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. 2011 Sep 20;33(10):2487–2498. doi: 10.1002/hbm.21385

Propofol disrupts functional interactions between sensory and high‐order processing of auditory verbal memory

Xiaolin Liu 1, Kathryn K Lauer 2, Barney D Ward 1, Stephen M Rao 3, Shi‐Jiang Li 1, Anthony G Hudetz 2,✉
PMCID: PMC3244539  NIHMSID: NIHMS320108  PMID: 21932265

Abstract

Current theories suggest that disrupting cortical information integration may account for the mechanism of general anesthesia in suppressing consciousness. Human cognitive operations take place in hierarchically structured neural organizations in the brain. The process of low‐order neural representation of sensory stimuli becoming integrated in high‐order cortices is also known as cognitive binding. Combining neuroimaging, cognitive neuroscience, and anesthetic manipulation, we examined how cognitive networks involved in auditory verbal memory are maintained in wakefulness, disrupted in propofol‐induced deep sedation, and re‐established in recovery. Inspired by the notion of cognitive binding, an functional magnetic resonance imaging‐guided connectivity analysis was utilized to assess the integrity of functional interactions within and between different levels of the task‐defined brain regions. Task‐related responses persisted in the primary auditory cortex (PAC), but vanished in the inferior frontal gyrus (IFG) and premotor areas in deep sedation. For connectivity analysis, seed regions representing sensory and high‐order processing of the memory task were identified in the PAC and IFG. Propofol disrupted connections from the PAC seed to the frontal regions and thalamus, but not the connections from the IFG seed to a set of widely distributed brain regions in the temporal, frontal, and parietal lobes (with exception of the PAC). These later regions have been implicated in mediating verbal comprehension and memory. These results suggest that propofol disrupts cognition by blocking the projection of sensory information to high‐order processing networks and thus preventing information integration. Such findings contribute to our understanding of anesthetic mechanisms as related to information and integration in the brain. Hum Brain Mapp33:2487–2498, 2012. © 2011 Wiley Periodicals, Inc.

Keywords: propofol sedation, verbal memory, information and integration, cognitive binding, fMRI‐guided connectivity analysis

INTRODUCTION

Memory and consciousness are two expressions of brain functioning that are suppressed by anesthetics [Hudetz and Pearce, 2010]. This brings great relief to surgical patients and clinicians, yet the precise mechanisms by which anesthetics produce their effects remain incompletely understood. Recent experimental data and theoretical formulations have suggested that a breakdown of cortical connectivity, and thus cortical information integration, underlies anesthesia‐induced loss of conscious perceptions [Alkire et al., 2008]. Particularly, it is not that neural activity is inhibited altogether by anesthetics, but that critical interactions between specialized neuronal groups are disrupted.

Cognitive processing takes place in hierarchically structured neural organizations in the brain [Baars and Gage, 2007]. Primary sensory cortices receive and develop meaningful representations of sensory stimuli. The registered sensory information is then transferred into and synthesized in high‐order cortical regions, leading to integrated cognitive expressions, such as perception, memory, and semantics. The process of low‐order neural representation of sensory stimuli becoming synthesized in high‐order brain regions is also described by the notion of cognitive binding [Singer, 1994, 1996; Teisman, 1999; von der Malsburg, 1981, 1996]. Thus, anesthesia‐induced disruption of cortical connectivity is also viewed as a form of cognitive unbinding [Mashour, 2004, 2006], which leads to cortical information disintegration [Alkire et al., 2008; Hudetz, 2006]. Yet, despite these experimental and theoretical advances, the exact manner in which anesthetics disrupt the binding of coordinated brain activities within and between different levels of a task‐defined cognitive hierarchy remains unclear.

The purpose of this study is to investigate, using functional imaging techniques, how the integrity of cognitive networks involved in an auditory verbal memory task is manifested and maintained in wakefulness, impaired during propofol‐induced deep sedation, and re‐established during subsequent recovery (i.e., recovery of conversational responsiveness from propofol sedation). Specifically, we focus on how propofol disrupts functional interactions (functional connectivity) within and between different levels of the task‐defined neural organizations in the brain. Temporal correlation derived from the blood oxygenation level‐dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) provides a measure of functional connectivity between spatially distinct brain regions. It characterizes how different neural organizations in the brain coordinate their activity under resting or task conditions, thus reflecting the degree of functional integration.

An element of novelty of the present study stems from its theory‐driven analytical approach, which allows the obtained results and the theoretical propositions to mutually inform each other. Specifically, we propose an fMRI‐guided connectivity analysis based on the conception of cognitive unbinding as a mechanism leading to the disintegration of neural information. The key concept of fMRI‐guided connectivity analysis is described by the process of empirically identifying the seed regions that represent cognitive functions performed at different levels of task‐defined cognitive processing in the brain, starting with the low‐order encoding of sensory stimuli in the primary sensory cortices and proceeding to sensory integration in high‐order association cortical areas that produce final cognitive expressions. The seed regions are determined by contrasting between the most prominent task‐related brain activations in wakefulness and the suppressing effects induced by propofol on brain activity and cognitive functions. For example, if the center area of a primary sensory cortex demonstrates robust and persistent BOLD reactivity to sensory stimuli in both wakefulness and sedation, regardless of the presence of anesthetics, then that brain area can be defined as a seed region representing the preserved low‐order encoding of sensory stimuli. In contrast, if a brain area in high‐order association cortices demonstrates the strongest activation in wakefulness but deactivation in sedation, which correlates with the presence and absence of cognitive functions, then that brain area can be defined as a seed region representing the high‐order processing of sensory integration that leads to cognitive formations. Connectivity analyses based on such a seed‐choosing strategy thus provide important information about the integrity of functional interactions within and between different levels of the task‐defined brain regions, and allow an assessment of anesthetic‐modulation of sensory and high‐order cognitive processing in the brain in line with the theoretical suggestions.

In the present study, healthy human volunteers underwent fMRI scans while listening to and attempting to remember (encode) a distinct set of English words presented during each of the four experimental sessions in wakefulness, light sedation, deep sedation, and recovery. After the scans, each subject's residual verbal memory corresponding to information received during each of the four experimental sessions was assessed by free‐recall and forced‐choice recognition tests. Imaging studies have consistently shown that the primary auditory cortex (PAC) preserves robust BOLD reactivity to auditory stimuli in sedated healthy human individuals; however, a set of left‐lateralized temporal and frontal areas, most consistently the inferior frontal gyrus (IFG), is deactivated by propofol in loss of word comprehension and memory [Davis et al., 2007; Heinke et al., 2004; Plourde et al., 2006]. Several comprehensive review studies suggest more widespread brain networks, consisting of a specific set of temporal, frontal, and parietal regions, that are involved in verbal processing and memory‐related functions [Baars and Gage, 2007; Binder et al., 2009; Curtis and D'Esposito, 2003; Wagner et al., 2005]. In addition, altered functional connectivity by anesthetics has also been evidenced in a range of specific brain networks, including the thalamocortical system [White and Alkire, 2003], interhemispheric networks [Peltier et al., 2005], default mode network [Greicius et al., 2008; Martuzzi et al. 2010; Peltier et al., 2006], and, in a very recent study, the resting‐state default and executive networks [Boveroux et al., 2010].

Motivated by these findings and the conceptual frameworks of consciousness and anesthesia, here we test the hypothesis that propofol‐induced loss of auditory verbal memory is associated with (1) preserved BOLD responses in the PAC but suppressed BOLD responses in the high‐order temporal and frontal regions and (2) disrupted functional connections between sensory processing and high‐order processing cortical regions in the temporal, frontal, and parietal lobes, which constitute part of the network that mediates verbal processing and memory.

METHODS

Study Participants

Eight subjects of both genders (25–35 years old; body mass index <25) provided written informed consent to participate in this study. Experimental protocols were approved by the Institutional Review Board of the Medical College of Wisconsin (MCW, WI). All subjects were native English speakers recruited from the MCW communities, free of drug administration and with no history of neurological or psychiatric conditions and structural brain abnormalities.

Auditory Verbal Memory Task and Propofol Administration

Lying in the scanner, all subjects were instructed to listen to and encode a distinct set of 40 high‐frequency English words (nouns) presented during each of the four experimental sessions in four different states of consciousness: wakefulness, light sedation, deep sedation, and recovery (see Fig. 1). Subjects were informed that their recall/recognition performance of words heard during each of these experimental sessions would be assessed after scanning. The experimental sessions were separated by approximately 15 min for experimental preparations.

Figure 1.

Figure 1

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Experimental paradigm. Study participants underwent fMRI scans while listening to and encoding a distinct set of 40 high‐frequency English words presented during each of the four experimental sessions in four different states of consciousness: wakefulness, light sedation, deep sedation, and recovery. The anesthetic agent, propofol, was administrated by computer‐controlled bolus plus continuous intravenous infusion to achieve plasma concentration of 0.5 μg/ml for light sedation and 0.75 or 1.0 μg/ml for deep sedation. The assessment of the level of sedation and OAAS scores were determined by an MCW faculty anesthesiologist with the standard ASA monitoring. All subjects lost conversational responsiveness in the scanner in deep sedation with OAAS scores between 3 and 4.

