QuNex 1.0.0 release

1

Hi,

We are in the process of releasing our probably biggest update to this day. Release notes are attached below. Since this upgrade includes a lot of new things, the chance of some bugs sneaking through our testing process is a bit larger, please let us know if something is not working as it should. QuNex 1.0.0 will be available over the next couple of days, or early next week if there are some unpredicted delays.

Best, Jure

1.0.0 [QIO]:

  • Replacement of run_turnkey with run_recipe, a much more flexible and powerful engine for transparent and reproducible chaining of QuNex commands (https://qunex.readthedocs.io/en/latest/wiki/UsageDocs/RunningQuNexRecipes.html).
  • run_qa, quality assurance (QA) functionality, which you can use to quickly validate raw neuroimaging data and metadata to check if there are discrepancies between acquired sessions or images. The commands generates input-friendly session lists that can be used in commands that follow and user-readable reports of the QA (https://qunex.readthedocs.io/en/latest/api/gmri/run_qa.html).
  • Updated core functions for functional connectivity analyses, enabling flexible extraction of timeseries and computation of seed-based, ROI-based, and global brain connectivity (GBC). Some of Specific improvements are: a) the ability to work with resting state and different types of task-based data, b) the ability to use different functional connectivity measures (r, rho, covariance, cross correlation, inverse covariance, coherence, mutual information and multivariate autoregressive model coefficients), c) a flexible specification of target ROI using different types of input data and masks, and d) the ability to process mulitple session in a single call, compute and store both session specific and group level results (https://qunex.readthedocs.io/en/latest/wiki/UsageDocs/BOLDFunctionalConnectivity.html).
  • The container now includes the recently released version of HCP Pipelines (v5.0.0), which includes a number of new functionalities and improvements (https://github.com/Washington-University/HCPpipelines).
  • Added support for the HCP longitudinal FreeSurfer pipeline (hcp_long_freesurfer and hcp_long_post_freesurfer commands).
  • Support for the HCP TransmitBias pipeline (hcp_transmit_bias_individual). Also added onboading functionalities for imaging data required by this pipeline (B1).
  • hcp_icafix now uses pyfix by default, to use legacy MATLAB fix, add the --hcp_legacy_fix flag to your command call
  • Simplification of the registration and access process.
  • You can now map HCP derivatives (e.g., denoised concatenated REST BOLDs) from HCP folder structure to QuNex with map_hcp_data.
  • Default value for the hcp_prefs_template_res parameter of hcp_pre_freesurfer is now read and set from the imaging data.
  • Made several optimizations that should make QuNex more user friendly (e.g., automatic setting of parameter values from JSON sidecars, more robust logic for automatic setting of parameter values when they are inferred from imaging data …).
  • Fixed some bugs in our data onboarding functions (import_dicom, import_bids, import_hcp).
  • Fixed a bug in the use of IntendedFor BIDS field.
  • You can now specify ROIs using a .roi file that defines ROIs by the center and radius of a sphere.
  • Easier to understand error reports at several locations.
  • Connectome Workbench updated to the latest version.
  • The commercial rights for the container of QuNex 1.0 (QIO) are transferd to a Yale startup called Manifest Technologies, Inc. For any QuNex container v1.0 commercial licensing inquiries please contact qunex@manifesttech.io.
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