Electrophysiological Markers of Consciousness: Measures of connectivity, complexity, and signal diversity in EEG for distinguishing between conscious and unconscious brain states

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Electrophysiological Markers of Consciousness:

Measures of connectivity, complexity, and signal diversity in EEG for distinguishing between conscious and unconscious brain states

Thesis for the degree of Philosophiae Doctor (PhD) by Bjørn Erik Juel

Institute of Basic Medical Sciences Faculty of Medicine

University of Oslo 2018

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Dear Reader,

This thesis is dedicated to you.

I hope you find what you are looking for!

If I can be of any further assistance, please do not hesitate to get in touch.

Best wishes,

Bjørn Erik Juel

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Acknowledgements

From before I can remember, you, my family and friends, have shaped me and made me into the person that I am today. As my mentors, colleagues, collaborators, and competitors, you have equipped me with the tools I needed to get to where I am. You have thought me all that I know, and much more than I realize. I am grateful for all that you have given, and I sincerely hope that the product of your collected efforts will make you proud. I can only aspire to one day mean as much to someone else as you have meant to me. Thank you.

Johan, I want to thank you for giving me the chance to work in your group and letting me be involved in setting up the lab to follow your dream of studying consciousness scientifically. You have thought me a lot over the four years I have known you, and if I ever end up in a situation where I supervise students, I hope I have the strength to give them the amount of freedom and responsibility you have given me. Your work ethic, scientific mind, writing skills, and kind heart have been truly inspiring to me.

Pål, thank you for holding me to my plans, for trying to keep me focused, and for supplying me with the tools and materials required to get to where we are today. I am keenly aware of the fact that your efforts (together with your team at the hospital) have been essential for getting our collected efforts to anything resembling a thesis worthy of the doctorate degree. In particular, though, I would like to thank you for our sporadic talks in your office – they were calming, motivating, intriguing, and educational all at once.

Thomas – not only were you my first true colleague in this endeavor, you became a good friend, and I often think back to when you used to educate me on ethics and morality over our morning coffees. André, thanks for widening my horizons, challenging my views, and for holding up a mirror to let me see my own good and bad sides for myself. Alessandro, thank you for showing me scientific integrity in action, and for bringing some southern etiquette to the lab. And a special thank you for showing me your childhood homes in Italy – see you in Portugal June 1^st 2024.

Sofia, thank you for being our guiding light through the HBP jungle and for keeping our moral up

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showed me how a master thesis actually should be done and were invaluable for getting the EEG lab into the shape it is. It was a pleasure to be part of your team! As for the newest additions to our lab, thanks to Benny, Sebastian, and Arnfinn for bringing new spirit and some much-needed experience to the EEG work in our lab.

Cecilie, Ricardo, and Nick: Even though I did not work so closely with you, the fact that you were around for my entire journey through the PhD gave me some much needed stability. I really enjoyed our short and sporadic conversations, and hope we will have more of them. An honorable mention to Pedro and Christoph, who showed me that it was in fact possible to graduate and move on both in and outside of academia. Furthermore, I would like to thank all the other people that have made my time in the lab all that it was. Hu and Fabian, all the students that have stopped by or stayed in the group, Lourdes and all the people working behind the scenes to make the system work.

I would also like to thank our collaborators and colleagues in Milano, Wisconsin, and Liege.

Marcello, Olivia, Silvia, Matteo, Giulio, and Larissa: you have been both inspiring and very helpful for my development throughout my period in Johan’s lab. Your practical and analytical training in using TMS and EEG for consciousness research and your patience in discussions about the theoretical underpinnings of the methods we used have been invaluable. In addition, I would like to thank Luis, our local anesthesiologist for his efforts, advice, and strict but fair feedback on everything anesthesia related.

A special thanks to all the participants that volunteered for our studies. In particular, I am very grateful to my brother Øyvind and my cousin Tønnes, who suffered through my stumbling first steps towards becoming a TMS-EEG practitioner. Without your trust and goodwill, I would not have even gotten out of the gates!

My family has been invaluable throughout my life in general, and over the last few years particularly. I cannot thank you enough for all your sacrifice, guidance, and support. A special thanks to my Portuguese family, who have shown me new sides of life, and have opened my eyes to a whole new world!

Ana. Coming home to you is a highlight of every day and the idea of you keeps me motivated in all that I do. I am so grateful for everything you are and all that you have given me. You keep me in check and bring out the best in me. I am looking forward to my life with you, wherever it may be.

Te amo para sempre!

Thank you all

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Summary

Can we know whether someone is conscious by looking solely at the activity of their brain? If so, what is it about the patterns of activity in someone’s brain that is particular for when they are having some conscious experience? Perhaps there is some marker in the brain activity that is always observable when someone is conscious, but that is never observable they are unconscious? In this thesis, and in the work leading up to it, we have tried to answer questions like these.

In our struggle to do so, we obtained recordings of electrical brain activity from patients and healthy volunteers in both conscious states (for example normal wakefulness or while dreaming) and unconscious states (for example under general anesthesia sufficient for surgery). Within the brain activity recordings, we tried to find particular properties that differed predictably between conscious and unconscious states, and that could be used to precisely and objectively classify individuals as conscious or unconscious. Thus, we have been looking for electrophysiological markers of consciousness.

Following emerging trends in empirical findings over the last few decades, as well as theoretical considerations about the phenomenon of consciousness, we focused our investigations on properties of connectivity, complexity, and signal diversity in the brain activity data. We quantified these properties using specific mathematical measures, and investigated which of the measures could be used to accurately classify individuals as conscious and unconscious. The ground truth about an individual’s (lack of) consciousness was taken to be their own report when available. And if such a report was not available, the judgement of a third party using standardized behavioral scores was used to define their conscious state.

Several of the measures we tested could be used to accurately classify the state of consciousness in patients and healthy controls. Apparently, they could even distinguish between states with and without reports of vivid dreams during anesthesia induced unresponsiveness. However, some of the measures were also affected by changes that are not necessarily related to the presence of consciousness as such. For example, closing one's eyes, while otherwise awake and aware, was sufficient to significantly alter certain measures of signal diversity to values closer to what is normally associated with unconscious states. That said, even though the potential markers of consciousness in their current form may not specifically track consciousness as such, and further work is required to improve them, we believe that some of them may become useful as future tools for objective, real-time monitoring of the conscious state in patients. Hopefully, the work required for their further development will also lead to new insights moving our scientific understanding of

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List of papers

This thesis covers work related to the following five research articles – one is published while the remaining four are still in manuscript form. Throughout the thesis text, I will refer to the papers by the names and roman numerals given below.

Paper I: Juel BE, Romundstad L, Kolstad F, Storm JF, and Larsson PG (2018) Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG. Frontiers of Human Neuroscience.

https://doi.org/10.3389/fnhum.2018.00040

Paper II: Juel BE, Romundstad L, Storm JF, and Larsson PG * (2018) Validation of a new approach for distinguishing anesthetized from awake state in patients using Directed Transfer Function applied to raw EEG. Manuscript.

Paper III: Juel BE, Bremnes TR, Gosseries O, Boly M, Nilsen AS, [...] Laureys S, Massimini M, Storm JF * (2018) Measures of complexity and connectivity calculated from spontaneous EEG can distinguish consciousness from apparent unconsciousness in unresponsive states. Manuscript.

