| 001 | 1047194 | ||
| 005 | 20251110202103.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1101/2025.09.02.673635 |
| 037 | _ | _ | |a FZJ-2025-04143 |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |0 P:(DE-Juel1)195856 |a Rauland, Amelie |b 0 |e Corresponding author |
| 245 | _ | _ | |a Benchmarking Orientation Distribution Function Estimation Methods for Tractometry in Single-Shell Diffusion Magnetic Resonance Imaging - An Evaluation of Test-Retest Reliability and Predictive Capability |
| 260 | _ | _ | |a Cold Spring Harbor |b Cold Spring Harbor Laboratory, NY |c 2025 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1762776272_26650 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5253 |a 5253 - Neuroimaging (POF4-525) |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Meisler, Steven L. |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Alexander-Bloch, Aaron F. |b 2 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Bagautdinova, Joëlle |b 3 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Baller, Erica B. |b 4 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Gur, Raquel E. |b 5 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Gur, Ruben C. |b 6 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Luo, Audrey C. |b 7 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Moore, Tyler M. |b 8 |
| 700 | 1 | _ | |0 P:(DE-Juel1)131880 |a Popovych, Oleksandr V. |b 9 |
| 700 | 1 | _ | |0 P:(DE-Juel1)177889 |a Reetz, Kathrin |b 10 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Roalf, David R. |b 11 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Shinohara, Russell T. |b 12 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sotardi, Susan |b 13 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sydnor, Valerie J. |b 14 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Vossough, Arastoo |b 15 |
| 700 | 1 | _ | |0 P:(DE-Juel1)131678 |a Eickhoff, Simon B. |b 16 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Cieslak, Matthew |b 17 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Satterthwaite, Theodore D. |b 18 |e Corresponding author |
| 773 | _ | _ | |0 PERI:(DE-600)2766415-6 |a 10.1101/2025.09.02.673635 |t bioRxiv beta |y 2025 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1047194/files/Rauland%20Benchmarking%20ODF%20Estimation%20Methods%20for%20Tractometry%20in%20Single-Shell%20dMRI%20Main.docx |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:1047194 |p VDB |
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| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)177889 |a Forschungszentrum Jülich |b 10 |k FZJ |
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| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-Juel1)131678 |a HHU Düsseldorf |b 16 |
| 913 | 1 | _ | |0 G:(DE-HGF)POF4-525 |1 G:(DE-HGF)POF4-520 |2 G:(DE-HGF)POF4-500 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |9 G:(DE-HGF)POF4-5253 |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |v Decoding Brain Organization and Dysfunction |x 0 |
| 914 | 1 | _ | |y 2025 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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