Background: Propofol is a commonly used anesthetic, and its impact on brain function has been a significant focus of neuroscience research. However, previous studies have primarily focused on the effects of propofol on gray matter function. White matter in the brain is a pathway for transmitting information between different brain regions. Recently, blood oxygen level-dependent signals in white matter have been shown to have physiological significance. However, the effects of propofol on white matter function remain unclear. The purpose of this study is to investigate changes in white matter functional connectivity during propofol-induced sedation.
Methods: Resting-state functional magnetic resonance imaging was performed on 21 healthy participants in four states: awake, mild propofol-induced sedation, deep propofol-induced sedation, and postsedation recovery. White matter functional connectivity, including white to gray matter functional connectivity and white to white matter functional connectivity, was compared between different states. The white matter tracts primarily affected by propofol were identified by calculating white matter functional connectivity strength from white to gray matter functional connectivity and performing a Friedman test across four states. Additionally, considering that white matter promotes gray matter communication, white matter-mediated functional networks were constructed through white to gray matter functional connectivity. The global efficiency of white matter-mediated functional networks across different states was studied.
Results: The white to gray matter functional connectivity and white to white matter functional connectivity significantly decreased during deep sedation compared to the awake state (P < .05). Several fiber tracts, including the posterior limb of the internal capsule, the cingulum near the cingulate gyrus, the genu of corpus callosum, and the retrolenticular part of the internal capsule, showed significant differences in white matter functional connectivity strength across the four states (P < .01). The global efficiency of the whole brain network, as well as the visual, somatomotor, attention, frontoparietal, limbic, and default mode networks, decreased during deep sedation and returned to the awake level after recovery (P < .05).
Conclusions: Propofol disrupts white matter functional connectivity, with deep sedation inducing widespread functional connectivity reductions, particularly in key tracts and networks. The disruption of white matter functional connectivity may reflect a breakdown in large-scale brain integration and could serve as a biomarker for deep propofol-induced sedation, although not necessarily its mechanistic driver.
Background: The stellate ganglion region is densely vascularized and innervated, making the stellate ganglion block (SGB) technically challenging under ultrasound, particularly for beginners. Deep learning can segment complex ultrasound anatomy, but its application to SGB has not been systematically assessed. We developed and validated a multilevel feature fusion UNet (MLF-UNet) to automatically delineate the SGB region on ultrasound, aiming to support accurate needle placement and improve procedural safety.
Methods: In this retrospective study, 370 patients who underwent ultrasound-guided SGB between March 1, 2023 and January 16, 2025 were included. Three expert anesthesiologists jointly annotated 730 videos (2190 images) to produce ground truth. Data were split 9:1 by patient into development and heldout test sets. MLF-UNet was trained and compared with 5 benchmark models using identical pipelines. Test-set performance was evaluated with Dice similarity coefficient (DSC), Intersection over Union (IoU), 95th percentile Hausdorff distance (95HD), and average symmetric surface distance (ASSD). Three blinded experts rated model outputs (0-2 scale) for topological integrity, boundary precision, and background accuracy. For clinical validation and human-machine comparison, 3 additional experts and 3 nonexperts independently delineated SGB regions on the test set; spatial agreement was visualized with heat maps and assessed by Bland-Altman analysis. Metrics (DSC, IoU, 95HD, and ASSD) were compared among MLF-UNet, experts, and nonexperts.
Results: MLF-UNet achieved the best test performance: DSC 0.856 (95% confidence interval [CI], 0.846-0.865), IoU 0.754 (95% CI, 0.740-0.768), 95HD 3.98 mm (95% CI, 3.44-4.52 mm), and ASSD 1.08 mm (95% CI, 0.99-1.18 mm). Expert ratings favored MLF-UNet over all benchmark models for topological integrity (all P < .001), boundary precision (all P < .001), background accuracy (P < .01 or P < .001), and total score (all P < .001). Bland-Altman analysis showed a mean segmentation area difference between MLF-UNet and ground truth of -38.1 mm² (limits of agreement -278 to +202 mm²). MLF-UNet outperformed the nonexpert group on region overlap (DSC, IoU; both P < .001) and boundary precision (95HD, ASSD; both P < .001). Compared with experts, MLF-UNet showed no significant difference in overlap (DSC P = .332; IoU P = .125) but had slightly larger boundary precision (95HD and ASSD: both P < .001).
Conclusions: MLFUNet outperforms 5 benchmark models and nonexpert clinicians for automated ultrasound segmentation of the SGB region, achieving expert‑level region overlap with a modest deficit in boundary precision.
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