The anesthetic agent, propofol, was administrated by a bolus plus continuous infusion coupled with the assessment of OAAS (observer's assessment of alertness/sedation) scores. Light sedation was induced using a computer‐controlled intravenous infusion of propofol [STANPUMP; Shafter, 1996] to achieve plasma concentration of 0.5 μg/ml (parameter that was set in the target‐controlled infusion (TCI) device) and OAAS scores between 1 and 2. Deep sedation was achieved by seeking plasma concentration of 0.75–1.0 μg/ml and OAAS scores between 3 and 4, chosen to produce the desired clinical endpoints of unresponsiveness and global loss of memory. Once a desired sedative state was achieved for an experimental session, the fMRI scans were initiated. During the scan, the sedation level was maintained by computer‐controlled propofol infusion with the preset plasma concentration. Right after fMRI scans in the last sedated session, propofol administration was stopped. Subjects were behaviorally assessed in terms of their conversational responsiveness in the scanner. Upon the confirmation that subjects had recovered conversational responsiveness, the last scanning session with the defined state of consciousness as “recovery” was initiated. Every subject had two intravenous catheters put in place for propofol administration and the withdrawal of blood samples for measuring plasma propofol concentration. However, due to a problem of red blood cell lysis, plasma propofol concentration could not be determined in this study. The order of administrating the low‐ and high‐dose propofol infusion was counterbalanced in subjects, with four subjects receiving a low dose before a high dose and the other four receiving a reversed order. The level of sedation was behaviorally assessed by an MCW faculty anesthesiologist by seeking the desired OAAS scores. Standard American Society of Anesthesiology (ASA) monitoring, including electrocardiogram, noninvasive blood pressure cuff, pulse oximetry, and end‐tidal CO2 gas analysis were also performed.

The auditory verbal input was presented using a headphone set (Koss Corporation) designed to work in the MR scanner environment. The word lists are matched for the maximum number of letters, frequency of usage in English, concreteness, and imageability and were presented in a counterbalanced order across subjects (Pavio‐Yule). Approximately 20–30 min after the completion of all the experiments (after study participants had been taken out of the scanner), all subjects completed a free‐recall test followed by a forced‐choice recognition test. The time separation between the memory tests and the last scan was intended to cancel out the primacy and recency effects that occur when the recognition memory test is administered immediately following the stimulus presentations. In the forced‐choice recognition test, subjects were presented auditorily with 320 words, of which 160 words were what they had heard during the experiment and the other 160 words were foils or distractors. Subjects were required to press a button if they thought they heard the word previously and another button if the word was new. Subjects were instructed to make a decision regarding each presented word as quickly as possible. Each subject's residual memory to words heard during each of the four experimental sessions was assessed based on a few performance indices: the percentage of recalled words, recognition ratio vs. chance, and the discriminability index (d′). Of these, the value of d′ is derived from hit (correct recognition) rate and false‐alarm rate, providing a criterion‐independent measure (i.e., regardless of how conservative or liberal subjects are in making decisions) of the internal response of subjects [Wickens, 2002]. A d′ = 0 indicates equal hit and false alarm rates; a d′ value significantly greater than zero indicates a higher hit than false‐alarm rate.

MRI Acquisition

Imaging acquisition was performed using a 1.5T GE Signa scanner with a locally designed gradient and RF coil. Potential head movements were minimized using a bite‐bar system developed at MCW. Functional echo‐planar images were obtained using whole‐brain imaging in the sagittal plane during each task session (repetition time (TR), 2000 ms; echo time (TE), 40 ms; thickness, 6 mm; in‐plane resolution, 3.75 × 3.75 mm2; 22 slices; flip angle, 90°; field of view (FOV), 24 cm; matrix size, 64 × 64). High‐resolution SPGR anatomical images were always acquired after the third experimental session for each subject (TR, 24 ms; TE, 5ms; slices thickness, 1.2 mm; flip angle, 40°; FOV, 24 cm; matrix size, 256 × 128).

Data Processing

Imaging data analysis was conducted in the environments of the Analysis of Functional NeuroImages (AFNI, http://afni.nimh.nih.gov/afni) and the Matlab (The MathWorks, Natick, MA). The high‐resolution anatomical images were first manually transformed into the standard Talairach space, followed by coregistering the functional data to the Talairach space in 2‐mm cubic voxels (adwarp in AFNI). The subsequent data preprocessing included despiking, detrending (3dDetrend in AFNI, using the Legendre polynomials with an order of 3), and motion correction (3dvolreg in AFNI using three translational and three rotational parameters obtained for each image). The first four points of the time series of each voxel were discarded to reduce the transient effects. Potential contaminating signals from the white matter (WM) and the cerebral spinal fluid (CSF) were extracted for each subject using the segments of WM and CSF manually defined according to each individual's anatomical images. We then constructed eight regressors using the signals corresponding to the six motion parameters obtained from volume registration and contributions from WM and CSF for the subsequent analysis.

To evaluate the voxel‐wise hemodynamic responses of the auditory verbal stimuli that were presented as an event‐related design during each experimental session, we used the area under the curve (AUC) of the estimated hemodynamic response functions (HRFs) of each voxel as a quantitative measure of the response magnitude [Ward, 2006]. The HRFs were estimated by AFNI 3dDeconvolve with a minimum time lag of 0 TR (2 s) and a maximum time lag of 7 TR, allowing for a variable shape of hemodynamic responses. Removing unwanted signals was conducted simultaneously using 3dDeconvolve by applying the earlier constructed eight regressors representing artifact contaminations. Spatial smoothing of the response magnitude across voxels was performed using a 3.5‐mm full‐width half maximum (FWHM) Gaussian kernel filter to compensate for intersubject variability. Finally, in the group analysis, stimuli‐induced activation maps for each experimental session were derived by applying voxel‐wise one‐sample t‐tests followed by transformations to z‐score (P = 0.05). The obtained statistical maps were corrected for multiple comparisons using the probability and cluster thresholding technique (AlphaSim in AFNI; a minimum cluster thresholding of 179 voxels of 2‐mm cubic in the Talairach space, for here and elsewhere).

