001047194 001__ 1047194
001047194 005__ 20251110202103.0
001047194 0247_ $$2doi$$a10.1101/2025.09.02.673635
001047194 037__ $$aFZJ-2025-04143
001047194 082__ $$a570
001047194 1001_ $$0P:(DE-Juel1)195856$$aRauland, Amelie$$b0$$eCorresponding author
001047194 245__ $$aBenchmarking Orientation Distribution Function Estimation Methods for Tractometry in Single-Shell Diffusion Magnetic Resonance Imaging - An Evaluation of Test-Retest Reliability and Predictive Capability
001047194 260__ $$aCold Spring Harbor$$bCold Spring Harbor Laboratory, NY$$c2025
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001047194 520__ $$aDeriving 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.
001047194 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
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001047194 7001_ $$0P:(DE-HGF)0$$aMeisler, Steven L.$$b1
001047194 7001_ $$0P:(DE-HGF)0$$aAlexander-Bloch, Aaron F.$$b2
001047194 7001_ $$0P:(DE-HGF)0$$aBagautdinova, Joëlle$$b3
001047194 7001_ $$0P:(DE-HGF)0$$aBaller, Erica B.$$b4
001047194 7001_ $$0P:(DE-HGF)0$$aGur, Raquel E.$$b5
001047194 7001_ $$0P:(DE-HGF)0$$aGur, Ruben C.$$b6
001047194 7001_ $$0P:(DE-HGF)0$$aLuo, Audrey C.$$b7
001047194 7001_ $$0P:(DE-HGF)0$$aMoore, Tyler M.$$b8
001047194 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr V.$$b9
001047194 7001_ $$0P:(DE-Juel1)177889$$aReetz, Kathrin$$b10
001047194 7001_ $$0P:(DE-HGF)0$$aRoalf, David R.$$b11
001047194 7001_ $$0P:(DE-HGF)0$$aShinohara, Russell T.$$b12
001047194 7001_ $$0P:(DE-HGF)0$$aSotardi, Susan$$b13
001047194 7001_ $$0P:(DE-HGF)0$$aSydnor, Valerie J.$$b14
001047194 7001_ $$0P:(DE-HGF)0$$aVossough, Arastoo$$b15
001047194 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b16
001047194 7001_ $$0P:(DE-HGF)0$$aCieslak, Matthew$$b17
001047194 7001_ $$0P:(DE-HGF)0$$aSatterthwaite, Theodore D.$$b18$$eCorresponding author
001047194 773__ $$0PERI:(DE-600)2766415-6$$a10.1101/2025.09.02.673635$$tbioRxiv beta$$y2025
001047194 8564_ $$uhttps://juser.fz-juelich.de/record/1047194/files/Rauland%20Benchmarking%20ODF%20Estimation%20Methods%20for%20Tractometry%20in%20Single-Shell%20dMRI%20Main.docx$$yRestricted
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001047194 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$aHHU Düsseldorf$$b16
001047194 9141_ $$y2025
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