PROBING THE BRAIN\ubfS CAPACITY FOR CONSCIOUSNESS THROUGH THE SPATIOTEMPORAL COMPLEXITY OF THE CORTICAL ACTIVITY EVOKED BY TRANSCRANIAL MAGNETIC STIMULATION

Neuroscience faces the challenging task of developing and implementing objective measures of consciousness that can be applied to patients who are unable to interact with their external environment. The standard clinical assessment of these patients relies heavily on the subjective distinction between voluntary and involuntary or reflexive movements and electrophysiological and neuroimaging protocols have been recently developed to improve diagnosis and probe for signs of awareness. However, because the ability to unambiguously infer the capacity for consciousness through these novel techniques is determined ultimately not by consciousness itself but the awareness of a specific stimulus, their use to diagnose consciousness at the single-patient level is challenged by difficulties related to the application and interpretation of results. This thesis addresses the possibility for investigating the brain\u2019s capacity for consciousness, instead of the neural correlates of particular conscious perceptions, following a path that has not yet been explored. General considerations about what constitutes the content of consciousness led us to hypothesize that consciousness depends on the brain\u2019s capacity to sustain complex patterns of causal interactions between different areas of the thalamocortical system. To investigate this hypothesis, we employed the combination of navigated transcranial magnetic stimulation (TMS) and high-density electroencephalography (hd-EEG) and developed a feasible measure of brain complexity, the Perturbational Complexity Index (PCI), that was calculated in healthy subjects during alert wakefulness, sleep and anesthesia; and at the bedside of brain-injured, non-communicating patients, who gradually recovered from coma. PCI is a measure of the spatiotemporal complexity of the cortical activity evoked by TMS and is high only if many regions of the cerebral cortex react to the initial perturbation quickly and in different ways. Remarkably, in a total of 116 TMS sessions collected from 19 healthy subjects and 17 brain-injured patients, we invariably found high PCI values in conditions in which consciousness was clearly present and low PCI values in conditions in which consciousness was unambiguously reduced. This difference was able to reliably discriminate between conscious and unconscious healthy subjects, producing disjoint distributions that were independent of the stimulation parameters, the strength and the extent of the cortical activation. Moreover, PCI was able to detect progressive changes in consciousness, such as those that occur while a subject is falling asleep, and to discriminate between ambiguous consciousness levels (minimally conscious state) in patients suffering from disorders of consciousness from both lower (vegetative state, sleep/anesthesia) and higher (locked-in syndrome, healthy wakefulness) levels of consciousness. The spatiotemporal complexity of the cortical activity evoked by TMS is a single number that can be calculated at the bedside with little a priori information. Because this measure aims at the brain\u2019s capacity for consciousness, instead of behavioral or neural correlations of conscious perception, this technique does not depend on the willingness or ability of the patient to engage in assessment protocols and can be employed bypassing sensory pathways and subcortical structures to directly probe the thalamocortical system. Our results support PCI as an appropriate tool to approximate an objective measure of the neural correlate of consciousness with the potential to assist the diagnosis and prognosis in brain-injured patients and with unique theoretical implications to a science of consciousness

Methods and models for brain connectivity assessment across levels of consciousness

The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics. In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes. Indeed, the network approach for studying the brain has several advantages: 1) it eases the study of collective behaviors and interactions between regions; 2) allows to map and study quantitative properties of its anatomical pathways; 3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions. The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)

SLEEPING WHILE AWAKE: A NEUROPHYSIOLOGICAL INVESTIGATION ON SLEEP DURING WAKEFULNESS.

