Drum training induces long-term plasticity in the cerebellum and connected cortical thickness

It is unclear to what extent cerebellar networks show long-term plasticity and accompanied changes in cortical structures. Using drumming as a demanding multimodal motor training, we compared cerebellar lobular volume and white matter microstructure, as well as cortical thickness of 15 healthy non-musicians before and after learning to drum, and 16 age matched novice control participants. After 8 weeks of group drumming instruction, 3 ×30 minutes per week, we observed the cerebellum significantly changing its grey (volume increase of left VIIIa, relative decrease of VIIIb and vermis Crus I volume) and white matter microstructure in the inferior cerebellar peduncle. These plastic cerebellar changes were complemented by changes in cortical thickness (increase in left paracentral, right precuneus and right but not left superior frontal thickness), suggesting an interplay of cerebellar learning with cortical structures enabled through cerebellar pathways

Extended field-of-view ultrathin microendoscopes with built-in aberration correction for high-resolution functional imaging with minimal invasiveness

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Evaluating the impact of intracortical microstimulation on distant cortical brain regions for neuroprosthetic applications

Enhancing functional motor recovery after localized brain injury is a widely recognized priority in healthcare as disorders of the nervous system that cause motor impairment, such as stroke, are among the most common causes of adult-onset disability. Restoring physiological function in a dysfunctional brain to improve quality of life is a primary challenge in scientific and clinical research and could be driven by innovative therapeutic approaches. Recently, techniques using brain stimulation methodologies have been employed to promote post-injury neuroplasticity for the restitution of motor function. One type of closed-loop stimulation, i.e., activity-dependent stimulation (ADS), has been shown to modify existing functional connectivity within either healthy or injured cerebral cortices and used to increase behavioral recovery following cortical injury. The aim of this PhD thesis is to characterize the electrophysiological correlates of such behavioral recovery in both healthy and injured cortical networks using in vivo animal models. We tested the ability of two different intracortical micro-stimulation protocols, i.e., ADS and its randomized open-loop version (RS), to potentiate cortico-cortical connections between two distant cortical locations in both anaesthetized and awake behaving rats. Thus, this dissertation has the following three main goals: 1) to investigate the ability of ADS to induce changes in intra-cortical activity in healthy anesthetized rats, 2) to characterize the electrophysiological signs of brain injury and evaluate the capability of ADS to promote electrophysiological changes in the damaged network, and 3) to investigate the long-term effects of stimulation by repeating the treatment for 21 consecutive days in healthy awake behaving animals. The results of this study indicate that closed-loop activity-dependent stimulation induced greater changes than open-loop random stimulation, further strengthening the idea that Hebbian-inspired protocols might potentiate cortico-cortical connections between distant brain areas. The implications of these results have the potential to lead to novel treatments for various neurological diseases and disorders and inspire new neurorehabilitation therapies

Neural principles underlying motor learning and adaptation

Animals, and especially humans, can learn to flexibly adjust their movements to changing environments. The neural principles underlying this remarkable capability are still not fully understood. Among the most prominent brain regions controlling movement is primary motor cortex (M1). Adapted motor behaviour can be related to a change in neural activity within this region. Yet, the rules guiding this activity change, and thus behavioural adaptation, remain unclear. The overall aim of this thesis is to investigate the learning process(es) governing the described change in activity in M1 and, with that, the change in behaviour. Computational modelling is used to study three specific aspects of learning: 1. What constrains learning to favour some neural activity patterns over others? 2. Can we identify where in a hierarchical pathway learning is happening? 3. How can sensory feedback guide the learning process? We start by investigating what kind of biological constraints differentially affect learning of new neural activity that either preserves coactivation patterns between neurons (within-manifold learning), or requires learning of new coactivation patterns (outside-manifold learning). We propose a new explanation - the learnability of feedback signals - for why within-manifold activity patterns can be easier learned than outside-manifold activity patterns. In the second part we develop a hierarchical model of the motor system to investigate whether we can derive where learning has happened from only measuring neural activity. Lastly, we investigate how the brain could implement a biologically plausible learning rule which allows it to correctly assign errors and update recurrent connectivity in a goal-driven manner. Overall, our work offers new perspectives on the role of M1 for motor learning and adaptation, challenges current beliefs, and puts a focus on the role of feedback signals for local plasticity in M1.Open Acces

