Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

Clinical Electroencephalography for Anesthesiologists

The widely used electroencephalogram-based indices for depth-of-Anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant TR01-GM104948

Studying Resting State and Stimulus Induced BOLD Activity using the Generalized Ising Model and Independent Component Graph Analysis

Although many technical advancements have been made, neuroscientists still struggle to explain the underlying behaviour of how brain regions communicate with each other to form large-scale functional networks. functional Magnetic Resonance Imaging (fMRI) has been commonly used to investigate changes between brain regions over time using the Blood Oxygen Level Dependent (BOLD) signal. The goal of this thesis is to show the applicability of novel techniques and tools, such as the generalized Ising model (GIM) and the independent component graph analysis (GraphICA), to obtain information on the functional connectivity of populations with altered perception of consciousness. The GIM was used to model brain activity in healthy brains during various stages of consciousness, as induced by an anesthetic agent, propofol, in the auditory paradigm. GraphICA, a tool that combines ICA and graph theory was used to investigate the functional connectivity of resting state networks (RSNs) in patients with altered perception caused by tinnitus and in patients with altered states of consciousness caused by severe brain injury. For the tinnitus patients, we examined whether a correlation exists between tinnitus behavioural scores and functional brain connectivity of RSNs. Moreover, for the severely brain injured patients, a multimodal neuroimaging approach with hybrid FDG-PET/MRI was implemented to study the functional connectivity changes of the RSNs. The GIM simulated with an external field was able to model the brain activity at different levels of consciousness under naturalistic stimulation, at a temperature in the super critical regime. Further, a strong correlation was observed between tinnitus distress and the connectivity pattern within and between the right executive control network and the other RSNs. This suggests that tinnitus distress is the consequence of a hyperactive attention condition. A variability was observed in the appearance and temporal/spatial patterns of RSNs among the two resting state fMRI acquisitions acquired within the same scanning session of the severely brain injured patients. This suggests the need for new strategies to be developed in order to pick the best RSN from each acquisition. Overall, this work demonstrated that the GIM and GraphICA were promising tools to investigate brain activity of populations with altered perception of consciousness and in future can be extended to investigate different neurological populations

Doctor of Philosophy

dissertationRecording the neural activity of human subjects is indispensable for fundamental neuroscience research and clinical applications. Human studies range from examining the neural activity of large regions of the cortex using electroencephalography (EEG) or electrocorticography (ECoG) to single neurons or small populations of neurons using microelectrode arrays. In this dissertation, microscale recordings in the human cortex were analyzed during administration of propofol anesthesia and articulate movements such as speech, finger flexion, and arm reach. Recordings were performed on epilepsy patients who required long-term electrocorticographic monitoring and were implanted with penetrating or surface microelectrode arrays. We used penetrating microelectrode arrays to investigate the effects of propofol anesthesia on action potentials (APs) and local field potentials (LFPs). Increased propofol concentration correlated with decreased high-frequency power in LFP spectra and decreased AP firing rates, as well as the generation of large amplitude spike-like LFP activity; however, the temporal relationship between APs and LFPs remained relatively consistent at all levels of propofol anesthesia. The propofol-induced suppression of neocortical network activity allowed LFPs to be dominated by low-frequency spike-like activity, and correlated with sedation and unconsciousness. As the low-frequency spike-like activity increased, and the AP-LFP relationship became more predictable, firing rate encoding capacity was impaired. This suggests a mechanism for decreased information processing in the neocortex that accounts for propofol-induced unconsciousness. We also demonstrated that speech, finger, and arm movements can be decoded from LFPs recorded with dense grids of microelectrodes placed on the surface of human cerebral cortex for brain computer interface (BCI) applications using LFPs recorded over face-motor area, vocalized articulations of ten different words and silence were classified on a trial-by-trial basis with 82.4% accuracy. Using LFPs recorded over the hand area of motor cortex, three individual finger movements and rest were classified on a trial-by-trial basis with 62% accuracy. LFPs recorded over the arm area of motor cortex were used to continuously decode the arm trajectory with a maximum correlation coefficient of 0.82 in the x-direction and 0.76 in the y-direction. These findings demonstrate that LFPs recorded by micro-ECoG grids from the surface of the cerebral cortex contain sufficient information to provide rapid and intuitive control a BCI communication or motor prosthesis

Anesthetic-induced unresponsiveness: Electroencephalographic correlates and subjective experiences

