A common neural code for similar conscious experiences in different individuals

Lorina Naci; Rhodri Cusack; Mimma Anello; Adrian M. Owen

doi:10.1073/pnas.1407007111

New Research In

Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved August 15, 2014 (received for review April 16, 2014)

Significance

Although in our daily lives we engage in many of the same activities as others, we are not privy to their conscious experiences, and can only understand them through their self-reports. Patients who are conscious, but are unable to speak or exhibit willful behavior, are, therefore, unable to report their conscious experiences to others. Indeed, in most cases, it is impossible to know whether they are conscious or not. We introduce a neural index that, in a group of healthy participants, predicted each individual’s conscious experience. Moreover, this approach provided strong evidence for intact conscious experiences in a brain-injured patient who had remained behaviorally nonresponsive for 16 y. These findings have implications for understanding the common basis of human consciousness.

Abstract

The interpretation of human consciousness from brain activity, without recourse to speech or action, is one of the most provoking and challenging frontiers of modern neuroscience. We asked whether there is a common neural code that underpins similar conscious experiences, which could be used to decode these experiences in the absence of behavior. To this end, we used richly evocative stimulation (an engaging movie) portraying real-world events to elicit a similar conscious experience in different people. Common neural correlates of conscious experience were quantified and related to measurable, quantitative and qualitative, executive components of the movie through two additional behavioral investigations. The movie’s executive demands drove synchronized brain activity across healthy participants’ frontal and parietal cortices in regions known to support executive function. Moreover, the timing of activity in these regions was predicted by participants’ highly similar qualitative experience of the movie’s moment-to-moment executive demands, suggesting that synchronization of activity across participants underpinned their similar experience. Thus we demonstrate, for the first time to our knowledge, that a neural index based on executive function reliably predicted every healthy individual’s similar conscious experience in response to real-world events unfolding over time. This approach provided strong evidence for the conscious experience of a brain-injured patient, who had remained entirely behaviorally nonresponsive for 16 y. The patient’s executive engagement and moment-to-moment perception of the movie content were highly similar to that of every healthy participant. These findings shed light on the common basis of human consciousness and enable the interpretation of conscious experience in the absence of behavior.

Although consciousness is a part of all of our lives, we are not privy to the conscious experiences of others. Indeed, our ability to understand and appreciate their experiences depends largely on their self-report, or ability to describe those experiences (1). However, in recent years a population of patients has been identified who are demonstrably conscious, but entirely unable to speak or move willfully in any way, precluding any systematic investigation of their conscious experience of the world (2 ⇓⇓⇓⇓⇓–8). It remains unknown whether there is a common neural code that can account for how different individuals might form similar conscious experiences and, if so, whether it could be used to interpret those experiences without recourse to self-report.

The “executive” function of the brain refers to those processes that coordinate and schedule a host of other more basic cognitive operations, such as monitoring and analyzing information from the environment and integrating it with internally generated goals, as well as planning and adapting new behavioral schemas to take account of this information (9 ⇓–11). As such, executive function is integral to our conscious experience of the world as prior knowledge is integrated into the current “state of play” to make predictions about likely future events. Accordingly, executive function may provide an empirical window by which the cognitive aspect of human conscious experience can be quantified. The behavioral and neuronal bases of executive function have been well studied in neuropsychological patients (12 ⇓–14) and with functional neuroimaging (9, 12, 15 ⇓–17), which confirm that executive function is supported by a network of brain regions, primarily involving the frontal lobes and the posterior parietal cortex (9, 12, 15 ⇓–17). However, the open-ended nature of our conscious experiences combined with the narrowly defined scope of most tests of executive function, which rely on responses to specific parameters of a study task, make it challenging to relate executive function to consciousness in real-world situations.

Movie viewing may provide a solution to this problem. By their very nature, engaging movies are designed to give viewers a shared conscious experience driven, in part, by the recruitment of similar executive processes, as each viewer continuously integrates their observations, analyses, and predictions, while filtering out any distractions, leading to an ongoing involvement in the movie’s plot. These cognitive, integrative processes are executive in the broad meaning of the word and go beyond processes directly related to planning and execution of motor behavior to encompass “second-order” or “meta” cognitive states that enable viewers to understand a movie. When different individuals watch the same movie, synchronized changes of brain activity across the individuals are observed (18 ⇓–20). However, it is not known whether any of these synchronized activity fluctuations reflect similar executive function across different individuals in response to the evolving executive demands of the movie plot.

We addressed this question in a series of studies by using a highly engaging short movie by Alfred Hitchcock, the so-called “master of suspense,” to drive the conscious experiences of three groups of healthy participants. Initially, the neural correlates of conscious experience common to different individuals were quantified using functional magnetic resonance imaging (fMRI). Subsequently, these correlates were related to measurable (quantitative and qualitative) executive components of the movie plot through two additional behavioral investigations in independent groups of healthy participants to determine the neural basis of executive processes common across individuals. We then applied the same approach in two behaviorally nonresponsive patients with unknown levels of consciousness to examine and quantify their experience of the world in the absence of self-report.

Results

Similar Neural Correlates of Conscious Experience Across Different Individuals.

In the first study, we acquired fMRI data from 12 healthy participants while they viewed the short (8 min) movie and also during a “resting state” scan—a scanning period of the same duration in which they were asked to just relax in the absence of any overt visual or auditory stimulation. We also acquired fMRI data from a second healthy group (n = 12) while they viewed a scrambled version of the movie, which was formally identical to the intact one, except for the absence of a detectable plot (Supporting Information). Consistent with previous studies (18 ⇓–20), we observed widespread and significant (P < 0.05; family wise error (FWE) cor) cross-subject synchronization between healthy participants, with sensory-driven (primary and association) visual and auditory cortex, as well as higher-order supramodal regions, showing activity peaks and dips at identical points of the movie in different individuals (Fig. 1A). By contrast, no synchrony was observed in any area of the brain in the resting state data (Fig. 1B). The scrambled movie elicited significant (P < 0.05; FWE cor) synchronization only in sensory-driven visual and auditory cortex (Fig. 1C). Formal comparison between the intact and scrambled movie conditions revealed that the intact movie elicited significantly (P < 0.05; FWE cor) more cross-subject synchronization than the scrambled movie bilaterally in parietal, temporal, motor, and dorsal/ventral frontal/prefrontal cortex (Fig. 1D). Together, these results confirmed that the cross-subject synchronization observed during movie viewing in high-order supramodal regions could not merely reflect modulations in the feed-forward processing load imposed by variations in the audiovisual information, nor any automatic attention effects triggered by the similarity of audiovisual stimuli across participants. Rather, the processing of higher-level properties of the movie itself, including its plot, must be driving the synchronized activity in these regions across participants.

