Caracterización de ambientes sonoros en recintos universitarios: influencia del ruido sobre procesos cognitivos básicos

El interés por el estudio de la problemática del ruido en las escuelas y sus efectos sobre los estudiantes a nivel universitarios, es un tema que no ha sido estudiado debidamente. Desgraciadamente, en el ámbito educativo universitario, no existen regulaciones específicas que permitan determinar parámetros preventivos, ni procedimientos de evaluación de ruido dentro de este tipo de instalaciones educativas. Debido a la importancia de los efectos que el ruido tiene sobre la salud y la calidad de vida de los estudiantes universitarios, y consecuentemente en el rendimiento académico; es de suma importancia desarrollar mecanismos que estudien y planteen soluciones que ayuden a garantizar la mejora de la calidad de vida de la población estudiantil. En este trabajo se ha presentado un extenso trabajo que incluye el estudio de los ambientes sonoros a los que los estudiantes universitarios se ven expuestos día a día y se proponen acciones que ayudan a mejorar la calidad acústica en instalaciones educativas. Así mismo se evidencian los efectos que tiene este contaminante sobre la salud psicológica y por consecuencia en el desarrollo intelectual de los estudiantes. Por un lado, se incluye una propuesta de metodología que ayuda a la correcta caracterización de los ambientes sonoros en los cuales se desarrollan los estudiantes a nivel universitario. Esta se realizó haciendo un completo registro de los niveles sonoros durante sus actividades diarias. Así mismo, una encuesta fue aplicada a estudiantes para conocer la percepción que se tiene sobre las condiciones sonoras en ambientes universitarios. Así mismo, se realizó un estudio de la calidad sonora en instalaciones universitarias, el cual deriva la valoración de la molestia al ruido. Se propone una escala de valoración de molestia al ruido, la cual deriva el diseño de una propuesta con acciones de bajo coste frente al ruido. Por otro lado, se evidencian los trastornos que ocasiona este contaminante sobre la salud psicológica de los estudiantes y que afectan el desarrollo académico de estos. Se realizó primeramente la valoración de la atención y la memoria por medio de test psicométricos estandarizados y otros diseñados para este estudio en particular. Por último, con la finalidad de obtener datos objetivos y confiables que permitieron relacionar la influencia negativa del ruido de fondo sobre procesos cognitivos básicos como la atención y la memoria, se llevó a cabo un estudio de la actividad cerebral. Para llevar a cabo esta evaluación se utilizó como principal herramienta el electroencefalograma (EEG), enfocándose en los cambios producidos con y sin exposición a ruido de fondo, específicamente en las bandas de frecuencia relacionadas con procesos cognitivos básicos como los son la atención y la memoria, en este caso la banda theta (4-7 Hz) y la banda beta (13-30 Hz). ABSTRACT The interest in the study of the problem of noise in schools and its impact on students at university level is a topic that has not been properly studied. Unfortunately, there are no specific regulations for determining preventive parameters or noise assessment procedures in university facilities. Due to the importance of the effects that noise has on health and on the quality of life of university students, and consequently on academic performance; is very important to develop mechanisms to evaluate and design solutions that help ensure an improvement in the quality of life of the student population. This thesis has presented an extensive work, which includes the study of the state of the art on the problem of noise in the sound environments to which university students are exposed every day, and the effects on students mainly on attention aspects. On one hand, a general study of the common noise environments of life of university students was carried out, where a methodological proposal is included and that helps in the correct characterization of the sound environments in which university students grow. This proposal includes the assessment of noise exposure, noise dose and a recording of the characteristic sound levels during their daily activities in and out spaces dedicated to their education. Also, a survey was conducted to know the perception that students have on noise conditions in university environments. Also, a method for evaluation of the noise annoyance is proposed, this is through the correlation of two known methods of evaluation. The first method is based on psychoacoustic parameters that allow the evaluation of the sound quality. These parameters were related, obtaining as a result the parameter known as psychoacoustics annoyance. The second method is based on a questionnaire in conjunction with listening tests in specific sound environments. Derived from the correlation of these two methods, a series of indicators of noise annoyance are proposed, which entails the design of a noise annoyance indicator. Furthermore, the effects of this pollutant on psychological health and therefore in the intellectual development of students has been shown. First, an evaluation of attention and memory using standardized psychometric tests were performed and others designed for this particular study. Because it has been evidenced that the use of these psychometric tests are not very reliable, we sought to obtain another objective and reliable data to show the relationship between the negative influence of background noise on basic cognitive processes such as attention and memory. This was achieved by carrying out a study of the brain activity. To carry out this evaluation the electroencephalogram (EEG) was used as the main tool, focusing on the changes produced with and without exposure to background noise, specifically in the frequency bands related to basic cognitive processes such as attention and are memory. In this case the band theta (4-7 Hz) and beta band (13-30 Hz) were studied. The purpose of this thesis is to establish the bases for future studies that allow go deep in the study of the sound conditions in school environments, and enable the design of strategies and measures against noise and the correct evaluation of the effects of noise on aspects for improving the psychological quality of life and academic performance of students.

