Reverse-Engineering the brain: The parts are as complex as the whole.

The purpose of this paper is to review the current state of neuroscience research with a focus on what has been achieved to date in unraveling the mysteries of brain operations, major research initiatives, fundamental challenges, and potentially realizable objectives. General research approaches aimed at constructing a wiring diagram of the brain (i.e., connectome), determining how the brain encodes and computes information, and whole brain simulation attempts are reviewed in terms of strategies employed and difficulties encountered. While promising advances have been made during the past 50 years due to electron microscopy, the development of new experimental methods, and the availability of computer-enabled high throughput imaging systems, brain research is still greatly encumbered by inadequate monitoring and recording capabilities. Four hypotheses relating to comprehension through the assembly of parts, formation of memories, influence of genes, and synapse formation are described as plausible explanations even though they cannot be validated at this time. By assessing the feasibility of overcoming the principal problems that beleaguer brain research in comparison with the potential benefits that can be derived from even partial achievement of the goals the author concludes that the significant investment of government funding is justified

MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities

Establishing a Framework for the development of Multimodal Virtual Reality Interfaces with Applicability in Education and Clinical Practice

The development of Virtual Reality (VR) and Augmented Reality (AR) content with multiple sources of both input and output has led to countless contributions in a great many number of fields, among which medicine and education. Nevertheless, the actual process of integrating the existing VR/AR media and subsequently setting it to purpose is yet a highly scattered and esoteric undertaking. Moreover, seldom do the architectures that derive from such ventures comprise haptic feedback in their implementation, which in turn deprives users from relying on one of the paramount aspects of human interaction, their sense of touch. Determined to circumvent these issues, the present dissertation proposes a centralized albeit modularized framework that thus enables the conception of multimodal VR/AR applications in a novel and straightforward manner. In order to accomplish this, the aforesaid framework makes use of a stereoscopic VR Head Mounted Display (HMD) from Oculus Rift©, a hand tracking controller from Leap Motion©, a custom-made VR mount that allows for the assemblage of the two preceding peripherals and a wearable device of our own design. The latter is a glove that encompasses two core modules in its innings, one that is able to convey haptic feedback to its wearer and another that deals with the non-intrusive acquisition, processing and registering of his/her Electrocardiogram (ECG), Electromyogram (EMG) and Electrodermal Activity (EDA). The software elements of the aforementioned features were all interfaced through Unity3D©, a powerful game engine whose popularity in academic and scientific endeavors is evermore increasing. Upon completion of our system, it was time to substantiate our initial claim with thoroughly developed experiences that would attest to its worth. With this premise in mind, we devised a comprehensive repository of interfaces, amid which three merit special consideration: Brain Connectivity Leap (BCL), Ode to Passive Haptic Learning (PHL) and a Surgical Simulator

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Adaptive extreme edge computing for wearable devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Methods for longitudinal complex network analysis in neuroscience

The study of complex brain networks, where the brain can be viewed as a system with various interacting regions that produce complex behaviors, has grown tremendously over the past decade. With both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for dynamic brain network analysis are needed. We first propose a paradigm for longitudinal brain network analysis over patient cohorts where we adapt the Stochastic Actor Oriented Model (SAOM) framework and model a subject's network over time as observations of a continuous time Markov chain. Network dynamics are represented as being driven by various factors, both endogenous (i.e., network effects) and exogenous, where the latter include mechanisms and relationships conjectured in the literature. We outline an application to the resting-state fMRI network setting, where we draw conclusions at the subject level and then perform a meta-analysis on the model output. As an extension of the models, we next propose an approach based on Hidden Markov Models to incorporate and estimate type I and type II error (i.e., of edge status) in our observed networks. Our model consists of two components: 1) the latent model, which assumes that the true networks evolve according to a Markov process as they did in the original SAOM framework; and 2) the measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for estimation. Lastly, we focus on the study of percolation - the sudden emergence of a giant connected component in a network. This has become an active area of research, with relevance in clinical neuroscience, and it is of interest to distinguish between different percolation regimes in practice. We propose a method for estimating a percolation model from a given sequence of observed networks with single edge transitions. We outline a Hidden Markov Model approach and EM algorithm for the estimation of the birth and death rates for the edges, as well as the type I and type II error rates.2018-07-25T00:00:00

The effect of using multiple connectivity metrics in brain Functional Connectivity studies

