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# ------------------------------------------------- # 3. Fit a GLM (event‑related design) # ------------------------------------------------- design = tio.load_events(bids_root, task='nback') glm = tstat.GLM() glm.fit(func_clean, design)

The suite is built around a , with optional C/C++ extensions for performance‑critical kernels. It follows the FAIR (Findable, Accessible, Interoperable, Re‑usable) principles and integrates seamlessly with other community tools such as Nilearn , MNE‑Python , FSL , SPM , and AFNI . 2. Historical Context | Year | Milestone | |------|-----------| | 2015 | Project conception at EPFL’s Laboratory for Cognitive Neuroimaging (LCN). | | 2016 | First public release (v0.1) on GitHub under the permissive BSD‑3‑Clause license. | | 2018 | Integration of a GPU‑accelerated diffusion‑tensor toolbox (via CUDA). | | 2020 | Introduction of the “Lausanne 2020 ” data‑standardisation layer, aligning with BIDS (Brain Imaging Data Structure). | | 2022 | Full support for containerised deployment (Docker, Singularity) and a cloud‑ready version for AWS/GCP. | | 2024 | Release of TWK Lausanne 2.0 , featuring a modular plugin architecture, a web‑based dashboard, and an extensive Python API. | twk lausanne download

# ------------------------------------------------- # 2. Preprocess functional runs # ------------------------------------------------- preproc = tpre.Pipeline() preproc.add_step('realign', reference='mean') preproc.add_step('slice_time_correction', method='interleaved') preproc.add_step('denoise', method='ica_aroma') func_clean = preproc.apply(dataset.func) # ------------------------------------------------- # 3

| Domain | Typical Use‑Cases | |--------|-------------------| | | Pre‑processing, statistical modelling, and visualisation of MRI, fMRI, and diffusion data. | | Computational Neuroscience | Large‑scale network simulations, dynamic causal modelling, and brain‑computer‑interface prototyping. | | Data‑Science & Machine Learning | Pipelines for feature extraction, classification, and clustering of high‑dimensional neuro‑datasets. | | Education & Training | Interactive notebooks, tutorials, and teaching modules for graduate‑level courses in brain science. | | | 2018 | Integration of a GPU‑accelerated

# ------------------------------------------------- # 1. Load a BIDS‑compliant dataset # ------------------------------------------------- bids_root = "/data/subject01" dataset = tio.load_bids(bids_root)

python -m pip install "twk-lausanne[cuda]" Pre‑built images are published on Docker Hub: