If the synaptic transmission is presumed a bionomial distribution, changes in release probability or number of release sites are associated with a change in the CV of the synaptic responses. In contrast, postsynaptic changes should have little effect on CV (S9 ). Means and CVs were calculated from 50–60 EPSPs immediately before induction and 15–25 min after the end of induction.
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Physiology
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Cell Function
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Synaptic Transmission
Synaptic Transmission
Synaptic Transmission: The process by which signaling molecules (neurotransmitters, neuromodulators, or hormones) released by the presynaptic neuron activate receptors on the postsynaptic cell, initiating a propagation of electrochemical signals.
This complex process is vital for the communication between neurons and plays a crucial role in neural function.
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This complex process is vital for the communication between neurons and plays a crucial role in neural function.
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Most cited protocols related to «Synaptic Transmission»
Excitatory Postsynaptic Potentials
Synaptic Transmission
Interneurons
neuro-oncological ventral antigen 2, human
Synaptic Transmission
In this study, the human upright posture is simply modeled by the motion of an inverted pendulum as where I represents the moment of inertia of human body around the ankle, θ the tilt angle, g the gravity acceleration, m the body mass, h the distance from the ankle joint to the body CoM (Center of Mass), T the ankle torque, and the gravitational toppling torque. The ankle joint torque T is modeled as where Δ is the neural transmission delay, and . The first two terms on the right hand side of the equation represent passive feedback torques, with no time delay, related to the intrinsic mechanical impedance of the ankle joint (K and B are the passive stiffness and viscosity parameters, respectively); the third and fourth terms represent the active neural feedback torques that are determined as functions of delay-affected tilt angle and angular velocity, respectively; the last term is a noise torque, modelled as an additive Gaussian white noise ξ(t) of intensity σ. By combining eq. 1 and eq. 2 we obtain a delay differential equation (DDE):
In the following we consider four different implementations of the active controllers fP and fD and analyze the corresponding properties and performance. In Models 1 and 2 the active feedback is linear and continuous in time. In Models 3 and 4 the active feedback is non-linear and intermittent.Figure 1 shows, for the four control models, the distribution of active and inactive regions in the phase plane ( vs. θ).
In the following we consider four different implementations of the active controllers fP and fD and analyze the corresponding properties and performance. In Models 1 and 2 the active feedback is linear and continuous in time. In Models 3 and 4 the active feedback is non-linear and intermittent.
Acceleration
Gravitation
Gravity
Homo sapiens
Human Body
Joints, Ankle
Nervousness
Synaptic Transmission
Torque
Viscosity
All simulations were performed using the NEURON simulation environment (v7.2) (Hines and Carnevale, 1997 (link)), with a previously published model of hippocampal CA1 pyramidal neurons (Bianchi et al., 2012 (link)). To implement synaptic inputs suitable to model changes in release probability and synaptic integration, for each synapse we used the kinetic scheme introduced by Tsodyks et al. (1998 (link)) and widely used to study synaptic transmission mechanisms and short-term plasticity effects (Tsodyks et al., 1998 (link)).
Briefly, in this model a synapse contains a finite amount of ‘resources’, which can be divided in three fractions: recovered (x), active (y), and inactive (z), in a dynamical relation to each other. At the arrival of a spike (at time tsp), a fraction p of recovered resource is activated, quickly inactivated with a time constant τin and then recovered with a time constant τrec according to the following equations:
with x + y + z = 1 and y0 = z0 = 0, x0 = 1.
p indicates the effective use of the synaptic resources of the synapses and can be seen as the average release probability of a quantal model. During repeated stimulation p can increase due to facilitation. In particular, at the arrival of a spike (t = tisp) p is incremented by a factor U(1 − p−), where p− is the last pre-spike value of p, so that the post-spike value is p+ = p− + U(1 − p−). After the spike, instead, p decays to baseline with a time constant τfacil, (Tsodyks et al., 1998 (link)), so that in the interval between the arrival of two spikes, t ∈ (tisp, tjsp):
U determines the increase in the value of p with each spike and coincides with the value of p reached upon the arrival of the first spike, i.e., p0 = p(t1sp) = U. It is worth noting that p is incremented before x is converted to y.
The resulting excitatory post-synaptic current (EPSC) is proportional to the active resource:
where A is the absolute synaptic strength, corresponding to the maximum EPSC obtained by activating all the resources.
The values used for all parameters are discussed in the Results section. Since the findings in Abramov et al. (2009 (link)) are principally mediated by AMPA receptors, we did not explicitly include NMDA receptors in the CA1 neuron model.
