Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.
corticospinal tract
diffusion magnetic resonance imaging
motor cortex
somatotopic organization
tractography
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 12 2023
01 12 2023
Historique:
revised:
09
09
2023
received:
07
06
2023
accepted:
13
09
2023
medline:
2
11
2023
pubmed:
4
10
2023
entrez:
4
10
2023
Statut:
ppublish
Résumé
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
Identifiants
pubmed: 37792280
doi: 10.1002/hbm.26497
pmc: PMC10619402
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
6055-6073Subventions
Organisme : NIH HHS
ID : R01MH119222
Pays : United States
Organisme : NIH HHS
ID : R01NS125307
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB028741
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015902
Pays : United States
Organisme : NIH HHS
ID : P41EB015902
Pays : United States
Organisme : NIH HHS
ID : R01MH125860
Pays : United States
Organisme : NIH HHS
ID : K24MH116366
Pays : United States
Organisme : NIH HHS
ID : R21DA042271
Pays : United States
Organisme : NIH HHS
ID : R01CA235589
Pays : United States
Organisme : NIH HHS
ID : R01AG042512
Pays : United States
Organisme : NIH HHS
ID : R01MH111917
Pays : United States
Organisme : NIH HHS
ID : R01MH112748
Pays : United States
Organisme : NIH HHS
ID : R01NS125781
Pays : United States
Organisme : NIH HHS
ID : R01NS125307
Pays : United States
Organisme : NIH HHS
ID : P41EB028741
Pays : United States
Organisme : NIH HHS
ID : P41EB015902
Pays : United States
Organisme : NIH HHS
ID : R01MH119222
Pays : United States
Organisme : NIH HHS
ID : R01MH125860
Pays : United States
Organisme : NCI NIH HHS
ID : HHSN261201500003I
Pays : United States
Informations de copyright
© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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