Brain-Computer Interfaces: Desarrollo de un sistema de identificación de estados mentales alfa

En el mundo actual las aplicaciones basadas en sistemas biométricos, es decir, aquellas que miden las señales eléctricas de nuestro organismo, están creciendo a un gran ritmo. Todos estos sistemas incorporan sensores biomédicos, que ayudan a los usuarios a controlar mejor diferentes aspectos de la rutina diaria, como podría ser llevar un seguimiento detallado de una rutina deportiva, o de la calidad de los alimentos que ingerimos. Entre estos sistemas biométricos, los que se basan en la interpretación de las señales cerebrales, mediante ensayos de electroencefalografía o EEG están cogiendo cada vez más fuerza para el futuro, aunque están todavía en una situación bastante incipiente, debido a la elevada complejidad del cerebro humano, muy desconocido para los científicos hasta el siglo XXI. Por estas razones, los dispositivos que utilizan la interfaz cerebro-máquina, también conocida como BCI (Brain Computer Interface), están cogiendo cada vez más popularidad. El funcionamiento de un sistema BCI consiste en la captación de las ondas cerebrales de un sujeto para después procesarlas e intentar obtener una representación de una acción o de un pensamiento del individuo. Estos pensamientos, correctamente interpretados, son posteriormente usados para llevar a cabo una acción. Ejemplos de aplicación de sistemas BCI podrían ser mover el motor de una silla de ruedas eléctrica cuando el sujeto realice, por ejemplo, la acción de cerrar un puño, o abrir la cerradura de tu propia casa usando un patrón cerebral propio. Los sistemas de procesamiento de datos están evolucionando muy rápido con el paso del tiempo. Los principales motivos son la alta velocidad de procesamiento y el bajo consumo energético de las FPGAs (Field Programmable Gate Array). Además, las FPGAs cuentan con una arquitectura reconfigurable, lo que las hace más versátiles y potentes que otras unidades de procesamiento como las CPUs o las GPUs.En el CEI (Centro de Electrónica Industrial), donde se lleva a cabo este TFG, se dispone de experiencia en el diseño de sistemas reconfigurables en FPGAs. Este TFG es el segundo de una línea de proyectos en la cual se busca obtener un sistema capaz de procesar correctamente señales cerebrales, para llegar a un patrón común que nos permita actuar en consecuencia. Más concretamente, se busca detectar cuando una persona está quedándose dormida a través de la captación de unas ondas cerebrales, conocidas como ondas alfa, cuya frecuencia está acotada entre los 8 y los 13 Hz. Estas ondas, que aparecen cuando cerramos los ojos y dejamos la mente en blanco, representan un estado de relajación mental. Por tanto, este proyecto comienza como inicio de un sistema global de BCI, el cual servirá como primera toma de contacto con el procesamiento de las ondas cerebrales, para el posterior uso de hardware reconfigurable sobre el cual se implementarán los algoritmos evolutivos. Por ello se vuelve necesario desarrollar un sistema de procesamiento de datos en una FPGA. Estos datos se procesan siguiendo la metodología de procesamiento digital de señales, y en este caso se realiza un análisis de la frecuencia utilizando la transformada rápida de Fourier, o FFT. Una vez desarrollado el sistema de procesamiento de los datos, se integra con otro sistema que se encarga de captar los datos recogidos por un ADC (Analog to Digital Converter), conocido como ADS1299. Este ADC está especialmente diseñado para captar potenciales del cerebro humano. De esta forma, el sistema final capta los datos mediante el ADS1299, y los envía a la FPGA que se encarga de procesarlos. La interpretación es realizada por los usuarios que analizan posteriormente los datos procesados. Para el desarrollo del sistema de procesamiento de los datos, se dispone primariamente de dos plataformas de estudio, a partir de las cuales se captarán los datos para después realizar el procesamiento: 1. La primera consiste en una herramienta comercial desarrollada y distribuida por OpenBCI, proyecto que se dedica a la venta de hardware para la realización de EEG, así como otros ensayos. Esta herramienta está formada por un microprocesador, un módulo de memoria SD para el almacenamiento de datos, y un módulo de comunicación inalámbrica que transmite los datos por Bluetooth. Además cuenta con el mencionado ADC ADS1299. Esta plataforma ofrece una interfaz gráfica que sirve para realizar la investigación previa al diseño del sistema de procesamiento, al permitir tener una primera toma de contacto con el sistema. 2. La segunda plataforma consiste en un kit de evaluación para el ADS1299, desde la cual se pueden acceder a los diferentes puertos de control a través de los pines de comunicación del ADC. Esta plataforma se conectará con la FPGA en el sistema integrado. Para entender cómo funcionan las ondas más simples del cerebro, así como saber cuáles son los requisitos mínimos en el análisis de ondas EEG se realizaron diferentes consultas con el Dr Ceferino Maestu, neurofisiólogo del Centro de Tecnología Biomédica (CTB) de la UPM. Él se encargó de introducirnos en los distintos procedimientos en el análisis de ondas en electroencefalogramas, así como la forma en que se deben de colocar los electrodos en el cráneo. Para terminar con la investigación previa, se realiza en MATLAB un primer modelo de procesamiento de los datos. Una característica muy importante de las ondas cerebrales es la aleatoriedad de las mismas, de forma que el análisis en el dominio del tiempo se vuelve muy complejo. Por ello, el paso más importante en el procesamiento de los datos es el paso del dominio temporal al dominio de la frecuencia, mediante la aplicación de la transformada rápida de Fourier o FFT (Fast Fourier Transform), donde se pueden analizar con mayor precisión los datos recogidos. El modelo desarrollado en MATLAB se utiliza para obtener los primeros resultados del sistema de procesamiento, el cual sigue los siguientes pasos. 