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Area of Science:

  • Neuroimaging
  • Neuroinformatics
  • Computational Neuroscience

Background:

  • The Human Connectome Project (HCP) preprocessing pipelines offer advanced neuroimaging analysis methods.
  • HCP pipelines utilize surface-based analysis and the CIFTI grayordinate format for enhanced statistical sensitivity and neuroanatomical accuracy.
  • A limitation is the requirement for specific imaging data (T2w, field map) not always present in legacy datasets.

Purpose of the Study:

  • To present the ciftify project, a framework for adapting HCP preprocessing pipelines to legacy neuroimaging data.
  • To enable analysis of existing functional datasets with accompanying anatomical data (T1w) in the CIFTI format.
  • To integrate ciftify with fMRIPrep within the BIDS-app framework for simplified usage.

Main Methods:

  • Developed ciftify as a parsimonious analytic framework.
  • Adapted key modules from HCP pipelines for integration with FreeSurfer's recon_all structural and existing functional preprocessing workflows.
  • Bundled the workflow with fMRIPrep under the BIDS-app framework for accessibility.

Main Results:

  • Established a framework enabling the analysis of legacy functional neuroimaging data using HCP-style methods.
  • Facilitated the conversion and analysis of data into the CIFTI grayordinate format, even without T2w or field map images.
  • Demonstrated the adaptability of HCP preprocessing to diverse datasets.

Conclusions:

  • The ciftify project provides a vital solution for leveraging legacy neuroimaging data with advanced HCP analytical approaches.
  • This framework promotes wider adoption of the CIFTI grayordinate standard in neuroimaging research.
  • Future neuroinformatics developments are expected to further streamline CIFTI-based analyses.