives: A Narrative Review. J. Clin. Med.
2022
,11, 67. [CrossRef]
11.
Wang, M.Y.; Li, N.; Jing, S.; Wang, C.M.; Sun, J.H.; Li, H.; Liu, J.L.; Chen, J.G. Schisandrin B exerts hypnotic effects in PCPA-treated
rats by increasing hypothalamic 5-HT and gamma-aminobutyric acid levels. Exp. Ther. Med. 2020,20, 7. [CrossRef]
12.
Lv, Y.B.; Zhou, Q.; Yan, J.X.; Luo, L.S.; Zhang, J.L. Enzymolysis peptides from Mauremys mutica plastron improve the disorder
of neurotransmitter system and facilitate sleep-promoting in the PCPA-induced insomnia mice. J. Ethnopharmacol.
2021
,274, 9.
[CrossRef] [PubMed]
13.
Mendiguren, A.; Aostri, E.; Pineda, J. Regulation of noradrenergic and serotonergic systems by cannabinoids: Relevance to
cannabinoid-induced effects. Life Sci. 2018,192, 115–127. [CrossRef] [PubMed]
14. Monti, J.M. Serotonin control of sleep-wake behavior. Sleep Med. Rev. 2011,15, 269–281. [CrossRef] [PubMed]
15. Diekelmann, S.; Born, J. The memory function of sleep. Nat. Rev. Neurosci. 2010,11, 114–126. [CrossRef]
16.
Anacker, C.; Hen, R. Adult hippocampal neurogenesis and cognitive flexibility-linking memory and mood. Nat. Rev. Neurosci.
2017,18, 335–346. [CrossRef] [PubMed]
17.
Dai, Z.H.; Xu, X.; Chen, W.Q.; Nie, L.N.; Liu, Y.; Sui, N.; Liang, J. The role of hippocampus in memory reactivation: An implication
for a therapeutic target against opioid use disorder. Curr. Addict. Rep. 2022, 1–13. [CrossRef] [PubMed]
18.
Leerssen, J.; Wassing, R.; Ramautar, J.R.; Stoffers, D.; Lakbila-Kamal, O.; Perrier, J.; Bruijel, J.; Foster-Dingley, J.C.; Aghajani, M.;
van Someren, E.J.W. Increased hippocampal-prefrontal functional connectivity in insomnia. Neurobiol. Learn. Mem.
2019
,
160, 144–150. [CrossRef]
Micromachines 2022,13, 488 14 of 15
19.
Emamian, F.; Mahdipour, M.; Noori, K.; Rostampour, M.; Mousavi, S.B.; Khazaie, H.; Khodaie-Ardakani, M.; Tahmasian, M.;
Zarei, M. Alterations of Subcortical Brain Structures in Paradoxical and Psychophysiological Insomnia Disorder. Front. Psychiatry
2021,12, 10. [CrossRef]
20.
Ren, S.; Wang, Y.; Yue, F.; Cheng, X.; Dang, R.; Qiao, Q.; Sun, X.; Li, X.; Jiang, Q.; Yao, J.; et al. The paraventricular thalamus is a
critical thalamic area for wakefulness. Science 2018,362, 429–434. [CrossRef]
21. Saper, C.B.; Fuller, P.M.; Pedersen, N.P.; Lu, J.; Scammell, T.E. Sleep state switching. Neuron 2010,68, 1023–1042. [CrossRef]
22.
Han, M.; Manoonkitiwongsa, P.S.; Wang, C.X.; McCreery, D.B.
In vivo
validation of custom-designed silicon-based microelectrode
arrays for long-term neural recording and stimulation. IEEE Trans. Biomed. Eng. 2012,59, 346–354. [CrossRef] [PubMed]
23.
Jun, J.J.; Steinmetz, N.A.; Siegle, J.H.; Denman, D.J.; Bauza, M.; Barbarits, B.; Lee, A.K.; Anastassiou, C.A.; Andrei, A.; Aydın, Ç.; et al.
Fully integrated silicon probes for high-density recording of neural activity. Nature 2017,551, 232–236. [CrossRef] [PubMed]
24.