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement

Functional and Structural Magnetic Resonance Imaging of Humans and Macaques

Magnetic resonance imaging (MRI) is a technique which finds use in the neurosciences both as an anatomical and functional localization tool. The traditional uses of MRI for structural analysis, such as are commonly found in medicine, can be adapted to serve in place of histological studies for identifying areas of interest in the cortex. Functional MRI (fMRI) is a rapidly developing tangent of MRI which can be used alone or in tandem with classical electrophysiological experiments to investigate neural activity. Although developed intensely for clinical and scientific studies in human subjects, MRI and fMRI have been used increasingly in the non-human primate. This document contains work exemplifying the use of fMRI in both species and methods for pre- and post-surgical anatomical MRI in the non-human primate. Serving as a solid foundation for learning the principles of block-design fMRI, a classic visual illusion, the motion aftereffect, is studied in the human by means of a hemifield visual stimulus using conventional blood oxygen level dependent (BOLD) fMRI. Primary response and levels of motion aftereffect are analyzed in visual cortex, areas pMT and pMST. A novel use of iron oxide nanoparticles as an intravascular contrast agent in the non-human primate is investigated as a method of boosting fMRI contrast, yielding an ultimate gain in contrast-to-noise at the expense of temporal resolution. While anatomical imaging served as a necessary tool for the localization of functional response in the human, further novel techniques were investigated in the non-human primate. A technique for MRI-guided implantation of multiple electrode arrays is considered, to aid the localization of sites of interest in the cortex. The use of MRI as a replacement for histological preparations for purposes of reconstructing electrode penetration sites is documented. These studies exist to aid in bridging the gap between human and non-human MRI and fMRI. Further application of these principles could be extended to the eventual placement of intracortical recording devices in the human, to benefit a patient population needing devices such as a neural prosthesis

The organizational principles of de-differentiated topographic maps in somatosensory cortex

Topographic maps are a fundamental feature of cortex architecture in the mammalian brain. One common theory is that the de-differentiation of topographic maps links to impairments in everyday behavior due to less precise functional map readouts. Here, we tested this theory by characterizing de-differentiated topographic maps in primary somatosensory cortex (SI) of younger and older adults by means of ultra-high resolution functional magnetic resonance imaging together with perceptual finger individuation and hand motor performance. Older adults' SI maps showed similar amplitude and size to younger adults' maps, but presented with less representational similarity between distant fingers. Larger population receptive field sizes in older adults' maps did not correlate with behavior, whereas reduced cortical distances between D2 and D3 related to worse finger individuation but better motor performance. Our data uncover the drawbacks of a simple de-differentiation model of topographic map function, and motivate the introduction of feature-based models of cortical reorganization

Learning From Geometry In Learning For Tactical And Strategic Decision Domains

Artificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs. The key insight in HyperNEAT is to make the algorithm aware of the geometry in which the ANNs are embedded and thereby exploit such domain geometry to evolve ANNs more effectively. The dissertation focuses on applying HyperNEAT to tactical and strategic decision domains. These domains involve simultaneously considering short-term tactics while also balancing long-term strategies. Board games such as checkers and Go are canonical examples of such domains; however, they also include real-time strategy games and military scenarios. The dissertation details three proposed extensions to HyperNEAT designed to work in tactical and strategic decision domains. The first is an action selector ANN architecture that allows the ANN to indicate its judgements on every possible action all at once. The second technique is called substrate extrapolation. It allows learning basic concepts at a low resolution, and then increasing the resolution to learn more advanced concepts. The iii final extension is geometric game-tree pruning, whereby HyperNEAT can endow the ANN the ability to focus on specific areas of a domain (such as a checkers board) that deserve more inspection. The culminating contribution is to demonstrate the ability of HyperNEAT with these extensions to play Go, a most challenging game for artificial intelligence, by combining HyperNEAT with UC