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5,448 results for “Neuron”
Phosphoglycerate kinase is a central leverage point in Parkinson's Disease driven neuronal metabolic deficits
<p><span>Although certain drivers of familial Parkinson’s Disease (PD) compromise mitochondrial integrity, whether metabolic deficits underly other idiopathic or genetic origins of PD is unclear. Here, we demonstrate that PGK1, a gene in the PARK12 susceptibility locus, <span> </span>is rate limiting in neuronal glycolysis and that modestly increasing PGK1 expression significantly boosts <span> </span>neuronal ATP production kinetics that is sufficient to suppress PARK20-driven synaptic dysfunction. We found that his activity enhancement depends on the molecular chaperone PARK7/DJ-1, whose loss of function significantly disrupts axonal bioenergetics. <em>In-vivo, </em>viral expression of PGK1 confers protection of striatal DA axons against metabolic lesions. These data support the notion that bioenergetic deficits may underpin PD associated pathologies and point to improving neuronal ATP production kinetics as a promising path forward in PD therapeutics.</span></p>
Fig. 4 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 4. The occurrence of 3 different antennal trichoid sensilla in relation to their widths (A, B and C) in male and female diamondback moth, Plutella xylostella. D shows the widths of the sensilla plotted against their lengths for the 3 trichoid sensilla types (Tr I, Tr II and Tr III). Data obtained from 3 females and 5 males.
Fig. 3 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 3. The number of each of 4 types of sensilla along various antennal segments (1–10 = proximal end, 11–20 = middle, 21–30 = distal end) of female (A, C) and male (B, D) diamondback moths, Plutella xylostella. The estimated number of sensilla (C, D) was calculated based on the surface area of each segment section, and by assuming that sensilla were present on 2/3 of the surface (mean ± S.E., n = 5–15 in females and 9–18 in males).
Fig. 7 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 7. Sensilla styloconica identified on the antennae of female (A, C and D) and male (B, E and F) Plutela xylostella. One sensillum styloconicum is located near the anterio-ventral end in each flagella subsegment (A, B). Usually 1 terminal sensory cone is present at the distal end in each sensillum styloconicum (C, D, E, F).
Fig. 2 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 2. Gross antennal morphology of female (A, B) and male (C) Plutella xylostella, and a part of a male antenna showing the presence of different morphological types of sensilla (D). Ac (sensilla auricillica); Cc (sensilla coeloconica); Ch (sensilla chaetica); Tr I (sensilla trichodea Type I); Tr II (sensilla trichodea Type II); Tr III (sensilla trichodea Type III).
Fig. 6 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 6. Detailed surface morphology of sensilla coeloconica (A, B and C) and sensilla auricillica (D, E and F) of the antennae of Plutela xylostella.
Fig. 8 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 8. Traces of the response spikes of the olfactory receptor neurons (ORNs) in class A trichoid sensilla of female Plutella xylostella in response to various green leaf volatiles. Each trace shows the extracellular signals for a period of 5 s. The scale bar indicates the stimulation for 0.1 s with corresponding test stimulus.
Fig. 9 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 9. Traces of the response spikes of the olfactory receptor neurons (ORNs) in class D trichoid sensilla of female Plutella xylostella in response to geraniol and (±)-linalool. Each trace shows the extracellular signals for a period of 5 s. The scale bar indicates the stimulation for 0.1 s with corresponding test stimulus.
Fig. 1 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 1. The length (triangles) and diam (squares) of each flagellar subsegment of the antennae of female (A) and male (B) diamondback moth, Plutella xylostella. Data obtained from 3 females and 5 males.
Fig. 5 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 5. Detailed surface morphology of the sensilla trichodea (A, B, C and D) and sensilla chaetica (A, B and E) of the antennae of Plutela xylostella.
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 2. Schematic of the same neuron N1, shared between 2 different patterns P1 and P2.