Fig. 1.

Brain-wide synchronization of neural activity across subjects. (A) Movie viewing elicited significant (P < 0.05; FWE cor) cross-subject correlation across the brain. (B) No cross-subject correlation was observed in the resting state. (C) The scrambled movie elicited significant (P < 0.05; FWE cor) cross-subject correlation only within primary and association visual and auditory cortex; none was observed in higher-order, supramodal cortex. (D) The intact movie elicited significantly (P < 0.05; FWE cor) more cross-subject correlation than the scrambled movie bilaterally in parietal, temporal, motor, and dorsal/ventral frontal/prefrontal cortex. Warmer colors depict higher t values of cross-subject correlation.

To relate the synchronized activity fluctuations to different and specific aspects of the movie-watching experience, we used Tensor Independent Components Analysis (ICA). Tensor ICA derives spatially orthogonal components, whose spatial and temporal features are common across different individuals (21 ⇓–23), thus isolating those neural patterns that may be driven from common aspects of the movie-watching experience. Group-level Tensor ICA revealed several spatially distinct networks (Fig. 2 A–F and Fig. S1) in sensory-specific (i.e., visual, auditory, and motor) cortex and regions of the frontal and parietal lobes that are known to support executive processing (9, 12, 15 ⇓–17). To further confirm that this frontoparietal network reflected synchronized activity across different participants, we performed single-subject ICAs, which revealed a high correlation between the single-subject time courses for the frontoparietal component [t(11) = 13.2; P < 0.00001; Fig. 2 G and H]. By contrast, single-subject ICAs demonstrated that brain activity was desynchronized among different participants when they were at rest (Fig. 2I).

Fig. 2.

Cross-subject synchronization of neural activity within different functional brain networks. (A) Time courses of group-level ICA components (ICs) clustered into five groups; they were correlated within groups and anticorrelated/less correlated across them (A, 1–5). (B–F) Individual ICs clustered into five spatially distinct brain networks (Fig. S1). (G) Individual time courses of independent components (IC) explaining the most variance within the frontoparietal network. (H) The single-subject time courses of IC explaining the most variance within each network were significantly synchronized across individuals. (I) No synchronization was observed between single-subject ICs of the resting state data, as, for example, shown by the auditory IC explaining the most variance overall in the resting state dataset. S1–12, Subject 1–12.

Neural Basis of Executive Processes Common to Different Individuals.

To test whether the cross-subject synchronization in frontal and parietal regions during movie viewing actually represented commonly experienced executive elements of the movie, two further studies in healthy participants were conducted outside of the scanner. First, the extent to which the movie made demands on executive functioning was quantified with a “dual-task” procedure that has been used previously to investigate executive performance, both in patients with frontal lobe damage (24 ⇓–26) and in healthy volunteers (27 ⇓–29). The dual-task framework assumes that, because executive function is a finite resource, in moments when the load on one executively demanding task is greatest, the performance of a second executively demanding task will be impaired, yielding a direct, quantitative measure of the executive demands of the first task across time (Supporting Information). Accordingly, we continually assessed performance on a demanding and widely used test of executive function [the Sustained Attention to Response Task (SART) (30 ⇓⇓–33), Supporting Information], while 27 healthy participants watched the same Alfred Hitchcock movie. During the SART, participants were required to respond with a key press to a series of randomly presented (“go”) digits (one–seven, nine) but withhold responses to one prespecified (“no go”) digit (eight) (Fig. 3A). To ensure that participants attended to both the SART and the movie, they were instructed to respond as quickly as they could, while minimizing errors and, in addition, to prepare to answer questions about the movie contents at its conclusion. Performance was assessed as the reaction time to make key-press responses throughout the movie, yielding a continuous measure of the movie’s executive load (Supporting Information).

Fig. 3.

Decoding shared executive engagement in healthy participants. (A) Performance on the SART (30) during simultaneous movie viewing. The probability density function, to the Right, shows that SART responses followed the canonical pattern (30); i.e., responses preceding the erroneous key press (where responding was automatic; pink) were significantly faster than those preceding the correct withhold (where executive processes were engaged; blue). (B and D) Overlay of the group-level frontoparietal IC (red) and the frontoparietal activity (P < 0.05; FWE cor.; green) predicted by SART performance (B) and frame ratings (D); overlap areas are displayed in yellow. (C, Middle) Group-averaged suspense ratings of movie stills. (Top, a–f) highest-rated and (Bottom, g–l) lowest-rated frames. Higher-rated frames predicted stronger activity within the frontoparietal network (D).

We then used statistical parametric mapping (SPM) to model the relationship between this quantitative measure of the movie’s executive load and changes in brain activity over time, assessed in the independent group of healthy participants who had watched the movie without a secondary task in the fMRI scanner. Group-averaged reaction times from participants who correctly performed both components of the dual task (n = 15) were used as a regressor in the SPM model of movie data. SART reaction times in one (behavioral) group of participants significantly (P < 0.05; FWE cor) predicted activity in a brain network involving frontal and parietal regions—which support executive function (9, 12, 15 ⇓–17)—in the other (fMRI) group of participants (Fig. 3B). This result confirmed that synchronized activity in these regions was driven by the executive load of the movie, as indexed by an entirely independent behavioral measure acquired in a separate group of healthy participants (Fig. S2).

To further confirm that these modulations in frontal and parietal activity reflected a common conscious experience across individuals, we developed a qualitative measure of the subjective experience of viewing the movie that reflected, albeit indirectly, its ongoing executive load. A third group of healthy participants (n = 15) watched the movie and were asked to rate how “suspenseful” it was every 2 s from “least” to “most” suspenseful (Fig. 3C). Beyond basic physical properties, such as the amplitude and tone of the musical soundtrack, “suspense” in classic Hitchcock movies, such as the one used in this study, arises through an understanding of the relevance of specific items intrinsic to the plot (e.g., a gun), their potential uses (e.g., to shoot people), the circumstances of the main protagonists (e.g., they are capable of shooting or being shot), and their own “theory of mind” (e.g., they are holding a gun, but they do not know that it is loaded). The ongoing understanding of the plot requires access to executive processing as the current features of the movie are compared with stored knowledge of the world (e.g., guns kill people), what has happened previously in the movie (e.g., the boy has acquired a gun), and what may happen in future (e.g., he may shoot someone). The single-subject suspense ratings throughout the movie showed significant intersubject correlation [t(14) = 25.3; P < 0.0001], once more confirming the common conscious experience of individuals watching it.

Again, we used SPM to model the relationship between this qualitative measure of the movie’s executive load and changes in brain activity over time, measured in the independent group of healthy participants who had watched the movie without a secondary task in the fMRI scanner. The group-averaged suspense ratings significantly (P < 0.05; FWE cor) predicted activity in a similar network of brain regions involving the frontal and parietal cortices, with frames rated as “highly suspenseful” predicting stronger activity in this network (Fig. 3D). This result confirmed further that activity in these frontal and parietal regions was driven by the common experience of the movie’s executive load, as indexed by a second independent behavioral measure acquired in a separate group of healthy participants (Fig. S2).

Decoding Conscious Experiences in Behaviorally Nonresponsive Patients.

The results of the previous three experiments in healthy participants suggested that synchronized activity fluctuations in the frontal and parietal regions tracked the common cognitive experience of different individuals while watching the same movie. In a final study, we asked whether these common patterns of brain activity in healthy individuals could be used to examine and quantify the movie-watching experiences of two entirely behaviorally nonresponsive, severely brain-injured patients with unknown levels of consciousness. In either case, the clinical diagnosis fluctuated at different time points since their injury between vegetative state (34) and minimally conscious state (35). A clinical diagnosis of vegetative state is made after repeated behavioral examinations have yielded no evidence of sustained, reproducible, purposeful or voluntary behavioral response to visual, auditory, tactile, or noxious stimuli (34). A clinical diagnosis of minimally conscious state describes a patient who shows inconsistent, but reproducible evidence of (minimal) awareness (35), ranging from basic (e.g., visual pursuit) to more complex behavioral responses (e.g., command following or communication). Although in isolated occasions since their injury, either patient demonstrated visual pursuit, neither exhibited any higher-order signs of awareness or any form of communication.

Previous neuroimaging studies have established that brain function can serve as a proxy for overt behavior in patients who are aware, yet unable to produce any overt physical responses (2 ⇓⇓⇓⇓⇓–8, 36). To date, these studies have relied on a response according to instruction, albeit a brain response, to establish that a significant minority of patients (∼19%) (6, 7) can follow commands and, in some cases, communicate with the outside world (2, 3, 7). However, even for this unique group of patients who are able to follow commands with their brain activity (2 ⇓⇓⇓⇓⇓–8, 36), it is not yet known how they perceive the world around them and whether their conscious experiences are comparable to those of healthy individuals. By contrast, the present study probed the naturalistic movie-watching experience in the absence of any structured instruction. We reasoned that if one of the patients engaged in executive processing while watching the movie, he would exhibit similar brain activity patterns in frontal and parietal regions to the healthy participants’. Conversely, we could use the healthy participants’ frontoparietal activity as a benchmark for assessing the presence of executive function and, therefore, as an index of conscious experience, in the two clinically similar, behaviorally nonresponsive patients. The stereotypicity of brain activity underlying similar executive function across individual healthy participants enabled model-based predictions that could be applied to individual nonresponsive patients.

fMRI data were acquired from the two patients as they freely viewed the Alfred Hitchcock movie. Subsequently, the healthy data served as a model for probing whether each patient showed auditory, visual, and, crucially, executive processing of events in the movie. Probing of brain function in individual brain-injured patients critically depended on the single-subject–level reliability of brain responses in healthy participants. A prior set of leave-one-out analyses in healthy participants had shown that each participant’s auditory, visual, and frontoparietal activity could be significantly (P < 0.05; FWE cor) predicted by the time course of the corresponding activity in the rest of the group (Fig. 4 A–C) (Supporting Information). Thus the same method could be used to test the similarity of each patient’s functional activity to the healthy participants’ by using the time course of the auditory/visual/frontoparietal network in the healthy group as a regressor in the SPM model of each patient’s movie data (Supporting Information).

Fig. 4.

Predicting sensory-driven and higher-order processes in individual participants. (A–C). Single-subject SPM analyses probe individual participants’ responses. The processing of the movie’s auditory (A), visual (B), and executive (C) information in each healthy participant was significantly (P < 0.05; FWE cor) predicted by the time course of the respective brain network in the leave-one-out group ICA. S1–S12, Subject 1–12.

Activity in Patient 1’s auditory cortex synchronized to that of the healthy group in this region (P < 0.05; FWE cor; Fig. 5A, Healthy Group vs. Patient 1), suggesting intact processing of the auditory information. No evidence of visual responses or executive function similar to the healthy participants’ was observed (Fig. 5 B and C, Healthy Group vs. Patient 1). Previous studies have revealed basic auditory responses in a significant minority (23%) of patients (37) who were diagnosed as vegetative in the absence of any detectible higher-level cognitive function. Moreover, because similar responses have been observed in healthy participants whose cognition was abolished under anesthesia (38), these basic auditory responses are unlikely to indicate conscious processing of the auditory stimuli.

Fig. 5.

Decoding executive function in one behaviorally nonresponsive patient. Healthy group: (A) Group-level auditory (purple) and visual (blue) ICs. (B and C) The healthy group’s activity predicted by the quantitative (B)/qualitative (C) executive measure (green) is overlaid on the group frontoparietal IC (red); overlap areas are displayed in yellow. Patient 1: (A) The healthy group’s auditory IC predicted significant activity in Patient 1’s auditory cortex (purple). (B and C) No evidence of visual responses or executive processing similar to the healthy participants’ was observed. Patient 2: (A) The healthy group’s auditory and visual ICs predicted significant activity in Patient 2’s auditory (purple) and visual (blue) cortex, respectively. (B and C) The quantitative (B) and qualitative (C) executive measures predicted activity (green) in the Patient’s frontal and parietal regions. Overlap with activity predicted by the healthy group’s frontoparietal IC (red) is displayed in yellow.

In stark contrast, activity in Patient 2’s auditory and visual cortex synchronized to that of the healthy group in these regions (P < 0.05; FWE cor), suggesting intact processing of both auditory and visual information in the movie; most importantly, activity in a network of frontal and parietal regions that are known to support executive processing (9, 12, 15 ⇓–17) significantly synchronized to that of healthy participants (P < 0.05; FWE cor) (Fig. 5 A–C, Healthy Group vs. Patient 2). To further test whether the frontal and parietal activity observed in Patient 2 truly reflected executive processes related to specific events in the movie, we assessed the extent to which it was explained by the quantitative and qualitative measures of the movie’s executive load. Both of these measures, derived in healthy participants, significantly (P < 0.05; FWE cor) predicted the patient’s activity in the same frontal and parietal regions, revealing analogous networks with bilateral spatial distributions, similar to the frontoparietal distribution of the quantitative and qualitative networks in the healthy participants (Fig. 5 B and C, Healthy Group vs. Patient 2).