1. Se captan los datos desde los electrodos y se escriben en una tabla de datos. 2. Se leen los datos de la tabla. 3. Se elige el tamaño temporal de la muestra a procesar. 4. Se aplica una ventana para evitar las discontinuidades al principio y al final del bloque analizado. 5. Se completa la muestra a convertir con con zero-padding en el dominio del tiempo. 6. Se aplica la FFT al bloque analizado con ventana y zero-padding. 7. Los resultados se llevan a una gráfica para ser analizados. Llegados a este punto, se observa que la captación de ondas alfas resulta muy viable. Aunque es cierto que se presentan ciertos problemas a la hora de interpretar los datos debido a la baja resolución temporal de la plataforma de OpenBCI, este es un problema que se soluciona en el modelo desarrollado, al permitir el kit de evaluación (sistema de captación de datos) actuar sobre la velocidad de captación de los datos, es decir la frecuencia de muestreo, lo que afectará directamente a esta precisión. Una vez llevado a cabo el primer procesamiento y su posterior análisis de los resultados obtenidos, se procede a realizar un modelo en Hardware que siga los mismos pasos que el desarrollado en MATLAB, en la medida que esto sea útil y viable. Para ello se utiliza el programa XPS (Xilinx Platform Studio) contenido en la herramienta EDK (Embedded Development Kit), que nos permite diseñar un sistema embebido. Este sistema cuenta con: Un microprocesador de tipo soft-core llamado MicroBlaze, que se encarga de gestionar y controlar todo el sistema; Un bloque FFT que se encarga de realizar la transformada rápida Fourier; Cuatro bloques de memoria BRAM, donde se almacenan los datos de entrada y salida del bloque FFT y un multiplicador para aplicar la ventana a los datos de entrada al bloque FFT; Un bus PLB, que consiste en un bus de control que se encarga de comunicar el MicroBlaze con los diferentes elementos del sistema. Tras el diseño Hardware se procede al diseño Software utilizando la herramienta SDK(Software Development Kit).También en esta etapa se integra el sistema de captación de datos, el cual se controla mayoritariamente desde el MicroBlaze. Por tanto, desde este entorno se programa el MicroBlaze para gestionar el Hardware que se ha generado. A través del Software se gestiona la comunicación entre ambos sistemas, el de captación y el de procesamiento de los datos. También se realiza la carga de los datos de la ventana a aplicar en la memoria correspondiente. En las primeras etapas de desarrollo del sistema, se comienza con el testeo del bloque FFT, para poder comprobar el funcionamiento del mismo en Hardware. Para este primer ensayo, se carga en la BRAM los datos de entrada al bloque FFT y en otra BRAM los datos de la ventana aplicada. Los datos procesados saldrán a dos BRAM, una para almacenar los valores reales de la transformada y otra para los imaginarios. Tras comprobar el correcto funcionamiento del bloque FFT, se integra junto al sistema de adquisición de datos. Posteriormente se procede a realizar un ensayo de EEG real, para captar ondas alfa. Por otro lado, y para validar el uso de las FPGAs como unidades ideales de procesamiento, se realiza una medición del tiempo que tarda el bloque FFT en realizar la transformada. Este tiempo se compara con el tiempo que tarda MATLAB en realizar la misma transformada a los mismos datos. Esto significa que el sistema desarrollado en Hardware realiza la transformada rápida de Fourier 27 veces más rápido que lo que tarda MATLAB, por lo que se puede ver aquí la gran ventaja competitiva del Hardware en lo que a tiempos de ejecución se refiere. En lo que al aspecto didáctico se refiere, este TFG engloba diferentes campos. En el campo de la electrónica:  Se han mejorado los conocimientos en MATLAB, así como diferentes herramientas que ofrece como FDATool (Filter Design Analysis Tool).  Se han adquirido conocimientos de técnicas de procesado de señal, y en particular, de análisis espectral.  Se han mejorado los conocimientos en VHDL, así como su uso en el entorno ISE de Xilinx.  Se han reforzado los conocimientos en C mediante la programación del MicroBlaze para el control del sistema.  Se ha aprendido a crear sistemas embebidos usando el entorno de desarrollo de Xilinx usando la herramienta EDK (Embedded Development Kit). En el campo de la neurología, se ha aprendido a realizar ensayos EEG, así como a analizar e interpretar los resultados mostrados en el mismo. En cuanto al impacto social, los sistemas BCI afectan a muchos sectores, donde destaca el volumen de personas con discapacidades físicas, para los cuales, este sistema implica una oportunidad de aumentar su autonomía en el día a día. También otro sector importante es el sector de la investigación médica, donde los sistemas BCIs son aplicables en muchas aplicaciones como, por ejemplo, la detección y estudio de enfermedades cognitivas.

