This app will generate images (jpgs) of data mapped to the cortical surface using MNE-python's (https://mne.tools/stable/index.html) build of pysurfer (https://pysurfer.github.io/index.html).

This app will convert important parcellation statistics, including volume of each individual ROI given in a parcellation. These can be used for group analyses. This app should be used whenever possible to compute parcellation statistics (uses freesurfer's mri_annotation_stats).

App on processing, analyzing eFC

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Simple brainlife app to create a network from a conmat matrix.

This is the official C-PAC (Configurable Pipeline for the Analysis of Connectomes) brainlife.io app. Currently this app runs either the default pipeline or one of our preconfigured 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.

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

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Filtering out of artifactual streamlines from a tractogram with a geometric deep learning model

Concatenates one or more diffusion images.

Create mid-axial, sagittal, and coronal slice png of T1 image with DWI outline overlayed

This app will map volumated measure files (i.e. tensor, NODDI) to the cortical surface following Fukutomi et al (2018; 10.1016/j.neuroimage.2018.02.017) using Connectome Workbench.

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This app will merge two rois within an ROI datatype dataset into one ROI. The user must specify which two rois should be merged. The app will output one roi that contains all of the voxels that were in the two original rois.

This app will perform fsl_anat (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat)

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

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

We present diffusion and anatomical (T1w) processed data and derivatives from a group of IU athletes (football, cross-country) and non-athletes (students). The raw data contains both anatomical (T1 weighted) and diffusion-weighted (dMRI) magnetic resonance imaging data. The processed data shares 14 types of derivatives across 42 participants, comprising of 84 brainmasks (2 per participant), 42 Freesurfer outputs (1 per participant), 168 fiber orientation dispersion (FOD) images (4 per participant), 588 diffusion parameter maps (14 per...

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This app will the individual tract tract profiles csv’s from the tractprofile dataype into a single summary csv for group analysis and machine learning. This app takes in a tractprofile datatype. The app will output a csv entitled ‘tracts.csv’ that can be used for group statistics and machine learning.

This app will generate optic radiations using MrTrix3's iFOD2 algorithm with the capability of using the Anatomically Constrained (ACT) framework

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DSI Studio scripts converting DWI file to SRC format.