To model the activation of a group of afferent fibers from CA3 pyramidal neurons through the Schaffer collaterals, ten synapses were distributed randomly on the apical trunk between 100 and 400 μm from the soma. Each synapse in the model represents the effect of a population of synapses activated in the oblique dendrites. A range of peak synaptic conductances (weights) was explored. All simulations were repeated 10 times redistributing synaptic location and weights. Results are expressed as mean ± SEM (standard error of the mean, SEM).
Briefly, in this model a synapse contains a finite amount of ‘resources’, which can be divided in three fractions: recovered (x), active (y), and inactive (z), in a dynamical relation to each other. At the arrival of a spike (at time tsp), a fraction p of recovered resource is activated, quickly inactivated with a time constant τin and then recovered with a time constant τrec according to the following equations:
with x + y + z = 1 and y0 = z0 = 0, x0 = 1.
p indicates the effective use of the synaptic resources of the synapses and can be seen as the average release probability of a quantal model. During repeated stimulation p can increase due to facilitation. In particular, at the arrival of a spike (t = tisp) p is incremented by a factor U(1 − p−), where p− is the last pre-spike value of p, so that the post-spike value is p+ = p− + U(1 − p−). After the spike, instead, p decays to baseline with a time constant τfacil, (Tsodyks et al., 1998 (link)), so that in the interval between the arrival of two spikes, t ∈ (tisp, tjsp):
U determines the increase in the value of p with each spike and coincides with the value of p reached upon the arrival of the first spike, i.e., p0 = p(t1sp) = U. It is worth noting that p is incremented before x is converted to y.
The resulting excitatory post-synaptic current (EPSC) is proportional to the active resource:
where A is the absolute synaptic strength, corresponding to the maximum EPSC obtained by activating all the resources.
The values used for all parameters are discussed in the Results section. Since the findings in Abramov et al. (2009 (link)) are principally mediated by AMPA receptors, we did not explicitly include NMDA receptors in the CA1 neuron model.
To model the activation of a group of afferent fibers from CA3 pyramidal neurons through the Schaffer collaterals, ten synapses were distributed randomly on the apical trunk between 100 and 400 μm from the soma. Each synapse in the model represents the effect of a population of synapses activated in the oblique dendrites. A range of peak synaptic conductances (weights) was explored. All simulations were repeated 10 times redistributing synaptic location and weights. Results are expressed as mean ± SEM (standard error of the mean, SEM).
AMPA Receptors
Dendrites
factor A
Kinetics
N-Methyl-D-Aspartate Receptors
Neurons
Pyramidal Cells
Schaffer Collaterals
Synapses
Synaptic Transmission
Vision
We can analytically calculate the firing rate and cross-correlation coefficient of each neuron by dividing excitatory synaptic inputs to the neuron into two parts, one consisting of weak and modestly strong synapses and one consisting of extremely strong synapses. In this approximation, we may treat inputs to the former excitatory synapses and inhibitory synapses by the diffusion approximation57 , in which Poisson spike inputs on (2) are replaced with a white Gaussian noise having the same mean and variance of the Poisson inputs. We then used the effective-time-constant approximation58 (link) to replace v−VE and v−VI with V0−VE and V0−VI to obtain linear stochastic differential equations where V0 = (VL/τm+VEgE0+VIgI0)τe and τe = 1/(1/τm+gE0+gI0) are the equilibrium membrane potential and the effective membrane time constant, respectively. The mean excitatory conductance of the first group and mean inhibitory conductance are and gI0 = τsMIGIrI, respectively, in terms of the firing rate of the i-th excitatory synaptic input ri, the synaptic transmission failure pi, firing rate at inhibitory synapses rI and the number of inhibitory synapses on the neuron MI. The fluctuation of the total synaptic current η is given as . The stationary probability density of the membrane potential and the output rate are obtained from equation (5) as59 (link)
up to the first order of , where erf(x) is the error function and , , , and ξ is the Riemann zeta function. Normalization constant C is calculated from the .
The contribution of extremely strong excitatory synapses to output firing is approximated as the sum of the conditional firing probabilities over inputs to these synapses. In the effective-time-constant approximation, the effective amplitude of EPSP evoked by i-th synapse is where v is the membrane potential just before the arrival of presynaptic input and Ei is the EPSP amplitude measured from the resting potential. Because the conditional probability of having an output spike given the i-th input is equal to the area of the stationary density function satisfying , the conditional probability is equal to Then, by summing these contributions, we obtain the firing rate of the neuron as Finally, by substituting Pi of the strongest synapse into and usingequation (10) , we obtain an analytical expression of the correlation coefficient given in equation (4) . To derive the analytical curve shown in Fig. 1c , we classified the five strongest synapses into the second group and the remaining ones into the first group.
up to the first order of , where erf(x) is the error function and , , , and ξ is the Riemann zeta function. Normalization constant C is calculated from the .