Discussion

In a series of studies, we tested whether a common neural basis can account for how different individuals form similar conscious experiences, in particular those invoking executive processes. We found that when participants attended to naturalistic stimuli evolving meaningfully over time, akin to real-world events—such as those present in a plot-driven movie—they displayed highly synchronized brain activity in supramodal frontal and parietal regions, which support executive function (9, 12, 15 ⇓–17). The movie’s executive demands, assessed quantitatively with a dual-task procedure (30 ⇓⇓–33), predicted activity in these frontal and parietal regions. Importantly, individual participants had a similar qualitative experience of the movie’s executive demands, which also predicted activity in these regions. Together, these results suggested that the movie’s executive demands drove brain activity in frontal and parietal regions and, further, that the synchronization of this activity across individuals underpinned their similar experience. By extension, the degree to which each individual’s frontoparietal brain activity could be predicted from the rest of the group’s represented a reliable neural index of how similar his/her cognitive experience was to the others’. Thus for the first time to our knowledge, these results demonstrate that similar conscious experiences in different individuals are supported by a common neural code, which can be used to interpret these experiences without recourse to self-report. This neural code does not read off the precise details of a person’s thoughts. Rather, it can reveal whether two individuals have a highly similar cognitive experience when exposed to the same information (e.g., a movie). Differently to previous methods (39, 40) that use behavioral states to determine how brain activity underlies conscious states in healthy individuals, this approach interprets brain activity and concomitant mental states without recourse to behavior. Thus it is uniquely suited to investigating conscious experience in individuals whose status as conscious agents is uncertain and cannot be tested through behavior or introspective report, as in the case of behaviorally nonresponsive patients.

Critically, this approach can be used to examine whether severely brain-injured patients, who may be conscious but are unable to speak, move, or exhibit any other willful behavior (2 ⇓⇓⇓⇓⇓–8), maintain conscious experiences similar to those of healthy individuals. We found that one patient who had remained behaviorally nonresponsive for a 16-y period before scanning demonstrated a highly similar brain response to that of the three independent groups of healthy participants. The patient’s brain activity in frontal and parietal regions was tightly synchronized with the healthy participants’ over time, and, crucially, it reflected the executive demands of specific events in the movie, as measured both qualitatively and quantitatively in healthy individuals. This suggested that the patient had a conscious cognitive experience highly similar to that of each and every healthy participant, while watching the same movie. These neuroimaging results were striking in light of the patient’s behavioral profile observed in repeated assessments at his bedside over the 16-y period. During that time the patient showed neither movement to command nor any behavioral signs of functional or nonfunctional communication. He displayed no signs of localization of sound and no visual recognition or interaction with objects or people in his environment, including his family members. In isolated instances since the injury, the patient displayed visual pursuit and, on that basis, was clinically diagnosed as minimally conscious on those occasions. However, the enduring absence of any signs of intermediate or complex auditory, visual, motor, verbal, or communication behavior rendered any “minimal” conscious experiences the patient might have had entirely uninterpretable. By the same token, it was impossible to determine, based on the patient’s behavior, whether, or how, he perceived the world around him.

By contrast, this fMRI approach provided strong evidence that the patient could continuously engage in complex thoughts about real-world events unfolding over time and thus that he was consciously aware. Importantly, this was consistent with the patient’s positive outcome in an independent, command-following task, the results of which were unknown at the time of the movie experiment. This task was performed on the same scanning visit and reported in a previously published study (2). During the command-following task (41), the patient was asked to pay attention to or ignore specific words, according to study instructions. Indeed, the patient demonstrated that he was able to follow commands by modulating his brain activity to selectively pay attention to some external events (i.e., words) and ignore others over the duration of the task. Furthermore, in the same scanning visit the patient was able to use this method to communicate factually correct answers to two binary (yes/no) questions (see Patient 2, ref. 2).

However, it was impossible to determine, based on the results of the command-following task and the patient’s behavioral assessments, whether he maintained conscious experiences comparable to those of healthy individuals in response to real-world events in his environment. By contrast, the patient’s brain response to the movie suggested that his conscious experience was highly similar to that of each and every healthy participant, including his moment-to-moment perception of the movie content, as well as his executive engagement with its plot. These processes are likely to include updating the contents of working memory (e.g., to follow the plot), relating events in the movie to past experiences (e.g., to appreciate that a gun is a dangerous weapon), and coding the foreshadowing cues (i.e., events that might have future relevance to the plot) characteristic of movies of this type. Thus, the patient’s brain response suggested that he could maintain much more complex mental processes than could ever be inferred based on his behavior or even based on his binary brain response to the fMRI command-following task.

Despite a highly similar clinical and behavioral profile, we found no fMRI evidence of executive processing and, therefore, conscious awareness, in Patient 1. The brain activity differences between the two patients cannot be attributed to differences in arousal, as both maintained the same state of wakefulness throughout the study (Supporting Information). Negative findings in nonresponsive patients must be interpreted with caution and cannot be used as conclusive evidence for lack of awareness, because false negative findings in functional neuroimaging studies may sometime occur even in healthy volunteers. Nevertheless, the aforementioned index of executive processing did reveal significant and similar changes in the frontoparietal network in each and every healthy participant who watched the movie, suggesting that its neural signature is reliably present in all adult and conscious humans. The lack of evidence for any responses in the executive frontoparietal network in Patient 1 was consistent with the lack of evidence for visual processing, a critical function for watching a movie. Moreover, Patient 1’s negative outcome in the movie experiment was consistent with his negative outcome in the aforementioned fMRI command-following task, which he performed on the same scanning visit.

To date, neuroimaging studies that have probed consciousness in behaviorally nonresponsive patients have tested whether any given patient could follow commands, and therefore demonstrate conscious awareness, via his/her brain activity (2 ⇓⇓⇓⇓⇓–8 and 42 ⇓–44; see 45 for a different approach). However, the requirement that a patient must be able to produce brain responses as prescribed by study instructions to demonstrate that he/she is aware is likely too stringent for many patients who are aware but, due to the effects of brain injury, fail to comply with structured instructions (35, 46, 47). Up to 43% of patients, who on the basis of routine bedside assessment are declared to be in a vegetative state (34), show inconsistent but reproducible behavioral signs of awareness to more careful/intensive bedside assessments (48) and are reclassified as being in a minimally conscious state (35). The discrepancy between the high proportion of nonresponsive patients who are routinely misdiagnosed through bedside assessments (48) and those who are able to demonstrate willful brain-based responses (17–19%) (6, 7) suggests that existing neuroimaging techniques lack the sensitivity to detect conscious awareness in a subset of patients.

