Spatiotemporal brain dynamics induced by propofol and ketamine in humans

Human brain dynamics are radically altered under the influence of anaesthetics. However, despite their widespread clinical use, the whole-brain mechanisms by which anaesthetics alter consciousness are still not fully understood and clinical translation of existing insights is limited. This thesis presents several lines of investigation aimed to improve our understanding of spatiotemporal brain states under the anaesthetics propofol and ketamine. First, slow-wave activity saturation (SWAS) was studied across the brain and in relation to existing depth of anaesthesia markers. Local propofol concentration needed to achieve SWAS in healthy volunteers correlated with GABA-A receptor density (Spearman ρ=-0.69, P=0.0018), providing more evidence for the importance of the neurophysiological state of SWAS. The average Bispectral Index at SWAS across volunteers was 49±4, but its value varied significantly over time. Second, relevant cortico-cardiac interactions were studied. A slow propofol infusion increased heart rate in a dose-dependent manner (increase of +4.2±1.5 bpm / (μg ml-1), P<0.001). Individual cortical slow waves were coupled to the heartbeat (P<0.001), with heartbeat incidence peaking about 450ms before slow-wave onset. A ketamine case study showed decreased amplitude of heartbeat-evoked potentials, suggesting impaired interoceptive signalling may have a part in dissociative phenomenology. Third, novel methodology was developed, validated, and applied throughout the thesis. Iterated Masking Empirical Mode Decomposition was used to identify three types of low-frequency propofol waves with different spatiotemporal maps and dose-responses. Hidden Markov Modelling of propofol showed a shift to anterior alpha states and a reduced switching rate (P<0.01); with ketamine states exhibiting low alpha power and decreased connectivity became more prominent (P<0.001). Fourth, the potential of translating electroencephalographic markers from high- to low- density montages was studied. Posterior montages were best at capturing the reduced state switching under propofol. A patient study of antidepressant ketamine treatment demonstrated reduced temporal lobe alpha and theta power were associated with dissociation (P=0.0109)

P300-Based Brain-Computer Interface Channel Selection using Swarm Intelligence

[ES] Los sistemas Brain-Computer Interface (BCI) se definen como sistemas de comunicación que monitorizan la actividad cerebral y traducen determinadas características, correspondientes a las intenciones del usuario, en comandos de control de un dispositivo. La selección de canales en los sistemas BCI es fundamental para evitar el sobre-entrenamiento del clasificador, reducir la carga computacional y aumentar la comodidad del usuario. A pesar de que se han desarrollado varios algoritmos con anterioridad para tal fin, las metaheurísticas basadas en inteligencia de enjambre aún no han sido suficientemente explotadas en los sistemas BCI basados en potenciales P300. En este estudio se muestra una comparativa entre cinco métodos de enjambre, basados en el comportamiento de sistemas biológicos, aplicados con el objetivo de optimizar la selección de canales en este tipo de sistemas. Los métodos se han evaluado sobre la base de datos de la “III BCI Competition 2005”, reportando precisiones similares o, en algunos casos, incluso más altas que las obtenidas sin realizar ningún tipo de selección. Dado que los cinco métodos se han demostrado capaces de disminuir drásticamente los 64 canales originales a menos de la mitad sin comprometer el rendimiento del sistema, así como de superar el conjunto típico de 8 canales y el método backward elimination, se concluye que todos ellos son adecuados para su aplicación en la selección de canales en sistemas P300-BCI.[EN] Brain-Computer Interfaces (BCI) are direct communication pathways between the brain and the environment that translate certain features, which correspond to users’ intentions, into device control commands. Channel selection in BCI systems is essential to avoid over-fitting, to reduce the computational cost and to increase the users’ comfort. Although several algorithms have previously developed for that purpose, metaheuristics based on swarm intelligence have not been exploited yet in P300-based BCI systems. In this study, a comparative among five different swarm methods, based on the behavior of biological systems, is shown. Those methods have been applied in order to optimize the channel selection procedure in this kind of systems, and have been tested with the ‘III BCI Competition 2005’ database II. Results show that the five methods can achieve similar or even higher accuracies than that obtained without performing any channel selection procedure. Owing to the fact that all the applied methods are able to drastically reduce the required number of channels without compromising the system performance, as well as to overcome the common 8-channel set and the backward elimination algorithm, we conclude that all of them are suitable for use in the P300-BCI systems channel selection procedure.Este estudio se ha financiado parcialmente mediante el proyecto TEC2014-53196-R del Ministerio de Economía y Competitividad (MINECO) y FEDER, y el proyecto VA037U16 de la Consejería de Educación de la Junta de Castilla y León. V. Martínez-Cagigal se encuentra financiado por un contrato de “Promocion de Empleo Joven e Implantación de la Garantía Juvenil del MINECO y la Universidad de Valladolid.Martínez-Cagigal, V.; Hornero, R. (2017). Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Inteligencia de Enjambre. 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