Paper IV: Nilsen AS, Juel BE, Storm JF (2018) Measures of conscious states during attentional and cognitive load. Manuscript.

Paper V: Farnes N, Juel BE, Nilsen AS, Romundstad L, Storm JF * (2018) Sub-anaesthetic ketamine increase spontaneous but not evoked EEG signal diversity. Manuscript.

*Final author list and order undecided.

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Abbreviations

ACE Amplitude coalition entropy ApEn Approximate entropy BIS Bispectral Index

DTF Directed transfer function

EEG Encephalography

ERP Event related potential

GNWT Global Neuronal Workspace Theory IFT Isolated forearm technique

IIT Integrated Information Theory LOOCV Leave-one-out cross-validation LZc Lempel-Ziv complexity

MCS Minimally conscious states

MOAAS Modified observer's alertness/sedation scale MRI Magnetic resonance imaging

NCC Neuronal correlates of consciousness PCI Perturbational complexity index PET Positron emission tomography SCE Synchrony coalition entropy TEP TMS evoked potential

TMS Transcranial magnetic stimulation UWS Unresponsive Wakefulness Syndrome

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Thesis at a glance

Word cloud created using tools by (Schubert, Spitz, Weiler, Geiß, & Gertz, 2017).

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1. Introduction

We don’t know what consciousness is. In fact, we cannot even quite agree on whether it even is anything at all! At the same time, every single person reading this text is presumably conscious right now, and the feeling of being an experiencer is something uniquely familiar to most of us. In some sense, our conscious experience is everything we have, and some even believe consciousness is all there is.

It is clear that we have yet to come to any clear consensus about even the most basic foundations of the phenomenon of study, indicating that there is much to be done in the quest to understand consciousness. To remedy this, with an aim of understanding of consciousness scientifically, the field of consciousness research is slowly coming of age. One sign of this was the large symposium devoted to presenting recent progress on theoretical, experimental, and clinical issues related to consciousness at the world’s largest neuroscience conference (Storm et al., 2017). However, the field is still young, and there are ongoing arguments about theoretically, empirically, and clinically fundamental aspects of consciousness.

With the work presented in this thesis, our team has tried to contribute to the field. Particularly, we have been trying to understand the link between brain activity and consciousness, by searching for what we call Electrophysiological markers of consciousness. The term ‘Electrophysiological markers of consciousness’ implies that we think there will be telltale measurable signs in the brain activity that are always apparent when some individual is conscious and is never there when the individual’s consciousness is gone. In what follows, I present the work we have done to find such a signature of consciousness in patterns of brain activity.

Together with our collaborators, we investigated specific properties of brain activity recordings, gathered from patients and healthy controls under conditions which are typically associated with altered states of consciousness. Specifically, we focused our work on measurements done using electroencephalography (EEG) and contrasted particular properties of the EEG signal between normal wakefulness and different states of general anesthesia (Papers I-III). In addition, we did two studies that can be considered controls, in which the same properties of the EEG signal were contrasted between distinct states where the EEG itself is affected, but where the participants are clearly conscious (Paper IV and V). The idea is that successful markers derived from the EEG should consistently change in conditions where consciousness is lost, but be unaffected by any change of state where consciousness remains.

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2. Background

In some sense, everything we feel, know, experience, value, remember about ourselves and the universe we are part of, requires consciousness to exist. If no one was (or ever could become) conscious, the universe might as well not have existed - no one would have been around to know the difference. Thus, in a way, the subjective existence of the universe and all its properties is conditional upon consciousness. If these statements have any truth to them at all, it seems reasonable to try to understand consciousness scientifically. Thus, we can learn what is required for physical systems to have a subjective perspective that brings the universe into existence for them.

The ability to accurately predict which objective properties are accompanied by subjective perspectives (and how they feel to the system itself) would not only be of theoretical philosophical interest, but could have direct impact on practical decision making and ethical questions we care about in daily life.

There are many hypothetical situations we could construct to discuss the relevance of understanding the necessary and sufficient conditions for consciousness in physical systems. For example, how can we know which animals are conscious (Boly et al., 2013)? Can machines be conscious (Aleksander, 2005)? Can lab-grown mini-brains become sentient (Lavazza & Massimini, 2018)?

However, there are also very real and challenging situations brought about by modern medicine, in which the question of the patient’s consciousness is central to the clinical decision making (Fins, 2011; Storm et al., 2017). For example, patients that are saved after traumatic brain injury and end up in a brief coma, may open their eyes and start spontaneously moving in the hospital bed without the ability to coherently communicate with the medical staff. Such patients are diagnosed with Unresponsive Wakefulness Syndrome (UWS), a state in which patients may persistently remain thanks to modern medical technology (Laureys et al., 2010). In such situation, decisions about how the patient should be treated, the type of patient care that should be administered, and for how long the treatment should go on depend strongly on how the clinical staff judge the patient’s state of consciousness (Giacino, Fins, Laureys, & Schiff, 2014; Giacino & Kalmar, 1997). Currently, estimates indicate that there are hundreds of thousands of patients with some such chronic disorder of consciousness (DOC) in the US alone (D. J. Strauss, Ashwal, Day, & Shavelle, 2000). With an approximate average annual incidence rate of 9 per 10 000 000 (Løvstad et al., 2014), questions regarding their state of consciousness is of moral, societal, and judicial relevance for a large (and growing) number individuals, families, and clinicians world-wide.

Another situation in which the patient’s state of consciousness is regularly of relevance is during procedures requiring general anesthesia (Bruhn, Myles, Sneyd, & Struys, 2006). Every day, around sixty thousand patients are rendered behaviorally unresponsive, and apparently unconscious, as a

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result of general anesthesia during surgical procedures in the US (Brown, Lydic, & Schiff, 2010).

And even with the most modern equipment for monitoring the state of the patients, reports indicate that more than 1 of every 1000 patients wake up and experience the surgery first hand without the clinical staff noticing (Sebel et al., 2004). In addition, many more patients experience vivid dreams during surgery (Leslie, Skrzypek, Paech, Kurowski, & Whybrow, 2007) that may seem confusing, frightening, or traumatic to them (Bischoff & Rundshagen, 2011; Leslie, 2017). There are strong recommendations that we learn how to notice the patients that do indeed have conscious experiences during general anesthesia, so that necessary steps can be taken during the surgery and the patient follow up to minimize risks of long lasting post traumatic disorders (Bischoff &

Rundshagen, 2011). Luckily, continuous brain activity monitoring using EEG is becoming the recommended standard in the clinic (Punjasawadwong, Phongchiewboon, & Bunchungmongkol, 2014; Purdon, Sampson, Pavone, & Brown, 2015), and it seems possible that particular properties of the brain’s activity pattern can be used to specifically track the conscious state of patients undergoing anesthesia (Purdon et al., 2013). If properties that are truly consciousness specific can be found in brain activity patterns, there are hopes that these may also be translationally used for distinguishing between DOC patients with and without (potentially covert) conscious experiences (Gosseries et al., 2011).

