Progression of auditory discrimination based on neural decoding predicts awakening from coma.

Auditory evoked potentials are informative of intact cortical functions of comatose patients. The integrity of auditory functions evaluated using mismatch negativity paradigms has been associated with their chances of survival. However, because auditory discrimination is assessed at various delays after coma onset, it is still unclear whether this impairment depends on the time of the recording. We hypothesized that impairment in auditory discrimination capabilities is indicative of coma progression, rather than of the comatose state itself and that rudimentary auditory discrimination remains intact during acute stages of coma. We studied 30 post-anoxic comatose patients resuscitated from cardiac arrest and five healthy, age-matched controls. Using a mismatch negativity paradigm, we performed two electroencephalography recordings with a standard 19-channel clinical montage: the first within 24 h after coma onset and under mild therapeutic hypothermia, and the second after 1 day and under normothermic conditions. We analysed electroencephalography responses based on a multivariate decoding algorithm that automatically quantifies neural discrimination at the single patient level. Results showed high average decoding accuracy in discriminating sounds both for control subjects and comatose patients. Importantly, accurate decoding was largely independent of patients' chance of survival. However, the progression of auditory discrimination between the first and second recordings was informative of a patient's chance of survival. A deterioration of auditory discrimination was observed in all non-survivors (equivalent to 100% positive predictive value for survivors). We show, for the first time, evidence of intact auditory processing even in comatose patients who do not survive and that progression of sound discrimination over time is informative of a patient's chance of survival. Tracking auditory discrimination in comatose patients could provide new insight to the chance of awakening in a quantitative and automatic fashion during early stages of coma.

Decoding decisions in healthy subjects and auditory discrimination in comatose patients through single-trial topographic EEG analyses

Neuroimaging studies analyzing neurophysiological signals are typically based on comparing averages of peri-stimulus epochs across experimental conditions. This approach can however be problematic in the case of high-level cognitive tasks, where response variability across trials is expected to be high and in cases where subjects cannot be considered part of a group. The main goal of this thesis has been to address this issue by developing a novel approach for analyzing electroencephalography (EEG) responses at the single-trial level. This approach takes advantage of the spatial distribution of the electric field on the scalp (topography) and exploits repetitions across trials for quantifying the degree of discrimination between experimental conditions through a classification scheme. In the first part of this thesis, I developed and validated this new method (Tzovara et al., 2012a,b). Its general applicability was demonstrated with three separate datasets, two in the visual modality and one in the auditory. This development allowed then to target two new lines of research, one in basic and one in clinical neuroscience, which represent the second and third part of this thesis respectively. For the second part of this thesis (Tzovara et al., 2012c), I employed the developed method for assessing the timing of exploratory decision-making. Using single-trial topographic EEG activity during presentation of a choice's payoff, I could predict the subjects' subsequent decisions. This prediction was due to a topographic difference which appeared on average at ~516ms after the presentation of payoff and was subject-specific. These results exploit for the first time the temporal correlates of individual subjects' decisions and additionally show that the underlying neural generators start differentiating their responses already ~880ms before the button press. Finally, in the third part of this project, I focused on a clinical study with the goal of assessing the degree of intact neural functions in comatose patients. Auditory EEG responses were assessed through a classical mismatch negativity paradigm, during the very early phase of coma, which is currently under-investigated. By taking advantage of the decoding method developed in the first part of the thesis, I could quantify the degree of auditory discrimination at the single patient level (Tzovara et al., in press). Our results showed for the first time that even patients who do not survive the coma can discriminate sounds at the neural level, during the first hours after coma onset. Importantly, an improvement in auditory discrimination during the first 48hours of coma was predictive of awakening and survival, with 100% positive predictive value. - L'analyse des signaux électrophysiologiques en neuroimagerie se base typiquement sur la comparaison des réponses neurophysiologiques à différentes conditions expérimentales qui sont moyennées après plusieurs répétitions d'une tâche. Pourtant, cette approche peut être problématique dans le cas des fonctions cognitives de haut niveau, où la variabilité des réponses entre les essais peut être très élevéeou dans le cas où des sujets individuels ne peuvent pas être considérés comme partie d'un groupe. Le but principal de cette thèse est d'investiguer cette problématique en développant une nouvelle approche pour l'analyse des réponses d'électroencephalographie (EEG) au niveau de chaque essai. Cette approche se base sur la modélisation de la distribution du champ électrique sur le crâne (topographie) et profite des répétitions parmi les essais afin de quantifier, à l'aide d'un schéma de classification, le degré de discrimination entre des conditions expérimentales. Dans la première partie de cette thèse, j'ai développé et validé cette nouvelle méthode (Tzovara et al., 2012a,b). Son applicabilité générale a été démontrée avec trois ensembles de données, deux dans le domaine visuel et un dans l'auditif. Ce développement a permis de cibler deux nouvelles lignes de recherche, la première dans le domaine des neurosciences cognitives et l'autre dans le domaine des neurosciences cliniques, représentant respectivement la deuxième et troisième partie de ce projet. En particulier, pour la partie cognitive, j'ai appliqué cette méthode pour évaluer l'information temporelle de la prise des décisions (Tzovara et al., 2012c). En se basant sur l'activité topographique de l'EEG au niveau de chaque essai pendant la présentation de la récompense liée à un choix, on a pu prédire les décisions suivantes des sujets (en termes d'exploration/exploitation). Cette prédiction s'appuie sur une différence topographique qui apparaît en moyenne ~516ms après la présentation de la récompense. Ces résultats exploitent pour la première fois, les corrélés temporels des décisions au niveau de chaque sujet séparément et montrent que les générateurs neuronaux de ces décisions commencent à différentier leurs réponses déjà depuis ~880ms avant que les sujets appuient sur le bouton. Finalement, pour la dernière partie de ce projet, je me suis focalisée sur une étude Clinique afin d'évaluer le degré des fonctions neuronales intactes chez les patients comateux. Des réponses EEG auditives ont été examinées avec un paradigme classique de mismatch negativity, pendant la phase précoce du coma qui est actuellement sous-investiguée. En utilisant la méthode de décodage développée dans la première partie de la thèse, j'ai pu quantifier le degré de discrimination auditive au niveau de chaque patient (Tzovara et al., in press). Nos résultats montrent pour la première fois que même des patients comateux qui ne vont pas survivre peuvent discriminer des sons au niveau neuronal, lors de la phase aigue du coma. De plus, une amélioration dans la discrimination auditive pendant les premières 48heures du coma a été observée seulement chez des patients qui se sont réveillés par la suite (100% de valeur prédictive pour un réveil).