The contribution of extremely strong excitatory synapses to output firing is approximated as the sum of the conditional firing probabilities over inputs to these synapses. In the effective-time-constant approximation, the effective amplitude of EPSP evoked by i-th synapse is where v is the membrane potential just before the arrival of presynaptic input and Ei is the EPSP amplitude measured from the resting potential. Because the conditional probability of having an output spike given the i-th input is equal to the area of the stationary density function satisfying , the conditional probability is equal to Then, by summing these contributions, we obtain the firing rate of the neuron as Finally, by substituting Pi of the strongest synapse into and using
Debility
Diffusion
Excitatory Postsynaptic Potentials
Membrane Potentials
Neurons
Psychological Inhibition
Resting Potentials
Synapses
Synaptic Transmission
Tissue, Membrane
Most recents protocols related to «Synaptic Transmission»
Macroscale whole-brain computational models represent regional activity in terms of two key ingredients: (i) a biophysical model of each region's local dynamics; and (ii) inter-regional anatomical connectivity. Thus, such in silico models provide a well-suited tool to investigate how the structural connectivity of the brain shapes the corresponding macroscale neural dynamics (Cabral et al., 2017 (link); Cofré et al., 2020 (link); Deco and Kringelbach, 2014 (link); Demirtaş et al., 2019 (link); Kringelbach and Deco, 2020 (link); Shine et al., 2021 (link); Wang et al., 2019 (link)). In particular, the Dynamic Mean Field (DMF) model employed here simulates each region (defined via an anatomical parcellation scheme) as a macroscopic neural field comprising mutually coupled excitatory and inhibitory populations (80% excitatory and 20% inhibitory), providing a neurobiologically plausible account of regional neuronal firing rate. Regions are then connected according to empirical anatomical connectivity obtained e.g. from DWI data (Deco et al., 2014 (link); G. 2013 (link); Deco and Jirsa, 2012 (link)). The reader is referred to (Deco et al., 2018 (link); Herzog et al., 2022 (link); 2020 (link); Luppi et al., 2022b (link)) for details of the DMF model and its implementation. Due to its multi-platform compatibility, low memory usage, and high speed, we used the recently developed FastDMF library (Herzog et al., 2022 (link)), available online at https://www.gitlab.com/concog/fastdmf .
The structural connectivity (SC) for the DMF model used here was obtained by following the procedure described by Wang et al. (2019) (link) to derive a consensus structural connectivity matrix. A consensus matrix A was obtained separately for each group (healthy controls, MCS patients, UWS patients) as follows: for each pair of regions i and j, if more than half of subjects had non-zero connection i and j, Aij was set to the average across all subjects with non-zero connections between i and j. Otherwise, Aij was set to zero.
The DMF model has one free parameter, known as “global coupling” and denoted by G, which accounts for differences in transmission between brain regions, considering the effects of neurotransmission but also synaptic plasticity mechanisms. Thus, separately for each group, we used a model informed by that group's consensus connectome to generate 40 simulations for each value of G between 0.1 and 2.5, using increments of 0.1. Finally, we set the G parameter to the value just before the one at which the simulated firing of each model became unstable, reflecting a near-critical regime.
Subsequently, for each group, 40 further simulations were obtained from the corresponding DMF model with the optimal G parameter. A Balloon-Windkessel hemodynamic model (Friston et al., 2003 (link)) was then used to turn simulated regional neuronal activity into simulated regional BOLD signal. Finally, simulated regional BOLD signal was bandpass filtered in the same range as the empirical data (0.008–0.09 Hz, or 0.04–0.07 Hz for the intrinsic ignition analysis).