O EFEITO DO CONTROLE RESPIRATÓRIO EM VARIÁVEIS ELETROFISIOLÓGICAS DA ATENÇÃO

A prática do ioga tem se tornado cada vez mais popular, não apenas pelos benefícios físicos, mas principalmente pelo bem-estar psicológico trazido pela sua prática. Um dos componentes do ioga é o Prãnãyama, ou controle da respiração. A atenção e a respiração são dois mecanismos fisiológicos e involuntários requeridos para a execução do Prãnãyama. O principal objetivo desse estudo foi verificar se variáveis contínuas do EEG (potência de diferentes faixas que o compõem) seriam moduladas pelo controle respiratório, comparando-se separadamente as duas fases do ciclo respiratório (inspiração e expiração), na situação de respiração espontânea e controlada. Fizeram parte do estudo 19 sujeitos (7 homens/12 mulheres, idade média de 36,89 e DP = ± 14,46) que foram convidados a participar da pesquisa nas dependências da Faculdade de Saúde da Universidade Metodista de São Paulo. Para o registro do eletroencefalograma foi utilizado um sistema de posicionamento de cinco eletrodos Ag AgCl (FPz, Fz, Cz, Pz e Oz) fixados a uma touca de posicionamento rápido (Quick-Cap, Neuromedical Supplies®), em sistema 10-20. Foram obtidos valores de máxima amplitude de potência (espectro de potência no domínio da frequência) nas frequências teta, alfa e beta e delta e calculada a razão teta/beta nas diferentes fases do ciclo respiratório (inspiração e expiração), separadamente, nas condições de respiração espontânea e de controle respiratório. Para o registro do ciclo respiratório, foi utilizada uma cinta de esforço respiratório M01 (Pletismógrafo). Os resultados mostram diferenças significativas entre as condições de respiração espontânea e de controle com valores das médias da razão teta/beta menores na respiração controlada do que na respiração espontânea e valores de média da potência alfa sempre maiores no controle respiratório. Diferenças significativas foram encontradas na comparação entre inspiração e expiração da respiração controlada com diminuição dos valores das médias da razão teta/beta na inspiração e aumento nos valores das médias da potência alfa, sobretudo na expiração. Os achados deste estudo trazem evidências de que o controle respiratório modula variáveis eletrofisiológicas relativas à atenção refletindo um estado de alerta, porém mais relaxado do que na situação de respiração espontânea.

