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Mapping the asynchrony of cortical maturation in the infant brain: A MRI multi-parametric clustering approach
While the main neural networks are in place at term birth, intense changes in cortical microstructure occur during early infancy with the development of dendritic arborization, synaptogenesis and fiber myelination. These maturational processes are thought to relate to behavioral acquisitions and the development of cognitive abilities. Nevertheless, in vivo investigations of such relationships are still lacking in healthy infants. To bridge this gap, we aimed to study the cortical maturation using non-invasive Magnetic Resonance Imaging, over a largely unexplored period (1–5 post-natal months). In a first univariate step, we focused on different quantitative parameters: longitudinal relaxation time (T1), transverse relaxation time (T2), and axial diffusivity from diffusion tensor imaging (λ//) These individual maps, acquired with echo-planar imaging to limit the acquisition time, showed spatial distortions that were first corrected to reliably match the thin cortical ribbon identified on high-resolution T2-weighted images. Averaged maps were also computed over the infants group to summarize the parameter characteristics during early infancy. In a second step, we considered a multi-parametric approach that leverages parameters complementarity, avoids reliance on pre-defined regions of interest, and does not require spatial constraints. Our clustering strategy allowed us to group cortical voxels over all infants in 5 clusters with distinct microstructural T1 and λ// properties The cluster maps over individual cortical surfaces and over the group were in sound agreement with benchmark post mortem studies of sub-cortical white matter myelination, showing a progressive maturation of 1) primary sensori-motor areas, 2) adjacent unimodal associative cortices, and 3) higher-order associative regions. This study thus opens a consistent approach to study cortical maturation in vivo.
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•The cortical maturation was studied in infants between 1 and 5 months of age.•Diffusion and relaxometry MRI were analyzed in univariate and multivariate manner.•Clustering analyses provided reliable results both at the subject and group levels.•Distinct maturation profiles were shown across regions of the infant cortex.
Journal Article
Big data, open science and the brain: lessons learned from genomics
The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (10(24)). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified \"big science\" for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this \"data driven\" paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new \"open neuroscience\" projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent \"open neuroscience\" movement.
Journal Article
Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme
Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme’s approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme’s conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.
Journal Article
A Self-Operating Time Crystal Model of the Human Brain: Can We Replace Entire Brain Hardware with a 3D Fractal Architecture of Clocks Alone?
Time crystal was conceived in the 1970s as an autonomous engine made of only clocks to explain the life-like features of a virus. Later, time crystal was extended to living cells like neurons. The brain controls most biological clocks that regenerate the living cells continuously. Most cognitive tasks and learning in the brain run by periodic clock-like oscillations. Can we integrate all cognitive tasks in terms of running clocks of the hardware? Since the existing concept of time crystal has only one clock with a singularity point, we generalize the basic idea of time crystal so that we could bond many clocks in a 3D architecture. Harvesting inside phase singularity is the key. Since clocks reset continuously in the brain–body system, during reset, other clocks take over. So, we insert clock architecture inside singularity resembling brain components bottom-up and top-down. Instead of one clock, the time crystal turns to a composite, so it is poly-time crystal. We used century-old research on brain rhythms to compile the first hardware-free pure clock reconstruction of the human brain. Similar to the global effort on connectome, a spatial reconstruction of the brain, we advocate a global effort for more intricate mapping of all brain clocks, to fill missing links with respect to the brain’s temporal map. Once made, reverse engineering the brain would remain a mere engineering challenge.
Journal Article
Responsible Data Governance of Neuroscience Big Data
Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of \"responsible data governance,\" applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).
Journal Article
Beyond Research Ethics: Dialogues in Neuro-ICT Research
The increasing use of information and communication technologies (ICTs) to help facilitate neuroscience adds a new level of complexity to the question of how ethical issues of such research can be identified and addressed. Current research ethics practice, based on ethics reviews by institutional review boards (IRB) and underpinned by ethical principlism, has been widely criticized. In this article, we develop an alternative way of approaching ethics in neuro-ICT research, based on discourse ethics, which implements Responsible Research and Innovation (RRI) through dialogues. We draw on our work in Ethics Support, using the Human Brain Project (HBP) as empirical evidence of the viability of this approach.
Journal Article
Ethical and Social Aspects of Neurorobotics
The interdisciplinary field of neurorobotics looks to neuroscience to overcome the limitations of modern robotics technology, to robotics to advance our understanding of the neural system’s inner workings, and to information technology to develop tools that support those complementary endeavours. The development of these technologies is still at an early stage, which makes them an ideal candidate for proactive and anticipatory ethical reflection. This article explains the current state of neurorobotics development within the Human Brain Project, originating from a close collaboration between the scientific and technical experts who drive neurorobotics innovation, and the humanities and social sciences scholars who provide contextualising and reflective capabilities. This article discusses some of the ethical issues which can reasonably be expected. On this basis, the article explores possible gaps identified within this collaborative, ethical reflection that calls for attention to ensure that the development of neurorobotics is ethically sound and socially acceptable and desirable.
Journal Article
Explanatory completeness and idealization in large brain simulations: a mechanistic perspective
The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: (1) Blue Brain, used for particular simulations of the cortical column in hybrid models, and (2) Eliasmith's SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I argue that largescale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multimodel integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a \"checklist\" for a modeler.
Journal Article
Combining Motor Primitives for Perception Driven Target Reaching With Spiking Neurons
Target reaching is one of the most important areas in robotics, object interaction, manipulation and grasping tasks require reaching specific targets. The authors avoid the complexity of calculating the inverse kinematics and doing motion planning, and instead use a combination of motor primitives. A bio-inspired architecture performs target reaching with a robot arm without planning. A spiking neural network represents motions in a hierarchy of motor primitives, and different correction primitives are combined using an error signal. In this article two experiments using a simulation of a robot arm are presented, one to extensively cover the working space by going to different points and returning to the start point, the other to test extreme targets and random points in sequence. Robotics applications—like target reaching—can provide benchmarking tasks and realistic scenarios for validation of neuroscience models, and also take advantage of the capabilities of spiking neural networks and the properties of neuromorphic hardware to run the models.
Journal Article