Caret7:Development/DataTypes

From Van Essen Lab

Revision as of 17:54, 2 August 2011 by Matt (Talk | contribs)
Jump to: navigation, search

Contents

Caret7 DataTypes

Scalar/Metric

  • Voxel and vertexwise continuous scalar values.
  • Can be surface (GIFTI), volume (NIFTI), or combined (CIFTI-Dense Timeseries format).
  • Represented internally as CIFTI with original header stored in an array for saving as original file type.
  • For volumes, use implicit or explicit masking (i.e. only store data not equal to zero, or user provides a binary mask file).
  • Ability to save as CIFTI dense timeseries or export to NIFTI volume or GIFTI scalar.
  • Do not composite columns unless requested to do so by user.
  • The user will choose Scalar/Metrics on a per window basis (allow at least 10)
  • Display settings on a per column basis
  • Ability to animate through columns/step through them one at a time (i.e. current dense timeseries functionality)
  • Ability to click on a voxel or vertex and display timeseries graph
  • Ability to display average and eigen timeseries graph for arbitrary ROI
  • Ability to export timeseries data


Label/Parcellation

  • Voxel and vertexwise labeled integer values
  • Each integer is associated with:
    • Name
    • Color
    • Metadata?
  • Can be surface (GIFTI), volume (NIFTI), or combined (CIFTI-Parcellation*).
  • Represented internally as CIFTI with original header stored in an array for saving as original file type.
  • For volumes, use implicit or explicit masking (i.e. only store data not equal to zero, or user provides a binary mask file).
  • Ability to save as CIFTI parcellation* or export to NIFTI volume or GIFTI label.
  • The user will choose Scalar/Metrics on a per window basis (allow at least 10)
  • Ability to turn on and off different parcels, edit their names, colors, metadata
  • Ability to override medial wall

*A CIFTI-parcellation is simply the header of a parcellated timeseries or connectome file. It stores the vertices and voxels that define each parcel, their names, their colors, and any relevant metadata.

Dense Connectivity

  • Voxel and vertexwise connectivity values
  • Too big to load into RAM (load on demand)
  • At least 8 connectivity entries
  • Point and click interaction on surface, volume, volume-surface-outline
  • Ability to freeze point and click and explore the values in the node
  • Ability to export connectivity map to Scalar/Metric
  • Ability to average connectivity across subjects or a arbitrary ROI
  • Consider Dense Timeseries for very high temporal density signals (e.g. lowTR BOLD, MEG/EEG) with very large files, otherwise dense timeseries functionality subsumed into metric/scalar
Personal tools
Sums Database