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Changelog

Source: NEWS.md

ravecore 0.1.2

New Features

  • Added yael_macaque() and macaque support in the YAEL preprocessing pipeline (#32dcb87, #6c864e2).
  • Added generate_atlas_YBA() to morph YBA690 / YBA696 atlases from the MNI152 symmetric template to the subject, with an atlas-overlay report (#f45c574).
  • Added streamline support: rave_brain() gains a streamlines argument, and the YAEL process can generate streamlines from a template (#fbd913a, #f79c06e, #6adff48).
  • yael_preprocess() accepts additional_images so that more image modalities can be used for co-registration (#e6d97ba).
  • Added realign_trials() to realign arrays by event; event can be an event string or a column name such as "Time" or "Event_*" (#f4fc653, #db09e00).
  • Added validate_condition_groupings() to validate and clean condition-group lists; the epoch table is ordered by trial number (#923c913, #509570b).
  • Added "db_zscore" baseline method to power_baseline() (#ea95dc4).
  • Repositories gain get_electrode_coordinate() to subset the electrode table by channel numbers and types (#9c7f5df).
  • RAVEEpoch supports an ExcludedHint column and an exclude_trials() method; saving produces a trimmed _OutlierRemoved epoch (#33c828c).
  • Added reg3d_rigid() and cmd_run_reg3d_rigid() to rigidly co-register CT to MRI with the built-in ravetools::register_volume3d() (YAEL-reg3d; no external program needed), with Rigid, DenseRigid and FastRigid presets and mutual-information or cross-correlation cost. Requires ravetools 0.3.1 or later.
  • Added YAELNativeProcess, as_yael_process(engine = "native"), yael_preprocess_native() and cmd_run_yael_preprocess_native(): the YAEL co-registration, template normalization and atlas mapping without Python, built on ravetools (N4, tissue segmentation with template priors, affine and SyN registration). They take the same arguments and write the same files as YAELProcess, and either engine reads the results of the other (checked against ANTs on toy images); YAELProcess stays the default. Requires ravetools 0.3.3 or later; with an older version the native functions stop with an explanation. Differences from YAELProcess (details in ?YAELNativeProcess):
    • No deep_atropos (use_antspynet is ignored): the tissue segmentation with the template priors always runs. A tissue class whose prior is empty inside the brain is left out (with a warning) instead of failing, and the template brain mask is made binary.
    • Every image of the non-linear stage uses local cross-correlation (no non-linear mutual information), also when normalize_affine is given.
    • ravetools runs a greedy single-field SyN with a fixed-point inverse. Its linear stages use an automatic step size, random (Bernoulli) sampling and no histogram matching, and its affine registration starts with a rigid pass.
    • The update-field smoothing takes the rpyANTs values (3 and 3.5) as a standard deviation in voxels, where ANTs reads a variance; for templates with more voxels per brain it is multiplied by up to 2 (the 0.5 mm 09b templates), since the 1 mm values folded the deformation in about 1.5% of the brain on mni_icbm152_nlin_asym_09b. The share of the brain where the final deformation folds is logged, with a warning above 1%.
    • When the k-means segmentation that finds the white matter fails on a few extreme intensities, it is run again with the intensities clipped at their 99.9% (then 99%) quantile.
    • Additional images are resampled onto the native T1w grid and left out (with a warning) when they cover less than 95% of the brain; the template tissue priors are resampled onto the template T1 grid; the outermost voxel layer of the stored warp fields has no displacement.
    • transform_image_from_template() and transform_image_to_template() return ieegio_volume objects, with nearest-neighbor or linear interpolation only, and interpolator = "auto" follows the documented rule.
    • Normalization results are replaced together once the new ones are complete, and stale ants<K> transforms of earlier layouts are removed; queries never create folders; set_input_image() reads the new image before removing the old one; nothing is written into the template folders.
    • Streamlines in VTK format are neither read nor written (they need Python), and TT files are saved as TCK.
    • Memory: with a 1 mm T1w image, the normalization to mni_icbm152_nlin_asym_09a peaked at about 8 GB of resident memory, and to mni_icbm152_nlin_asym_09b at about 15 GB (22 GB with compressed memory). The script of cmd_run_yael_preprocess_native() therefore switches from 09b to 09a below 32 GB of memory (12 GB for cmd_run_yael_preprocess()), and map_to_template() warns for such templates below 32 GB.
  • Added YAELProcess$write_talairach_xfm() to write ants/mri/transforms/talairach.xfm from an existing template normalization.
  • YAELProcess$generate_atlas_from_template() gains smooth_method = "explicit" to smooth atlas surfaces with ravetools::mris_smooth(), and max_vertices (500,000 by default) to reduce very large atlas surfaces after smoothing; both are passed to ieegio::volume_to_surface() when the installed ieegio accepts them.
  • RAVESubject gains format(), which print() now shows: the subject’s blocks, its electrodes by signal type with sample rates and which ones are Notch-filtered or have Wavelet power, its epoch and reference names (with the subject’s defaults marked), and its imaging state (native MRI, MNI normalization templates, CT-MRI co-registration, FreeSurfer). Only metadata are read.

