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Perioperative Medicine  |   March 2017
Dexmedetomidine Disrupts the Local and Global Efficiencies of Large-scale Brain Networks
Author Notes
  • From the Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, Halifax, Nova Scotia, Canada (J.A.H.); Department of Radiology, Martinos Center for Biomedical Imaging (M.L.L., J.K., V.N.), Department of Neurology (S.K.), and Department of Anesthesia, Critical Care and Pain Medicine (L.G., E.N.B., O.A.), Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts; Clinical Research Division, Korea Institute for Oriental Medicine, Daejeon, Korea (J.K.); and Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts (E.N.B.).
  • Corresponding article on page 366.
    Corresponding article on page 366.×
  • Submitted for publication April 25, 2016. Accepted for publication November 11, 2016.
    Submitted for publication April 25, 2016. Accepted for publication November 11, 2016.×
  • Address correspondence to Dr. Hashmi: Dalhousie University, 4086, Pain Management Unit, 4th Floor Dickson Building, 5820 University Ave, Halifax, NS, Canada, B3H 1V7. javeria.hashmi@dal.ca. Information on purchasing reprints may be found at www.anesthesiology.org or on the masthead page at the beginning of this issue. Anesthesiology’s articles are made freely accessible to all readers, for personal use only, 6 months from the cover date of the issue.
Article Information
Perioperative Medicine / Clinical Science / Central and Peripheral Nervous Systems / Pharmacology
Perioperative Medicine   |   March 2017
Dexmedetomidine Disrupts the Local and Global Efficiencies of Large-scale Brain Networks
Anesthesiology 3 2017, Vol.126, 419-430. doi:10.1097/ALN.0000000000001509
Anesthesiology 3 2017, Vol.126, 419-430. doi:10.1097/ALN.0000000000001509
Abstract

Background: A clear understanding of the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia. Recently, decreased efficiency of information integration was suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to dexmedetomidine, which has a different molecular target.

Methods: Dexmedetomidine was administered as a 1-μg/kg bolus over 10 min, followed by a 0.7-μg · kg−1 · h−1 infusion to healthy human volunteers (age range, 18 to 36 yr; n = 15). Resting-state functional magnetic resonance imaging data were acquired during baseline, dexmedetomidine-induced altered arousal, and recovery states. Zero-lag correlations between resting-state functional magnetic resonance imaging signals extracted from 131 brain parcellations were used to construct weighted brain networks. Network efficiency, degree distribution, and node strength were computed using graph analysis. Parcellated brain regions were also mapped to known resting-state networks to study functional connectivity changes.

Results: Dexmedetomidine significantly reduced the local and global efficiencies of graph theory–derived networks. Dexmedetomidine also reduced the average brain connectivity strength without impairing the degree distribution. Functional connectivity within and between all resting-state networks was modulated by dexmedetomidine.

Conclusions: Dexmedetomidine is associated with a significant drop in the capacity for efficient information transmission at both the local and global levels. These changes result from reductions in the strength of connectivity and also manifest as reduced within and between resting-state network connectivity. These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.

What We Already Know about This Topic
  • Decreased efficiency in information integration has been suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to anesthetics with different molecular targets.

What This Article Tells Us That Is New
  • Using resting-state functional magnetic resonance in healthy human volunteers, it was shown that dexmedetomidine significantly reduced the local and global efficiencies of graph theory–derived networks, reduced the mean strength of network connectivity without impairing the degree distribution, and modulated functional connectivity within and between all resting-state networks.

  • These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.

