This service uses Automated fiber quantification AFQ and fe structure output from LiFE to identify major tract segments and quantify tissue properties along their trajectories. You can choose to have the zero weighted fibers (as determined by LiFE) removed before or after AFQ is applied. useinterhemisphericsplit is a variable from AFQ, which if set to true will cut fibers crossing between hemispheres with a midsaggital plane below z=-10. This is to get rid of CST...
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 segments tracts using pairs of (potentially multiple) specified atlas regions to output a set of tracts.
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.
App to plot the fibers outputted from AFQ
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.
This application will create a 3D surface for 87 freesurfer labels from the aparc+aseg.mgz file.
Fit MAPMRI model (with optional Laplacian regularization). Generates rtop, lapnorm, msd, qiv, rtap, rtpp, non-gaussian (ng), parallel ng, perpendicular ng. Either the laplacian or positivity or both must be set to True. (dipy_fit_mapmri)
Classifies streamlines into known anatomical tracts.
Provides useful information about different files used in medical imaging.
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.)
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.
Plot ROC-AUC curve for multi-LAp and multi-NN
RecoBundles - Automatic extraction of brain pathways
App for running high resolution structural connectome processing from Pierre Besson
This service uses MRtrix 0.2.12 to do ensemble tracking using tensor and constrained spherical deconvolution (csd) algorithms. It generates a large set of candidate streamlines using a tensor-based deterministic model, csd-based deterministic model, and csd-based probabilistic model. The csd-based models can be computed at lmax values of 2, 4, 6, 8, 10, and 12. All candidate streamlines are combined into a single track.mat file. If you know the max lmax value for your data input...
Computes n equally spaced offset surfaces between the white and pial surfaces with equal ratios between areas of successive surfaces, which samples the same layers in gyri and sulci.