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Neurotransmitters

Neurotransmitters are chemical messengers that facilitate communication between neurons in the nervous system.
These signaling molecules play a crucial role in regulating physiological processes, influencing mood, cognition, and behavior.
Explore the diverse classes of neurotransmitters, including monoamines, amino acids, and neuropeptides, and their involvment in conditions such as depression, Parkinson's disease, and addiction.
Understand the mechanisms of neurotransmitter synthesis, release, and reuptake, as well as the receptors and signaling pathways they activate.
Leverage PubCompare.ai's AI-driven platform to enhance the reproducibility and accuracy of your neuroscience research, locating relevant protocols and identifying the best products and methods for your studies.
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Most cited protocols related to «Neurotransmitters»

Advancing White Matter Imaging Techniques

Though the vast majority of recent MRI studies of white matter have focused on diffusion, MT or relaxometry, there are other techniques that may provide complementary information. One of the oldest methods is MR spectroscopy, which may be used to characterize specific metabolites in the tissue including NAA (N-acetylaspartate), creatine, choline and neurotransmitters like GABA and glutamine/glutamate. Each of these metabolites reflects different physiological processes and have unique spectral signatures. Of significant interest in white matter is NAA, which is a marker of the presence, density and health of neurons including the axonal processes. In fact, NAA may be one of the most specific markers of healthy axons and, as such, it is surprising that it is not used more widely for the investigation of white matter in the brain. This may be due in part to the fact that MR spectroscopy is extremely sensitive to the homogeneity of the magnetic field, which makes it challenging to apply in areas near air or bone interfaces. The concentrations of the metabolites are also in the micromolar range (compare with multiple molar for water), thus, large voxels must be used and the acquisition speed is slow. Therefore, MR spectroscopy studies are often limited by poor coverage, poor resolution, and long scan times.
The recent push towards ever higher magnetic fields makes quantitative MRI methods more challenging. Imaging distortions in DTI studies increase proportional to the field strength. The RF power deposition (SAR – specific absorption rate) increases quadratically with the magnetic field strength, which limits the application of MT pulses and can also limit the flip angles used in steady state imaging. However, susceptibility weighted imaging is one method that greatly benefits from higher magnetic field strengths. Recent studies have observed interesting contrast in white matter tracts as a function of orientation and degree of myelination (Liu et al., 2011 ). Stunning images of white matter tracts have recently been obtained in ex vivo brain specimens (Sati et al., 2011 ). Techniques for characterizing white matter in the human brain are only beginning to be developed.
Other white matter cellular components are the glia, which include oligodendrocytes, astrocytes, and microglia. In general, there are no specific markers of changes in either oligodendrocytes or astrocytes. Recent evidence suggests that hypointense white matter lesions on T1w imaging may indicate reactive astrocytes (Sibson et al., 2008 (link)). Increases in microglia often accompany inflammation, which can be detected using contrast agents, either gadolinium or superparamagnetic iron oxide (SPIO) particles. Recent studies have suggested that SPIO particles are preferentially taken up by macrophages in inflammatory regions. The impact of these contrast agents on other quantitative MRI measures have not (Oweida et al., 2004 (link)) been widely studied, thus multimodal imaging studies must be designed carefully.

Alexander A.L., Hurley S.A., Samsonov A.A., Adluru N., Hosseinbor A.P., Mossahebi P., Tromp D.P., Zakszewski E, & Field A.S. (2011). Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains. Brain Connectivity, 1(6), 423-446.

Publication 2011

Astrocytes Axon Bones Brain Cellular Structures Choline Contrast Media Creatine Diffusion ferric oxide Gadolinium gamma Aminobutyric Acid Glutamate Glutamine Homo sapiens Inflammation Macrophage Magnetic Fields Magnetic Resonance Spectroscopy Microglia Molar Myelin Sheath N-acetylaspartate Neuroglia Neurons Neurotransmitters Oligodendroglia Physiological Processes Pulses Radionuclide Imaging Susceptibility, Disease Tissues White Matter