UNDERSTANDING the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia.1  However, we are yet to clearly decipher how the human brain mediates consciousness. This is because the brain is a complex biologic system composed of components that interact dynamically to give rise to higher brain functions.2,3  For example, the sensory experience of a painfully hot object comprises various composite experiences4  (i.e., intensity, duration, quality, and emotional distress) that are integrated by the brain and represented as a unified experience of pain.2,3,5–9 
Synchronization of resting-state functional magnetic resonance imaging (rsfMRI) slow signals is a proxy for the putative neural syntax that integrates various brain regions into networks mediating higher brain functions.10–13  Numerous investigations of anesthesia-induced changes to rsfMRI networks have been conducted. For a review, see Hudetz.14  However, less studied is how anesthetics alter the capacity of information transfer in rsfMRI brain networks, and how this relates to the brain state in question. This graph-theoretic approach, which is distinct from standard functional connectivity analyses, is based on the fundamental premise that the brain is topologically organized to maximize information transfer within and between networks.15,16  This approach parallels theories of consciousness that stress integrated information processing.8,9 
Network efficiency is a graph-theoretical measure of information exchange. A recent study of the γ-aminobutyric acid receptor agonist propofol described decreased efficiency of information transfer as a key differentiating feature of conscious and unconscious brain states.17  Similarly, decreased efficiency of information transfer has also been suggested to account for decreased consciousness during nonrapid eye movement sleep.18,19  Thus, altered states of arousal may be associated with network reconfigurations that favor decreased efficiency of communication. However, the generalizability of this finding is unclear. Thus, studies of anesthetics from different drug classes are necessary to help refine our knowledge of information processing correlates of unconsciousness.
Using a graph-theoretical approach, we studied network changes associated with dexmedetomidine, an α2-adrenergic agonist that activates endogenous sleep pathways.20–24  We hypothesized that dexmedetomidine would result in decreased capacity for information integration as represented by surrogate markers of local and global network communication (local and global efficiencies). Furthermore, we hypothesized that this decrease in information integration results from reduced synchronization strength between brain regions. In a previous study, we found that dexmedetomidine preferentially decreased blood flow and metabolism in the thalamus, default mode network, and bilateral frontoparietal networks.1  Therefore, we also hypothesized that between-network disruption in these and other resting-state networks (RSNs) is associated with dexmedetomidine.
To explore these hypotheses, blood oxygen level–dependent (BOLD) signals obtained during baseline, dexmedetomidine-induced altered arousal, and recovery states were analyzed (n = 15, 18 to 36 yr of age). Zero-lag correlations between rsfMRI signals extracted from 131 brain parcellations were used to construct weighted brain networks. Network efficiency and node strength were computed using graph analysis. Parcellated brain regions were also mapped to known RSNs (table 1). This data set was previously reported in a within-network analysis of the default mode and frontoparietal networks.1 
Table 1.
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates×
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Table 1.
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates×
×
Materials and Methods
Imaging Visit
The Human Research Committee at the Massachusetts General Hospital (Boston, Massachusetts) approved this study (protocol no. 2011P002333; clinical trial no. NCT01485380). Written informed consent was obtained after the nature and possible study consequences of the study were explained to each healthy volunteer (18 to 36 yr; n = 15). All volunteers were required to be American Society of Anesthesiologists Physical Status I. Brain imaging was performed with the Biograph mMR scanner (Siemens Healthcare, Germany), which allows simultaneous acquisition of whole-body positron emission tomography and 3-Tesla magnetic resonance imaging data. At the beginning of the imaging visit, structural magnetic resonance imaging (magnetization-prepared rapid acquisition with gradient echo volume, repetition time/echo time = 2,100/3.24 ms; flip angle = 7°, voxel size = 1 mm isotropic) was acquired for the purpose of anatomical localization and spatial normalization of the imaging data. BOLD functional magnetic resonance imaging data were collected using a whole-brain T2*-weighted gradient echo BOLD; echo planar imaging pulse sequence was used (repetition time/echo time = 3,000/35 ms, flip angle = 90°, voxel size = 2.3 × 2.3 × 3.8 mm, number of slices = 35).
Dexmedetomidine was administered as a 1-μg/kg loading bolus over 10 min, followed by a 0.7-μg · kg−1 · h−1 infusion. During the infusion period, an anesthesiologist monitored cuff blood pressure, capnography, electrocardiogram, and pulse oximetry. Volunteers were instructed to keep their eyes open during the course of the study. During the start of the dexmedetomidine infusion (i.e., bolus), a 20-min induction pulsed arterial spin label (pASL) scan was acquired. Altered arousal was defined as the onset of sustained eye closure and the lack of response to a verbal request to open the eyes at 1-min intervals during the induction pASL scan. Sustained eye closure and the lack of response to verbal stimuli were confirmed in all subjects during the induction pASL. The dexmedetomidine-induced altered arousal BOLD rsfMRI scans were acquired after the induction pASL scan. At the end of the dexmedetomidine infusion, a 20-min recovery pASL scan was also acquired. During this period, arousal was also periodically assessed verbally at 1-min intervals. Spontaneous eye opening and a positive response to give a thumbs-up signal were used to determine recovery. Arousability to the verbal stimulus was confirmed in all subjects during the 20-min recovery pASL scan. The recovery BOLD rsfMRI scan was acquired after the pASL scan. The resting-state nature of our data acquisition precluded continuous assessments of arousal during rsfMRI BOLD data acquisition.
