COMT genotype is differentially associated with single trial variability of ERPs as a function of memory type.

Previous research on the association between intra-subject variability (ISV) in reaction times (RTs) and the Val(158)Met polymorphism of the catechol-o-methyltransferase gene (COMT; rs4680) has yielded mixed results. The present study compared the associations between COMT genotype and ISV in P3b latency measured during working and secondary memory tasks using residue iteration decomposition (RIDE) of single trial latencies. We compared the outcome of the present analyses with a previous analysis of the same data (N = 70, n-back tasks) using an alternative single-trial method. Additionally, we used RIDE to analyse the association between COMT genotype and ISV in an independent sample performing a different task (N = 91, face-recognition task). Analyses reconfirmed previous results from the n-back tasks, showing that Val alleles are associated with lower ISV. In the face recognition tasks, genotype interacted with task conditions, so Val homozygotes had higher ISV to unfamiliar faces than familiar ones but Met carriers showed no effect of familiarity. Moreover, in both datasets trial-by-trial RTs were predicted by P3b latencies. Therefore, the present data suggests that associations between COMT genotype and ISV depend on the type of cognitive processes, which may explain heterogeneity in previous results

Biological Mechanisms underlying Inter- and Intra-Individual Variability of Face Cognition

In dieser Arbeit untersuche ich der Gesichterkognition zugrundeliegende biologischen Mechanismen auf der genetischen, neuronalen und verhaltensbasierten Ebene. Die neuronale Aktivität wurde mittels ereigniskorrelierter Potenziale (EKPs) untersucht und ihre Latzenzvariabilität innerhalb der Person wurde durch eine innovative Methode, Residue Iteration Decomposition (RIDE), gemessen. Die erste Studie demonstriert die Reliabilität von RIDE für die Extraktion von Einzeltrialparametern der P3b Komponente, welche in der zweiten Studie die Basis für die Untersuchung der Innen-Subjekt-Variabilität (ISV) bei der Geschwindigkeit der Gesichterkognition bildet. Die zweite Studie untersucht individuelle Unterschiede in ISV in ihrer genetischen Variation, gemessen an der Verhaltens- und neuronalen Ebene während einer Gesichterkognitionsaufgabe. Die Ergebnisse zeigen, dass ISV nicht nur mit dem COMT Val158Met Polymorphismus zusammenhängt, sondern auch von der geforderten kognitiven Verarbeitung abhängt. Zudem ist die ISV in der Reaktionszeit teilweise durch die ISV in der Geschwindigkeit zentralkognitiver Prozesse erklärbar. Studie 3 liefert neuartige Informationen für die N1/N170 Forschung. Mit einem differentialpsychologischen Ansatz konnten wir nicht nur vorangegangene Ergebnisse zur Vorhersagekraft der N170 für individuelle Unterschiede in der Gesichterkognition replizieren, sondern auch die individuellen Unterschiede in der N170 in einen allgemeinen und einen gesichtsspezifischen Teil mit unterschiedlicher Vorhersagekraft zerlegen. Darüber hinaus konnten wir zeigen, dass top-down Modulationen der N170 unterscheidbare und qualitativ unterschiedliche Beziehungen zu Fähigkeiten der Gesichterkognition aufweisen. Insgesamt zeigen die integrierten Ergebnisse der Studien meiner Dissertation die psychologische Bedeutsamkeit der intra- und interindividuellen Variabilität in der Gesichterkognition für die Erforschung der ihr zugrundeliegenden biologischen Mechanismen.The biological mechanisms underlying face cognition from an inter- and intra-individual variability perspective at the genetic, neural, and behavioral levels are investigated. The neural activities related to face processing are measured by event-related potentials (ERPs) and their trial-by-trial latency variability are estimated using a novel and well-established method, Residue Iteration Decomposition (RIDE). Study 1 demonstrates the reliability of RIDE in extracting single-trial parameters of the P3b component. In the Study 2, individual differences in ISV of face processing speed, measured at both behavioral and neural levels during a face processing task, are studied in their genetic variation. The results suggest that individual differences in ISV are related not only to the COMT Val158Met polymorphism, but also to the type of cognitive processing (e.g., memory domain). Moreover, we showed that ISV in reaction time can be partially explained by ISV in the speed of central cognitive processes. Furthermore, the individual differences approach in Study 3, provided valuable and novel information beyond the common group-mean approach applied in the N1/N170-related research. Based on this approach, not only we could replicate previous findings that the N170 predicts individual differences in face cognition abilities, but also we could decompose individual differences in the N170 into a domain-general and a face-specific part with different predictive powers. Moreover, we showed that top-down modulations on the N170 have separable and qualitatively different relationships to face cognition abilities. In summary, the integrated results from different studies in my dissertation demonstrate the psychological importance of the information provided by inter- and intra-individual variability in face processing in the investigation of its underlying biological mechanisms

Sex-specific relationships between face memory and the N170 component in event-related potentials

At the group level, women consistently perform better in face memory tasks than men and also show earlier and larger N170 components of event-related brain potentials (ERP), considered to indicate perceptual structural encoding of faces. Here we investigated sex differences in the relationship between the N170 and face memory performance in 152 men and 141 women at group mean and individual differences levels. ERPs and performance were measured in separate tasks, avoiding statistical dependency between the two. We confirmed previous findings about superior face memory in women and a—sex-independent—negative relationship between N170 latency and face memory. However, whereas in men, better face memory was related to larger N170 components, face memory in women was unrelated with the amplitude or latency of the N170. These data provide solid evidence that individual differences in face memory within men are at least partially related to more intense structural face encoding.Peer Reviewe

Dissociating the Influence of Affective Word Content and Cognitive Processing Demands on the Late Positive Potential

© 2015, Springer Science+Business Media New York. The late positive potential (LPP) elicited by affective stimuli in the event-related brain potential (ERP) is often assumed to be a member of the P3 family. The present study addresses the relationship of the LPP to the classic P3b in a published data set, using a non-parametric permutation test for topographical comparisons, and residue iteration decomposition to assess the temporal features of the LPP and the P3b by decomposing the ERP into several component clusters according to their latency variability. The experiment orthogonally manipulated arousal and valence of words, which were either read or judged for lexicality. High-arousing and positive valenced words induced a larger LPP than low-arousing and negative valenced words, respectively, and t he LDT elicited a larger P3b than reading. The experimental manipulation of arousal, valence, and task yielded main effects without any interactions on ERP amplitude in the LPP/P3b time range. The arousal and valence effects partially differed from the task effect in scalp topography; in addition, whereas the late positive component elicited by affective stimuli, defined as LPP, was stimulus-locked, the late positive component elicited by task demand, defined as P3b, was mainly latency-variable. Therefore LPP and P3b manifest different subcomponents.Link_to_subscribed_fulltex

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality