Generate FA profiles
Brainlife.io app that computes the signal-to-noise ratio in the corpus callosum
Converts a tck that was made using MRtrix 2 into a trk in the dwi space.
clever computes principal Components LEVERage (CLEVER) and robust distance, measures of outlyingness for high-dimensional data such as fMRI data.
Compute and plot total number of fibers, total number of nodes and average length of some tracts.
This app performs automatic example-based tract segmentation using the Linear Assignment Problem (LAP) algorithm with a single example. The user can specify up to eight tracts of interest, otherwise all the tracts of the given segmentation are considered as input.
Takes a white matter classification resulting from a tract segmentation app and outputs one trk file for each segmented tract.
brainlife.io version of multi atlas transfer tools (maTT). Specifically, this is an implemenation of maTT2, which uses the gcs files trained on the mindboggle-101 data. The GCS files are automatically pulled from the maTT fishare directory if not present locally. Inputs include: freesurfer, t1, atlas. Outputs include: parc.nii.gz (for each atlas indicated) & remapped LUT text file (for each atlas indicated). These outputs are in the space of the T1w that was submitted to FreeSurfer's...
This application will correct for bias field issues in T1 images using ANTs N4BiasFieldCorrection algorithm
Tesor reconstruction and compute DTI metrics using weighted least-squares (dipy_fit_dti)
Classifies streamlines into known anatomical tracts.
BIDS app for HCP post-FreeSurfer for BrainLife. Outputs include surface topologies and features resampled onto the high (164k) dimensional atlas space as well as myelin mappings. (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.)
This application checks and reports the orientation (neurological or radiological) of a nifti file.
This brainlife.io App implements the white matter segmentation of the vertical tracts described in Bullock et al. 2019. It segments 8 (4 per hemisphere) white matter tracts, the posterior Arcuate (pArc), temporal-parietal connection (TPC), middle-longitudinal fasciculus (MdLF-SPL MdLF-Ang) and the vertical occipital fasciculus (VOF).
This app will generate nifti files for specific ROIs, or every ROI, for a parcellation (either freesurfer or atlas).
Index specific connections for a project
Convert trk (trackvis) file to tck (mrtrix) format
This app segments tracts using pairs of (potentially multiple) specified rois from an roi directory to output a set of tracts.
This app has been deprecated. It will only work with wmc_deprecated datatype.
This app will perform tracking between 2 cortical regions of interest (ROIs) from either a freesurfer parcellation or an atlas parcellation. Inputs include: parcellation (freesurfer; atlas optional) with ROI niftis (generated from app-roiGeneration), dt6 from dtiinit, and ROI pairings. Outputs include a track.tck for each pairing of ROIs, a classification structure, and a fg_classified structure which can then be fed into other apps on the website (example: Clean WMC Output).
This app performs automatic example-based tract segmentation using the Linear Assignment Problem (LAP) algorithm having multiple examples.
This service uses mrtrix 2.0 to track using three methods DTI-based Deterministic, CSD-based Probabilistic and Deterministic. It generates three separate tractograms (TCK), one for each algorithm.
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.
This service runs mrtrix 2.0 tracking spanning over multiple tracking methods and parameters (Probabilistic and Deterministic tracking). It generates three separate tracking outputs for each algorithm.