We propose a novel approach that is unconstrained by any task commands but, rather, captures attention naturally and therefore might be more effective for detecting conscious awareness. This approach can determine not only whether any given patient is conscious but also infer what the contents of that conscious experience might actually be, thus revealing important practical and ethical implications for the patient’s standard of care and quality of life.

Methods

Participants.

Ethical approval was obtained from the Health Sciences Research Ethics Board and the Psychology Research Ethics Board of Western University. All healthy volunteers were right-handed and native English speakers and had no history of neurological disorders. They signed informed consent before participating and were remunerated for their time. The respective surrogate decision makers gave informed written consent for each patient’s participation. Twenty-four (19–31 y; 12 males), 27 (19–30 y; 13 males), and 15 (19–29 y; 5 males) healthy volunteers participated in experiment 1, 2, and 3, respectively. Two nonresponsive patients (20, 34 y; 1 male) participated in experiment 4.

Procedure and Design.

An edited sequence of the black and white TV episode, “Alfred Hitchcock Presents—Bang! You’re Dead” and its (visually and auditorily) scrambled version were presented in the intact and scrambled movie conditions, respectively (experiment 1). Participants were asked to simply watch each and follow it as best they could (Supporting Information). The SART stimuli consisted of a pseudorandomized sequence of spoken numbers (one–nine), each repeated 45 times (stimulus-onset asynchrony = 1.5 s) superimposed on the movie soundtrack, throughout its duration (experiment 2) (Supporting Information). Participants rated 239 consecutive stills created from the movie—one per 2 s of film—on an eight-point scale ranging from least to most suspenseful (experiment 3). Two nonresponsive patients (for clinical histories, see Supporting Information) were asked to simply watch the Hitchcock movie and follow it as best they could (experiment 4).

Data Acquisition and Analysis of fMRI Time Series.

Functional images were acquired on a Siemens Tim Trio 3 Tesla MRI scanner. Model-based analyses, including standard preprocessing procedures, were performed with SPM8 (Supporting Information). Fixed-effect analyses were performed in each subject, corrected for temporal autocorrelation. The regressors were generated by convolving boxcar functions with the canonical hemodynamic response function. Also included in the general linear model were nuisance variables, comprising the movement parameters in the three directions of motion and three degrees of rotation, as well as the mean of each session. Linear contrasts were used to obtain subject-specific estimates for each effect of interest. Linear contrast coefficients for each participant were entered into the second-level random-effects analysis. The second-level effects of the intact and scrambled movie conditions were directly compared with a two-sample t test. Significant clusters/voxels survived the P < 0.05 threshold, corrected for multiple comparisons with the FWE.

Behavioral Analyses.

SART performance was analyzed in 19/27 participants who paid adequate attention to the movie (>70% accuracy in the postmovie questionnaire). Only the reaction times of the participants who, in addition to attending to the movie, correctly performed the SART component of the dual task were included in the fMRI data analysis (Supporting Information).

Acknowledgments

We are grateful to Leah Sinai for her help with the first movie experiment. This research was supported by the Canada Excellence Research Chairs program.

Footnotes

↵¹To whom correspondence should be addressed. Email: lorina.clare{at}gmail.com.

Author contributions: L.N., R.C., and A.M.O. designed research; L.N. and M.A. performed research; R.C. contributed new reagents/analytic tools; L.N., R.C., and M.A. analyzed data; and L.N., R.C., and A.M.O. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1407007111/-/DCSupplemental.

Freely available online through the PNAS open access option.