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis à vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.

Decoding auditory EEG responses in healthy and clinical populations: A comparative study.

BACKGROUND: Analyses of brain responses to external stimuli are typically based on the means computed across conditions. However in many cognitive and clinical applications, taking into account their variability across trials has turned out to be statistically more sensitive than comparing their means. NEW METHOD: In this study we present a novel implementation of a single-trial topographic analysis (STTA) for discriminating auditory evoked potentials at predefined time-windows. This analysis has been previously introduced for extracting spatio-temporal features at the level of the whole neural response. Adapting the STTA on specific time windows is an essential step for comparing its performance to other time-window based algorithms. RESULTS: We analyzed responses to standard vs. deviant sounds and showed that the new implementation of the STTA gives above-chance decoding results in all subjects (in comparison to 7 out of 11 with the original method). In comatose patients, the improvement of the decoding performance was even more pronounced than in healthy controls and doubled the number of significant results. COMPARISON WITH EXISTING METHOD(S): We compared the results obtained with the new STTA to those based on a logistic regression in healthy controls and patients. We showed that the first of these two comparisons provided a better performance of the logistic regression; however only the new STTA provided significant results in comatose patients at group level. CONCLUSIONS: Our results provide quantitative evidence that a systematic investigation of the accuracy of established methods in normal and clinical population is an essential step for optimizing decoding performance.

Quantifying the time for accurate EEG decoding of single value-based decisions.

BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for 'easy' vs. ∼700ms for 'hard' decisions, well before subjects' response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S): We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Reliability and validity of nonverbal thin slices in social interactions

Four studies investigated the reliability and validity of thin slices of nonverbal behavior from social interactions including (1) how well individual slices of a given behavior predict other slices in the same interaction; (2) how well a slice of a given behavior represents the entirety of that behavior within an interaction; (3) how long a slice is necessary to sufficiently represent the entirety of a behavior within an interaction; (4) which slices best capture the entirety of behavior, across different behaviors; and (5) which behaviors (of six measured behaviors) are best captured by slices. Notable findings included strong reliability and validity for thin slices of gaze and nods, and that a 1.5 min slice from the start of an interaction may adequately represent some behaviors. Results provide useful information to researchers making decisions about slice measurement of behavior.

Nonverbal social sensing in action: Unobtrusive recording and extracting of nonverbal behavior in social interactions illustrated with a research example

Nonverbal behavior coding is typically conducted by "hand". To remedy this time and resource intensive undertaking, we illustrate how nonverbal social sensing, defined as the automated recording and extracting of nonverbal behavior via ubiquitous social sensing platforms, can be achieved. More precisely, we show how and what kind of nonverbal cues can be extracted and to what extent automated extracted nonverbal cues can be validly obtained with an illustrative research example. In a job interview, the applicant's vocal and visual nonverbal immediacy behavior was automatically sensed and extracted. Results show that the applicant's nonverbal behavior can be validly extracted. Moreover, both visual and vocal applicant nonverbal behavior predict recruiter hiring decision, which is in line with previous findings on manually coded applicant nonverbal behavior. Finally, applicant average turn duration, tempo variation, and gazing best predict recruiter hiring decision. Results and implications of such a nonverbal social sensing for future research are discussed.

Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior

Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.

A cellular system for spatial signal decoding in chemical gradients.

Directional cell growth requires that cells read and interpret shallow chemical gradients, but how the gradient directional information is identified remains elusive. We use single-cell analysis and mathematical modeling to define the cellular gradient decoding network in yeast. Our results demonstrate that the spatial information of the gradient signal is read locally within the polarity site complex using double-positive feedback between the GTPase Cdc42 and trafficking of the receptor Ste2. Spatial decoding critically depends on low Cdc42 activity, which is maintained by the MAPK Fus3 through sequestration of the Cdc42 activator Cdc24. Deregulated Cdc42 or Ste2 trafficking prevents gradient decoding and leads to mis-oriented growth. Our work discovers how a conserved set of components assembles a network integrating signal intensity and directionality to decode the spatial information contained in chemical gradients.

Evaluation de la douleur chez les personnes cérébrolésées non-communicantes aux soins intensifs