As an alternative way of finding the most suitable value of G for the simulation of each condition, we adopted the approach previously described (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)) which aims to obtain the best match between empirical and simulated functional connectivity dynamics. First, we quantified empirical functional connectivity dynamics (FCD) in terms of Pearson correlation between regional BOLD timeseries, computed within a sliding window of 30 TRs with increments of 3 TRs (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)). Subsequently, the resulting matrices of functional connectivity at times tx and ty were themselves correlated, for each pair of timepoints tx and ty, thereby obtaining an FCD matrix of time-versus-time correlations. Thus, each entry in the FCD matrix represents the similarity between functional connectivity patterns at different points in time. This procedure was repeated for each subject of each group (controls, MCS, and UWS). For each simulation at each value of G, we used the Kolmogorov-Smirnov distance to compare the histograms of empirical (group-wise) and simulated FCD values (obtained from the upper triangular FCD matrix). Finally, we set the model's G parameter to the value that was observed to minimize the mean KS distance - corresponding to the model that is best capable of simulating the temporal dynamics of resting-state brain functional connectivity observed in the corresponding group (Figure S8). After having found the value of G for each condition, simulated BOLD signals were obtained as described above. This same procedure was also used for fitting the DMF model based on each individual's structural connectome, simulating BOLD signals to fit their own empirical FCD.
The structural connectivity (SC) for the DMF model used here was obtained by following the procedure described by Wang et al. (2019) (link) to derive a consensus structural connectivity matrix. A consensus matrix A was obtained separately for each group (healthy controls, MCS patients, UWS patients) as follows: for each pair of regions i and j, if more than half of subjects had non-zero connection i and j, Aij was set to the average across all subjects with non-zero connections between i and j. Otherwise, Aij was set to zero.
The DMF model has one free parameter, known as “global coupling” and denoted by G, which accounts for differences in transmission between brain regions, considering the effects of neurotransmission but also synaptic plasticity mechanisms. Thus, separately for each group, we used a model informed by that group's consensus connectome to generate 40 simulations for each value of G between 0.1 and 2.5, using increments of 0.1. Finally, we set the G parameter to the value just before the one at which the simulated firing of each model became unstable, reflecting a near-critical regime.
Subsequently, for each group, 40 further simulations were obtained from the corresponding DMF model with the optimal G parameter. A Balloon-Windkessel hemodynamic model (Friston et al., 2003 (link)) was then used to turn simulated regional neuronal activity into simulated regional BOLD signal. Finally, simulated regional BOLD signal was bandpass filtered in the same range as the empirical data (0.008–0.09 Hz, or 0.04–0.07 Hz for the intrinsic ignition analysis).
As an alternative way of finding the most suitable value of G for the simulation of each condition, we adopted the approach previously described (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)) which aims to obtain the best match between empirical and simulated functional connectivity dynamics. First, we quantified empirical functional connectivity dynamics (FCD) in terms of Pearson correlation between regional BOLD timeseries, computed within a sliding window of 30 TRs with increments of 3 TRs (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)). Subsequently, the resulting matrices of functional connectivity at times tx and ty were themselves correlated, for each pair of timepoints tx and ty, thereby obtaining an FCD matrix of time-versus-time correlations. Thus, each entry in the FCD matrix represents the similarity between functional connectivity patterns at different points in time. This procedure was repeated for each subject of each group (controls, MCS, and UWS). For each simulation at each value of G, we used the Kolmogorov-Smirnov distance to compare the histograms of empirical (group-wise) and simulated FCD values (obtained from the upper triangular FCD matrix). Finally, we set the model's G parameter to the value that was observed to minimize the mean KS distance - corresponding to the model that is best capable of simulating the temporal dynamics of resting-state brain functional connectivity observed in the corresponding group (Figure S8). After having found the value of G for each condition, simulated BOLD signals were obtained as described above. This same procedure was also used for fitting the DMF model based on each individual's structural connectome, simulating BOLD signals to fit their own empirical FCD.
Brain
Connectome
Diencephalon
DNA Library
Hemodynamics
Memory
Nervousness
Neuronal Plasticity
Neurons
Patients
Psychological Inhibition
Strains
Synaptic Transmission
Transmission, Communicable Disease
Acute hippocampal slices were prepared from 4-week-old NT−/− and NT+/+ mice. Each mouse was killed by cervical dislocation, followed by decapitation. The brain was removed from the skull and transferred into ice-cold artificial cerebrospinal fluid (ACSF) saturated with carbogen (95% O2/5% CO2) containing (in mM) 250 sucrose, 25.6 NaHCO3, 10 glucose, 4.9 KCl, 1.25 KH2PO4, 2 CaCl2, and 2.0 MgSO4 (pH = 7.3). Both hippocampi were dissected out and sliced transversally (400 µm) using a tissue chopper with a cooled stage (custom-made by LIN, Magdeburg, Germany). Slices were kept at room temperature in carbogen-bubbled ACSF (95% O2 /5% CO2) containing 124 mM NaCl instead of 250 mM sucrose for at least 2 h before recordings were initiated.