Khoshnevisan, K.; Maleki, H.; Honarvarfard, E.; Baharifar, H.; Gholami, M.; Faridbod, F.; Larijani, B.; Faridi Majidi, R.;
Khorramizadeh, M.R. Nanomaterial based electrochemical sensing of the biomarker serotonin: A comprehensive review.
Mikrochim. Acta 2019,186, 49. [CrossRef] [PubMed]
25.
Lecomte, A.; Descamps, E.; Bergaud, C. A review on mechanical considerations for chronically-implanted neural probes. J. Neural
Eng. 2018,15, 031001. [CrossRef]
26.
Murray, N.M.; Buchanan, G.F.; Richerson, G.B. Insomnia Caused by Serotonin Depletion is Due to Hypothermia. Sleep
2015
,38, 1985–1993.
[CrossRef]
27.
Shi, R.; Han, Y.; Yang, Y.; Qiao, H.Y.; He, J.; Lian, W.W.; Xia, C.Y.; Li, T.L.; Zhang, W.K.; Xu, J.K. Loganin Exerts Sedative and
Hypnotic Effects via Modulation of the Serotonergic System and GABAergic Neurons. Front. Pharmacol.
2019
,10, 11. [CrossRef]
28.
Sun, Y.J.; Zhang, N.; Qu, Y.X.; Cao, Y.J.; Li, J.H.; Yang, Y.W.; Yang, T.G.; Sun, Y.K. Shuangxia decoction alleviates p-chlorophenylalanine
induced insomnia through the modification of serotonergic and immune system. Metab. Brain Dis. 2020,35, 315–325. [CrossRef]
29.
Si, Y.P.; Wang, L.L.; Lan, J.X.; Li, H.W.; Guo, T.; Chen, X.H.; Dong, C.H.; Ouyang, Z.; Chen, S.Q. Lilium davidii extract alleviates
p-chlorophenylalanine-induced insomnia in rats through modification of the hypothalamic-related neurotransmitters, melatonin
and homeostasis of the hypothalamic-pituitary-adrenal axis. Pharm. Biol. 2020,58, 915–924. [CrossRef]
30.
Browne, C.J.; Abela, A.R.; Chu, D.; Li, Z.X.; Ji, X.D.; Lambe, E.K.; Fletcher, P.J. Dorsal raphe serotonin neurons inhibit operant
responding for reward via inputs to the ventral tegmental area but not the nucleus accumbens: Evidence from studies combining
optogenetic stimulation and serotonin reuptake inhibition. Neuropsychopharmacology 2019,44, 793–804. [CrossRef]
31.
Jiang, Q.; Liu, B.C.; Qu, M.Z.; Zhou, G.M.; Zhang, B.L.; Yu, Z.L. A study on relations between structure and electrochemical
capacitance of multi-walled carbon nanotubes. Acta Chim. Sin. 2002,60, 1539–1542.
32.
Modak, S.; Roy, S.S.; Bose, R.; Chatterjee, S. Focal Epileptic Area Recognition Employing Cross EEG Rhythm Spectrum Images
and Convolutional Neural Network. IEEE Sens. J. 2021,21, 23335–23343. [CrossRef]
33.
Vyazovskiy, V.V.; Riedner, B.A.; Cirelli, C.; Tononi, G. Sleep homeostasis and cortical synchronization: II. A local field potential
study of sleep slow waves in the rat. Sleep 2007,30, 1631–1642. [CrossRef] [PubMed]
34.
Hubbard, J.; Gent, T.C.; Hoekstra, M.M.B.; Emmenegger, Y.; Mongrain, V.; Landolt, H.P.; Adamantidis, A.R.; Franken, P. Rapid
fast-delta decay following prolonged wakefulness marks a phase of wake-inertia in NREM sleep. Nat. Commun.
2020
,11, 3130.
[CrossRef] [PubMed]
35.
Lee, H.C.; Gaire, J.; McDowell, S.P.; Otto, K.J. The effect of site placement within silicon microelectrodes on the long-term
electrophysiological recordings. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; Volume 2014, pp. 465–468. [CrossRef] [PubMed]
36.