La douleur est fréquente en milieu de soins intensifs et sa gestion est l'une des missions des infirmières. Son évaluation est une prémisse indispensable à son soulagement. Cependant lorsque le patient est incapable de signaler sa douleur, les infirmières doivent se baser sur des signes externes pour l'évaluer. Les guides de bonne pratique recommandent chez les personnes non communicantes l'usage d'un instrument validé pour la population donnée et basé sur l'observation des comportements. A l'heure actuelle, les instruments d'évaluation de la douleur disponibles ne sont que partiellement adaptés aux personnes cérébrolésées dans la mesure où ces personnes présentent des comportements qui leur sont spécifiques. C'est pourquoi, cette étude vise à identifier, décrire et valider des indicateurs, et des descripteurs, de la douleur chez les personnes cérébrolésées. Un devis d'étude mixte multiphase avec une dominante quantitative a été choisi pour cette étude. Une première phase consistait à identifier des indicateurs et des descripteurs de la douleur chez les personnes cérébrolésées non communicantes aux soins intensifs en combinant trois sources de données : une revue intégrative des écrits, une démarche consultative utilisant la technique du groupe nominal auprès de 18 cliniciens expérimentés (6 médecins et 12 infirmières) et les résultats d'une étude pilote observationnelle réalisée auprès de 10 traumatisés crâniens. Les résultats ont permis d'identifier 6 indicateurs et 47 descripteurs comportementaux, vocaux et physiologiques susceptibles d'être inclus dans un instrument d'évaluation de la douleur destiné aux personnes cérébrolésées non- communicantes aux soins intensifs. Une deuxième phase séquentielle vérifiait les propriétés psychométriques des indicateurs et des descripteurs préalablement identifiés. La validation de contenu a été testée auprès de 10 experts cliniques et 4 experts scientifiques à l'aide d'un questionnaire structuré qui cherchait à évaluer la pertinence et la clarté/compréhensibilité de chaque descripteur. Cette démarche a permis de sélectionner 33 des 47 descripteurs et valider 6 indicateurs. Dans un deuxième temps, les propriétés psychométriques de ces indicateurs et descripteurs ont été étudiés au repos, lors de stimulation non nociceptive et lors d'une stimulation nociceptive (la latéralisation du patient) auprès de 116 personnes cérébrolésées aux soins intensifs hospitalisées dans deux centres hospitaliers universitaires. Les résultats montrent d'importantes variations dans les descripteurs observés lors de stimulation nociceptive probablement dues à l'hétérogénéité des patients au niveau de leur état de conscience. Dix descripteurs ont été éliminés, car leur fréquence lors de la stimulation nociceptive était inférieure à 5% ou leur fiabilité insuffisante. Les descripteurs physiologiques ont tous été supprimés en raison de leur faible variabilité et d'une fiabilité inter juge problématique. Les résultats montrent que la validité concomitante, c'est-à-dire la corrélation entre l'auto- évaluation du patient et les mesures réalisées avec les descripteurs, est satisfaisante lors de stimulation nociceptive {rs=0,527, p=0,003, n=30). Par contre la validité convergente, qui vérifiait l'association entre l'évaluation de la douleur par l'infirmière en charge du patient et les mesures réalisés avec les descripteurs, ainsi que la validité divergente, qui vérifiait si les indicateurs discriminent entre la stimulation nociceptive et le repos, mettent en évidence des résultats variables en fonction de l'état de conscience des patients. Ces résultats soulignent la nécessité d'étudier les descripteurs de la douleur chez des patients cérébrolésés en fonction du niveau de conscience et de considérer l'hétérogénéité de cette population dans la conception d'un instrument d'évaluation de la douleur pour les personnes cérébrolésées non communicantes aux soins intensifs. - Pain is frequent in the intensive care unit (ICU) and its management is a major issue for nurses. The assessment of pain is a prerequisite for appropriate pain management. However, pain assessment is difficult when patients are unable to communicate about their experience and nurses have to base their evaluation on external signs. Clinical practice guidelines highlight the need to use behavioral scales that have been validated for nonverbal patients. Current behavioral pain tools for ICU patients unable to communicate may not be appropriate for nonverbal brain-injured ICU patients, as they demonstrate specific responses to pain. This study aimed to identify, describe and validate pain indicators and descriptors in brain-injured ICU patients. A mixed multiphase method design with a quantitative dominant was chosen for this study. The first phase aimed to identify indicators and descriptors of pain for nonverbal brain- injured ICU patients using data from three sources: an integrative literature review, a consultation using the nominal group technique with 18 experienced clinicians (12 nurses and 6 physicians) and the results of an observational pilot study with 10 traumatic brain injured patients. The results of this first phase identified 6 indicators and 47 behavioral, vocal and physiological descriptors of pain that could be included in a pain assessment tool for this population. The sequential phase two tested the psychometric properties of the list of previously identified indicators and descriptors. Content validity was tested with 10 clinical and 4 scientific experts for pertinence and comprehensibility using a structured questionnaire. This process resulted in 33 descriptors to be selected out of 47 previously identified, and six validated indicators. Then, the psychometric properties of the descriptors and indicators were tested at rest, during non nociceptive stimulation and nociceptive stimulation (turning) in a sample of 116 brain-injured ICLI patients who were hospitalized in two university centers. Results showed important variations in the descriptors observed during the nociceptive stimulation, probably due to the heterogeneity of patients' level of consciousness. Ten descriptors were excluded, as they were observed less than 5% of the time or their reliability was insufficient. All physiologic descriptors were deleted as they showed little variability and inter observer reliability was lacking. Concomitant validity, testing the association between patients' self report of pain and measures performed using the descriptors, was acceptable during nociceptive stimulation (rs=0,527, p=0,003, n=30). However, convergent validity ( testing for an association between the nurses' pain assessment and measures done with descriptors) and divergent validity (testing for the ability of the indicators to discriminate between rest and a nociceptive stimulation) varied according to the level of consciousness These results highlight the need to study pain descriptors in brain-injured patients with different level of consciousness and to take into account the heterogeneity of this population forthe conception of a pain assessment tool for nonverbal brain-injured ICU patients.