This app will extracts diffusion metrics (tensor) from within ROIs for ROI-level analysis using FSL. Will output metric x ROI NIFTI images. Takes in DWI, tensor, NODDI, and ROIs inputs and outputs a directory with the metric x ROI NIFTI images. Inflation can be performed on the ROIs using SPM.

Compute the strength of evidence for white matter tracts in a classification given the measured diffusion-weighted MRI data

convert an NxT time series to an NxN functional connectivity matrix

This app will generate ROIs from a parcellation, subcortical structures found in the Freesurfer aseg, cortical structures found in the Freesurfer parcellations, and population receptive field visual area segmentations. The ROIs will be resliced into diffusion space in of this, merging ROIs is allowed.

Create mid-axial, sagittal, and coronal slice png of the brainmask overlaid on the DWI image

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This app generates simple 2D static visualizations for networks by using a force-directed algorithm. The current implementation uses the Large Graph Layout (LGL) algorithm.

This is copy of the mergeTCK BL app (https://doi.org/10.25663/brainlife.app.219) modified to take two tractograms as input. This is a temporary workaround for the lack of functionality for multi-input datatypes in brainilfe Pipelines.

This app will generate ROIs that can be used for visual white matter tracking, including optic radiation tracking. This includes V1 from a pRF visual area segmentation, the left and right LGNs from a thalamic nuclei segmentation, and exclusion ROIs from freesurfer. The ROIs will be resliced into diffusion space in of this.

Brainlife App (wrapper) for dMRI based fiber tracking between ROIs using parallel transport tractography

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

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Create mid-axial, sagittal, and coronal slice pngs of the NODDI

Brainlife.io app for Noah Benson's neuropythy library, retinotopy from T1 anatomy

Dataset of expert-based segmented white matter bundles connecting the human dorsal and posterior cortices. The bundles included in this dataset are: the posterior arcuate fasciculus (pArc), the temporo-parietal connection to the superior parietal lobule (TP-SPL), the middle longitudinal fasciculus–superior parietal lobule component (MdLF-SPL), and the middle longitudinal fasciculus–superior angular gyrus component (MdLF-Ang).

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This application takes an input tractography amalgum (i.e. whole brain fiber group (WBFG) or composite) and, using the categories designated in the White Matter Category (WMC) input, extracts and outputs a tck file for each category into the output directory. Intended to facilitate users performing analyses offline/outside of the brainlife platform. WARNING: Submitting mismatched input TCK and WMC will result in an error, but may also result in garbage output in the event that the...

Automated Fiber Quantification ... in Python

This app uses [Trekker](https://dmritrekker.github.io) to track between multiple ROIs.

This application is a port of the NODDI Cortex Mapping code (https://github.com/RIKEN-BCIL/NoddiSurfaceMapping) developed by Fukutomi et al. The description of methodologies can be found in their 2018 Neuroimage paper (https://www.sciencedirect.com/science/article/pii/S1053811918301058?via%3Dihub). If used, please cite: Fukutomi, H., Glasser, M.F., Zhang, H., Autio, J.A., Coalson, T.S., Okada, T., Togashi, K., Van Essen, D.C., Hayashi, T., 2018. Neurite imaging reveals microstructural variations in human cerebral cortical gray matter. Neuroimage.

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This app uses [Trekker](https://dmritrekker.github.io) to Track the Human Optic Radiation.

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

This is data related to the upcoming article by Vinci-Booher et al. The dataset contains diffusion-weighted and T1-weighted magnetic resonance imaging from 24 children (age 4.5-9 years of age) and 12 adults (18-21 years of age).