However, it is not straightforward to know whether properties of brain activity that seem to track consciousness in specific situations (such as general anesthesia) really are specifically reflecting consciousness as such, or if they are related to some confounder or are spuriously correlated with our phenomenon of interest. To increase the likelihood of finding the relevant properties, and narrow down the search space for necessary and sufficient conditions for consciousness, making efforts to improve our theoretical understanding of the phenomenon is important (Seth, Izhikevich, Reeke, & Edelman, 2006). Since the 1990’s, research programs for grappling with fundamental questions have been alive and thriving, both attempting to solve consciousness from the bottom up with strong empirical approaches (Crick & Koch, 1990; Searle, 1998) and from the top down by building theories of consciousness with scientifically testable predictions (Baars, 1997; Dehaene, Changeux, & Naccache, 2011; G. Tononi & Edelman, 1998; Giulio Tononi, 2004). However, the extent to which we are (or can expect to be) making progress on the hard problem of consciousness - why some constellations of physical matter are associated with subjective, inner, qualitative experiences, at all (Chalmers, 1995) - is debated (Searle, 1998). Furthermore, it is still not uncommon to assume that there is an “explanatory gap” between phenomenal consciousness and the physical world (Levine, 1983), making attempts at explaining the phenomenal from the physical

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suggestions have been made for which structural and functional properties should be considered neuronal correlates of consciousness (NCCs (Crick & Koch, 1990); see (Chalmers, 1998) for a list of suggestions that had emerged within the first 10 years)). The NCC is defined to comprise “the minimum neural mechanisms jointly sufficient for any one specific conscious experience” (Koch, Massimini, Boly, & Tononi, 2016), and although there is no final consensus it is assumed that the structural and functional properties of (thalamo)cortical networks are centrally involved (Alkire &

Miller, 2005; Chalmers, 1998; Rees, Kreiman, & Koch, 2002). Thus, not everyone is convinced that science cannot hope to explain the existence of consciousness as such, maintaining the hope that careful investigations of systems in the brain and its functions related to consciousness (such as sensory systems, attention, executive functions, future simulations, self-other distinctions and so on) will lead the ethereal phenomenon of consciousness to no longer seem mystical (Searle, 1998; Seth, 2016).

2.1 Consciousness

Over the last several decades, efforts to understand consciousness seem to have evolved from being a predominantly philosophical endeavor, towards becoming a scientifically approachable area of research (Chalmers, 1998; Crick & Koch, 2003; Searle, 2000). In fact, around the turn of the millennium, understanding consciousness was proclaimed to be one of the “major unsolved problems in biology” (Koch, 2004) and the “ultimate intellectual challenge” for the sciences in the new millennium (Dehaene & Changeux, 2004). It is beyond the scope of this thesis to give a complete overview of the developments in the field, but it should be noted that it is only as a result of the combined efforts of a number of prominent philosophers and scientists that the field has grown into what it is today. And their relevance can be seen, if nothing else, by the continued focus in the field on ideas from several decades ago such as the NCC (Crick & Koch, 1990), the hard problem of consciousness (Chalmers, 1995), consciousness and complexity (G. Tononi & Edelman, 1998), the global workspace (Baars, 1997), and many more. With the work presented here, we can only hope to contribute to the growing literature aiming to shed light on one of the most elusive, yet subjectively salient, features of the world - consciousness.

2.1.1

Why do we study consciousness

For me, there are at least two reasons to study consciousness: We study it to gain an understanding of the phenomenon as such, and to aid in development of applications for distinguishing between conscious and unconscious individuals in situations where our intuition fails us (Sanders, Tononi, Laureys, & Sleigh, 2012). Acknowledging that consciousness is a natural phenomenon that exists in our universe, it seems reasonable to try to understand how it comes about in systems such as human

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brains. Such an understanding could help us predict which other systems may be endowed with consciousness and how they experience the world when they do.

For clinical applications

By now, it is quite clear that unresponsiveness is not equal to unconsciousness (Noreika et al., 2011;

Sanders et al., 2012). Every night, we experience dreams (even though some of us struggle to remember when we wake up) that can be anything from complex experiences like normal wakefulness to strange free-floating experiences such as pure-thought (Dement & Kleitman, 1957;

Siclari et al., 2017). And in the clinic, while apparently unconscious under the effect of general anesthetics, a significant number of patients report having had dreamlike experiences (Leslie, 2017;

Leslie et al., 2007). Some patients even report waking up during surgery, unbeknownst to the clinical staff (Goddard & Smith, 2013). Furthermore, patients that survive brief periods of coma after brain injury due to trauma or anoxia may wake up and be diagnosed with UWS (Laureys et al., 2010). This is a state (previously known as vegetative state) in which patients open their eyes spontaneously and have reflex behavior, but show no sign of coherent command following (Giacino et al., 2014). However, also here there is evidence of patients being aware of their surroundings in spite of their behavioral unresponsiveness and apparent unconsciousness (Monti et al., 2010; Owen et al., 2006; Schnakers et al., 2009; Vanhaudenhuyse et al., 2018). In other words, there are many situations in which our intuition is insufficient and an understanding of the link between brain and consciousness may aid development of tools that can help clinicians to correctly diagnose patients.

Every year, tens of millions of patients undergo general anesthesia in the US alone (Avidan et al., 2008). Going by estimates indicating that 1-2 of every thousand patients being anesthetized in the clinic experience an unintended awakening during surgery (Sebel et al., 2004), hundreds of patients must be assumed to be affected on average per day (Mashour, Orser, & Avidan, 2011). As the experience of waking up, paralyzed and confused, during surgery without the clinical staff noticing may be very stressful, this is an issue that should be taken seriously (Goddard & Smith, 2013).

Patients suspected of having had such experiences should be followed up extensively, with explanations of the situation and counselling, to avoid post-traumatic stress and the like (Goddard &

Smith, 2013). Thankfully, the number of (reported) unintended awakenings is relatively low, indicating that the current clinical monitoring of patients conscious state is normally sufficient.

However, there is still room for improvement, and real-time monitoring of brain signals using EEG equipment has been suggested as a logical next step for routine anesthesia monitoring (Musialowicz

& Lahtinen, 2014; Palanca, Mashour, & Avidan, 2009; Purdon et al., 2015). In fact, several commercially available EEG-based tools have been developed for this purpose, and are

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However, it is not clear to what extent these tools are in fact tracking the conscious state of the patients, as opposed to changes in muscle activity, general arousal, or other consciousness non- specific changes that are captured by the EEG systems (Musialowicz & Lahtinen, 2014; Schuller, Newell, Strickland, & Barry, 2015). Thus, research focused on understanding the brain mechanisms relevant for consciousness per se could further improve the tools, and be specifically useful for capturing the patients that wake up unbeknownst to clinical staff that are busy monitoring a slew of physiological signals and parameters (Mashour et al., 2011).

Brain-activity related markers of conscious awareness resulting from such research could also be applicable to other clinical situations. In particular, the hard cases of patients suffering from DOC after waking up from a coma (Giacino et al., 2014). Currently, the gold standard for diagnosing DOC patients involves testing the behavioral responsiveness of patients using structured tests for their ability to coherently communicate - for example the Coma Recovery Scale (Giacino, Kalmar,

& Whyte, 2004). Patients that spontaneously open their eyes, will typically first be diagnosed as UWS, before potentially developing into one of the Minimally conscious states (MCS) or recovered, depending on their level of communication and command following (Giacino et al., 2002, 2014).

