brainlife/app-snr_in_cc

Stars
1
Language
Python
Created about 6 years ago
Updated about 1 year ago

brainlife/app-snr_in_cc

brainlife

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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

brainlife

Free cloud platform for secure neuroscience data analysis.

ezbids

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

BCT

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

warehouse

Service that allows data warehousing and workfow orchestration

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.

abcd-spec

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

docs

Brainlife.io Documentation

ui-tractview

HTML5 Tract Viewer - used to visualize output from AFQ

amaretti

Lightweight task orchestration service for ABCD-specification compatible apps.

app-mrtrix3-act

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

app-hcp-acpc-alignment

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.

brainlife.github.io

brainlife.io home page

app-mrtrix3-preproc

Run the recommended preprocessing procedure provided by mrtrix3. The options available mostly reflect the optimal DESIGNER pipeline that was recently proposed. This App runs for >15 on topup if both PA and AP dwi files are provided. It detects bvecs flipping (dwigradcheck) and update the gradient table accordingly.

app-networkneuro

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

ui-3dsurfaces

Surfaces viewer based on VTK

app-dtiinit

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

app-connectome-evaluator

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

app-convert-tck-to-trk

Convert a tractogram in tck format to a trk format file

cli

brainlife.io Command Line Interface (CLI)

pybrainlife

Library to access brainlife.io data objects and jupyter notebooks

app-documentation-template

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.

dockerfiles

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

app-wmaSeg

Classifies streamlines into known anatomical tracts.

app-myelin-mapping

app-life

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.

ui-networkneuro-tracts

networkneuro fiber/roi visualizer

ui-conn

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

ui-itksnap

ITK-SNAP Visualizer for Brainlife

app-abcd-hcp-pipeline

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

app-mouse_seg

Pipeline for mouse brain skull-stripping and ROI segmentation

app-epi-t1-registration

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-denoise-tensorflow

app-fslDTIFIT

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

app-bold-time-series

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

app-tractanalysisprofiles

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-cortex-tissue-mapping-stats

app-tractseg

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-noddi-amico

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-qsiprep

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-tractclassification

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