| 001 | 1049582 | ||
| 005 | 20251215162549.0 | ||
| 024 | 7 | _ | |a 10.1002/hbm.70429 |2 doi |
| 024 | 7 | _ | |a 1065-9471 |2 ISSN |
| 024 | 7 | _ | |a 1097-0193 |2 ISSN |
| 037 | _ | _ | |a FZJ-2025-05386 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Rauland, Amelie |0 P:(DE-Juel1)195856 |b 0 |
| 245 | _ | _ | |a White Matter Bundle Reconstruction From Single‐Shell Diffusion Magnetic Resonance Imaging: Test–Retest Reliability and Predictive Capability Across Orientation Distribution Function Reconstruction Methods |
| 260 | _ | _ | |a New York, NY |c 2025 |b Wiley-Liss |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1765812087_9854 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 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 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. |
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| 536 | _ | _ | |a 5253 - Neuroimaging (POF4-525) |0 G:(DE-HGF)POF4-5253 |c POF4-525 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Meisler, Steven L. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Alexander-Bloch, Aaron F. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Bagautdinova, Joëlle |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Baller, Erica B. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Gur, Raquel E. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Gur, Ruben C. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Luo, Audrey C. |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Moore, Tyler M. |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Popovych, Oleksandr V. |0 P:(DE-Juel1)131880 |b 9 |
| 700 | 1 | _ | |a Reetz, Kathrin |0 P:(DE-Juel1)177889 |b 10 |
| 700 | 1 | _ | |a Roalf, David R. |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Shinohara, Russell T. |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Sotardi, Susan |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Sydnor, Valerie J. |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Vossough, Arastoo |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 16 |
| 700 | 1 | _ | |a Cieslak, Matthew |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Satterthwaite, Theodore D. |0 P:(DE-HGF)0 |b 18 |e Corresponding author |
| 773 | _ | _ | |a 10.1002/hbm.70429 |g Vol. 46, no. 17, p. e70429 |0 PERI:(DE-600)1492703-2 |n 17 |p e70429 |t Human brain mapping |v 46 |y 2025 |x 1065-9471 |
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