Data Preprocessing
Both FMRIB Software Library v5.0 (FSL, University of Oxford, United Kingdom) and Analysis of Functional Neuro-Images (National Institute of Mental Health Scientific and Statistical Computing Core, USA) software were used to preprocess data in line with procedures adapted for the 1,000 Functional Connectomes project.25  Data were slice-time corrected for interleaved acquisitions using Fourier interpolation, motion corrected using least squares alignment of each volume to the eighth image using Fourier interpolation, despiked of extreme time series outliers using a continuous transformation function, temporal band-pass filtered between 0.009 and 0.3 Hz using Fourier transformation, and further filtered to remove linear and quadratic trends using analysis of functional neuro-images. In addition, FSL was used for spatially smoothing the images (Gaussian kernel full width half maximum = 6 mm) and for normalizing the mean-based intensity by the same factor (10,000). Next, eight nuisance signals (time courses of white matter and cerebrospinal fluid, and six motion parameters) were used as regressors of no interest. White matter and cerebrospinal fluid time series were extracted from masks obtained by segmenting each individual’s high-resolution structural image using FMRIB’s Automated Segmentation Tool (University of Oxford, United Kingdom), thresholded at 80% tissue-type probability. The six motion parameters were generated in the FSL-based motion correction step. These six vectors included rotational movement around three axes (pitch, yaw, and roll) and movement in each of the three cardinal directions (X, Y, and Z). All of these steps were conducted in native functional space.
For registration, FMRIB’s Linear and Non-LINEAR Image Registration Tools were used for transformations from native functional and structural space to the Montreal Neurological Institute (Montréal, Quebec, Canada) MNI152 template with 2 × 2 × 2 mm resolution. First, the high-resolution structural image was registered to the MNI152 2-mm template with a 12 degrees of freedom linear affine transformation. The transformation was further refined using FMRIB’s Non-LINEAR Image Registration Tool. Next, each participant’s functional data were registered to their high-resolution structural image using a linear transformation with 6 degrees of freedom. The structural-to-standard nonlinear transformation matrix was used to register the functional volume to MNI152 standard space.
Brain Parcellation and Time Course Extraction
The brain was anatomically parcellated using a previously published parcellation scheme.26  Briefly, the Harvard Oxford Atlas was refined by increasing the anatomical partitioning of the cingulate, medial, and lateral prefrontal cortices. In addition, anatomical partitioning of the insular label was also performed. Thus, instead of the single region of interest spanning the entire insula in the Harvard Oxford Atlas, the insula was subdivided into posterior, middle, dorsal anterior, and ventral anterior regions based on a previously published scheme.27  Regions spanning the right and left hemispheres were separated to create hemisphere-specific regions. The complete parcellation scheme consisted of a total of 131 regions (table 1). A list of these regions, their coordinates, and a description of the anatomical landmarks used for the subdivision of the Harvard Oxford labels in smaller parcellates have previously been published.26  Each region of interest was designated as a node. Thus, from each node, the BOLD time series were extracted and averaged to generate a 131 time series for each subject.
RSN Network Construction
Each parcellated brain region or node was classified as belonging to a designated RSN based on spatial overlap with a specific RSN map. The RSN maps were identified using functional connectivity maps in the neurosynth framework (last accessed February 15, 2016).28  Each region was represented in only one RSN, based on maximum overlap.