Modeling Direction Selectivity in Starburst Amacrine Cells

We constructed models of an individual SAC and a network of 7 SACs using the simulation language Neuron-C^{45 (link)}. We digitized a SAC morphology from a confocal stack of a labeled SAC, but included a multiplicative “diameter factor” set for each dendritic region based on the dendritic diameters measured from the EM reconstructions (Figure S7A). The SAC network was assembled with an algorithm that synaptically interconnected the SAC dendrites based on their location and orientation. Each SAC typically made a total of 120–250 inhibitory synapses onto its neighbors. The central SAC received about twice the number of inhibitory synapses as the surrounding SACs because of the “edge effect.” Therefore, to achieve a balance between inhibition in the central SAC and its 6 surrounding SACs, we reduced the conductance of the surround → central inhibitory synapses by 50%. BCs were created in a semi-random pattern and were connected to SACs with ribbon synapses if they were within a criterion distance. Synapses were modeled as Ca²⁺-driven neurotransmitter release that bound to a postsynaptic channel defined by a ligand-activated Markov sequential-state machine^{45 (link),46 (link)}. The excitatory conductances were typically set to 230 pS and inhibitory conductances were typically 80–160 pS. Membrane ion channels were defined by a voltage-gated Markov state machine and were placed at densities specified for each region of the cell. See Extended Data Table 1 for biophysical parameters.
The contrast of the stimulus presented to the SAC models was achieved by varying the strength of excitatory input from BCs. This was accomplished by voltage-clamping a presynaptic compartment that represented each BC according to the spatio-temporal pattern of the stimulus. The presynaptic holding potential in the BCs was just above the threshold for synaptic release, typically ~ −45 mV.
The synaptic connectivity of the SAC output synapses was set automatically by an algorithm based on the orientation of presynaptic and possible candidates for the postsynaptic dendrite. When the orientations of both dendrites were within a specific angular range, a synaptic connection was made. This synaptic placement depended on several other criteria, e.g. whether the presynaptic point fit within the allowable spacing and radial distribution on the presynaptic dendrite, and also whether the closest point on the postsynaptic dendrite was within a specified distance. The orientations were computed as the absolute angle from the prospective presynaptic point on the distal dendrite to the soma.
Direction selectivity indices were calculated based on the calcium concentration at a location along a central SAC dendrite using the following equation: DSI = (PD – ND)/PD, where PD is the response in the CF direction and ND is the response in the CP direction.
Models were run on an array of 3.2 GHz AMD Opteron CPUs interconnected by Gigabit ethernet, with a total of 220 CPU cores. Simulations of the 7-SAC model took 4 – 48 hours, depending on the model complexity and duration of simulated time. The simulations were run on the Mosix parallel distributed task system under the Linux operating system.

Ding H., Smith R.G., Poleg-Polsky A., Diamond J.S, & Briggman K.L. (2016). Species-specific wiring for direction selectivity in the mammalian retina. Nature, 535(7610), 105-110.

Publication 2016

actinomycin D2 Calcium Carisoprodol Cells Dendrites Genetic Selection Ion Channel Ligands Mental Orientation Neurons Neurotransmitters Psychological Inhibition Reconstructive Surgical Procedures Synapses Tissue, Membrane

Voltammetric Recordings for Neurotransmitter Monitoring

Procedures for FSCV recordings are identical to those previously described.^{8 (link),17 (link)} Briefly, electrodes were made by aspirating a carbon fiber into a glass pipette, which was pulled in a vertical micropipette puller. These electrodes, which had a length of carbon fiber protruding from the glass seal, were examined under light microscopy and the fiber was cut to 75–100 µm using a scalpel. Electrodes were then loaded into custom made manipulators (UIC Research Resources Center). Using these manipulators, which are designed to interface with the guide cannula implanted in the brain of experimental rat subjects, electrodes were lowered into the channel of the µFC. Electrodes were held at −0.4 V against Ag/AgCl between voltammetric scans and then driven to +1.3 V and back at 400 V/s. This triangle waveform causes oxidation and reduction of chemical species at the electrode resulting in a large background current. Background current is digitally subtracted so that changes in current produced by the oxidation/reduction of transient signals (e.g. neurotransmitter) can be identified. Dopamine is electroactive within this potential range and is identified by plotting current against the applied potential used to produce a background-subtracted voltammogram color plot.

Sinkala E., McCutcheon J.E., Schuck M., Schmidt E., Roitman M.F, & Eddington D.T. (2012). Electrode Calibration with a Microfluidic Flow Cell for Fast-scan Cyclic Voltammetry. Lab on a chip, 12(13), 2403-2408.

Publication 2012

ARID1A protein, human Brain Cannula Carbon Carbon Fiber Dopamine Fibrosis Light Microscopy Neurotransmitters Oxidation-Reduction Phocidae Radionuclide Imaging Transients