Chromatic contrast in luminance-defined images affects performance and neural activity during a shape classification task

Funded by BBSRC funded grant, BB/H019731/1.

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Developmental disorder associated with increased cellular nucleotidase activity

Four unrelated patients are described with a syndrome that included developmental delay, seizures, ataxia, recurrent infections, severe language deficit, and an unusual behavioral phenotype characterized by hyperactivity, short attention span, and poor social interaction. These manifestations appeared within the first few years of life. Each patient displayed abnormalities on EEG. No unusual metabolites were found in plasma or urine, and metabolic testing was normal except for persistent hypouricosuria. Investigation of purine and pyrimidine metabolism in cultured fibroblasts derived from these patients showed normal incorporation of purine bases into nucleotides but decreased incorporation of uridine. De novo synthesis of purines and cellular phosphoribosyl pyrophosphate content also were moderately decreased. The distribution of incorporated purines and pyrimidines did not reveal a pattern suggestive of a deficient enzyme activity. Assay of individual enzymes in fibroblast lysates showed no deficiencies. However, the activity of cytosolic 5′-nucleotidase was elevated 6- to 10-fold. Based on the possibility that the observed increased catabolic activity and decreased pyrimidine salvage might be causing a deficiency of pyrimidine nucleotides, the patients were treated with oral pyrimidine nucleoside or nucleotide compounds. All patients showed remarkable improvement in speech and behavior as well as decreased seizure activity and frequency of infections. A double-blind placebo trial was undertaken to ascertain the efficacy of this supplementation regimen. Upon replacement of the supplements with placebo, all patients showed rapid regression to their pretreatment states. These observations suggest that increased nucleotide catabolism is related to the symptoms of these patients, and that the effects of this increased catabolism are reversed by administration of uridine.

An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds

In this paper, we demonstrate an approach by which some evoked neuronal events can be probed by functional MRI (fMRI) signal with temporal resolution at the time scale of tens of milliseconds. The approach is based on the close relationship between neuronal electrical events and fMRI signal that is experimentally demonstrated in concurrent fMRI and electroencephalographic (EEG) studies conducted in a rat model with forepaw electrical stimulation. We observed a refractory period of neuronal origin in a two-stimuli paradigm: the first stimulation pulse suppressed the evoked activity in both EEG and fMRI signal responding to the subsequent stimulus for a period of several hundred milliseconds. When there was an apparent site–site interaction detected in the evoked EEG signal induced by two stimuli that were primarily targeted to activate two different sites in the brain, fMRI also displayed signal amplitude modulation because of the interactive event. With visual stimulation using two short pulses in the human brain, a similar refractory phenomenon was observed in activated fMRI signals in the primary visual cortex. In addition, for interstimulus intervals shorter than the known latency time of the evoked potential induced by the first stimulus (≈100 ms) in the primary visual cortex of the human brain, the suppression was not present. Thus, by controlling the temporal relation of input tasks, it is possible to study temporal evolution of certain neural events at the time scale of their evoked electrical activity by noninvasive fMRI methodology.

Diazepam-induced changes in sleep: Role of the α1 GABAA receptor subtype

Ligands acting at the benzodiazepine (BZ) site of γ-aminobutyric acid type A (GABAA) receptors currently are the most widely used hypnotics. BZs such as diazepam (Dz) potentiate GABAA receptor activation. To determine the GABAA receptor subtypes that mediate the hypnotic action of Dz wild-type mice and mice that harbor Dz-insensitive α1 GABAA receptors [α1 (H101R) mice] were compared. Sleep latency and the amount of sleep after Dz treatment were not affected by the point mutation. An initial reduction of rapid eye movement (REM) sleep also occurred equally in both genotypes. Furthermore, the Dz-induced changes in the sleep and waking electroencephalogram (EEG) spectra, the increase in power density above 21 Hz in non-REM sleep and waking, and the suppression of slow-wave activity (SWA; EEG power in the 0.75- to 4.0-Hz band) in non-REM sleep were present in both genotypes. Surprisingly, these effects were even more pronounced in α1(H101R) mice and sleep continuity was enhanced by Dz only in the mutants. Interestingly, Dz did not affect the initial surge of SWA at the transitions to sleep, indicating that the SWA-generating mechanisms are not impaired by the BZ. We conclude that the REM sleep inhibiting action of Dz and its effect on the EEG spectra in sleep and waking are mediated by GABAA receptors other than α1, i.e., α2, α3, or α5 GABAA receptors. Because α1 GABAA receptors mediate the sedative action of Dz, our results provide evidence that the hypnotic effect of Dz and its EEG “fingerprint” can be dissociated from its sedative action.

Intracortical and corticothalamic coherency of fast spontaneous oscillations.

We report that fast (mainly 30- to 40-Hz) coherent electric field oscillations appear spontaneously during brain activation, as expressed by electroencephalogram (EEG) rhythms, and they outlast the stimulation of mesopontine cholinergic nuclei in acutely prepared cats. The fast oscillations also appear during the sleep-like EEG patterns of ketamine/xylazine anesthesia, but they are selectively suppressed during the prolonged phase of the slow (<1-Hz) sleep oscillation that is associated with hyperpolarization of cortical neurons. The fast (30- to 40-Hz) rhythms are synchronized intracortically within vertical columns, among closely located cortical foci, and through reciprocal corticothalamic networks. The fast oscillations do not reverse throughout the depth of the cortex. This aspect stands in contrast with the conventional depth profile of evoked potentials and slow sleep oscillations that display opposite polarity at the surface and midlayers. Current-source-density analyses reveal that the fast oscillations are associated with alternating microsinks and microsources across the cortex, while the evoked potentials and the slow oscillation display a massive current sink in midlayers, confined by two sources in superficial and deep layers. The synchronization of fast rhythms and their high amplitudes indicate that the term "EEG desynchronization," used to designate brain-aroused states, is incorrect and should be replaced with the original term, "EEG activation" [Moruzzi, G. & Magoun, H.W. (1949) Electroencephalogr. Clin. Neurophysiol. 1, 455-473].

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.

Proactive and reactive inhibition during overt and covert actions. An electrical neuroimaging study.

Response inhibition is the ability to suppress inadequate but automatically activated, prepotent or ongoing response tendencies. In the framework of motor inhibition, two distinct operating strategies have been described: “proactive” and “reactive” control modes. In the proactive modality, inhibition is recruited in advance by predictive signals, and actively maintained before its enactment. Conversely, in the reactive control mode, inhibition is phasically enacted after the detection of the inhibitory signal. To date, ample evidence points to a core cerebral network for reactive inhibition comprising the right inferior frontal gyrus (rIFG), the presupplementary motor area (pre-SMA) and the basal ganglia (BG). Moreover, fMRI studies showed that cerebral activations during proactive and reactive inhibition largely overlap. These findings suggest that at least part of the neural network for reactive inhibition is recruited in advance, priming cortical regions in preparation for the upcoming inhibition. So far, proactive and reactive inhibitory mechanisms have been investigated during tasks in which the requested response to be stopped or withheld was an “overt” action execution (AE) (i.e., a movement effectively performed). Nevertheless, inhibitory mechanisms are also relevant for motor control during “covert actions” (i.e., potential motor acts not overtly performed), such as motor imagery (MI). MI is the conscious, voluntary mental rehearsal of action representations without any overt movement. Previous studies revealed a substantial overlap of activated motor-related brain networks in premotor, parietal and subcortical regions during overtly executed and imagined movements. Notwithstanding this evidence for a shared set of cerebral regions involved in encoding actions, whether or not those actions are effectively executed, the neural bases of motor inhibition during MI, preventing covert action from being overtly performed, in spite of the activation of the motor system, remain to be fully clarified. Taking into account this background, we performed a high density EEG study evaluating cerebral mechanisms and their related sources elicited during two types of cued Go/NoGo task, requiring the execution or withholding of an overt (Go) or a covert (MI) action, respectively. The EEG analyses were performed in two steps, with different aims: 1) Analysis of the “response phase” of the cued overt and covert Go/NoGo tasks, for the evaluation of reactive inhibitory control of overt and covert actions. 2) Analysis of the “preparatory phase” of the cued overt and covert Go/NoGo EEG datasets, focusing on cerebral activities time-locked to the preparatory signals, for the evaluation of proactive inhibitory mechanisms and their related neural sources. For these purposes, a spatiotemporal analysis of the scalp electric fields was applied on the EEG data recorded during the overt and covert Go/NoGo tasks. The spatiotemporal approach provide an objective definition of time windows for source analysis, relying on the statistical proof that the electric fields are different and thus generated by different neural sources. The analysis of the “response phase” revealed that key nodes of the inhibitory circuit, underpinning inhibition of the overt movement during the NoGo response, were also activated during the MI enactment. In both cases, inhibition relied on the activation of pre-SMA and rIFG, but with different temporal patterns of activation in accord with the intended “covert” or “overt” modality of motor performance. During the NoGo condition, the pre-SMA and rIFG were sequentially activated, pointing to an early decisional role of pre-SMA and to a later role of rIFG in the enactment of inhibitory control of the overt action. Conversely, a concomitant activation of pre-SMA and rIFG emerged during the imagined motor response. This latter finding suggested that an inhibitory mechanism (likely underpinned by the rIFG), could be prewired into a prepared “covert modality” of motor response, as an intrinsic component of the MI enactment. This mechanism would allow the rehearsal of the imagined motor representations, without any overt movement. The analyses of the “preparatory phase”, confirmed in both overt and covert Go/NoGo tasks the priming of cerebral regions pertaining to putative inhibitory network, reactively triggered in the following response phase. Nonetheless, differences in the preparatory strategies between the two tasks emerged, depending on the intended “overt” or “covert” modality of the possible incoming motor response. During the preparation of the overt Go/NoGo task, the cue primed the possible overt response programs in motor and premotor cortex. At the same time, through preactivation of a pre-SMA-related decisional mechanism, it triggered a parallel preparation for the successful response selection and/or inhibition during the subsequent response phase. Conversely, the preparatory strategy for the covert Go/NoGo task was centred on the goal-oriented priming of an inhibitory mechanism related to the rIFG that, being tuned to the instructed covert modality of the motor performance and instantiated during the subsequent MI enactment, allowed the imagined response to remain a potential motor act. Taken together, the results of the present study demonstrate a substantial overlap of cerebral networks activated during proactive recruitment and subsequent reactive enactment of motor inhibition in both overt and covert actions. At the same time, our data show that preparatory cues predisposed ab initio a different organization of the cerebral areas (in particular of the pre-SMA and rIFG) involved with sensorimotor transformations and motor inhibitory control for executed and imagined actions. During the preparatory phases of our cued overt and covert Go/NoGo tasks, the different adopted strategies were tuned to the “how” of the motor performance, reflecting the intended overt and covert modality of the possible incoming action.

