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CANlab workflows

A workflow is an end-to-end recipe that chains several CanlabCore methods together to accomplish a common analysis goal. Where the per-class method pages (fmri_data, atlas, region, …) document individual methods, a workflow shows how those pieces fit together — and which method to reach for when several can do a similar job.

Each workflow comes in two complementary parts:

  • a roadmap — a conceptual overview that names the available approaches, explains how they relate, and helps you choose the right one; and
  • a walkthrough — a runnable, didactic guide with copy-pasteable sample code on built-in datasets, including the figures it produces.

Start with the roadmap to orient yourself, then follow the walkthrough to run it.

Available workflows

Workflow What it does Roadmap (overview) Walkthrough (code)
ROI / atlas data extraction Pull region-of-interest, pattern, parcel, tissue-compartment, and sphere/coordinate summaries out of brain images, then visualize them (bar plots, line plots, multi-subject slope plots). ROI extraction roadmap Extract & visualize ROI data — how-to
First-level fMRI time-series modeling (glm_map) Build a single-subject event-related model from onsets (FSL tables, SPM-style cells, or an SPM.mat), choose a basis set, add nuisance covariates and contrasts, screen the design (VIF/cVIF, efficiency, high-pass filter), simulate data, fit (AR errors), threshold, and visualize. First-level roadmap First-level how-to
Second-level fMRI group analysis (glm_map) Group regression on contrast images: build the object via fmri_data.regress or the glm_map estimator API, handle outliers and WM/CSF nuisance signals (normalize_gm_by_wm_csf), fit OLS or robust, screen the design, threshold, and visualize. Second-level roadmap Second-level how-to

More workflows will be added here over time.

The two glm_map workflows share the glm_map object page (full method reference) and the fmri_glm_design_matrix design-building object.

Visualizing results

Most workflows end at a thresholded statistic_image or a region object. The same map can be rendered several ways — pick by output medium:

Static figures (MATLAB):

  • montage(t) — slice montage on a canonical anatomical underlay; the workhorse for figures and reports.
  • surface(t) — render activation on 3-D cortical surfaces. Style presets include 'foursurfaces_hcp' (lateral + medial views of both hemispheres with brainstem, on HCP pial surfaces), 'inflated', and cutaways; isosurface gives 3-D blobs.
  • canlab_results_fmridisplay(t) — pre-built montage + surface scaffolds ('compact2', 'full', …) returning a registered fmridisplay whose blob layers you can swap without re-rendering the anatomy.
  • table(t) / region(t) — atlas-labeled results table and the region objects behind it.

Interactive viewers (point-and-click):

  • canlab_orthviews(t) — SPM-style three-plane viewer in MATLAB. Click/drag to navigate; the bottom strip names the atlas region under the crosshair (attach any atlas with canlab_orthviews('AddAtlasLabel', atl)).
  • canlab_niivue(t) — a portable, self-contained .html web viewer (NiiVue) with colormap/threshold/opacity controls, a crosshair coordinate + value readout, and an atlas region readout that outlines the region under the crosshair. Email it or embed it in an HTML report. orthviews_niivue(t) is a one-liner shortcut that writes the page to a temp folder and opens it in your browser.

See Visualizing images and results for the full set of visualization entry points.