Graph Analysis
Whole-brain networks were constructed and network measures were assessed using the Brain Connectivity Toolbox (MATLAB, USA).29  For each patient, the BOLD time series in each region was correlated with every other region to create a 131 × 131 weighted connectivity or adjacency matrix. The adjacency matrices were thresholded at connectivity densities of 0.05, 0.1, 0.15, 0.2, 0.25, and 0.3. The following graph functions were computed from each matrix:
Strength.
This function measures the strength of connections in the graph. Node strength is the sum of weights of links connected to the node.
where CIJ is the undirected weighted connection matrix. Mean connectivity strength of the graph was measured by averaging str from all nodes.
Global and Local Efficiencies.
Efficiency is a measure of the network’s capacity for parallel information transfer between nodes through multiple series of edges. The average global efficiency of information transfer in graph g having n nodes can be calculated from the inverse of the edge distances dij
The quantity above is a measure of the global efficiency of information transfer for the whole graph g. There is also a local efficiency for each vertex vi, measuring how efficiently its neighbors can communicate when vertex vi is removed. If the subgraph of all neighbors of vi is denoted by gi, then its local efficiency E(gi) is approximately equivalent to the clustering coefficient Ci.10 
Statistical Comparisons
Comparison between the local and global efficiencies and the mean connectivity strength between the three states was first conducted using repeated-measures ANOVA (table 2). Post hoc comparisons were conducted on significant ANOVA findings using the paired t test. Significance was set at P < 0.05, and Bonferroni correction for multiple comparisons was employed. Statistics on graph results and graphical presentations of networks were performed with custom code written in MATLAB (USA).
Table 2.
Results from Repeated-measures ANOVA
Results from Repeated-measures ANOVA×
Results from Repeated-measures ANOVA
Table 2.
Results from Repeated-measures ANOVA
Results from Repeated-measures ANOVA×
×
RSN functional connectivity analyses were also conducted using custom code written in MATLAB. First, we present mean networks for the three conditions (awake, dexmedetomidine, and recovery). To assess statistical differences, each network was held at a threshold proportional to 50% of strongest connections, and these matrices were compared using a paired t test. Each test was corrected for multiple comparisons setting the false discovery rate to P < 0.05. The regions that showed significant changes in connectivity are shown as binarized maps where nodes are sorted based on membership to RSN groupings. To calculate the percentage of modulated connections within- and between-RSN networks as a discrete value, the total number of significantly modulated connections that survived correction for multiple comparisons were summed for within-RSN network and between-network connections. This value was then divided by the number of total possible connections for any given combination and multiplied by one hundred to calculate the percentage of modulated connections. The final values were represented as a heat map to qualitatively assess network modulations.
Results
Dexmedetomidine Disrupts the Local and Global Efficiencies of Brain Networks
The local efficiency of weighted brain networks was significantly disrupted during the dexmedetomidine-induced altered arousal state compared to the awake state (fig. 1A; 0.15: P = 0.003, 0.2: P = 0.004, 0.25: P = 0.004, and 0.3: P = 0.004). During the recovery state, the local efficiency reverted to higher values compared to the dexmedetomidine-induced state. However, these findings did not meet our conservative Bonferroni-adjusted threshold for significance (fig. 1A; 0.15: P = 0.03, 0.2: P = 0.029, 0.25: P = 0.029, and 0.3: P = 0.038). There were no statistically significant differences between awake and recovery state comparisons (fig. 1A; P > 0.025).
Fig. 1.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
Fig. 1.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
×
The global efficiency of weighted brain networks was significantly disrupted during the dexmedetomidine-induced altered arousal state compared to the awake state (fig. 1B; 0.1: P = 0.006, 0.15: P = 0.001, 0.2: P = 0.002, 0.25: P = 0.002, and 0.3: P = 0.002). During the recovery state, the global efficiency was significantly increased for most, but not for all network thresholds (fig. 1B; 0.1: P = 0.004, 0.15: P = 0.005, 0.2: P = 0.01, 0.25: P = 0.017, and 0.3: P = 0.026). There were no statistically significant differences between awake and recovery state comparisons (fig. 1B; P > 0.025)
Dexmedetomidine Reduces the Strength of Synchronizations in Brain Networks
Next, using a graph-theoretic approach, a significant reduction in the mean strength of nodal connectivity was found during the dexmedetomidine-induced altered arousal state compared to the awake state (fig. 2A; 0.1: P = 0.006, 0.15: P = 0.005, 0.2: P = 0.005, 0.25: P = 0.005, and 0.3: P = 0.005). The recovery state was associated with increased mean strength of nodal connectivity compared to the dexmedetomidine-induced altered arousal state. However, this change in strength was not significant (fig. 2A; 0.05: P = 0.052, 0.1: P = 0.051, 0.15: P = 0.053, 0.2: P = 0.055, 0.25: = 0.055, and 0.3: P = 0.056). There were no statistically significant differences between awake and recovery state comparisons (fig. 2A; P > 0.025).