Estudio de la actividad cerebral durante la percepción y procesamiento afectivo de las expresiones faciales de la emoción

Las expresiones faciales de la emoción constituyen estímulos altamente relevantes en la interacción humana, dado que son señales comunicativas que nos permiten inferir el estado interno de otras personas. La función comunicativa de las expresiones faciales de la emoción ha sido objeto de gran interés y existe abundante literatura sobre el tema. Muchos autores han investigado los mecanismos involucrados en la percepción y decodificación de las expresiones faciales desde distintas perspectivas. En estudios realizados con medidas de la actividad cerebral de alta resolución temporal (electroencefalografía-EEG- y magnetoencefalografía-MEG) que se centran en el curso temporal del procesamiento perceptivo de las expresiones faciales de la emoción se ha encontrado una sensibilidad temprana a diversas emociones. Por ejemplo, el componente N170 ha mostrado sensibilidad diferenciada a las expresiones faciales de la emoción (ver revisión de Hinojosa, Mercado & Carretié, 2015). Un procedimiento utilizado habitualmente para investigar el procesamiento afectivo es el paradigma de priming afectivo, en el que primes y targets emocionales se presentan secuencialmente. La técnica de potenciales evocados (event-related potentials-ERP) se ha empleado habitualmente para explorar estos procesos y los estudios se han centrado en dos componentes principales: el N400 y el Potencial Tardío Positivo (Late Positive Potential-LPP). Se ha encontrado que el N400 es altamente sensible a la incongruencia semántica, mientras que su sensibilidad a la incongruencia afectiva no está tan clara. Por el contrario, se ha observado modulación del LPP debida a la incongruencia afectiva en ausencia de efectos en N400 (Herring et al., 2011)...

