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vcog_hps_ad

This repository contains packages, scripts, and notebooks for the following article:

Angela Tam, Christian Dansereau, Yasser Iturria-Medina, Sebastian Urchs, Pierre Orban, Hanad Sharmarke, John Breitner, Pierre Bellec, Alzheimer's Disease Neuroimaging Initiative, A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia, GigaScience, Volume 8, Issue 5, May 2019, giz055, https://doi.org/10.1093/gigascience/giz055

Click the following link to reproduce the analysis with simulated data on binder: 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: DOI

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packages, scripts, and notebooks for paper about cognitive and atrophy signatures in Alzheimer's disease prediction

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