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2022Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to assist as a diagnostic or prognostic tool for a diverse set of neurological and neuropsychiatric disorders, which are often difficult to differentiate. fMRI focuses on the study of the brain functional Connectome, which is characterized by the functional connections and neuronal activity among different brain regions, also interpreted as communications between pairs of regions. This Functional Connectivity (FC) is quantified through the statistical dependences between brain regions’ blood-oxygen-level-dependent (BOLD) signals time-series, being traditionally evaluated by correlation coefficient metrics and represented as FC matrices. However, several studies underlined limitations regarding the use of correlation metrics to fully capture information from these signals, leading investigators towards different statistical metrics that would fill those shortcomings. Recently, investigators have turned their attention to Deep Learning (DL) models, outperforming traditional Machine Learning (ML) techniques due to their ability to automatically extract relevant information from high-dimensional data, like FC data, using these models with rs-fMRI data to improve diagnostic predictions, as well as to understand pathological patterns in functional Connectome, that can lead to the discovery of new biomarkers. In spite of very encouraging performances, the black-box nature of DL algorithms makes difficult to know which input information led the model to a certain prediction, restricting its use in clinical settings. The objective of this dissertation is to exploit the power of DL models, understanding how FC matrices created from different statistical metrics can provide information about the brain FC, beyond the conventionally used correlation family. Two publicly available datasets where studied, the ABIDE I dataset, composed by healthy and autism spectrum disease (ASD) individuals, and the ADHD-200 dataset, with typically developed controls and individuals with attention-deficit/hyperactive disorder (ADHD). The computation of the FC matrices of both datasets, using different statistical metrics, was performed in MATLAB using MULAN’s toolbox functions, encompassing the correlation coefficient, non-linear correlation coefficient, mutual information, coherence and transfer entropy. The classification of FC data was performed using two DL models, the improved ConnectomeCNN model and the innovative ConnectomeCNN-Autoencoder model. Moreover, another goal is to study the effect of a multi-metric approach in classification performances, combining multiple FC matrices computed from the different statistical metrics used, as well as to study the use of Explainable Artificial Intelligence (XAI) techniques, namely Layer-wise Relevance Propagation method (LRP), to surpass the black-box problem of DL models used, in order to reveal the most important brain regions in ADHD. The results show that the use of other statistical metrics to compute FC matrices can be a useful complement to the traditional correlation metric methods for the classification between healthy subjects and subjects diagnosed with ADHD and ASD. Namely, non-linear metrics like h2 and mutual information, achieved similar and, in some cases, even slightly better performances than correlation methods. The use of FC multi-metric, despite not showing improvements in classification performance compared to the best individual method, presented promising results, namely the ability of this approach to select the best features from all the FC matrices combined, achieving a similar performance in relation to the best individual metric in each of the evaluation measures of the model, leading to a more complete classification. The LRP analysis applied to ADHD-200 dataset proved to be promising, identifying brain regions related to the pathophysiology of ADHD, which are in broad accordance with FC and structural study’s findings.A ressonância magnética funcional em estado de repouso (rs-fMRI) tem o potencial de ser uma ferramenta auxiliar de diagnóstico ou prognóstico para um conjunto diversificado de distúrbios neurológicos e neuropsiquiátricos, que muitas vezes são difíceis de diferenciar. A análise de dados de rs-fMRI recorre muitas vezes ao conceito de conectoma funcional do cérebro, que se caracteriza pelas conexões funcionais entre as diferentes regiões do cérebro, sendo estas conexões interpretadas como comunicações entre diferentes pares de regiões cerebrais. Esta conectividade funcional é quantificada através de dependências estatísticas entre os sinais fMRI das regiões cerebrais, sendo estas tradicionalmente calculadas através da métrica coeficiente de correlação, e representadas através de matrizes de conectividade funcional. No entanto, vários estudos demonstraram limitações em relação ao uso de métricas de correlação, em que estas não conseguem capturar por completo todas as informações presentes nesses sinais, levando os investigadores à procura de diferentes métricas estatísticas que pudessem preencher essas lacunas na obtenção de informações mais completas desses sinais. O estudo destes distúrbios neurológicos e neuropsiquiátricos começou por se basear em técnicas como mapeamento paramétrico estatístico, no contexto de estudos de fMRI baseados em tarefas. Porém, essas técnicas apresentam certas limitações, nomeadamente a suposição de que cada região cerebral atua de forma independente, o que não corresponde ao conhecimento atual sobre o funcionamento do cérebro. O surgimento da rs-fMRI permitiu obter uma perspetiva mais global e deu origem a uma vasta literatura sobre o efeito de patologias nos padrões de conetividade em repouso, incluindo tentativas de diagnóstico automatizado com base em biomarcadores extraídos dos conectomas. Nos últimos anos, os investigadores voltaram a sua atenção para técnicas de diferentes ramos de Inteligência Artificial, mais propriamente para os algoritmos de Deep Learning (DL), uma vez que são capazes de superar os algoritmos tradicionais de Machine Learning (ML), que foram aplicados a