Il sonno e la veglia vengono comunemente considerati come due stati distinti. L\u2019alternanza tra essi, la cui presenza \ue8 stata dimostrata in ogni specie animale studiata fino ad oggi, sembra essere una delle caratteristiche che definisce la nostra vita. Allo stesso tempo, per\uf2, le scoperte portate alla luce negli ultimi decenni hanno offuscato i confini tra questi due stati. I meccanismi del sonno hanno sempre affascinato i neurofisiologi, che infatti, nell\u2019ultimo secolo, li hanno caratterizzati in dettaglio: ora sappiamo che all\u2019attivit\ue0 del sonno sottost\ue0 una specifica attivit\ue0 neuronale chiamata slow oscillation. La slow oscillation, che \ue8 costituita da (ancora una volta) un\u2019alternanza tra periodi di attivit\ue0 e periodi di iperpolarizzazione e silenzio neuronale (OFF-periods), \ue8 la modalit\ue0 base di attivazione del cervello dormiente. Questa alternanza \ue8 dovuta alla tendenza dei neuroni surante lo stato di sonno, di passare ad un periodo silente dopo un\u2019attivazione iniziale, una tendenza a cui viene dato il nome di bistabilit\ue0 neuronale. Molti studi hanno dimostrato come la bistabilit\ue0 neuronale tipica del sonno ed i relativi OFF-periods, possano accadere anche durante la veglia in particolari condizioni patologiche, nelle transizioni del sonno e durante le deprivazioni di sonno. Per questo motivo, se accettassimo che la bistabilit\ue0 neuronale e gli OFF-periods rappresentino una caratteristica fondamentale del sonno, allora dovremmo ammettere che stiamo assistendo ad un cambio di paradigma: da una prospettiva neurofisiologica il sonno pu\uf2 intrudere nella veglia. In questa tesi ho analizzato i nuovi -fluidi- confini tra sonno e veglia e le possibili implicazioni di questi nel problema della persistenza personale attraverso il tempo. Inoltre, ho studiato le implicazioni cliniche dell\u2019intrusione di sonno nella veglia in pazienti con lesioni cerebrali focali di natura ischemica. In particolare, i miei obiettivi sono stati: 1) Dimostrare come la bistabilit\ue0 neuronale possa essere responsabile della perdita di funzione nei pazienti affetti da ischemia cerebrale e come questo potrebbe avere implicazioni nello studio della patofisiologia dell\u2019ischemia cerebrale e nella sua terapia; 2) Stabilire le basi per un modello di sonno locale presente nella vita di tutti i giorni: la sensazione di sonnolenza. Infatti, essa potrebbe riflettere la presenza di porzioni di corteccia in stato di sonno, ma durante lo stato di veglia; 3) Difendere il criterio biologico di identit\ue0, che troverebbe nell\u2019attivit\ue0 cerebrale la continuit\ue0 necessaria al mantenimento della nostra identit\ue0 nel tempo.Sleep and wakefulness are considered two mutually exclusive states. The alternation between those two states seems to be a defining characteristic of our life, a ubiquitous phenomenon demonstrated in every animal species investigated so far. However, during the last decade, advances in neurophysiology have blurred the boundaries between those states. The mechanisms of sleep have always intrigued neurophysiologists and great advances have been made over the last century in understanding them: we now know that the defining characteristic underlying sleep activity is a specific pattern of neuronal activity, namely the slow oscillation. The slow oscillation, which is characterized by the periodic alternation between periods of activity (ON-periods) and periods of hyperpolarization and neuronal silence (OFF-periods) is the default mode of activity of the sleeping cortex. This alternation is due to the tendency of neurons to fall into a silent period after an initial activation; such tendency is known as \u201cbistability\u201d. There is accumulating evidence that sleep-like bistability, and the ensuing OFF-periods, may occur locally in the awake human brain in some pathological conditions, in sleep transition, as well as after sleep deprivation. Therefore, to the extent that bistability and OFF periods represents the basic neuronal features of sleep, a paradigm shift is in place: from a neurophysiological perspective sleep can intrude into wakefulness. In this thesis, I explore the fluid boundaries between sleep and wakefulness and investigate their possible implications on the problem of personal persistence over time. Moreover, I study the clinical implications of the intrusion of sleep into wakefulness in patients with focal brain injury due to stroke. Specifically, I aim to: 1) show how the sleep-like bistability can be responsible for the loss of function in stroke patients. This may have implications for understanding the pathophysiology of stroke and helping to foster recovery; 2) establish the basis for a model of local sleep that might be present in the everyday life, id est the sensation of sleepiness. Indeed, sleepiness could reflect islands of sleep during wakefulness; 3) advocate the biological criterion of identity, in which the continuity necessary for maintaining ourselves over time could be represented by never resting activity in the brain

