Skip to main content

Modular pipeline for brain stimulation modelling (tDCS/TMS) with SimNIBS

Project description

simnibs-analyze

Post-processing and analysis pipeline for SimNIBS e-field outputs. Built for non-invasive brain stimulation studies (TMS/tDCS).

What it does

Starting from SimNIBS outputs, the pipeline covers the full analysis workflow:

  • Target definition — generate ROI masks in MNI and subject space from MNI coordinates or atlas parcels (sphere, atlas-based)
  • E-field preparation — coregister, skull-strip, smooth, and mask NIfTI volumes; intra/extra-ROI decomposition
  • E-field analysis — extract scalar features (mean, max, percentiles, focality ratio) per subject and condition
  • Single-subject optimisation assessment — evaluate how well a given montage targets the intended ROI
  • Simulation robustness — assess sensitivity of the e-field distribution to input variability
  • Stimulation method comparison — contrast montages or stimulation parameters across conditions
  • Group-level analysis — inter-subject summary statistics, condition comparisons, and effect-size reporting
  • Visualisation — 2D slice overlays, 3D surface rendering, histograms, and group bar plots

Installation

# TODO: publication sur PyPI
pip install simnibs-pipeline

En attendant, cloner le dépôt et installer les dépendances manuellement :

git clone <repo>
cd simnibs-pipeline
pip install -e .

Documentation

Ressource Description
Documentation API Classes et fonctions (généré par pdoc)
Référence config.yaml Toutes les clés du fichier de configuration
Structure des outputs Fichiers générés dans simnibs_output/ et results_dir/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

simnibs_analyze-0.0.1.tar.gz (52.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

simnibs_analyze-0.0.1-py3-none-any.whl (63.7 kB view details)

Uploaded Python 3

File details

Details for the file simnibs_analyze-0.0.1.tar.gz.

File metadata

  • Download URL: simnibs_analyze-0.0.1.tar.gz
  • Upload date:
  • Size: 52.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for simnibs_analyze-0.0.1.tar.gz
Algorithm Hash digest
SHA256 3ee823504800323333f384615bd88395e470f55ec935ff82dd3228d12eff6310
MD5 b59521b8d51900cfdd15307830599e23
BLAKE2b-256 9d6f5f91da816ea82cfc68ad768aa0769fd43090c552d63f2df898218862074d

See more details on using hashes here.

File details

Details for the file simnibs_analyze-0.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for simnibs_analyze-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 037891778a49680e16f6a50d0900ca6b10818355fcf06a743666ce51cb7e6854
MD5 bce636cc8ff73a1a9e6c0a06bb6c9afb
BLAKE2b-256 009d54a05c5120f6442af128488984c35e3a2a11779fd0c3dcd0e1160aa59b1a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page