🧠 Fast and Slow Cortical High-Frequency Oscillations During NonREM Sleep

This repository contains code and analyses for the project:

Fast and slow cortical high frequency oscillations for cortico-cortical and cortico-hippocampal network consolidation during NonREM sleep.
📖 Preprint on bioRxiv

This project later became the published paper:

Sleep deprivation and hippocampal ripple disruption after one-session learning eliminate memory expression the next day.
📖 PNAS paper

Authors:
Adrián Alemán-Zapata¹, Richard G.M. Morris², Lisa Genzel*¹²
*Corresponding author: lgenzel@donders.ru.nl

Affiliations:

1. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
2. Centre for Cognitive and Neural Systems, University of Edinburgh, UK

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⚙️ Requirements

This repository relies on:

📦 Toolboxes and Dependencies

• MATLAB toolboxes:

  • Image Processing Toolbox
  • Signal Processing Toolbox
  • Statistics and Machine Learning Toolbox
  • Mapping Toolbox
  • Deep Learning Toolbox
  • Symbolic Math Toolbox
  • Bioinformatics Toolbox
  • Computer Vision Toolbox
  • Fixed-Point Designer
  • MATLAB Coder
  • Simulink
  • Parallel Computing Toolbox
  • MATLAB Parallel Server
  • Polyspace Bug Finder

• External libraries:

  • FieldTrip — tested with 2019 version to avoid plotting issues
  • ADRITOOLS
  • FMA Toolbox — specific analysis functions included in subfunctions/
  • YASA (used in the 2020 version only)

⚠️ FieldTrip and ADRITOOLS must be added to the MATLAB path manually.

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📂 Main Scripts by Figure

🧮 Co-occurrence and Event Counting

• Figure 2B, 2D – Count co-occurring/single events, slow/fast counts and rates:
  GL_hfos_counts.m

• Analysis mentioned in text – Ripple-HFO shuffling controls:
  GL_ripples_hfos_control.m
GL_plusmaze_control.m

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📈 Spectral Power & Granger Causality (Figure 3)

• Figure 3A–D – Spectral power:
  GL_spectral_power.m (2020 version)
  GL_spectral_power_2021.m (2022 version)

• Figure 3E–G – Granger causality:
  GL_granger.m
GL_granger_Nayanika.m (all rats combined)

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🌊 Delta and Spindle Analyses (Figure 4)

• Figure 4A – Delta counts:
  GL_delta_counts.m

• Figure 4B – Spindle counts:
  GL_spindles_counts.m (2020)
  GL_spindles_counts_nayanika.m (2022)

• Figure 4C – Delta–Spindle co-occurrence (inspired by Kim et al., Cell, 2019):
  GL_delta_spindles.m

• Figure 4D–F – Spindle co-occurrence (before/after counts):
  GL_spindles.m (2020)
  GL_spindles_Nayanika.m (2022)

• Analysis mentioned in text – Spindle co-occurrence control:
  GL_spindles_control.m

• Figure 4I – SWR disruption:
  GL_swr_disruption.m

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🔄 Spindle Detection Versions

• 2022 (Current):
  Spindles detected using a modified version of FindSpindles.m from the Zugaro Lab's FMA Toolbox.

• 2020 (Old):
  Spindles detected using YASA (Python):

  1. Export NREM epochs using GL_spindle_matlab2python.m
  2. Run spindle detection with GL_yasa_spindles.py
     Results are saved as .mat files

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🧪 Related Work (Outdated)

You can find an outdated fork focused on Plusmaze analysis here:
🔗 Plusmaze 2022 fork by Nayanika

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📬 Contact

For questions or collaboration inquiries, feel free to reach out:

• 👨‍🔬 Adrian Aleman-Zapata: GitHub | Email
• 📧 Lisa Genzel: lgenzel@donders.ru.nl

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