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32 results for “entorhinal cortex”

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zenodo48/100

Phase coding of spatial representations in the human entorhinal cortex

<p>Supporting Preprocessed Electrophysiology Data for the article titled &quot;Phase coding of spatial representations in the human entorhinal cortex&quot;.</p> <p>Data were preprocessed and analyzed using Matlab.</p> <p>Data, after decompression, are organized hierarchically in folders and subfolders (two levels).</p> <p>Main folder names are composed as subjectID_date_EC_DATA_taskNo</p> <p>Sub-folders named as: &nbsp;CHn_single-unit ID&nbsp;</p> <p>[subjectID, date, task num] + [Electrode channel, and single-unit ID]&nbsp;</p> <p>subjectID: (subject1, subject2)</p> <p>date: mmm-dd</p> <p>task: (1,2,3,4) Virtual environments (1: backyard, 2: Louvre, 3: Luxor, 4: desert)</p> <p>electrode Channel: CH1,CH2, CH3, CH4, CH5</p> <p>single-unit ID: 0, 1, 2, 3, 4, 5, 6</p> <p>For each channel and single-unit, the following 9 datasets were computed and saved. For instance, for the first electrode and first single-unit class (CH=1, cell ID= 0):</p> <p>CH1_Clu0.mat&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- Summary of firing features of this single-unit including firing rate, and grid score of the cell. (In Matlab mat format)<br> CH1_Clu0_MeanPhaseMap.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- Mean spike phase map relative to gamma-band LFP<br> CH1_Clu0_Phase.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- Spike phase CH1_Clu0_spikeData.csv<br> CH1_Clu0_spkT.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- Spike times in increental order<br> CH1_Clu0_VarPhaseMap.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- Map of the variance of spike times in increental order<br> CH1_Clu0_xval.mat&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; --&nbsp; Summary of firing features of 50% of single-unit spikes (every other spike) for cross-validation purposes. (In Matlab mat format)<br> CH1_Clu0_xyPos.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- X,Y coordinates of the avatar&#39;s position in 1 ms resolution. In other words, the path taken by the avatar sampled at 1 kHz.<br> CH1_Clu0_XYspkT.csv&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -- X,Y coordinates of the avatar at moments of spikes. In other words, the location in space where a spike was fired by the putative neuron.</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2022View details →
dryad40/100

The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin

<p>Although the neural circuitry underlying fear memory formation is important in fear-related mental disorders, it is incompletely understood. Here, we utilized trace fear conditioning to study the formation of trace fear memory. We identified the entorhinal cortex (EC) as a critical component of sensory signaling to the amygdala. Moreover, we used the loss of function and rescue experiments to demonstrate that release of the neuropeptide cholecystokinin (CCK) from the EC is required for trace fear memory formation. We discovered that CCK-positive neurons extend from the EC to the lateral nuclei of the amygdala (LA), and inhibition of CCK-dependent signaling in the EC prevented long-term potentiation of sensory signals to the LA and formation of trace fear memory. Altogether, we suggest a model where sensory stimuli trigger the release of CCK from EC neurons, which potentiates sensory signals to the LA, ultimately influencing neural plasticity and trace fear memory formation.</p>

opencc-zeroNov 2021View details →
dryad40/100

The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin

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publicNov 2021View details →
dryad40/100

Spatial coding dysfunction and network instability in the aging medial entorhinal cortex

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publicAug 2025View details →
dryad36/100

Data from: ASHS-OAP atlas for automatic entorhinal cortex segmentation

<p>Early stages of Alzheimer's disease (AD) are associated with volume reductions in specific subregions of the medial temporal lobe (MTL). Using a manual segmentation method—the Olsen-Amaral-Palombo (OAP) protocol— previous work in healthy older adults showed that reductions in grey matter volumes in MTL subregions were associated with lower scores on the Montreal Cognitive Assessment (MoCA), suggesting atrophy may occur prior to diagnosis of mild cognitive impairment, a condition that often progresses to AD. However, current manual segmentation methods are labour intensive and time consuming. Here, we examined the utility of Automatic Segmentation of Hippocampal Subfields (ASHS) to detect volumetric differences in MTL subregions of healthy older adults who varied in cognitive status as determined by the MoCA. We trained ASHS on the OAP protocol to create the ASHS-OAP atlas, and then examined how well automated segmentation replicated the ground truth of manual segmentation. Volumetric measures obtained from the ASHS-OAP atlas were also contrasted against those from the ASHS-PMC atlas, a widely used atlas provided by the ASHS team. Volumetrics from the ASHS-OAP atlas aligned well with those from manual segmentation, suggesting ASHS-OAP is a viable alternative to current manual segmentation methods. In addition, while some subtle differences were observed, results from the ASHS-PMC and ASHS-OAP atlases aligned well with each other overall. Our findings highlight the utility of automated segmentation methods but still underscore the need for a unified and harmonized MTL segmentation atlas.</p>

opencc-zeroFeb 2024View details →
dryad36/100

Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex

<p>This dataset contains iEEG neural recordings and behavior movement direction in a navigation task from human subjects undergoing inpatient monitoring for seizure localization. The experimental design, including an explanation of metrics of interest, is detailed in Chen et al (2018) <em>Current Biology</em> and Chen et al (2021) <em>Science Advances</em>. EXAMPLE electrode data from the vmPFC and EC ROIs are included. These data are used for grid-like modulation of theta power in vmPFC and EC.</p>

opencc-zeroNov 2021View details →
dryad36/100

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

<p><span>Structural, functional, and molecular reorganization of denervated neural networks is often observed in neurological conditions. The loss of input is accompanied by homeostatic synaptic adaptations, which can affect the reorganization process. A major challenge of denervation-induced homeostatic plasticity operating in complex neural networks is the specialization of neuronal inputs. It remains unclear whether neurons respond similarly to the loss of distinct inputs. Here, we used <em>in</em> <em>vitro</em> entorhinal cortex lesion (ECL) and Schaffer collateral lesion (SCL) in mouse organotypic entorhino-hippocampal tissue cultures to study denervation-induced plasticity of CA1 pyramidal neurons. We observed microglia accumulation, presynaptic bouton degeneration, and a reduction in dendritic spine numbers in the denervated layers three days after SCL and ECL. Transcriptome analysis of the CA1 region revealed complex changes in differential gene expression following SCL and ECL compared to non-lesioned controls with a specific enrichment of differentially expressed synapse-related genes observed after ECL. Consistent with this finding, denervation-induced homeostatic plasticity of excitatory synapses was observed three days after ECL but not after SCL. Chemogenetic silencing of the EC but not CA3 confirmed the pathway-specific induction of homeostatic synaptic plasticity in CA1. Additionally, increased RNA oxidation was observed after SCL and ECL. These results reveal important commonalities and differences between distinct pathway lesions and demonstrate a pathway-specific induction of denervation-induced homeostatic synaptic plasticity. </span></p>

opencc-zeroMar 2023View details →
ClinicalTrials.gov36/100

The Entorhinal Cortex and Aerobic Exercise in Aging

ClinicalTrials.gov study NCT02775760. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
dryad36/100

Data from: ASHS-OAP atlas for automatic entorhinal cortex segmentation

Open the record for dataset details and reuse information.

publicFeb 2024View details →
dryad36/100

Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex

Open the record for dataset details and reuse information.

publicNov 2021View details →
dryad36/100

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

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publicMar 2023View details →
dryad28/100

Data from: Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex

Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.

opencc-zeroDec 2015View details →
dryad28/100

Data from: Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex

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publicJul 2017View details →
geo24/100

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

GEO Series GSE223096. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2023View details →
geo24/100

Spatial and single cell transcriptomics in the human Entorhinal Cortex across diverse risk of Alzheimer’s disease

GEO Series GSE308007. Homo sapiens. 31 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenNov 2025View details →
geo24/100

Real-time quantitative PCR analysis of retrogradely labeled stellate neurons in LII of the rat medial entorhinal cortex

GEO Series GSE85752. Rattus norvegicus. 12 samples. Type: Other.

openGEO-OpenAug 2017View details →
geo24/100

Spatial and single cell transcriptomics in the human Entorhinal Cortex across diverse risk of Alzheimer’s disease [Visium]

GEO Series GSE307990. Homo sapiens. 31 samples. Type: Other.

openGEO-OpenNov 2025View details →
geo24/100

Spatial Coding Dysfunction & Network Instability in the Aging Medial Entorhinal Cortex

GEO Series GSE281777. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJul 2025View details →
geo24/100

Genome-wide H3K27ac profiles in post-mortem entorhinal cortex samples from Alzheimer's disease cases and controls

GEO Series GSE102538. Homo sapiens. 47 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenAug 2018View details →
geo24/100

RNAseq anaysis of entorhinal cortex layer II neurons upon Ptbp1 modulation

GEO Series GSE151356. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMay 2020View details →

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