Perform the Track-Weighted Dynamic Functional Connectivity (TW-dFC) method


tckdfc [ options ]  tracks fmri output
  • tracks: the input track file.
  • fmri: the pre-processed fMRI time series
  • output: the output TW-dFC image


This command generates a Track-Weighted Image (TWI), where the contribution from each streamline to the image is the Pearson correlation between the fMRI time series at the streamline endpoints.

The output image can be generated in one of two ways (note that one of these two command-line options MUST be provided):

  • “Static” functional connectivity (-static option): Each streamline contributes to a static 3D output image based on the correlation between the signals at the streamline endpoints using the entirety of the input time series.
  • “Dynamic” functional connectivity (-dynamic option): The output image is a 4D image, with the same number of volumes as the input fMRI time series. For each volume, the contribution from each streamline is calculated based on a finite-width sliding time window, centred at the timepoint corresponding to that volume.

Note that the -backtrack option in this command is similar, but not precisely equivalent, to back-tracking as can be used with Anatomically-Constrained Tractography (ACT) in the tckgen command. However, here the feature does not change the streamlines trajectories in any way; it simply enables detection of the fact that the input fMRI image may not contain a valid timeseries underneath the streamline endpoint, and where this occurs, searches from the streamline endpoint inwards along the streamline trajectory in search of a valid timeseries to sample from the input image.


Options for toggling between static and dynamic TW-dFC methods; note that one of these options MUST be provided

  • -static generate a “static” (3D) output image.
  • -dynamic shape width generate a “dynamic” (4D) output image; must additionally provide the shape and width (in volumes) of the sliding window.

Options for setting the properties of the output image

  • -template image an image file to be used as a template for the output (the output image will have the same transform and field of view).
  • -vox size provide either an isotropic voxel size (in mm), or comma-separated list of 3 voxel dimensions.
  • -stat_vox type define the statistic for choosing the final voxel intensities for a given contrast type given the individual values from the tracks passing through each voxel
    Options are: sum, min, mean, max (default: mean)

Other options for affecting the streamline sampling & mapping behaviour

  • -backtrack if no valid timeseries is found at the streamline endpoint, back-track along the streamline trajectory until a valid timeseries is found
  • -upsample factor upsample the tracks by some ratio using Hermite interpolation before mapping (if omitted, an appropriate ratio will be determined automatically)

Standard options

  • -info display information messages.
  • -quiet do not display information messages or progress status; alternatively, this can be achieved by setting the MRTRIX_QUIET environment variable to a non-empty string.
  • -debug display debugging messages.
  • -force force overwrite of output files (caution: using the same file as input and output might cause unexpected behaviour).
  • -nthreads number use this number of threads in multi-threaded applications (set to 0 to disable multi-threading).
  • -config key value (multiple uses permitted) temporarily set the value of an MRtrix config file entry.
  • -help display this information page and exit.
  • -version display version information and exit.


Calamante, F.; Smith, R.E.; Liang, X.; Zalesky, A.; Connelly, A Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity. Brain Struct Funct, 2017, doi: 10.1007/s00429-017-1431-1

Tournier, J.-D.; Smith, R. E.; Raffelt, D.; Tabbara, R.; Dhollander, T.; Pietsch, M.; Christiaens, D.; Jeurissen, B.; Yeh, C.-H. & Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137

Author: Robert E. Smith (

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