Differential Cortical Network Engagement During States of Un/Consciousness

doi:10.21203/rs.3.rs-2006868/v1

Download PDF

Article

Differential Cortical Network Engagement During States of Un/Consciousness

https://doi.org/10.21203/rs.3.rs-2006868/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 31 Aug, 2023

Read the published version in Neuron →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

The neural basis of consciousness remains a major unresolved issue in human neuroscience. To better understand how cortical networks are engaged during different states of consciousness, we utilized multi-region intracranial single-pulse direct electrical stimulation to examine circuit and network interactions during three canonical states of consciousness: wake, sleep, and under propofol-induced general anesthesia. Increased variability in cortical responses, reduced information transfer, and reduced complexity characterized states of diminished consciousness. Notably, however, these metrics differed in different brain regions and types of unconscious states. Anesthesia induced more overall changes in brain responses than sleep. Brain activity changes were largely anatomically uniform during sleep, contrasting with a substantial and selective disconnection of the prefrontal cortices during anesthesia. These results suggest that the obliteration of consciousness during anesthesia results not from just altered overall physiology but from a disconnection between prefrontal areas and other brain areas.

Consciousness

Cortico-cortical evoked potential

Intracranial EEG

Propofol-induced general anesthesia

Sleep

Sleep and anesthesia showed decreased complexity, connectivity, and response amplitude with increased response variability compared to wake states in the human brain.
These changes in brain response to stimulation were more pronounced during propofol-induced general anesthesia than natural sleep.
During sleep, changes were homogeneously distributed across the brain.
During anesthesia, there was a substantial disconnection of the frontal cortices.

Understanding the neuronal mechanisms underlying consciousness is one of the greatest challenges in human neuroscience. Despite decades of research, there are more controversies than agreements. One of the most powerful ways to study consciousness is to compare the intra-individual physiology of the conscious awake state with two of the most obvious non-pathological unconscious states: natural sleep and general anesthesia. Dissecting similarities and differences between these uniquely defined states at the neural network level and the corresponding regional involvement in the human brain is paramount to obtaining a deeper understanding of consciousness.

Theories of consciousness^1–7 and experimental studies^8–10 differ concerning which brain regions are necessary for consciousness. In particular, the role of the prefrontal cortex (PFC) in consciousness is at the center of avid debate^11,12. On the one hand, the Global Neuronal Workspace (GNW³) hypothesis proposes that an individual only becomes conscious when information is widely broadcasted across the brain, with the PFC being a central, albeit not the exclusive, node³. On the other hand, Information Integration Theory (IIT^6,13,14) and its corollaries propose that the PFC is not necessary for consciousness but instead postulate that a posterior hot spot, comprised of temporoparietal-occipital regions, is the fundamental nexus^1,2,11. Direct experimental evidence to resolve this debate requires identifying the global, network, and regional involvement during wake vs. arousable unconsciousness (e.g., sleep) vs. non-arousable unconsciousness (e.g., propofol-induced general anesthesia). Despite both being states of unconsciousness and being similar in some physiological parameters, they also have important differences^15–17. In both states we are unresponsive, and both are reversible. However, we are arousable during sleep but not during anesthesia. One challenge is understanding whether anesthesia is sleep amplified (a “deep sleep”) or if different neuronal mechanisms are involved. If the former, similar neural networks should be disrupted under both conditions, albeit to a different degree. If the latter, cortical regions would be differentially altered between the two states.

A robust approach for understanding brain networks and their sensitivity to state is to examine responses to perturbations. This can be done safely and in a controlled fashion using direct electrical stimulation (DES). Moreover, as brain stimulation is increasingly used as a chronic therapeutic approach in neurological^18–24 and neuropsychiatric disorders^25–28, understanding the brain’s response to stimulation during different brain states is important in its own right. Since Penfield’s seminal mapping of the homunculus²⁹, DES with simultaneous intracranial EEG recordings has become invaluable as a research and clinical tool. Brief single pulses of electrical stimulation (SPES³⁰) produce consistent cortico-cortical evoked potentials (CCEP^31,32). CCEP connectivity measures the way the response to stimulation spreads³³, likely a combination of anatomical and functional networks³⁴, revealing human brain connections^35,36, epileptic networks^37–40, and allowing intraoperative language mapping⁴¹.

Studies to date aimed at differentiating consciousness from altered consciousness states^42–45, implicitly focusing on common characteristics across unconscious states during spontaneous activity⁴² or following stimulation^45–48. Interestingly, details of those studies suggest potential differences across types of loss of consciousness. Taking this into account as well as the general theories of consciousness^1–6, animal stimulation studies^8,9,49,50 we hypothesize that the brain’s response to SPES perturbations is different while awake, during natural sleep, and under propofol-induced general anesthesia. We also hypothesize that the differences between arousable vs. non-arousable unconsciousness are region specific. Specifically, we hypothesize that the PFC will be less responsive to SPES during anesthesia than during sleep, while posterior regions will be similarly affected regardless of the cause of loss of consciousness (LOC).

We probe human neural reactivity with SPES while recording via intracranial electrodes to infer the global, network, and intrinsic neural correlates of un/consciousness. Specifically, complexity, a surrogate of contextual consciousness, summarizes the brain’s global response to stimulation^43,51,52. CCEP connectivity, sometimes referred to as effective connectivity^33,53, represents stimulation-evoked networks of information transfer^36,54. Intrinsic characteristics were measured by the amplitude and latency of the responses, as well as inter-stimulation variability, a measure of underlying stability^55–57.

Global complexity, stimulation connectivity, and the response’s amplitude were reduced while variability increased during both unconscious states, compared to wake. During sleep, changes were mostly homogenous across brain regions. In contrast, during anesthesia, the PFC was the most disrupted, with a deepened decrease in complexity and CCEP response and increase in variability. These results provide direct evidence of increased cortical variability, reduced information transfer, and reduced complex neural responses during LOC from human intracranial recordings. However, these metrics differ depending on arousable (sleep) vs. unarousable (anesthesia) LOC as well as brain regions examined.

