Propofol disrupts the functional core-matrix architecture of the thalamus in humans

Abstract

Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. Here we show a significant shift in this geometry during deep sedation, marked by a transmodal-deficient geometry. This alteration is closely linked to the spatial variations in the matrix cell composition within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Fig. 1. Thalamic correlates of the unimodal-transmodal cortical gradient.

a A voxel-based method was developed to analyze the unimodal-transmodal functional geometry of the thalamocortical circuits aligned with the unimodal-transmodal functional geometry of the cortex. Pair-wise correlations were computed between the subcorticocortical connectivity values of each subcortical voxel and the cortical gradient values. b Reproducible patterns were seen across diverse datasets. c High spatial similarity across different datasets was found. Spin permutation tests were used to determine p values (two-sided), accounting for potential inflation of significance due to spatial autocorrelations. Abbreviations: HCP Human Connectome Project. Detailed statistics are provided in Supplementary Data 1. Source data are provided as a Source Data file.

Fig. 2. Changes of thalamocortical gradient correlation during deep sedation.

a The study involved healthy volunteers undergoing fMRI scans during conscious baseline, deep sedation, and recovery conditions, each comprising 16 min of resting state (n = 27) and 16 min of music listening (n = 27). Topographical maps of the gradient correlation coefficients are shown for each condition. b Gradient correlation coefficients were extracted from predefined thalamic areas. Abbreviations: VAs superior ventroanterior thalamus, DAm medial dorsoanterior thalamus, VAi inferior ventroanterior thalamus, DAl lateral dorsoanterior thalamus, VPm medial ventroposterior thalamus, DP dorsoposterior thalamus, and VPl lateral ventroposterior thalamus. Results are FDR–corrected for multiple comparisons at α = 0.05 (two-sided). An asterisk signifies FDR-corrected p < 0.05 when comparing conscious baseline to deep sedation. A pound signifies FDR-corrected p < 0.05 when comparing recovery to deep sedation. Detailed statistics are provided in Supplementary Data 1. Source data are provided as a Source Data file.

Fig. 3. Relationship between core-matrix thalamic mRNA expression and gradient correlation coefficient.

a Core and matrix cell types were inferred from the mRNA expression levels of two calcium-binding proteins, Calbindin (CALB1) and Parvalbumin (PVALB), sourced from the Allen Human Brain Atlas. The relative weighting of the difference between CALB1 and PVALB levels was defined by the CP_T metric. The left panel displays a z-scored heatmap of CP_T values across thalamic voxels, indicating the relative expression of CALB1 and PVALB. The right panel shows the extracted CP_T values for predefined thalamic areas, corresponding to the color-coded parcellation scheme in the bottom right. Abbreviations: VAs superior ventroanterior thalamus, DAm medial dorsoanterior thalamus, VAi inferior ventroanterior thalamus, DAl lateral dorsoanterior thalamus, VPm medial ventroposterior thalamus, DP dorsoposterior thalamus, and VPl lateral ventroposterior thalamus. b CP_T values were correlated with the group-averaged gradient correlation coefficients (GCC) across all thalamic voxels (n = 467). The group-averaged GCC maps were derived from those combining the results obtained from both resting-state (n = 27) and music listening data (n = 27). c CP_T values were correlated with the group-averaged changes in GCC (ΔGCC) for deep sedation vs. conscious, and for recovery vs. deep sedation across all thalamic voxels. For both (b) and (c), spin permutation tests were used to determine p values (two-sided). Linear trend lines with 95% confidence intervals are shown. Detailed statistics are provided in Supplementary Data 1. Source data are provided as a Source Data file.