Determination of Seed Regions in fMRI‐Guided Connectivity Analysis

As described earlier, a key step of fMRI‐guided connectivity analysis is the identification of seed regions that represent the cognitive functions expressed at different levels of the task‐defined cognitive processing in the brain. From the first‐stage analysis of task‐related BOLD responses (see Fig. 2), we found that auditory verbal stimuli‐induced BOLD activations persisted in the PAC across all experimental sessions, regardless of the propofol infusion. In contrast, BOLD activations in the IFG and premotor areas of the frontal lobe that were present in wakefulness were suppressed in deep sedation, accompanying the loss of responsiveness and memory. By contrasting the BOLD activation maps, we identified two seed regions located, respectively, in the PAC and IFG, which can be considered representative of the low‐order encoding of sensory stimuli and the high‐order processing of sensory integration, respectively. To enhance the power of the follow‐up connectivity analysis, the voxels included in each of the seed regions were determined by applying more stringent thresholds (P = 0.005 for PAC seed in deep sedation; P = 0.025 for IFG seed in wakefulness; P = 0.01 for IFG seed in recovery) to the group BOLD activation maps, resulting in approximately 173 ± 53 (mean ± SD) voxels included in each of the defined seed regions. The PAC seed (Fig. 4, the rightmost column, center of mass in the Talairach space: [−52.7, 27.6, 6.5]) was determined using the activation map obtained in deep sedation, because voxels included in this seed region represent the most commonly preserved hemodynamic activations in the PAC across the states of wakefulness, deep sedation, and recovery. The IFG seeds, however, were firstly determined using activation maps obtained in wakefulness and recovery, respectively. At the above‐mentioned P‐values, the voxels included in the IFG seeds are the only ones left showing statistical significance among all those voxels in the frontal lobe whose BOLD activation are suppressed in deep sedation. As the IFG seed regions identified in wakefulness and recovery show significant spatial overlap and nearly identical connectivity patterns, we combined these seed voxels into one single IFG seed (Fig. 5, the rightmost column, center of mass in the Talairach space: [−43.7, 8.6, 22.2]) in the subsequent connectivity analysis.

Figure 2.

Figure 2

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Brain areas showing significant verbal stimuli evoked BOLD responses measured by the area under the curve (AUC) of the estimated HRFs in wakefulness (left), deep sedation (middle), and recovery (right). BOLD activations and deactivations are indicated by warm and blue colors, respectively. (One‐sample t‐tests followed by transformations to Z‐sore; P < 0.05, after correction for multiple comparisons here and elsewhere).

Figure 4.

Figure 4

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Brain regions showing significant functional connectivity with the PAC seed in wakefulness (left), deep sedation (middle), and recovery (right). The seed region in the PAC is shown in the rightmost column.

Figure 5.

Figure 5

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Brain regions showing significant functional connectivity with the IFG seed region in wakefulness (left), deep sedation (middle), and recovery (right). The center area of the PAC, which shows a loss of functional connections with the IFG seed in deep sedation but not in other two conditions, is indicated by the red ellipses. The seed region in the IFG is shown in the rightmost column.

Functional Connectivity Analysis

Upon the determination of the seed regions for connectivity analysis, the original fMRI data of all experimental sessions were processed again with the general linear regression (3dDeconvolve in AFNI). This new regression analysis takes into consideration only the earlier constructed eight regressors to remove the potential noisy components from the motion artifacts, WM, and CSF. The residual signals were considered as the data containing task‐induced BOLD responses with potential contamination minimized. Functional connectivity analysis was then conducted by computing the voxel‐wise cross‐correlation (3dfim+ in AFNI) of the low‐frequency (<0.1 Hz) filtered BOLD time courses with the mean voxel time course of each of the two seed regions in the PAC and IFG. The Fisher transformation, m = 0.5 × ln(1 + r)/(1 − r)), was applied to the obtained correlation coefficients (r) to normalize the output. Spatial smoothing of the m‐values was then performed using a 3.5‐mm FWHM Gaussian kernel filter. In the end, the group effect of connectivity maps was constructed by one‐sample t‐tests followed by transformations to z‐score. We report results that survived P<0.05 after correction for multiple comparisons.

Exclusion of Imaging Data in Light Sedation From Analysis

From the activation map obtained during the low propofol dose, we were able to identify a distinct pattern of hemodynamic responses, particularly in the dorsal medial prefrontal cortex (Supporting Information Fig. 1). This is presumably associated with propofol‐induced excitation, which is common at low propofol doses [McCarthy et al., 2008]. Behavioral observations during experiments and post‐scan memory tests also suggest the existence of a certain degree of excitation during the low propofol dose, indicating incomplete sedation. Because of the inherent difficulty in interpreting the state of consciousness and the pharmacological effects in the low propofol dose, the imaging data obtained in light sedation were not further analyzed in the present study.