Fig. 2.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (DF) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (D–F) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
Fig. 2.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (DF) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
×
To study the nodes that principally contributed to the changes in the mean strength of connectivity, nodes were first sorted (from the lowest to the highest strength of connectivity during the awake state) and then plotted by the strength of connectivity (fig. 2B). The largest decreases in connectivity strength were most evident in strongly connected nodes. During the recovery state, the mean strength of connectivity reverted to higher values compared to the dexmedetomidine-induced altered arousal state. The degree distribution (number of connection at each node) was studied to understand whether the changes in the strength of connectivity at the nodal level primarily resulted from a reduction in a number of connections versus a reduction in the strength of connection. Dexmedetomidine did not strongly modulate the number of connections at the nodal level (fig. 2C).
To graphically illustrate our results, plots of network graphs where nodes with common connections are plotted as clusters and the distances between nodes represent the strength of connectivity were made (fig. 2, D–F). This network representation more clearly depicts that changes in network strength of connections are a significant driver of topologic changes during the dexmedetomidine-induced altered arousal state.
Dexmedetomidine Alters Connection Within and Between Resting-state Networks
Parcellated brain regions were sorted based on composition to RSNs. During the dexmedetomidine-induced altered arousal state, the strength of connectivity within and between networks held at the same link density appeared different compared to the baseline and recovery states (fig. 3, A–C). Contrasts between the states were analyzed to characterize connectivity changes associated with dexmedetomidine (contrast 1: awake vs. dexmedetomidine; fig. 3D) and recovery (contrast 2: recovery vs. dexmedetomidine; fig. 3E). Although volunteers were responsive to verbal commands during the recovery period, they remained mildly sedated due to residual drug effects. Therefore, contrasts between the awake and recovery states (contrast 3: awake vs. recovery; fig. 3F) were also analyzed. Only connectivity changes in contrast 1 were significant after correction for multiple comparisons. Therefore, corrected maps for contrast 1 and uncorrected maps (P < 0.01) for contrasts 2 and 3 are shown (fig. 3, D–F).
Fig. 3.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (AC) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (DF) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (GI) Matrices showing percentage of nodes modulated in DF. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (A–C) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (D–F) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (G–I) Matrices showing percentage of nodes modulated in D–F. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
Fig. 3.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (AC) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (DF) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (GI) Matrices showing percentage of nodes modulated in DF. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
×
The percentages of modulated connections scaled by the total number of possible connections are presented for qualitative assessment (fig. 3, G–I). The dexmedetomidine-induced altered arousal state was associated with significant reductions in functional connectivity between regions that comprise all RSNs (fig. 3D; awake > dexmedetomidine; false discovery rate corrected P < 0.05). Recovery was associated with partial restoration of connectivity within the default mode network and increased functional connectivity between subcortical regions and all other networks (fig. 3E; recovery > dexmedetomidine; uncorrected P < 0.01). Functional connectivity changes associated with dexmedetomidine did not fully revert to baseline values during the recovery state (fig. 3F; awake > recovery; uncorrected P < 0.01). The awake state, relative to the recovery state, had more within-network connectivity in attention/executive networks (fig. 3, F and I). Also, the language/memory networks had more between-network connectivity with subcortical regions, sensory network, default mode network, and attention/executive (fig. 3, F and I).
