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 * '''General:''' If you want to process multiple subjects in parallel by submitting jobs for each one on a compute cluster, add '''-jobs <filename>''' to any of the trac-all command lines above. This will not actually run the analysis. It will process the configuration file, set up the analysis for all subjects included in it, and write the commands that need to be run into a text file called '''<filename>.''' You can then submit each line in this text file as a separate job on your cluster, using your cluster's job submission commands.
 * '''For Martinos Center users (and others with a cluster running torque):''' You can run trac-all directly on the command line of the cluster, and it will do the job submission for you. If run on your local machine, trac-all will run all analyses on that machine. If run on the cluster, however, trac-all will submit the analysis of each subject listed in your [[dmrirc|configuration file]] as a job on the cluster. (Note that, if you submit trac-all as a job with qsub or pbsubmit, it will run all subjects in the configuration file serially on a single node.)
 * '''General:''' If you want to process multiple subjects in parallel by submitting jobs for each one on a compute cluster, add '''-jobs <filename>''' to any of the trac-all command lines above. This will ''not actually run the analysis.'' It will process the configuration file, set up the analysis for all subjects included in it, and write the commands that need to be run into a text file called '''<filename>.''' You can then submit each line in this text file as a separate job on your cluster, using your cluster's job submission commands.
 * '''For Martinos Center users (and others with a cluster running torque):''' You can run trac-all ''directly on the command line of the cluster,'' and it will do the job submission for you. If run on your local machine, trac-all will run all analyses on that machine. If run on the cluster, however, trac-all will submit the analysis of each subject listed in your [[dmrirc|configuration file]] as a job on the cluster. (Note that, if you submit trac-all itself as a job with qsub or pbsubmit, it will run all subjects in the configuration file serially on a single node.)
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||-c&nbsp;<dmrirc> ||[[dmrirc|configuration file]] that specifies analysis options || ||-c&nbsp;<dmrirc> ||[[dmrirc|Configuration file]] that specifies analysis options ||
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||-s&nbsp;<subjectname> ||name of the subject to be analyzed (if not specified via a configuration file) ||
||-i&nbsp;<file> ||path to the input DWIs (if not specified via a configuration file) ||
||-s&nbsp;<subjectname> ||Name of the subject to be analyzed (if not specified via a configuration file) ||
||-i&nbsp;<file> ||Path to the input DWIs (if not specified via a configuration file) ||
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    * Convert the input DWI files to NIfTI.     * Convert the input DWI files to NIfTI, using [[mri_convert]].

Index

Name

trac-all: White-matter pathway reconstruction from diffusion-weighted images (DWIs) using TRACULA

Usage

Using a configuration file to set analysis options:

trac-all -[step] -c <configfile>

Using only mandatory inputs with all default options (no configuration file needed):

trac-all -[step] -s <subjectname> -i <dicomfile>

In the above, -[step] is one or more command-line options that specify which steps of the processing to run (see details below).

Parallel processing

  • General: If you want to process multiple subjects in parallel by submitting jobs for each one on a compute cluster, add -jobs <filename> to any of the trac-all command lines above. This will not actually run the analysis. It will process the configuration file, set up the analysis for all subjects included in it, and write the commands that need to be run into a text file called <filename>. You can then submit each line in this text file as a separate job on your cluster, using your cluster's job submission commands.

  • For Martinos Center users (and others with a cluster running torque): You can run trac-all directly on the command line of the cluster, and it will do the job submission for you. If run on your local machine, trac-all will run all analyses on that machine. If run on the cluster, however, trac-all will submit the analysis of each subject listed in your configuration file as a job on the cluster. (Note that, if you submit trac-all itself as a job with qsub or pbsubmit, it will run all subjects in the configuration file serially on a single node.)

Input image formats

  • The input DWIs can be in any format recognized by mri_convert. If the format is DICOM, mri_convert will attempt to extract the b-values and the diffusion gradient vectors from the DICOM header. In any other scenario, you will have to supply them in separate text files and specify them in the trac-all configuration file. To see the required format of the b-value table and gradient table files, please refer to the tutorial.

Arguments

Required Arguments

-c <dmrirc>

Configuration file that specifies analysis options

OR:

-s <subjectname>

Name of the subject to be analyzed (if not specified via a configuration file)

-i <file>

Path to the input DWIs (if not specified via a configuration file)

In addition to the above, one of the processing step options below must be provided to specify which parts of the analysis to run.

Processing step options

Choosing which part of the analysis to do:

-prep

Run all preprocessing (steps 1.1-1.7, see below)

-bedp

Run FSL's bedpost (step 2)

-path

Run pathway reconstruction (step 3)

-stat

Assemble pathway measures from multiple subjects (step 4)

Performing a part of the preprocessing or skipping a part:

-corr

Run image corrections (step 1.1)

-nocorr

Skip step 1.1

-qa

Run image quality assessment (step 1.2)

-noqa

Skip step 1.2

-intra

Run intra-subject registration (step 1.3)

-nointra

Skip step 1.3

-inter

Run inter-subject registration (step 1.4)

-nointer

Skip step 1.4

-masks

Run mask creation (step 1.5)

-nomasks

Skip step 1.5

-tensor

Run tensor fit (step 1.6)

-notensor

Skip step 1.6

-prior

Run estimation of pathway priors (step 1.7)

-noprior

Skip step 1.7

Optional arguments

Parallel processing

-jobs <file>

Write a text file with command lines that can be run in parallel but do not run them - the user can then submit each line as a job on a compute cluster

Status and log files

-log <file>

Log file (default: $SUBJECTS_DIR/<your_subjectid>/scripts/trac-all.log)

-cmd <file>

Command file (default: $SUBJECTS_DIR/<your_subjectid>/scripts/trac-all.cmd)

-noappendlog

Start new log files instead of appending to existing files

Other arguments

-no-isrunning

Do not check whether subjects are currently being processed

-sd <dir>

Specify subjects dir (default: $SUBJECTS_DIR)

-umask <umask>

Set unix file permission mask (default: 002)

-grp <groupid>

Check that current group is alpha groupid

-allowcoredump

Set coredump limit to unlimited

-debug

Generate much more output

-dontrun

Do everything except executing commands

-onlyversions

Print version of each binary and exit

-version

Print version of this script and exit

-help

Print full contents of help

Processing steps

  1. Preprocessing

    • 1.1 Image corrections

      • This step does the following:
        • Convert the input DWI files to NIfTI, using mri_convert.

        • Correct for B0 inhomogeneities (optional). This is done using epidewarp.fsl. This step can be turned on or off in the configuration file. To run this step, B0 field map files for each subject must be specified in the configuration file.

        • Correct for eddy currents and simple head motion (optional). This is done using FSL's eddy_correct. This step can be turned on or off in the configuration file.

        • Create a brain mask from the low-b diffusion images. This done using FSL's bet. The threshold can be specified in the configuration file. The configuration file can also be used to specify if this brain mask will actually be used in the following processing steps or if the anatomical brain mask from recon-all will be used instead.

      1.2 Image quality assessment

      • This step computes the four measures of head motion from Yendiki et al. 2013, based on the DWIs and the output of the eddy-current correction of step 1.1.

      1.3 Intra-subject registration

      • This step performs an affine registration between the individual's low-b diffusion and T1 images. Depending on what has been specified in the configuration file, this can be done either with bbregister or with FSL's flirt.

      1.4 Inter-subject registration

      • This step does the following:
        • Register the individual's T1 image to a template. Depending on what has been specified in the configuration file, this can be done either with affine registration to the MNI template (using FSL's flirt), or with non-linear registration to the CVS template (using mri_cvs_register).

        • Compose the diffusion-to-T1 transformation (from step 1.3) and the T1-to-template transformation to get the diffusion-to-template transformation.

      1.5 Mask creation

      • This step does the following:
        • Create a white-matter (WM) mask. This is done by extracting the cerebral WM, cerebellar WM, ventral DC, and brainstem from the individual's FreeSurfer cortical parcellation and subcortical segmentation (mri/aparc+aseg.mgz).

