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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Streamlined neuroimaging analysis +with enhanced infrastructure +and surface API integration + Yasmin Mzayek, Himanshu Aggarwal, Pierre Bellec, Ahmad Chamma, Alexandre Cionca, Jelle Roelof, Dalenberg, Jérôme Dockès, Mathieu Dugré, Elizabeth DuPre, Rémi Gau, Nicolas Gensollen, Mathias Goncalves, Anne-Sophie Kieslinger, Alisha Kodibagkar, Steven Meisler, François Paugam, Julio A. Peraza, Jean-Baptiste Poline, Patrick Sadil, Taylor Salo, Kevin Sitek, Maximilian Cosmo Sitter, Alexis Thual, Mohammad Torabi, Konrad Wagstyl, Hao-Ting Wang, Michelle Wang, Bertrand Thirion and Nilearn contributors + + + + - Python package for analysis of brain images + – Connectivity analysis (resting-state) + – Decoding (MVPA) + – GLM (stats) + – Plotting volumetric and surface data +- Well documented +- Supportive community +- Open-source and community driven + What is Nilearn ? + + - Run decoding directly on surfaces +- Input / output for surface data +- Improved user-guide + + + - Check the documentation + nilearn.github.io + +- Ask questions on + neurostars.org/tag/nilearn + +- Contribute GitHub + github.com/nilearn/nilearn + +- Weekly drop-in hour, Wednesday 4pm UTC + meet.jit.si/nilearn-drop-in-hours + + Join the community!!! + Future directions + + + + + DOI + 10.5281/zenodo.8397156 + + + + + + RRID + SCR_001362 + + + + + + + + New surface API + + + # experimental module with surface datasets +>>> from nilearn.experimental import surface +>>> surf_time_series = surface.fetch_nki()[0] +>>> print(f"NKI image: {surf_time_series}") +NKI image: <SurfaceImage (895, 20484)> + + + + + + # mesh and data in the same surface object +>>> print(surf_time_series.data.parts) +{'left': array([[-0.4938, ...] ...], + dtype=object), + 'right': array([[-0.5474, ...] ...], + dtype=object)} + +>>> print(surf_time_series.mesh.parts) +{'left': <FileMesh with 10242 vertices>, + 'right': <FileMesh with 10242 vertices>} + + + + + + # surface maskers to perform signal extraction +>>> masker = surface.SurfaceMasker() +>>> data = masker.fit_transform(surf_time_series) +>>> print(f"Masked data shape: {data.shape}") +Masked data shape: (895, 20484) + +>>> first_data = data[0] +>>> first_img = masker.inverse_transform(first_data) +>>> print(f"First timepoint: {first_data}") +First timepoint: <SurfaceImage (20484,)> + + + + + + # easy plotting and reporting +>>> from nilearn.experimental import plotting +>>> plotting.plot_surf(first_img, ...) +>>> report = masker.generate_report() + + + + + + + Releases 0.10.2 - 0.10.4 + - support for CompCor confounds from fmriprep +- easily load fmriprep confounds for GLM analysis +- improved HTML reports +- fixes for many old (and new) bugs +- improved codebase (formating, linting, automation) + + + + + + + + + + + + + + + # quickly run GLM on surface data +>>> from nilearn.glm.first_level import FirstLevelModel +>>> glm = FirstLevelModel(t_r).fit(surface_time_series, + events) +>>> z_scores = glm.compute_contrast("language-string") +>>> plotting.plot_surf_stat_map(stat_map=z_scores, + hemi="left", ...) + + + + + + + + + + + + + + + contrast: language-string + + + diff --git a/publications/ohbm_2024/qr-code-doc-logo.svg b/publications/ohbm_2024/qr-code-doc-logo.svg new file mode 100644 index 0000000..091eaaf --- /dev/null +++ b/publications/ohbm_2024/qr-code-doc-logo.svg @@ -0,0 +1,1307 @@ + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + \ No newline at end of file diff --git a/publications/ohbm_2024/tmp.py b/publications/ohbm_2024/tmp.py new file mode 100644 index 0000000..b499133 --- /dev/null +++ b/publications/ohbm_2024/tmp.py @@ -0,0 +1,24 @@ +from nilearn.experimental import surface, plotting +from nilearn.plotting import show + +img = surface.fetch_nki(mesh_type="inflated", n_subjects=1)[0] + +masker = surface.SurfaceMasker() +masked_data = masker.fit_transform(img) +first_data = masked_data[0] +first_data = masker.inverse_transform(first_data) + +bg_data = surface.load_fsaverage_data(data_type="curvature", mesh_type="inflated") + +# easy plotting and reporting +report = masker.generate_report() +plotting.plot_surf(first_data, threshold=0.5) +show() + + + +from nilearn.glm.first_level import FirstLevelModel + +glm = FirstLevelModel(t_r).fit(surface_time_series, events) +z_scores = glm.compute_contrast("language-string") +plotting.plot_surf_stat_map(stat_map=z_scores, hemi="left", threshold=2.0, ...) \ 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