Volume electron microscopy reveals unique laminar synaptic characteristics in the human entorhinal cortex
- Instituto Cajal (CSIC), Spain
Abstract
The entorhinal cortex (EC) plays a pivotal role in memory function and spatial navigation, connecting the hippocampus with the neocortex. The EC integrates a wide range of cortical and subcortical inputs, but its synaptic organization in the human brain is largely unknown. We used volume electron microscopy to perform a 3D analysis of the microanatomical features of synapses in all layers of the medial EC (MEC) from the human brain. Using this technology, 12,974 synapses were fully 3D reconstructed at the ultrastructural level. The MEC presented a distinct set of synaptic features, differentiating this region from other human cortical areas. Furthermore, ultrastructural synaptic characteristics within the MEC was predominantly similar, although layers I and VI exhibited several synaptic characteristics that were distinct from other layers. The present study constitutes an extensive description of the synaptic characteristics of the neuropil of all layers of the EC, a crucial step to better understand the connectivity of this cortical region, in both health and disease.
Data availability
Most data are available in the main text or the supplementary files. Raw data used for analysis are provided in Supplementary File 4.Stack of images are available at BossDB.org as project page:https://10.60533/boss-2024-ekc7The datasets used and analyzed during the current study are published in the EBRAINS Knowledge Graph:https://doi.org/10.25493/3GMJ-FEZhttps://doi.org/10.25493/QGH8-MTShttps://doi.org/10.25493/CSB1-Q07
Article and author information
Author details
Lidia Alonso-Nanclares
Funding
Ministerio de Ciencia e Innovación (PID2021-127924NB-I00)
- Javier DeFelipe
CSIC Interdisciplinary Thematic Platform (Cajal Blue Brain)
- Javier DeFelipe
European Union's Horizon 2020 Framework Programme for Research and Innovation (Grant Agreement No. 945539 (Human Brain Project SGA3))
- Javier DeFelipe
Ministerio de Ciencia e Innovación (PRE2019-089228)
- Nicolas Cano-Astorga
Ministerio de Ciencia, Innovación y Universidades (FPU19/00007)
- Sergio Plaza-Alonso
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Human brain tissue was obtained from 3 autopsies with no recorded neurological or psychiatric alterations (supplied by Unidad Asociada Neuromax, Laboratorio de Neuroanatomía Humana, Facultad de Medicina, Universidad de Castilla-La Mancha, Albacete, Spain). The consent of the individuals was obtained and the sampling procedure was approved by the Institutional Ethical Committee of the Albacete University Hospital. The tissue was obtained following national laws and international ethical and technical guidelines on the use of human samples for biomedical research purposes. Human brain tissue from the same subjects has been used in previous studies (Cano-Astorga et al., 2021, 2023; Domínguez-Álvaro et al., 2021a; Montero-Crespo et al., 2020).
Copyright
© 2025, Plaza-Alonso et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 197
- views
-
- 45
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Volume electron microscopy reveals unique laminar synaptic characteristics in the human entorhinal cortex
eLife 14:e96144.
https://doi.org/10.7554/eLife.96144
Further reading
-
- Neuroscience
The neural noise hypothesis of dyslexia posits an imbalance between excitatory and inhibitory (E/I) brain activity as an underlying mechanism of reading difficulties. This study provides the first direct test of this hypothesis using both electroencephalography (EEG) power spectrum measures in 120 Polish adolescents and young adults (60 with dyslexia, 60 controls) and glutamate (Glu) and gamma-aminobutyric acid (GABA) concentrations from magnetic resonance spectroscopy (MRS) at 7T MRI scanner in half of the sample. Our results, supported by Bayesian statistics, show no evidence of E/I balance differences between groups, challenging the hypothesis that cortical hyperexcitability underlies dyslexia. These findings suggest that alternative mechanisms must be explored and highlight the need for further research into the E/I balance and its role in neurodevelopmental disorders.
-
- Neuroscience
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