Anesthetic drugs can induce reversible alterations in responsiveness, connectedness and consciousness. The measures based on electroencephalogram (EEG) have marked potential for monitoring the anesthetized state because of their relatively easy use in the operating room. In this study, 79 healthy young men participated in an awake experiment, and 47 participants continued to an anesthesia experiment where they received either dexmedetomidine or propofol as target-controlled infusion with stepwise increments until the loss of responsiveness. The participants were roused during the constant drug infusion and interviewed. The drug dose was increased to 1.5-fold to achieve a deeper unresponsive state. After regaining responsiveness, the participants were interviewed. EEG was measured throughout the experiment and the N400 event-related potential component and functional and directed connectivity were studied. Prefrontal-frontal connectivity in the alpha frequency band discriminated the states that differed with respect to responsiveness or drug concentration. The net direction of connectivity was frontal-to-prefrontal during unresponsiveness and reversed back to prefrontal-to-frontal upon return of responsiveness. The understanding of the meaning of spoken language, as measured with the N400 effect, was lost along with responsiveness but, in the dexmedetomidine group, the N400 component was preserved suggesting partial preservation of the processing of words during anesthetic-induced unresponsiveness. However, the N400 effect could not be detected in all the awake participants and the choice of analysis method had marked impact on its detection rate at the individual-level. Subjective experiences were common during unresponsiveness induced by dexmedetomidine and propofol but the experiences most often suggested disconnectedness from the environment. In conclusion, the doses of dexmedetomidine or propofol minimally sufficient to induce unresponsiveness do not render the participants unconscious and dexmedetomidine does not completely abolish the processing of semantic stimuli. The local anterior EEG connectivity in the alpha frequency band may have potential in monitoring the depth of dexmedetomidine- and propofol-induced anesthesia.Anesteettien aiheuttama vastauskyvyttömyys: aivosähkökäyräpohjaiset korrelaatit ja subjektiiviset kokemukset Anestesialääkkeillä voidaan saada aikaan palautuvia muutoksia vastauskykyisyydessä, kytkeytyneisyydessä ja tajunnassa. Aivosähkökäyrään (EEG) pohjautuvat menetelmät tarjoavat lupaavia mahdollisuuksia mitata anestesian vaikutusta aivoissa, sillä niitä on suhteellisen helppo käyttää leikkaussalissa. Tässä tutkimuksessa 79 tervettä nuorta miestä osallistui valvekokeeseen ja 47 heistä jatkoi anestesiakokeeseen. Anestesiakokeessa koehenkilöille annettiin joko deksmedetomidiinia tai propofolia tavoiteohjattuna infuusiona nousevia annosportaita käyttäen, kunnes he menettivät vastauskykynsä. Koehenkilöt herätettiin tasaisen lääkeinfuusion aikana ja haastateltiin. Koko kokeen ajan mitattiin EEG:tä, josta tutkittiin N400-herätevastetta sekä toiminnallista ja suunnattua konnektiivisuutta. Prefrontaali-frontaalivälillä mitattu konnektiivisuus alfa-taajuuskaistassa erotteli toisistaan tilat, jotka erosivat vastauskykyisyyden tai lääkepitoisuuden suhteen. Konnektiivisuuden vallitseva suunta oli frontaalialueilta prefrontaalialueille vastauskyvyttömyyden aikana, mutta se kääntyi takaisin prefrontaalisesta frontaaliseen kulkevaksi koehenkilöiden vastauskyvyn palatessa. N400-efektillä mitattu puhutun kielen ymmärtäminen katosi vastauskyvyn menettämisen myötä. Deksmedetomidiiniryhmässä N400-komponentti säilyi, mikä viittaa siihen, että anesteettien aiheuttaman vastauskyvyttömyyden aikana sanojen prosessointi voi säilyä osittain. Yksilötasolla N400-efektiä ei kuitenkaan havaittu edes kaikilla hereillä olevilla henkilöillä, ja analyysimenetelmän valinnalla oli suuri vaikutus herätevasteen havaitsemiseen. Subjektiiviset kokemukset olivat yleisiä deksmedetomidiinin ja propofolin aiheuttaman vastauskyvyttömyyden aikana, mutta kokemukset olivat usein ympäristöstä irtikytkeytyneitä. Yhteenvetona voidaan todeta, että deksmedetomidiini- ja propofoliannokset, jotka juuri ja juuri riittävät aikaansaamaan vastauskyvyttömyyden, eivät aiheuta tajuttomuutta. Deksmedetomidiini ei myöskään täysin estä merkityssisällöllisten ärsykkeiden käsittelyä. Frontaalialueen sisällä EEG:llä mitattu konnektiivisuus alfataajuuskaistassa saattaa olla tulevaisuudessa hyödyllinen menetelmä deksmedetomidiini- ja propofolianestesian syvyyden mittaamiseksi

Investigating the role of Gamma-aminobutyric acid (GABA) in sedation: a combinedelectrophysiological, haemodynamicand spectroscopic study in humans