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Contribution différentielle de Neuroligine‐1 et d’EphA4 à la régulation du sommeil

Le sommeil est un besoin vital et le bon fonctionnement de l’organisme dépend de la quantité et de la qualité du sommeil. Le sommeil est régulé par deux processus : un processus circadien qui dépend de l’activité des noyaux suprachiasmatiques de l’hypothalamus et qui régule le moment durant lequel nous allons dormir, et un processus homéostatique qui dépend de l’activité neuronale et se reflète dans l’intensité du sommeil. En effet, le sommeil dépend de l’éveil qui le précède et plus l’éveil dure longtemps, plus le sommeil est profond tel que mesuré par des marqueurs électroencéphalographiques (EEG). Des études ont montré que le bon fonctionnement de ces deux processus régulateurs du sommeil dépend de la plasticité synaptique. Ainsi, les éléments synaptiques régulant la communication et la force synaptique sont d’importants candidats pour agir sur la physiologie de la régulation du sommeil. Les molécules d’adhésion cellulaire sont des acteurs clés dans les mécanismes de plasticité synaptique. Elles régulent l’activité et la maturation des synapses. Des études ont montré que leur absence engendre des conséquences similaires au manque de sommeil. Le but de ce projet de thèse est d’explorer l’effet de l’absence de deux familles de molécule d’adhésion cellulaire, les neuroligines et la famille des récepteur Eph et leur ligand les éphrines dans les processus régulateurs du sommeil. Notre hypothèse est que l’absence d’un des membres de ces deux familles de molécule affecte les mécanismes impliqués dans le processus homéostatique de régulation du sommeil. Afin de répondre à notre hypothèse, nous avons étudié d’une part l’activité EEG chez des souris mutantes n’exprimant pas Neuroligine‐1 (Nlgn1) ou le récepteur EphA4 en condition normale et après une privation de sommeil. D’autre part, nous avons mesuré les changements moléculaires ayant lieu dans ces deux modèles après privation de sommeil. Au niveau de l’activité EEG, nos résultats montrent que l’absence de Nlgn1 augmente la densité des ondes lentes en condition normale et augment l’amplitude et la pente des ondes lentes après privation de sommeil. Nlgn1 est nécessaire au fonctionnement normal de la synchronie corticale, notamment après une privation de sommeil, lui attribuant ainsi un rôle clé dans l’homéostasie du sommeil. Concernant le récepteur EphA4, son absence affecte la durée du sommeil paradoxal ainsi que l’activité sigma qui dépendent du processus circadien. Nos résultats suggèrent donc que ce récepteur est un élément important dans la régulation circadienne du sommeil. Les changements transcriptionnels en réponse à la privation de sommeil des souris n’exprimant pas Nlgn1 et EphA4 ne sont pas différents des souris sauvages. Toutefois, nous avons montré que la privation de sommeil affectait la distribution des marques épigénétiques sur le génome, tels que la méthylation et l’hydroxyméthylation, et que l’expression des molécules régulant ces changements est modifiée chez les souris mutantes pour le récepteur EphA4. Nos observations mettent en évidence que les molécules d’adhésion cellulaire, Nlgn1 et le récepteur EphA4, possèdent un rôle important dans les processus homéostatique et circadien du sommeil et contribuent de manière différente à la régulation du sommeil.

Unistructed BIAT faking when ego depleted or in normal state: Differential effect on brain and behavior

Background: Deception can distort psychological tests on socially sensitive topics. Understanding the cerebral processes that are involved in such faking can be useful in detection and prevention of deception. Previous research shows that faking a brief implicit association test (BIAT) evokes a characteristic ERP response. It is not yet known whether temporarily available self-control resources moderate this response. We randomly assigned 22 participants (15 females, 24.23 ± 2.91 years old) to a counterbalanced repeated-measurements design. Participants first completed a Brief-IAT (BIAT) on doping attitudes as a baseline measure and were then instructed to fake a negative doping attitude both when self-control resources were depleted and non-depleted. Cerebral activity during BIAT performance was assessed using high-density EEG. Results: Compared to the baseline BIAT, event-related potentials showed a first interaction at the parietal P1, while significant post hoc differences were found only at the later occurring late positive potential. Here, significantly decreased amplitudes were recorded for ‘normal’ faking, but not in the depletion condition. In source space, enhanced activity was found for ‘normal’ faking in the bilateral temporoparietal junction. Behaviorally, participants were successful in faking the BIAT successfully in both conditions. Conclusions: Results indicate that temporarily available self-control resources do not affect overt faking success on a BIAT. However, differences were found on an electrophysiological level. This indicates that while on a phenotypical level self-control resources play a negligible role in deliberate test faking the underlying cerebral processes are markedly different. Electronic supplementary material: The online version of this article (doi:10.1186/s12868-016-0249-8) contains supplementary material, which is available to authorized users.