floc ROIs

Subject-specific functional regions of interest (ROIs) derived from the fLoc dataset using a first-level GLM and Kanwisher-group group parcels as spatial priors.

Task

Four participants (sub-01, sub-02, sub-03, sub-05) completed six sessions of the fLoc functional localizer task, based on pyfLoc (adapted from the Stanford VPNL fLoc task). Each session included two functional runs (~3.85 min) of ~6 s blocks of rapidly presented images from five categories: faces, places, bodies, objects, and characters. Subjects performed a one-back repetition-detection task while fixating a central red dot. Baseline blocks (fixation only) were intermixed throughout. The two runs per session used complementary sub-category sets (e.g., house vs. corridor for places; adult faces in both).

Analysis pipeline

ROIs are derived through a six-step pipeline; full scripts and instructions are in code/README.md inside the floc/rois submodule.

  1. Design matrices — GLM design matrices are built from each subject’s *events.tsv files across all fLoc sessions (~6 sessions, 2 runs each).

  2. First-level GLM — A nilearn first-level GLM is run on the fLoc BOLD data, producing t-score and beta maps for all nine contrasts (see below).

  3. Warp Kanwisher parcels to subject space — Group parcels (n=40, CVS space) from the Kanwisher lab are warped CVS → MNI (FreeSurfer/FSL) → T1w (ANTs using fMRIPrep transforms) for each subject.

  4. Parcel masks — Binary parcel masks (face, scene, body, object) are created as the intersection of each warped group parcel and above-threshold voxels in the subject’s contrast map (α = 0.0001).

  5. ROI masks — Subject-specific ROI masks (FFA, OFA, pSTS, PPA, OPA, MPA, EBA) are derived by ranking voxels within an enlarged group-derived ROI mask by t-score and selecting the top fraction proportional to the group ROI size.

  6. Manual flat-map ROIs — For sub-01, sub-02 and sub-03, ROI boundaries were additionally drawn manually on cortical flat maps using Inkscape and PyCortex.

GLM contrasts

Nine contrasts are estimated per subject from all fLoc sessions pooled together:

  • Kanwisher-style: faceMinObject, sceneMinObject, bodyMinObject, objectMinRest

  • NSD-style (each category vs. all others): faces, places, bodies, characters, objects

ROIs

Seven bilateral ROIs are derived by intersecting subject t-score maps with group-level Kanwisher parcels warped from CVS to native (T1w) space:

ROI

Full name

Contrast

FFA

Fusiform Face Area

face

OFA

Occipital Face Area

face

pSTS

Posterior Superior Temporal Sulcus

face

PPA

Parahippocampal Place Area

scene

OPA

Occipital Place Area

scene

MPA

Medial Place Area

scene

EBA

Extrastriate Body Area

body

Each ROI is available as left-hemisphere, right-hemisphere and bilateral binary masks in native subject (T1w) space. For sub-06, who did not complete the fLoc task, ROI masks were derived from voxelwise noise ceilings from the main THINGS task.

For sub-01, sub-02 and sub-03, ROI boundaries were additionally drawn manually on cortical flat maps and are available via anat/pycortex.