207 Works


Giulia Berto
White matter bundle segmentation as multiple Linear Assignment Problems (multi-LAP).

White matter tracts statistics and quality control

Franco Pestilli & Daniel Bullock
This brainlife.io App will compute aggregrate statistics on the white matter tracts produced on brainlife.io. Statistics such as fiber count, tract volume, etc can be used to compare across tracts, across subjects either for scientific analyses or quality control of the results.

Tensor Mask

Soichi Hayashi, Serge Koudoro & Aman Arya
generate a mask from FA

Check T1 Orientation

Soichi Hayashi & Lindsey Kitchell
This application checks and reports the orientation (neurological or radiological) of a nifti file.

Prepare PRF output for visualization

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

DP Fit Model with dtiInit

Soichi Hayashi, Franco Pestilli & Cesar F. Caiafa
Runs FitFullModelSampleAllTracts to find the optimal parameters and fit model with the optimal parameters.

Clean WMC output

Soichi Hayashi, Lindsey Kitchell & Daniel Bullock
This service cleans the output from AFQ and WMA using AFQ's AFQ_removeFiberOutliers function. For more information on the inputs of this application, please read the documentation at the top of the function: https://github.com/yeatmanlab/AFQ/blob/master/functions/AFQ_removeFiberOutliers.m

Noddi Amico (w/ dtiinit)

Brad Caron
This app will fit the Neurite Orientation Dispersion and Density Imaging (NODDI; Zhang et al, 2012) model to multi-shell, normalized DWI data using the Accelerated Microstructure Imaging via Convex Optimization (AMICO; Daducci et al, 2015) toolbox. Requires normalized, multi-shell DWI data (including bvals and bvecs), and the single shell dwi file that has been aligned to the subject's T1 (i.e. dtiinit output) as input. The app will align the multi-shell data to the single-shell data...

3D Tract Surfaces

Lindsey Kitchell
app to generate surfaces of each fiber tract

Convert trk to tck in T1 space

Soichi Hayashi & Paolo Avesani
Convert a tractogram in tck format to a trk format file in T1 space

Tract Profiles

Soichi Hayashi & Aman Arya
This app takes in tracking data from the White Matter Segmentation app well as the nifti files of the user and gives tract profiles for each tracking file in a json format.

The major white matter tracts tissue microstructure predict collison-sport participation

Franco Pestilli & Brad Caron
We present processed data from an investigation of the effects of long-term exposure to repetitive head impacts and brain tissue microstructure. Participants included 4th and 5th year varsity IU football players, 4th and 5th year varsity IU cross-country runners, and senior non-athlete IU students. We publish this dataset in hopes TBI researchers will be able to reproduce our results and perform new analyses to accelerate understanding.

Non Local Means Denoising

Serge Koudoro
Applies nlmeans denoise (dipy / dipy_nlmeans)

Connectome Mapper for BIDS datasets

David Hunt
Generates connectome matrices and for T1 and BIDS datasets

Reconstruct Surfaces from LB Eigenfunctions

Lindsey Kitchell
This application will reconstruct the surfaces of each 3D model based on the selected number of eigenfunctions.

Test Gradient Flip for dtiInit processing

Soichi Hayashi & Lindsey Kitchell
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.

ANTs transformation

Giulia Berto
Compute ANTs transformation between two subjects based on T1 and FA volumes.

LiFE (dwi)

Soichi Hayashi & Franco Pestilli
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.


Soichi Hayashi & Franco Pestilli
Brainlife.io tutorial App

Registration Year

  • 2018
  • 2019

Resource Types

  • Software

Data Centers

  • Brainlife Production