Discover brainlife/app-networkneuro Open Source project by @brainlife

A web service for semi-automated conversion of raw imaging data to BIDS

ezbids

Vue

BCT

The Brain Connectivity Toolbox: https://sites.google.com/site/bctnet/

Service that allows data warehousing and workfow orchestration

warehouse

CSS

app-freesurfer

Freesurfer segments the t1w anatomical data into functionally different parts of the brain. Segmentation/parcellation can then be fed to many other subsequent analysis.

Application for Big Computational Data Specification (v1.1). This specification provide information on how to write an Application that can run on the open platform

abcd-spec

Brainlife.io Documentation

docs

HTML

ui-tractview

HTML5 Tract Viewer - used to visualize output from AFQ

Vue

amaretti

Lightweight task orchestration service for ABCD-specification compatible apps.

Runs mrtrix3 ACT (Anatomically Constrained Tractography) using either single- or multi-shell diffusion-weighted MRI data.

app-mrtrix3-act

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.

app-hcp-acpc-alignment

brainlife.github.io

brainlife.io home page

HTML

app-mrtrix3-preproc

Surfaces viewer based on VTK

ui-3dsurfaces

Runs vistasoft/dtiInit to preprocess and register dwi to anat/t1

app-dtiinit

Estimate the quality of your diffusion-weighted data to map human connectomes.

app-connectome-evaluator

Convert a tractogram in tck format to a trk format file

app-convert-tck-to-trk

brainlife.io Command Line Interface (CLI)

cli

Library to access brainlife.io data objects and jupyter notebooks

pybrainlife

This is a minimal template for brainlife.io Apps readme files. Use this as a start for your readme file and please cite the funding sources and articles reported here.

app-documentation-template

dockerfiles

repository to store various dockerfiles used to build docker containers that are used across multiple brainlife apps

Dockerfile

app-wmaSeg

Classifies streamlines into known anatomical tracts.

app-myelin-mapping

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.

app-life

networkneuro fiber/roi visualizer

ui-networkneuro-tracts

Brainlife's nvidia-docker UI container for CONN toolbox; functional connectivity toolbox

ui-conn

ITK-SNAP Visualizer for Brainlife

ui-itksnap

ABCD-BIDS pipeline used to process the BIDS input data (DCAN Labs' modified HCP pipeline)

app-abcd-hcp-pipeline

Pipeline for mouse brain skull-stripping and ROI segmentation

app-mouse_seg

Align a Diffusion weighted MRI image to a T1 image using FSL's epi_reg. INPUTS: DWI data and a T1 image. OUTPUTS aligned DWI filed

app-epi-t1-registration

app-denoise-tensorflow

This app will fit the diffusion tensor model (DTI) to a diffusion MRI image using FSL's dtifit commmand.

app-fslDTIFIT

an app to get nuisance regressed time series from a preprocessed bold, confounds, and a parc

app-bold-time-series

Create plots of diffusion metrics (i.e. FA, MD, RD, AD) for each of the segmented tracts from AFQ, known as Tract Profiles. Obtains streamline positions from segmented tracts and plots the metrics of interest along "nodes" of the tract, allowing for comparison of individual subject tracts. Requires the dt6 output from dtiinit and a white matter classification output from AFQ or WMA

app-tractanalysisprofiles

app-cortex-tissue-mapping-stats

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

app-tractseg

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 an optional brainmask of the DWI. Will output the four NODDI output files: neurite density index (ndi), orientation dispersion index (odi), isotropic volume fraction (isovf), and the directions (dirs).

app-noddi-amico

Preprocessing of diffusion MRI data. It includes automatically generated preprocessing pipelines that correctly group, distortion correct, motion correct, denoise, coregister and resample your scans, producing visual reports and QC metrics.

app-qsiprep

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 fibers that appear to cross at the level of the brainstem. For more information about AFQ see https://github.com/yeatmanlab/AFQ/wiki

app-tractclassification

Brainlife.io app that computes the signal-to-noise ratio in the corpus callosum

app-snr_in_cc