Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.
Challenge
Connectivity
Diffusion MRI
Microstructure
Monte carlo simulation
Numerical substrates
Tractography
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
15 08 2023
15 08 2023
Historique:
received:
02
03
2023
revised:
12
05
2023
accepted:
14
06
2023
medline:
23
10
2023
pubmed:
18
6
2023
entrez:
17
6
2023
Statut:
ppublish
Résumé
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
Identifiants
pubmed: 37330025
pii: S1053-8119(23)00382-8
doi: 10.1016/j.neuroimage.2023.120231
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
120231Subventions
Organisme : NIMH NIH HHS
ID : R01 MH125479
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS092870
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB017230
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB022883
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS105646
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG059312
Pays : United States
Organisme : NICHD NIH HHS
ID : P50 HD105353
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS126806
Pays : United States
Organisme : NICHD NIH HHS
ID : U54 HD090256
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS111022
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS082436
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG037639
Pays : United States
Organisme : NIBIB NIH HHS
ID : K01 EB032898
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS123378
Pays : United States
Organisme : NIAID NIH HHS
ID : P01 AI132132
Pays : United States
Organisme : NIDA NIH HHS
ID : R34 DA050258
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS124920
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB017183
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG033514
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS117568
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI138647
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB028774
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102665
Pays : United States
Organisme : NIA NIH HHS
ID : UF1 AG051216
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG027161
Pays : United States
Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no competing interests.