A better understanding of the mechanisms of anaesthesia and sedation are expected not only to improve the understanding of the neural correlates of consciousness but also to help improve safety from the complications of anaesthesia/ sedation and develop safer drugs and objective brain function monitoring systems. Neuroimaging modalities such as functional MRI, magnetoencephalography and MR spectroscopy provide complimentary information about brain functions and can help interrogate brain activity in a living human brain. Most anaesthetic drugs act by enhancing the inhibitory actions of GABA in the brain. Most neuroimaging research has focused on anaesthetic-induced unconsciousness, with only few investigating the earliest levels of sedation-induced altered consciousness. The work in this thesis used a range of advanced neuroimaging modalities to investigate the role of GABA (through a GABA-ergic drug, propofol), during mild sedation, in humans. This was performed as a series of experiments within two, sequential, scanning sessions, MEG followed by fMRI, in the same participants. Propofol resulted in a dissociation of the visual gamma band response (decreased evoked, increased induced power). This was related to a reduced BOLD fMRI response but there were no changes in MRS detectable GABA concentration. Response to multisensory stimulation also revealed interesting changes with MEG and fMRI. Functional connectivity analyses showed changes in connectivities of the posterior cingulate cortex (key hub of default-mode network) and thalamus with each other and other key brain regions. Resting state networks were identified with MEG too, which revealed interesting increases in connectivity in certain band- limited networks while motor networks showed no change. Perfusion fMRI using arterial spin labelling revealed a global and regional reduction in perfusion, highlighting some of the key regions (frontal cortex, precuenus, PCC and thalamus) involved in sedation

Probing resting-state functional connectivity in the infant brain: methods and potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Moreover, potent postnatal brain plasticity engenders increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Recently, resting-state functional magnetic resonance imaging (fMRI) emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Its application has expanded to infant populations in the past decade, providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal/ disease states. However, rapid extension of the resting-state technique to infant populations leaves many methodological issues need to be resolved prior to standardization of the technique. The purpose of this thesis is to describe a protocol for intrinsic functional connectivity analysis, and extraction of resting-state networks in infants <12 months of age using the data-driven approach independent component analysis (ICA). To begin, we review the evolution of resting-state fMRI application in infant populations, including the biological premise for neural networks. Next, we present a protocol designed such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature. Presented protocol provides detailed, albeit basic framework for RSN analysis, with interwoven discussion of basic theory behind each technique, as well as the rationale behind selecting parameters. The overarching goal is to catalyze efforts towards development of robust, infant-specific acquisition and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used. Finally, we review the literature, current methodological challenges and potential future directions for the field of infant resting-state fMRI

Resting State Network Dynamics Across Wakefulness and Sleep

The function of sleep is a longstanding mystery of the brain. By contrast, the function of resting state networks (RSNs) is one of its most recent mysteries. The relationship between RSNs and neuronal activity has been unclear since RSNs were discovered during the advent of functional magnetic resonance imaging (fMRI). Somewhat paradoxically, investigating these enigmatic phenomena in parallel can help to illuminate the function of both. The three studies described as part of this thesis all involve an evaluation of RSN dynamics across wakefulness and sleep. They are all based on the same dataset, derived from an experimental paradigm in which healthy, non sleep-deprived participants (N=36, 21 female) slept in an MRI scanner, as their brain activity was recorded using simultaneous electroencephalography (EEG)-fMRI. An independent component analysis (ICA) was performed in the first study. Spatial boundaries of components in each sleep stage were compared with those of wakefulness, in the first attempt to catalogue RSNs across all healthy alternate functional modes of the brain. Against expectations, all non-wake-RSN components were positively identified as noise. This indicated that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. In the second study, between-RSN functional connectivity (FC) dynamics were evaluated across wakefulness and sleep, in order to determine whether they reflect known cortical neurophysiological dynamics. This was confirmed, highlighting the connection between RSNs and neuronal activity. Moreover, the dynamic pattern suggested that one of the functions of sleep may be to homeostatically counterbalance wakefulness RSN FC. A further pattern, indicating increased FC of “higher-order” RSNs (e.g., default mode network), suggested that slow wave sleep might manifest an altered, rather than a reduced state of awareness, in contrast to historical depictions. Finally, the third study correlated frequency-banded oscillatory activity, as measured by EEG, with RSN activity, as measured with fMRI. This was done in order to track changes in representations of frequency-banded neuronal activity in each RSN across stages. It was discovered that the pattern of frequency band representation dynamics reflects the aforementioned cortical neurophysiological dynamics, further strengthening the connection between RSNs and neuronal activity