Pseudo-random multi-region intracranial SPES (at seven mA, biphasic, bipolar, charge-balanced, duration 233 µsec) was delivered while simultaneously recording intracranial EEG (iEEG) in 20 patients with semi-chronic depth-electrodes implanted for clinical reasons to localize the focus of their seizures (median age= 37.5, range: [18 61]; 10 women). SPES was delivered in the epilepsy monitoring unit (EMU) while participants were awake (Wake_EMU; N=17) or asleep (Sleep; N=14) and during the electrode explanation procedure in the operating room (OR) while awake (Wake_OR; N=16) and following propofol-induced general anesthesia (Anesthesia; N=14). Wake_EMU and wake_OR corresponded to conscious states under different environments; a normalizing element to allow direct comparison of sleep vs. anesthesia.

Only contacts outside the clinically determined epileptic network were included. After excluding stimulation channels that did not evoke CCEPs (see “Methods - Channel selection”), a total of s=115 stimulation channels (median: 5 range: [1 12] per participant) and r=2413 bipolar recording channels (120 [29 177] per participant) were included across the 20 participants (Table S1; distribution in Fig. 1.a & Fig. S1). Brain’s responses were compared during wake_EMU vs. sleep (N=13, s=99, r=1618), wake_OR vs. anesthesia (N=14, s=35, r=1736), and wake_EMU vs. wake_OR (N=13, s=34, r=1534). In seven participants, SPES was delivered during all four states (s=19; r= 941).

1. Brain responses decreased and variability increased during loss of consciousness.

Conscious (wake_EMU and wake_OR) and unconscious states (sleep and anesthesia) were compared with global complexity, CCEP network connectivity, CCEP response features, and response variability (Fig. 1). We chose these measures as they are highly representative of overall, network, and local circuit level responses likely to be altered differently.

Complexity, a measure of overall information integration, was estimated with the perturbation complexity index (PCI⁴³) adapted for intracranial or sparse recordings (PCI^st ⁵¹). Complexity was reduced during sleep compared to wake_EMU (p<<0.001, m_WakeEMU= 49.9, m_Sleep= 30.9, s=99; Wilcoxon test; Table S2; Fig 2.a & 2.c) in participants whose recordings were acquired in the EMU. The same was true, in the OR, for anesthesia compared to wake_OR (p<<0.001, m_WakeOR= 51.8, m_Anesth=24.7, s=35). There was no significant difference between wake_OR and wake_EMU (p= 0.12, m_WakeEMU=53.6, m_WakeOR=51.3, s=34). The maximum PCI^st value per participant and state, corresponding to the stimulation site producing the most complex response⁵⁸, was higher in wakefulness than either sleep (p=0.0002, m_WakeEMU=70.3, m_Sleep=42.7, N=13) or anesthesia (p=0.0001, m_WakeOR=66.3, m_Anest=29.2, N=14; Wilcoxon test; Fig. 2.d & S2). There was no significant difference between wake states(p=0.22; N=13). These differences in complexity suggest that overall complex neural processing was disrupted during both sleep and anesthesia.

To understand how stimulation connectivity, which represents network information transfer³³, was affected by state, we compared the percentage of recording channels with CCEP responses during each state (Fig. 1.b-e). For each stimulation site, channels with CCEP responses were channels with peak amplitude or area under the curve larger than a null distribution created from baseline segments (see “Methods – CCEP connectivity” for details). CCEP connectivity was reduced during sleep compared to wake_EMU(p<<0.0001, m_WakeEMU=8%, m_Sleep=6%, s=99, Wilcoxon test) and during anesthesia compared to wake_OR (p<<0.0001, m_WakeOR=7%, m_Anesthesia=2%, s=35; Table S5; Fig 2.e). Again, there was no statistical difference between wake_OR and wake_EMU(p=0.49, m_WakeOR=8%, m_WakeEMU=8%, s=34).

The variability of the responses following stimulation was measured as the median absolute deviation (MAD) for individual trials, averaged across trials (Fig. 2.b). Overall, response variability was larger for unconscious compared to conscious states. Variability was significantly higher during sleep than wake_EMU (p<<0.001, m_WakeEMU=17.4, m_Sleep=28.7; r=1472 recording channels with CCEP response in any state; permutation test) and during anesthesia than wake_OR (p<<0.001, m_WakeOR=15.4, m_Anesthesia=27.7, r=535; Table S8; Fig. 2.f). There was no significant difference between wake_EMU and wake_OR (p=0.12; r=632).

The peak-to-peak amplitude (P2P) and the latency of the first peak of the CCEP responses were computed as well. Overall, P2P was smaller for unconscious compared to conscious states. There were significant differences between wake_OR and anesthesia (p<<0.001, m_WakeOR=10.3, m_Anesthesia=5.1, r=99 recording channels with CCEP response in both states, permutation test) and between wake_EMU and sleep (p<<0.001, m_WakeEMU=9.2, m_Sleep=5.6; r=654; Table S11; Fig 2.g). Surprisingly, the responses’ P2P amplitudes during wake in the EMU and OR were also significantly different (p<<0.001; m_WakeEMU=8.5, m_WakeOR=7.0; r=338). A closer look at the anatomical distribution showed that this difference was driven by temporal lobe channels (Table S11; Fig. 3.g), likely due to the auditory task used to assess LOC in the OR. There was no significant difference in the latency of the first peak for any of the comparisons including regional partitions (Table S11; Fig. 3.h).

2. Brain responses are more disrupted during anesthesia than during sleep

The preceding comparisons focused on the physiology of conscious vs. both unconscious states. We now compare arousable (sleep) and unarousable (under anesthesia) states of unconsciousness. To allow a direct comparison between sleep and anesthesia, relative measures were computed by normalizing to their corresponding wake state (see “Methods – Relative measures” for details). These normalized measures, bounded between -1 and +1 with zero representing no difference between states, controlled for heterogeneities due to patient etiologies, electrode location, recording environments, head position, and medication levels.