Recordings were performed in the same solution in a submerged chamber that was continuously superfused with carbogen-bubbled ACSF (1.2 ml/min) at 32 °C. Recordings of field excitatory postsynaptic potentials (fEPSPs) were performed in CA1a and CA1c with a glass pipette filled with ACSF to activate synapses in the CA1b stratum radiatum. The resistance of the pipette was 1–4 MΩ. Stimulation pulses were applied to Schaffer collaterals via a monopolar, electrolytically sharpened and lacquer-coated stainless-steel electrode located approximately 300 mm closer to the CA3 subfield than to the recording electrode. Basal synaptic transmission was monitored at 0.05 Hz and collected at 3 pulses/min. The spaced LTP protocol was performed as previously described (Kramár et al., 2012). LTP was induced by applying 5xTBS with an interval of 20 s. One TBS consisted of a single train of ten bursts (four pulses at 100 Hz) separated by 200 ms and the width of the single pulses was 0.2 ms. To induce spaced LTP, we applied two trains of TBS (TBS1/TBS2) separated by 1 h. The stimulation strength was set to provide baseline fEPSPs with slopes of approximately 50% of the subthreshold maximum. The data were recorded at a sampling rate of 10 kHz and then filtered (0–5 kHz) and analyzed using IntraCell software (custom-made, LIN Magdeburg, Germany).
Recordings were performed in the same solution in a submerged chamber that was continuously superfused with carbogen-bubbled ACSF (1.2 ml/min) at 32 °C. Recordings of field excitatory postsynaptic potentials (fEPSPs) were performed in CA1a and CA1c with a glass pipette filled with ACSF to activate synapses in the CA1b stratum radiatum. The resistance of the pipette was 1–4 MΩ. Stimulation pulses were applied to Schaffer collaterals via a monopolar, electrolytically sharpened and lacquer-coated stainless-steel electrode located approximately 300 mm closer to the CA3 subfield than to the recording electrode. Basal synaptic transmission was monitored at 0.05 Hz and collected at 3 pulses/min. The spaced LTP protocol was performed as previously described (Kramár et al., 2012). LTP was induced by applying 5xTBS with an interval of 20 s. One TBS consisted of a single train of ten bursts (four pulses at 100 Hz) separated by 200 ms and the width of the single pulses was 0.2 ms. To induce spaced LTP, we applied two trains of TBS (TBS1/TBS2) separated by 1 h. The stimulation strength was set to provide baseline fEPSPs with slopes of approximately 50% of the subthreshold maximum. The data were recorded at a sampling rate of 10 kHz and then filtered (0–5 kHz) and analyzed using IntraCell software (custom-made, LIN Magdeburg, Germany).
Bicarbonate, Sodium
Brain
carbogen
Cerebrospinal Fluid
Cold Temperature
Cranium
Decapitation
Excitatory Postsynaptic Potentials
Glucose
Joint Dislocations
Mus
Neck
Pulse Rate
Schaffer Collaterals
Seahorses
Sodium Chloride
Stainless Steel
Sucrose
Sulfate, Magnesium
Synapses
Synaptic Transmission
Tissues
As a typical model to study the development and function of the NMJ [43 (link)–45 (link)], diaphragm muscle was dissected with special care to preserve phrenic nerve connectivity. Isolated nerve–muscle preparations were immersed in Ringer’s solution and maintained at 26 °C.
One hemidiaphragm was used as a treatment, and the other served as its paired untreated control. All treatments were performed ex vivo. Muscles were stimulated through the phrenic nerve at 1 Hz, which allows the maintenance of different tonic functions without depleting synaptic vesicles, for 30 min using the A-M Systems 2100 isolated pulse generator (A-M System) as in previous studies [38 (link)–40 (link)]. We designed a protocol of stimulation that preserves the nerve stimulation and the associated neurotransmission mechanism. This method prevents other mechanisms associated with non-nerve-induced (direct) muscle contraction [46 –48 (link)]. To verify muscle contraction, a visual checking was done. Two main experiments were performed to distinguish the effects of synaptic activity from those of muscle activity (Fig.1 ).![]()
Reagents ”). Nevertheless, before immersing these muscles in μ-CgTx-GIIIB, a visual checking of the correct contraction of the muscle was done [39 (link)].
