https://github.com/khanlab/scattr

BIDS app for structural connectivity between targeted regions
https://github.com/khanlab/scattr

Last synced: 6 months ago
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BIDS app for structural connectivity between targeted regions

• Host: GitHub
• URL: https://github.com/khanlab/scattr
• Owner: khanlab
• License: mit
• Created: 2022-03-10T19:12:40.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2025-12-10T20:28:57.000Z (8 months ago)
• Last Synced: 2025-12-11T07:38:39.047Z (8 months ago)
• Language: Python
• Homepage: https://scattr.readthedocs.io/en/stable/
• Size: 42.9 MB
• Stars: 6
• Watchers: 5
• Forks: 5
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/contributing/contributing.md
  • License: LICENSE

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README

          
# Structural Connectivity Applied To Targeted Regions (SCATTR)

[![Documentation Status](https://readthedocs.org/projects/scattr/badge/?version=stable)](https://scattr.readthedocs.io/en/stable/?badge=stable)

![Version](https://img.shields.io/github/v/tag/khanlab/scattr?label=version)

![Python3](https://img.shields.io/badge/python-3.8_|_3.9_|_3.10_|_3.11-blue.svg)

[![Tests](https://github.com/khanlab/scattr/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/khanlab/scattr/actions/workflows/test.yml?query=branch%3Amain)

![Docker Pulls](https://img.shields.io/docker/pulls/khanlab/scattr)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7636506.svg)](https://doi.org/10.5281/zenodo.7636506)

SCATTR is a BIDS App that performs a tractography processing workflow to 

identify connections between targetted structures of interest (extracted from

various atlases) in the brain, using some common neuroimaging tools like 

`prepdwi`, `Freesurfer`, and `Mrtrix3`.

![Example outputs](https://raw.githubusercontent.com/khanlab/scattr/main/docs/images/ex_output.png)

This is useful for:

* Analyzing structural connections of known brain circuits

* Examining specific connections of interest

* Comparison across different groups (e.g. controls vs patients)

## Example use of workflow 

This workflow was used to process and analyze the data from

[`hcp_subcortical_repo`](https://github.com/kaitj/hcp_subcortical_repro) 

(see Kai et al., 2022). 

## Workflow 

A brief summary of the workflow can be found below (see documentation for

a detailed summary):

_Note: The workflow assumes Freesurfer has already been run on the dataset, as 

well as diffusion preprocessing (e.g. distortion correction)._

![Workflow example](https://raw.githubusercontent.com/khanlab/scattr/main/docs/images/workflow.png)

1. Merge the segmentations of structures in a standard template space from 

various sources (if necessary) via 

[labelmerge](https://zenodo.org/record/7636410) into a combined atlas, which is 

used downstream to identify targeted connections.

1. Estimate and apply transformations from standard template space to 

subject-specific space.

1. Further process the pre-processed diffusion data to enable tractography

(e.g. compute response functions, fibre orientation distribution, 

normalization).

1. Perform whole-brain tractography from computed files, applying filtering 

(e.g. SIFT2) to computed tractogram. Once filtered, connections between the 

merged segmentations (from step 1) are identified, generating a connectome map.

1. Analysis can then be performed on the map to examine and explore the 

connectome of interest.

### **Full documentation:** [here](https://scattr.readthedocs.io/en/stable/)

## Relevant Papers

* Kai, J., Khan, A.R., Haast, R.A.M., Lau, J.C. (2022).

Mapping the subcortical connectome using in vivo diffusion MRI: feasibility

and reliability. Terra incognita: diving into the human subcortex,

special issue of NeuroImage. doi: 

[10.1016/j.neuroimage.2022.119553](https://doi.org/10.1016/j.neuroimage.2022.119553).