RESULTS

Propofol‐Induced Loss of Cognitive Performance

In contrast to conscious reactions that were present during wakefulness, light sedation, and recovery, all subjects failed to produce conversational responses in deep sedation with assessed OAAS scores between 3 and 4. The post‐scan memory tests show that subjects could freely recall significant percentages of the words heard in the scanner during wakefulness, light sedation, and recovery (one sample t‐test, P < 0.05 in all cases), but not during deep sedation (Table I). The auditory forced‐choice recognition tests performed outside the scanner further show that subjects were able to distinguish target words from foils originally heard in the wakefulness, light sedation, and recovery periods at a level significantly better than chance (i.e., 50%, indicating the probability of random guessing) (one sample t‐test, P < 0.05 in all cases). However, such recognition performance is not present for words heard in deep sedation. The corresponding averaged d′ values of the recognition tests for the four experimental sessions are listed in Table I (after correction for the response bias measured on the foils). These results indicate that subjects have at least partially preserved capability of verbal processing and memory in light sedation, whereas these functionalities are suppressed in deep sedation.

Table I.

Post‐scan free recall performance (row 1) and forced‐choice recognition performance measured by d′ value (row 2, mean ± SD for both cases)

Wakefulness Light sedation Deep sedation Recovery
Recall percentage 9% ± 0.3 10% ± 0.5 2% ± 0.15 (N.S.) 14% ± 0.35
d′ value 0.9 ± 0.25 0.6 ± 0.24 0.2 ± 0.15 1.5 ± 0.42
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Task‐Evoked Activation Map

In wakefulness, auditory verbal stimuli induced significant but mainly left‐lateralized BOLD activations in multiple temporal and frontal regions, including the middle and superior temporal gyrus (BA 21, 22, 41), IFG, anterior insular, middle frontal gyrus, and a small fraction of the premotor areas (BA 6) (Fig. 2 left; Table II). In contrast, these hemodynamic responses were suppressed in deep sedation except that a small fraction of brain areas in the center of the PAC (BA 41, 22 in the superior temporal gyrus) still preserved prominent reactivity (Fig. 2 middle; Table III). In comparison with these two earlier states, the brain activation map in recovery was reinstated to a very similar pattern as observed in wakefulness (Fig. 2 right).

Table II.

Talairach coordinates of brain regions showing significant auditory stimuli‐induced activation and deactivation of BOLD response in wakefulness

Brain regions Side BA Talairach coordinates (LPI) Z‐score
x y z
Inferior frontal gyrus L 9 −43 13 22 3.34
Middle frontal gyrus L 6 −41 3 39 2.84
Insula L 13 −40 13 11 3.17
Precentral gyrus L 6 −37 −2 39 2.92
Superior temporal gyrus L 41 −46 −28 9 3.30
R 41 54 −19 6 3.35
Middle temporal gyrus L 22 −60 −43 −5 3.29
R 21 52 −9 −14 3.02
Inferior temporal gyrus L 20 −62 −33 −15 2.60
R 20 49 −6 −21 3.18
Medial frontal gyrus L 32 −2 7 45 3.29
R 32 6 7 40 2.95
Precuneus L 7 −1 −59 43 −3.91
R 7 2 −55 45 −3.55
PCC L 31 −3 37 25 −2.96
R 31 3 −45 27 −2.40
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L = left, R = right, BA = Broadman's area, p < 0.05.

Table III.

Talairach coordinates of brain regions showing significant auditory stimuli‐induced activation and deactivation of BOLD response in deep sedation

Brain regions Side BA Talairach coordinates (LPI) Z‐score
x y z
Superior temporal gyrus L 41 −46 −26 6 3.51
R 41 53 −21 8 2.98
Middle temporal gyrus L 21 −55 −30 1 2.26
Precentral gyrus L 4 −38 −19 53 −2.77
R 6 32 −10 53 −3.68
Postcentral gyrus L 3/4 −26 −24 67 −2.80
R 3/4 35 −29 61 −2.57
Precuneus L 7 −7 −61 34 −3.33
R 7 6 −60 39 −3.87
PCC L 23 −4 −51 23 −2.35
R 31 6 −53 26 −3.21
Medial frontal gyrus L 9 −5 41 19 −3.26
R 9 4 48 16 −3.35
Middle frontal gyrus R 6 34 12 52 −3.34
Cuneus L 18 −3 −73 14 −2.79
R 18 4 −70 14 −2.45
Lingual gyrus L 18 −5 −78 −3 −3.07
R 18 5 −83 −5 −3.77
Inferior temporal gyrus R 37 47 −68 −1 −2.64
Middle occipital gyrus R 19 47 −70 8 −3.98
Middle temporal gyrus R 39 48 −62 21 −4.14
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L = left, R = right, BA = Broadman's Area, p < 0.05.