Unfortunately, tests such as the Coma Recovery Scale diagnose the patients based on their behavioral state, and the approach has been shown to mischaracterize patients with covert consciousness and ability to establish communication using brain imaging techniques (Monti et al., 2010). Furthermore, the tests are time consuming and must be applied repeatedly to correctly diagnose patients that fluctuate in their ability to communicate (Wannez et al., 2017). Thus, markers of consciousness derived directly from patterns of passively recorded brain activity may be useful for monitoring these patients continuously and could at least be used for notifying clinical personnel about the optimal time for applying more intuitive behavioral tests (Schnakers et al., 2008). Finally, behavioral markers of consciousness (at least for standard behavioral tests like the Coma Recovery Scale) have been shown to correlate with the prognosis for DOC patients (Bruno et al., 2012;

Giacino et al., 2014). Thus, there seems to be some situations in which reliably measuring the mere presence of consciousness is useful for accurate prognosis as well.

In recent years, a multitude of measures and markers have been proposed, tested, and applied for distinguishing between states of (un)consciousness in patients and healthy controls (for some examples, see e.g. (Casali et al., 2013; Gosseries et al., 2011; Kirschner, Cruse, Chennu, Owen, &

Hampshire, 2015; Lee, Blain-Moraes, & Mashour, 2015; Nicolaou, Hourris, Alexandrou, &

Georgiou, 2012)). In particular, measures that quantify properties thought to reflect the brain’s capacity for global information integration have shown particular promise in this regard (Mashour

& Hudetz, 2018). This has been shown using a wide range of brain imaging techniques (for

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example using EEG (Lee, Mashour, Kim, Noh, & Choi, 2009), positron emission tomography (PET) (White & Alkire, 2003), or magnetic resonance imaging (MRI) (Schrouff et al., 2011)) and several different techniques of quantification (for example Granger causality (Nicolaou et al., 2012), directed connectivity or transfer entropy (Lee et al., 2013), causal density (Seth, Barrett, & Barnett, 2011), and measures of complexity (Ferenets, Vanluchene, Lipping, Heyse, & Struys, 2007) to mention a few) regardless of how the change in state or level of consciousness came about (in natural sleep (Massimini et al., 2005), using anesthetic agents (M. Schartner et al., 2015), or due to DOC (Boly, Massimini, et al., 2012)). Thus, the prospect of finding some particular objectively quantifiable property of brain activity that can be used to precisely track individual’s state and level of consciousness has seemed plausible for some time. And indeed, measures quantifying the brain’s capacity for information integration are among the most promising candidates. Not only do they work well empirically, but they also seem to capture characteristics predicted to be important by leading theories of consciousness (Dehaene et al., 2011; Oizumi, Albantakis, & Tononi, 2014).

However, a clinically viable tool for tracking consciousness in patients may require more than just the capacity for notifying clinicians of when a patient is likely to have some sort of conscious experience. Specifically, the monitors may be required to be specific for clinically relevant contents of consciousness, as opposed to capturing consciousness as such. For example, it may be irrelevant for the surgeon or anesthesiologist if a patient has vivid dreams during a clinical procedure, while even fuzzy experiences of the operation room or pain related to surgery may be important signals for changing the anesthetic protocol. Furthermore, questions regarding the importance of memory and recall of experience, in contrast to the experience itself, should be considered. For example, under the effects of general anesthesia, apparently unconscious patients were able to communicate experience of pain using the isolated forearm technique (IFT), but could not remember this after the anesthesia protocol was ended (Sanders et al., 2017). Does it matter if a patient is experiencing discomfort or pain if they will not remember after reemerging from anesthesia?

For theoretical understanding

An apparently complicating factor in the field of consciousness research is the fact that there is no clear consensus about what consciousness is. Some conceive it as a fundamental property of nature, others as an emergent property of complex systems, still others prefer to view it as a higher order process of representation of some lower order state or a necessary result of active inference about the world in agents interacting with the environment. Others, still, speculate that it is something altogether different from all known phenomena in nature (see for example (Van Gulick, 2016) for an introductory overview). Furthermore, there are disagreements about what the mechanisms for

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human beings. One suggestion is that self-sustaining activity in long range pyramidal neurons making feedback projections to sensory areas is required for conscious perception of some stimulus (Dehaene et al., 2011; van Vugt et al., 2018). Others argue that the substrate itself is irrelevant, as long as there is a system of causally interacting elements that constrain each other in such a way that the system as a whole can be seen as existing above and beyond its parts (Oizumi et al., 2014).

Whether or not any of the current hypotheses turn out to be correct, only time will tell.

However, to push our understanding of consciousness further - beyond purely empirical determination of brain regions apparently involved in consciousness (Chalmers, 1998; Crick &

Koch, 1995; Koch et al., 2016) - we need to continue to develop such theories for consciousness (Crick & Koch, 1990). As in science more generally, theory building can help guide our investigation of the phenomenon of interest (here: consciousness), it can generate relevant hypotheses to be tested, and can give a framework for interpretation of the (otherwise blind) data we have available. Successful theories of consciousness should be able to explain why it feels like something rather than nothing to exist or at least why it, for example, feels like something to see color (Crick & Koch, 1990). Furthermore, a theory must account for what is particular about consciousness, be able to reject supernatural assumptions about it, and provide a framework for the design and interpretation of experiments (Edelman, 2003). Ideally, theories should propose testable hypotheses about neural mechanisms that account for the various features of consciousness (see for example (Seth et al., 2006) for an overview of features that require theoretical explanation). For now, we must avoid becoming bound to any one theory, and be open for rejections of, or changes to, the theories in the face of new data to allow them to become more inclusive and refined in the end (Crick & Koch, 1990). We can hope for a theory that can unify the observations associated to (un)consciousness within a common explanatory framework, and guide our search for mechanisms underpinning conscious experience (Boly et al., 2013). As theories become more refined, and capable of explaining more predictions about human consciousness, we might even begin to apply theories to other species without the human ability to report about their contents of consciousness (Boly et al., 2013; Seth, Baars, & Edelman, 2005; Tsuchiya, 2017) and hope to understand consciousness from an evolutionary perspective (Mashour & Alkire, 2013).

An improved theoretical understanding of consciousness may also directly interact with the search for clinically relevant electrophysiological markers of consciousness. Theoretical frameworks can help us narrow the search space, and as we will see, theories have already inspired some of the more promising markers (e.g. (Bekinschtein et al., 2009; Casali et al., 2013; M. Schartner et al., 2015)).

Furthermore, on a more general basis, theories can be used to provide reasons for caution in situations where our intuition tells us there is no consciousness but theoretical considerations

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indicate otherwise. For example, there are now patients surviving large scale brain damage in a way that leaves “islands” of functional cortex totally dissociated from the world outside (Storm et al., 2017). Does it feel like something to be such an island in a sea of cortex dominated by slow waves?

Furthermore, advances in science and technology is allowing for growing of cerebral organoids, or

‘mini-brains’ (Bae & Walsh, 2013), from embryonic stem cells in vitro (Lancaster et al., 2013).

