Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients.

Electroencephalographic activity in the context of disorders of consciousness is a swiss knife like tool that can evaluate different aspects of cognitive residual function, detect consciousness and provide a mean to communicate with the outside world without using muscular channels. Standard recordings in the neurological department offer a first global view of the electrogenesis of a patient and can spot abnormal epileptiform activity and therefore guide treatment. Although visual patterns have a prognosis value, they are not sufficient to provide a diagnosis between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) patients. Quantitative electroencephalography (qEEG) processes the data and retrieves features, not visible on the raw traces, which can then be classified. Current results using qEEG show that MCS can be differentiated from VS/UWS patients at the group level. Event Related Potentials (ERP) are triggered by varying stimuli and reflect the time course of information processing related to the stimuli from low-level peripheral receptive structures to high-order associative cortices. It is hence possible to assess auditory, visual, or emotive pathways. Different stimuli elicit positive or negative components with different time signatures. The presence of these components when observed in passive paradigms is usually a sign of good prognosis but it cannot differentiate VS/UWS and MCS patients. Recently, researchers have developed active paradigms showing that the amplitude of the component is modulated when the subject's attention is focused on a task during stimulus presentation. Hence significant differences between ERPs of a patient in a passive compared to an active paradigm can be a proof of consciousness. An EEG-based brain-computer interface (BCI) can then be tested to provide the patient with a communication tool. BCIs have considerably improved the past two decades. However they are not easily adaptable to comatose patients as they can have visual or auditory impairments or different lesions affecting their EEG signal. Future progress will require large databases of resting state-EEG and ERPs experiment of patients of different etiologies. This will allow the identification of specific patterns related to the diagnostic of consciousness. Standardized procedures in the use of BCIs will also be needed to find the most suited technique for each individual patient.Peer reviewe

Consciousness level assessment in completely locked-in syndrome patients using soft-clustering

Brain-computer interfaces (BCIs) are very convenient tools to assess locked-in (LIS) and completely locked-in state (CLIS) patients' hidden states of consciousness. For the time being, there is no ground-truth data in respect to these states for above-mentioned patients. This lack of gold standard makes this problem particularly challenging. In addition to consciousness assessment, BCIs also provide them with a communication device that does not require the presence of motor responses, which they are lacking. Communication plays an important role in the patients' quality of life and prognosis. Significant progress have been made to provide them with EEG-based BCIs in particular. Nonetheless, the majority of existing studies directly dive into the communication part without assessing if the patient is even conscious. Additionally, the few studies that do essentially use evoked brain potentials, mostly the P300, that necessitates the patient's voluntary and active participation to be elicited. Patients are easily fatigued, and would consequently be less successful during the main communication task. Furthermore, when the consciousness states are determined using resting state data, only one or two features were used. In this thesis, different sets of EEG features are used to assess the consciousness level of CLIS patients using resting-state data. This is done as a preliminary step that needed to be succeeded in order to engage to the next step, communication with the patient. In other words, the 'conversation' is initiated only if the patient is sufficiently conscious. This variety of EEG features is utilised to increase the probability of correctly estimating the patients' consciousness states. Indeed, each of them captures a particular signal attribute, and combining them would allow the collection of different hidden characteristics that could have not been obtained from a single feature. Furthermore, the proposed method should allow to determine if communication shall be initiated at a specific time with the patient. The EEG features used are frequency-based, complexity related and connectivity metrics. Besides, instead of analysing results from individual channels or specific brain regions, the global activity of the brain is assessed. The estimated consciousness levels are then obtained by applying two different soft-clustering analysis methods, namely Fuzzy c-means (FCM) and Gaussian Mixture Models (GMM), to the individual features and ensembling their results using their average or their product. The proposed approach is first applied to EEG data recorded from patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) (patients with disorders of consciousness (DoC)) to evaluate its performance. It is subsequently applied to data from one CLIS patient that is unique in its kind because it contains a time frame during which the experimenters affirmed that he was conscious. Finally, it is used to estimate the levels of consciousness of nine other CLIS patients. The obtained results revealed that the presented approach was able to take into account the variations of the different features and deduce a unique output taking into consideration the individual features contributions. Some of them performed better than others, which is not surprising since each person is different. It was also able to draw very accurate estimations of the level of consciousness under specific conditions. The approach presented in this thesis provides an additional tool for diagnosis to the medical staff. Furthermore, when implemented online, it would enable to determine the optimal time to engage in communication with CLIS patients. Moreover, it could possibly be used to predict patients' cognitive decline and/or death

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

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

Interaction Paradigms for Brain-Body Interfaces for Computer Users with Brain Injuries

In comparison to all types of injury, those to the brain are among the most likely to result in death or permanent disability. Some of these brain-injured people cannot communicate, recreate, or control their environment due to severe motor impairment. This group of individuals with severe head injury have received limited help from assistive technology. Brain-Computer Interfaces have opened up a spectrum of assistive technologies, which are particularly appropriate for people with traumatic brain injury, especially those who suffer from “locked-in” syndrome. The research challenge here is to develop novel interaction paradigms that suit brain-injured individuals, who could then use it for everyday communications. The developed interaction paradigms should require minimum training, reconfigurable and minimum effort to use. This thesis reports on the development of novel interaction paradigms for Brain-Body Interfaces to help brain-injured people to communicate better, recreate and control their environment using computers despite the severity of their brain injury. The investigation was carried out in three phases. Phase one was an exploratory study where a first novel interaction paradigm was developed and evaluated with able-bodied and disabled participants. Results obtained were fed into the next phase of the investigation. Phase two was carried out with able participants who acted as development group for the second novel interaction paradigm. This second novel interaction paradigm was evaluated with non-verbal participants with severe brain injury in phase three. An iterative design research methodology was chosen to develop the interaction paradigms. A non-invasive assistive technology device named Cyberlink™ was chosen as the Brain-Body Interface. This research improved previous work in this area by developing new interaction paradigms of personalised tiling and discrete acceleration in Brain- Body Interfaces. The research hypothesis of this study ‘that the performance of the Brain-Body Interface can be improved by the use of novel interaction paradigms’ was successfully demonstrated

The Neural Correlates of Consciousness - An Update

This review examines recent advances in the study of brain correlates of consciousness. First, we briefly discuss some useful distinctions between consciousness and other brain functions. We then examine what has been learned by studying global changes in the level of consciousness, such as sleep, anesthesia, and seizures. Next we consider some of the most common paradigms used to study the neural correlates for specific conscious percepts and examine what recent findings say about the role of different brain regions in giving rise to consciousness for that percept. Then we discuss dynamic aspects of neural activity, such as sustained versus phasic activity, feedforward versus reentrant activity, and the role of neural synchronization. Finally, we briefly consider how a theoretical analysis of the fundamental properties of consciousness can usefully complement neurobiological studies

Transition from the locked in to the completely locked-in state: A physiological analysis

h i g h l i g h t s This represents the first documentation of transition of a patient with ALS from the Locked In State the to Completely Locked In State, and the first EMG documentation of loss of all muscle activities, including sphincter function, but with retained cognition as measured with ERPs. In this patient, any stimulation, communication or learning using visual and tactile stimuli was lost. Visual BCI was useless. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways. a b s t r a c t Objective: To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication. Methods: Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS. Results: At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brain potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response. Conclusions: The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways. Significance: Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting