Fast and slow cortical high frequency oscillations for cortico-cortical and corticohippocampal network consolidation during NonREM sleep.

Adrian Aleman-Zapata 1, Richard GM Morris 2, Lisa Genzel *1,2

*corresponding author: lgenzel@donders.ru.nl 📫

1, Donders Institute for Brain Cognition and Behavior, Radboud University, Postbus 9010, 6500GL Nijmegen/Netherlands.

2, Centre for Cognitive and Neural Systems, Edinburgh Neuroscience, University of Edinburgh, 1 George Square, Edinburgh EH8 9JZ, UK.

Reference: doi.org/10.1101/765149

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⚠️ Requirements: Makes use of functions from the YASA (only used in the older version of the manuscript 2020), Fieldtrip (Suggest to use 2019 version to avoid plotting issues) and ADRITOOLS repositories.

These last two need to be added to the path.

We also include in the subfunctions folder some functions borrowed from FMA toolbox

Required Matlab built-in 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

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Main scripts: 📁

Figure 2B and 2D: Count of coocurring and single events, as well as slow and fast counts and rates.

• GL_hfos_counts.m

Mentioned in Text: Shuffling co-occurrence control.

• GL_ ripples_hfos _control.m

Mentioned in Text: Shuffling Plusmaze co-occurrence control.

• GL_plusmaze_control.m

Figure 3 (A,B,C,D): Spectral power during events.

• GL_spectral_power.m (Older version 2020)
• GL_spectral_power_2021.m (Updated version 2022)

Figure 3 (E,F,G): Granger causality during events.

• GL_granger.m
• GL_granger_Nayanika.m (Computes spectral Granger Causality analysis after combining events of all rats).

Figure 4A:

• GL_delta_counts.m

Figure 4B: Spindles counts

• GL_spindles_counts.m * (Older version 2020)
• GL_spindles_counts_nayanika.m * (Updated version 2022)

Figure 4C:

• GL_delta_spindles.* (Computes the co-occurrence of deltas an spindles from PPC and PFC as done by Kim et al.,Cell, 2019)

Figure 4 (D,E,F): Spindle co-occurrence. Before & After counts.

• GL_spindles.m * (Older version 2020)
• GL_spindles_Nayanika.m * (Updated version 2022)

Mentioned in Text: Spindle co-occurrence shuffling control

• GL_spindles_control.m *

Figure 4I:

• GL_swr_disruption.m

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*In the current version (2022): Spindles were computed using an adaptation of the FindSpindles.m function from FMA toolbox (Zugaro lab).

*In the older version (2020): Spindles were previously detected using the YASA algorithm. The steps to do this were:

1. Run GL_spindle_matlab2python.m for every session per condition to export NREM epochs to python.
2. Run GL_yasa_spindles.py for every session per condition to save detections in a .mat file.

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Outdated fork.

Plusmaze 2022 update (Nayanika)