Large-Scale Cerebellar Granular Layer Model

The network had a size sufficient to reproduce a functionally relevant portion of the cerebellar granular layer, i.e. a cube with 100 μm edge length (Table 1 and Figure 1). The model included 315 mfs, 4393 neurons (4096 GrCs, 27 GoCs and 270 SCs/BCs) and more than 40000 synapses. The number of cells and synapses was large enough to maintain realistic convergence/divergence ratios (Eccles et al., 1967 ). On this scale, mf branching was not implemented (see Sultan and Heck, 2003 (link)). Moreover, to achieve inhibitory control over GoCs, a partial representation of the SC/BC was also included.
Network connections were constructed using precise rules, yet allowing the number of connections and synaptic weights to show statistical variability (Gaussian distribution: mean = 1, s.d. = 0.4; see Medina and Mauk, 2000 (link)). No systematic differences were observed using different seeds for parameter randomization, so that in several cases the same network configuration was used to facilitate data comparison. Background noise in the network was generated by random spike patterns in mfs and pacemaking in GoCs and SC/BCs (see e.g. Häusser and Clark, 1997 (link); Chadderton et al., 2004 (link); Rancz et al., 2007 (link)). Neurons and synapses were endowed with multiple receptor and ionic channel-based mechanisms, allowing an accurate representation of neuronal firing. The synapses were endowed with neurotransmitter diffusion mechanisms and with a representation of vesicle cycling, generating spillover and developing short-term facilitation and depression. However, no molecular noise (e.g. from ionic diffusion, channel gating or receptor binding) or synaptic noise (e.g. from stochastic vesicle fusion) were introduced.
The model was written with NEURON-7.1. The simulation of 3 s of activity required about 20 h on a Pentium-5 dual-core but just 30 min using 80 CPUS on the CASPUR parallel cluster (http://www.caspur.it/en/). A graphical interface was written to represent the data as in MEA and VSD experiments.

Solinas S., Nieus T, & D'Angelo E. (2010). A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties. Frontiers in Cellular Neuroscience, 4, 12.

Publication 2009

Cerebellum Diffusion Ion Channel Ions Neurons Neurotransmitters Plant Embryos Psychological Inhibition Sultan Synapses

Immunohistochemistry for Renal Denervation

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Publication 2014

Anabolism Arteries Calcitonin Gene-Related Peptide Catecholamines Cells Denervation Dissection Dopa Dopa Decarboxylase Dopamine Elastica Elastic Fibers Enzymes Ganglia Glial Fibrillary Acidic Protein High Blood Pressures Kidney Nerve Fibers Nervousness Neurofilament Proteins Neuroglia Neurons Neurotransmitters Norepinephrine S100 Proteins Substance P Tissues trichrome stain Tyrosine Tyrosine 3-Monooxygenase

Most recents protocols related to «Neurotransmitters»

RNA-seq Analysis of Bipolar Disorder and Schizophrenia

We used the Illumina RiboZero TruSeq Stranded Total RNA Library Prep Kit (Illumina) to construct the RNA-seq library and used the Illumina NovaSeq6000 platform for sequencing in the 100 nt, paired-end configuration, as described previously [20 (link), 21 (link)]. We obtained an average of 60 million reads for each sample. The reads were trimmed with Cutadapt and aligned to the reference genome (hg38 UCSC assembly) to analyze gene expression using TopHat v2.0.14 and Bowtie v2.10 with default parameters and RefSeq annotation (genome-build GRCh38.p9) [22 (link)]. We used Cufflinks v2.2.1 to analyze distribution of alignments and quantile normalized FPKM (fragments per kilobase of exon model per million reads mapped) values [23 (link), 24 (link)]. We utilized Cuffdiff v2.2.1 to perform differential expression testing. We did not consider sex differences for this analysis. The false discovery rate (FDR) was 0.05. The raw data analysis is included in Supplementary file 1, in sheet 3 titled significant genes. Tables 1, 2 and 3 show q values represent FDR-adjusted p-value of the test statistic. RT-PCR was used to validate a number of key relevant genes.Table 1

