This repository contains the code used to compute 1/f slopes and E/I predictions from EEG/MEG data recordings that supports the findings of the following study:
Martínez-Cañada, P, Perez-Valero, E, Minguillon, J, Pelayo, F, López-Gordo, MA, Morillas, C. Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease. Alzheimer's Dement. 2023; 15:e12477. https://doi.org/10.1002/dad2.12477
Scripts to process neuroimaging data and generate slope and E/I prediction results of the research article above can be found as Jupyter notebooks in folders EEG and MEG with the file name slopes_EI_computation.ipynb. The folder regression_coefficients contains coefficients of the polynomial regression model used to infer the E/I ratio. Simulation data from the cortical network model used to fit the regression model are located in the folder spectral_features.
Raw EEG data recordings have been included in EEG/data/PLOSONE2020_DATA_v1.1.csv. Raw MEG data recordings are available on request from the Dementia Platform UK (DPUK) (https://portal.dementiasplatform.uk/Apply). However, we include here the 1/f slopes and power spectra computed from the raw MEG data, in the folders MEG/slopes/BioFind_data and MEG/spectra/BioFind_data.
The scripts included in this repository have the following required Python package's dependencies (in brackets, the package's version used):
- numpy (1.22.4)
- scipy (1.8.1)
- sklearn (1.1.1)
- matplotlib (3.5.2)
- pymp (0.2)
- pandas (1.4.2)
- csv (1.0)