Fine-Grained Parcellation of Brain Connectivity Improves Differentiation of States of Consciousness During Graded Propofol Sedation

Abstract

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.

Keywords: brain parcellation; loss of consciousness; propofol sedation; resting-state fMRI; state classification.

FIG. 1.

Illustration of the anatomical-functional brain parcellation. (A) A combination of regional hierarchical clustering and global thresholding of dendrograms determines the spatial granularity of parcellation as shown for 300, 500, and 2000 nodes. (B) Glass-brain images of 116 anatomical regions (Tzourio-Mazoyer et al., 2002) and functional parcellations at 500 and 2000 nodes. Nodes in six major anatomical divisions are color coded. The plots are from one participant as an example.

FIG. 2.

Functional connectivity changes between various states of consciousness. (A–G) Seven state comparisons from group paired t-test at p = 3.0 × 10⁻⁵ with panels showing decreased node functional connectivity with deepening of sedation (red lines, A–D) and increased connectivity in deep sedation (blue lines, E–G). The contrasts between wakefulness and deep sedation (C and F) were used to determine the collections of node pairs for computing DCI and ICI. Different dot colors distinguish major brain divisions, as indicated in Figure 1. (H) The number of connections at three different p-thresholds for the seven comparisons corresponding to panels (A–G). The trend of changes in the number of node pairs is similar at the three p-thresholds. DCI, decreased connectivity index; ICI, increased connectivity index.

FIG. 3.

ROC curve analysis of state classification. (A–D) Distribution of feature vectors defined by the DCI and ICI in the two-dimensional feature space. Color-coded symbols of feature vectors represent the four states of consciousness in all individual participants at the parcellation of 116, 300, 500, and 2000 nodes, respectively. The color-coded ellipses depict a two-dimensional Gaussian fitting of spatial distribution of feature vectors in each state. (E–H) The ROC curves describing true-positive versus false-positive rates by a quadratic discriminant analysis and leave-one-out cross-validation tests at increasing parcellation granularity in four conditions. AUC scores are shown for each parcellation. State classification in individual participants is distinctly better at 2000-node parcellation. AUC, area under curve; ROC, receiver operating characteristic.