Gene set enrichment analysis

	Size	ES	NES	NOM p-val	FDR q-val	FWER p-val	Rank at max	Leading edge
Reactome
Reactome_Influenza_Infection	154	− 0.59132	− 2.80241	0	0	0	10,503	tags = 73%, list = 27%, signal = 99%
Reactome_Mitochondrial_Translation	93	− 0.60753	− 2.62088	0	0	0	13,391	tags = 86%, list = 34%, signal = 131%
Reactome_Respiratory_Electron_Transport	89	− 0.60034	− 2.61611	0	0	0	12,856	tags = 84%, list = 33%, signal = 125%
Reactome_Infectious_Disease	371	− 0.49008	− 2.60519	0	0	0	13,579	tags = 68%, list = 35%, signal = 103%
Reactome_Cell_Cycle_Checkpoints	282	− 0.51048	− 2.60441	0	0	0	15,024	tags = 74%, list = 39%, signal = 120%
Reactome_Naplus_Cl_Dependent_Neurotransmitter_Transporters	19	0.515474	1.541765	0.038388	0.66119	1	1466	tags = 32%, list = 4%, signal = 33%
Reactome_Role_Of_Phospholipids_In_Phagocytosis	33	0.452627	1.529319	0.032136	0.623584	1	4477	tags = 24%, list = 11%, signal = 27%
Reactome_Plasma_Lipoprotein_Assembly	19	0.504823	1.504998	0.043222	0.652722	1	2605	tags = 32%, list = 7%, signal = 34%
Reactome_Xenobiotics	24	0.466743	1.485898	0.049904	0.666401	1	8655	tags = 50%, list = 22%, signal = 64%
Reactome_Long_Term_Potentiation	23	0.474369	1.470371	0.046693	0.608513	1	8104	tags = 48%, list = 21%, signal = 60%
Hallmark
Hallmark_Myogenesis	199	− 0.55399	− 2.73616	0	0	0	5986	tags = 43%, list = 15%, signal = 51%
Hallmark_E2F_Targets	198	− 0.51894	− 2.56449	0	0	0	13,107	tags = 65%, list = 34%, signal = 98%
Hallmark_Oxidative_Phosphorylation	185	− 0.52698	− 2.53241	0	0	0	13,844	tags = 71%, list = 36%, signal = 109%
Hallmark_Unfolded_Protein_Response	111	− 0.43549	− 1.96044	0	0	0	14,339	tags = 65%, list = 37%, signal = 102%
Hallmark_Glycolysis	198	− 0.38222	− 1.88766	0	0	0	13,694	tags = 57%, list = 35%, signal = 87%
Hallmark_Pancreas_Beta_Cells	40	0.268541	0.960266	0.515539	1	1	2039	tags = 10%, list = 5%, signal = 11%
Hallmark_Spermatogenesis	132	0.17472	0.771267	0.937394	1	1	9809	tags = 27%, list = 25%, signal = 36%
Hallmark_Xenobiotic_Metabolism	200	0.164059	0.76698	0.98087	0.93739	1	5051	tags = 12%, list = 13%, signal = 13%
KEGG
KEGG_Ribosome	86	− 0.66314	− 2.80034	0	0	0	10,890	tags = 93%, list = 28%, signal = 129%
KEGG_Parkinsons_Disease	99	− 0.50716	− 2.26377	0	0	0	13,694	tags = 75%, list = 35%, signal = 115%
KEGG_Oxidative_Phosphorylation	100	− 0.51326	− 2.26148	0	0	0	13,763	tags = 71%, list = 35%, signal = 109%
KEGG_Small_Cell_Lung_Cancer	84	− 0.50382	− 2.18709	0	0	0	8917	tags = 50%, list = 23%, signal = 65%
KEGG_P53_Signaling_Pathway	67	− 0.52866	− 2.14187	0	9.17E−04	0.003	12,063	tags = 64%, list = 31%, signal = 93%
KEGG_Taste_Transduction	51	0.457469	1.688169	0.001692	0.234029	0.361	7615	tags = 49%, list = 20%, signal = 61%
KEGG_Linoleic_Acid_Metabolism	28	0.510943	1.677292	0.011858	0.129439	0.388	8626	tags = 43%, list = 22%, signal = 55%
KEGG_Type_I_Diabetes_Mellitus	42	0.433637	1.558308	0.012797	0.188571	0.766	8960	tags = 43%, list = 23%, signal = 56%
KEGG_Asthma	29	0.486376	1.629667	0.013619	0.132661	0.538	8960	tags = 52%, list = 23%, signal = 67%
KEGG_Retinol_Metabolism	64	0.358277	1.385493	0.043956	0.410179	0.994	7681	tags = 28%, list = 20%, signal = 35%

Gene set enrichment analysis (GSEA) data for DEGs using three different databases (Hallmark, KEGG, Reactome) tabulated according to normalized enrichment score (NES) and False discovery rate (FDR) q-value. Positive correlation indicates relative association with gene expression in bipolar disorder and a negative correlation indicates relative association with gene expression in schizophrenia

