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C-PAC 1.8.8.dev1 Beta documentation
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C-PAC 1.8.8.dev1 Beta documentation
  • User Guide
    • Latest Release: Version 1.8.7 Beta (May 03, 2024)
    • 1. C-PAC Quickstart
    • 2. Specify Your Data
    • 3. Select Your Pipeline
      • Pre-configured Pipelines
      • Configurable Settings
        • ALFF and f/ALFF
        • ReHo
        • Network Centrality
        • SCA/Dual Regression
        • VMHC
        • PyPEER Integration
      • Data Management and Environment Settings
        • Computer Settings
        • Output Settings
      • Pre- and post-processing
        • Anatomical Preprocessing
        • Functional Preprocessing
        • Nuisance Corrections
        • Timeseries Extraction
        • Configuring ROI Time Series Extraction
        • After Warp Settings
      • Derivatives
        • Seed-based Correlation Analysis (SCA) and Dual Regression - Analyze the connectivity between brain regions.
        • Voxel-mirrored Homotopic Connectivity (VMHC) - Investigate connectivity between hemispheres.
        • Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) - Measure the power of slow fluctuations in brain activity.
        • Regional Homogeneity (ReHo) - Measure the similarity of activity patterns across neighboring voxels.
        • Network Centrality - Analyze the structure of functional networks.
      • Quality Control
        • QC Pages - Visual Data Quality Control
        • XCP QC files - eXtensible Connectivity Pipeline-style quality control files
    • 4. Pre-Process Your Data
      • Anatomical Preprocessing
      • Functional Preprocessing
      • Longitudinal Preprocessing
      • Nuisance Corrections
      • ICA Denoising
      • Timeseries Extraction
      • Configuring ROI Time Series Extraction
      • After Warp Settings
    • 5. Compute Derivatives
      • ALFF and f/ALFF
      • ReHo
      • Network Centrality
      • SCA/Dual Regression
      • VMHC
      • PyPEER Integration
    • 6. All Run Options
    • 7. Run Group Analysis
      • FSL FEAT & Randomise
      • Bootstrapped Analysis of Stable Clusters (PyBASC)
      • Multivariate Distance Matrix Regression (MDMR)
      • Inter-subject Correlation (ISC) & Inter-subject Functional Correlation (ISFC)
      • Quasi-Periodic Patterns (QPP)
    • 8. Check Your Outputs
    • 9. Tutorials
      • 1. Tutorial: optimizing memory estimation
    • 10. Troubleshoot
    • 11. Release Notes
    • Appendix
    • Benchmark Package
    • C-PAC versions
  • Developer Documentation
    • Installing CPAC
    • Pipeline Development
    • Templates and Atlases
    • Nodes
    • Random State
    • Logging
    • Workflow Documentation
    • Workflows
      • C-PAC Pipeline Construction
      • Anatomical Preprocessing
      • Functional Preprocessing
      • Segmentation Workflow
      • Seed Based Correlation Analysis
      • Bootstrap Analysis of Stable Clusters
      • Nuisance Signal Removal
      • Amplitude of Low Frequency Fluctuations(ALFF) and fractional ALFF
      • Voxel Mirrored Homotopic Connectivity Analysis
      • Regional Homogeneity Approach to fMRI data analysis
      • Generate Motion and Power Statistics
      • Scrubbing
      • Group Analysis
      • Easy thresh
      • Multivariate Distance Matrix Regression (MDMR)
      • Median Angle Correction
      • Registration
      • Timeseries Analysis
      • Network Centrality
    • Indices and tables
    • eXtensible Connectivity Pipeline-style quality control files
    • Utilities
    • Testing CPAC
    • Continuous Integration
    • Deprecating a version of C-PAC
  • License
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C-PAC Tutorials¶

These tutorials can be viewed online here, or downloaded from the FCP-INDI/C-PAC_tutorials repository on GitHub.

  • 1. Tutorial: optimizing memory estimation
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1. Tutorial: optimizing memory estimation
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