Article ; Online: Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability.
2022 Volume 262, Page(s) 119553
Abstract: Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) ... ...
Abstract | Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo. |
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MeSH term(s) | Cerebral Cortex ; Connectome ; Diffusion Magnetic Resonance Imaging ; Feasibility Studies ; Humans ; Reproducibility of Results |
Language | English |
Publishing date | 2022-08-09 |
Publishing country | United States |
Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
ZDB-ID | 1147767-2 |
ISSN | 1095-9572 ; 1053-8119 |
ISSN (online) | 1095-9572 |
ISSN | 1053-8119 |
DOI | 10.1016/j.neuroimage.2022.119553 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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