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@ARTICLE{Rauland:1049582,
      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        = {{W}hite {M}atter {B}undle {R}econstruction {F}rom
                      {S}ingle‐{S}hell {D}iffusion {M}agnetic {R}esonance
                      {I}maging: {T}est–{R}etest {R}eliability and {P}redictive
                      {C}apability {A}cross {O}rientation {D}istribution
                      {F}unction {R}econstruction {M}ethods},
      journal      = {Human brain mapping},
      volume       = {46},
      number       = {17},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2025-05386},
      pages        = {e70429},
      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 modern 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 a
                      state-of-the-art bundle-specific reconstruction method
                      generalizes to this data, we evaluated the test–retest
                      reliability of WM bundles reconstructed from the two
                      32-direction scans 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 when comparing the reconstructed bundles to
                      atlas bundles, a high intra-class correlation of extracted
                      features, more plausible bundles, and strong predictive
                      performance. More broadly, these results demonstrate that
                      bundle-specific reconstruction 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 / INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-11-20170113},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1002/hbm.70429},
      url          = {https://juser.fz-juelich.de/record/1049582},
}