Table 2

Mitochondrial genes upregulated in schizophrenia compared to bipolar disorder

Gene	Log2(fold_change)	P_value	Q_value
B2M	inf	0.00005	0.0168915
SFTPB	5.47445	0.00005	0.0168915
P2RX3	4.09903	0.00015	0.041525
PKHD1L1	4.00139	0.00005	0.0168915
THSD7B	3.9018	0.00005	0.0168915
EBF2	3.73444	0.00005	0.0168915
ACTA1	3.71003	0.00005	0.0168915
SHOX2	3.54632	0.00005	0.0168915
BCL6B	3.40011	0.00005	0.0168915
MUC5B	3.27599	0.00005	0.0168915
CD5	3.27526	0.00005	0.0168915
CASQ2	3.27468	0.00005	0.0168915
MYBPC2	3.20693	0.00005	0.0168915
NPTX2	3.17192	0.0001	0.0297493
NHLH1	3.15736	0.00005	0.0168915
MYBPH	3.05216	0.00005	0.0168915
ASB4	3.01621	0.0001	0.0297493
MUC12	2.87732	0.00005	0.0168915
SHD	2.83455	0.00005	0.0168915
TRIM55	2.73832	0.00005	0.0168915
ACTC1	2.72941	0.00005	0.0168915
MYH3	2.66443	0.00005	0.0168915
EBF1	2.6039	0.00005	0.0168915
CHRND	2.60343	0.00005	0.0168915
APLN	2.54672	0.00005	0.0168915
GRID2	2.50442	0.0002	0.0480289
MYL4	2.42199	0.0001	0.0297493
KRT1	2.38926	0.0002	0.0480289
TNNT2	2.26791	0.00005	0.0168915
NEFM	2.25293	0.00005	0.0168915
AFAP1L1	2.2312	0.00005	0.0168915
EYA1	2.10989	0.0002	0.0480289
RBM24	2.05014	0.0002	0.0480289
RYR1	2.00653	0.00005	0.0168915
C7	1.92159	0.00005	0.0168915
COL19A1	1.87283	0.0001	0.0297493
DUOX2	1.86384	0.0001	0.0297493
SERPINA3	1.79983	0.00005	0.0168915
TNNC1	1.798	0.0002	0.0480289
NTRK2	1.73311	0.00005	0.0168915
CAPN6	1.60497	0.00005	0.0168915
NEUROD1	1.53609	0.00005	0.0168915
MCAM	1.52598	0.00005	0.0168915
SPOCK2	1.43396	0.00005	0.0168915
ARHGAP29	1.29965	0.00005	0.0168915
CDR1	1.0788	0.0001	0.0297493
PEG10	1.07848	0.0002	0.0480289
FREM2	1.00581	0.0002	0.0480289

List of mitochondria-associated genes in the MitoCarta 2.0 database that were upregulated in SCZ organoids vis-à-vis BPI organoids and in BPI organoids vis-à-vis SCZ organoids

Table 3

Mitochondrial genes upregulated in bipolar disorder compared to schizophrenia

Gene	Log2(fold_change)	P_value	Q_value
CCL25	4.42404	0.00005	0.0168915
GIP	4.04306	5.00E−05	0.0168915
HLA-DRB1	3.66592	0.00005	0.0168915
OPRK1	3.58192	0.00005	0.0168915
CX3CR1	3.33744	0.00005	0.0168915
ASAH2	3.32305	0.00005	0.0168915
LCT	3.24615	0.00005	0.0168915
APOC3	3.05208	0.00005	0.0168915
SLC2A2	2.78622	0.0001	0.0297493
FOLH1	2.75393	0.00005	0.0168915
GATA4	2.37677	0.0002	0.0480289
ANXA13	2.13002	0.00005	0.0168915
COL2A1	1.98566	0.00005	0.0168915
SI	1.92321	0.00005	0.0168915
PIGR	1.76004	0.00005	0.0168915
SLC5A1	1.70496	0.0001	0.0297493
APOB	1.69844	0.00005	0.0168915
SULT2A1	1.69654	0.0002	0.0480289
MTTP	1.66128	0.00005	0.0168915
MALRD1	1.6595	0.00005	0.0168915
OLFM4	1.61724	0.00005	0.0168915
GSTA1	1.3839	0.00005	0.0168915
OAT	1.24558	0.00015	0.041525
FOS	1.2401	0.00005	0.0168915
PRLR	1.21405	0.00015	0.041525
XDH	1.21051	0.00005	0.0168915
MME	1.07592	0.00005	0.0168915

Kathuria A., Lopez-Lengowski K., McPhie D., Cohen B.M, & Karmacharya R. (2023). Disease-specific differences in gene expression, mitochondrial function and mitochondria-endoplasmic reticulum interactions in iPSC-derived cerebral organoids and cortical neurons in schizophrenia and bipolar disorder. Discover Mental Health, 3(1), 8.