Discussion
We previously reported that impaired thalamic information processing—loss of functional connectivity between the default mode network and the thalamus and bidirectional changes between the frontoparietal networks and cerebellar clusters—is a neural correlate of dexmedetomidine-induced altered arousal.1  In this investigation, we applied network metrics of information processing to study differences between baseline, dexmedetomidine-induced altered arousal, and recovery states. First, we found that dexmedetomidine reversibly reduced the local and global efficiencies of brain networks. Second, dexmedetomidine was associated with a reduction in the mean strength of nodal connectivity but did not alter the relative distribution of connections between nodes. Third, by using a global network approach, we show that these changes are not specific to any given RSN. Taken together, these findings parallel the decreased efficiency of information transfer within the brain that has been reported for propofol-induced unconsciousness17,30  and nonrapid eye movement sleep18,19  and strengthen the hypothesis that conscious processing in the brain relies on an efficient system of information transfer.
The local and global efficiencies are both measures of information integration that are derived from the characteristic path length (the average shortest path length between all possible pairs of nodes).31  Because the paths in this investigation represent statistical dependency or functional connectivity between nodes, our efficiency and mean node strength findings are consistent with our previous report of dexmedetomidine-induced functional connectivity changes.1  Although our approach of representing each brain region in only one RSN based on the maximum overlap was different from the independent component analysis method we previously employed, our finding that functional connectivity was reduced between the default mode network and subcortical regions is also consistent with our previous report.1  However, our current results extend upon those previously reported by showing that functional connectivity changes between subcortical regions are not specific to any RSN. Since sleep slow-delta (0.1 to 4 Hz) and spindle oscillations (13 to 16 Hz) reflect altered sensory information processing in the brainstem and thalamus,32  it follows that dexmedetomidine-induced altered arousal, which is also associated with slow-delta and spindle oscillations,33–35  should manifest with the altered subcortical–cortical functional connectivity. We speculate that other anesthesia-induced slow-delta oscillations (propofol, sevoflurane, and nitrous oxide),34,36–40  Theta oscillations (4 to 8 Hz; ketamine and sevoflurane),36,37  frontal alpha oscillations (8 to 12 Hz; propofol and sevoflurane),34,36–40  and gamma oscillations (less than 40 Hz; ketamine)41  may also manifest as altered subcortical–cortical and corticocortical fMRI BOLD network connectivity. A likely mechanism for this speculated finding is the disruption of normal sensory processing gamma oscillations (greater than 40 Hz) that have been related to fMRI BOLD signals.42 
Recovery from the dexmedetomidine-induced altered arousal state was associated with partially recovered connectivity between brain regions that comprise the default mode network. Notably, within-network alterations in default mode network connectivity have been reported for dexmedetomidine,1  sevoflurane,43  propofol,44  and ketamine.45  Thus, this RSN, which is associated with stimulus-independent thought and self-consciousness,46–48  may play a significant role in recovery from altered states of arousal. Our findings also suggest that key differences between the baseline and recovery states were increased within-network connectivity in attention/executive and language/memory networks. Thus, recovery from altered states of arousal may follow a graded pathway where within-network restoration of default mode functional connectivity precedes within-network connectivity in attention/executive and language/memory networks. This finding, which suggests gradations in the level of arousal, is consistent with an information integration theory of consciousness.9  It may result from time-varying disruption (altered arousal) or reintegration (recovery) of hub nodes.
Hubs are crucial for efficient information transmission in brain networks.49  Although a detailed analysis of network hub dynamics was beyond the scope of this investigation, our finding of reduced strength of connectivity in highly connected nodes suggests that dexmedetomidine disrupts hub nodes. Importantly, a consistent finding from graph-theoretical studies of electroencephalogram50  and rsfMRI17,30  data during propofol-induced unconsciousness is the disruption of hub nodes. Further confirming the role of hubs in information transfer, a simulation of hub disruption using various anesthetics (propofol, sevoflurane, and ketamine) resulted in disrupted surrogates of information transfer.51  This suggests that even though anesthetics have distinct pharmacologic targets—reflected by differences in behavioral states and oscillatory dynamics—preferential disruption of information flow at network hubs is a common macrocircuit dynamic. An open question is whether unique patterns of hub disruption may explain the pharmacologic and behavioral diversity inherent to these anesthetics.