        • Create a mask of the cortex. This is done by mapping the cortical parcellation labels to the volume, growing them into the WM by 2mm and combining all the grown cortical labels into a mask.
        • Create an anatomical brain mask. This is done by binarizing and dilating the entire cortical parcellation and subcortical segmentation.
        • Transform all the above masks from individual T1 space to individual diffusion space and to the template space. This is done using the registrations that were computed in steps 1.3 and 1.4.
        • Transform the diffusion brain mask created in step 1.1 from the individual diffusion space to individual T1 space and to the template space.

      1.6 Tensor fit

      • This step does the following:
        • Perform least-squares tensor estimation using FSL's dtifit.

        • Map all scalar output volumes of the tensor fit (FA, MD, etc.) from diffusion space to the template space. This is done using the registrations that were computed in steps 1.3 and 1.4. (One could use these transformed volumes to do voxel-based statistical analyses in the template space, if one is so inclined.)

      1.7 Estimation of pathway priors

      • This step does the following:
        • Compute pathway priors. This is done in template space by combining the atlas data (training subjects' manually labeled pathways and anatomical segmentations) with the individual's own masks from step 1.5. The training data is used to estimate a priori probabilities that each pathway intersects/neighbors each of the labels in the cortical parcellation and subcortical segmentation, at each point along the pathway's trajectory. The training set is also used to obtain ROIs for the two endings of each pathway, as well as an initial guess of the location of the control points of each pathway, to be used in the tractography of step 3.

        • Map the selected initial control points from the template space to individual diffusion space, using the registrations that were computed in steps 1.3 and 1.4.
  2. Ball-and-stick model fitting

    • This step runs FSL's bedpostx to fit the ball-and-stick model of diffusion to the DWIs. One isotropic and two anisotropic compartments are assumed by default to model the diffusion signal in each voxel. Parallel processing on a computer cluster is highly recommended for this step.

  3. Pathway reconstruction

    • This step does the following:
      • Estimate the a posteriori probability distribution of the location of each pathway in the individual. This distribution consists of a likelihood term (the fit of the pathway orientation to the anisotropic compartments of the ball-and-stick model at each voxel) and a prior term (computed in step 1.7 from the atlas). The estimation is done by an MCMC algorithm and several parameters of that algorithm can be set in the configuration file.

      • Use the estimated pathway distributions to extract statistics on standard diffusion measures (FA, MD, etc.) for each of the pathways
  4. Assemble pathway measures from all subjects

    • This step can be run after each of the subjects has been processed with all of the previous steps. It will combine all subjects' diffusion measures (FA, MD, etc.) along the each pathway and output a table for each diffusion measure (FA, MD, etc.) and each pathway. In these tables, each row is a different position along the trajectory of the pathway and each column is a different subject. The user can then use these tables to perform "along-the-tract" group analyses.

Output directories and files

When trac-all runs, it generates output files for each subject under a directory that is denoted by <subjid> in the following. This subject name is provided either on the command line via the -s option, where only one subject can be specified, or in the configuration file, where multiple subjects can be specified.

By default the <subjid> directory is the same as the one under $SUBJECTS_DIR, where the subject's FreeSurfer recon is saved. An alternative location for trac-all subject directories can be specified either in the configuration file or with the -sd command-line option.

The most basic output of trac-all is the concatenation of the volumetric distributions of all the pathways that were specified in the configuration file (by default all 18 pathways included in the atlas). This output is called merged_*.mgz (the actual name depends on processing options). It can be visualized with freeview's -tv option. More information on this can be found in the TRACULA tutorial.

A detailed list of output files from each step of trac-all is given below.

Outputs from trac-all -corr

  • <subjid>/dlabel/diff/lowb_brain_mask.nii.gz -- Diffusion brain mask

  • <subjid>/dmri/dwi_orig.nii.gz -- Original DWI file converted to NIfTI format

  • <subjid>/dmri/dwi_orig.mghdti.bvals -- Original list of b-values (generated by mri_convert or specified by the user)

  • <subjid>/dmri/dwi_orig.mghdti.bvecs -- Original list of gradient vectors (generated by mri_convert or specified by the user)

  • <subjid>/dmri/dwi_orig_flip.nii.gz -- DWI converted to the orientation preferred by FSL