Bug Fixes

  • YAELProcess$generate_atlas_from_template() reports each atlas surface it fails to generate as a warning naming the volume; failures were hidden by a silent try(). Generating the surface of a whole-brain parcellation such as YBA690 (millions of vertices) crashed R inside ravetools::vcg_smooth_implicit(); with ravetools 0.3.3 the full-resolution surface is smoothed in about 20 seconds and then reduced to max_vertices.
  • YAELProcess$construct_ants_folder_from_template() wrote ants/mri/transforms/talairach.xfm from the template-to-native ANTs affine without inverting it or converting LPS to RAS, and applied the MNI152-to-MNI305 correction to every template. For subjects without rave-imaging/fs, MNI305/MNI152 electrode coordinates derived from this file could be off by several centimeters. The transform is now inverted, converted to RAS, and corrected only for MNI152 templates; failures are logged and the stale file is removed. To repair a subject, run as_yael_process("<project>/<subject>")$write_talairach_xfm("<template>"), then re-save the electrode localization, or re-import an electrode table without the MNI305_*/MNI152_* columns (or with them set to zero); existing MNI columns are kept as they are.
  • cmd_run_dcm2niix() now remembers the imported image source under the yael_preprocess module (it used the old module ID surface_reconstruction), as a path relative to the subject’s raw folder (or BIDS raw folder), so the module loader can select it (#57c7684).
  • Subject pipeline listing is backward compatible with old pipeline paths, and listing all pipelines works again (#f428fc0, #e9ebb8e, #7922564).
  • filearray partitions are created before parallel workers start during power baseline, preventing a rare write race (#9dadfec).
  • rave_slices is no longer copied when normalization fails (#2dd9c41).
  • Fixed a read_mat2() call signature in the per-channel HDF5/MATLAB importer (#6872bfc).
  • Fixed a syntax issue in the YAEL process (#61016f5).

Other Changes

  • Added tests that check function-call signatures through asNamespace() (#633cde9).

ravecore 0.1.1

CRAN release: 2026-04-02

New Features

  • Added generate_atlases_from_template() to generate brain atlases from a template; supports exporting STL meshes in LPS coordinates (#d2b0f60, #c42dbb9).
  • Added use_antspynet option in the YAEL preprocessing pipeline (#7795023).
  • Added snapshot report for projects and subjects (#f91eb3b).
  • Added install_openneuro() helper to install OpenNeuro subjects (#1cdffc2).
  • Added quick mode for faster subject preparation (#70388b4).
  • Added spike diagnostic plots and graphic options for the spike visualizer (#b53887d, #f1cc332).
  • Added debug message output for spike sorter workflows (#fc25b2f).
  • Added utility functions for SpikeInterface integration (#71db36d).
  • Spike analyzer now uses band-passed signal for improved accuracy (#bb021d4).
  • Parallel workers are used to save spike data (#b4c1b46).
  • Python environment is auto-installed when missing (#5985f46).
  • Spike sorters are now cached to avoid redundant computation (#2068ce0).
  • Baseline firing rate computation now supports sub-1 rates (#292f2fe, #48f3760).
  • Spike histogram bins now cover the entire epoch range (#d49f9db, #a9e94ea).
  • Spike train is filtered to ensure times are within the requested bin range (#ca2c1e4).
  • Repository object format has been updated and improved (#ea2513a).

Bug Fixes

  • Fixed HDF5 links not being closed promptly, which could cause resource leaks (#a5e95fe).
  • Fixed LFP_reference serialization error and added additional validation checks (#93ad452, #f39f51a).
  • Fixed error message displayed when rpymat is not configured (#ff5a6b2).
  • Fixed ensure_py_package() to install spikeinterface via pip correctly (#1bcd358).
  • Fixed ordering issue when the electrode table is empty (#37904c3).
  • Fixed syntax errors (#fb14366).
  • Handle missing electrodes gracefully (#3f6e381).
  • threeBrain template is now ensured without throwing an error (#f91eb3b).
  • Invalid projects now display without broken links (#048c91e).
  • Spike iteration now processes per channel instead of manually computing iteration chunks (#1580465).
  • Only spike data (not raw signals) is stored during sorter runs (#2aa1cff).
  • Reference path is now correctly prioritized (#39c75bb).

Other Changes

  • Lint and code style improvements (#afae460).
  • Minor documentation fixes (#990fc40).
  • CRAN submission comment fixes (#004b775, #8804222).

ravecore 0.1.0

CRAN release: 2025-09-23

  • Initial CRAN submission.