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(2010) The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends Cogn Sci 14(4):172–179.
OpenUrl CrossRef PubMed
↵
1. Elliott R
(2003) Executive functions and their disorders. Br Med Bull 65:49–59.
OpenUrl Abstract/FREE Full Text
↵
1. Shallice T
(1988) From Neuropsychology to Mental Structure (Cambridge Univ Press, Cambridge, UK).
↵
1. Ptak R
(2012) The frontoparietal attention network of the human brain: Action, saliency, and a priority map of the environment. Neuroscientist 18(5):502–515.
OpenUrl Abstract/FREE Full Text
↵
1. Woolgar A,
2. et al.
(2010) Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex. Proc Natl Acad Sci USA 107(33):14899–14902.
OpenUrl Abstract/FREE Full Text
↵
1. Owen AM,
2. Downes JJ,
3. Sahakian BJ,
4. Polkey CE,
5. Robbins TW
(1990) Planning and spatial working memory following frontal lobe lesions in man. Neuropsychologia 28(10):1021–1034.
OpenUrl CrossRef PubMed
↵
1. Barbey AK,
2. et al.
(2012) An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain 135(Pt 4):1154–1164.
OpenUrl Abstract/FREE Full Text
↵
1. Hampshire A,
2. Owen AM
(2006) Fractionating attentional control using event-related fMRI. Cereb Cortex 16(12):1679–1689.
OpenUrl Abstract/FREE Full Text
↵
1. Sauseng P,
2. Klimesch W,
3. Schabus M,
4. Doppelmayr M
(2005) Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. Int J Psychophysiol 57(2):97–103.
OpenUrl CrossRef PubMed
↵
1. Hasson U,
2. Malach R,
3. Heeger DJ
(2010) Reliability of cortical activity during natural stimulation. Trends Cogn Sci 14(1):40–48.
OpenUrl CrossRef PubMed
↵
1. Hasson U,
2. et al.
(2004) Neurocinematics: The neuroscience of film. Projections 2(1):1–26.
OpenUrl
↵
1. Hasson U,
2. Nir Y,
3. Levy I,
4. Fuhrmann G,
5. Malach R
(2004) Intersubject synchronization of cortical activity during natural vision. Science 303(5664):1634–1640.
OpenUrl Abstract/FREE Full Text
↵
1. Calhoun VD,
2. Liu J,
3. Adali T
(2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(Suppl 1):S163–S172.
OpenUrl CrossRef PubMed
↵
1. Beckmann CF,
2. Smith SM
(2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25(1):294–311.
OpenUrl CrossRef PubMed
↵
1. Beckmann CF,
2. Smith SM
(2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23(2):137–152.
OpenUrl CrossRef PubMed
↵
1. Pettit LD,
2. et al.
(2013) Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis. Brain 136(Pt 11):3290–3304.
OpenUrl Abstract/FREE Full Text
↵
1. Baddeley A,
2. Della Sala S,
3. Papagno C,
4. Spinnler H
(1997) Dual-task performance in dysexecutive and nondysexecutive patients with a frontal lesion. Neuropsychology 11(2):187–194.
OpenUrl CrossRef PubMed
↵
1. Vilkki J,
2. Virtanen S,
3. Surma-Aho O,
4. Servo A
(1996) Dual task performance after focal cerebral lesions and closed head injuries. Neuropsychologia 34(11):1051–1056.
OpenUrl CrossRef PubMed
↵
1. Mizuno K,
2. Tanaka M,
3. Tanabe HC,
4. Sadato N,
5. Watanabe Y
(2012) The neural substrates associated with attentional resources and difficulty of concurrent processing of the two verbal tasks. Neuropsychologia 50(8):1998–2009.
OpenUrl CrossRef PubMed
↵
1. Collette F,
2. et al.
(2005) Involvement of both prefrontal and inferior parietal cortex in dual-task performance. Brain Res Cogn Brain Res 24(2):237–251.
OpenUrl CrossRef PubMed
↵
1. D’Esposito M,
2. et al.
(1995) The neural basis of the central executive system of working memory. Nature 378(6554):279–281.
OpenUrl CrossRef PubMed
↵
1. Robertson IH,
2. Manly T,
3. Andrade J,
4. Baddeley BT,
5. Yiend J
(1997) ‘Oops!’: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia 35(6):747–758.
OpenUrl CrossRef PubMed
↵
1. Datta A,
2. et al.
(2007) The p300 as a marker of waning attention and error propensity. Comput Intell Neurosci 2007:1–9.
OpenUrl
↵
1. Whyte J,
2. Grieb-Neff P,
3. Gantz C,
4. Polansky M
(2006) Measuring sustained attention after traumatic brain injury: Differences in key findings from the sustained attention to response task (SART) Neuropsychologia 44(10):2007–2014.
OpenUrl CrossRef PubMed
↵
1. Manly T,
2. Robertson IH,
3. Galloway M,
4. Hawkins K
(1999) The absent mind: Further investigations of sustained attention to response. Neuropsychologia 37(6):661–670.
OpenUrl CrossRef PubMed
↵
1. The Multi-Society Task Force on PVS
(1994) Medical aspects of the persistent vegetative state (1) N Engl J Med 330(21):1499–1508.
OpenUrl CrossRef PubMed
↵
1. Giacino JT,
2. et al.
(2002) The minimally conscious state: Definition and diagnostic criteria. Neurology 58(3):349–353.
OpenUrl CrossRef
↵
1. Bardin JC,
2. Schiff ND,
3. Voss HU
(2012) Pattern classification of volitional functional magnetic resonance imaging responses in patients with severe brain injury. Arch Neurol 69(2):176–181.
OpenUrl CrossRef PubMed
↵
1. Coleman MR,
2. et al.
(2009) Towards the routine use of brain imaging to aid the clinical diagnosis of disorders of consciousness. Brain 132(Pt 9):2541–2552.
OpenUrl Abstract/FREE Full Text
↵
1. Davis MH,
2. et al.
(2007) Dissociating speech perception and comprehension at reduced levels of awareness. Proc Natl Acad Sci USA 104(41):16032–16037.
OpenUrl Abstract/FREE Full Text
↵
1. Soon CS,
2. Brass M,
3. Heinze HJ,
4. Haynes JD
(2008) Unconscious determinants of free decisions in the human brain. Nat Neurosci 11(5):543–545.
OpenUrl CrossRef PubMed
↵
1. Kamitani Y,
2. Tong F
(2005) Decoding the visual and subjective contents of the human brain. Nat Neurosci 8(5):679–685.
OpenUrl CrossRef PubMed
↵
1. Naci L,
2. Cusack R,
3. Jia VZ,
4. Owen AM
(2013) The brain’s silent messenger: Using selective attention to decode human thought for brain-based communication. J Neurosci 33(22):9385–9393.
OpenUrl Abstract/FREE Full Text
↵
1. Owen AM,
2. Naci L
, Decoding thoughts in behaviorally non-responsive patients. Consciousness Without Overt Behavior (Duke Univ Press), in press.
↵
1. Naci L,
2. et al.
(2012) Brain-computer interfaces for communication with nonresponsive patients. Ann Neurol 72(3):312–323.
OpenUrl CrossRef PubMed
↵
1. Boly M,
2. et al.
(2011) Preserved feedforward but impaired top-down processes in the vegetative state. Science 332(6031):858–862.
OpenUrl Abstract/FREE Full Text
↵
1. Casali AG,
2. et al.
(2013) A theoretically based index of consciousness independent of sensory processing and behaviour. Sci Transl Med 5(198):198ra105.
OpenUrl Abstract/FREE Full Text
↵
1. Chung CS,
2. Pollock A,
3. Campbell T,
4. Durward BR,
5. Hagen S
(2013) Cognitive rehabilitation for executive dysfunction in adults with stroke or other adult non-progressive acquired brain damage. Cochrane Database Syst Rev 4:CD008391.
OpenUrl PubMed
↵
1. McDowell S,
2. Whyte J,
3. D’Esposito M
(1997) Working memory impairments in traumatic brain injury: Evidence from a dual-task paradigm. Neuropsychologia 35(10):1341–1353.
OpenUrl CrossRef PubMed
↵
1. Schnakers C,
2. et al.
(2009) Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment. BMC Neurol 9(1):35.
OpenUrl CrossRef PubMed

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Proceedings of the National Academy of Sciences: 111 (39)

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Article Classifications

Biological Sciences
Neuroscience

[1] ↵
Plum F,
Posner JB
(1966) The Diagnosis of Stupor and Coma (F.A. Davis Company, Philadelphia), 1st Ed.

[2] Plum F,

[3] Posner JB

[4] ↵
Naci L,
Owen AM
(2013) Making every word count for nonresponsive patients. JAMA Neurol 70(10):1235–1241.
OpenUrl PubMed

[5] Naci L,

[6] Owen AM

[7] ↵
Fernández-Espejo D,
Owen AM
(2013) Detecting awareness after severe brain injury. Nat Rev Neurosci 14(11):801–809.
OpenUrl CrossRef PubMed

[8] Fernández-Espejo D,

[9] Owen AM

[10] ↵
Chennu S,
et al.
(2013) Dissociable endogenous and exogenous attention in disorders of consciousness. Neuroimage Clin 3:450–461.
OpenUrl CrossRef PubMed

[11] Chennu S,

[12] et al.

