The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is an open-source software pipeline for automated preprocessing and analysis of resting-state fMRI data. C-PAC builds upon a robust set of existing software packages including AFNI, FSL, and ANTS, and makes it easy for both novice users and experts to explore their data using a wide array of analytic tools. Users define analysis pipelines by specifying a combination of preprocessing options and analyses to be run on an arbitrary number of subjects. Results can then be compared across groups using the integrated group statistics feature.
Optimized For Large Datasets
Researchers can now explore functional connectome data from thousands of subjects made public through releases by the International Neuroimaging Data-Sharing Initiative (INDI) and 1000 Functional Connectomes Project. With this in mind, C-PAC has been designed to reliably preprocess and analyize data for hundreds of subjects in a single run, either on a single machine or on a compute cluster using Sun Grid Engine, HTCondor, or Portable Batch System.
Ensure Robust Results
Different analysis pipelines can produce significantly different results, raising questions about the replicability and reliability of brain imaging findings. C-PAC makes it easy to explore the impact of particular processing decisions by allowing users to run a factorial number of analysis pipelines, each with a different set of preprocessing and analysis options. An integrated Quality Control interface facillitates rapid manual examination of pipeline outputs and selection of subjects to be included in group comparisons.
- Skull Stripping
- Template-Based Registration
- Automatic Tissue Segmentation
- Anatomical / Functional Coregistration
- Volume Realignment
- Slice Timing Correction
- Intensity Normalization
- Temporal Filtering
- Nuisance Signal Correction
- Median Angle Correction
- Spatial Smoothing
- Motion Scrubbing
- Seed-based Correlation Analysis
- Amplitude of Low Frequency Fluctuations (ALFF) and fALFF
- Regional Homogeneity
- Voxel-Mirrored Homotopic Connectivity
- Timeseries Extraction
- Network Centrality
- Connectome-Wide Association Studies
- Dual Regression
- Bootstrap Analysis of Stable Clusters
- Group Comparisons
Ask us at the CPAC Support Forum.