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

Citation

Feng, Hemin et al. (2021), The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf217

Abstract

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.

Methods

For fear conditioning, all videos (baseline, training, and testing) were recorded with a webcam (Logitech C270) set in the ceiling of the chamber. Videos were analyzed with a custom program based on an open-source platform (Lopes et al., 2015) (https://bonsai-rx.org). Briefly, the centroid of the animal was extracted from the videos. By comparing the coordinates of the centroid frame by frame, we then calculated the distance moved between two frames. The instant velocity of the animal was calculated by dividing this distance by the time span between two adjacent frames. The freezing percentage was defined as the percentage of frames with an instant velocity lower than the threshold of all frames in an observed time window.

For electrophysiological recording, responses were recorded and passed to a pre-amplifier (PZ5, TDT) and an acquisition system (RZ5D, TDT). Signals were filtered for field potential or spikes with respective bandwidth ranges of 10–500 Hz and 1–5000 Hz. All recordings were stored using TDT software (OpenEx, TDT). For LTP experiment, field potential data were extracted and processed in the MATLAB program. Slope of fEPSP was generated and normalized to the mean value of baseline session before induction.

Usage Notes

Please see our ReadMe files for dataset description and using notes.

Funding

Hong Kong Research Grants Council, Award: T13-605/18-W

National Natural Science Foundation of China, Award: 31671102

Health and Medical Research Fund, Award: 06172456

Innovation and Technology Fund, Award: MRP/101/17X

Hong Kong Research Grants Council, Award: 11102417M

Hong Kong Research Grants Council, Award: 11101818M

Hong Kong Research Grants Council, Award: 11103220

Health and Medical Research Fund, Award: 31571096

Innovation and Technology Fund, Award: MPF/053/18X

Innovation and Technology Fund, Award: GHP_075_19GD