From 90b65aa6e63bb468f8393ad8994ba5eb2ddd939f Mon Sep 17 00:00:00 2001 From: Valerie Sydnor Date: Fri, 2 Feb 2024 15:10:50 -0500 Subject: [PATCH] [DOC] Describe mrtrix connectivity matrix outputs --- docs/reconstruction.rst | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/docs/reconstruction.rst b/docs/reconstruction.rst index ea7cbb7c..b13846ce 100644 --- a/docs/reconstruction.rst +++ b/docs/reconstruction.rst @@ -250,6 +250,27 @@ Pre-configured recon_workflows were calculated using SIFT2_ [Smith2015]_ and were included for while estimating the structural connectivity matrix. + The MRtrix workflows generate a variety of outputs. These include: + + * WM FOD, GM-like, and CSF-like compartment files. Both original (wmFOD, gmFOD, csfFOD .mif.gz + files) and bias field corrected/intensity normalized (wmFODmtnormed, gmFODmtnormed, + csfFODmtnormed .mif.gz files) files are written. These are the outputs of dwi2fod and + mtnormalise, respectively + * atlas files of interest in .mig.gz and .nii.gz formats, with corresponding txt files + * a .mat file, which contains cortico-cortical and cortical-subcortical structural connectivity + matrices for numerous atlases. This mat file contains information about regional + numbers/labels for each atlas as well as 4 structural connectome outputs for each atlas. + The 4 connectivity matrix outputs are + *radius#.count.connectivity*: raw streamline count based matrix + *sift.radius#.count.connectivity*: sift-weighted streamline count based matrix + *radius#.meanlength.connectivity*: a matrix containing mean length of raw streamlines + *sift.radius#.meanlenght.connectivity*: a matrix containing mean length of sifted output + + The number # in radius# indicates how many mm the algorithm would search up from a + given streamline's endpoint for a cortical region. E.g., a radius of 2 indicates that + if a streamline ended before hitting gray matter, the search for a cortical + termination region could be up to 2mm from the endpoint. + .. warning:: We don't recommend using ACT with FAST segmentations. The full benefits of ACT