This repository contains packages, scripts, and notebooks for the following article A signature of cognitive deficits and brain atrophy that is highly predictive of progression to Alzheimer’s dementia.
Click the following link to reproduce the analysis with simulated data on binder:
Here is a brief description of each item in the repository:
- adas13_mixed_effects.ipynb - a Jupyter notebook that gives the linear mixed effects models for cognitive trajectories of different groups
- adni_bl_vbm_pipeline_20171201.m - an Octave script that runs a segmentation pipeline from SPM12 inside a NIAK container
- adni_csv_merging.ipynb - a Jupyter notebook that merges ADNI spreadsheets together
- adni_filter_mci_csv.ipynb - a Jupyter notebook that filters eligible MCI subjects
- cog_hpc_prediction.ipynb - a Jupyter notebook containing analyses that give a highly predictive signature (HPS) of Alzheimer's disease dementia using cognitive features that were derived from real data
- Proteus - a Python package by Christian Dansereau. Proteus was built on scikit-learn and it offers machine learning tools to make highly confident predictions
- vbm_hpc_prediction.ipynb - a Jupyter notebook containing analyses that give a highly predictive signature (HPS) of Alzheimer's disease dementia using structural features that were derived from real data
- vbm_subtypes_glm.ipynb - a Jupyter notebook that provides univariate tests between vbm subtypes and diagnosis
- vbm_subtypes_pipeline.m - an Octave script to build subtypes of grey matter atrophy and extract weights from structural T1 images
- vcog_hpc_prediction.ipynb - a Jupyter notebook containing analyses that give a highly predictive signature (HPS) of Alzheimer's disease dementia from cognitive and structural brain features that were derived from real data
- vcog_hpc_prediction_simulated_data.ipynb - a Jupyter notebook containing analyses that give a highly predictive signature (HPS) of Alzheimer's disease dementia from cognitive and structural features using simulated data
- simulation_script.py - a Python script that generates simulated data from raw data
- simulated_data.csv - a comma separated value file that contains simulated data
- spm_container - an Octave package containing wrappers for SPM12 functions for segmentation and DARTEL
This repository has also been archived on Zenodo:
