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The striatum is the principal input structure of the basal ganglia. Numerically, the great majority of neurons in the striatum are spiny projection neurons, which produce the inhibitory output of the striatum to the globus pallidum and substantia nigra. The major glutamatergic afferents to the striatum from the cerebral cortex make monosynaptic contact with spiny projection neurons. The dopaminergic afferents from the substantia nigra also synapse directly on the spiny projection neurons. Thus, the spiny projection neurons play a crucial role in the input–output operations of the striatum by integrating glutamatergic cortical inputs with dopaminergic inputs and producing the output to other basal ganglia nuclei. Anatomical observations made nearly 30 years ago suggested that inhibitory interactions among the spiny projection neurons of the striatum are very pr- able. Individual spiny projection neurons produce a local axonal plexus in the spheroidal space occupied by their own dendritic trees [1, 2]. Based on the GABAergic nature of these neurons and their synaptic contacts with other spiny neurons, several authors have proposed that the spiny projection neurons form a lateral inhibition type of neural network [3–5]. In the idealised concept of lateral inhibition, each output neuron makes inhibitory synaptic contact with its neighbours [5]. However, there are physical limitations set by the extent of axonal and dendritic trees, and the number of synaptic sites, which mean that lateral inhibition is limited to a local domain of inhibition.
The striatum is the principal input structure of the basal ganglia. Numerically, the great majority of neurons in the striatum are spiny projection neurons, which produce the inhibitory output of the striatum to the globus pallidum and substantia nigra. The major glutamatergic afferents to the striatum from the cerebral cortex make monosynaptic contact with spiny projection neurons. The dopaminergic afferents from the substantia nigra also synapse directly on the spiny projection neurons. Thus, the spiny projection neurons play a crucial role in the input–output operations of the striatum by integrating glutamatergic cortical inputs with dopaminergic inputs and producing the output to other basal ganglia nuclei. Anatomical observations made nearly 30 years ago suggested that inhibitory interactions among the spiny projection neurons of the striatum are very pr- able. Individual spiny projection neurons produce a local axonal plexus in the spheroidal space occupied by their own dendritic trees [1, 2]. Based on the GABAergic nature of these neurons and their synaptic contacts with other spiny neurons, several authors have proposed that the spiny projection neurons form a lateral inhibition type of neural network [3–5]. In the idealised concept of lateral inhibition, each output neuron makes inhibitory synaptic contact with its neighbours [5]. However, there are physical limitations set by the extent of axonal and dendritic trees, and the number of synaptic sites, which mean that lateral inhibition is limited to a local domain of inhibition.
In this study, we explored the use of functional connectivity patterns in fMRI data to classify subjects on the basis of Parkinson's disease. We explore various brain networks and features. We partition our fMRI data in 5 filtered frequency ranges. We use a proximal support vector machine paired with a minimum-redundancy and maximum-relevance feature selection method on each frequency range. We use a majority voting ensemble classification method on the results of the proximal support vector machine classification results. We use a double 5-fold cross validation scheme for model validation. We achieve 84% accuracy 74% sensitivity, and 93% specificity. Our results indicate that the ensemble method is effective compared to a single broad frequency range, and that Bonferroni correction may enhance classification results. We produce brain graphs to illustrate the brain networks of Parkinson's and control subjects.
"The mind is the music that neural networks play." This quote from computational neurobiologist T.J. Sejnowski underscores a growing scientific consensus that studying the structure and function of vast networks of connections between brain regions is essential to understanding cognitive and affective state maintenance, sensorimotor information processing and control, etiologies and remedies for numerous neuropathologies, as well as a host of other facets of our conscious (and non-conscious) experience. Towards this goal, an ongoing challenge lies in identifying - in vivo in humans - spatiotemporal cortical network dynamics, at the level of individuals and groups, across experimental task conditions, and at the level of single trials. In the opening chapter of this dissertation, I introduce the Source Information Flow Toolbox (SIFT), a novel open-source software package for identification of neuronal dynamics and causal interactions in electrophysiological source and sensor data. The software integrates with the widely used EEGLAB analysis suite, addressing a need for robust tools for identifying single- and multi-trial multivariate brain network dynamics across time, frequency, anatomical source location, and subjects. I then introduce and assess two new methods (Measure Projection Analysis and Multi-view Hierarchical Bayesian Learning) for statistical analysis of source-level dynamics (including connectivity) across groups of subjects in the presence of missing data. The remaining chapters focus on applications of dynamical modeling approaches in SIFT to open problems within the fields of cognitive neuroscience, clinical neuroscience and neuroengineering. I first present three studies examining single-trial time-varying spatiotemporal network dynamics underlying generation and maintenance of epileptic seizures. Next I present a case study examining the effect of visual feedback on an occipito-parietal-motor network in freezing-of-gait in patients with Parkinson's disease. The final chapters focus on new directions in neuroengineering and brain-computer interfaces (BCI) leveraging neural dynamical system identification. We first review the history and state of the BCI field and summarize important new directions in BCI design. I then present a novel system for real-time mobile brain imaging, artifact rejection, neuronal system identification, and cognitive state prediction, and demonstrate its application in predicting response error commission from cortical network dynamics using a new high-density mobile dry EEG system.