These mini-brains exhibit properties similar enough to naturally developing brains to warrant raising the question of whether they are (or have the capacity for) consciousness (Lavazza &

Massimini, 2018). If they do, can we know what it feels like to be a lab-grown mini-brain? Such questions are far too outlandish to be answerable using normal, everyday intuition. However, as theories of consciousness mature, we may be able to pass judgment about such cases in a principled and scientifically sound way.

There are many different theoretical frameworks that can be used to guide efforts to explain consciousness and its place in nature (Van Gulick, 2016). I am not going to go into detail about any of them here, but suffice it to say there are enough of them for anyone to find their own personal favorite (Chalmers, 1998, 2015; Dehaene et al., 2011; Hameroff & Penrose, 1996; Lamme, 2006;

Lau & Rosenthal, 2011; Tegmark, 2015; Giulio Tononi, Boly, Massimini, & Koch, 2016). To me, it seems like most of the theories are not mutually exclusive, several of them have at least some empirical support, and very few (if any) make practically testable predictions that would falsify the theory. There is also considerable overlap in what properties each of them predict for the neuronal substrate of consciousness. In particular, a recurring theme is the idea that the substrate must somehow be densely connected to itself to allow information in any part of the system to be globally distributed and have direct effects on all other parts of the substrate for it to be consciously accessible. The two theories that are currently most highly regarded in the field (as far as I understand) are the Global Neuronal Workspace Theory (GNWT; (Dehaene et al., 2011)) and the Integrated Information Theory (IIT; (Oizumi et al., 2014; Giulio Tononi, 2004, 2008; Giulio Tononi et al., 2016)). The GNWT has been developed by Dehaene and Changeux and colleagues over the last few decades, and was inspired by Bernard Baars’ ideas about consciousness (Baars, 1997). It contends that some stimulus becomes consciously accessible if it is strong enough to cause patterns of global, self-sustaining activity in the so called “global neuronal workspace”. If the stimulus is somehow hindered from reaching the global workspace, and therefore does not cause such an

“ignition” of self-sustaining activity, it remains unconscious (Dehaene & Changeux, 2011; van Vugt et al., 2018). IIT is, on the face of it, a very different theory all together. It has been developed mainly by Giulio Tononi (Oizumi et al., 2014; Giulio Tononi, 2004, 2008), and was inspired in part

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by Tononi and colleagues (see (Giulio Tononi et al., 2016) for the latest review, or (Giulio Tononi

& Koch, 2015) for a more accessible introduction). Briefly, the theory starts from phenomenology and introspection, proposes a set of axioms that is assumed to be true about any conscious experience, and postulates a set of properties that any physical system in a state must fulfill to be conscious. It goes on to equate conscious experience with intrinsic existence (or having cause-effect power upon oneself (Albantakis & Tononi, 2015)), and provides a mathematical framework that, in principle, can be used to determine to what extent some physical system (like the brain) is conscious, and the precise way the particular conscious state feels (Oizumi et al., 2014).

2.1.2

How can we study consciousness?

There are many ways we can study consciousness, and the choice of methods should depend on the particular questions asked. Once we have decided on a question, we can attempt to explain how brain processes cause the phenomenon of interest using standard neuroscientific tools such as electrophysiological recordings, modulation of activity, lesion studies etc. Searle advocated approaching consciousness head on in such a way, and thus taking consciousness seriously as a biological phenomenon caused by neuronal processes in the brain (Searle, 1998). However, many of the standard neuroscientific tools (involving animal models, slices, and cultures) may not seem viable in this case because of a lack of consensus regarding what counts as consciousness in non- human systems (Seth et al., 2005). However, there is significant evolutionary conservation between different species, both with regards to anatomy and physiology as well as behavior and apparent cognitive capacities, to indicate that animal models would be very useful for a mechanistic understanding of the brain and its relation to consciousness (Boly et al., 2013; Mashour & Alkire, 2013). And, over the last few decades, there has been significant progress in understanding mechanisms and systems related to consciousness using animal models alongside studies of humans (Koch et al., 2016; Storm et al., 2017). Starting from research on humans, preferably driven by theoretical predictions, we can generate hypotheses about the relation between brain mechanisms and states commonly related with consciousness in humans. These hypotheses may be investigated with precise (and more invasive) interventional techniques in animal models to move our understanding further.

There are two main aspects that are typically studied in the science of consciousness: 1) whether or not there is any consciousness at all in some state - that is whether it feels like anything at all to be in that state or not (e.g. (Casali et al., 2013)) - and 2) whether some particular content is present within an otherwise clearly conscious state (e.g. (Dehaene & Changeux, 2004)). When working with humans, we have the benefit of having verbal reports available, so we can ask our participants if they 1) had any experience at all or 2) if some particular content was present in their conscious

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experience. Even though reports have their flaws (Tsuchiya, Wilke, Frässle, & Lamme, 2015), to me, they are still the closest thing we have to objective proof of consciousness in an individual.

Both types of studies can be done using either within-state or between-state designs (Koch et al., 2016). If possible, and if the target of study is consciousness per se, it is recommended to use within-state paradigms to avoid confounding effects related to physiological changes due to changing state (e.g. a lot changes in the body and brain when we go from wakefulness to dreamless sleep, and not all the changes are necessarily relevant for consciousness) (Koch et al., 2016). If theoretical predictions and empirical observations indicate that some set of properties in the brain is associated with what some test subject will reports about their conscious experience, we can move on to animal models and simulation studies to try to understand the underlying mechanisms for those properties better. Hopefully, these studies will shed light on brain properties hypothesized to be relevant for consciousness, and provide novel ways of testing theoretical predictions in humans or force updates to theories. Through a back-and-forth interaction between theoretical development, human studies, and work on animal models, we can hope to progress towards an understanding of the relationship between consciousness and the brain (Boly et al., 2013).

A lot of such progress has been made, and a set of properties have emerged as being supported both by theoretical ideas (Dehaene et al., 2011; Oizumi et al., 2014) as well as empirical evidence (see (Koch et al., 2016) for a review). In particular, properties related to the brain’s capacity for integrating information efficiently have been seen to differ between conscious and (assumed) unconscious states (e.g. effective brain connectivity (Alkire, 2008; Boly et al., 2011; Massimini et al., 2005), symbolic information transfer across the scalp (J.-R. King et al., 2013; Ku, Lee, Noh, Jun,

& Mashour, 2011), complexity or compressibility of brain activity (Casali et al., 2013; Shaw, Chen, Tsao, & Yen, 1999), integrated information (Seth et al., 2011; Giulio Tononi & Sporns, 2003), and measures quantifying causal interactions in a system (Nicolaou et al., 2012)). In brief, brains that lose their strong, but balanced, global integration, or have a reduced repertoire of dynamically available states, typically fall into unconsciousness (Mashour & Hudetz, 2018). Building on this progress, the work presented in this thesis has been focused on applying measures related to connectivity and complexity in the electrical brain activity to distinguish conscious from unconscious states in humans. We have applied measures inspired by theories of consciousness to EEG data, and tested their capacity for accurately distinguishing normal wakefulness from different types of altered states of consciousness in both patients and healthy volunteers.