The relative complexity for sleep and for anesthesia compared to wake in the same participant and environment was negative for most stimulated channels (Fig 3.a), in line with complexity reduction during unconscious states (Fig 2.c). The median relative complexity of wake states, which corresponds to the difference between wake in different environments, was zero, indicating similar overall complexity, but the variance was large, emphasizing the importance of using normalized measures to compare sleep and anesthesia. Overall, relative complexity in anesthesia was significantly lower than during sleep (unpaired Mann-Whitney U-test: p=0.001, m_RelSleep=-0.26, m_RelAnesth=-0.38, n_Sleep=99, n_Anesth=35; paired Wilcoxon test: p=0.005, s=19; Table S3; Fig 3.a).

The relative CCEP connectivity was also mainly negative for sleep and anesthesia, indicating fewer connections when not awake (Fig. 3.c), in line with CCEP connectivity decrease during unconscious states (Fig. 2.e). The median relative CCEP connectivity between wake states in different environments was zero, suggesting similar overall effective connectivity, but with large variance. Again, this measure of relative connectivity was not equivalent for both unconscious states. The relative CCEP connectivity was significantly smaller for anesthesia than for sleep (unpaired Mann-Whitney U-test: p<<0.001, m_RelSleep= -0.14, m_RelAnesth= -0.60, n_Sleep= 99, n_Anest= 35; paired Wilcoxon test: p=0.0004, s=19; Table S6; Fig. 3.c).

When analyzing relative CCEP features, only channels with responses in all states were considered. The median relative P2P amplitude for sleep and anesthesia was negative (Fig. 3.e), indicating diminished CCEP responses, in line with decreased P2P during unconsciousness (Fig 2.g). There was a large variance of relative P2P for wake states. The relative P2P was significantly smaller for anesthesia than for sleep (p <<0.001, m_RelSleep=-0.23, m_RelAnest=-0.37; r_RelSleep=654, r_RelAnest=99; permutation test; Fig. 3.e; Table S12).

The relative variability was mostly positive for anesthesia and sleep (Fig. 3.g), in line with the variability increase during unconsciousness (Fig. 2.f). The median variability between wake states was zero, but with wide distribution dispersion. Variability was significantly larger for anesthesia than for sleep (p=0.03; m_RelSleep= 0.26, m_RelAnest= 0.33; r_Sleep=1472, r_Anest=535; permutation test; Fig. 3.g; Table S9).

Together, these relative measures allowed a direct comparison of arousable vs. unarousable states of unconsciousness. These results suggest that although there was loss of information in any form of unconsciousness, this was more profound with anesthesia than with sleep. However, there was more variance in the aggregate responses than expected, and we wondered if regional differences could explain this.

3. The anatomical distribution of differs between sleep and anesthesia.

To understand whether decreased complexity, connectivity, and response amplitude, as well as increased variability, were uniformly distributed across the brain or if they were defined anatomically, these relative measures were visualized on a template brain^59,60. Anatomical regions were parcellated in each participant’s native space, relative measures averaged for each sub-lobular region, and plotted on the stereotactic brain surface.

Complexity was uniformly reduced across brain regions for sleep but not for anesthesia. In anesthesia, the frontal and temporal lobes were especially affected (Fig. 3.b). In terms of CCEP connectivity, there was a greater reduction of relative CCEP connectivity during anesthesia than during sleep across the brain. This reduced CCEP connectivity during anesthesia was more pronounced for stimulation in frontal and temporal channels than for posterior channels (Fig 3.d).

P2P amplitude of the responses was uniformly reduced during sleep compared to wake. During anesthesia, there was a more considerable reduction in frontal regions than in any other region (Fig. 3.f). Note that P2P was computed on the detected CCEP responses, which were generally uncommon during anesthesia in temporal and frontal regions (Fig 3.d). The P2P differed between wake_EMU and wake_OR in temporal regions (Table S14), further emphasizing the importance of comparing conscious vs. unconscious states within the same environment.

Relative variability uniformly increased across responsive channels during sleep. However, during anesthesia, there was an apparent dichotomy between anterior and posterior regions, with a marked increase in variability in the PFC. In contrast, no increase was observed in the posterior or temporal regions (Fig. 3.h).

4. The PFC was particularly affected during anesthesia.

Not all brain regions were altered by the different states of consciousness equally. The PFC was particularly interesting as the four measures differentiated anesthesia from sleep (Fig. 3). The PFC (including lateral and orbitofrontal cortices) was compared to posterior (posterior cingulate, parietal, and occipital cortices, as part of the posterior hot zone^2,61) and temporal (mesial and lateral temporal) regions.

During sleep, compared to wake_EMU, activity across brain regions was predominately uniformly altered (Fig. 3). Complexity decreased during sleep irrespective of where in the brain stimulation was delivered (Fig. 3.b; Table S2). There was no significant difference when comparing PFC to posterior or temporal regions (Fig. 4.a; Table S4). CCEP connectivity was slightly different. It was lower during sleep than wake_EMU for PFC (p=0.0002, s=30; Table S5) and temporal lobe (p=0.002, s=30), but not for posterior stimulation (p=0.22, s=23) and relative connectivity was lower for PFC stimulation compared to posterior stimulation (Fig. 4.b; Table S7). Even though there were regional differences in the CCEP connectivity, the P2P amplitudes and the latency of those responses were similar regardless of the recorded region (Fig. 4.d; Tables S11, S13). Similarly, variability was higher regardless of the response region with only small regional differences (Tables S8, S10, Fig. 3.h). Thus, there were small differences across regions but with an overall homogenous disengagement during sleep.

In contrast, during anesthesia, the PFC was exceptionally affected across measures. Complexity, CCEP connectivity, and P2P amplitude all decreased more for PFC stimulation than posterior stimulation (Tables S4, S7, S13, Fig. 4). Inter-stimulation variability of the recording channels was significantly larger when comparing anesthesia vs. wake_OR for PFC (p<<0.001, r=239; Table S8) and temporal lobe (p=0.007, r=73) but not for posterior recordings (p=0.65, r=109). Relative variability was also higher in the PFC than in posterior or temporal regions (Fig. 4.e; Table S10).

Most importantly, when directly comparing sleep and anesthesia with normalized measures, the relative complexity was lower for anesthesia than for sleep for PFC but not for posterior or temporal regions (U-Mann test: p=0.01, n_Sleep =33, n_Anesth =16; Wilcoxon test: p=0.03, s=7; Fig 4.a, Table S3). The relative CCEP connectivity was lower for anesthesia than for sleep for each stimulated region, but it was the lowest for PFC stimulation (Fig. 4.b; Table S6). The P2P amplitude of those CCEPs was significantly smaller under anesthesia than during sleep only in the PFC (Fig. 4.d Table S13). The relative variability was higher during anesthesia than sleep when recording in PFC (p<<0.001, r_Sleep=497, r_Anest=239; Fig. 4.C; Table S9) while the opposite was observed for posterior and temporal channels, for which the relative variability was lower during anesthesia than sleep (posterior: p<<0.001, r_Sleep=284, r_Anest=109; temporal: p<<0.001, r_Sleep=457, r_Anest=73; Fig. 4.C, Table S9). For posterior channels, there was also a large spread of values (Fig. 4.C). These differences in regional relative variability could explain why the overall relative variability was only p=0.03 between sleep and anesthesia (Fig. 3.f). Thus, the PFC was specifically disconnected during anesthesia, but not during sleep.

We studied the electrophysiological response of the human brain to stimulation during conscious, arousable unconscious (sleep), and unarousable unconscious (propofol-induced general anesthesia) states with a 1) global measure of information integration (Complexity), 2) network measures of information transfer (CCEP connectivity and CCEP features), and 3) a measure of response stability (inter-trial variability). These measures were first compared between conscious and unconscious states. Then, relative measures were computed to directly compare arousable and unarousable unconscious states, normalizing against the environment and participant-wise heterogeneities. Finally, informed by the brain-wide distribution of the measures, prefrontal, posterior, and temporal regions were contrasted. Together our results suggest a distinct response to stimulation for different states and brain regions, with the PFC particularly affected by anesthetic drugs.

Overall complexity and connectivity decreased when consciousness was lost, responses were fewer and smaller, and variability was larger. However, these results were not identical for different states of unconsciousness. Relative measures, controlling for heterogeneities and environment, showed that these differences were more pronounced during anesthesia than sleep. These results point to an increase in response variability and a decrease in information transfer capabilities during anesthesia and align with behavioral and physiological differences between sleep and propofol-induced anesthesia^15,16,62,63. In addition, the measures showed different anatomical distributions during sleep and anesthesia. Complexity, reflecting contextual consciousness, was similarly reduced in posterior regions during sleep or anesthesia, but during anesthesia, the reduction was significantly more pronounced in the PFC. Similarly, not only was CCEP connectivity more reduced during anesthesia than sleep across all regions but the decrease was most pronounced in PFC during anesthesia. Correspondingly, variability increased uniformly during sleep, but, during anesthesia, variability was higher in the PFC.

How do we interpret these differences? The regional differences during anesthesia suggest an overall reduction of information integration, with loss of effective connections in anterior areas much more than in posterior regions. Sleep shows a more homogenous disengagement and one of lesser magnitude. The overall uniform changes during sleep suggest that even though sleep oscillations are to some extent local phenomena^64–67, the response to perturbations across the brain is similar. This might correspond to thalamocortical synchronization during sleep^68–70. In contrast, the substantial disconnection of the PFC during anesthesia, also manifested by the disappearance of correlation between complexity and connectivity (Fig. S3), may reflect thalamocortical communication disruption to frontal regions. Studies of spontaneous electrophysiology suggested that propofol disrupts thalamocortical connections^71–76 by enhancing GABA_A inhibition^62,77, imposing a unique rhythm predominantly observed on anterior scalp electrodes^71,72,78,79, with reduced synchronization⁷². This also aligns with studies that show that stimulation of the PFC prelimbic area but not stimulation of posterior regions restored consciousness in anesthetized rodents⁸. When directly stimulating the thalamus, consciousness was briefly restored in monkeys^50,80,81 and in rodent models of focal epilepsy⁸². Therapeutically, in humans, central thalamic stimulation partially restored consciousness after seizures in patients with epilepsy⁸³. In patients with disorders of consciousness^84,85, transient improvement was observed with thalamic stimulation, or when indirectly targeting the thalamus via vagal nerve stimulation^86,87 or with transcranial direct current stimulation to the PFC⁸⁸. However, other connections may play a role; for example, frontal-posterior cortico-cortical synchronization was modulated during LOC in animal models^9,50. Our data could not separate between these possible pathways, but future studies based on our protocol in participants with electrodes in the PFC and in the thalamus^89–91 could help dissociate these mechanisms in humans.

Moreover, brain regions were grouped based on insight from theories of consciousness^1–7 and animal studies^8–10. Our results suggest the overall frontal disruption during anesthesia. Statistical differences hold if we would have included the anterior cingulate and/or central regions in the frontal group. Future studies building up on ours and including a larger number of patients and perhaps higher density recordings could help further understand in more detail the subregional involvement.

Complexity and CCEP connectivity provide a single value per stimulation channel while response amplitude and variability were computed per recording channel, regardless of where stimulation was delivered. Variability is a measure that likely captures different aspects of neuronal activity than connectivity and amplitude analysis. It does not always correspond with the other measures, does not correlate with the distance between stimulation and recording (Fig. S4.a), and changes during spontaneous activity outside of stimulation (Fig. S4.b). Therefore, variability more likely reflects mechanisms downstream from the stimulation itself such as pre/post-synaptic mechanisms, and dendritic or intrinsic neuronal properties. The increase in variability observed here was present in both sleep and anesthesia but, like other measures, was most robust in PFC during anesthesia. Thus, not only is the communication from/to the PFC heavily disrupted during anesthesia but intrinsic neuronal mechanisms are as well. Thus, there is a change in information transfer and in local information processing.

Inherent to any intracranial study in patients with epilepsy are heterogeneities due to etiology, medication, brain lesions, or previous surgeries. Depth electrode spatial sampling is always limited and defined by clinical needs. Specific to SPES studies in patients with epilepsy is the possibility of evoking delayed (>100ms) interictal epileptiform activity^30,39,92. To ensure a comparison of physiological activity, we excluded channels within clinical epileptogenic networks and channels with observed post-stimulation evoked or spontaneous interictal discharges. Our strict criteria ensured the comparison of physiological activity but reduced the number of channels studied. Moreover, background noise was different in different environments (such as OR vs. EMU) and an auditory task was performed to evaluate LOC during the OR experiments, while no study-related audio was present in the EMU. To mitigate these sources of variability, we compared brain states within each patient in the same environment using relative measures. The importance of this normalization was evident in the comparison of wake in the EMU vs. OR. The amplitude of the CCEP responses and variability had wide distributions (Fig. 3.e & 3.g) and there were dissimilarities for different recording regions (Fig. 3.f & 3.h). In particular, the difference in temporal regions was likely attributed to the auditory task, while the overall dispersion was probably due to EEG noise level variations due to equipment re-connection in the OR.

Understanding the brain’s distinct response to stimulation during different states not only improves our understanding of the mechanisms of consciousness but also has therapeutic implications, as chronic neuromodulation is increasingly common for movement disorders^{18–20,93–96}, epilepsy^22,24, and psychiatric disorders^25,97,98. Moreover, deep brain stimulation using stimulation trains affects sleep^99–101. Our results showed that even a single brief electrical impulse resulted in different relative connectivity depending on the state of the brain during delivery and where it was delivered, highlighting the need for further research on the effect of stimulation on sleep and other unconscious states.

At first glance, theoretical and modeling approaches to our understanding of consciousness^3,6,102,103 seem to contradict each other, including a lively debate about whether the PFC is a fundamental node for consciousness or not^11,12. As an example of the debate, early lesion studies suggested that the PFC might not be necessary to preserve consciousness (as emphasized in^2,11). However, the extent of the lesions was argued not to be clear¹². A key aspect to consider is that the definition of consciousness might not be consistent. It is essential to separate content-specific consciousness from the possibility of being aroused from an unconscious state. Furthermore, non-invasive and animal studies suggest that LOC under anesthesia might not be a singular phenomenon but rather involve several distinct shifts in a brain state that disrupts both posterior cortical processing of the “content” of consciousness and prefrontal arousal and awareness of that content. In this regard, the posterior hot zone is an excellent candidate for locating content-specific consciousness's neural correlates^2,61, corresponding to regions needed to be aware of an experience. Indeed, in a scalp EEG study, low dose anesthesia only affected posterior areas, disrupting contents of consciousness while high dose propofol resulted in frontal area disruption of the mechanisms of arousal from consciousness¹⁰⁴. Similarly, primate work shows that the PFC is disconnected during deep anesthesia⁹. Our results endorse this differentiation.

Moreover, although there is consideration of regional differences in trying to understand the mechanisms of consciousness, most theories highlight an integrated, distributed, and interdependent model of consciousness¹⁰⁵. While we emphasized the lack of PFC responses during propofol-induced anesthesia, suggestive of its role in the unarousable nature of anesthesia, we observed a reduction of information integration and transfer with respect to wake in all regions. Given the complexity of the human brain, it is likely that there are multiple pathways through which consciousness could be disrupted. We studied the effect of propofol, the most commonly used anesthetic during surgery, but other drugs likely have different neural correlates¹⁰⁶. Subanesthetic doses of Ketamine, disrupted posterior rhythms¹⁰⁷, dexmedetomidine had similar functional network involvement to deep sleep^108,109, and other sedatives (including low dose propofol) shared metabolic patterns with slow-wave sleep¹¹⁰. A possible future direction to further disentangle and unify the theories of consciousness is to perform a similar protocol as in this study while participants perform different perception tasks, during restful wake, during sleep, and under various drug-induced LOC. Having the entire spectrum with the appropriate normalizing factors could further help understand the neural correlates of consciousness.

In summary, we showed differential cortical network engagement dependent on the state of consciousness, with the PFC particularly affected by propofol anesthesia. Compared to wake, information integration and transfer were reduced, and response variability increased during any type of loss of consciousness. Still, these differences were more pronounced during anesthesia than sleep with distinct cortical involvement. Indeed, relative inter-trial variability increased, while complexity and response amplitude decreased during anesthesia compared to sleep predominantly in the PFC. This is suggestive of different neural correlations and mechanisms for arousable and unarousable unconscious states, which is a step towards a better understanding of the neural correlates of loss of consciousness, with significant therapeutic and diagnostic implications.

Acknowledgments

We would like to thank Dan Soper, Constantin Krempp, and Pariya Salami for their help in data collection. We would like to thank Simone Russo for his advice on PCI^st implementation and Tal Benoliel Berman for her help reviewing electrode locations. We would like to especially thank the patients for participating in the study.

Funding

This work was supported by the Tiny Blue Dot Foundation (RZ, ACP, SSC), NIH grant K24-NS088568 (SSC), and the CURE Epilepsy Taking Flight award (RZ

CCEP – Cortico-cortical evoked potential

DES – Direct electrical stimulation

EEG – Electroencephalogram

iEEG – Intracranial EEG

LOC – Loss of consciousness

PFC – The prefrontal cortex

SPES – Single pulse electrical stimulation

1. Tononi, G., Boly, M., Massimini, M. & Koch, C. Integrated information theory: From consciousness to its physical substrate. Nat. Rev. Neurosci. 17, 450–461 (2016).

2. Koch, C., Massimini, M., Boly, M. & Tononi, G. Neural correlates of consciousness: progress and problems. Nat. Rev. Neurosci. 17, 307–321 (2016).