This book focuses on our current understanding of brain dynamics in various brain disorders (e.g. epilepsy, Alzheimer’s and Parkinson’s disease) and how the multi-scale, multi-level tools of computational neuroscience can enhance this understanding. In recent years, there have been significant advances in the study of the dynamics of the disordered brain at both the microscopic and the macroscopic levels. This understanding can be furthered by the application of multi-scale computational models as integrative principles that may link single neuron dynamics and the dynamics of local and distant brain regions observed using human EEG, ERPs, MEG, LFPs and fMRI. Focusing on the computational models that are used to study movement, memory and cognitive disorders as well as epilepsy and consciousness related diseases, the book brings together physiologists and anatomists investigating cortical circuits; cognitive neuroscientists studying brain dynamics and behavior by means of EEG and functional magnetic resonance imaging (fMRI); and computational neuroscientists using neural modeling techniques to explore local and large-scale disordered brain dynamics. Covering topics that have a significant impact on the field of medicine, neuroscience and computer science, the book appeals to a diverse group of investigators.
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Homeostatic Control of Brain Function offers a broad view of brain health and diverse perspectives for potential treatments, targeting key areas such as mitochondria, the immune system, epigenetic changes, and regulatory molecules such as ions, neuropeptides, and neuromodulators. Loss of homeostasis becomes expressed as a diverse array of neurological disorders. Each disorder has multiple comorbidities - with some crossing over several conditions - and often disease-specific treatments remain elusive. When current pharmacological therapies result in ineffective and inadequate outcomes, therapies to restore and maintain homeostatic functions can help improve brain health, no matter the diagnosis. Employing homeostatic therapies may lead to future cures or treatments that address multiple comorbidities. In an age where brain diseases such as Alzheimer's or Parkinson's are ever present, the incorporation of homeostatic techniques could successfully promote better overall brain health. Key Features include · A focus on the homeostatic controls that significantly depend on the way one lives, eats, and drinks. · Highlights from emerging research in non-pharmaceutical therapies including botanical medications, meditation, diet, and exercise. · Incorporation of homeostatic therapies into existing basic and clinical research paradigms. · Extensive scientific basic and clinical research ranging from molecules to disorders. · Emerging practical information for improving homeostasis. · Examples of homeostatic therapies in preventing and delaying dysfunction. Both editors, Detlev Boison and Susan Masino, bring their unique expertise in homeostatic research to the overall scope of this work. This book is accessible to all with an interest in brain health; scientist, clinician, student, and lay reader alike.
Research on brain oscillations and event-related electroencephalography (EEG) and event-related (de-) synchronization (ERD/ERS) in particular became a rapidly growing field in the last decades. A large number of laboratories worldwide are using ERD/ERS to study cognitive and motor brain function and the importance of this tool in neurocognitive research is widely recognized. This book is a summary of the most current research, methods, and applications of the study of event-related dynamics of brain oscillations. Facing the rapid progress in this field, it brings together, on the one side, fundamental questions of the underlying events, which still remain to be clarified and, on the other side, some of the most significant novel findings, which point to the key topics for future research. In particular, the chapters of this volume cover the neurophysiological fundamentals and models (Section I), new methodological approaches (Section II), current ERD research related to cognitive (Section III) and sensorimotor brain function (Section IV), invasive approaches and clinical applications (Section V), and novel developments of EEG-based brain-computer interfaces and neurofeedback (Section IV).