Introducing FASTANS - Fast Targeted Functional Network Stimulation

🐍 Python implementation of the TANS approach (Lynch et al.).
🎯 Optimizes TMS coil placements for precise, personalized network targeting within minutes! 🚀

Code, instructions, and example data: github.com/mlueckel/FAS...

Optional: one score to guide choices – weight metrics into a composite map that ranks candidate placements.

Plan early: explore targeting feasibility & hardware requirements (e.g., focal depth) on MNI templates or open-access datasets even before you collect individual MRIs.

Integrates nicely with other software: export selected placements to BabelBrain, k-Wave, or k-Plan for simulation and to Localite or BrainSight for neuronavigation.

Click to choose a spot, preview the transducer placement and a simplified acoustic focus, then export the placement for validation.

What it shows for every scalp point: distance to target, straight-path intersection with target, required transducer tilt, skin-skull angle, skull thickness – so you can quickly spot promising transducer placements. Some examples:

🎯 Meet 𝐏𝐥𝐚𝐧𝐓𝐔𝐒: a fast, open-source tool to pick where to place a transducer for transcranial ultrasonic stimulation (TUS) on an individual head – before you run heavy simulations.

Paper: doi.org/10.1016/j.br...

Code & instructions: plan-tus.org