Differential Effects of Simulated Neural Network's Lesions on Synchrony and EEG Complexity

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi’s Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity. </jats:p

Human brain distinctiveness based on EEG spectral coherence connectivity

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherencebased connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match score fusio

Sistema de predicción epileptogenica en lazo cerrado basado en matrices sub-durales

The human brain is the most complex organ in the human body, which consists of approximately 100 billion neurons. These cells effortlessly communicate over multiple hemispheres to deliver our everyday sensorimotor and cognitive abilities. Although the underlying principles of neuronal communication are not well understood, there is evidence to suggest precise synchronisation and/or de-synchronisation of neuronal clusters could play an important role. Furthermore, new evidence suggests that these patterns of synchronisation could be used as an identifier for the detection of a variety of neurological disorders including, Alzheimers (AD), Schizophrenia (SZ) and Epilepsy (EP), where neural degradation or hyper synchronous networks have been detected. Over the years many different techniques have been proposed for the detection of synchronisation patterns, in the form of spectral analysis, transform approaches and statistical based studies. Nonetheless, most are confined to software based implementations as opposed to hardware realisations due to their complexity. Furthermore, the few hardware implementations which do exist, suffer from a lack of scalability, in terms of brain area coverage, throughput and power consumption. Here we introduce the design and implementation of a hardware efficient algorithm, named Delay Difference Analysis (DDA), for the identification of patient specific synchronisation patterns. The design is remarkably hardware friendly when compared with other algorithms. In fact, we can reduce hardware requirements by as much as 80% and power consumption as much as 90%, when compared with the most common techniques. In terms of absolute sensitivity the DDA produces an average sensitivity of more than 80% for a false positive rate of 0.75 FP/h and indeed up to a maximum of 90% for confidence levels of 95%. This thesis presents two integer-based digital processors for the calculation of phase synchronisation between neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters or adders. In fact, the first introduced processor was fabricated in a 0.18μm CMOS process and only occupies 0.05mm2 and consumes 15nW from a 0.5V supply voltage at a signal input rate of 1024S/s. These low-area and low-power features make the proposed circuit a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for measuring functional connectivity maps between different recording sites in the brain. A second VLSI implementation was designed and integrated as a mass integrated 16-channel design. Incorporated into the design were 16 individual synchronisation processors (15 on-line processors and 1 test processor) each with a dedicated training and calculation module, used to build a specialised epileptic detection system based on patient specific synchrony thresholds. Each of the main processors are capable of calculating the phase synchrony between 9 independent electroencephalography (EEG) signals over 8 epochs of time totalling 120 EEG combinations. Remarkably, the entire circuit occupies a total area of only 3.64 mm2. This design was implemented with a multi-purpose focus in mind. Firstly, as a clinical aid to help physicians detect pathological brain states, where the small area would allow the patient to wear the device for home trials. Moreover, the small power consumption would allow to run from standard batteries for long periods. The trials could produce important patient specific information which could be processed using mathematical tools such as graph theory. Secondly, the design was focused towards the use as an in-vivo device to detect phase synchrony in real time for patients who suffer with such neurological disorders as EP, which need constant monitoring and feedback. In future developments this synchronisation device would make an good contribution to a full system on chip device for detection and stimulation.El cerebro humano es el órgano más complejo del cuerpo humano, que consta de aproximadamente 100 mil millones de neuronas. Estas células se comunican sin esfuerzo a través de ambos hemisferios para favorecer nuestras habilidades sensoriales y cognitivas diarias. Si bien los principios subyacentes de la comunicación neuronal no se comprenden bien, existen pruebas que sugieren que la sincronización precisa y/o la desincronización de los grupos neuronales podrían desempeñar un papel importante. Además, nuevas evidencias sugieren que estos patrones de sincronización podrían usarse como un identificador para la detección de una gran variedad de trastornos neurológicos incluyendo la enfermedad de Alzheimer(AD), la esquizofrenia(SZ) y la epilepsia(EP), donde se ha detectado la degradación neural o las redes hiper sincrónicas. A lo largo de los años, se han propuesto muchas técnicas diferentes para la detección de patrones de sincronización en forma de análisis espectral, enfoques de transformación y análisis estadísticos. No obstante, la mayoría se limita a implementaciones basadas en software en lugar de realizaciones de hardware debido a su complejidad. Además, las pocas implementaciones de hardware que existen, sufren una falta de escalabilidad, en términos de cobertura del área del cerebro, rendimiento y consumo de energía. Aquí presentamos el diseño y la implementación de un algoritmo eficiente de hardware llamado “Delay Difference Aproximation” (DDA) para la identificación de patrones de sincronización específicos del paciente. El diseño es notablemente compatible con el hardware en comparación con otros algoritmos. De hecho, podemos reducir los requisitos de hardware hasta en un 80% y el consumo de energía hasta en un 90%, en comparación con las técnicas más comunes. En términos de sensibilidad absoluta, la DDA produce una sensibilidad promedio de más del 80% para una tasa de falsos positivos de 0,75 PF / hr y hasta un máximo del 90% para niveles de confianza del 95%. Esta tesis presenta dos procesadores digitales para el cálculo de la sincronización de fase entre señales neuronales. Se basa en la medición de los períodos de tiempo entre dos mínimos consecutivos. La simplicidad del enfoque permite el uso de bloques digitales elementales, como registros, contadores o sumadores. De hecho, el primer procesador introducido se fabricó en un proceso CMOS de 0.18μm y solo ocupa 0.05mm2 y consume 15nW de un voltaje de suministro de 0.5V a una tasa de entrada de señal de 1024S/s Estas características de baja área y baja potencia hacen que el procesador propuesto sea un valioso elemento informático en prótesis neurales de circuito cerrado para el tratamiento de trastornos neuronales, como la epilepsia, o para medir mapas de conectividad funcional entre diferentes sitios de registro en el cerebro. Además, se diseñó una segunda implementación VLSI que se integró como un diseño de 16 canales integrado en masa. Se incorporaron al diseño 16 procesadores de sincronización individuales (15 procesadores en línea y 1 procesador de prueba), cada uno con un módulo de entrenamiento y cálculo dedicado, utilizado para construir un sistema de detección epiléptico especializado basado en umbrales de sincronía específicos del paciente. Cada uno de los procesadores principales es capaz de calcular la sincronización de fase entre 9 señales de electroencefalografía (EEG) independientes en 8 épocas de tiempo que totalizan 120 combinaciones de EEG. Cabe destacar que todo el circuito ocupa un área total de solo 3.64 mm2. Este diseño fue implementado teniendo en mente varios propósitos. En primer lugar, como ayuda clínica para ayudar a los médicos a detectar estados cerebrales patológicos, donde el área pequeña permitiría al paciente usar el dispositivo para las pruebas caseras. Además, el pequeño consumo de energía permitiría una carga cero del dispositivo, lo que le permitiría funcionar con baterías estándar durante largos períodos. Los ensayos podrían producir información importante específica para el paciente que podría procesarse utilizando herramientas matemáticas como la teoría de grafos. En segundo lugar, el diseño se centró en el uso como un dispositivo in-vivo para detectar la sincronización de fase en tiempo real para pacientes que sufren trastornos neurológicos como el EP, que necesitan supervisión y retroalimentación constantes. En desarrollos futuros, este dispositivo de sincronización sería una buena base para desarrollar un sistema completo de un dispositivo chip para detección de trastornos neurológicos