[13] ↵
Cruse D,
et al.
(2012) Detecting awareness in the vegetative state: Electroencephalographic evidence for attempted movements to command. PLoS ONE 7(11):e49933.
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[14] Cruse D,

[15] et al.

[16] ↵
Cruse D,
et al.
(2011) Bedside detection of awareness in the vegetative state: A cohort study. Lancet 378(9809):2088–2094.
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[17] Cruse D,

[18] et al.

[19] ↵
Monti MM,
et al.
(2010) Willful modulation of brain activity in disorders of consciousness. N Engl J Med 362(7):579–589.
OpenUrl CrossRef PubMed

[20] Monti MM,

[21] et al.

[22] ↵
Owen AM,
et al.
(2006) Detecting awareness in the vegetative state. Science 313(5792):1402.
OpenUrl Abstract/FREE Full Text

[23] Owen AM,

[24] et al.

[25] ↵
Duncan J
(2010) The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends Cogn Sci 14(4):172–179.
OpenUrl CrossRef PubMed

[26] Duncan J

[27] ↵
Elliott R
(2003) Executive functions and their disorders. Br Med Bull 65:49–59.
OpenUrl Abstract/FREE Full Text

[28] Elliott R

[29] ↵
Shallice T
(1988) From Neuropsychology to Mental Structure (Cambridge Univ Press, Cambridge, UK).

[30] Shallice T

[31] ↵
Ptak R
(2012) The frontoparietal attention network of the human brain: Action, saliency, and a priority map of the environment. Neuroscientist 18(5):502–515.
OpenUrl Abstract/FREE Full Text

[32] Ptak R

[33] ↵
Woolgar A,
et al.
(2010) Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex. Proc Natl Acad Sci USA 107(33):14899–14902.
OpenUrl Abstract/FREE Full Text

[34] Woolgar A,

[35] et al.

[36] ↵
Owen AM,
Downes JJ,
Sahakian BJ,
Polkey CE,
Robbins TW
(1990) Planning and spatial working memory following frontal lobe lesions in man. Neuropsychologia 28(10):1021–1034.
OpenUrl CrossRef PubMed

[37] Owen AM,

[38] Downes JJ,

[39] Sahakian BJ,

[40] Polkey CE,

[41] Robbins TW

[42] ↵
Barbey AK,
et al.
(2012) An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain 135(Pt 4):1154–1164.
OpenUrl Abstract/FREE Full Text

[43] Barbey AK,

[44] et al.

[45] ↵
Hampshire A,
Owen AM
(2006) Fractionating attentional control using event-related fMRI. Cereb Cortex 16(12):1679–1689.
OpenUrl Abstract/FREE Full Text

[46] Hampshire A,

[47] Owen AM

[48] ↵
Sauseng P,
Klimesch W,
Schabus M,
Doppelmayr M
(2005) Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. Int J Psychophysiol 57(2):97–103.
OpenUrl CrossRef PubMed

[49] Sauseng P,

[50] Klimesch W,

[51] Schabus M,

[52] Doppelmayr M

[53] ↵
Hasson U,
Malach R,
Heeger DJ
(2010) Reliability of cortical activity during natural stimulation. Trends Cogn Sci 14(1):40–48.
OpenUrl CrossRef PubMed

[54] Hasson U,

[55] Malach R,

[56] Heeger DJ

[57] ↵
Hasson U,
et al.
(2004) Neurocinematics: The neuroscience of film. Projections 2(1):1–26.
OpenUrl

[58] Hasson U,

[59] et al.

[60] ↵
Hasson U,
Nir Y,
Levy I,
Fuhrmann G,
Malach R
(2004) Intersubject synchronization of cortical activity during natural vision. Science 303(5664):1634–1640.
OpenUrl Abstract/FREE Full Text

[61] Hasson U,

[62] Nir Y,

[63] Levy I,

[64] Fuhrmann G,

[65] Malach R

[66] ↵
Calhoun VD,
Liu J,
Adali T
(2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(Suppl 1):S163–S172.
OpenUrl CrossRef PubMed

[67] Calhoun VD,

[68] Liu J,

[69] Adali T

[70] ↵
Beckmann CF,
Smith SM
(2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25(1):294–311.
OpenUrl CrossRef PubMed

[71] Beckmann CF,

[72] Smith SM

[73] ↵
Beckmann CF,
Smith SM
(2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23(2):137–152.
OpenUrl CrossRef PubMed

[74] Beckmann CF,

[75] Smith SM

[76] ↵
Pettit LD,
et al.
(2013) Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis. Brain 136(Pt 11):3290–3304.
OpenUrl Abstract/FREE Full Text

[77] Pettit LD,

[78] et al.

[79] ↵
Baddeley A,
Della Sala S,
Papagno C,
Spinnler H
(1997) Dual-task performance in dysexecutive and nondysexecutive patients with a frontal lesion. Neuropsychology 11(2):187–194.
OpenUrl CrossRef PubMed

[80] Baddeley A,

[81] Della Sala S,

[82] Papagno C,

[83] Spinnler H

[84] ↵
Vilkki J,
Virtanen S,
Surma-Aho O,
Servo A
(1996) Dual task performance after focal cerebral lesions and closed head injuries. Neuropsychologia 34(11):1051–1056.
OpenUrl CrossRef PubMed

[85] Vilkki J,

[86] Virtanen S,

[87] Surma-Aho O,

[88] Servo A

[89] ↵
Mizuno K,
Tanaka M,
Tanabe HC,
Sadato N,
Watanabe Y
(2012) The neural substrates associated with attentional resources and difficulty of concurrent processing of the two verbal tasks. Neuropsychologia 50(8):1998–2009.
OpenUrl CrossRef PubMed

[90] Mizuno K,

[91] Tanaka M,

[92] Tanabe HC,

[93] Sadato N,

[94] Watanabe Y

[95] ↵
Collette F,
et al.
(2005) Involvement of both prefrontal and inferior parietal cortex in dual-task performance. Brain Res Cogn Brain Res 24(2):237–251.
OpenUrl CrossRef PubMed

[96] Collette F,

[97] et al.