Furthermore, to assess the effect of PKA blocking, three different experiments have been performed:
One hemidiaphragm was used as a treatment, and the other served as its paired untreated control. All treatments were performed ex vivo. Muscles were stimulated through the phrenic nerve at 1 Hz, which allows the maintenance of different tonic functions without depleting synaptic vesicles, for 30 min using the A-M Systems 2100 isolated pulse generator (A-M System) as in previous studies [38 (link)–40 (link)]. We designed a protocol of stimulation that preserves the nerve stimulation and the associated neurotransmission mechanism. This method prevents other mechanisms associated with non-nerve-induced (direct) muscle contraction [46 –48 (link)]. To verify muscle contraction, a visual checking was done. Two main experiments were performed to distinguish the effects of synaptic activity from those of muscle activity (Fig.
Presynaptic stimulation (Ctrl versus ES): to show the impact of the synaptic activity, we compared presynaptically stimulated muscles whose contraction was blocked by μ-CgTx-GIIIB with nonstimulated muscles also incubated with μ-CgTx-GIIIB to control for nonspecific effects of the blocker.
Contraction (ES versus ES + C): to estimate the effect of nerve-induced muscle contraction, we compared stimulated/contracting muscles with stimulated/noncontracting muscles whose contraction was blocked by μ-CgTx-GIII. By comparing the presynaptic stimulation with or without postsynaptic activity, we separate the effect of contraction. However, one should consider that postsynaptic contraction experiments also contain presynaptic activity.
Design of experimental treatment for the study of effects of presynaptic activity and nerve-induced muscle contraction. μ-CgTx-GIIIB, μ-conotoxin GIIIB
Furthermore, to assess the effect of PKA blocking, three different experiments have been performed:
To estimate the effect of PKA inhibition under synaptic activity, we compared presynaptically stimulated muscles whose contraction was blocked by μ-CgTx-GIIIB with and without H-89: ES versus ES + H-89.
To show the impact of the PKA inhibition under muscle contraction, we compared stimulating and contracting muscles with and without H-89: (ES + C) versus (ES + C) + H-89.
To demonstrate if degradation or redistribution along the axon is involved, the diaphragm muscle was dissected with special care to preserve phrenic nerve connectivity. We compared stimulating and contracting muscles with and without protease inhibitor (Prot.Inh.) cocktail 1% (10 μl/ml; Sigma, Saint Louis, MO, USA): (ES + C) versus (ES + C) + Prot.Inh.
Axon
Conotoxins
Muscle Contraction
Muscle Tissue
Nerve-Muscle Preparation
Nervousness
Phrenic Nerve
Protease Inhibitors
Psychological Inhibition
Pulse Rate
Ringer's Solution
Synaptic Transmission
Synaptic Vesicles
Therapies, Investigational
Vaginal Diaphragm
HAL (Sigma-Aldrich, St. Louis, MO) was diluted with 0.05% dimethyl sulfoxide (DMSO; Dojindo Laboratories, Japan) in sterile saline (vehicle). The drug is a traditional antipsychotic agent used primarily to treat schizophrenia and other psychoses (Gomes et al. 2013 (link); Vaz et al. 2018 (link); Magno et al. 2015 (link); Bruni et al. 2016 (link)) by relieving the symptoms of delusions and hallucinations commonly associated with schizophrenia. Haloperidol competitively blocks post-synaptic dopamine D2 receptors, eliminating dopamine neurotransmission while partially inhibiting 5-hydroxy-tryptamine (5-HT2) and α1-receptors. However, there is negligible activation of dopamine D1-receptors (Seibt et al. 2010 (link)).
CBD (Cayman Chemical, Ann Arbor, MI) was diluted with 0.05% methanol and sterile saline. CBD, one of the major compounds present in the marijuana (C. sativa) plant, has some medicinal properties; however, its mechanism is not well known (Andreza et al. 2016 (link); Jeong et al. 2019 (link)).
Ropinirole hydrochloride (ROP; KYOWA Pharmaceutical Industry Co., Ltd, Osaka, Japan) was diluted with 0.05% dimethyl sulfoxide (DMSO; Dojindo Laboratories, Kumamoto, Japan) and sterile saline. The drug is a novel non-ergoline dopamine agonist, has selective affinity for dopamine D2 receptors, and is indicated for the treatment of early and advanced Parkinson’s disease (Pahwa et al. 2004 (link)).
CBD (Cayman Chemical, Ann Arbor, MI) was diluted with 0.05% methanol and sterile saline. CBD, one of the major compounds present in the marijuana (C. sativa) plant, has some medicinal properties; however, its mechanism is not well known (Andreza et al. 2016 (link); Jeong et al. 2019 (link)).
