Functional Fusion Framework

The function fusion framework is a data structure / code based adopted by the Diedrichsenlab to learn brain organisation across functional imaging datasets. The current emphasis is on the cerebellum and neocortex, but the framework is general and can be applied to any brain region.

Licence and Acknowledgements

Documentation

Contents:

• Overview
• Atlases, Regions, and Parcellations
  • Atlas Spaces
  • Standard Atlases
  • Getting Atlases
  • Defining new Atlases or Regions
  • Reading data from NIFTI, GIFTI, or CIFTI files
  • Writing atlas data to NIFTI, GIFTI, or CIFTI files
  • Using parcellations
  • Saving parcellation results as pscalar CIFTI files
  • Transforming data between atlas spaces
• Atlasmaps
  • Example of volume-based ROI analysis
  • Example of surface-based ROI analysis
  • Data Extraction using atlas maps
• Datasets
  • Contributing datasets
  • Using dataset
  • Datasets that are available in the framework
• Data Import
  • Directory structure
  • participant.tsv file
  • reginfo.tsv file
  • Import Anatomical and MNI normalization parameters from SPM (Segement)
  • Import Cortical surfaces from Freesurfer reconstruction
  • Import SUIT normalization
  • Import functional estimates and design matrix from SPM
• Data Extraction
  • Spatial resampling
  • Datafile specification
  • Data aggregation
  • Optimal contrast
• Using Datasets
  • Loading Data
  • Aggregating data
  • Group averaging data
• Reliability
  • Reliability within individual
  • Reliability across individuals
  • Decomposing variances (reliability within and across subject)
  • Mean substraction
  • Separating the analysis by voxel and condition
  • Leave-one-out reliability
• API reference
  • Atlas module
  • Dataset module
  • Utility modules
  • Reliability module

Indices and tables

• Index

• Module Index

• Search Page