Dendritic Na+ spikes enable cortical input to drive action potential output from hippocampal CA2 pyramidal neurons
- Howard Hughes Medical Institute, Columbia University, United States
Abstract
Synaptic inputs from different brain areas are often targeted to distinct regions of neuronal dendritic arbors. Inputs to proximal dendrites usually produce large somatic EPSPs that efficiently trigger action potential (AP) output whereas inputs to distal dendrites are greatly attenuated and may largely modulate AP output. In contrast to most other cortical and hippocampal neurons, hippocampal CA2 pyramidal neurons show unusually strong excitation by their distal dendritic inputs from entorhinal cortex (EC). Here we demonstrate that the ability of these EC inputs to drive CA2 AP output requires the firing of local dendritic Na+ spikes. Furthermore we find that CA2 dendritic geometry contributes to the efficient coupling of dendritic Na+ spikes to AP output. These results provide a striking example of how dendritic spikes enable direct cortical inputs to overcome unfavorable distal synaptic locale to trigger axonal AP output, and thereby enable efficient cortico-hippocampal information flow.
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Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All mouse studies were approved by the Institutional Animal Care and Use Committee (IACUC) of Columbia University (protocol Number AC-AAAF6104). Animal housing, husbandry, and euthanasia were conducted under the guidelines of the Institute of Comparative Medicine, Columbia University.
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© 2014, Sun et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Dendritic Na+ spikes enable cortical input to drive action potential output from hippocampal CA2 pyramidal neurons
eLife 3:e04551.
https://doi.org/10.7554/eLife.04551
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Background:
Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.
Methods:
Medical records, imaging reports, and laboratory test results from 21,459 ischemic stroke patients were collected and analyzed. Univariable and multivariable statistical analyses identified key predictive factors. The dataset was split into a 70% training set and a 30% testing set. To address the class imbalance, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors was employed. Nine widely used machine learning algorithms were evaluated using relevant prediction metrics, with SHAP (SHapley Additive exPlanations) used to interpret the model and assess the contributions of different features.
Results:
Regression analyses revealed that complications such as hydrocephalus, cerebral hernia, and deep vein thrombosis, as well as specific brain regions (frontal, parietal, and temporal lobes), significantly contributed to PSE. Factors such as age, gender, NIH Stroke Scale (NIHSS) scores, and laboratory results like WBC count and D-dimer levels were associated with increased PSE risk. Tree-based methods like Random Forest, XGBoost, and LightGBM showed strong predictive performance, achieving an AUC of 0.99.
Conclusions:
The model accurately predicts PSE risk, with tree-based models demonstrating superior performance. NIHSS score, WBC count, and D-dimer were identified as the most crucial predictors.
Funding:
The research is funded by Central University basic research young teachers and students research ability promotion sub-projec t(2023CDJYGRH-ZD06), and by Emergency Medicine Chongqing Key Laboratory Talent Innovation and development joint fund project (2024RCCX10).