Propofol-induced unresponsiveness is associated with impaired feedforward connectivity in cortical hierarchy

peer reviewedBackground: Impaired consciousness has been associated with impaired cortical signal propagation after transcranial magnetic stimulation (TMS). We hypothesised that the reduced current propagation under propofol-induced unresponsiveness is associated with changes in both feedforward and feedback connectivity across the cortical hierarchy. Methods: Eight subjects underwent left occipital TMS coupled with high-density EEG recordings during wakefulness and propofol-induced unconsciousness. Spectral analysis was applied to responses recorded from sensors overlying six hierarchical cortical sources involved in visual processing. Dynamic causal modelling (DCM) of induced time–frequency responses and evoked response potentials were used to investigate propofol's effects on connectivity between regions. Results: Sensor space analysis demonstrated that propofol reduced both induced and evoked power after TMS in occipital, parietal, and frontal electrodes. Bayesian model selection supported a DCM with hierarchical feedforward and feedback connections. DCM of induced EEG responses revealed that the primary effect of propofol was impaired feedforward responses in cross-frequency theta/alpha–gamma coupling and within frequency theta coupling (F contrast, family-wise error corrected P<0.05). An exploratory analysis (thresholded at uncorrected P<0.001) also suggested that propofol impaired feedforward and feedback beta band coupling. Post hoc analyses showed impairments in all feedforward connections and one feedback connection from parietal to occipital cortex. DCM of the evoked response potential showed impaired feedforward connectivity between left-sided occipital and parietal cortex (T contrast P=0.004, Bonferroni corrected). Conclusions: Propofol-induced loss of consciousness is associated with impaired hierarchical feedforward connectivity assessed by EEG after occipital TMS. © 2018 British Journal of AnaesthesiaARC-06/11-34

MVAR ANALYSIS OF IEEG SIGNALS TO DIFFERENTIATE CONSCIOUS STATES

Neuroscience is a highly multidisciplinary and rapidly evolving research field. An important recent challenge of this discipline is the investigation of the so-called connectome. According to its original meaning, connectome is the map of the all brain neural connections. In this framework, the cognitive processes are not seen as localized in specific loci, but stored and processed in a distributed manner. Connectome aims to map and under-stand the organization of neural interactions trying, at the same time, to explain the role of functional units within the brain system. In particular, one of the most difficult and un-solved tasks in neuroscience is the identification of the areas, connections or brain func-tions that are called neuronal correlates of consciousness (NCCs). In this thesis the neural activity was explored by analysing human brain signals ac-quired during medical procedure. Signals from patients with drug resistant epilepsy were acquired by means of electrodes placed deep in the cortex (intracranial electroencephalog-raphy, EEG-iEEG), positioned in order to localize the epileptogenic focus. The technique, called stereotactic EEG (SEEG), guided and flanked by detailed 3D images, also pro-vides for periodical intracranial single-pulse electrical stimulation (SPES) to highlight are-as of interest. The continuous recording of the EEG activity took place for several days, and signals were grouped in two datasets: one acquired during wakefulness (WAKE) and the other one during the Non-Rapid Eye Movement sleep (NREM), stage 3. The signals were processed by means of two methods based on a multivariate auto-regressive model (MVAR). The first method was DTF (Directed Transfer Function), that is an estimator of the information flow between structures, depending on the signal fre-quency; it is able to describe which structure influences another. The second one was ADTF (Adaptive DTF) that permits to study the time-variant signal features, capturing their temporal dynamics. In addition to these connectivity analysis, feature extraction and classification techniques have been employed. The main aim of the dissertation is to evaluate methods and carry out analyses useful to distinguish between conscious and unconscious states, corresponding to WAKE and NREM respectively, studying at the same time the brain connectivity in response to Single Pulse Electrical Stimulation in intracranial EEG data. Massimini\u2019s group (Department of Biomedical and Clinical sciences \u201cL. Sacco\u201d, Uni-versit\ue0 degli Studi di Milano) revealed a different behavior for signals from the two states, WAKE and NREM: they noted a reactivation of the signal around 300 ms after the system perturbation in WAKE and, in contrast, a period of neural silence (down-state) in NREM condition. A hypothesis about the origin of the reactivation phenomenon is a feedback activity, i.e. the result of the activity from the rest of the network. In the thesis, the ADTF method was chosen to shed light on the down-state effect, paying attention to a defined temporal slice of data. The analysis was completed by the application of the DTF procedure, that was chosen to compare the two consciousness states and underline their differences in the frame of network connectivity. The analysis carried out lead to the following results: \uf0a7 Indication of useful combinations of features and techniques able to distinguish the states of interest \uf0a7 Observations of neural connection changes over frequency and time consider-ing causal relationships \uf0a7 Comparison of connectivity results using different re-referencing styles \uf0a7 Endorsement of the anatomical-functional importance of some channels corre-sponding to specialized brain areas. As conclusion of the analysis it was possible to identify a series of anatomical-functional brain features useful to discriminate the two mentioned states, therefore to speculate on the possibility to differentiate conscious and unconscious states with computational tools