Altered states of consciousness

To study the relation between consciousness and the brain, a starting point that seems reasonable is

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In particular, comparing normal wakefulness and some state of altered consciousness might shed light the properties of the brain that are important for consciousness. When using such between- state paradigms to study consciousness, there are several different states one could potentially compare. Perhaps the most common approach is to contrast normal wakefulness with states of apparent unconsciousness, such as sleep or anesthesia in healthy participants or patients (e.g.

(Massimini et al., 2005; M. Schartner et al., 2015)), or to compare brains of DOC patients with some matched control group (e.g. (Gosseries et al., 2011)). However, these types of contrasts, if applied naively in an attempt to understand what distinguishes consciousness from unconsciousness, suffer from the fact that unresponsiveness does not equal unconsciousness (Sanders et al., 2012).

For example, you might recall that dreams have been seen to occur frequently in all stages of sleep (Stickgold, Malia, Fosse, Propper, & Hobson, 2001) and anesthesia (Leslie et al., 2007), and that some patients diagnosed with UWS retain consciousness covertly (Monti et al., 2010). In fact, to what extent (or in what way) we are unconscious at all during sleep and anesthesia is disputed (Hudetz, 2008; Windt, Nielsen, & Thompson, 2016). Thus, we should be careful about concluding anything about consciousness as such from properties that distinguish conscious from apparently unconscious states, unless we have strict control of the person’s subjective state in both conditions.

That said, there have been a number of studies done, contrasting conscious and (apparently) unconscious states, which have revealed details about what happens in the brain under conditions when consciousness is altered and even lost as judged from the third person perspective. In the next few paragraphs, I will go into a little bit of detail about how certain properties of the brain seem to change when consciousness is apparently altered in anesthesia, sleep, DOC, and under the peculiar effects of psychedelic compounds.

Anesthesia

In clinical situations, the main aim for an anesthetists is to induce a state of amnesia, analgesia, unconsciousness, and immobilization (Forman & Chin, 2008), and the induced state of general anesthesia can be described as “a reversible drug-induced coma” (Brown et al., 2010). Even though general anesthetics have been common practice in clinical work for a long time (Antkowiak, 2001), the mechanisms of action for inducing all these effects have only begun to be properly understood quite recently (Franks, 2008; Rudolph & Antkowiak, 2004; Villars, Kanusky, & Dougherty, 2004).

Due to empirical observations indicating a link between a compound’s solubility in fats and and its anesthetic potency (the Meyer-Overton correlation), the idea that anesthetics acted within the lipid bilayer of biological membranes “was universally accepted for more than 60 years” (Antkowiak, 2001). However, this started to change when Franks and Lieb presented evidence suggesting that the anesthetic act, at least in part, on proteins to generate their effects (Franks & Lieb, 1978). Now,

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combination of facilitation of inhibitory transmission and blocking of excitatory transmission (Villars et al., 2004). Each anesthetic agent has its own particular set of effects on different ion channels (Alkire, Hudetz, & Tononi, 2008), leading to partially distinct changes to the global activity patterns of the brain (Purdon et al., 2015). However, they have in common the capacity for inducing a reversible state of unresponsiveness, from which humans are intended to have no memories (Forman & Chin, 2008).

The use of general anesthesia is not only common practice in the clinic; it is also a popular tool to use for the study of consciousness in humans (Alkire et al., 2008). In fact, general anesthesia has been used in scientific investigations of how brain function changes when consciousness is lost for as long as brain activity recordings have been available (Alkire et al., 2008; Gibbs, Gibbs, &

Lennox, 1937). From the beginning, it was clear that brain activity changed profoundly as patients were gradually induced from wakefulness, through sedation, to general anesthesia by various drugs.

Specifically, Gibbs et al. (1937) reported observing the emergence of high amplitude slow oscillations typical for deep sleep (so called slow waves (Sanchez-Vives, Massimini, & Mattia, 2017)), as patients became unresponsive and apparently unconscious. Still today, three quarters of a century later, the change from low amplitude, asynchronous, and high frequency brain activity (in normal wakefulness) to high amplitude activity dominated by low frequencies, is one of the surest signs of sufficient anesthesia for surgery (Koch et al., 2016; Purdon et al., 2015). However, with the advent of more advanced neuroimaging technology, other changes in the brain and its activity patterns have also become apparent. For example, general anesthesia induced unresponsiveness have been seen to be linked to a suppression of glucose consumption in thalamus, and a breakdown of the corticothalamic functional networks (White & Alkire, 2003). Multiple newer studies have shown related effects on measures of connectivity (Ferrarelli et al., 2010; Kim, Hwang, Kang, Kim,

& Choi, 2012; Lee, Kim, et al., 2009; Lewis et al., 2012; Schrouff et al., 2011), and the evidence is mounting for the idea that the apparent breakdown of brain connectivity is a potentially specific marker for anesthesia induced unconsciousness. Interestingly, there is some evidence that the spectral changes (that is, changes in frequency content such as the occurrence of slow waves) seen in the brain activity in response to anesthesia-induced loss of consciousness may be caused by changes in connectivity in the thalamo-cortical networks of the brain (Boly, Moran, et al., 2012).

Thus, even though the changes in spectral properties may be the most obvious change in brain activity related to loss of consciousness, the changes in brain connectivity may be more fundamental.

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Sleep

Another state that is commonly used to investigate difference between consciousness and unconsciousness is sleep. Sleep is a naturally occurring cyclic phenomenon involving large changes to the physiology of the brain, which is typically characterized by the loss (or at least massive suppression) of conscious awareness of the world around us (Nir, Massimini, Boly, & Tononi, 2013). Under normal conditions, a sleeping individual will cycle through different sleep stages that are distinguishable by their distinct activation patterns in the brain, as well as differences in muscle tone and presence of eye movements (Dement & Kleitman, 1957; Nir et al., 2013). The different patterns of brain activity that occur during sleep were described already in the 1930’s (Loomis, Harvey, & Hobart, 1936, 1937), and as was the case in the anesthesia field, the early findings (such as appearance of large slow waves, so-called K-complexes and spindle activity in deep sleep, and wake-like low amplitude random-looking activity in paradoxical (REM) sleep) are still considered to be among the most reliable markers of sleep stages. Already by the mid 1950’s, work towards understanding the cyclic variations in EEG and their relation to different behavioral states were well on their way, and sleep studies had shown that dream reports could be obtained from all sleep stages (all though they were seen to be dominant in REM-sleep) (Aserinsky & Kleitman, 1955; Dement &

Kleitman, 1957).

Interestingly, there are some similarities between changes occurring in the different stages of sleep and under the effects of certain anesthetic agents - both at the level of mechanisms of action (Vacas, Kurien, & Maze, 2013) and in the patterns of EEG activity (Brown et al., 2010; Murphy et al., 2011). Also, as might be expected, similar changes are seen in estimates of brain connectivity during deep sleep as in general anesthesia. For example, effective connectivity is seen to break down in non-REM sleep (Massimini et al., 2005) like in midazolam anesthesia (Ferrarelli et al., 2010), and it returns to resemble wakefulness during REM sleep (Casali et al., 2013) as was seen for participants undergoing ketamine anesthesia associated with vivid dreams (Sarasso et al., 2015).

Interestingly, though, the directionality of functional connectivity measures along the anterior- posterior axis has been seen to go from being predominantly back-to-front in wakefulness, to being more symmetric or even predominantly front-to-back in non-REM sleep (De Gennaro et al., 2004;

Lioi, Bell, Smith, & Simpson, 2017). In contrast, in anesthesia studies, inhibition of front-to-back connectivity has been seen to be a sign of unconsciousness during anesthesia (Ku et al., 2011; Lee et al., 2013). These differences may be artefacts of applying different measures of connectivity (especially given that the studies differ in their description of front-to-back connectivity in wakefulness), but this requires further work to clarify.

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Finally, another way to characterize changes in brain activity between wakefulness and different stages of sleep is to investigate how the complexity of brain activity is affected. Measures that quantify complexity in brain signals have repeatedly been shown to drop in non-REM sleep compared to wakefulness, but to return to wakefulness-levels in REM sleep associated with dreams (this has been shown using multiple measures quantifying complexity, e.g. using Approximate entropy (ApEn) (Burioka et al., 2005), Lempel-Ziv complexity (LZc) (Abásolo, Simons, Morgado da Silva, Tononi, & Vyazovskiy, 2015; M. M. Schartner, Pigorini, et al., 2017), or Perturbational Complexity Index (PCI) (Casali et al., 2013)). Again, this is comparable to findings in anesthesia experiments where complexity drops when participants are under the influence of anesthetic agents typically associated with unconsciousness (again using ApEn (Bruhn, Röpcke, & Hoeft, 2000), LZc (M. Schartner et al., 2015; Zhang, Roy, & Jensen, 2001), and PCI (Casali et al., 2013)), but does not drop if the anesthetic state is associated with vivid dreams (e.g. during carefully titrated ketamine anesthesia (Sarasso et al., 2015)). That said, I think it is important to reiterate here that most states of assumed unconsciousness (e.g. non-REM sleep or propofol anesthesia), are well known to sometimes be associated with dream-experiences (Leslie, 2017; Siclari et al., 2017). Therefore, measures that consistently indicate unconsciousness in such states may not be specifically sensitive to consciousness as such, but to be more closely related to the general state of the brain in states classically assumed to be unconscious.

Disorders of consciousness

Also among patients suffering from different DOCs, particular properties of brain activity have been observed to predictably differ between apparent levels of consciousness. Specifically, as was the case with sleep and anesthesia, the frequency content, connectivity, and complexity of brain signals are among the most reliable signatures of patients’ conscious state (Sitt et al., 2014). Again, changes in these properties are similar to what was observed in sleep and anesthesia with reduction in complexity (e.g. as quantified by ApEn (Sarà & Pistoia, 2010) and PCI (Casarotto et al., 2016)), and a disruption of connectivity patterns (e.g. using information transfer metrics (J.-R. King et al., 2013) or dynamic causal modeling (Boly et al., 2011)) in states associated with unconsciousness.

Interestingly, these changes appear to be reversed as patients improve, and their diagnosis changes from UWS to MCS and recovered (Casarotto et al., 2016; Marinazzo et al., 2014; Rosanova et al., 2012; Sitt et al., 2014). Thus, even for patient groups with potentially vast damage to the brain, the measures related to the brain’s capacity for information sharing and complex dynamics seem to be the dominant markers for consciousness.

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Psychedelics

Of course, I cannot write a section in my thesis titled ‘altered states of consciousness’ without mentioning psychedelic substances. After several decades of being a taboo topic in science, the study of psychedelic compounds has become an accepted endeavor. Along with the basis for their illegalization being disputed (Nutt, King, Phillips, & Independent Scientific Committee on Drugs, 2010), their usefulness as a tool to study the brain’s true repertoire of conscious states has yielded some interesting results (Carhart-Harris et al., 2012, 2014; Muthukumaraswamy et al., 2015;

Roseman, Leech, Feilding, Nutt, & Carhart-Harris, 2014; M. M. Schartner, Carhart-Harris, Barrett, Seth, & Muthukumaraswamy, 2017; Tagliazucchi, Carhart-Harris, Leech, Nutt, & Chialvo, 2014).

Several studies have gone into detail about the different compound’s phenomenological fingerprint (Studerus, Gamma, & Vollenweider, 2010), trying to understand how different mechanisms in the brain map on to the fantastical subjective effects of the compounds (T. A. Bowdle et al., 1998; Li &

Vlisides, 2016). Observations of changes in brain dynamics in response to a number of psychedelic states has given rise to new theoretical ideas about the relationship between consciousness and the brain (Carhart-Harris et al., 2014), and the effect of psychedelics on proposed markers of consciousness has fueled the fire of viewing the psychedelic state as one of increased consciousness (M. M. Schartner, Carhart-Harris, et al., 2017; Tagliazucchi et al., 2014). If nothing else, it is clear that psychedelics have a profound effect on conscious phenomenology, and the states and content they induce must be taken into consideration when we try to understand consciousness as such.

2.1.3

Some clarification of terminology

“As George Miller wrote in 1962, ‘Consciousness is a word worn smooth by a million tongues’.

Almost 50 years later, little has changed. The term means many different things to many different people, and no universally agreed ‘core meaning’ exists.” (Velmans, 2009)

The field of consciousness research is haunted by issues related to specific terms being used in ambiguous ways within the field (Antony, 2001), as well as words having distinct meaning within the academic field compared to their typical use in the general public. Such issues relate not only to the word consciousness itself, but also to concepts such as awareness, attention, perception, wakefulness and many others. Furthermore, it is unclear along which dimensions consciousness can be said to vary, and to which extent different categories or types of consciousness are useful to consider. In the following paragraphs, I will try to clarify how some of these terms are used in this particular thesis, to avoid unnecessary confusion.

In this thesis, consciousness is to be understood as that which seemingly disappears when we fall into dreamless sleep, and returns when we dream or wake up in the morning (Searle, 1998; G.

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Tononi & Edelman, 1998). On this view, being conscious is equivalent to having any experience at all, so that anyone (or anything) that would (if it could) truthfully answer ‘yes’ to the question ‘does it feel like something to be you?’, would be considered conscious. This is akin to the understanding of consciousness put forth by Nagel in his seminal paper ‘What is it like to be a bat’ (Nagel, 1974), and fits well with Searle’s working definition - “Consciousness consists of inner, qualitative, subjective states and processes of sentience or awareness” (Searle, 2000). Thus, any state of being with a subjective perspective is considered conscious, regardless of whether the contents of the experience are externally or internally generated. However, this is by no means the only way the word ‘consciousness’ can be understood (Antony, 2001). For example, it is quite common to subdivide consciousness into apparently distinct properties or dimensions such as wakefulness, awareness, alertness, responsiveness, attentiveness, and so on. Particularly, a distinction between wakefulness (as related to the so-called level of consciousness, indicated by spontaneous eye opening) and awareness (as related to the contents of consciousness, indicated by ability to report about experiences) has gained popularity in the field (Laureys, 2005).