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Publication 2023

All-Trans-Retinol Bipolar Disorder cDNA Library Cell Cycle Cells Diabetes Mellitus, Insulin-Dependent DNA, Mitochondrial E2F2 protein, human Electrons Exons Gene Expression Genes Genes, vif Genome Infection Influenza Linoleic Acid Lipoproteins Lung Mitochondria Neurotransmitters Organoids Pancreas Phospholipids Plasma Proteins Respiratory Rate Reverse Transcriptase Polymerase Chain Reaction RNA-Seq Schizophrenia Taste Xenobiotics

Neural Decoding Techniques for Neural Bypass

There is a variety of methods to filter noise, determine stimulation, decode, and predict neural activity. Some authors have attempted neural decoding through training sessions, while others have determined arbitrary thresholds which can be used as a neural ‘switch’ to enable neural plasticity to activate desired movements. When EEG is used in the setting of neural bypasses, recording thresholds are determined which then translate to effector stimulation, often with FES. EEG thresholds to stimulate FES are typically obtained through motor imagery as measured by attention with sensorimotor rhythm and beta/theta oscillation ratios, or Common Spatial Patterns based on Event De/Synchronization [21 (link)–41 ]. Others have used steady-state visual evoked potentials (SSVEP) to trigger FES stimulation [42 (link)]. Furthermore, some studies have used alpha rhythms as a deactivating signal following a stimulation event [43 (link)]. When single-cell recordings are used, the threshold for stimulating FES has typically been cell firing rate [5 (link)]. When ECoG is used, the rate of high-gamma oscillations has been selected as a threshold for effector muscle stimulation [3 (link)]. In studies with microelectrode arrays, neuronal action potential rate, or average spectral high-frequency power, have typically been used as thresholds for stimulation of effector muscles [7 (link)]. Microelectrode arrays have also been used to record mean wavelet power after artifact removal during trials of imagined movements in paralyzed patients [4 (link), 44 (link)]. Microelectrode arrays are often used during training sessions prior to paralysis or with simulated motor tasks to create predictive models of neuronal control of muscle activity [19 (link), 45 (link), 46 (link)].
In some studies, daily calibration is required to train neural decoding algorithms which presents a limitation for the translation of these technologies to real-world environments. One possible approach to this problem is a neural network capable of decoding without daily training sessions [18 (link)]. Other decoding methods consist of gradient boosted trees, support vector machines, and linear methods [12 (link), 47 ]. A drawback to any decoding method is the group of associated assumptions. For example, assumptions made with a regularized linear regression are that outputs are proportional to input changes, additional noise is assumed to be Gaussian noise, and that the regression coefficients are from a Gaussian distribution [47 ]. Bouton et al. suggest that nonlinear methods of decoding may help to increase robustness and accuracy of specific decoders [48 ]. As methods increase in complexity, so do their associated assumptions. Glaser et al. states that a crucial assumption built into decoders is the form of the relation between the input and output. With machine learning methods, multiple decoding models can be organized in ensembles [47 ]. Bouton et al. demonstrates this in their finding that Long Short-Term Memory-based deep learning networks, used in tandem with repeatability-based feature selection based on temporal correlation, results in positive outcomes and accurate decoding [12 (link)].
Developing devices to maximize spatial resolution, temporal resolution, and biocompatibility will be crucial in developing robust neural interfaces. Additionally, a number of unidirectional recording or stimulation devices exist that have not yet been combined into neural bypasses, including novel spinal cord stimulators or neurotransmitter-sensing electrodes [49 (link), 50 (link)]. There exists a lack of comparative analysis across studies with different methodologies to determine the most efficacious methods of achieving neural bypass.

Zuccaroli I., Lucke-Wold B., Palla A., Eremiev A., Sorrentino Z., Zakare-Fagbamila R., McNulty J., Christie C., Chandra V, & Mampre D. (2023). Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections. OBM neurobiology, 7(1), 158.

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Publication 2023

Action Potentials Alpha Rhythm Attention Cells Electrocorticography Gamma Rays Imagery, Guided Medical Devices Memory, Long-Term Microelectrodes Movement Muscle Tissue Nervousness Neuronal Plasticity Neurons Neurotransmitters Patients Precipitating Factors Spinal Cord Trees Visual Evoked Potential