A Quest for Meaning in Spontaneous Brain Activity - From fMRI to Electrophysiology to Complexity Science

The brain is not a silent, complex input/output system waiting to be driven by external stimuli; instead, it is a closed, self-referential system operating on its own with sensory information modulating rather than determining its activity. Ongoing spontaneous brain activity costs the majority of the brain\u27s energy budget, maintains the brain\u27s functional architecture, and makes predictions about the environment and the future. I have completed three separate studies on the functional significance and the organization of spontaneous brain activity. The first study showed that strokes disrupt large-scale network coherence in the spontaneous functional magnetic resonance imaging: fMRI) signals, and that the degree of such disruption predicts the behavioral impairment of the patient. This study established the functional significance of coherent patterns in the spontaneous fMRI signals. In the second study, by combining fMRI and electrophysiology in neurosurgical patients, I identified the neurophysiological signal underlying the coherent patterns in the spontaneous fMRI signal, the slow cortical potential: SCP). The SCP is a novel neural correlate of the fMRI signal, most likely underlying both spontaneous fMRI signal fluctuations and task-evoked fMRI responses. Some theoretical considerations have led me to propose a hypothesis on the involvement of the neural activity indexed by the SCP in the emergence of consciousness. In the last study I investigated the temporal organization across a wide range of frequencies in the spontaneous electrical field potentials recorded from the human brain. This study demonstrated that the arrhythmic, scale-free brain activity often discarded in human and animal electrophysiology studies in fact contains rich, complex structures, and further provided evidence supporting the functional significance of such activity

