neurodatascience / compare-surf-tools

Project: compare_surf_tools

An extension of BrainHack Project (https://github.com/companat/compare-surf-tools) to compare thickness outputs from different pipelines run on ABIDE-I

Objectives

• Compare output of preprocessing pipelines for structural MR imaging
  • Software: Freesurfer (v5.1, v5.3, v6.0) ANTs, CIVET2.1
  • Atlases: ROI-wise (surface parcellations: DKT40, Destrieux, Glasser)
  • analytic comparisons: classifier performance (individual predictions), statistical inference (biological group differences)
• Quality control
  • Manual / visual outlier detection
  • Automatic outlier detection

Data

Consolidated from the analysis results provided at http://preprocessed-connectomes-project.org/abide/Description. We provide the unified data tables in the 'data' directory. The subject lists for various analyses can be generated using software specific data tables and QC lists.

• ABIDE_Phenotype.csv : phenotypic data for the subjects
• ANTs, CIVET, FS* : preproc software output (ROI-wise)
• QC : QC lists from manual and automatic outlier detection

Legacy:

• see https://github.com/companat/compare-surf-tools for details

Code

• notebooks: driver code to run analysis
• lib: helper functions for parsing and analysis
• scripts: code to read software output (civet2.1, fs6.0)

.
├──  notebooks           
│   ├── run_atlas_comparisons.ipynb
│   ├── run_software_comparisons.ipynb
│   ├── import_QC_data.ipynb
│   ├── Outlier_QC_analysis.ipynb
│   ├── generate_plots_individual_and_aggregates.ipynb
│   └── learn_pipeline_transforms.ipynb 
└── lib
│   ├── data_handling.py
│   ├── deeplearning.py
│   ├── data_stats.py
│   └── plot_utils.py
└── scripts
    ├── get_vertex_data_fs.py
    ├── get_dkt_data_civet.py
    ├── get_roi_data_fs.py
    └── check_vertex_data.py

Steps (see ./compute_workflow.png)

Prereq: Processed output from a given software: e.g. FreeSurfer

A. Data parsing

• run scripts/get_vertex_data_fs.py on a FS subject dir to get vertext-wise summay CSV for all subjects.

python get_vertex_data_fs.py -s ../data/subjects/ -k '.fwhm20.fsaverage.mgh' -o ../data/sample_output/fs_fsaverage_vout

• run scripts/get_roi_data_fs.py on a FS subject dir to get ROI-wise summay CSV for all subjects. This script uses aparcstats2table command.

python get_roi_data_fs.py -s ../data/subjects -l ../data/subject_list.txt -m thickness -p a2009s -o ../data/sample_output/

B. Data standardization and comparative analyses

• run_atlas_comparisons.ipynb
• run_software_comparisons.ipynb

C. QC and outlier analysis

• import_QC_data.ipynb
• Outlier_QC_analysis.ipynb

D. Visualization of brainmaps

• generate_plots_individual_and_aggregates.ipynb