Flexible Polymer-Based Electrodes for Detecting Depression-Related Theta Oscillations in the Medial Prefrontal Cortex
This study investigates neural activity changes in the medial prefrontal cortex (mPFC) of a lipopolysaccharide (LPS)-induced acute depression mouse model using flexible polymer multichannel electrodes, local field potential (LFP) analysis, and a convolutional neural network-long short-term memory (C...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Chemosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9040/12/12/258 |
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| Summary: | This study investigates neural activity changes in the medial prefrontal cortex (mPFC) of a lipopolysaccharide (LPS)-induced acute depression mouse model using flexible polymer multichannel electrodes, local field potential (LFP) analysis, and a convolutional neural network-long short-term memory (CNN-LSTM) classification model. LPS treatment effectively induced depressive-like behaviors, including increased immobility in the tail suspension and forced swim tests, as well as reduced sucrose preference. These behavioral outcomes validate the LPS-induced depressive phenotype, providing a foundation for neurophysiological analysis. Flexible polymer-based electrodes enabled the long-term recording of high-quality LFP and spike signals from the mPFC. Time-frequency and power spectral density (PSD) analyses revealed a significant increase in theta band (3–8 Hz) amplitude under depressive conditions. Using theta waveform features extracted via empirical mode decomposition (EMD), we classified depressive states with a CNN-LSTM model, achieving high accuracy in both training and validation sets. This study presents a novel approach for depression state recognition using flexible polymer electrodes, EMD, and CNN-LSTM modeling, suggesting that heightened theta oscillations in the mPFC may serve as a neural marker for depression. Future studies may explore theta coupling across brain regions to further elucidate neural network disruptions associated with depression. |
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| ISSN: | 2227-9040 |