Figure 1: Two figures indicating some authors’ understanding of the relation between levels and contents of consciousness. To the left, in panel A, a simplistic (but clinically relevant) distinction between the level of consciousness (as related to objectively observable behavior) and content of consciousness (understood as subjective awareness). Reprinted from “The neural correlate of (un)awareness: lessons from the vegetative state”, by Steven Laureys (2005). Copyright 2018 Copyright Clearance Center, Inc. To the right, in panel B, a visualization of a different understanding of the level of consciousness (as a function of the intensity, precision, and fragmentation of the contents of consciousness). Reprinted from “Multidimensional Models of Degrees and Levels of Consciousness”, by Peter Fazekas and Morten Overgaard (2016).Copyright 2018 Copyright Clearance Center, Inc.

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Even though the distinction between contents and levels of consciousness may be useful, not everyone is convinced of its relevance for properly describing fundamental aspects of consciousness (Bayne, Hohwy, & Owen, 2016; Fazekas & Overgaard, 2016). The idea of consciousness as graded on a scale from total unconsciousness to vivid wakefulness has been questioned, as it seems to be at odds with the conception of consciousness as being an either-or phenomenon defined by whether or not a creature has any subjective perspective at all (Bayne et al., 2016). The notion of graded levels of consciousness has also been proposed to be better understood as determined by the contents of consciousness (Bachmann, 2012; Overgaard & Overgaard, 2010), and that the apparent grading may be characterized by changes along one of multiple dimensions related to the contents (Fazekas

& Overgaard, 2016). On this view, the overall state of consciousness would be characterized by the contents that comprise it at the time, and level would relate to the intensity, precision, and fragmentation of these contents.

As the consensus is out on the exact relation between states, contents, and levels of consciousness, I will refrain from taking sides, but will try to be clear about my meaning when I use the terms. That said, to my understanding, any conscious being necessarily has some ‘state of consciousness’

(meaning that it feels in some particular way), and that state has some particular set of contents which distinguishes it from all other states of consciousness. Furthermore, I understand the concept of ‘level of consciousness’ to be a useful (but probably overly simplistic) descriptor of a state of consciousness, indicating something about the vividness, complexity, intensity, coherency, numerosity of the phenomenal contents that are accessible to the being. I will also use the terms

‘conscious state’ (or ‘unconscious state’), which will be used to refer to the full state of the being (such as ‘normal wakefulness’, ‘dreamless sleep’, ‘general anesthesia’, etc.). Finally, when I use terms such as ‘consciousness as such’ or ‘consciousness per se’, the statement should be interpreted as being about whether or not it feels like anything at all to be whatever state the statement is about.

2.2 Electrophysiological markers of consciousness

As we have seen, many measurement techniques that can be used to understand how brain properties change when the state of consciousness changes (EEG (Murphy et al., 2011), MRI (Gómez et al., 2013), PET (White & Alkire, 2003), etc.), and depending on the particular problem one is trying to solve (or aspect one wants to understand) the optimal technique will differ. For the sake of brevity, I will focus on a small subset of the potential properties and measurement techniques in this thesis. In the section that follows I will introduce properties that are derivable from brain activity recorded using non-invasive electrophysiological measurements in humans with EEG. I will start by giving a brief introduction to the EEG, where it comes from, and how it can be analyzed. Then I will give some examples of how properties of the EEG have been observed to

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change with the apparent state of consciousness, focusing on changes that occur when individuals are assumed to lose consciousness due to general anesthesia. Finally, I will introduce some of the EEG-based electrophysiological markers that have been applied in an attempt to monitor consciousness in the past, mention some interesting and theoretically relevant developments over the last few decades, and end by briefly introducing the specific markers that we have used in our work.

2.2.1

The electroencephalogram - EEG

The EEG is a graphical representation of fluctuating electric fields that is generated by the activity of the brain, and can be recorded from the scalp of human subjects (Gibbs, Davis, & Lennox, 1935).

The fact that currents of variable strength and direction could be measured along the surface of the brain, and between the gray matter and the scalp of animals, was well known already in the 19th century (see e.g. comments by (Caton, 1875)). However, the first relatively comprehensive study of how these currents, and their related electrical fields measured at the scalp, varied across different conditions in humans was carried out by Berger in the late 1920’s and early 30’s (Berger, 1929).

Berger also introduced the name EEG, as far as I am aware, and his work was soon followed up by researchers investigating how the EEG behaved in patients during epileptic seizures and under the effects of general anesthetics (Gibbs et al., 1935, 1937). It was also EEG technology that was used to observe the predictable and periodic manner in which brain activity patterns changed throughout the night, in what came to be known as sleep stages (Loomis et al., 1937). Even though the EEG has

Figure 2: Example of EEG (top) and electrocorticogram (ECG; bottom) recordings presented by Berger (1929) in his original article. The top graph shows the brain waves of a 38 year old female, from a bipolar recording with needle electrodes placed 6.5cm apart (inserted under the skin, over a crack in the bone over the motor region). Reprinted from ‘Über das

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been used as a tool to understand the brain for almost a century, and the main contributors to the field fluctuations recorded are understood (Buzsáki, Anastassiou, & Koch, 2012), the complete set of sources for the electric fields measured on the scalp is still unknown (Cohen, 2017).

Physical underpinnings, measurement, and analysis of the EEG signal

The EEG is a technology developed to measure the electrical activity of the brain, is thought to reflect the sum of electrical events in the brain, and can be used to investigate brain activity with a relatively coarse spatial resolution (Cohen, 2017). However, the exact way in which the neuronal activity (together with other electrical currents in the brain) translates to the measurable field fluctuations at the scalp is still not completely understood (Pesaran et al., 2018). That said, scalp EEG signals are classically believed to be dominated by the postsynaptic currents flowing along parallel dendrites in large populations of pyramidal neurons being synchronously activated (Buzsáki et al., 2012). The summed flux of ions across dendritic membranes in multiple neurons generates a sufficiently large concentration difference of ions between dendritic (superficial) and somatic (deeper) layers of the cortex to generate an effective electric dipoles measurable from the scalp.

Their effect can be measured at multiple levels - intracortically (using wire electrodes), intracranially (using electrocorticograms), or extracranially (using EEG) - and even though there are relatively good models and estimates for how the voltages measured at different levels relate to one another (and to the brain activity itself), there is currently no way to exactly determine what neural events caused some voltage fluctuation observed at the scalp level (Grech et al., 2008). In addition to complicating factors (e.g. spatial blurring due to varying conductivities in different tissue types and signal mixing due to volume conduction (Akhtari et al., 2002)), the problem of inferring the true sources of activity measured on the scalp is intractable without imposing massive constraints on our models. This is in part due to the low number of recording sites in any existing EEG apparatus relative to the number of (independent) sources of electric activity in the brain (Mahjoory et al., 2017).

Still, the EEG is a useful non-invasive tool for getting (at least some) information about the ongoing activity in the human brain. It is easy to use and has been developed over many decades to ensure relatively high signal-to-noise ratio in recordings. Furthermore, it can be applied in a variety of situations, and does not confine subject’s posture, position, or ability to move to the extent other tools for studying brain activity do (e.g. MRI, PET, MEG). In the normal research setting, the main components required for successful registration of brain signals with the EEG are 1) electrodes (and conductive medium) for reading signals from the head surface, 2) an amplifier to bring the signal into a range that can be accurately digitized, 3) a converter that can digitize the signal, and 4) a

Electrophysiological Markers of Consciousness: Measures of connectivity, complexity, and signal diversity in EEG for distinguishing between conscious and unconscious brain states