Ropinirole hydrochloride (ROP; KYOWA Pharmaceutical Industry Co., Ltd, Osaka, Japan) was diluted with 0.05% dimethyl sulfoxide (DMSO; Dojindo Laboratories, Kumamoto, Japan) and sterile saline. The drug is a novel non-ergoline dopamine agonist, has selective affinity for dopamine D2 receptors, and is indicated for the treatment of early and advanced Parkinson’s disease (Pahwa et al. 2004 (link)).
Antipsychotic Agents
Caimans
Cannabis
Delusions
Dopamine
Dopamine Agonists
Dopamine D1 Receptor
Dopamine D2 Receptor
Ergoline
Hallucinations
Haloperidol
Methanol
Pharmaceutical Preparations
Plants
Psychotic Disorders
ropinirole hydrochloride
Saline Solution
Schizophrenia
Serotonin
Sterility, Reproductive
Sulfoxide, Dimethyl
Synaptic Transmission
Electrophysiological recording was performed as previously described (Zhang et al., 2019 (link)). Briefly, the stimulating electrode was positioned at the perforant path branch at 4.5 mm posterior to the bregma, 3.0 mm lateral to the midline, and 1.5–2.0 mm from the surface of the cortex. The recording electrode was inserted into the molecular layer of the hippocampal dentate gyrus (DG) area at 2.1 mm posterior to the bregma, 1.5 mm lateral to the midline, and at a depth of 1.75–2.25 mm from the surface of the cortex. A reference electrode was attached to the head skin.
We generated the input–output curve (I–O curve) by gradually increasing the intensity of the stimulation in the DG area before recording LTP, and the field excitatory postsynaptic potential (fEPSP) was evoked and recorded via electrical stimulation with a wave width of 0.6 ms and frequency of 0.067 Hz. The rising slope of the fEPSP in each mouse was obtained to evaluate the basic function of synaptic transmission. The electrical stimulation intensity that induced 30% of the maximum amplitude of the fEPSP was set as the stimulus intensity for the baseline fEPSP recording. After 15 min of stable baseline recording, high-frequency stimuli (HFS) (100 Hz; 100 trains; 1 s) were delivered to induce LTP. Then, fEPSPs were continuously monitored for 90 min. The fEPSPs were recorded using Clampex 10.2 software and analyzed using Clampfit 10.2 software (Molecular Devices Corporation, California, USA). The obtained data are presented as the percentage of the population spike (PS) amplitude of fEPSPs to the baseline and expressed as the mean ± standard error (SE).
We generated the input–output curve (I–O curve) by gradually increasing the intensity of the stimulation in the DG area before recording LTP, and the field excitatory postsynaptic potential (fEPSP) was evoked and recorded via electrical stimulation with a wave width of 0.6 ms and frequency of 0.067 Hz. The rising slope of the fEPSP in each mouse was obtained to evaluate the basic function of synaptic transmission. The electrical stimulation intensity that induced 30% of the maximum amplitude of the fEPSP was set as the stimulus intensity for the baseline fEPSP recording. After 15 min of stable baseline recording, high-frequency stimuli (HFS) (100 Hz; 100 trains; 1 s) were delivered to induce LTP. Then, fEPSPs were continuously monitored for 90 min. The fEPSPs were recorded using Clampex 10.2 software and analyzed using Clampfit 10.2 software (Molecular Devices Corporation, California, USA). The obtained data are presented as the percentage of the population spike (PS) amplitude of fEPSPs to the baseline and expressed as the mean ± standard error (SE).
Cortex, Cerebral
Excitatory Postsynaptic Potentials
Gyrus, Dentate
Head
Medical Devices
Mus
Perforant Pathway
Skin
Stimulations, Electric
Synaptic Transmission
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PClamp 10 software is a data acquisition and analysis platform for electrophysiology research. It provides tools for recording, analyzing, and visualizing electrical signals from cells and tissues.
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The Multiclamp 700B amplifier is a versatile instrument designed for electrophysiology research. It provides high-quality amplification and signal conditioning for a wide range of intracellular and extracellular recording applications. The Multiclamp 700B offers advanced features and precise control over signal acquisition, enabling researchers to obtain reliable and accurate data from their experiments.
Sourced in United States, United Kingdom, Germany, France, Sao Tome and Principe, Canada, Israel, Australia, Italy, Japan
Picrotoxin is a chemical compound that acts as a GABA antagonist. It is primarily used in scientific research as a tool to study the function of GABA receptors.
