Chapter 4 Automated Fiber Quantification

If you have used results from this software for a publication, please use this citation (Yeatman, Dougherty, Myall, Wandell, & Feldman, 2012).

Automated Fiber Quantification version 1.2 (https://github.com/yeatmanlab/AFQ) identifies twenty major fiber tracts that include the corticospinal tract, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, anterior thalamic radiation, cingulum cingulate gyrus and hippocampal bundles, superior longitudinal fasciculus, arcuate fasciculus, and forceps major and forceps minor of the corpus callosum. For each identified pathway, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) are extracted along 100 segments of the tract.

From the AFQ wiki:

At present, AFQ uses functions from mrDiffusion, a component of vistasoft. We plan to implement AFQ in a docker container that can be invokved as a standalone routine. The AFQ pipeline is briefly described below. The main steps in the pipeline are linked to a detailed description of the function.

Preprocessing steps involve computing the means B0 if there are more than one B0 collected. Next step is eddy current correction which involves aligning all the volumes back to the average B0. Images are then aligned to a T1 which help correct EPI distortions. Images are resampled to 2-mm isotropic. The bvecs have to be reoriented before tensors are calculated since the volumes have moved. Finally, whole brain tensors are calculated.

If you need more information about preprocessing: http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/DTI_Preprocessing

AFQ requires that the data be [preprocessed in mrDiffusion with dtiInit to create a dt6.mat file (or processed in another software program and imported into mrDiffusion format). AFQ_run is the main routine to analyze group data. This function runs the full automated pipeline and is extensively documented. The example in help AFQ_run analyzes a data set that is included in the repository. For single subject analysis, see AFQ_example.m.

Inputs into AFQ_run are a cell array of paths to subject’s DTI data and, if there are two groups of subjects, a binary vector of 0s and 1s identifying subjects as patients (1) or controls (0). If two groups are provided AFQ_run will output plots of the normal range of the desired diffusion measurement (FA, RD etc.) along each of 20 major fiber tracts. Each subject that is outside this normal range on a given tract will be flagged and plotted with respect to the controls. It will also automatically perform group comparisons for each Tract Profile and plot out the results. If only one group of subjects is provided then the segmented fiber groups and Tract Profiles will be returned.

If you need more information about AFQ: https://github.com/yeatmanlab/AFQ/wiki

subjid dtiscalar tract node1 node… node100
sub-001 ad callosum_forceps_major 1.38360 " " 1.70300
sub-001 fa callosum_forceps_major 0.45431 0.42063
sub-001 md callosum_forceps_major 0.89892 1.14860
sub-001 rd callosum_forceps_major 0.65660 0.87134
sub-001 ad callosum_forceps_minor 1.28500 1.31690
sub-001 fa callosum_forceps_minor 0.54511 0.55843
sub-001 md callosum_forceps_minor 0.76290 0.77091
sub-001 rd callosum_forceps_minor 0.50185 0.49790
sub-001 ad left_arcuate 1.13020 1.20240
sub-001 fa left_arcuate 0.52412 0.55669
sub-001 md left_arcuate 0.70549 0.72109
sub-001 rd left_arcuate 0.49315 0.48043
sub-001 ad left_cingulum_cingulate 1.17870 1.21050
sub-001 fa left_cingulum_cingulate 0.52182 0.56583
sub-001 md left_cingulum_cingulate 0.72352 0.70770
sub-001 rd left_cingulum_cingulate 0.49593 0.45630
sub-001 ad left_corticospinal 1.44240 1.48040
sub-001 fa left_corticospinal 0.62557 0.63471
sub-001 md left_corticospinal 0.79361 0.80862
sub-001 rd left_corticospinal 0.46921 0.47273
sub-001 ad left_ifof 1.69960 1.74040
sub-001 fa left_ifof 0.44588 0.43685
sub-001 md left_ifof 1.12360 1.16910
sub-001 rd left_ifof 0.83554 0.88343
sub-001 ad left_ilf 1.49300 1.46740
sub-001 fa left_ilf 0.60612 0.59457
sub-001 md left_ilf 0.82855 0.82505
sub-001 rd left_ilf 0.49633 0.50388
sub-001 ad left_slf 0.97465 0.99455
sub-001 fa left_slf 0.37366 0.38691
sub-001 md left_slf 0.69242 0.69709
sub-001 rd left_slf 0.55130 0.54836
sub-001 ad left_thalamic_radiation 1.15050 1.16940
sub-001 fa left_thalamic_radiation 0.26111 0.27743
sub-001 md left_thalamic_radiation 0.89864 0.89830
sub-001 rd left_thalamic_radiation 0.77272 0.76276
sub-001 ad left_uncinate 1.47840 1.49040
sub-001 fa left_uncinate 0.49604 0.50775
sub-001 md left_uncinate 0.91180 0.90856
sub-001 rd left_uncinate 0.62851 0.61762
sub-001 ad right_cingulum_cingulate 1.17930 1.12320
sub-001 fa right_cingulum_cingulate 0.55716 0.57049
sub-001 md right_cingulum_cingulate 0.69598 0.65822
sub-001 rd right_cingulum_cingulate 0.45433 0.42571
sub-001 ad right_cingulum_hippocampus 1.15770 1.15230
sub-001 fa right_cingulum_hippocampus 0.43977 0.42784
sub-001 md right_cingulum_hippocampus 0.75542 0.76074
sub-001 rd right_cingulum_hippocampus 0.55428 0.56498
sub-001 ad right_corticospinal 1.36440 1.39920
sub-001 fa right_corticospinal 0.66548 0.68178
sub-001 md right_corticospinal 0.72347 0.72975
sub-001 rd right_corticospinal 0.40301 0.39503
sub-001 ad right_ifof 1.31130 1.31910
sub-001 fa right_ifof 0.55673 0.56644
sub-001 md right_ifof 0.76970 0.76890
sub-001 rd right_ifof 0.49889 0.49379
sub-001 ad right_ilf 1.52820 1.53210
sub-001 fa right_ilf 0.57411 0.58081
sub-001 md right_ilf 0.87883 0.87491
sub-001 rd right_ilf 0.55412 0.54630
sub-001 ad right_slf 1.14550 1.16680
sub-001 fa right_slf 0.44595 0.45703
sub-001 md right_slf 0.75316 0.75757
sub-001 rd right_slf 0.55699 0.55295
sub-001 ad right_thalamic_radiation 1.07180 1.04850
sub-001 fa right_thalamic_radiation 0.25754 0.28755
sub-001 md right_thalamic_radiation 0.84185 0.80448
sub-001 rd right_thalamic_radiation 0.72688 0.68248
sub-001 ad right_uncinate 1.11940 1.13370
sub-001 fa right_uncinate 0.30999 0.31395
sub-001 md right_uncinate 0.83524 0.84168
sub-001 rd right_uncinate 0.69316 0.69565

4.1 Files

List of files will include DTI measures from each of the 20 fiber tracts at 100 segments along each tract:

  • afq-FA.csv
  • afq-RD.csv
  • afq-MD.csv
  • afq-AD.csv

Head motion:

  • afq-motion.csv

References

Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: Automating fiber-tract quantification. PLoS One, 7(11), e49790. https://doi.org/10.1371/journal.pone.0049790