  • <subjid>/dmri/bvals.norot -- List of b-values in FSL format

  • <subjid>/dmri/bvecs.norot -- List of gradient vectors in FSL format

  • <subjid>/dmri/dwi.ecclog -- Log file generated by eddy_correct, if eddy-current correction is performed

  • <subjid>/dmri/dwi.nii.gz -- DWI after all corrections, if any are performed

  • <subjid>/dmri/bvals -- List of b-values in FSL format

  • <subjid>/dmri/bvecs -- List of gradient vectors in FSL format (rotated to account for eddy-current correction, if this option is specified)

  • <subjid>/scripts/trac-all.cmd -- Command file containing all the commands executed by trac-all. This file is constantly appended to every time that trac-all is run unless a new command file is specified using the -cmd flag.

  • <subjid>/scripts/trac-all.local-copy -- A local copy of the actual trac-all script with which all the steps were run.

  • <subjid>/scripts/trac-all.log -- Complete log of all the commands run and terminal output generated while running trac-all. This file is constantly appended to every time that trac-all is run unless a new log file is specified using the -log flag.

  • <subjid>/scripts/trac-preproc.local-copy -- A local copy of the actual trac-preproc script with which all the steps were run.

Outputs from trac-all -qa

  • <subjid>/dmri/dwi_motion.txt -- Four measures of head motion in the DWIs (see Yendiki et al. 2013): Average volume-to-volume translation, average volume-to-volume rotation, percentage of slices with excessive intensity drop-out, and average drop-out score for slices with excessive drop-out.

Outputs from trac-all -intra

In the following, <intra> is used to denote the intra-subject registration method. Depending on what configuration options were used, it can be flt (flirt) or bbr (bbregister).

  • <subjid>/dmri/brain_anat_orig.nii.gz -- Original skull-stripped anatomical from FreeSurfer recon

  • <subjid>/dmri/brain_anat.nii.gz -- Anatomical converted to the orientation preferred by FSL

  • <subjid>/dmri/xfms/anatorig2anat.mat(.dat) -- Registration matrix from original anatomical space to converted anatomical space

  • <subjid>/dmri/xfms/anat2anatorig.mat(.dat) -- Registration matrix from converted anatomical space to original anatomical space

  • <subjid>/dmri/lowb_brain_anat.<intra>.nii.gz -- Low-b diffusion image in converted anatomical space

  • <subjid>/dmri/xfms/diff2anat.<intra>.mat -- Registration matrix from diffusion space to converted anatomical space

  • <subjid>/dmri/xfms/anat2diff.<intra>.mat -- Registration matrix from converted anatomical space to diffusion space

  • <subjid>/dmri/xfms/diff2anatorig.<intra>.mat -- Registration matrix from diffusion space to original anatomical space

  • <subjid>/dmri/xfms/anatorig2diff.<intra>.mat -- Registration matrix from original anatomical space to diffusion space

Outputs from trac-all -inter

  • <subjid>/dmri/brain_anat_mni.nii.gz -- Anatomical in MNI space

  • <subjid>/dmri/xfms/anat2mni.mat -- Registration matrix from converted anatomical space to MNI space

  • <subjid>/dmri/xfms/mni2anat.mat -- Registration matrix from MNI space to converted anatomical space

  • <subjid>/dmri/xfms/diff2mni.<intra>.mat -- Registration matrix from diffusion to MNI space

  • <subjid>/dmri/xfms/mni2diff.<intra>.mat -- Registration matrix from MNI to diffusion space

  • <subjid>/dmri/xfms/anatorig2mni.mat -- Registration matrix from original anatomical space to MNI

  • <subjid>/dmri/xfms/mni2anatorig.mat -- Registration matrix from MNI to original anatomical space

  • <subjid>/dmri/xfms/cvs -- Symbolic link to the subject's CVS registration output directory (if applicable)

Outputs from trac-all -masks

In the following, <space> is used to denote the space that the volume is in. It can be anatorig (original anatomical space), anat (converted anatomical space in the orientation preferred by FSL), diff (diffusion space), mni (MNI template space) or cvs (CVS template space).

