How a LaBGAS neuroimaging study goes from raw scanner output to a set of archived, date-stamped result reports. Each page below covers one stage: what it is for, what it consumes and produces, the canonical scripts involved, and where to read further.

Read the CANlab documentation first. These pages describe our layer. The object model, the methods, and most of the underlying machinery are CANlab’s, and are documented far more thoroughly at canlab.github.io: Setup · Interactive fMRI philosophy · Object & method docs · Walkthroughs · Batch system

Where a CANlab walkthrough already covers something, we link to it rather than restate it.

How the pipeline fits together

raw DICOM
   │
   ├─ prep/          BIDS conversion → fMRIPrep → event timing files → smoothing
   │
   ├─ firstlevel/    SPM + CANlab GLM per subject → contrast images → diagnostics
   │
   └─ secondlevel/   group inference, MVPA, signature responses
                     │
                     └─ CANlab batch system → date-stamped HTML reports

Everything upstream of secondlevel/ is owned by LaBGAScore. Second-level analysis is a mix: the batch machinery and reporting come from the CANlab_help_examples fork, while the multivariate pipelines (PLS-DA, PLSR, Elastic Net) are ours.

Working principles

A few conventions shape all of the below, and are worth knowing before you start.

Scripts are templates, not a library. LaBGAScore is copied into a study repository and adapted. Numbered prefixes (s0, s1, s2 …) mark pipeline order. This keeps analysis code versioned with the study it belongs to.

Reports are the deliverable. Analyses end in date-stamped HTML containing the figures, statistics and the code that produced them. The point is that a result can be traced back months later without re-running anything.

Data lives under version control. DataLad — built on git and git-annex — tracks both code and data, with code pushed to GitHub and data to GIN.

Analyses run on a shared Linux server, not on laptops.

Pages

Stage Page
Prerequisites, paths, conventions Setup & dependencies
1 · Raw data → BIDS → prepared inputs BIDS conversion & prep
2 · Subject-level GLM First-level models
3 · Group inference & reporting Second-level analysis
Classification PLS-DA
Continuous prediction PLSR
Regularised regression Elastic Net
Receptor and kinetic imaging PET
Metabolite quantification MR spectroscopy
Networks and representational similarity Connectivity & RSA
Parcellations, masks, signatures Atlases & masks

These pages summarise the repository as it stood when they were written. For anything operational — exact arguments, current script names — treat the repository as the source of truth, and in particular LaBGAS_fMRI_analysis_workflow.md, which walks the fMRI workflow end to end and is maintained alongside the code it describes.