[98] ↵
D’Esposito M,
et al.
(1995) The neural basis of the central executive system of working memory. Nature 378(6554):279–281.
OpenUrl CrossRef PubMed

[99] D’Esposito M,

[100] et al.

[101] ↵
Robertson IH,
Manly T,
Andrade J,
Baddeley BT,
Yiend J
(1997) ‘Oops!’: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia 35(6):747–758.
OpenUrl CrossRef PubMed

[102] Robertson IH,

[103] Manly T,

[104] Andrade J,

[105] Baddeley BT,

[106] Yiend J

[107] ↵
Datta A,
et al.
(2007) The p300 as a marker of waning attention and error propensity. Comput Intell Neurosci 2007:1–9.
OpenUrl

[108] Datta A,

[109] et al.

[110] ↵
Whyte J,
Grieb-Neff P,
Gantz C,
Polansky M
(2006) Measuring sustained attention after traumatic brain injury: Differences in key findings from the sustained attention to response task (SART) Neuropsychologia 44(10):2007–2014.
OpenUrl CrossRef PubMed

[111] Whyte J,

[112] Grieb-Neff P,

[113] Gantz C,

[114] Polansky M

[115] ↵
Manly T,
Robertson IH,
Galloway M,
Hawkins K
(1999) The absent mind: Further investigations of sustained attention to response. Neuropsychologia 37(6):661–670.
OpenUrl CrossRef PubMed

[116] Manly T,

[117] Robertson IH,

[118] Galloway M,

[119] Hawkins K

[120] ↵
The Multi-Society Task Force on PVS
(1994) Medical aspects of the persistent vegetative state (1) N Engl J Med 330(21):1499–1508.
OpenUrl CrossRef PubMed

[121] The Multi-Society Task Force on PVS

[122] ↵
Giacino JT,
et al.
(2002) The minimally conscious state: Definition and diagnostic criteria. Neurology 58(3):349–353.
OpenUrl CrossRef

[123] Giacino JT,

[124] et al.

[125] ↵
Bardin JC,
Schiff ND,
Voss HU
(2012) Pattern classification of volitional functional magnetic resonance imaging responses in patients with severe brain injury. Arch Neurol 69(2):176–181.
OpenUrl CrossRef PubMed

[126] Bardin JC,

[127] Schiff ND,

[128] Voss HU

[129] ↵
Coleman MR,
et al.
(2009) Towards the routine use of brain imaging to aid the clinical diagnosis of disorders of consciousness. Brain 132(Pt 9):2541–2552.
OpenUrl Abstract/FREE Full Text

[130] Coleman MR,

[131] et al.

[132] ↵
Davis MH,
et al.
(2007) Dissociating speech perception and comprehension at reduced levels of awareness. Proc Natl Acad Sci USA 104(41):16032–16037.
OpenUrl Abstract/FREE Full Text

[133] Davis MH,

[134] et al.

[135] ↵
Soon CS,
Brass M,
Heinze HJ,
Haynes JD
(2008) Unconscious determinants of free decisions in the human brain. Nat Neurosci 11(5):543–545.
OpenUrl CrossRef PubMed

[136] Soon CS,

[137] Brass M,

[138] Heinze HJ,

[139] Haynes JD

[140] ↵
Kamitani Y,
Tong F
(2005) Decoding the visual and subjective contents of the human brain. Nat Neurosci 8(5):679–685.
OpenUrl CrossRef PubMed

[141] Kamitani Y,

[142] Tong F

[143] ↵
Naci L,
Cusack R,
Jia VZ,
Owen AM
(2013) The brain’s silent messenger: Using selective attention to decode human thought for brain-based communication. J Neurosci 33(22):9385–9393.
OpenUrl Abstract/FREE Full Text

[144] Naci L,

[145] Cusack R,

[146] Jia VZ,

[147] Owen AM

[148] ↵
Owen AM,
Naci L
, Decoding thoughts in behaviorally non-responsive patients. Consciousness Without Overt Behavior (Duke Univ Press), in press.

[149] Owen AM,

[150] Naci L

[151] ↵
Naci L,
et al.
(2012) Brain-computer interfaces for communication with nonresponsive patients. Ann Neurol 72(3):312–323.
OpenUrl CrossRef PubMed

[152] Naci L,

[153] et al.

[154] ↵
Boly M,
et al.
(2011) Preserved feedforward but impaired top-down processes in the vegetative state. Science 332(6031):858–862.
OpenUrl Abstract/FREE Full Text

[155] Boly M,

[156] et al.

[157] ↵
Casali AG,
et al.
(2013) A theoretically based index of consciousness independent of sensory processing and behaviour. Sci Transl Med 5(198):198ra105.
OpenUrl Abstract/FREE Full Text

[158] Casali AG,

[159] et al.

[160] ↵
Chung CS,
Pollock A,
Campbell T,
Durward BR,
Hagen S
(2013) Cognitive rehabilitation for executive dysfunction in adults with stroke or other adult non-progressive acquired brain damage. Cochrane Database Syst Rev 4:CD008391.
OpenUrl PubMed

[161] Chung CS,

[162] Pollock A,

[163] Campbell T,

[164] Durward BR,

[165] Hagen S

[166] ↵
McDowell S,
Whyte J,
D’Esposito M
(1997) Working memory impairments in traumatic brain injury: Evidence from a dual-task paradigm. Neuropsychologia 35(10):1341–1353.
OpenUrl CrossRef PubMed

[167] McDowell S,

[168] Whyte J,

[169] D’Esposito M

[170] ↵
Schnakers C,
et al.
(2009) Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment. BMC Neurol 9(1):35.
OpenUrl CrossRef PubMed

[171] Schnakers C,

[172] et al.

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A common neural code for similar conscious experiences in different individuals

Significance

Abstract

Results

Similar Neural Correlates of Conscious Experience Across Different Individuals.

Neural Basis of Executive Processes Common to Different Individuals.

Decoding Conscious Experiences in Behaviorally Nonresponsive Patients.

Discussion

Methods

Participants.

Procedure and Design.

Data Acquisition and Analysis of fMRI Time Series.

Behavioral Analyses.

Acknowledgments

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A common neural code for similar conscious experiences in different individuals

Significance

Abstract

Results

Similar Neural Correlates of Conscious Experience Across Different Individuals.

Neural Basis of Executive Processes Common to Different Individuals.

Decoding Conscious Experiences in Behaviorally Nonresponsive Patients.

Discussion

Methods

Participants.

Procedure and Design.

Data Acquisition and Analysis of fMRI Time Series.

Behavioral Analyses.

Acknowledgments

Footnotes

References

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