Sourced in United States, United Kingdom
The Multiclamp 700B is a versatile electrophysiology amplifier designed to support a wide range of applications. It provides high-quality signal conditioning and amplification for both intracellular and extracellular recording techniques. The Multiclamp 700B offers advanced features such as programmable gain and filtering, allowing researchers to optimize signal acquisition for their specific experimental needs.
Sourced in United States, Germany, Jamaica, United Kingdom, Australia
Igor Pro is a graphical data analysis and visualization software developed by WaveMetrics. It is a powerful tool for scientific data processing, analysis, and presentation. Igor Pro provides a wide range of features for data acquisition, processing, and visualization, catering to the needs of researchers, scientists, and engineers across various fields.
Sourced in United States, France, Canada
PClamp 9 software is a comprehensive data acquisition and analysis software designed for electrophysiology research. It provides a suite of tools for recording, visualizing, and analyzing electrical signals from cells and tissues.
Sourced in United States, United Kingdom, Brazil, Mexico
4-aminopyridine (4-AP) is a chemical compound used in research and laboratory settings. It serves as a potassium channel blocker, a function that is utilized in various scientific applications. The core purpose of 4-AP is to provide a tool for researchers to investigate and study physiological and biochemical processes.
Sourced in United States, Germany, France, Italy, United Kingdom
Kynurenic acid is a chemical compound that is commonly used in laboratory research. It is a metabolite of the amino acid tryptophan and is known to have various biological functions. The core function of kynurenic acid is to serve as a biochemical tool for scientific investigation and analysis, particularly in the fields of neuroscience and immunology.
Sourced in United Kingdom
Gabazine is a laboratory reagent used in biochemical research. It functions as a GABAA receptor antagonist, blocking the inhibitory effects of gamma-aminobutyric acid (GABA) on neuronal activity. Gabazine is commonly utilized in experimental settings to investigate the role of GABA-mediated signaling in various biological processes.
Sourced in United Kingdom, United States
SR95531 is a GABA(A) receptor antagonist. It binds to and blocks the GABA(A) receptor, a type of receptor for the neurotransmitter GABA in the central nervous system.
More about "Synaptic Transmission"
Synaptic transmission is a crucial process in neural communication, where signaling molecules released by the presynaptic neuron activate receptors on the postsynaptic cell, initiating a cascade of electrochemical signals.
This complex process is vital for the proper functioning of the nervous system.
Optimizing synaptic transmission research can be greatly enhanced by leveraging AI-driven platforms like PubCompare.ai.
This cutting-edge tool allows researchers to easily locate the best protocols from literature, preprints, and patents, using intelligent search and comparison capabilities.
By harnessing the power of AI, PubCompare.ai helps identify the optimal products and protocols to drive synaptic transmission research forward.
Key aspects of synaptic transmission include neurotransmitter release, receptor activation, and signal propagation.
Commonly used tools and techniques in this field include the PClamp 10 software, Multiclamp 700B amplifier, and various pharmacological agents like Picrotoxin, 4-aminopyridine (4-AP), Kynurenic acid, Gabazine, and SR95531.
Researchers can utilize these resources, along with the PubCompare.ai platform, to streamline their synaptic transmission experiments, optimize experimental protocols, and ultimately accelerate their discoveries in this critical area of neuroscience.
Experience the power of intelligent protocol optimization today and unlock new insights into the complex world of synaptic transmission.
This complex process is vital for the proper functioning of the nervous system.
Optimizing synaptic transmission research can be greatly enhanced by leveraging AI-driven platforms like PubCompare.ai.
This cutting-edge tool allows researchers to easily locate the best protocols from literature, preprints, and patents, using intelligent search and comparison capabilities.
By harnessing the power of AI, PubCompare.ai helps identify the optimal products and protocols to drive synaptic transmission research forward.
Key aspects of synaptic transmission include neurotransmitter release, receptor activation, and signal propagation.
Commonly used tools and techniques in this field include the PClamp 10 software, Multiclamp 700B amplifier, and various pharmacological agents like Picrotoxin, 4-aminopyridine (4-AP), Kynurenic acid, Gabazine, and SR95531.
Researchers can utilize these resources, along with the PubCompare.ai platform, to streamline their synaptic transmission experiments, optimize experimental protocols, and ultimately accelerate their discoveries in this critical area of neuroscience.
Experience the power of intelligent protocol optimization today and unlock new insights into the complex world of synaptic transmission.