Task and Propofol Induced Negative BOLD Effects

In addition to BOLD activations, auditory stimuli and propofol also introduced prominent negative BOLD effects in a specific set of brain regions, respectively. Specifically, the auditory stimuli‐induced negative BOLD effects are mainly located in the posterior cingulate cortex (PCC) and precuneus, which constitute part of the parietal default mode network (Fig. 2 left) [Gusnard and Raichle, 2001]. With the presence of propofol in deep sedation, the negative BOLD effects expanded to more widespread brain areas, including the PCC, precuneus, bilateral middle frontal gyrus (BA 6), pre/postcentral gyrus, a small fraction of the inferior and middle temporal gyrus, and scattered spots of the lingual gyrus and cuneus in the occipital lobe (Fig. 2 middle). In contrast, in recovery, the negative BOLD effects were reversed to an almost identical pattern as observed in wakefulness, showing deactivations of BOLD signal only in the areas of the PCC and precuneus (Fig. 2 right).

Characteristic Hemodynamic Responses in the Seed Regions

The hemodynamic responses of the IFG seed exhibit distinct differences among deep sedation, wakefulness, and recovery. As shown by the data from a typical subject (Fig. 3A, left), in wakefulness, the mean voxel time course of the IFG seed demonstrates consistent increase or decrease of amplitude corresponding to the presence or absence of the auditory stimuli. As a result, the averaged HRF over voxels demonstrate a typical shape of activation over time delays (Fig. 3A, right). However, in deep sedation, the changes of the BOLD response of these voxels show no obvious correlation with the input of the auditory stimuli (Fig. 3B, left). Consequently, the averaged HRF over voxels just demonstrates flat fluctuations around the baseline zero (Fig. 3B, right). In a sharp contrast, across the experimental sessions, the estimated HRFs of the PAC seed always show a typical shape of activation in response to the presence of the auditory stimuli. Group comparisons of the mean HRF of the PAC seed across time delays exhibit no significant differences among wakefulness, deep sedation, and recovery, as indicated by both visual inspection and the statistical comparisons (Fig. 3C; paired t‐test, P>0.42 in all cases). However, for the IFG seed, there are qualitative differences demonstrated with respect to the shape of HRFs among the three conditions. Specifically, in wakefulness, the averaged HRF over subjects shows a typical activation shape of a gradual increase to a peak value followed by a gradual decrease over time delays (Fig. 3D, the red line). The same trend of the averaged HRF is also replicated in recovery (Fig. 3D, the green line) except that there is a small delay of the increase to the peak value, which has a relatively smaller magnitude than wakefulness. In contrast, the averaged HRF in deep sedation simply shows weak fluctuations with no statistical differences from the zero baseline (Fig. 3D, the blue line, P>0.276). Further paired group t‐tests indicate that the amplitude of HRFs under wakefulness and recovery are significantly greater than the ones in deep sedation (P<0.014 in all cases).

Figure 3.

Figure 3

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BOLD responses and the estimated corresponding HRFs in the seed regions. A: The mean voxel time course of the IFG seed region (left) and the estimated HRFs (right) from a typical subject in wakefulness. The presence of auditory verbal stimuli in time is indicated by red marks along with the BOLD signal. B: The same type of information as noted in A for the subject in deep sedation. C: The average HRFs of the PAC seed demonstrate a consistent profile of activation regardless of the states of consciousness across subjects. D: The average HRFs of the IFG seed exhibited differential state‐dependent shapes across subjects in wakefulness, deep sedation, and recovery.

Functional Connectivity With the PAC Seed

In wakefulness, brain regions that maintain significant functional connectivity with the PAC seed region include the middle and superior temporal gyrus (MTG and STG), IFG, anterior insular, middle and superior frontal gyrus (MFG and SFG), supplementary motor area (SMA), pre/postcentral gyrus, and the thalamus (Fig. 4, left). In contrast, in deep sedation, all these functional connections between the PAC seed and the frontal cortices and the thalamus are lost. Some remaining connections were found only in the middle and superior temporal gyrus, and the surrounding areas of the PAC seed (Fig. 4, middle). However, in recovery, these lost functional connections in the frontal cortices and the thalamus were restored, and the overall connectivity map with the PAC seed was reinstated to a similar pattern as observed in wakefulness (Fig. 4, right).

Functional Connectivity With the IFG Seed

The IFG seed demonstrates robust functional connectivity with widespread frontoparietal and temporal regions across the states of wakefulness, deep sedation, and recovery (see Fig. 5). The connectivity maps are nearly identical in wakefulness and recovery, comprising of a large area of the IFG, anterior insular, MFG and SFG, SMA, pre/postcentral gyrus, MTG, STG, precuneus, inferior and superior parietal lobule (IPL and SPL) (Fig. 5, left and right). Notably, in deep sedation, all these functional connections manifested in the wakefulness and recovery are still preserved, only except that the connectivity between the IFG seed and the center area of the PAC is lost (Fig. 5, middle; the brain areas showing the disrupted functional connections with the IFG seed in deep sedation are marked by red ellipses).

