SleepFC

Introduction:

This page contains the necessary code to run the analyses from the paper "Sleep network functional connectivity, hyperexcitability, and cognition in Alzheimer’s disease".
Link: https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.13861

"Preprocessing_Features_and_Matrices.ipynb": Preprocess raw EEG timeseries files and sleep staging files to obtain output features and functional connectivity matrices.
"Manuscript_Figures.ipynb": Plot the main figures from the paper including FC matrix statistical comparisons, regressions, and box plots.
"Cross_sectional_MoCA.ipynb": Cognitive score analysis for the cross-sectional regression.
"Longitudinal_decline.R": Longitudinal cognitive score decline analysis.
"Preprocess_2nd_night.ipynb": Obtain features and matrices for the 2nd night reproducibility analysis.
"ML_group_classification.ipynb": Machine Learning classifier for groups (AD-NoEp, AD-Ep, and HC).
"Classifier_MoCA_decline.ipynb": Machine Learning classifier for fast cognitive decline.

Python 3.9.7
mne 1.2.1
numpy 1.22.4
pandas 1.5.3
pickleshare 0.7.5
plotly 5.14.1
scikit-learn 0.24.2
scipy 1.7.1
seaborn 0.11.2
statsmodels 0.12.2

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
Classifier_MoCA_decline.ipynb		Classifier_MoCA_decline.ipynb
Cross_sectional_MoCA.ipynb		Cross_sectional_MoCA.ipynb
Longitudinal_decline.R		Longitudinal_decline.R
ML_group_classification.ipynb		ML_group_classification.ipynb
Manuscript_Figures.ipynb		Manuscript_Figures.ipynb
Preprocess_2nd_night.ipynb		Preprocess_2nd_night.ipynb
Preprocessing_Features_and_Matrices.ipynb		Preprocessing_Features_and_Matrices.ipynb
README.md		README.md