estes estudos numa fase inicial, devido à sua capacidade de extrair automaticamente informações relevantes de dados de alta dimensão, como é o caso dos dados de conectividade funcional. Esses modelos utilizam os dados obtidos da rs-fMRI para melhorar as previsões de diagnóstico em relação às técnicas usadas atualmente em termos de precisão e rapidez, bem como para compreender melhor os padrões patológicos nas conexões funcionais destes distúrbios, podendo levar à descoberta de novos biomarcadores. Apesar do notável desempenho destes modelos, a arquitetura natural em caixa-preta dos algoritmos de DL, torna difícil saber quais as informações dos dados de entrada que levaram o modelo a executar uma determinada previsão, podendo este utilizar informações erradas dos dados para alcançar uma dada inferência, restringindo o seu uso em ambientes clínicos. O objetivo desta dissertação, desenvolvida no Instituto de Biofísica e Engenharia Biomédica, é explorar o poder dos modelos DL, de forma a avaliar até que ponto matrizes de conectividade funcional criadas a partir de diferentes métricas estatísticas podem fornecer mais informações sobre a conectividade funcional do cérebro, para além das métricas de correlação convencionalmente usadas neste tipo de estudos. Foram estudados dois conjuntos de dados bastante utilizados em estudos de Neurociência e que estão disponíveis publicamente: o conjunto de dados ABIDE-I, composto por indivíduos saudáveis e indivíduos com doenças do espectro do autismo (ASD), e o conjunto de dados ADHD-200, com controlos tipicamente desenvolvidos e indivíduos com transtorno do défice de atenção e hiperatividade (ADHD). Numa primeira fase foi realizada a computação das matrizes de conetividade funcional de ambos os conjuntos de dados, usando as diferentes métricas estatísticas. Para isso, foi desenvolvido código de MATLAB, onde se utilizam as séries temporais dos sinais BOLD obtidas dos dois conjuntos de dados para criar essas mesmas matrizes de conectividade funcional, incorporando funções de diferentes métricas estatísticas da caixa de ferramentas MULAN, compreendendo o coeficiente de correlação, o coeficiente de correlação não linear, a informação mútua, a coerência e a entropia de transferência. De seguida, a classificação dos dados de conectividade funcional, de forma a avaliar o efeito do uso de diferentes métricas estatísticas para a criação de matrizes de conectividade funcional na discriminação de sujeitos saudáveis e patológicos, foi realizada usando dois modelos de DL. O modelo ConnectomeCNN melhorado e o modelo inovador ConnectomeCNN-Autoencoder foram desenvolvidos com recurso à biblioteca de Redes Neuronais Keras, juntamente com o seu backend Tensorflow, ambos em Python. Estes modelos, desenvolvidos previamente no Instituto de Biofísica e Engenharia Biomédica, tiveram de ser otimizados de forma a obter a melhor performance, onde vários parâmetros dos modelos e do respetivo treino dos mesmos foram testados para os dados a estudar. Pretendeu-se também estudar o efeito de uma abordagem multi-métrica nas tarefas de classificação dos sujeitos de ambos os conjuntos de dados, sendo que, para estudar essa abordagem as diferentes matrizes calculadas a partir das diferentes métricas estatísticas utilizadas, foram combinadas, sendo usados os mesmos modelos que foram aplicados às matrizes de conectividade funcional de cada métrica estatística individualmente. É importante realçar que na abordagem multi-métrica também foi realizada a otimização dos parâmetros dos modelos utilizados e do respetivo treino, de modo a conseguir a melhor performance dos mesmos para estes dados. Para além destes dois objetivos, estudou-se o uso de técnicas de Inteligência Artificial Explicável (XAI), mais especificamente o método Layer-wise Relevance Propagation (LRP), com vista a superar o problema da caixa-preta dos modelos de DL, com a finalidade de explicar como é que os modelos estão a utilizar os dados de entrada para realizar uma dada previsão. O método LRP foi aplicado aos dois modelos utilizados anteriormente, usando como dados de entrada o conjunto de dados ADHD-200, permitindo assim revelar quais as regiões cerebrais mais importantes no que toca a um diagnóstico relacionado com o ADHD. Os resultados obtidos mostram que o uso de outras métricas estatísticas para criar as matrizes de Conectividade Funcional podem ser um complemento bastante útil às métricas estatísticas tradicionalmente utilizadas para a classificação entre indivíduos saudáveis e indivíduos como ASD e ADHD. Nomeadamente métricas estatísticas não lineares como o h2 e a informação mútua, obtiveram desempenhos semelhantes e, em alguns casos, desempenhos ligeiramente melhores em relação aos desempenhos obtidos por métodos de correlação, convencionalmente usados nestes estudos de conectividade funcional. A utilização da multi-métrica de conectividade funcional, apesar de não apresentar melhorias no desempenho geral da classificação em relação ao melhor método das matrizes de conectividade funcional individuais do conjunto de métricas estatísticas abordadas, apresenta resultados que justificam a exploração mais aprofundada deste tipo de abordagem, de forma a compreender melhor a complementaridade das métricas e a melhor maneira de as utilizar. O uso do método LRP aplicado ao conjunto de dados do ADHD-200 mostrou a sua aplicabilidade a este tipo de estudos e a modelos de DL, identificando as regiões cerebrais mais relacionadas à fisiopatologia do diagnóstico do ADHD que são compatíveis com o que é reportado por diversos estudos de conectividade funcional e estudos de alterações estruturais associados a esta doença. O facto destas técnicas de XAI demonstrarem como é que os modelos de DL estão a usar os dados de entrada para efetuar as previsões, pode significar uma mais rápida e aceite adoção destes algoritmos em ambientes clínicos. Estas técnicas podem auxiliar o diagnóstico e prognóstico destes distúrbios neurológicos e neuropsiquiátricos, que são na maioria das vezes difíceis de diferenciar, permitindo aos médicos adquirirem um conhecimento em relação à previsão realizada e poder explicar a mesma aos seus pacientes