Bad Baby reanalysis

This repo is for reanalysis of the "Bad Baby" data, using the MNE-BIDS-Pipeline.

Notes on data sources and data munging

• local folder /media/<REDACTED>/Untitled contains badbaby data with prebad files defined and many missing emptyroom files tracked down. This is our starting point, rsync'd to ./local-data.

• server folder /mnt/brainstudio/badbaby/ has lots of anomalies / redundancies. It gets rsync'd to ./server-data.

• file and folder naming anomalies and known-bad-file exclusions are handled by prep-dataset/select-files-from-*.py

Data prep

There is a Makefile in the prep-dataset folder. make all will:

1. rsync data from local and remote sources.
2. sort through the copied file trees, generate a mapping from filenames we want to keep to new file locations in ./data (correcting folder- or file-names along the way), and make a hardlink to each "kept" file at the new location.
3. write summaries of how much data we have for each subject/session.
4. generate logs / lists of missing or unexpected files.

BIDSification and processing

Once all the original data files are in place, we can proceed with a 3-step conversion process. To restart from scratch, you can do:

$ rm -Rf ./anat ./bids-data

1. Anatomical mapping

The script prep-dataset/rerun-coreg.py will load the participant digitization, coregister, and create ./anat and subdirectories with scaled MRIs for each subject and session.

2. Converting to BIDS

The script prep-dataset/bidsify.py will convert the dataset in ./data to BIDS format in ./bids-data. It also checks/validates the events found in the FIF files against the TAB files from the stimulus presentation script (enabled in the bidsify.py script via a boolean flag verify_events_against_tab_files). Any failures to match up events from the FIF and TAB files will be flagged in prep-dataset/qc/log-of-scoring-issues.txt.

3. Running the Pipeline

Files related to the preprocessing pipeline are in ./pipeline. It can be invoked using standard MNE-BIDS-Pipeline mechanics:

• mne_bids_pipeline --config=pipeline/config.py will process all data
• View the pipeline reports at ./bids-data/derivatives/mne-bids-pipeline/sub-XXX/ses-Z/meg/sub-XXX_ses-Y_report.html