% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Rauland:1047194,
      author       = {Rauland, Amelie and Meisler, Steven L. and Alexander-Bloch,
                      Aaron F. and Bagautdinova, Joëlle and Baller, Erica B. and
                      Gur, Raquel E. and Gur, Ruben C. and Luo, Audrey C. and
                      Moore, Tyler M. and Popovych, Oleksandr V. and Reetz,
                      Kathrin and Roalf, David R. and Shinohara, Russell T. and
                      Sotardi, Susan and Sydnor, Valerie J. and Vossough, Arastoo
                      and Eickhoff, Simon B. and Cieslak, Matthew and
                      Satterthwaite, Theodore D.},
      title        = {{B}enchmarking {O}rientation {D}istribution {F}unction
                      {E}stimation {M}ethods for {T}ractometry in {S}ingle-{S}hell
                      {D}iffusion {M}agnetic {R}esonance {I}maging - {A}n
                      {E}valuation of {T}est-{R}etest {R}eliability and
                      {P}redictive {C}apability},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {FZJ-2025-04143},
      year         = {2025},
      abstract     = {Deriving white matter (WM) bundles in-vivo has thus far
                      mainly been applied in research settings, leveraging high
                      angular resolution, multi-shell diffusion MRI (dMRI)
                      acquisitions that enable advanced reconstruction methods.
                      However, these advanced acquisitions are both time-consuming
                      and costly to acquire. The ability to reconstruct WM bundles
                      in the massive amounts of existing single-shelled, lower
                      angular resolution data from legacy research studies and
                      healthcare systems would offer much broader clinical
                      applications and population-level generalizability. While
                      legacy scans may offer a valuable, large-scale complement to
                      contemporary research datasets, the reliability of white
                      matter bundles derived from these scans remains unclear.
                      Here, we leverage a large research dataset where each
                      64-direction dMRI scan was acquired as two independent
                      32-direction runs per subject. To investigate how recently
                      developed bundle segmentation methods generalize to this
                      data, we evaluated the test-retest reliability of the two
                      32-direction scans, of WM bundle extraction across three
                      orientation distribution function (ODF) reconstruction
                      methods: generalized q-sampling imaging (GQI), constrained
                      spherical deconvolution (CSD), and single-shell three-tissue
                      CSD (SS3T). We found that the majority of WM bundles could
                      be reliably extracted from dMRI scans that were acquired
                      using the 32-direction, single-shell acquisition scheme. The
                      mean dice coefficient of reconstructed WM bundles was
                      consistently higher within-subject than between-subject for
                      all WM bundles and ODF reconstruction methods, illustrating
                      preservation of person-specific anatomy. Further, when using
                      features of the bundles to predict complex reasoning
                      assessed using a computerized cognitive battery, we observed
                      stable prediction accuracies ( r : 0.15-0.36) across the
                      test-retest data. Among the three ODF reconstruction
                      methods, SS3T had a good balance between sensitivity and
                      specificity in external validation, a high intra-class
                      correlation of extracted features, more plausible bundles,
                      and strong predictive performance. More broadly, these
                      results demonstrate that bundle segmentation can achieve
                      robust performance even on lower angular resolution,
                      single-shell dMRI, with particular advantages for ODF
                      methods optimized for single-shell data. This highlights the
                      considerable potential for dMRI collected in healthcare
                      settings and legacy research datasets to accelerate and
                      expand the scope of WM research.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2025.09.02.673635},
      url          = {https://juser.fz-juelich.de/record/1047194},
}