LiFE (Linear Fasicle Evaluation) 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.

Segments all streamlines from an input tractogram in to general categories (i.e. fronto-temporal, parieto-occipital etc).

Classifies streamlines into known anatomical tracts.

We present cortical endpoint termination data from a series of associative white matter tracts connecting the posterior human brain cortex. These tracts include the vertical occipital fasciculus (VOF), the posterior arcuate fasciculus (pArc), the temporo-parietal connection (TP-SPL), and the middle longitudinal fasciculus (MdLF).

Generate FA profiles

Applies nlmeans denoise (dipy / dipy_nlmeans)

This app has been deprecated. It will only work with wmc_deprecated datatype.

This application takes in a full tractography (Multishell Tractography w/CSA or Multishell Tractography w/ SHORE) and the output from the White Matter Segmentation and uses the Recobundles algorithm to find the corresponding white matter segmentation in the full tractogram.

Classifies streamlines into known anatomical tracts.

BIDS app for HCP pre-FreeSurfer for BrainLife. Performs ACPC alignment, FNIRT-based brain extraction, and bias field correction. (Please see https://github.com/Washington-University/HCPpipelines/wiki/v3.4.0-Release-Notes,-Installation,-and-Usage#structural-preprocessing for more information on HCP structural pipelines.)

Run HCP Pipeline powered by BIDS app

This service takes in a nifti and tck output from Multishell Tractography w/ CSA or SHORE and outputs the CST and ILF segmentations in .tck file format of both the left and right hemispheres.

Brainlife App for MIC-DKFZ/TractSeg. A tool for fast and accurate white matter bundle segmentation from Diffusion MRI using pretrained pytorch ML model.

Build structural brain networks using diffusion-weighted MRI, tractography and a brain atlas for cortical and subcortical parcellation.

Brain Extraction via FSL's BET command

A brain life app to prepare PRF and freesurfer data to be visualized with ui-3dsurfaces

This app performs automatic example-based tract segmentation using the Linear Assignment Problem (LAP) algorithm having multiple examples.

This application converts Freesurfer's pial and white matter surfaces to different file types. It will convert the lh/rh.pial, lh/rh.white, lh/rh.smoothwm, lh/rh.inflated files into your choice of file type (stl, vtk, gii, mgz)

Generates specified ROIS (in nifti format) from an input atlas.

This app will align a T1w image to the ACPC plane (specifically, the MNI152_T1_1mm template from FSL using a 6 DOF alignment via FSL commands. This protocol was adapted from the HCP Preprocessing Pipeline (https://github.com/Washington-University/HCPpipelines.git). Requires a T1w image input and outputs an acpc_aligned T1w image.

copy of ensemble tracking that runs from the master branch for testing new changes

repeat of fixed parameters for OHBM 2019 submission

This app is an example for the brainhack 2018 tutorial. Should be deleted

This application will provide a recommendation on which axis you should flip the bvecs of your data, if it is necessary. It will perform fiber tracking using 4 different gradient flip options (no flip, x flip, y flip, and z flip) and report the most likely flip needed for your data. The flip recommendation is made based on the flip direction with the highest number of long fibers.

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