Fig. 4. Relative importance of core vs. matrix cell compositions in predicting the changes of gradient correlation coefficient.

a Z-scored heatmaps illustrating the mRNA expression levels of Calbindin (CALB1, matrix-enriched) and Parvalbumin (PVALB, core-enriched). b Dominance analysis was employed to assess the relative importance of core vs. matrix cell compositions (PVALB and CALB1, respectively) in predicting changes in group-averaged gradient correlation coefficient (ΔGCC) during transitions between conscious and deep sedated states. The analysis quantifies the contribution of each cell composition to the overall model fit (adjusted R²) in a multiple linear regression model (two-sided). Scatter plots (n = 467) depict partial linear regression, plotting residuals against each other, with linear trend lines and 95% confidence intervals shown. Detailed statistics are available in Supplementary Data 1, and source data are provided as a Source Data file.

Fig. 5. Relative importance of thalamic cell compositions, neurotransmitter receptors and transporters in predicting the changes of gradient correlation coefficient.

a Z-scored heatmaps illustrating the densities of neurotransmitter receptors and transporters in the thalamus, derived from collated and averaged PET tracer images. b Dominance analysis was employed to assess the relative importance of core vs. matrix cell compositions (PVALB and CALB1, respectively), along with 19 neurotransmitter receptors, transporters, and receptor-binding sites across nine neurotransmitter systems, in predicting changes in group-averaged GCC (ΔGCC) during transitions between conscious and deep sedated states. The analysis quantifies the contribution of each predictor to the overall model fit (adjusted R²) in a multiple linear regression model. Due to the computational intensity of dominance analysis, the top 10 predictors were selected based on F-regression. Abbreviations: serotonin receptors (5-HT_1A, 5-HT_1B, 5-HT_2A, 5-HT₄, 5-HT₆), serotonin transporter (5-HTT), dopamine receptors (D1, D2), dopamine transporter (DAT), norepinephrine transporter (NET), histamine receptor H3 (H3), nicotinic acetylcholine receptor α₄β₂ subtype (α₄β₂), muscarinic acetylcholine receptor M1 (M1), vesicular acetylcholine transporter (VAChT), cannabinoid receptor 1 (CB₁), μ-opioid receptor (MOR), N-Methyl-D-aspartate receptor (NMDA), metabotropic glutamate receptor 5 (mGluR₅), GABA-A receptor/benzodiazepine binding site (GABA_A/BZ). Detailed statistics are available in Supplementary Data 1, and source data are provided as a Source Data file.

Fig. 6. Relative alterations of thalamocortical functional connectivity during deep sedation.

a Functional connectivity was computed between each pre-defined thalamic area and the cortical areas associated with both unimodal (brain areas in the visual and somatomotor networks) and transmodal (brain areas in the frontoparietal and default-mode networks) functions. Global signal regression was applied to the data. The dataset consists of 16 min of resting state and 16 min of music listening data, each with 27 participants. b Radar plots illustrate the relative alterations in thalamocortical functional connectivity between unimodal and transmodal networks. Abbreviations: VAs superior ventroanterior thalamus, DAm medial dorsoanterior thalamus, VAi inferior ventroanterior thalamus, DAl lateral dorsoanterior thalamus, VPm medial ventroposterior thalamus, DP dorsoposterior thalamus, and VPl lateral ventroposterior thalamus. Results are FDR–corrected for multiple comparisons at α = 0.05 (two-sided). An asterisk signifies FDR-corrected p < 0.05 when comparing conscious baseline to deep sedation. A pound signifies FDR-corrected p < 0.05 when comparing recovery to deep sedation. Detailed statistics are provided in Supplementary Data 1. Source data are provided as a Source Data file.

Fig. 7. Schematic illustration of the primary conclusion.

The thalamus exhibits a heterogeneous cytoarchitecture with core and matrix cells that send differential projections to the cortex^–,. Within the thalamus, these cells coexist in varying proportions. Thalamic regions rich in matrix cells demonstrate heightened functional connectivity with transmodal cortical areas, while those enriched with core cells exhibit stronger functional connectivity with unimodal cortical areas. Our findings imply that propofol-induced loss of consciousness is associated with the functional disruption of thalamic matrix cell connectivity.