Usage¶

All scripts are run from the project root. Each script accepts -h for the full flag reference.

Scripts cache intermediate results as TSV/CSV in data/dataframes/ and skip recomputation on subsequent runs. Outputs are written to results/figures/ as SVG/PNG. Run logs are saved to logs/.

Scripts¶

Script	Figure panel	Description
`figure_1a_t1map.py`	1a	T1 microstructural gradient within the salience network
`figure_1b_contextualisation.py`	1b	Multi-modal contextualization of the T1 gradient
`figure_1c_cortical_types.py`	1c	Von Economo cortical type distribution
`figure_2_distance.py`	2	Structural connectivity at MPC gradient extremes
`figure_3_ieeg_mni.py`	3	iEEG spectral analysis — MNI open atlas
`figure_3_ieeg_mica.py`	3	iEEG spectral analysis — MICA dataset

Figure 1a¶

scripts/figure_1a_t1map.py — Processes MICA-PNI micapipe derivatives to extract T1 intensity profiles, compute MPC gradients within the salience network, and visualize the relationship between T1 profiles and gradient values.

python scripts/figure_1a_t1map.py \
  -pni_deriv /path/to/BIDS_PNI/derivatives/micapipe_v0.2.0 \
  -mics_deriv /path/to/BIDS_MICs/derivatives/micapipe_v0.2.0 \
  -hemi LH

Flag	Required	Default	Description
`-pni_deriv`	yes	—	Path to PNI micapipe derivatives directory
`-mics_deriv`	yes	—	Path to MICs micapipe derivatives directory
`-hemi`	no	`both`	Hemisphere: `both`, `LH`, or `RH`

Outputs

results/figures/figure_1a_profiles.svg
results/figures/figure_1a_brain.svg
data/dataframes/df_1a_<hemi>.tsv (cache)

Figure 1b¶

scripts/figure_1b_contextualisation.py — Correlates the T1 gradient with BigBrain, AHEAD Bielschowsky, and AHEAD Parvalbumin histological profiles using spin permutation tests.

python scripts/figure_1b_contextualisation.py -hemi LH

Flag	Required	Default	Description
`-hemi`	no	`both`	Hemisphere: `both`, `LH`, or `RH`

No external derivatives are required. All histological profiles are bundled in data/parcellations/.

Outputs

results/figures/figure_1b_correlations.svg
results/figures/figure_1b_correlations_circle.svg
results/figures/figure_1b_brain_t1map.svg
results/figures/figure_1b_brain_bigbrain.svg
results/figures/figure_1b_brain_biel.svg
results/figures/figure_1b_brain_parva.svg

Figure 1c¶

scripts/figure_1c_cortical_types.py — Maps Von Economo cortical types onto the salience network and tests for non-random type distributions using spin permutations.

python scripts/figure_1c_cortical_types.py -pni_deriv /path/to/BIDS_PNI/derivatives/micapipe_v0.2.0

Flag	Required	Default	Description
`-pni_deriv`	no	—	Path to PNI micapipe derivatives (for surface loading)

Outputs

results/figures/figure_1c_brain_economo.svg
results/figures/figure_1c_type_salience.svg

Figure 2¶

scripts/figure_2_distance.py — Tests whether structural connectivity (SC), navigation path length, and Euclidean distance differ between parcels at the high vs. low end of the MPC gradient, within and across all 7 Yeo networks.

python scripts/figure_2_distance.py \
  -pni_deriv /path/to/BIDS_PNI/derivatives/micapipe_v0.2.0

Flag	Required	Default	Description
`-pni_deriv`	yes	—	Path to PNI micapipe derivatives directory

Outputs

results/figures/figure_2a_distance_metric.svg
results/figures/figure_2a_brain_{SC,Nav,Dist}_diff.svg
results/figures/figure_2b_distance_network.svg
results/figures/figure_2b_brain_SC_diff_<network>.svg
data/dataframes/df_2b_label_<hemi>.csv (cache)

Figure 3 MNI¶

scripts/figure_3_ieeg_mni.py — Computes PSD and band power from the MNI open iEEG atlas, maps channels to fsLR-32k surface vertices, and correlates band power with the T1 gradient.

python scripts/figure_3_ieeg_mni.py \
  -pni_deriv /path/to/BIDS_PNI/derivatives/micapipe_v0.2.0 \
  -ieeg_deriv /path/to/ieeg/derivatives

Flag	Required	Default	Description
`-pni_deriv`	no	—	Path to PNI micapipe derivatives
`-ieeg_deriv`	no	—	Path to MNI iEEG derivatives

Outputs — results/figures/figure_3a_ieeg_mni_*.svg

Figure 3 MICA¶

scripts/figure_3_ieeg_mica.py — Same pipeline as Figure 3 MNI, applied to the MICA intracranial EEG dataset with subject-specific leadfield sensitivity maps.

python scripts/figure_3_ieeg_mica.py \
  -ieeg_deriv /path/to/BIDS_iEEG/derivatives/electroMICA \
  -hemi RH

Flag	Required	Default	Description
`-ieeg_deriv`	yes	—	Path to electroMICA derivatives directory
`-hemi`	no	`RH`	Hemisphere: `both`, `LH`, or `RH`

Outputs — results/figures/figure_3b_ieeg_mica_*.svg

External data paths¶

Scripts that require micapipe or iEEG derivatives expect the following directory structure:

micapipe_v0.2.0/
└── sub-<id>/
    └── ses-<id>/
        ├── mpc/        # T1 intensity profiles (.shape.gii)
        ├── dwi/        # tractography and connectome files
        └── surf/       # cortical surface reconstructions

See data/README.md for download instructions.