  • <subjid>/dlabel/<space>/White-Matter.nii.gz -- White matter mask extracted from FreeSurfer's aparc+aseg.mgz file

  • <subjid>/dlabel/<space>/White-Matter++.nii.gz -- White matter mask including ventral DC and brainstem

  • <subjid>/dlabel/<space>/notventricles.nii.gz -- White matter mask excluding lateral ventricles

  • <subjid>/dlabel/<space>/aparc+aseg+2mm.nii.gz -- Cortical parcellation grown into the WM by 2mm + subcortical segmentation

  • <subjid>/dlabel/<space>/cortex.nii.gz -- Mask of the cortex

  • <subjid>/dlabel/<space>/cortex+2mm.nii.gz -- Mask of the cortex grown into the WM by 2mm

  • <subjid>/dlabel/<space>/Brain-Stem.nii.gz -- Brainstem from FreeSurfer's aparc+aseg.mgz file

  • <subjid>/dlabel/<space>/cortex+2mm+bs.nii.gz -- Mask of grown cortex with brainstem

  • <subjid>/dlabel/<space>/aparc+aseg_mask.nii.gz -- Anatomical brain mask obtained by dilating FreeSurfer's aparc+aseg.mgz

  • <subjid>/dmri/dwi_snr.txt -- SNR of DWI intensities within the WM mask

  • <subjid>/dlabel/<space>/lowb_brain_mask.<intra>.nii.gz -- Diffusion brain mask (in template space or original anatomical space)

Outputs from trac-all -tensor

  • <subjid>/dmri/dtifit_FA.nii.gz -- Fractional anisotropy

  • <subjid>/dmri/dtifit_V1.nii.gz -- Primary eigenvector

  • <subjid>/dmri/dtifit_V2.nii.gz -- Secondary eigenvector

  • <subjid>/dmri/dtifit_V3.nii.gz -- Tertiary eigenvector

  • <subjid>/dmri/dtifit_L1.nii.gz -- Primary eigenvalue

  • <subjid>/dmri/dtifit_L2.nii.gz -- Secondary eigenvalue

  • <subjid>/dmri/dtifit_L3.nii.gz -- Tertiary eigenvalue

  • <subjid>/dmri/dtifit_MD.nii.gz -- Mean diffusivity

  • <subjid>/dmri/dtifit_MO.nii.gz -- Mode of the anisotropy (oblate ~ -1; isotropic ~ 0; prolate ~ 1)

  • <subjid>/dmri/dtifit_S0.nii.gz -- Raw T2 signal with no diffusion weighting

  • <subjid>/dmri/<inter>/dtifit_*.<intra>.nii.gz -- All of the above files, mapped to the common template space, where <inter> is the inter-subject registration method (mni or cvs) and <intra> is the intra-subject registration method (flt or bbr)

Outputs from trac-all -prior

In the following, <tract> is used to denote the name of the pathway, <nsubj> the number of training subjects used to calculate the priors (by default all 33 subjects in the atlas), <inter> the inter-subject registration method (mni for the MNI template or cvs for the CVS template), and <intra> the intra-subject registration method (flt for flirt or bbr for bbregister).

Pathway initialization:

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_cpts_all.nii.gz -- Streamline that was chosen from the atlas to initialize tractography in subject <subjid>.

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_cpts_all.txt -- Coordinates (in template space) of all points of the above streamline.

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_cpts_<npts>.nii.gz -- Spline with <npts> control points that was fit to the initial streamline above.

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_cpts_<npts>.txt -- Coordinates (in template space) of the <npts> control points of the initial spline.

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_cpts_<npts>_std.txt -- Standard deviation of the streamlines in the atlas around the <npts> control points above (in template space).

  • <subjid>/dlabel/diff/<tract>_avg<nsubj>_<inter>_<intra>_cpts_<npts>.txt -- Coordinates (in diffusion space) of the <npts> control points of the initial spline.

  • <subjid>/dlabel/diff/<tract>_avg<nsubj>_<inter>_<intra>_cpts_<npts>_std.txt -- Standard deviation of the streamlines in the atlas around the <npts> control points above (in diffusion space)

Pathway end ROIs:

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_end[1,2].nii.gz -- End points of all the streamlines included in the atlas.