Lu, Z.; Xu, S.; Wang, H.; Liu, J.; Dai, Y.; Xie, J.; Song, Y.; Wang, Y.; Wang, Y.; Qu, L.; et al. Total sleep deprivation-induced
electrophysiological activities changes in rat hippocampal CA1 detected by microelectrode arrays. Sens. Actuators A Phys.
2021
,
331, 112983. [CrossRef]
37.
Penington, N.J.; Fox, A.P. Effects of LSD on Ca++ currents in central
5
-HT-containing neurons-5-HT
1
A receptors may play a role
in hallucinogenesis. J. Pharmacol. Exp. Ther. 1994,269, 1160–1165. [PubMed]
38. Bean, B.P. The action potential in mammalian central neurons. Nat. Rev. Neurosci. 2007,8, 451–465. [CrossRef] [PubMed]
39.
Jacklet, J.W.; Grizzaffi, J.; Tieman, D.G. Serotonin and cAMP induce excitatory modulation of a serotonergic neuron. J. Neurobiol.
2006,66, 499–510. [CrossRef]
40. Parker, D. Serotonergic modulation of locust motor-neurons. J. Neurophysiol. 1995,73, 923–932. [CrossRef]
41.
Guillaumin, M.C.C.; McKillop, L.E.; Cui, N.; Fisher, S.P.; Foster, R.G.; de Vos, M.; Peirson, S.N.; Achermann, P.; Vyazovskiy, V.V.
Cortical region-specific sleep homeostasis in mice: Effects of time of day and waking experience. Sleep
2018
,41, zsy079. [CrossRef]
42.
Vassalli, A.; Franken, P. Hypocretin (orexin) is critical in sustaining theta/gamma-rich waking behaviors that drive sleep need.
Proc. Natl. Acad. Sci. USA 2017,114, E5464–E5473. [CrossRef]
43.
Jones, S.G.; Vyazovskiy, V.V.; Cirelli, C.; Tononi, G.; Benca, R.M. Homeostatic regulation of sleep in the white-crowned sparrow
(Zonotrichia leucophrys gambelii). BMC Neurosci. 2008,9, 47. [CrossRef] [PubMed]
44.
Martinez-Gonzalez, D.; Lesku, J.A.; Rattenborg, N.C. Increased EEG spectral power density during sleep following short-term
sleep deprivation in pigeons (Columba livia): Evidence for avian sleep homeostasis. J. Sleep Res. 2008,17, 140–153. [CrossRef]
45.
Watson, B.O.; Ding, M.X.; Buzsaki, G. Temporal coupling of field potentials and action potentials in the neocortex. Eur. J. Neurosci.
2018,48, 2482–2497. [CrossRef]
46. Mander, B.A.; Winer, J.R.; Walker, M.P. Sleep and Human Aging. Neuron 2017,94, 19–36. [CrossRef] [PubMed]
Micromachines 2022,13, 488 15 of 15
47.
Medic, G.; Wille, M.; Hemels, M.E.H. Short- and long-term health consequences of sleep disruption. Nat. Sci. Sleep
2017
,9, 151–161.
[CrossRef] [PubMed]
48.
Son, Y.D.; Kang, J.M.; Cho, S.J.; Lee, J.S.; Hwang, H.Y.; Kang, S.G. fMRI brain activation in patients with insomnia disorder during
a working memory task. Sleep Breath. 2018,22, 487–493. [CrossRef]
49.
Dai, X.J.; Jiang, J.; Zhang, Z.Q.; Nie, X.; Liu, B.X.; Pei, L.; Gong, H.H.; Hu, J.P.; Lu, G.M.; Zhan, Y. Plasticity and Susceptibility of
Brain Morphometry Alterations to Insufficient Sleep. Front. Psychiatry 2018,9, 16. [CrossRef]
50.
Kim, C.R.; Chun, M.H.; Han, E.Y. Effects of Hypnotics on Sleep Patterns and Functional Recovery of Patients with Subacute
Stroke. Am. J. Phys. Med. Rehabil. 2010,89, 315–322. [CrossRef]
