v0.1.6

This release resolves an issue with mapping high-resolution connectomes while computing the mean statistic for input weights.

v0.1.5

Important bug fix (sklearn change name to scikit-learn)

v0.1.4

This version consists of a range of new features and bug fixes:

added support for weights (sift2, FA, length, etc.)
added possibility to disable verbosity
added support for computing mean instead of sum (e.g. mean FA in high-resolution)
added support for automated pypi push upon release (needs testing)
fixed a bug with subcortical parcellations (needs testing)

Connectome Spatial Smoothing v.0.1.3

This version fixes a bug introduced by the previous release.

Connectome Spatial Smoothing v.0.1.2

This release includes updates to the code to add support for subcortical volumetric representation and fix small bugs in the previous code.

Connectome Spatial Smoothing v.0.1.1

The first release of code and sample scripts to use for Connectome Spatial Smoothing.

Structural connectomes computed from diffusion MRI tractography are increasingly mapped at higher spatial resolutions comprising thousands of network nodes. High-resolution connectomes refer to those mapped at the resolution of vertices forming the cortical surface mesh. These connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We've implemented a connectome analogue of image smoothing to increase the robustness of mapped high-resolution structural connectomes against noise and registration errors. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen bivariate smoothing kernel to the two endpoints of all tractography streamlines, yielding a spatially smoothed connectivity matrix. CSS proposes a computationally efficient method using a matrix congruence transformation on sparse connectome representations to smooth connectivity matrices. Our analyses indicate that connectome spatial smoothing is particularly important for the reliability of high-resolution structural connectomes, but can also provide benefits to connectomes mapped at a lower parcellation resolution. Given that CSS is a fast operation relative to the time required to perform whole-brain tractography, we anticipate that spatial smoothing will be integrated as a common step in established connectome mapping workflows.

To find out more about the method's detail and benefits check the following article:

Mansour L., S., Seguin, C., Smith, R.E. and Zalesky, A. Connectome Spatial Smoothing (CSS): concepts, methods, and evaluation.