Accuracy and reliability of diffusion imaging models.
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 07 2022
01 07 2022
Historique:
received:
23
12
2021
revised:
01
03
2022
accepted:
22
03
2022
pubmed:
28
3
2022
medline:
11
5
2022
entrez:
27
3
2022
Statut:
ppublish
Résumé
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
Identifiants
pubmed: 35339687
pii: S1053-8119(22)00266-X
doi: 10.1016/j.neuroimage.2022.119138
pmc: PMC9841915
mid: NIHMS1840704
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
119138Subventions
Organisme : CSRD VA
ID : IK2 CX001680
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS088590
Pays : United States
Organisme : NIMH NIH HHS
ID : K99 MH129616
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD094381
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH100019
Pays : United States
Organisme : NINDS NIH HHS
ID : P30 NS098577
Pays : United States
Organisme : NINDS NIH HHS
ID : T32 NS115672
Pays : United States
Organisme : NIMH NIH HHS
ID : R44 MH121276
Pays : United States
Organisme : NINDS NIH HHS
ID : F31 NS110332
Pays : United States
Organisme : NINDS NIH HHS
ID : R25 NS090978
Pays : United States
Organisme : NIMH NIH HHS
ID : R00 MH121518
Pays : United States
Organisme : NICHD NIH HHS
ID : U54 HD087011
Pays : United States
Organisme : NIMH NIH HHS
ID : K01 MH104592
Pays : United States
Organisme : NIMH NIH HHS
ID : K99 MH121518
Pays : United States
Organisme : NIMH NIH HHS
ID : R25 MH112473
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH096773
Pays : United States
Organisme : NIMH NIH HHS
ID : R44 MH124567
Pays : United States
Organisme : NIMH NIH HHS
ID : R44 MH122066
Pays : United States
Informations de copyright
Copyright © 2022. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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