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_end[1,2]_dil.nii.gz -- End ROIs used to constrain tractography solutions (obtained by dilating the end points of all the streamlines included in the atlas and masking with the anatomy of subject <subjid>).

Pathway histograms:

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_histo_str.nii.gz -- Histogram of the streamlines from the atlas that were used to compute the priors (number of atlas streamlines in each voxel).

  • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_histo.nii.gz -- Histogram of the streamlines from the atlas that were used to compute the priors (number of atlas subjects in each voxel).

Priors on the underlying anatomy of the pathways

Before computing these, each pathway is split into segments along its arc length. The number of segments depends on the average length of the pathway. Prior information on the underlying anatomy is extracted from the training subjects for each segment along the length of each pathway. In the following text files, each row corresponds to a different segment along the pathway, i.e., the number of rows equals the number of segments. The information in the text files relates to the frequency with which each label in the aparc+aseg is found in the immediate neighborhood of each segment of the pathway, in the left, right, anterior, posterior, superior, and inferior direction. There is a different text file for each of these directions, denoted by the <x>, <y>, and <z> suffixes for the L-R, A-P, and I-S direction respectively. Specifically, x=1: Left; x=-1: Right; y=1: Anterior; y=-1: Posterior; z=1: Superior; z=-1: Inferior.

  • Local neighbor anatomical priors:

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fsids_<x>_<y>_<z>.txt -- List of the aparc+aseg label IDs that were found in the training subjects in the immediate neighborhood of the pathway (0 or 1 voxels away) in the direction denoted by <x>, <y>, <z>.

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fshisto_<x>_<y>_<z>.txt -- Histogram (number of occurrences) of the label IDs above.

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fsprior_<x>_<y>_<z>.txt -- Prior probability of each of the label IDs, calculated from the histogram above.

    Nearest neighbor anatomical priors:

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fsnnids_<x>_<y>_<z>.txt -- List of the aparc+aseg label IDs that were found in the training subjects, adjacent to but different than the label traversed by the pathway, in the direction denoted by <x>, <y>, <z>.

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fsnnhisto_<x>_<y>_<z>.txt -- Histogram (number of occurrences) of the label IDs above.

    • <subjid>/dlabel/<inter>/<tract>_avg<nsubj>_<inter>_<intra>_fsnnprior_<x>_<y>_<z>.txt -- Prior probability of each of the label IDs, calculated from the histogram above.

Outputs from trac-all -bedp

See bedpostX documentation for a detailed explanation of the bedpostX algorithm and its outputs. Every voxel in the diffusion volume is modeled as a combination of two anisotropic compartments and one isotropic compartment. In the following <i> denotes the anisotropic compartment (1 or 2).

  • <subjid>/dmri.bedpostX/merged_th<i>samples.nii.gz - Samples from the distribution on the orientation angle theta of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/merged_ph<i>samples.nii.gz - Samples from the distribution on the orientation angle phi of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/merged_f<i>samples.nii.gz - Samples from the distribution on the volume fraction of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/mean_th<i>samples.nii.gz - Mean orientation angle theta of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/mean_ph<i>samples.nii.gz - Mean orientation angle phi of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/mean_f<i>samples.nii.gz - Mean volume fraction of each anisotropic compartment.

  • <subjid>/dmri.bedpostX/mean_dsamples.nii.gz - Mean diffusivity.

  • <subjid>/dmri.bedpostX/mean_S0samples.nii.gz - Mean baseline signal intensity.

  • <subjid>/dmri.bedpostX/dyads<i>.nii.gz - Mean of PDD (Principal Diffusion Direction) distribution in a vector form.

  • <subjid>/dmri.bedpostX/dyads<i>_dispersion.nii.gz - Uncertainty on the estimated fiber orientation.

  • <subjid>/dmri.bedpostX/nodif_brain_mask.nii.gz - Brain mask from low-b diffusion image.

  • <subjid>/dmri.bedpostX/bvals - List of b-values (copy of <subjid>/dmri/bvals).

  • <subjid>/dmri.bedpostX/bvecs - List of gradient vectors (copy of <subjid>/dmri/bvecs).

Outputs from trac-all -path

In the following, <tract> is used to denote the name of the pathway, <nsubj> the number of training subjects used to calculate the priors (by default all 33 subjects in the atlas), <inter> the inter-subject registration method (mni for the MNI template or cvs for the CVS template), and <intra> the intra-subject registration method (flt for flirt or bbr for bbregister).

All volumes and coordinate files below are in the native diffusion space of subject <subjid>. Tractography is run in diffusion space, even though the anatomical priors are derived in the common template (MNI or CVS) space.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/cpts.map.txt -- Text file containing the coordinates of the control points of the maximum a posteriori (highest-probability) path.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/cpts.samples.txt -- Text file containing the coordinates of the control points of all path samples drawn by the MCMC algorithm and used to estimate the path distribution.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/endpt[1,2].pd.nii.gz -- Posterior distribution of the two end points of the path.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/length.samples.txt -- Text file containing the length of all path samples drawn by the MCMC algorithm and used to estimate the path distribution.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/pd.samples.txt -- Text file containing the likelihood and anatomical prior of all path samples drawn by the MCMC algorithm and used to estimate the path distribution.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/log.txt -- Text file containing a list of the input files that were used to generate the path distribution.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/path.map.nii.gz -- Volume of the maximum a posteriori (highest-probability) path. This is the spline whose control points are given in cpts.map.txt.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/path.pd.nii.gz -- Posterior distribution of the path.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/pathstats.byvoxel.txt -- Text file containing the values of various diffusion measures (axial diffusivity, radial diffusivity, mean diffusivity, fractional anisotropy) at each voxel along the highest-probability path.

  • <subjid>/dpath/<tract>_avg<nsubj>_<inter>_<intra>/pathstats.overall.txt -- Text file containing the average values of the diffusion measures above over the entire posterior distribution of the path.

  • <subjid>/dpath/merged_avg<nsubj>_<inter>_<intra>.mgz -- Merged 4D volume of all the pathway distributions that were estimated. Opening this file in freeview will display the distributions as isosurfaces thresholded at 20% of their maximum value.

For more information on the stats files, see Tract statistics in the TRACULA tutorial.

For more information on how to visualize the merged 4D volume, see Visualizing the posterior distribution of all reconstructed tracts in the TRACULA tutorial.

Outputs from trac-all -stat

Outputs from this step will be saved in a subdirectory named stats, directly under the root TRACULA output directory. Outputs are saved separately for each WM pathway and each diffusion measure (FA, MD, etc.)

In the following, <meas> denotes the name of the diffusion measure, <tract> the name of the pathway, <nsubj> the number of training subjects used to calculate the priors (by default all 33 subjects in the atlas), <inter> the inter-subject registration method (mni for the MNI template or cvs for the CVS template), and <intra> the intra-subject registration method (flt for flirt or bbr for bbregister).

  • stats/<tract>.avg<nsubj>_<inter>_<intra>.inputs.txt -- Complete list of input files from all study subjects that were processed to create the group tables

  • stats/<tract>.avg<nsubj>_<inter>_<intra>.log -- Log file, which also contains information about which inputs were flagged as outliers (potential failed pathway reconstructions) and excluded from the group tables

  • stats/<tract>.avg<nsubj>_<inter>_<intra>.<meas>.txt -- Group table for this measure and pathway, where each row is a different position along the trajectory of the pathway and each column is a different subject (this file can then be used for group analyses)

  • stats/<tract>.avg<nsubj>_<inter>_<intra>.path.mean.txt -- Mean pathway in template space (this file can be used to visualize the results of group analyses in freeview)

See Also

dmrirc

Links

TRACULA

References

Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J, Wang R, Salat D, Ehrlich S, Behrens T, Jbabdi S, Gollub R and Fischl B (2011). Front. Neuroinform. 5:23. doi: 10.3389/fninf.2011.00023

Reporting Bugs

Report bugs to < analysis-bugs@nmr.mgh.harvard.edu >

Author/s

Anastasia Yendiki

trac-all (last edited 2021-12-08 16:06:06 by AnastasiaYendiki)