Quantifying GABA, Glutamine, and APT in the Hippocampus

Mescher–Garwood point resolved spectroscopy (MEGA-PRESS) data were analyzed using GANNET 3.0 (http://www.gabamrs.com/) in Matlab 2020b (Mathworks) (Edden et al., 2014 (link)). Moreover, macromolecules (MM) and homocarnosine (Rothman et al., 1997 (link)) contribute to the GABA signal at 3 ppm; therefore, it is referred to as GABA+ rather than GABA. Since the glutamine (Gln) signal was not separated from the glutamine (Glu) signal, we reported the Glx (the combined signals of Glu and Gln) level in our study. The ratios of the integrals of neurotransmitters (GABA+ or Glx) and water signals, corrected with T₁/T₂ relaxation and tissue composition, were used to calculate water-scaled GABA+ or Glx levels in institutional units (IUs) (Mullins et al., 2014 (link); Harris et al., 2015 (link)).
Based on 3D T1-weighted brain images, the fractional gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) content within each spectroscopic voxel were calculated using an automatic brain segmentation program, FAST (FMRIB's automated segmentation tool) in the FSL package (Oxford University, Oxford, UK) (Zhang et al., 2001 (link)).
Amide proton transfer-weighted (APTw) images were generated directly from the scanner, and they were co-registered and overlaid with geometrically identically acquired 3D T1WI images on a dedicated workstation called “IntelliSpace Portal” (Philips Healthcare, Best, the Netherlands) (Figure 2). The regions of interest (ROIs) were manually drawn on the fused image by two radiologists with 10 years of experience in neurological imaging blinded to clinical and cognitive information of all participants, to delineate the segments of the right hippocampus in the maximum cross-sectional layer; to avoid areas of infarction, necrosis, and hemorrhage; and to calculate the average value. The MTR asymmetry (MTRasym) map at the offset of 3.5 ppm is called the APTw image: APTw = MTRasym (3.5 ppm) = MTR (3.5 ppm) – MTR (−3.5 ppm) = [Ssat (−3.5 ppm) – Ssat (3.5 ppm)]/S0.

Chen X., Gong T., Chen T., Xu C., Li Y., Song Q., Lin L., Oeltzschner G., Edden R.A., Xia Z, & Wang G. (2023). Altered glutamate–glutamine and amide proton transfer-weighted values in the hippocampus of patients with amnestic mild cognitive impairment: A novel combined imaging diagnostic marker. Frontiers in Neuroscience, 17, 1089300.

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Publication 2023

Amides Brain Cerebrospinal Fluid Cognition gamma Aminobutyric Acid Glutamine Gray Matter Hemorrhage homocarnosine Infarction Necrosis Neurotransmitters Protons putrescine N-acetyltransferase Radiologist Seahorses Spectrum Analysis Tissues White Matter

Quantification of Enteric Neurotransmitters 5-HT and VIP

Two main enteric neurotransmitters: 5-hydroxytryptamine (5-HT) and VIP were determined by the enzyme-linked immunosorbent assay (ELISA). The serum concentration of 5-HT and the VIP content in the colon tissue was detected by the ELISA kits. ELISA kits of 5-HT (Cat No.: JYM0433Mo) and VIP (Cat No.: JYM0436Mo) were purchased from Colorful-Gene Biotechnology Co., Ltd. (www.jymbio.com, Wuhan, China). All assays were performed rigorously according to the manufacturer’s instructions. The Synergy H1 Hybrid Reader (Biotech, United States) was applied to measure the relative optical density of 5-HT and VIP spectrophotometrically at a wavelength of 450 nm.

Jiang J.G., Luo Q., Li S.S., Tan T.Y., Xiong K., Yang T, & Xiao T.B. (2023). Cinnamic acid regulates the intestinal microbiome and short-chain fatty acids to treat slow transit constipation. World Journal of Gastrointestinal Pharmacology and Therapeutics, 14(2), 4-21.

Publication 2023

Biological Assay Colon Enzyme-Linked Immunosorbent Assay Genes Hybrids Neurotransmitters Serum Tissues Vision

Transradial Amputee Myoelectric Prosthesis Study

Two transradial amputees (amputee 1: a 55-year-old male with electric shock amputation in 1989, amputee 2: a 60-year-old male with explosion amputation in 1980) were recruited for this research. Five able-bodied subjects (1 male, 4 females, 20∼25 years old) were recruited. All subjects met the following requirements: (a) not taking drugs that affect hormones or neurotransmitters in the last 30 days, (b) no electromagnetic hypersensitivity, (c) no psychiatric or cognitive disorder, and (d) experience using a myoelectric prosthesis. The experimental procedure was approved by the Chongqing University Three Gorges Hospital Ethics Committee (2021-KY-24). All subjects signed informed consent forms before the experiments, which includes the stimulation and prompts they would receive and what operations they needed to perform in the experiment.

Han Y., Lu Y., Zuo Y., Song H., Chou C.H., Wang X., Li X., Li L., Niu C.M, & Hou W. (2023). Substitutive proprioception feedback of a prosthetic wrist by electrotactile stimulation. Frontiers in Neuroscience, 17, 1135687.

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Publication 2023

Amputation Amputees Blast Injuries Cognition Disorders Electricity Electromagnetics Ethics Committees, Clinical Females Hormones Hypersensitivity Limb Prosthesis Males Neurotransmitters Pharmaceutical Preparations Shock

Top products related to «Neurotransmitters»

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Dopamine is a laboratory reagent used in various biochemical and analytical applications. It is a naturally occurring neurotransmitter that plays a crucial role in the human body. Dopamine is often used as a standard in the measurement and analysis of compounds with similar chemical structures and properties.

