Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

Improving Emotion Recognition Systems by Exploiting the Spatial Information of EEG Sensors

Electroencephalography (EEG)-based emotion recognition is gaining increasing importance due to its potential applications in various scientific fields, ranging from psychophysiology to neuromarketing. A number of approaches have been proposed that use machine learning (ML) technology to achieve high recognition performance, which relies on engineering features from brain activity dynamics. Since ML performance can be improved by utilizing 2D feature representation that exploits the spatial relationships among the features, here we propose a novel input representation that involves re-arranging EEG features as an image that reflects the top view of the subject’s scalp. This approach enables emotion recognition through image-based ML methods such as pre-trained deep neural networks or "trained-from-scratch" convolutional neural networks. We have employed both of these techniques in our study to demonstrate the effectiveness of our proposed input representation. We also compare the recognition performance of these methods against state-of-the-art tabular data analysis approaches, which do not utilize the spatial relationships between the sensors. We test our proposed approach using two publicly available benchmark datasets for EEG-based emotion recognition tasks, namely DEAP and MAHNOB-HCI. Our results show that the "trained-from-scratch" convolutional neural network outperforms the best approaches in the literature, achieving 97.8% and 98.3% accuracy in valence and arousal classification on MAHNOB-HCI, and 91% and 90.4% on DEAP, respectively

MATHEMATICAL MODELING OF THE INTERPLAY BETWEEN CENTRAL AND AUTONOMOUS NERVOUS SYSTEMS

In the course of this doctoral work, methodological advancements were pursued in the field of brain-heart interplay, for the understanding of the role of such interplay in different aspects of human cognition. The role of the body on brain functions and consciousness was hypothesized in ancient philosophical postulates and emphasized in the explanation of human emotions. Nevertheless, the role of these visceral signals has been undervalued, as brain imaging techniques have drastically improved, the evolving neuroscientific research has focused mostly in exploring neural dynamics at brain level only. The scientific evidence has shown a steady interaction between central and autonomous nervous systems, which is disrupted in several pathological conditions. For instance, on the effects of brain trauma on cardiac activity, or on the assessment of cardiac activity as a marker of psychological and psychiatric conditions. More recent evidence depicts that brain-heart interplay predicts or reflects perception at different tasks, supporting hypotheses of an embodied view of cognition. In predictive coding framework, the brain integrates interoceptive and exteroceptive information to perform predictions of the ongoing environmental changes. Other hypotheses state that visceral activity is inherent for the brain to experience subjectivity and it would be required to raise awareness of inner mental and physical states, and the external world. My work in this doctoral project provides with methodological advancements to assess in a non-invasive manner the ongoing brain-heart interactions, which can be applied to different cognitive paradigms. I investigated the impact of using different electroencephalographic references on the outputs of different markers of brain-heart interplay. I provide with guidelines for a robust estimation, showing that an average reference from a wide scalp coverage is a reliable estimate of the electrical reference. I developed a new Integral Pulse Frequency Modulation Model for synthetic data generation of RR series, namely Sympatho-Vagal Modulation Model. The introduced model leverages on orthonormal Laguerre expansions of RR series, instead of the classical sinusoidal expansion, which addresses the problem of an inaccurate estimation of sympathetic and parasympathetic outflow. I propose a methodology to measure brain-heart interplay, embedding the aforementioned model with a Markovian model of synthetic data generation of EEG series. The mutual modulations between EEG and heart rate oscillations are estimated through these coupled models. I focus on the brain-heart interplay in two different conditions: under emotion elicitation and cold-pressor test. From our results, I show that ascending cardiac inputs to the brain, as measured with EEG, initiate and shape arousal at cortical level in both emotional and physical arousing conditions. These results are relevant in a methodological and neuroscientific point of view, as the proposed framework may help to understand the brain-heart interplay at different conditions, but also provide with evidence supporting long-lasting hypotheses of physiological feelings, in which the visceral activity prompts the emotional processing. The present work is structured in five chapters: Introduction, reviewing the theoretical landscape of embodied cognition and the experimental evidence supporting it; Methodological description and pre-processing, in which I detail the methods used for the data analysis, and the state-of-the-art on the analysis of brain-heart interplay is reviewed; Methodological advancements in the measurement of brain-heart interplay, in which the proposed methodology for synthetic data generation of RR series and the new framework to estimate brain-heart interplay is explained; Results and Discussion, where the results on brain-heart interplay during emotion elicitation and cold-pressor test are presented and discussed. These results are presented in a critical point of view, meaning that the proposed analysis has advantages, but also limitations, which can be addressed in future research to continue the understanding of why visceral activity may be necessary for the brain functions we observe in our daily lives

Caracterización de la respuesta emocional ante estímulos visuales en registros electroencefalográficos

Ingeniero Civil EléctricoEl presente trabajo de título tiene por objetivo caracterizar la respuesta en la actividad neuronal de sujetos que han sido expuestos a estímulos visuales con contenido emocional mediante el análisis de series de tiempo de los registros electroencefalográficos (EEG). En particular, se comparan tres estados emocionales en base a sus diferencias en los valores de valencia y excitación emocional. La hipótesis de este trabajo es que la respuesta emocional ante estímulos visuales puede ser caracterizada en registros EEG en las dimensiones de tiempo, frecuencia y topografía en el cuero cabelludo. Para esto se introduce un enfoque metodológico en el que se analizan canales individuales de EEG descompuestos en bandas de frecuencia. La base de datos utilizada consiste en nueve sujetos, cuyos registros fueron pre-procesados para eliminar el ruido y artefactos oculares. La metodología propuesta consiste en extracción de características, y la construcción de modelos predictivos de emociones basados en Máquinas de Soporte Vectorial y Bosques Aleatorios. De los nueve sujetos, seis fueron utilizados como conjunto de entrenamiento para construir los modelos predictivos y los tres sujetos restantes fueron usados como conjunto de prueba. Los resultados obtenidos fueron una completa discriminación entre emociones positivas y negativas. Para la distinción entre las tres emociones a la vez se obtuvo una precisión de 2/3. Las 20 características utilizadas para la clasificación incluyen canales de distintos lóbulos del cerebro y frecuencias que van desde la banda delta hasta la gamma. Se observó además una alta influencia de la actividad de la banda alfa en los estados emocionales. Los resultados sugieren que el registro de la actividad neuronal a través de EEG permite obtener signos del estado emocional en respuesta a estímulos visuales, pero para obtener una mayor precisión se deben combinar características de múltiples canales y frecuencias

Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal.

A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain–heart interplay under emotion elicitation in publicly available data from 62 healthy subjects using a computational model based on synthetic data generation of electroencephalography and electrocardiography signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains the processing of emotional arousal. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions

Neural responses to heartbeats detect residual signs of consciousness during resting state in post-comatose patients

International audienc