Schematic Summary of Functional Connectivities

To facilitate the comparison of the changes of functional connectivity during wakefulness, deep sedation, and recovery, we summarized schematically the anatomical neural organizations described in Figures 4 and 5 that show significant temporal correlations with both seed regions and in all task conditions (see Fig. 6). All red connecting lines (Fig. 6A,B, left) indicate the functional connections observed in wakefulness and recovery (the non‐sedated states), regardless of whether they are marked by white backslash signs. The disrupted functional connections in deep sedation (the sedated state) are indicated by red connecting lines with two white backslash signs. The corresponding voxel counts of the involved brain regions are provided (Fig. 6A,B, right), showing a consistent quantitative description of the qualitative differences described on the left. The voxel counts of the same anatomical region of wakefulness and recovery are averaged in the right panel of Figure 6.

Figure 6.

Figure 6

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Schematic summary of the neural organizations showing significant functional connectivity of the BOLD signal with the PAC and IFG seeds under non‐sedated (wakefulness and recovery) and sedated (deep sedation) conditions. A: Left: brain regions functionally connected with the PAC seed in wakefulness and recovery (all red lines, regardless of whether they are marked by white backslash signs). The lost functional connectivities in deep sedation are shown by connecting lines marked by two white backslash signs. Right: the corresponding region‐specific voxel counts of the functional connectivity shown on the left. B: Left: the same type of information as noted in A for brain regions showing significant functional connectivity with the IFG seed. Right: the corresponding voxel counts. (TH: thalamus; I/SPL: inferior/superior parietal lobule; pre/post‐CG: pre/post‐central gyrus).

DISCUSSION

Effects of Propofol on Sensory and High‐Order Processing of Auditory Verbal Memory

Combining functional imaging, cognitive neuroscience, and anesthetic manipulation, the present study addresses two important questions: (1) how the integrity of cognitive networks sustaining auditory verbal processing and memory are maintained in wakefulness and reinstated in recovery from propofol sedation, and (2) how propofol selectively disrupts functional interactions between sensory and high‐order processing of the memory task in the brain, which leads to disintegration of neural information and, therefore, suppressed cognition.

From a theoretical perspective, neural processes giving rise to conscious perception and memory require sufficient complexity of neuronal information processing and adequate integration among distributed neural systems in the global workspace [Baars and Franklin, 2003; Tononi, 2004, 2008]. A breakdown of cortical functional connectivity evidenced by fMRI or electrophysiological recordings has been often taken as an indication of disrupted information integration in the brain [Alkire, 2008; Hudetz, 2006]. The association between the loss of consciousness in anesthesia and a breakdown of effective cortical connectivity has been observed in a range of human and experimental studies involving transcranial magnetic stimulation (TMS) [Ferrarelli et al. 2010], event‐related potentials [Hudetz and Imas, 2007], functional brain imaging [White and Alkire, 2003], and computational simulations [Steyn‐Ross et al., 2001]. Yet, the exact nature of these disrupted functional connectivities and their theoretical implications as related to information and integration in the brain remains to be further elucidated. In this study, we argue that by comparing BOLD response maps obtained during different stages of anesthetic manipulation, it is possible to identify seed regions that represent cognitive functions performed at different levels of the cognitive processing; connectivity analyses based on such a seeding strategy therefore provide insight into the nature of propofol‐disrupted functional interactions in the brain, and allow the discussion of the results in light of the theoretical suggestions.

A prominent effect of propofol is the disruption of functional connections from the PAC seed to the thalamus and a set of frontal regions in deep sedation (Fig. 6A). These suppressed connections were subsequently reinstated in recovery, further confirming the observations. Although our current analytical method is limited in addressing whether these lost connections are due primarily to a direct influence of propofol on cortico‐cortical connections, or they result indirectly from disruption of cortico‐thalamic and thalamo‐cortical interactions, the results suggest that propofol inhibited cognitive formations by preventing the projection of sensory information into the high‐order processing association areas. In this manner, the received sensory information is not functionally integrated, and cognitive formations are inhibited.

In comparison, the IFG seed, which is mostly activated in wakefulness and recovery but deactivated in deep sedation with suppressed cognition, maintains persistent functional connectivity with a set of widely distributed frontoparietal and temporal neural organizations, regardless of the propofol administration (Fig. 6B). The only anatomically meaningful brain region whose connections with the IFG seed are impaired by propofol is the center area of the PAC. The preserved functional connectivity in the high‐order cortical regions is a surprising result, given the fact that these regions no longer show task‐induced activations in deep sedation. Consistently, these specific left‐lateralized neural organizations distributed in the frontal, parietal, and temporal lobes have been explicitly implicated in supporting verbal processing and memory [Baars and Gage, 2007; Binder et al., 2009; Curtis and D'Esposito, 2003; Wagner et al., 2005]. The results thus suggest a preserved high‐order processing network for verbal processing and memory, which, however, becomes disconnected with the sensory stimuli in deep propofol sedation. Notice that there are also minor functional connections present in the right hemisphere (Figs. 4 and 5). These connections are likely associated with the activity of information retrieval, which is usually associated with the right hemisphere of the brain [Habib et al., 2003] and could be present in our task.

