A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain

White matter fibres dissection in the human brain

PhD ThesisIntroduction: lesion to white matter fibres can induce permanent neurological deficits due to the induction of disconnection syndromes. Knowledge of white matter fibre anatomy is therefore relevant to the neurosurgeon in order to minimise the risk of causing neurological damage when approaching lesions in eloquent areas of the brain. Aim: to investigate the 3D anatomy of white matter fibres with particular attention to the associative tracts, including short arcuate fibres and intralobar fibres. The results obtained will be used to provide insights in brain connectivity, delineating networks important for specific brain functions. Methods: The Klingler technique for white matter dissection was followed. Brain specimens were collected and prepared at the Newcastle Brain Tissue Resource, Newcastle University. Brains were initially fixed in 10% formalin for at least 4 weeks. After removing the pia-mater and arachnoid, the brains were frozen at -15C° for 2 weeks. The water crystallisation induced by the freezing process separates the white matter fibres, facilitating the dissection of the tracts. Dissection was performed with wooden spatulas and blunt metallic dissectors, removing the cortex and exposing the white matter. The short associative (U-shaped) fibres were initially exposed. Long associative, commissural and projection fibres were demonstrated as the dissection proceeded. Results: five papers form the main body of the present work: 1) “Raymond de Vieussens and his contribution to the study of white matter anatomy”. This historical paper reviewed the history of white matter dissection, focusing on the work of Raymond de Vieussens, who gave the first account of the centrum ovale and of the continuity of the corticospinal tract from the centrum ovale to the brainstem. 2) “The white matter of the human cerebrum: part I The occipital lobe by Heinrich Sachs “ ; 3) “Intralobar fibres of the occipital lobe: A post mortem dissection study”. These joint papers were dedicated to the white matter anatomy of the occipital lobe. A rich network of association fibres, arranged around the ventricular wall, was demonstrated. A new white matter tract, connecting the cuneus to the lingula, was also described. Our original data I II were compared to the atlas of occipital fibres produced by the German anatomist Heinrich Sachs. 4) “White matter connections of the Supplementary Motor Area (SMA) in humans”. This study demonstrated that the SMA shows a wide range of connections with motor, language and limbic areas. Features of the SMA syndrome (akinesia and mutism) can be better understood on the basis of these findings. 5) “Anatomical connections of the Subgenual Cingulate Region” (SCG). This study showed that the SCG is at the centre of a large network, connecting prefrontal, limbic and mesotemporal regions. The connectivity of this region can help explain the clinical effect of neuromodulaton of the SCG in Deep Brain Stimulation for neuropsychiatric disorders. Conclusions: Klingler dissection provided original data about the connections of different brain regions that are relevant to neurosurgical practice, along with the description of a new white matter tract, connecting the cuneus to the lingula

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

Registration and Analysis of Developmental Image Sequences

Mapping images into the same anatomical coordinate system via image registration is a fundamental step when studying physiological processes, such as brain development. Standard registration methods are applicable when biological structures are mapped to the same anatomy and their appearance remains constant across the images or changes spatially uniformly. However, image sequences of animal or human development often do not follow these assumptions, and thus standard registration methods are unsuited for their analysis. In response, this dissertation tackles the problems of i) registering developmental image sequences with spatially non-uniform appearance change and ii) reconstructing a coherent 3D volume from serially sectioned images with non-matching anatomies between the sections. There are three major contributions presented in this dissertation. First, I develop a similarity metric that incorporates a time-dependent appearance model into the registration framework. The proposed metric allows for longitudinal image registration in the presence of spatially non-uniform appearance change over time—a common medical imaging problem for longitudinal magnetic resonance images of the neonatal brain. Next, a method is introduced for registering longitudinal developmental datasets with missing time points using an appearance atlas built from a population. The proposed method is applied to a longitudinal study of young macaque monkeys with incomplete image sequences. The final contribution is a template-free registration method to reconstruct images of serially sectioned biological samples into a coherent 3D volume. The method is applied to confocal fluorescence microscopy images of serially sectioned embryonic mouse brains.Doctor of Philosoph

AI Adoption in Real-World Clinical Neuroimaging Applications: Practical Challenges and Solutions

Deep learning has demonstrated a capacity to revolutionise human life in the last several years. Medical imaging, which has a vast data footprint, has emerged as a pioneering area that has seen rapid adoption of deep learning approaches to the domain. The development of new imaging-based algorithms is directed toward improved efficiency and accuracy of disease diagnosis and prognosis; and enhanced disease progression monitoring. These models also have the potential to provide insights into disease pathomechanisms. However, the translation rate of newly described deep learning models into real-world clinical practice is extraordinarily low, despite an exponential increase in the number of research publications over the past few years. The cost of of data collection and annotation required to achieve model performance sufficient for clinical use; and to provide persuasive evidence of utility in real-world settings are significant roadblocks. This thesis investigates solutions to the challenges of adapting deep neural networks to real-world settings. To improve the performance of algorithms, while reducing the costs of meticulous labelling, a novel model Masked Multi-Task Network is proposed for classification using only case-level labels; and a new training approach is proposed to tackle the issue of noisy labels in a federated learning setting. Furthermore, an in-depth analysis of the requirements for sample size used for training is conducted, to guide the development of deep learning models for large-scale adoption. The research presented in this thesis encompasses the clinical validation and technical steps required for the commercialisation of two exemplary neuroimaging deep learning algorithms based on above works. This work also offers valuable insight into the compilation of requisite documentation for medical device registration, providing a valuable resource for researchers who wish to translate their models from the bench to the bedside