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Norepinephrine by Merck Group

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Norepinephrine is a laboratory product produced by Merck Group. It is a neurotransmitter and hormone that plays a role in the sympathetic nervous system. The core function of Norepinephrine is to regulate physiological processes such as heart rate, blood pressure, and pupil dilation.

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What are the different types of neurotransmitters?

Neurotransmitters can be broadly classified into several major categories, including monoamines (e.g., dopamine, serotonin, norepinephrine), amino acids (e.g., glutamate, GABA), and neuropeptides (e.g., endorphins, substance P). Each class of neurotransmitter has unique functions and physiological effects, influencing processes like mood, cognition, motor control, and pain perception. Understanding the diverse roles of these signaling molecules is crucial for studying conditions associated with neurotransmitter imbalances, such as depression, Parkinson's disease, and addiction.

How do neurotransmitters affect the brain and body?

Neurotransmitters play a pivotal role in regulating a wide range of physiological and psychological processes. For example, dopamine is involved in reward, motivation, and movement; serotonin is linked to mood, sleep, and appetite; and glutamate is the primary excitatory neurotransmitter, essential for learning and memory. Imbalances or dysregulation of neurotransmitter systems have been implicated in numerous neurological and psychiatric disorders, underscoring the importance of understanding their mechanisms of action and developing targeted therapies.

What are some common challenges in researching neurotransmitters?

Studying neurotransmitters can be comicplicated due to the complexity of the nervous system, the multifaceted roles of these signaling molecules, and the need for highly specialized techniques and equipment. Researchers often face challenges in accurately measuring neurotransmitter levels, identifying the most appropriate assays and detection methods, and ensuring the reproducibility of their findings. PubCompare.ai can assist by helping researchers efficiently screen relevant protocols from the literature, leverage AI to identify critical insights, and compare the effectiveness of different experimental approaches to optimize their neurotransmitter research.

How can PubCompare.ai help with neurotransmitter research?

PubCompare.ai is a powerful tool that can enhance the efficiency and accuracy of neurotransmitter research. The platform allows researchers to quickly screen protocol literature, leveraging AI to pinpoint critical insights that can inform their experimental design. By comparing the effectiveness of different protocols related to neurotransmitter measurement, synthesis, or regulation, PubCompare.ai helps researchers identify the most suitable methods for their specific research goals, improving reproducibility and data quality. This data-driven approach enables seamless collaboration and accelerates the advancement of our understanding of this fasinating field.

What are some of the key applications of neurotransmitter research?

Neurotransmitter research has a wide range of applications in both basic science and clinical settings. Understanding the mechanisms of neurotransmitter synthesis, release, and signaling is crucial for developing targeted therapies for neurological and psychiatric disorders, such as depression, anxiety, Parkinson's disease, and addiction. Additionally, neurotransmitter research has implications for studying cognitive function, learning, memory, and the overall regulation of physiological processes. By leveragien the tools and insights provided by PubCompare.ai, researchers can more efficiently conduct high-quality studies that advance our knowledge of these critical signaling molecules and their impact on human health and behavior.

More about "Neurotransmitters"

Neurotransmitters are the chemical messengers that facilitate communication between neurons in the nervous system.
These signaling molecules play a crucial role in regulating physiological processes, influencing mood, cognition, and behavior.
The diverse classes of neurotransmitters include monoamines like dopamine, serotonin, and norepinephrine, amino acids like glutamate and GABA, and neuropeptides.
These neurotransmitters are involved in various neurological and psychiatric conditions, such as depression, Parkinson's disease, and addiction.
The synthesis, release, and reuptake of neurotransmitters, as well as the receptors and signaling pathways they activate, are complex processes that are crucial to understanding their functions.
Leveraging AI-driven platforms like PubCompare.ai can enhance the reproducibility and accuracy of neuroscience research by helping researchers locate relevant protocols from literature, preprints, and patents, and identify the best products and methods for their studies.
Seamless collaboration and data-driven insights can advance the understanding of this fascinating field.
Researchers can utilize tools like Prism 8, SAS 9.4, and the BCA protein assay kit to analyze and quantify neurotransmitter levels, while the FlexStation 3 can be used for functional assays.
Formic acid is a common additive in sample preparation for mass spectrometry analysis of neurotransmitters.
By incorporating these resources and techniques, scientists can gain deeper insights into the complex roles of neurotransmitters in the brain and body, and develop more effective treatments for neurological and psychiatric disorders.