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Widefield Imaging Reveals Distinct Whole-Cortex Dynamics During Anesthetic-Induced Unconsciousness
Muchao Xia1, Chengyu Wang1, Ling Jiang1
1From the Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Background:
Anesthesia-induced loss of consciousness (LOC) is associated with alterations in cortical synchronization and desynchronization in various cortical regions. However, the precise spatiotemporal dynamics across the entire cortex and their role in the LOC remain unclear. This study utilized advanced imaging techniques to investigate these cortical dynamics under the action of different anesthetics.
Methods:
We used Thy1-GCaMP6s mice with surgically prepared transparent skulls to observe cortical Ca 2+ signals using widefield microscopy. Functional connectivity analysis was performed, and the primary spatial representations of the different frequency signal components were examined under anesthesia induced by ketamine, propofol, and isoflurane.
Results:
All drugs enhanced 1.5 to 2.5 Hz signal oscillations in the retrosplenial cortex (RSC), making the 1.5 to 2.5 Hz signal oscillation power in the retrosplenial cortex significantly higher than that in other cortical regions (ketamine (mean ± SD: 1. 58 ± 0.06 vs 0. 27 ± 0.10, -0. 44 ± 0.02 and -0. 36 ± 0.06 for the retrosplenial cortex (RSC) vs the primary motor cortex (MOp), the primary somatosensory cortex (SSp) and the primary visual cortex (VISp), P < .001; propofol: 1. 36 ± 0.06 vs 0. 50 ± 0.09, -0. 34 ± 0.04 and -0. 20 ± 0.11 isoflurane: 1. 39 ± 0.12 vs 0. 30 ± 0.07, -0. 14 ± 0.19 and -0. 20 ± 0.14). Moreover, ketamine and isoflurane was associated with a structured signaling pattern.
Conclusions:
The general anesthetics induced unique cortical signal patterns, with the RSC emerging as a key region in which specific oscillatory patterns manifested. The observed 1.5 to 2.5 Hz oscillations in the RSC under different anesthetic conditions suggest a common underlying mechanism for anesthesia-induced LOC. Understanding these spatiotemporal patterns can help improve the monitoring and management of anesthesia.
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