Resting-state functional connectivity in early postanaesthesia recovery is characterised by globally reduced anticorrelations

Abstract

Background: A growing body of literature addresses the possible long-term cognitive effects of anaesthetics, but no study has delineated the normal trajectory of neural recovery attributable to anaesthesia alone in adults. We obtained resting-state functional MRI scans on 72 healthy human volunteers between ages 40 and 80 (median: 59) yr before, during, and after general anaesthesia with sevoflurane, in the absence of surgery, as part of a larger study on cognitive function postanaesthesia.

Methods: Region-of-interest analysis, independent component analysis, and seed-to-voxel analysis were used to characterise resting-state functional connectivity and to differentiate between correlated and anticorrelated connectivity before, during, and after general anaesthesia.

Results: Whilst positively correlated functional connectivity remained essentially unchanged across these perianaesthetic states, anticorrelated functional connectivity decreased globally by 35% 1 h after emergence from general anaesthesia compared with baseline, as seen by the region-of-interest analysis. This decrease corresponded to a consistent reduction in expression of canonical resting-state networks, as seen by independent component analysis. All measures returned to baseline 1 day later.

Conclusions: The normal perianaesthesia trajectory of resting-state connectivity in healthy adults is characterised by a transient global reduction in anticorrelated activity shortly after emergence from anaesthesia that returns to baseline by the following day.

Clinical trial registration: NCT02275026.

Keywords: MRI; consciousness; functional MRI; functional connectivity; general anaesthesia; recovery.

Copyright © 2020 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

Figures

Fig 1

Consolidated Standards of Reporting Trials diagram.

Fig 2

Global ROI-to-ROI analysis. ROI-to-ROI analysis was performed using 106 cortical and subcortical ROIs (predefined in CONN). Functional connectivity was characterised as the number of statistically significant edges connecting ROIs and further divided into correlated and anticorrelated, represented by positive and negative edges, respectively (P<0.001, false discovery rate corrected for multiple comparisons). (a) Quantification of correlated (positive) functional connectivity as average edges per ROI is shown schematically for each time point along the perianaesthesia trajectory. (b) Connectome map of correlated functional connectivity. (c) and (d) Anticorrelated (negative) edges are similarly characterised. Correlated functional connectivity does not change significantly across the perianaesthesia trajectory. Changes in anticorrelated functional connectivity were significant: average anticorrelated edges per ROI increased under anaesthesia and decreased postanaesthesia compared with baseline (PRE) (∗P<E−10; paired t-test). ANA, during anaesthesia; D1, 1 day later; POST, 1 h after emergence; PRE, baseline before anaesthesia; ROI, region of interest.

Fig 3

Return to baseline connectivity at day 1. For each ROI, connectivity is plotted for D1 (y-axis) vs baseline (x-axis) values. (a) Correlated connectivity and (b) anticorrelated connectivity. The best-fit regression line is plotted as well (with intercept set to 0). Slope is 0.96 (R2=0.87) for correlated connectivity and 0.93 (R2=0.65) for anticorrelated connectivity. D1, 1 day later; PRE, baseline before anaesthesia; ROI, region of interest.

Fig 4

Resting-state network expression along the perianaesthesia trajectory. (a) Quantification of volume (statistically significant voxels) of the ICA component matching the DMN (correlated volume in red; anticorrelated volume in blue), with (b) multiple slices illustrated. Seed-to-voxel analysis of the DMN is presented as a control for the adequacy of the ICA component, with (c) quantification of statistically significant voxels and (d) multiple slices illustrated. For seed voxel, the DMN is represented by medial prefrontal cortex, posterior cingulate cortex, and parietal cortex ROIs. Statistical threshold was set at P<0.001, false discovery rate (FDR) corrected. ICA analysis: normalised (e) positive and (f) negative components of various resting state networks at each time point (with voxel counts normalised to baseline value for each RSN). (g) Average volume across RSNs (red, positive; blue, negative). Statistically significant changes (paired t-test) are shown (∗P<0.05; ∗∗P<0.01). ANA, during anaesthesia; D1, 1 day later; DA, dorsal attention; DMN, default mode network; FP, frontoparietal; ICA, independent component analysis; POST, 1 h after emergence; PRE, baseline before anaesthesia; ROI, region of interest; RSN, resting-state network; SAL, salience; SM, sensory motor.