Few neuroimaging studies have been previously performed to examine the nature of propofol‐disrupted functional interactions in the brain from a similar angle. A recent study examined resting‐state fMRI connectivity using a similar anesthesia paradigm but at higher propofol dose [Boveroux et al., 2010]. The study demonstrates that, in the resting‐state, functional connections in specific frontoparietal networks (i.e., the default and executive control networks) are partially reduced during propofol‐induced unconsciousness. In contrast, functional connections with seed regions defined in the primary visual and auditory cortices are preserved in the low‐level sensory cortices. Although the study by Boveroux et al. examines network connectivity in the resting‐state of the brain, the investigators come to a similar conclusion that propofol‐induced unconsciousness could be linked to a breakdown of network connectivity that prevents communication between sensory and high‐order frontoparietal cortices. With respect to other forms of loss of conscious perception to the environment, the similar pattern of evidence was also identified in one early study of rapid eye movement (REM) sleep [Llinas and Ribary, 1993]. The 40‐Hz oscillations of cerebral neural activities measured by magnetoencephalography (MEG) exhibited a similar pattern to that observed in wakefulness; however, such activities were no longer modulated by sensory stimuli in REM sleep. Anesthesia and sleep are not the same in terms of the underlying mechanisms and neural correlates [Vanini et al., 2010], yet these results suggest a common manifestation of high‐order cerebral cortices becoming unbound from sensory modalities in suppressed conscious perception to the environment.

Task and Propofol Related Brain Activity

Task‐related BOLD activations in the left‐lateralized frontal and temporal cortices and propofol‐induced suppressing effects are highly consistent with what several other studies have evidenced using similar experimental settings, especially with respect to the significance of the IFG activation to verbal comprehension and memory [Davis et al., 2007; Heinke et al., 2004; Plourde et al., 2006]. Task‐induced negative BOLD effects in the parietal regions (Fig. 2 middle) are in accordance with the commonly observed deactivation of the default mode network in cognitively demanding and externally cued tasks. Of interest is that such negative BOLD effects expand to more widespread brain areas in deep propofol sedation. These findings confirm and extend the earlier PET studies showing that propofol preferentially decreased regional cerebral blood flow in a specific set of frontoparietal regions and the thalamus [Alkire and Miller, 2005; Fiset et al., 1999; Kaisti et al., 2002]. Significant overlap with respect to the deactivated brain regions is obvious, especially in the parietal default mode network (i.e., the precuneus and PCC) [Gusnard and Raichle, 2001]. Of note is that these deactivated brain regions are also commonly identified in association with degraded consciousness in sleep [Kaufmann et al., 2006], coma [Laureys et al., 2004], and vegetative state [Laureys, 2005], suggesting a common set of neural substrates that might be affected across pathological conditions of reduced consciousness.

The Nature of the Involvement of Parietal Regions

The nature of the involvement of a specific set of parietal regions in memory‐related functions, particularly the lateral posterior parietal areas and precuneus, has generated a significant amount of interest [Hutchinson et al., 2009; Wagner et al., 2005]. These regions have been particularly implicated in a specific set of memory‐related functions, such as identifying old/new items and recollective‐orienting operations [Bucker and Wheeler, 2001; Hutchinson et al., 2009; Rugg et al., 2002; Wagner et al., 2005]. Arguing about the nature of the observed parietal involvement, Wagner et al. hypothesized that parietal involvement is associated with internally generated mnemonic representations and information accumulation, as opposed to being a marker of external stimulus‐directed attention [Wagner et al., 2005]. The present study provides supporting evidence to this hypothesis. A set of parietal brain organizations, specifically, the precuneus and the left‐lateralized inferior and superior parietal lobules, are exclusively connected with the IFG seed (Figs. 5 and Fig. 6B); there are no identified functional interactions of these regions with the PAC seed. These results suggest that the parietal involvement in the cognitive processing of our task does not function as a direct consequence of the auditory verbal stimuli, but rather relies only on internally generated information in high‐order verbal memory processing. The proposed fMRI‐guided connectivity analysis appears particularly beneficial in understanding this neuroscientific inquiry.

In summary, this study provides direct imaging evidence suggesting that propofol suppresses verbal processing and memory by disrupting sensory information from being projected into and integrated by a high‐order processing network, which has been repeatedly implicated in mediating the desired cognitive functions. The functional connectivity patterns derived from the seed regions that represent sensory and high‐order processing of the memory task convey particular relevance in understanding the effects of the anesthetic as related to information and integration in the brain. Despite the significance and theoretical implications of these results, we would like to limit our discussions of the significance of the findings within the context defined by the task, dose‐level, and the type of the anesthetic in the current study. A complete delineation of the anesthetic mechanisms of propofol with respect to cognitive suppression is beyond the scope of a single study, and requires detailed examination of different types of cognitive tasks at various levels of the anesthetic dose.

Supporting information

Additional Supporting Information may be found in the online version of this article.

Supporting Information

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Acknowledgements

Authors thank Ms. Carrie O'Connor, MA, for editorial assistance. The assistance from Drs. Richard C. Mulligan, Catherine L. Elsinger, Roland J. Erwin, Thomas E. Prieto, and Sally J. Durgerian to the early part of this work is greatly appreciated.

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