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| 001 | 1010399 | ||
| 005 | 20231027114412.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2023.120292 |2 doi |
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| 037 | _ | _ | |a FZJ-2023-03039 |
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| 100 | 1 | _ | |a Antonopoulos, Georgios |0 P:(DE-Juel1)180946 |b 0 |
| 245 | _ | _ | |a A systematic comparison of VBM pipelines and their application to age prediction |
| 260 | _ | _ | |a Orlando, Fla. |c 2023 |b Academic Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better. |
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| 700 | 1 | _ | |a Raimondo, Federico |0 P:(DE-Juel1)185083 |b 2 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 3 |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 4 |
| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2023.120292 |g p. 120292 - |0 PERI:(DE-600)1471418-8 |p 120292 - |t NeuroImage |v 279 |y 2023 |x 1053-8119 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1010399/files/1-s2.0-S1053811923004433-main.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1010399/files/Acomparison_of_VBM_pipelines_reviewed.pdf |
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