3. Mashour, G. A., Roelfsema, P., Changeux, J. P. & Dehaene, S. Conscious Processing and the Global Neuronal Workspace Hypothesis. Neuron 105, 776–798 (2020).

4. Dehaene, S. & Changeux, J. P. Experimental and Theoretical Approaches to Conscious Processing. Neuron 70, 200–227 (2011).

5. Dehaene, S. & Naccache, L. Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79, 1–37 (2001).

6. Tononi, G. Integrated information theory of consciousness: an updated account. Arch. Ital. Biol. 150, 293–329 (2012).

7. Dehaene, S., Kerszberg, M. & Changeux, J. P. A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl. Acad. Sci. U. S. A. 95, 14529–14534 (1998).

8. Pal, D. et al. Differential Role of Prefrontal and Parietal Cortices in Controlling Level of Consciousness. Curr. Biol. 28, 2145-2152.e5 (2018).

9. Bastos, A. M. et al. Neural effects of propofol-induced unconsciousness and its reversal using thalamic stimulation. Elife 10, (2021).

10. Pitkänen, A., Ekolle Ndode-Ekane, X., Lapinlampi, N. & Puhakka, N. Epilepsy biomarkers – Toward etiology and pathology specificity. Neurobiology of Disease 123, 42–58 (2019).

11. Boly, M. et al. Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? Clinical and neuroimaging evidence. J. Neurosci. 37, 9603–9613 (2017).

12. Odegaard, B., Knight, R. T. & Lau, H. Should a few null findings falsify prefrontal theories of conscious perception? J. Neurosci. 37, 9593–9602 (2017).

13. Tononi, G. Information measures for conscious experience. Arch. Ital. Biol. 139, 367–371 (2001).

14. Oizumi, M., Albantakis, L. & Tononi, G. From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput. Biol. 10, (2014).

15. Brown, E. N., Lydic, R. & Schiff, N. D. General anesthesia, sleep, and coma. New England Journal of Medicine 363, 2638–2650 (2010).

16. Bonhomme, V. et al. Linking sleep and general anesthesia mechanisms: This is no walkover. Acta Anaesthesiol. Belg. 62, 161–171 (2011).

17. Mashour, G. A. & Pal, D. Interfaces of Sleep and Anesthesia. Anesthesiol. Clin. 30, 385–398 (2012).

18. Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. 15, 148–160 (2019).

19. Bronstein, J. M. et al. Deep Brain Stimulation for Parkinson Disease: An Expert Consensus and Review of Key Issues. Arch. Neurol. 68, 165–165 (2011).

20. Herrington, T. M., Cheng, J. J. & Eskandar, E. N. Mechanisms of deep brain stimulation. J. Neurophysiol. 115, 19–38 (2016).

21. Kokkinos, V., Sisterson, N. D., Wozny, T. A. & Richardson, R. M. Association of Closed-Loop Brain Stimulation Neurophysiological Features With Seizure Control Among Patients With Focal Epilepsy. JAMA Neurol. 76, 800 (2019).

22. Jarosiewicz, B. & Morrell, M. The RNS System: brain-responsive neurostimulation for the treatment of epilepsy. Expert Rev. Med. Devices 18, 129–138 (2021).

23. Ryvlin, P., Rheims, S., Hirsch, L. J., Sokolov, A. & Jehi, L. Neuromodulation in epilepsy: state-of-the-art approved therapies. Lancet. Neurol. 20, 1038–1047 (2021).

24. Salanova, V. et al. The SANTÉ study at 10 years of follow-up: Effectiveness, safety, and sudden unexpected death in epilepsy. Epilepsia 62, 1306–1317 (2021).

25. Holtzheimer, P. E. & Mayberg, H. S. Deep brain stimulation for psychiatric disorders. Annu. Rev. Neurosci. 34, 289–307 (2011).

26. Mayberg, H. S. et al. Deep brain stimulation for treatment-resistant depression. Neuron 45, 651–660 (2005).

27. Widge, A. S., Malone, D. A. & Dougherty, D. D. Closing the Loop on Deep Brain Stimulation for Treatment-Resistant Depression. Front. Neurosci. 12, 175 (2018).

28. Basu, I. et al. Closed-loop enhancement and neural decoding of cognitive control in humans. Nat. Biomed. Eng. (2021). doi:10.1038/s41551-021-00804-y

29. Penfield , Jasper, Herbert H., Penfield, Wilder,, W. Epilepsy and the functional anatomy of the human brain. (Little, Brown, 1954).

30. Valentín, A. et al. Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo. Brain 125, 1709–18 (2002).

31. Matsumoto, R. et al. Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain 127, 2316–30 (2004).

32. Matsumoto, R. et al. Functional connectivity in human cortical motor system: a cortico-cortical evoked potential study. Brain 130, 181–197 (2006).

33. Keller, C. J. et al. Corticocortical evoked potentials reveal projectors and integrators in human brain networks. J. Neurosci. 34, 9152–63 (2014).

34. Crocker, B. et al. Local and distant responses to single pulse electrical stimulation reflect different forms of connectivity. Neuroimage 237, (2021).

35. David, O. et al. Probabilistic functional tractography of the human cortex. Neuroimage 80, 307–317 (2013).

36. Trebaul, L. et al. Probabilistic functional tractography of the human cortex revisited. Neuroimage (2018). doi:10.1016/j.neuroimage.2018.07.039

37. Matsumoto, R., Kunieda, T. & Nair, D. Single pulse electrical stimulation to probe functional and pathological connectivity in epilepsy. Seizure 44, 27–36 (2017).

38. Kokkinos, V., Alarcón, G., Selway, R. P. & Valentín, A. Role of single pulse electrical stimulation (SPES) to guide electrode implantation under general anaesthesia in presurgical assessment of epilepsy. Seizure 22, 198–204 (2013).

39. Valentín, A. et al. Single-pulse electrical stimulation identifies epileptogenic frontal cortex in the human brain. Neurology 65, 426–435 (2005).

40. Flanagan, D., Valentín, A., García Seoane, J. J., Alarcón, G. & Boyd, S. G. Single-pulse electrical stimulation helps to identify epileptogenic cortex in children. Epilepsia 50, 1793–1803 (2009).

41. Yamao, Y. et al. Intraoperative Brain Mapping by Cortico-Cortical Evoked Potential. Front. Hum. Neurosci. 15, 635453 (2021).

42. Bourdillon, P. et al. Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study. Sci. Rep. 10, (2020).

43. Casali, A. G. et al. A theoretically based index of consciousness independent of sensory processing and behavior. 5, 198ra105-198ra105 (2013).

44. Sarasso, S. et al. Quantifying cortical EEG responses to TMS in (un)consciousness. Clin. EEG Neurosci. 45, 40–49 (2014).

45. Massimini, M., Boly, M., Casali, A., Rosanova, M. & Tononi, G. A perturbational approach for evaluating the brain’s capacity for consciousness. Prog. Brain Res. 177, 201–214 (2009).

46. Pigorini, A. et al. Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep. Neuroimage 112, 105–113 (2015).

47. Sarasso, S. et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Curr. Biol. 25, 3099–3105 (2015).

48. Rosanova, M. et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain 135, 1308–20 (2012).

49. Pal, D., Silverstein, B. H., Lee, H. & Mashour, G. A. Neural Correlates of Wakefulness, Sleep, and General Anesthesia: An Experimental Study in Rat. Anesthesiology 125, 929–942 (2016).

50. Redinbaugh, M. J. et al. Thalamus Modulates Consciousness via Layer-Specific Control of Cortex. Neuron 106, 66-75.e12 (2020).

51. Comolatti, R. et al. A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations. Brain Stimul. (2019). doi:10.1016/j.brs.2019.05.013

52. Sarasso, S. et al. Consciousness and complexity: a consilience of evidence. Neurosci. Conscious. 2021, 1–24 (2021).

53. Matsumoto, R., Kunieda, T. & Nair, D. Single pulse electrical stimulation to probe functional and pathological connectivity in epilepsy. Seizure 44, 27–36 (2017).

54. Matsumoto, R., Kunieda, T. & Nair, D. Single pulse electrical stimulation to probe functional and pathological connectivity in epilepsy. Seizure 44, 27–36 (2017).

55. Sergent, C. et al. Bifurcation in brain dynamics reveals a signature of conscious processing independent of report. Nat. Commun. 12, (2021).

56. Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).

57. Fox, M. D., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Intrinsic Fluctuations within Cortical Systems Account for Intertrial Variability in Human Behavior. Neuron 56, 171–184 (2007).

58. Casarotto, S. et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann. Neurol. 80, 718–729 (2016).

59. Holmes, C. J. et al. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22, 324–333 (1998).

60. Aubert-Broche, B., Evans, A. C. & Collins, L. A new improved version of the realistic digital brain phantom. Neuroimage 32, 138–145 (2006).

61. Siclari, F. et al. The neural correlates of dreaming. Nat. Neurosci. 20, 872–878 (2017).

62. Brown, E. N., Purdon, P. L. & Van Dort, C. J. General anesthesia and altered states of arousal: a systems neuroscience analysis. Annu. Rev. Neurosci. 34, 601–628 (2011).

63. Goodchild, C. S. & Serrao, J. M. Cardiovascular effects of propofol in the anaesthetized dog. Br. J. Anaesth. 63, 87–92 (1989).

64. Song, C., Boly, M., Tagliazucchi, E., Laufs, H. & Tononi, G. fMRI spectral signatures of sleep. Proc. Natl. Acad. Sci. U. S. A. 119, (2022).

65. von Ellenrieder, N. et al. How the Human Brain Sleeps: Direct Cortical Recordings of Normal Brain Activity. Ann. Neurol. 87, 289–301 (2020).

66. Frauscher, B. & Gotman, J. Sleep, oscillations, interictal discharges, and seizures in human focal epilepsy. Neurobiology of Disease 127, 545–553 (2019).

67. Siclari, F. & Tononi, G. Local aspects of sleep and wakefulness. Curr. Opin. Neurobiol. 44, 222–227 (2017).

68. Steriade, M., McCormick, D. A. & Sejnowski, T. J. Thalamocortical oscillations in the sleeping and aroused brain. 262, 679–685 (1993).

69. Steriade, M. & Timofeev, I. Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron 37, 563–576 (2003).

70. Piantoni, G., Halgren, E. & Cash, S. S. The Contribution of Thalamocortical Core and Matrix Pathways to Sleep Spindles. Neural Plast. 2016, (2016).

71. Lewis, L. D. et al. Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness. Proc. Natl. Acad. Sci. U. S. A. 109, (2012).

72. Flores, F. J. et al. Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness. Proc. Natl. Acad. Sci. U. S. A. 114, E6660–E6668 (2017).

73. Schwartz, R. S., Brown, E. N., Lydic, R. & Schiff, N. D. Mechanisms of Disease General Anesthesia, Sleep, and Coma. N Engl J Med 363, (2010).

74. Ma, L., Liu, W. & Hudson, A. E. Propofol Anesthesia Increases Long-range Frontoparietal Corticocortical Interaction in the Oculomotor Circuit in Macaque Monkeys. Anesthesiology 130, 560–571 (2019).

75. Liu, X., Lauer, K. K., Ward, B. D., Li, S. J. & Hudetz, A. G. Differential effects of deep sedation with propofol on the specific and nonspecific thalamocortical systems: a functional magnetic resonance imaging study. Anesthesiology 118, 59–69 (2013).

76. Mashour, G. A. & Alkire, M. T. Consciousness, Anesthesia, and the thalamocortical system. Anesthesiology 118, 13–15 (2013).

77. Purdon, P. L., Sampson, A., Pavone, K. J. & Brown, E. N. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology 123, 937–960 (2015).

78. Purdon, P. L. et al. Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc. Natl. Acad. Sci. U. S. A. 110, (2013).

79. Ching, S. N. & Brown, E. N. Modeling the dynamical effects of anesthesia on brain circuits. Curr. Opin. Neurobiol. 25, 116–122 (2014).

80. Bastos, A. M. & Schoffelen, J. M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience 9, (2016).

81. Tasserie, J. et al. Deep brain stimulation of the thalamus restores signatures of consciousness in a nonhuman primate model. 8, 5547 (2022).

82. Xu, J. et al. Thalamic stimulation improves postictal cortical arousal and behavior. J. Neurosci. 40, 7343–7354 (2020).

83. Gummadavelli, A. et al. Thalamic stimulation to improve level of consciousness after seizures: evaluation of electrophysiology and behavior. Epilepsia 56, 114–124 (2015).

84. Schiff, N. D. Central thalamic contributions to arousal regulation and neurological disorders of consciousness. Ann. N. Y. Acad. Sci. 1129, 105–118 (2008).

85. Schiff, N. D. et al. Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature 448, 600–603 (2007).

86. Bourdillon, P., Hermann, B., Sitt, J. D. & Naccache, L. Electromagnetic Brain Stimulation in Patients With Disorders of Consciousness. Front. Neurosci. 13, (2019).

87. Corazzol, M. et al. Restoring consciousness with vagus nerve stimulation. 27, R994–R996 (2017).

88. Thibaut, A., Bruno, M. A., Ledoux, D., Demertzi, A. & Laureys, S. tDCS in patients with disorders of consciousness: sham-controlled randomized double-blind study. Neurology 82, 1112–1118 (2014).

89. Arthuis, M. et al. Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. 132, 2091–2101 (2009).

90. Chaitanya, G., Sinha, S., Narayanan, M. & Satishchandra, P. Scalp high frequency oscillations (HFOs) in absence epilepsy: An independent component analysis (ICA) based approach. Epilepsy Res. 115, 133–140 (2015).

91. Blumenfeld, H. Arousal and Consciousness in Focal Seizures. Epilepsy Curr. 21, 353–359 (2021).

92. Nayak, D., Valentín, A., Selway, R. P. & Alarcón, G. Can single pulse electrical stimulation provoke responses similar to spontaneous interictal epileptiform discharges? Clin. Neurophysiol. 125, 1306–1311 (2014).

93. Lozano, A. M., Hutchison, W. D. & Kalia, S. K. What Have We Learned About Movement Disorders from Functional Neurosurgery? Annu. Rev. Neurosci. 40, 453–477 (2017).

94. Eisinger, R. S., Cernera, S., Gittis, A., Gunduz, A. & Okun, M. S. A review of basal ganglia circuits and physiology: Application to deep brain stimulation. Parkinsonism Relat. Disord. 59, 9–20 (2019).

95. Krack, P., Volkmann, J., Tinkhauser, G. & Deuschl, G. Deep Brain Stimulation in Movement Disorders: From Experimental Surgery to Evidence-Based Therapy. Mov. Disord. 34, 1795–1810 (2019).

96. Benabid, A. L. et al. Functional neurosurgery for movement disorders: a historical perspective. Prog. Brain Res. 175, 379–391 (2009).

97. Widge, A. S. et al. Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach. Exp. Neurol. 287, 461–472 (2017).

98. Lee, M. B. et al. Clinical neuroprosthetics: Today and tomorrow. J. Clin. Neurosci. (2019). doi:10.1016/j.jocn.2019.07.056

99. Dafsari, H. S. et al. Beneficial effect of 24-month bilateral subthalamic stimulation on quality of sleep in Parkinson’s disease. J. Neurol. 267, 1830–1841 (2020).

100. Jost, S. T. et al. Subthalamic stimulation improves quality of sleep in Parkinson disease: A 36-month controlled study. J. Parkinsons. Dis. 11, 323–335 (2021).

101. Zuzuárregui, J. R. P. & Ostrem, J. L. The Impact of Deep Brain Stimulation on Sleep in Parkinson’s Disease: An update. J. Parkinsons. Dis. 10, 393–404 (2020).

102. Brown, R., Lau, H. & LeDoux, J. E. Understanding the Higher-Order Approach to Consciousness. Trends Cogn. Sci. 23, 754–768 (2019).

103. Schif, N. D. Making waves in consciousness research. Science Translational Medicine 5, 198fs32-198fs32 (2013).

104. Stephen, E. P. et al. Broadband slow-wave modulation in posterior and anterior cortex tracks distinct states of propofol-induced unconsciousness. (2020). doi:10.1038/s41598-020-68756-y

105. Northoff, G. & Lamme, V. Neural signs and mechanisms of consciousness: Is there a potential convergence of theories of consciousness in sight? Neurosci. Biobehav. Rev. 118, 568–587 (2020).

106. Brown, E. N., Pavone, K. J. & Naranjo, M. Multimodal General Anesthesia: Theory and Practice. Anesth. Analg. 127, 1246–1258 (2018).

107. Vesuna, S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 586, 87–94 (2020).

108. Liaquat, Z., Xu, X., Zilundu, P. L. M., Fu, R. & Zhou, L. The Current Role of Dexmedetomidine as Neuroprotective Agent: An Updated Review. Brain Sci. 11, (2021).

109. Guldenmund, P. et al. Brain functional connectivity differentiates dexmedetomidine from propofol and natural sleep. Br. J. Anaesth. 119, 674–684 (2017).

110. Scheinin, A. et al. Foundations of Human Consciousness: Imaging the Twilight Zone. J. Neurosci. 41, 1769–1778 (2021).

There is NO Competing Interest.

Download PDF

Journal Publication

published 31 Aug, 2023

Read the published version in Neuron →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Differential Cortical Network Engagement During States of Un/Consciousness

Status:

Journal Publication

Version 1

Abstract

Figures

Highlights

Introduction

Results

1. Brain responses decreased and variability increased during loss of consciousness.

2. Brain responses are more disrupted during anesthesia than during sleep

3. The anatomical distribution of differs between sleep and anesthesia.

4. The PFC was particularly affected during anesthesia.

Discussion

Declarations

Acknowledgments

Funding

Abbreviations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1