A limitation of this study is that the level of arousal was not experimentally manipulated in a graded manner. Therefore, studies of dexmedetomidine and other anesthetics with graded manipulations of level of arousal levels are needed to more clearly delineate how the efficiency measures described in this article covary with the level of arousal. Another limitation of the current study is that the current results cannot be directly translated to molecular or neurophysiologic function at the neuronal level. We note that although statistically significant, our effect sizes were small in spite of clinically significant alterations to the level of arousal. However, this finding is likely a reflection of the mathematical construct underlying efficiency measures (i.e., global and local efficiencies are not exponential functions).10  Finally, it is important to note that the use of graph-theoretical metrics as a proxy for higher cognitive processes is not definitive.
Our findings demonstrate that dexmedetomidine is associated with a significant drop in the capacity for efficient information transfer in functional networks at both the local and global levels. These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.
Research Support
Supported by grant no. R01 AG053582 (to Dr. Akeju); grant no. TRO1 GM104948 (to Dr. Brown); and grant nos. OT2 OD023867, P01 AT006663, R61 AT009306, R01 AR064367 (to Dr. Napadow) from the National Institutes of Health (Bethesda, Maryland) and by the Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Competing Interests
The authors declare no competing interests.
References
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Fig. 1.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
Fig. 1.
Local and global efficiencies of neural information transfer are disrupted by dexmedetomidine (A and B). Local and global efficiencies are significantly decreased during the dexmedetomidine-induced altered arousal state and reverted to higher values during the recovery state. This observation was consistent at a range of network densities. *P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM.
×
Fig. 2.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (DF) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (D–F) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
Fig. 2.
Dexmedetomidine modulates the strength of connectivity in brain networks. (A) Mean strength of nodal connectivity in weighted networks reported for different network sparsities (*P < 0.025 for awake vs. dexmedetomidine, error bars represent ±SEM). (B) Mean strength of connection presented for each node. Nodes are ordered in ascending order of strength of connections to show that the most strongly connected nodes were the most disrupted by dexmedetomidine. (C) Mean degree (total number of connections) of each node is presented in ascending order. (DF) Representations of large-scale network topology, illustrating how connectivity strength (weights) modulated by dexmedetomidine contributes to alterations in network architecture. The positioning of nodes is topologic rather than anatomical. The algorithm positions network nodes based on the strength of connections, so that regions with common connections are placed in a group and the physical distance between the nodes is adjusted based on the weight of the connection. The awake state shows an ordered modular structure with a large number of within-module connections. The modules are held together by global connections. In the dexmedetomidine-induced state, the strengths of connection (weights) are reduced at both the local (within-module) and global (between-module) levels. In the recovery state, the strength of connections is increased, but does not fully revert to the awake state. All three representative networks were constructed at a threshold proportional to 0.25 connections.
×
Fig. 3.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (AC) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (DF) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (GI) Matrices showing percentage of nodes modulated in DF. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (A–C) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (D–F) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (G–I) Matrices showing percentage of nodes modulated in D–F. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
Fig. 3.
Dexmedetomidine modulates within-network and between-network connections when networks are organized based on meta-analytic resting-state networks. (AC) Adjacency matrices representing mean connectivity pattern in (A) awake, (B) dexmedetomidine, and (C) recovery states. The adjacency matrices consist of parcellation nodes ordered based on their membership in five resting-state networks listed in boxed legend. Resting-state networks were identified based on node overlap with connectivity maps in the neurosynth meta-analytic framework. (DF) Contrast maps showing regions that significantly changed in connectivity within and between the five listed networks. Results are based on Student’s t tests, where contrast shown in D is false discovery rate corrected at P < 0.05. (E and F) are uncorrected at P < 0.01. (GI) Matrices showing percentage of nodes modulated in DF. The percent values in G, H, and I are based on the sum of significantly modulated nodes observed in D, E, and F, respectively, scaled by the total number of possible connections.
×
Table 1.
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates×
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Table 1.
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates
Resting-state Networks, Anatomic Brain Regions, and Associated Montreal Neurological Institute Coordinates×
×
Table 2.
Results from Repeated-measures ANOVA
Results from Repeated-measures ANOVA×
Results from Repeated-measures ANOVA
Table 2.
Results from Repeated-measures ANOVA
Results from Repeated-measures ANOVA×
×