Linear Fascicle Evaluation (LiFE)

Method for encoding and evaluating the quality of tractography using multidimensional arrays.

This software implements a framework to encode structural brain connectomes into multidimensional arrays. These arrays are commonly referred to as tensors. Encoding Connectomes provides an agile framework for computing over connectome edges and nodes efficiently. We provide several examples of operations that can be performed using the framework.

One major application of the tensor encoding is the implementation of the Linear Fascicle Evaluation method, in short LiFE. The tensor encoding method allows implementing LiFE with a dramatic reduction in storage requirements, up to 40x compression factors. Furthermore, connectome encoding allows performing multiple computational neuroanatomy operations such as tract-dissections, virtual lesions, and connectivity estimates very efficiently using machine-friendly array operators.

We provide demos to explain how to:
(1) Load and encode diffusion-weighted data and tractography models of white matter fascicles, as well as perform multidimensional arrays operations.
(2) Build and optimize a Linear Fascicle Evaluation model.
(3) Perform neuroanatomical segmentation, computational neuroanatomy operations, and virtual lesions using the connectome encoding framework.
(4) Reproduce some of the figures of an article describing the method implemented in this toolbox:

Caiafa, C.F., Pestilli, F. Multidimensional encoding of brain connectomes. Sci Rep 7, 11491 (2017). https://doi.org/10.1038/s41598-017-09250-w

Application.
Encoding of brain connectome and associated phenotypes into multidimensional arrays.

Evaluate the evidence supporting white-matter connectomes generated using magnetic resonance diffusion-weighted imaging and computational tractography.
Perform statistical inference on white-matter connectomes: Compare white-matter connectomes, show the evidence for white-matter tracts and connections between brain areas.