ArticleBrain and behavior2024
Correlation of diffusion tensor tractography with obstructive sleep apnea severity.
Article in Brain and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The micro-structural changes in white matter fibers associated with anxiety and depression in moderate-severe obstructive sleep apnea.Scientific reports · 2026Article
- Tractography-based correlation of diffusion anisotropy metrics in chronic post-stroke aphasia.BMC medical imaging · 2026Article
- Connectometry reveals differing associations of cortisol and PACAP with dorsal cingulum microstructure in posttraumatic stress.medRxiv : the preprint server for health sciences · 2025Article
- Correlation of diffusion tensor tractography with obstructive sleep apnea severity.Brain and behavior · 2024Article
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Authors and funding
8 authors.
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Abstract
introductionUsing correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity.
methodsWe enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections.
resultsConnectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI.
conclusionSeveral WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.
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