Neuronal dynamics and connectivity analysis of neuronal cultures on multi electrode arrays

Despite a number of attempts over the past two decades, research into reliable, controlled induction of long term evoked responses, mimicking low level learning and memory in dissociated cell cultures remains challenging. In addition, a full understanding of the stimulus-response relationships that underlie synaptic plasticity has not yet been achieved, and many of the underlying principles remain largely unknown. Plasticity studies have been predominantly limited to low density Multi/Micro Electrode Arrays (MEAs). With the advent of complementary metal-oxide-semiconductor (CMOS) based High-Density (HD) MEAs, unprecedented spatial and temporal resolution is now possible. In this thesis, an attempt to bridge the gap between studies of neural plasticity and the use of CMOS based HD-MEAs with thousands of electrodes, is reported. Additionally, since such HD-MEAs generate a large volume of data and require advanced analytics to efficiently process and analyse recordings, computational tools and novel algorithms to infer connectivity during plasticity have been developed. The study showed that the responsiveness, stability and initial firing rate of neuronal cultures are the deciding factors to reliably induce evoked responses. With multi-site stimulation, sustained long term potentiation was achieved, which was validated both by evoked response plots and overall firing rates measured at five different time points - before and after repeated stimulation, and at a three day time points. In contrast, while depression responses were observed, it was found that the effects were not sustained over many days. The findings of the study suggest that appropriate selection of neuronal cultures is crucial for inducing desired evoked responses and criteria for this have been developed. Furthermore, it is concluded that the initial responses to test stimuli can be used to determine whether potentiated or depressed responses are to be expected. To analyse the recordings, pipeline of computational tools was developed. Firstly, neuronal synchrony metrics were adapted for the first time for large HD-MEA recordings and shown to correspond effectively to the firing dynamics. To analyse functional connectivity, an information theoretic approach, Transfer Entropy(TE), was utilised. The method showed accurate estimation of functional connectivity with mid 80th percentile accuracy on simulated data. A superimposition method was proposed to enhance confidence in the connectivity estimation. To statistically evaluate connectivity estimation, a new surrogate method, based on ISI distribution approach, was proposed and validated with a simulated Izhikevich network. The method achieved improved accuracy, compared to the existing ISI shuffling method. This newly developed method was later utilised to infer connectivity and refine connections during the learning process of real neuronal cultures over many days of stimulation. The connectivity inference corresponded accurately to both the spontaneous and stimulated networks during evoked responses and the proposed method permitted observation of the evolution of connections for the potentiated network

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses