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Published April 28, 2021 | Version 0.0.10
Software Open

ME-ICA/tedana: 0.0.10

  • 1. Mayo Clinic
  • 2. National Institutes of Health
  • 3. Florida International University
  • 4. Basque Center on Cognition, Brain and Language
  • 5. Center for Magnetic Resonance Research, University of Minnesota
  • 6. Montreal Neurological Institute, McGill University
  • 7. Eindhoven University of Technology
  • 8. Mount Sinai Hospital
  • 9. Stanford University
  • 10. Big Data Institute, University of Oxford
  • 11. National Institutes of Mental Health, Section on Functional Imaging Methods
  • 12. Laboratory of Brain and Cognition, National Institute of Mental Health
  • 13. The Alan Turing Institute

Description

Release Notes

The 0.0.10 release of tedana includes a number of bug fixes over the previous stable release, and drops support for Python 3.5, as well as adding formal support for Python 3.8 and 3.9. As always, we encourage users to review our documentation (at tedana.readthedocs.io) which includes information for theoretical background for multi-echo, acquisition-related guidance, and documentation for our :sparkles: interactive reports. :sparkles:

The complete changelog since the last alpha release is included below. Here, we briefly summarize the significant changes since our last stable release.

:wrench: Breaking changes
  • PCA is now normalized over time, which may change number of PCA components retained
  • A bug-fix for ICA f-statistic thresholding may change some component classifications and metric calculations.
  • For datasets with more than 3 echoes, a bug was fixed where we required all echoes to be "good" instead of just the minimum three needed for accurate metric calculation. This may significantly impact classifications on datasets with more than 3 echoes.
:sparkles: Enhancements
  • Formal support added for Python 3.8 and 3.9.
  • We now normalize PCA over time.
:bug: Bug fixes
  • In prior releases, f-statistic maps were thresholded just before kappa/rho calculation, such that the metric maps related to T2 and S0 were not aligned with the values used to calculate kappa and rho. All T2 and S0 maps are now thresholded at calculation, so that their derivative metrics reflect this thresholding as well.
  • In previous releases, there was a bug where datasets required all echoes be considered "good" for a voxel to be included in denoising. However, in datasets with more than three echoes, this is too conservative. This release requires only the minimal 3 echoes in order to perform accurate metric calculations.
Changes since last stable release
  • [MAINT] Modifies actions to run on release publish (#725) @jbteves
  • [DOC] Add warning about not using release-drafter releases to developer instructions (#718) @tsalo
  • [FIX] Bumps (down) sklearn and scipy (#723) @emdupre
  • [MAINT] Drop 3.5 support and begin 3.8 and 3.9 support (#721) @tsalo
  • [FIX] Calculate Kappa and Rho on full F-statistic maps (#714) @tsalo
  • [FIX] Adds f_max to right place (#712) @jbteves
  • [DOC] Added MAPCA to list of dependencies (#709) @handwerkerd
  • [DOC] Add references to HTML report (#695) @tsalo
  • [FIX] Enable normalization in mapca call (#705) @notZaki
  • [REF] Replace MAPCA code with mapca library (#641) @tsalo
  • [REF] Normalize over time in MAPCA (#702) @tsalo
  • [ENH] Match BokehJS with BokehPy version (#703) @notZaki
  • [MAINT] Update Kirstie affiliation in zenodo file (#694) @KirstieJane
  • [MAINT] Add Javier Gonzalez-Castillo to Zenodo file (#682) @javiergcas
  • [DOC] Harmonizes Governance Documents (#678) @jbteves

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