LiFE - Linear Fascicle Evaluation

Evaluation and statistical inference for living connectomes.

Project maintained by francopestilli Hosted on GitHub Pages — Theme by mattgraham

Linear Fascicle Evaluation (LiFE)

A new version of the LiFE code is developed at:

Caiafa, Cesar and Pestilli, Franco Multidimensional encoding of brain connectome. Scientific Reports 7, Article number: 11491 (2017)

Standard tractography can use diffusion measurements from a living brain to generate a large collection of candidate white-matter fascicles; the connectome. Linear Fascicle Evaluation (LiFE) takes any connectome and uses a forward modelling approach to predict diffusion measurements in the same brain. LiFE predicts the measured diffusion signal using the orientation of the fascicles present in a connectome. LiFE uses the difference between the measured and predicted diffusion signals to measure prediction error. The connectome model prediction error is used to compute two metrics to evaluate the evidence supporting properties of the connectome. One metric -the strength of evidence - compares the mean prediction error between alternative hypotheses. The second metric - the earth movers distance - compares full distributions of prediction error. These metrics can be used for: 1. Comparing tractography algorithms 2. Evaluating the quality of tractography solutions for individual brains or group of brains and 3. Testing hypotheses about white-matter tracts and connections.






Stable code release.

How to cite LiFE.

Pestilli, Franco, Jason D. Yeatman, Ariel Rokem, Kendrick N. Kay, and Brian A. Wandell. Evaluation and statistical inference for human connectomes. Nature methods 11, no. 10 (2014): 1058-1063.

Caiafa, Cesar and Pestilli, Franco Multidimensional encoding of brain connectome. Scientific Reports 7, Article number: 11491 (2017) doi:10.1038/s41598-017-09250-w


This work was supported by grants by US National Science Fundation (NSF BCS-1228397; NSF IIS-1636893; NSF BCS-1734853) and National Institute for Health (NEI EY015000).


  1. Download LiFE.
  2. Start MatLab.
  3. Add LiFE to the matlab search path.


Getting started.

Learn about LiFE by using life_demo.m in MatLab.

1. Download LiFE.

2. Download vistasoft.

3. Download LiFE Data Demo.

4. Read the life_demo documentation.

Read the description of the calculations in the documentation inside the file, life_demo.m by typing the following in the matlab prompt:

  >>  edit life_demo

5. Run the life_demo code.

This final step will run the life_demo code. The code will perform the operations described here.

  >>  life_demo

life_demo.m runs in about 30 minutes on a modern Intel processor with 8GB of RAM. This code has been tested with MatLab 2012b on Ubuntu 12.10 and Mac OSX 10.9.

LiFE in Python : Dipy

The LiFE algorithm has been recently implemented in Python by Ariel Rokem and is now available as part of the Dipy software: LiFE @ Dipy.