lipsia

Getting started

  • Overview
    • Installation
    • Lipsia file format
    • Getting started
    • Laminar-specific fMRI analysis (Cylarim) - under construction
    • Statistical inference (LISA) in examples:
    • Semi-blind machine learning (SML):
  • Installation
    • How to install LIPSIA using Singularity
    • Install LIPSIA from source (Linux/Unix/MacOS)
      • 1) Install the necessary compilers and libraries
    • Ubuntu:
    • Fedora:
    • MacOS:
      • 2) Clone the git repository:
      • 3) Execute the script “lipsia-setup.sh” (in a shell)
      • 4) Compile lipsia
      • 5) Change bash profile
    • Install LIPSIA using Docker
      • 1) Install docker on your local machine
      • 3) Build the Dockerfile

Laminar-specific fMRI (Cylarim)

  • Cylarim overview
    • Initial steps
    • The main steps
    • Example workflow
    • Contents
      • vcylarim
        • Example:
        • Parameters of ‘vcylarim’
      • vcylarim_stats
        • Examples:
        • Parameters of ‘vcylarim_stats’
      • vcylarim_getmask
        • Example:
        • Parameters of ‘vcylarim_getmask’
      • vcylarim_plot
        • Example:
        • Example output:
        • Parameters of ‘vcylarim_plot’
      • vrim
        • Example:
        • Parameters of ‘vrim’
      • vmetric
        • Example:
        • Parameters of ‘vmetric’
  • vcylarim
    • Example:
    • Parameters of ‘vcylarim’
  • vcylarim_stats
    • Examples:
    • Parameters of ‘vcylarim_stats’
  • vcylarim_plot
    • Example:
    • Example output:
    • Parameters of ‘vcylarim_plot’
  • vcylarim_getmask
    • Example:
    • Parameters of ‘vcylarim_getmask’
  • vmetric
    • Example:
    • Parameters of ‘vmetric’
  • vrim
    • Example:
    • Parameters of ‘vrim’

LISA statistical inference

  • LISA generic framework (vlisa_generic)
    • Parameters of ‘vlisa_generic’:
      • References
  • LISA second-level GLM (vlisa_2ndlevel)
    • Parameters of ‘vlisa_2ndlevel’:
      • References
  • LISA onesample test (vlisa_onesample)
    • Parameters of ‘vlisa_onesample’:
      • References
  • LISA twosample test (vlisa_twosample)
    • Parameters of ‘vlisa_twosample’:
      • References
  • LISA single subject analysis using precoloring (vlisa_precoloring)
    • Parameters of ‘vlisa_precoloring’:
      • References
  • LISA single subject analysis using prewhitening (vlisa_prewhitening)
    • Parameters of ‘vlisa_prewhitening’:
      • References

Semi-blind machine learning (SML):

  • Semi-blind machine learning - ensemble learning (SML-EL)
    • Example:
    • Parameters of ‘vsml’:
      • Reference:
  • Reading data for use in Semi-blind machine learning (SML-EL)
    • Example:
    • Parameters of ‘vreadconnectome’:
  • Semi-blind machine learning - ensemble learning (SML-EL)
    • Example:
    • Parameters of ‘vsml’:
    • Reference:

Task-related edge density (TED)

  • Cut functional data into trials
    • Example:
    • Parameters of ‘vcuttrials’
  • Task-based edge density (TED)
    • Example:
    • Parameters of ‘vted’
  • Hubness maps resulting from TED
    • Example:
    • Parameters of ‘vhubness’
  • False discovery rates in TED
    • Example:
    • Parameters of ‘vtedfdr’

Network tools

  • Bipartite connectivity mapping (BCM)
    • Example:
    • Parameters of ‘vbcm’
    • References
  • Connectivity concordance mapping (CCM)
    • Example:
    • Parameters of ‘vccm’
  • Eigenvector centrality mapping (ECM)
    • Example:
    • Parameters of ‘vecm’
    • References

Preprocessing

  • Detrending
    • Parameters of ‘vdetrend’
  • Spatial smoothing and temporal filtering
    • Parameters of ‘vpreprocess’

File format converters

  • Convert from dicom files
    • Parameters of ‘vdicom’
  • Convert to/from nifti files
    • Parameters of vnifti

Utilities

  • Masking an image
    • Parameters of ‘vapplymask’
  • Denoising
  • Speckle filter
  • Design file format
  • First level design files
  • Higher-level level design files
    • One-sample test
    • Two-sample test
    • Paired test
  • ANOVA (interactions and main effects, repeated measures)
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