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

Neural correlates of consciousness in the complexity of brain networks

How do we define consciousness? Besides philosophical endeavours, the development of modern neuroimaging techniques fostered a principled way of quantifying the neural correlates of consciousness. Acquiring and analysing resting-state functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data, has allowed neuroscientists to noninvasively map the brain’s functional interactions (or functional connectivity). Based on data obtained during controlled loss of consciousness and in cases of patients with disorders of consciousness, it has now been suggested that multiple, functionally specialized/segregated areas need to interact and integrate information in order to support consciousness. Thus an emerging idea in neuroscience is that the brain needs to balance the coexistence of functional segregation and integration, a property often termed as brain complexity, in order to produce consciousness. A resulting hypothesis is that consciousness is abolished when the balance between segregation and integration is lost and brain complexity is attenuated. In that regard, I use complexity of functional connectivity, an aggregate measure of segregation and integration, as a marker of consciousness. This effort consists of two parts. First, I provide evidence that complexity in the healthy, awake brain is critical in the sense that it reflects a critical balance of segregation and integration designed to support efficient information communication. In turn, I provide evidence that loss of consciousness is associated with decreased complexity i.e. that functional connectivity departs from the critical complexity of the healthy, awake brain towards a more segregated configuration. The structure of this thesis follows accordingly. In the first experimental chapter (3), I show the importance of the critical balance of complexity in the healthy, awake brain by using a structure-to-function association model. Specifically, I show that complexity can be derived upon certain optimal, structural connections (computed as the Nash equilibrium between regions), which promote efficient communication in the brain from the regional to the whole-brain level. Chapter 4 focuses on capturing alterations of complexity in cases of sedation, anaesthesia and disorders of consciousness. Specifically, I show that as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness, functional connectivity becomes less complex and more segregated. A refined approach that quantifies complexity in different parts of the brain allowed me to see whether this reduction in complexity is more evident in specific regions and networks. Under this framework, at the regional level I provide evidence that sparsely connected regions linking different parts of the brain play a critical role in whole-brain complexity. At the network level I show the importance of the default mode network in whole-brain complexity. Even during rest, the brain is not static and displays rich temporal dynamics. Thus it is not only the complexity at each snapshot of time but also how complexity changes across time that can help us understand loss of consciousness. In chapter 5 I use a dynamic framework to derive and characterize the dynamics of functional connectivity during loss of consciousness. In turn, I provide evidence that brains become less temporally complex as one goes from the awake state to anaesthetic-induced unconsciousness and disorders of consciousness. Moreover, my goal is to see whether the principle of complexity reduction can be also applied to the developing brain. Towards this direction, in chapter 6 I use complexity on EEG connectivity data to examine anaesthetic-induced loss of consciousness in infants. Specifically, I show that complexity in anaesthetised infants aged 0-3 years is reduced compared to a state of emergence from anaesthesia, indicating its importance in supporting consciousness and brain function since infancy. Taken together, these findings show that while the complexity of the healthy, awake brain during rest is critically configured, the unconscious brain is characterized by reduced complexity. Based on the results presented in this work, I propose that consciousness can be assessed on the basis of complexity of resting-state functional connectivity data

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as “Integrated Information Decomposition,” which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems — including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients’ structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence

Consciousness unbound: social simulation theory of dreaming

Every night during sleep we experience an immersive world of dreams, woven together by our sleeping brain unbound by external stimulation. Despite considerable effort the question of why we dream has eluded a conclusive answer. Understanding dreams also arguably makes progress toward answering the broader question of consciousness: why do we experience anything at all? I attempt to illuminate these questions by concentrating on the quintessentially social nature of dreams. First, in Study I a novel theoretical account —the Social Simulation Theory of dreaming (SST)—is proposed, together with the first outlines of a research program for its empirical study. SST suggests the world simulation form of dreams provides clues for its function by preferentially simulating certain kinds of scenariosnamely social interactions. Second, in Studies II and III specific hypotheses derived from the SST in Study I are empirically evaluated. These provide evidence for dreams to contain more social content than corresponding waking life and to remain so even when social interactions are removed from waking life (Sociality Bias). Furthermore, the Strengthening Hypothesis that suggests dreams serve to maintain and/or increase social bonding with close others gains partial support. The Practise and Preparation Hypothesis gained support as dreams simulated positive interactions in one fifth of dream interactions and overall simulate complex social behaviours. The Compensation Hypothesis suggests dreams simulations to increase when waking social contacts are abolished, but this was not supported in the data as dream sociality remained stable despite social seclusion. When excluded from others our dreams reconfigure to decrease simulations of interactions with strangers. However, dreams during normal day-to-day life do not preferentially simulate bond-strengthening interactions with close others. In opposition to previous findings, Study II found no differences in social dream contents between either stage of sleep or time of night. In Study III a short social seclusion showed not only differences in dream content, but also in sleep structure, with an increase in REM sleep. Third, methodological development was undertaken by, both, developing a content analysis method for extracting social episodes in narrative reports (Social Content Scale, SCS; Study II), and by assessing the validity of a novel home sleep monitor device, the Beddit Sleep Tracker (BST). While the SCS proved useful for categorizing the social features in both studies II and III, BST failed to provide accurate sleep data as measured against a polysomnogram. Overall, the development of SST and the initial empirical evidence for some of its hypotheses brings us closer to understanding the twin problems of dreaming and consciousness.Kahlitsematon tajunta: unennäön sosiaalisen simulaation teoria Nukkuvat aivomme kehittävät joka yö ajankohtaisesta aistitiedosta riippumattoman monipuolisen ja todentuntuisen kokemuksen maailmasta—unen. Kysymykseen siitä miksi koemme unia ei ole yrityksistä huolimatta vielä saatu kattavaa vastausta. Unien luonteen ymmärtäminen toisi meitä todennäköisesti lähemmäs myös suuremman, tajunnan luonnetta koskevan kysymyksen ratkaisua: miksi ylipäänsä koemme mitään? Pyrin valottamaan näitä kysymyksiä keskittymällä erityisesti unien sosiaaliseen luonteeseen. Osatutkimuksessa I kehitämme uuden sosiaalisen simulaation teorian (SST) sekä esittelemme tutkimusohjelman sen väitteiden empiiriseen arviointiin. SST hyödyntää näkemystä unien maailma-simulaatio-muodosta ymmärtääkseen niiden funktiota, keskittyen erityisesti unien taipumukseen painottaa sosiaalisten tilanteiden simulointia. Osatutkimuksissa II ja III tutkimme SST:n hypoteeseja empiirisen unitutkimuksen keinoin. Sosiaalisuusvinouma unista poikkeuksellisen sosiaalisina kokemuksina saa vahvistusta löydöksestä, jossa unissa havaitaan olevan merkittävästi vastaavaa valvetta enemmän sosiaalisia tilanteita (II), ja vaikka sosiaalisia tilanteita ei esiintyisi arjessa, pysyy niiden määrä unissa ennallaan. Lisäksi vahvistushypoteesi, jonka mukaan unet vahvistavat erityisesti läheisiä ihmissuhteitamme, saa osittaista tukea. Osatutkimus III:ssa lyhyt sosiaalinen eristys johtaa muutoksiin unihahmojen luonteessa, unien alkaessa sisältää vähemmän tuntemattomien kanssa koettuja vuorovaikutustilanteita. Harjoitushypoteesi sai osin tukea unien simuloidessa monimutkaisia, ja viidenneksen positiivisia vuorovaikutustilanteita. Kompensaatio-hypoteesin mukaan vuorovaikutusunet lisääntyvät arjen sosiaalisten suhteiden poistuessa, mutta tämä ei saanut tukea unisosiaalisuuden pysyessä entisellään eristyksestä huolimatta. Normaalisti unissa emme kuitenkaan erityisesti simuloi vahvistavia vuorovaikutustilanteita läheisten kanssa, eivätkä unien sosiaaliset sisällöt eroa univaiheen tai nukkumisen keston mukaan (II). Sosiaalinen eristys kuitenkin lisäsi myös REM-unen osuutta. Lopuksi, väitöskirjassa menetelmäkehitystä edistettiin sekä luomalla uusi sisällönanalyysimenetelmä sosiaalisten tilanteiden luokitteluun (SCS) että tutkimalla unta mittaavan Beddit-unimittarin (BST) tarkkuutta mitata nukkumista ja univaiheita. Siinä missä SCS osoittautui käyttökelpoiseksi menetelmäksi sosiaalisten tilanteiden kategorisointiin, BST ei kyennyt esittämään luotettavaa tietoa unimuuttujista verrattuna unipolygrafiaan. Lopputulemana, SST ja sen ensimmäisten hypoteesien tutkimus tuo meidät lähemmäs unennäön ja tajunnan kaksoisongelmien ratkaisua