<p>It can be assumed that only one pattern fires during each time unit, where Rule 1 of the Introduction states that if a neuron fires a stronger signal for a pattern, it sends feedback to ask it to fire again, the next time unit. If it fires a weaker signal, the request is not to fire next time. Stronger and weaker relates to a more variable type of on or off. As the mechanism is not final yet, there could be some flexibility with it. Rule 1 also states that if the neuron does not fire for the current pattern, it receives the switch command from the last pattern it did fire for. Note that the actual firing event can produce either zero or some input and so an earlier event can also force a weak feedback signal. Rule 2 states that this particular feedback is only the on/off switch command and does not affect the size of the output value. A schematic of the idea is illustrated in Figure 2, which relates to the same neuron shared between 2 pattern events, at times t1 and t2. The schematic is described further in section 0. </p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
New Ideas for Brain Modelling 4-Figure 3. Neuron Pairing: an ensemble neuron links with a hierarchal neuron. Also figure 4 in Greer (2016)
<p>The model is also based on the idea of an auto-associative neural network. The Hopfield neural network (Hopfield, 1982), and its stochastic equivalents are auto-associative or memory networks. With the memory networks, information is sent between the input and the output until a stable state is reached, when the information does not then change. These are resonance networks, such as bidirectional associative memory (BAM), or others (Rojas, 1996), but they can only provide a memory recall – they map the input pattern directly to the output pattern. If some of the input pattern is missing however, they can still provide an accurate recall of the whole pattern. They also prefer the data vectors to be orthogonal without overlap. This is however ideal for the binding that only wants to reproduce the base ensemble in the hierarchy.</p>
Changes in neuronal representations of phonemes in the ascending auditory system and their role speech recognition
<p>This dataset comprises neural responses to a set of speech sounds from several brain regions. Auditory nerve data was simulated using a computational model of the auditory nerve. Also included are multi-unit extracellular recordings or responses to the same stimuli in the inferior colliculus and auditory cortex of anaethetised guinea pigs.</p>
Molecular and functional variation in iPSC-derived sensory neurons
<p>These files contain summary statistics for gene expression QTLs and splicing QTLs from an RNA-sequencing study of iPSC-derived sensory neurons from 97 individuals.</p>
Dataset related to article "Glia-to-neuron transfer of miRNAs via extracellular vesicles: a new mechanism underlying inflammation-induced synaptic alterations"
<p>This record contains raw data related to article "Glia-to-neuron transfer of miRNAs via extracellular vesicles: a new mechanism underlying inflammation-induced synaptic alterations"</p> <p>Recent evidence indicates synaptic dysfunction as an early mechanism affected in neuroinflammatory diseases, such as multiple sclerosis, which are characterized by chronic microglia activation. However, the mode(s) of action of reactive microglia in causing synaptic defects are not fully understood. In this study, we show that inflammatory microglia produce extracellular vesicles (EVs) which are enriched in a set of miRNAs that regulate the expression of key synaptic proteins. Among them, miR-146a-5p, a microglia-specific miRNA not present in hippocampal neurons, controls the expression of presynaptic synaptotagmin1 (Syt1) and postsynaptic neuroligin1 (Nlg1), an adhesion protein which play a crucial role in dendritic spine formation and synaptic stability. Using a Renilla-based sensor, we provide formal proof that inflammatory EVs transfer their miR-146a-5p cargo to neuron. By western blot and immunofluorescence analysis we show that vesicular miR-146a-5p suppresses Syt1 and Nlg1 expression in receiving neurons. Microglia-to-neuron miR-146a-5p transfer and Syt1 and Nlg1 downregulation do not occur when EV-neuron contact is inhibited by cloaking vesicular phosphatidylserine residues and when neurons are exposed to EVs either depleted of miR-146a-5p, produced by pro-regenerative microglia, or storing inactive miR-146a-5p, produced by cells transfected with an anti-miR-146a-5p. Morphological analysis reveals that prolonged exposure to inflammatory EVs leads to significant decrease in dendritic spine density in hippocampal neurons in vivo and in primary culture, which is rescued in vitro by transfection of a miR-insensitive Nlg1 form. Dendritic spine loss is accompanied by a decrease in the density and strength of excitatory synapses, as indicated by reduced mEPSC frequency and amplitude. These findings link inflammatory microglia and enhanced EV production to loss of excitatory synapses, uncovering a previously unrecognized role for microglia-enriched miRNAs, released in association to EVs, in silencing of key synaptic genes.</p>
Spatiotemporal dysregulation of neuron-glia related genes and pro-/anti-inflammatory miRNAs in the 5xFAD mouse model of Alzheimer's disease - Supplementary data
<p><strong>Supplementary Table 1. </strong> Gene expression profile by RT-qPCR analysis revealed no significant differences when simultaneously considering the genotype (WT/5xFAD), age (6/9 months) and brain region (HPC, hippocampus/PFC, prefrontal cortex). </p> <p><strong>Supplementary Table 2.</strong> miRNA-target table for the Analyzed microRNAs and targets selected for this study. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 3. </strong> Node table for the analyzed microRNAs and targets selected for this study. We only considered miRNAs and/or targets with a node degree of at least 2. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 4.</strong> Bivariate Pearson’s correlation coefficients and respective p-values obtained between all miRNAs and genes.</p> <p><strong>Supplementary Table 5.</strong> List of microRNAs analyzed by RT-qPCR and their primer sequences.</p> <p><strong>Supplementary Table 6.</strong> List of genes and respective primer sequences used for mRNA analysis by RT-qPCR.</p> <p><strong>Supplementary Table 7.</strong> Raw data used for correlational analysis in hippocampus (HPC) and prefrontal cortex (PFC) using the cor function in RStudio software.</p>
Processed snRNAseq data from female Aedes aegypti antennal neurons
<p>Single-nucleus RNA sequencing data accompanying Adavi et al. 2024 <em>bioRxiv </em>preprint: https://doi.org/10.1101/2024.08.21.608847</p> <p>For analysis scripts see: https://github.com/mcbridelab/Adavi_2024_snRNAseqAaegAntennae</p> <p>For raw sequencing files see NCBI BioProject: PRJNA1138769</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.